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abstract—we introduce learned attention models into the radio machine learning domain for the task of modulation recognition by leveraging spatial transformer networks and introducing
new radio domain appropriate transformations. this attention
model allows the network to learn a localization network capable
of synchronizing and normalizing a radio signal blindly with zero
knowledge of the signal’s structure based on optimization of the
network for classification accuracy, sparse representation, and
regularization. using this architecture we are able to outperform
our prior results in accuracy vs signal to noise ratio against an
identical system without attention, however we believe such an
attention model has implication far beyond the task of modulation
recognition.
| 3 |
abstract
planar ornaments, a.k.a. wallpapers, are regular repetitive patterns which exhibit translational symmetry
in two independent directions. there are exactly 17 distinct planar symmetry groups. we present a fully
automatic method for complete analysis of planar ornaments in 13 of these groups, specifically, the groups
called p6m, p6, p4g, p4m, p4, p31m, p3m, p3, cmm, pgg, pg, p2 and p1. given the image of an ornament
fragment, we present a method to simultaneously classify the input into one of the 13 groups and extract
the so called fundamental domain (fd), the minimum region that is sufficient to reconstruct the entire
ornament. a nice feature of our method is that even when the given ornament image is a small portion
such that it does not contain multiple translational units, the symmetry group as well as the fundamental
domain can still be defined. this is because, in contrast to common approach, we do not attempt to first
identify a global translational repetition lattice. though the presented constructions work for quite a wide
range of ornament patterns, a key assumption we make is that the perceivable motifs (shapes that repeat)
alone do not provide clues for the underlying symmetries of the ornament. in this sense, our main target is
the planar arrangements of asymmetric interlocking shapes, as in the symmetry art of escher.
keywords: ornaments, wallpaper groups, mosaics, regular patterns, escher style planar patterns
| 1 |
abstract—recently, an idling mechanism has been introduced
in the context of distributed first order methods for minimization
of a sum of nodes’ local convex costs over a generic, connected
network. with the idling mechanism, each node i, at each
iteration k, is active – updates its solution estimate and exchanges
messages with its network neighborhood – with probability pk ,
and it stays idle with probability 1 − pk , while the activations
are independent both across nodes and across iterations. in
this paper, we demonstrate that the idling mechanism can be
successfully incorporated in distributed second order methods
also. specifically, we apply the idling mechanism to the recently
proposed distributed quasi newton method (dqn). we first
show theoretically that, when pk grows to one across iterations
in a controlled manner, dqn with idling exhibits very similar
theoretical convergence and convergence rates properties as
the standard dqn method, thus achieving the same order of
convergence rate (r-linear) as the standard dqn, but with
significantly cheaper updates. simulation examples confirm the
benefits of incorporating the idling mechanism, demonstrate the
method’s flexibility with respect to the choice of the pk ’s, and
compare the proposed idling method with related algorithms
from the literature.
index terms—distributed optimization, variable sample
schemes, second order methods, newton-like methods, linear
convergence.
| 7 |
abstract
this paper considers recovering l-dimensional vectors w, and x1 , x2 , . . . , xn from their
circular convolutions yn = w ∗ xn , n = 1, 2, 3, . . . , n . the vector w is assumed to be s-sparse
in a known basis that is spread out in the fourier domain, and each input xn is a member of a
known k-dimensional random subspace.
we prove that whenever k + s log2 s . l/ log4 (ln ), the problem can be solved effectively
by using only the nuclear-norm minimization as the convex relaxation, as long as the inputs are
sufficiently diverse and obey n & log2 (ln ). by “diverse inputs”, we mean that the xn ’s belong
to different, generic subspaces. to our knowledge, this is the first theoretical result on blind
deconvolution where the subspace to which w belongs is not fixed, but needs to be determined.
we discuss the result in the context of multipath channel estimation in wireless communications. both the fading coefficients, and the delays in the channel impulse response w are
unknown. the encoder codes the k-dimensional message vectors randomly and then transmits
coded messages xn ’s over a fixed channel one after the other. the decoder then discovers all of
the messages and the channel response when the number of samples taken for each received message are roughly greater than (k + s log2 s) log4 (ln ), and the number of messages is roughly
at least log2 (ln ).
| 7 |
abstract
this is an account of the theory of jsj decompositions of finitely generated groups,
as developed in the last twenty years or so.
we give a simple general definition of jsj decompositions (or rather of their bassserre trees), as maximal universally elliptic trees. in general, there is no preferred jsj
decomposition, and the right object to consider is the whole set of jsj decompositions,
which forms a contractible space: the jsj deformation space (analogous to outer
space).
we prove that jsj decompositions exist for any finitely presented group, without
any assumption on edge groups. when edge groups are slender, we describe flexible vertices of jsj decompositions as quadratically hanging extensions of 2-orbifold
groups.
similar results hold in the presence of acylindricity, in particular for splittings
of torsion-free csa groups over abelian groups, and splittings of relatively hyperbolic
groups over virtually cyclic or parabolic subgroups. using trees of cylinders, we obtain
canonical jsj trees (which are invariant under automorphisms).
we introduce a variant in which the property of being universally elliptic is replaced
by the more restrictive and rigid property of being universally compatible. this yields
a canonical compatibility jsj tree, not just a deformation space. we show that it
exists for any finitely presented group.
we give many examples, and we work throughout with relative decompositions
(restricting to trees where certain subgroups are elliptic).
| 4 |
abstract
we study the problem of compressing recurrent neural networks (rnns). in particular, we focus on the compression
of rnn acoustic models, which are motivated by the goal
of building compact and accurate speech recognition systems
which can be run efficiently on mobile devices. in this work,
we present a technique for general recurrent model compression that jointly compresses both recurrent and non-recurrent
inter-layer weight matrices. we find that the proposed technique allows us to reduce the size of our long short-term
memory (lstm) acoustic model to a third of its original size
with negligible loss in accuracy.
index terms— model compression, lstm, rnn, svd,
embedded speech recognition
1. introduction
neural networks (nns) with multiple feed-forward [1, 2] or
recurrent hidden layers [3, 4] have emerged as state-of-theart acoustic models (ams) for automatic speech recognition
(asr) tasks. advances in computational capabilities coupled
with the availability of large annotated speech corpora have
made it possible to train nn-based ams with a large number
of parameters [5] with great success.
as speech recognition technologies continue to improve,
they are becoming increasingly ubiquitous on mobile devices:
voice assistants such as apple’s siri, microsoft’s cortana,
amazon’s alexa and google now [6] enable users to search
for information using their voice. although the traditional
model for these applications has been to recognize speech
remotely on large servers, there has been growing interest
in developing asr technologies that can recognize the input speech directly “on-device” [7]. this has the promise to
reduce latency while enabling user interaction even in cases
where a mobile data connection is either unavailable, slow
or unreliable. some of the main challenges in this regard
are the disk, memory and computational constraints imposed
by these devices. since the number of operations in neural
† equal contribution. the authors would like to thank haşim sak and
raziel alvarez for helpful comments and suggestions on this work, and chris
thornton and yu-hsin chen for comments on an earlier draft.
| 9 |
abstract. we present a probabilistic extension of the description logic alc for
reasoning about statistical knowledge. we consider conditional statements over
proportions of the domain and are interested in the probabilistic-logical consequences of these proportions. after introducing some general reasoning problems
and analyzing their properties, we present first algorithms and complexity results
for reasoning in some fragments of statistical alc.
| 2 |
abstract
we classify the linearly reductive finite subgroup schemes g of
sl2 = sl(v ) over an algebraically closed field k of positive characteristic, up to conjugation. as a corollary, we prove that such g is in oneto-one correspondence with an isomorphism class of two-dimensional
f -rational gorenstein complete local rings with the coefficient field k
by the correspondence g 7→ ((sym v )g )b.
| 0 |
abstract
we show that the question whether a term is typable is decidable for type systems combining inclusion polymorphism with parametric polymorphism provided
the type constructors are at most unary. to prove this result we first reduce the
typability problem to the problem of solving a system of type inequations. the
result is then obtained by showing that the solvability of the resulting system of
type inequations is decidable.
| 6 |
abstract
this report presents jartege, a tool which allows random generation of unit tests for java
classes specified in jml. jml (java modeling language) is a specification language for
java which allows one to write invariants for classes, and pre- and postconditions for
operations. as in the jml-junit tool, we use jml specifications on the one hand to
eliminate irrelevant test cases, and on the other hand as a test oracle. jartege randomly
generates test cases, which consist of a sequence of constructor and method calls for the
classes under test. the random aspect of the tool can be parameterized by associating
weights to classes and operations, and by controlling the number of instances which are
created for each class under test. the practical use of jartege is illustrated by a small case
study.
keywords
testing, unit testing, random generation of test cases, java, jml
| 2 |
abstract—this work is motivated by the problem of error
correction in bit-shift channels with the so-called (d, k) input
constraints (where successive 1’s are required to be separated
by at least d and at most k zeros, 0 ≤ d < k ≤ ∞). bounds
on the size of optimal (d, k)-constrained codes correcting a
fixed number of bit-shifts are derived. the upper bound is
obtained by a packing argument, while the lower bound follows
from a construction based on a family of integer lattices.
several properties of (d, k)-constrained sequences that may be
of independent interest are established as well; in particular,
the capacity of the noiseless channel with (d, k)-constrained
constant-weight inputs is characterized. the results are relevant
for magnetic and optical storage systems, reader-to-tag rfid
channels, and other communication models where bit-shift errors
are dominant and where (d, k)-constrained sequences are used
for modulation.
index terms—bit-shift channel, peak shift, timing errors,
runlength-limited code, integer compositions, manhattan metric,
asymmetric distance, magnetic recording, inductive coupling.
| 7 |
abstract—ant colony system (acs) is a distributed (agentbased) algorithm which has been widely studied on the
symmetric travelling salesman problem (tsp). the optimum
parameters for this algorithm have to be found by trial and
error. we use a particle swarm optimization algorithm (pso)
to optimize the acs parameters working in a designed subset
of tsp instances. first goal is to perform the hybrid pso-acs
algorithm on a single instance to find the optimum parameters
and optimum solutions for the instance. second goal is to
analyze those sets of optimum parameters, in relation to
instance characteristics. computational results have shown
good quality solutions for single instances though with high
computational times, and that there may be sets of parameters
that work optimally for a majority of instances.
i. introduction
| 9 |
abstract— the analysis and interpretation of relationships
between biological molecules is done with the help of networks.
networks are used ubiquitously throughout biology to represent
the relationships between genes and gene products. network
models have facilitated a shift from the study of evolutionary
conservation between individual gene and gene products towards
the study of conservation at the level of pathways and complexes.
recent work has revealed much about chemical reactions inside
hundreds of organisms as well as universal characteristics of
metabolic networks, which shed light on the evolution of the
networks. however, characteristics of individual metabolites
have been neglected in this network. the current paper provides
an overview of bioinformatics software used in visualization of
biological networks using proteomic data, their main functions
and limitations of the software.
keywords- metabolic network; protein interaction network;
visualization tools.
| 5 |
abstract. we note a generalization of whyte’s geometric solution to the von
neumann problem for locally compact groups in terms of borel and clopen
piecewise translations. this strengthens a result of paterson on the existence
of borel paradoxical decompositions for non-amenable locally compact groups.
along the way, we study the connection between some geometric properties of
coarse spaces and certain algebraic characteristics of their wobbling groups.
| 4 |
abstract
this paper focuses on the modal analysis of laminated glass beams. in these
multilayer elements, the stiff glass plates are connected by compliant interlayers with frequency/temperature-dependent behavior. the aim of our study
is (i) to assess whether approximate techniques can accurately predict the
behavior of laminated glass structures and (ii) to propose an easy tool for
modal analysis based on the enhanced effective thickness concept by galuppi
and royer-carfagni.
to this purpose, we consider four approaches to the solution of the related nonlinear eigenvalue problem: a complex-eigenvalue solver based on
the newton method, the modal strain energy method, and two effective
thickness concepts. a comparative study of free vibrating laminated glass
beams is performed considering different geometries of cross-sections, boundary conditions, and material parameters for interlayers under two ambient
temperatures. the viscoelastic response of polymer foils is represented by
the generalized maxwell model.
we show that the simplified approaches predict natural frequencies with
an acceptable accuracy for most of the examples. however, there is a considerable scatter in predicted loss factors. the enhanced effective thickness
approach adjusted for modal analysis leads to lower errors in both quantities
compared to the other two simplified procedures, reducing the extreme error
in loss factors to one half compared to the modal strain energy method or to
one quarter compared to the original dynamic effective thickness method.
keywords: free vibrations, laminated glass, complex dynamic modulus,
dynamic effective thickness, enhanced effective thickness, modal strain
preprint submitted to arxiv
| 5 |
abstract. unsupervised learning permits the development of algorithms that are
able to adapt to a variety of different data sets using the same underlying rules
thanks to the autonomous discovery of discriminating features during training.
recently, a new class of hebbian-like and local unsupervised learning rules for
neural networks have been developed that minimise a similarity matching costfunction. these have been shown to perform sparse representation learning. this
study tests the effectiveness of one such learning rule for learning features from
images. the rule implemented is derived from a nonnegative classical multidimensional scaling cost-function, and is applied to both single and multi-layer
architectures. the features learned by the algorithm are then used as input to a
svm to test their effectiveness in classification on the established cifar-10 image dataset. the algorithm performs well in comparison to other unsupervised
learning algorithms and multi-layer networks, thus suggesting its validity in the
design of a new class of compact, online learning networks.
keywords: classification; competitive learning; feature learning; hebbian learning; online algorithm; neural networks; sparse coding; unsupervised learning.
| 1 |
abstract. throughout the last decade, we have seen much progress towards characterising and
computing the minimum hybridisation number for a set p of rooted phylogenetic trees. roughly
speaking, this minimum quantifies the number of hybridisation events needed to explain a set of
phylogenetic trees by simultaneously embedding them into a phylogenetic network. from a mathematical viewpoint, the notion of agreement forests is the underpinning concept for almost all results
that are related to calculating the minimum hybridisation number for when |p| = 2. however, despite various attempts, characterising this number in terms of agreement forests for |p| > 2 remains
elusive. in this paper, we characterise the minimum hybridisation number for when p is of arbitrary
size and consists of not necessarily binary trees. building on our previous work on cherry-picking
sequences, we first establish a new characterisation to compute the minimum hybridisation number
in the space of tree-child networks. subsequently, we show how this characterisation extends to the
space of all rooted phylogenetic networks. moreover, we establish a particular hardness result that
gives new insight into some of the limitations of agreement forests.
key words. agreement forest, cherry-picking sequence, minimum hybridisation, phylogenetic
networks, reticulation, tree-child networks
ams subject classifications. 05c05; 92d15
| 8 |
abstract
phylogenetic networks are a generalization of phylogenetic trees that allow for the
representation of evolutionary events acting at the population level, like recombination between genes, hybridization between lineages, and lateral gene transfer. while
most phylogenetics tools implement a wide range of algorithms on phylogenetic trees,
there exist only a few applications to work with phylogenetic networks, and there are no
open-source libraries either. in order to improve this situation, we have developed a perl
package that relies on the bioperl bundle and implements many algorithms on phylogenetic networks. we have also developed a java applet that makes use of the aforementioned perl package and allows the user to make simple experiments with phylogenetic
networks without having to develop a program or perl script by herself. the perl
package has been accepted as part of the bioperl bundle. it can be downloaded from
the url http://dmi.uib.es/~gcardona/bioinfo/bio-phylonetwork.tgz. the webbased application is available at the url http://dmi.uib.es/~gcardona/bioinfo/.
the perl package includes full documentation of all its features.
| 5 |
abstract—various hand-crafted features and metric learning
methods prevail in the field of person re-identification. compared
to these methods, this paper proposes a more general way that
can learn a similarity metric from image pixels directly. by
using a “siamese” deep neural network, the proposed method
can jointly learn the color feature, texture feature and metric in
a unified framework. the network has a symmetry structure with
two sub-networks which are connected by cosine function. to
deal with the big variations of person images, binomial deviance
is used to evaluate the cost between similarities and labels, which
is proved to be robust to outliers.
compared to existing researches, a more practical setting is
studied in the experiments that is training and test on different
datasets (cross dataset person re-identification). both in “intra
dataset” and “cross dataset” settings, the superiorities of the
proposed method are illustrated on viper and prid.
index terms—person re-identification, deep metric learning,
convolutional network, cross dataset
| 9 |
abstract
the class of ℓq -regularized least squares (lqls) are considered for estimating β ∈ rp from its n noisy
linear observations y = xβ + w. the performance of these schemes are studied under the high-dimensional
asymptotic setting in which the dimension of the signal grows linearly with the number of measurements.
in this asymptotic setting, phase transition diagrams (pt) are often used for comparing the performance
of different estimators. pt specifies the minimum number of observations required by a certain estimator
to recover a structured signal, e.g. a sparse one, from its noiseless linear observations. although phase
transition analysis is shown to provide useful information for compressed sensing, the fact that it ignores
the measurement noise not only limits its applicability in many application areas, but also may lead to
misunderstandings. for instance, consider a linear regression problem in which n > p and the signal is
not exactly sparse. if the measurement noise is ignored in such systems, regularization techniques, such
as lqls, seem to be irrelevant since even the ordinary least squares (ols) returns the exact solution.
however, it is well-known that if n is not much larger than p then the regularization techniques improve the
performance of ols.
in response to this limitation of pt analysis, we consider the low-noise sensitivity analysis. we show that
this analysis framework (i) reveals the advantage of lqls over ols, (ii) captures the difference between
different lqls estimators even when n > p, and (iii) provides a fair comparison among different estimators
in high signal-to-noise ratios. as an application of this framework, we will show that under mild conditions
lasso outperforms other lqls even when the signal is dense. finally, by a simple transformation we
connect our low-noise sensitivity framework to the classical asymptotic regime in which n/p → ∞ and
characterize how and when regularization techniques offer improvements over ordinary least squares, and
which regularizer gives the most improvement when the sample size is large.
key words: high-dimensional linear model, ℓq -regularized least squares, ordinary least squares,
lasso, phase transition, asymptotic mean square error, second-order expansion, classical asymptotics.
| 10 |
abstract—periodic event-triggered control (petc) [13] is a
version of event-triggered control (etc) that only requires to
measure the plant output periodically instead of continuously. in
this work, we present a construction of timing models for these
petc implementations to capture the dynamics of the traffic they
generate. in the construction, we employ a two-step approach.
we first partition the state space into a finite number of regions.
then in each region, the event-triggering behavior is analyzed
with the help of lmis. the state transitions among different
regions result from computing the reachable state set starting
from each region within the computed event time intervals.
index terms—systems abstractions; periodic event-triggered
control; lmi; formal methods; reachability analysis.
| 3 |
abstract. submodularity is one of the most important property of combinatorial optimization, and k-submodularity is a generalization of submodularity. maximization of a k-submodular function is np-hard, and
approximation algorithm has been studied. for monotone k-submodular
functions, [iwata, tanigawa, and yoshida 2016] gave k/(2k−1)-approximation
algorithm. in this paper, we give a deterministic algorithm by derandomizing that algorithm. our algorithm is k/(2k−1)-approximation and runs
in polynomial time.
| 8 |
abstract—in this technical note, we study the controllability of
diffusively coupled networks from a graph theoretic perspective.
we consider leader-follower networks, where the external control
inputs are injected to only some of the agents, namely the leaders.
our main result relates the controllability of such systems to the
graph distances between the agents. more specifically, we present
a graph topological lower bound on the rank of the controllability
matrix. this lower bound is tight, and it is applicable to systems
with arbitrary network topologies, coupling weights, and number
of leaders. an algorithm for computing the lower bound is also
provided. furthermore, as a prominent application, we present
how the proposed bound can be utilized to select a minimal set
of leaders for achieving controllability, even when the coupling
weights are unknown.
| 3 |
abstract: in this paper, we consider hands-off control via minimization of the clot
(combined l-one and two) norm. the maximum hands-off control is the l0 -optimal (or the
sparsest) control among all feasible controls that are bounded by a specified value and transfer
the state from a given initial state to the origin within a fixed time duration. in general, the
maximum hands-off control is a bang-off-bang control taking values of ±1 and 0. for many real
applications, such discontinuity in the control is not desirable. to obtain a continuous but still
relatively sparse control, we propose to use the clot norm, a convex combination of l1 and
l2 norms. we show by numerical simulation that the clot control is continuous and much
sparser (i.e. has longer time duration on which the control takes 0) than the conventional en
(elastic net) control, which is a convex combination of l1 and squared l2 norms.
keywords: optimal control, convex optimization, sparsity, maximum hands-off control,
bang-off-bang control
1. introduction
sparsity has recently emerged as an important topic in
signal/image processing, machine learning, statistics, etc.
if y ∈ rm and a ∈ rm×n are specified with m < n, then
the equation y = ax is underdetermined and has infinitely
many solutions for x if a has rank m. finding the sparsest
solution (that is, the solution with the fewest number of
nonzero elements) can be formulated as
min kzk0 subject to az = b.
z
| 3 |
abstract. the compressed zero-divisor graph γc (r) associated with a commutative ring r has
vertex set equal to the set of equivalence classes {[r] | r ∈ z(r), r 6= 0} where r ∼ s whenever
ann(r) = ann(s). distinct classes [r], [s] are adjacent in γc (r) if and only if xy = 0 for all
x ∈ [r], y ∈ [s]. in this paper, we explore the compressed zero-divisor graph associated with quotient
rings of unique factorization domains. specifically, we prove several theorems which exhibit a method
of constructing γ(r) for when one quotients out by a principal ideal, and prove sufficient conditions
for when two such compressed graphs are graph-isomorphic. we show these conditions are not
necessary unless one alters the definition of the compressed graph to admit looped vertices, and
conjecture necessary and sufficient conditions for two compressed graphs with loops to be isomorphic
when considering any quotient ring of a unique factorization domain.
| 0 |
abstract—in this paper, an efficient control strategy for
physiological interaction based anaesthetic drug infusion model is
explored using the fractional order (fo) proportional integral
derivative (pid) controllers. the dynamic model is composed of
several human organs by considering the brain response to the
anaesthetic drug as output and the drug infusion rate as the
control input. particle swarm optimisation (pso) is employed to
obtain the optimal set of parameters for pid/fopid controller
structures. with the proposed fopid control scheme much less
amount of drug-infusion system can be designed to attain a
specific anaesthetic target and also shows high robustness for
±50% parametric uncertainty in the patient’s brain model.
keywords—anaesthetic drug; dosage control; fractional order
pid controller; physiological organs; pso
| 3 |
abstract. the main result is an elementary proof of holonomicity for a-hypergeometric
systems, with no requirements on the behavior of their singularities, originally due to
adolphson [ado94] after the regular singular case by gelfand and gelfand [gg86]. our
method yields a direct de novo proof that a-hypergeometric systems form holonomic families over their parameter spaces, as shown by matusevich, miller, and walther [mmw05].
| 0 |
abstract— we propose a top-down approach for formation
control of heterogeneous multi-agent systems, based on the
method of eigenstructure assignment. given the problem of
achieving scalable formations on the plane, our approach
globally computes a state feedback control that assigns desired closed-loop eigenvalues/eigenvectors. we characterize the
relation between the eigenvalues/eigenvectors and the resulting
inter-agent communication topology, and design special (sparse)
topologies such that the synthesized control may be implemented locally by the individual agents. moreover, we present
a hierarchical synthesis procedure that significantly improves
computational efficiency. finally, we extend the proposed approach to achieve rigid formation and circular motion, and
illustrate these results by simulation examples.
| 3 |
abstract. we define the notion of limit set intersection property for a collection of subgroups of a hyperbolic group; namely, for a hyperbolic group g and
a collection of subgroups s we say that s satisfies the limit set intersection
property if for all h, k ∈ s we have λ(h) ∩ λ(k) = λ(h ∩ k). given a hyperbolic group admitting a decomposition into a finite graph of hyperbolic groups
structure with qi embedded condition, we show that the set of conjugates of
all the vertex and edge groups satisfy the limit set intersection property.
| 4 |
abstract
localization performance in wireless networks has been traditionally benchmarked using the cramérrao lower bound (crlb), given a fixed geometry of anchor nodes and a target. however, by endowing
the target and anchor locations with distributions, this paper recasts this traditional, scalar benchmark
as a random variable. the goal of this work is to derive an analytical expression for the distribution of
this now random crlb, in the context of time-of-arrival-based positioning.
to derive this distribution, this work first analyzes how the crlb is affected by the order statistics
of the angles between consecutive participating anchors (i.e., internodal angles). this analysis reveals
an intimate connection between the second largest internodal angle and the crlb, which leads to
an accurate approximation of the crlb. using this approximation, a closed-form expression for the
distribution of the crlb, conditioned on the number of participating anchors, is obtained.
next, this conditioning is eliminated to derive an analytical expression for the marginal crlb
distribution. since this marginal distribution accounts for all target and anchor positions, across all
numbers of participating anchors, it therefore statistically characterizes localization error throughout an
entire wireless network. this paper concludes with a comprehensive analysis of this new network-widecrlb paradigm.
| 7 |
abstract.
load balancing is a well-studied problem, with balls-in-bins being the primary framework. the
greedy algorithm greedy[d] of azar et al. places each ball by probing d > 1 random bins and placing
the ball in the least loaded of them. with high probability, the maximum load under greedy[d]
is exponentially lower than the result when balls are placed uniformly randomly. vöcking showed
that a slightly asymmetric variant, left[d], provides a further significant improvement. however, this
improvement comes at an additional computational cost of imposing structure on the bins.
here, we present a fully decentralized and easy-to-implement algorithm called firstdiff[d] that
combines the simplicity of greedy[d] and the improved balance of left[d]. the key idea in firstdiff[d]
is to probe until a different bin size from the first observation is located, then place the ball. although
the number of probes could be quite large for some of the balls, we show that firstdiff[d] requires
only at most d probes on average per ball (in both the standard and the heavily-loaded settings).
thus the number of probes is no greater than either that of greedy[d] or left[d]. more importantly,
we show that firstdiff[d] closely matches the improved maximum load ensured by left[d] in both the
standard and heavily-loaded settings. we further provide a tight lower bound on the maximum load
up to o(log log log n) terms. we additionally give experimental data that firstdiff[d] is indeed as
good as left[d], if not better, in practice.
key words.
allocation
| 8 |
abstract
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| 5 |
abstract
this paper focuses on effectivity aspects of the lüroth’s theorem in differential
fields. let f be an ordinary differential field of characteristic 0 and f hui be the field
of differential rational functions generated by a single indeterminate u. let be given
non constant rational functions v1 , . . . , vn ∈ f hui generating a differential subfield
g ⊆ f hui. the differential lüroth’s theorem proved by ritt in 1932 states that there
exists v ∈ g such that g = f hvi. here we prove that the total order and degree of a
generator v are bounded by minj ord(vj ) and (nd(e + 1) + 1)2e+1 , respectively, where
e := maxj ord(vj ) and d := maxj deg(vj ). as a byproduct, our techniques enable us
to compute a lüroth generator by dealing with a polynomial ideal in a polynomial
ring in finitely many variables.
| 0 |
abstract. the concept of a c-approximable group, for a class of finite
groups c, is a common generalization of the concepts of a sofic, weakly
sofic, and linear sofic group.
glebsky raised the question whether all groups are approximable
by finite solvable groups with arbitrary invariant length function. we
answer this question by showing that any non-trivial finitely generated
perfect group does not have this property, generalizing a counterexample
of howie. on a related note, we prove that any non-trivial group which
can be approximated by finite groups has a non-trivial quotient that can
be approximated by finite special linear groups.
moreover, we discuss the question which connected lie groups can
be embedded into a metric ultraproduct of finite groups with invariant
length function. we prove that these are precisely the abelian ones,
providing a negative answer to a question of doucha. referring to a
problem of zilber, we show that a the identity component of a lie group,
whose topology is generated by an invariant length function and which
is an abstract quotient of a product of finite groups, has to be abelian.
both of these last two facts give an alternative proof of a result of
turing. finally, we solve a conjecture of pillay by proving that the
identity component of a compactification of a pseudofinite group must
be abelian as well.
all results of this article are applications of theorems on generators
and commutators in finite groups by the first author and segal. in section 4 we also use results of liebeck and shalev on bounded generation
in finite simple groups.
| 4 |
abstract
this paper addresses the question of emotion classification. the
task consists in predicting emotion labels (taken among a set of
possible labels) best describing the emotions contained in short
video clips. building on a standard framework – lying in describing
videos by audio and visual features used by a supervised classifier
to infer the labels – this paper investigates several novel directions.
first of all, improved face descriptors based on 2d and 3d convolutional neural networks are proposed. second, the paper explores
several fusion methods, temporal and multimodal, including a novel
hierarchical method combining features and scores. in addition, we
carefully reviewed the different stages of the pipeline and designed
a cnn architecture adapted to the task; this is important as the size
of the training set is small compared to the difficulty of the problem,
making generalization difficult. the so-obtained model ranked 4th
at the 2017 emotion in the wild challenge with the accuracy of
58.8 %.
| 1 |
abstract—the randles circuit (including a parallel
resistor and capacitor in series with another resistor)
and its generalised topology have widely been employed
in electrochemical energy storage systems such as batteries, fuel cells and supercapacitors, also in biomedical
engineering, for example, to model the electrode-tissue
interface in electroencephalography and baroreceptor
dynamics. this paper studies identifiability of generalised randles circuit models, that is, whether the
model parameters can be estimated uniquely from the
input-output data. it is shown that generalised randles circuit models are structurally locally identifiable.
the condition that makes the model structure globally
identifiable is then discussed. finally, the estimation
accuracy is evaluated through extensive simulations.
index terms—randles circuit, identifiability, system
identification, parameter estimation.
| 3 |
abstract. we prove that iterated toric fibre products from a finite collection of
toric varieties are defined by binomials of uniformly bounded degree. this implies
that markov random fields built up from a finite collection of finite graphs have
uniformly bounded markov degree.
| 0 |
abstract. we propose a simple global computing framework, whose main concern is
code migration. systems are structured in sites, and each site is divided into two parts: a
computing body, and a membrane which regulates the interactions between the computing
body and the external environment. more precisely, membranes are filters which control
access to the associated site, and they also rely on the well-established notion of trust
between sites. we develop a basic theory to express and enforce security policies via
membranes. initially, these only control the actions incoming agents intend to perform
locally. we then adapt the basic theory to encompass more sophisticated policies, where
the number of actions an agent wants to perform, and also their order, are considered.
| 6 |
abstract—objective: to present the first real-time a posteriori error-driven adaptive finite element approach for realtime simulation and to demonstrate the method on a needle
insertion problem. methods: we use corotational elasticity and a
frictional needle/tissue interaction model. the problem is solved
using finite elements within sofa1 . the refinement strategy
relies upon a hexahedron-based finite element method, combined
with a posteriori error estimation driven local h-refinement, for
simulating soft tissue deformation. results: we control the local
and global error level in the mechanical fields (e.g. displacement
or stresses) during the simulation. we show the convergence
of the algorithm on academic examples, and demonstrate its
practical usability on a percutaneous procedure involving needle
insertion in a liver. for the latter case, we compare the force
displacement curves obtained from the proposed adaptive algorithm with that obtained from a uniform refinement approach.
conclusions: error control guarantees that a tolerable error level
is not exceeded during the simulations. local mesh refinement
accelerates simulations. significance: our work provides a first
step to discriminate between discretization error and modeling
error by providing a robust quantification of discretization error
during simulations.
index terms—finite element method, real-time error estimate,
adaptive refinement, constraint-based interaction.
| 5 |
abstract
the task of a neural associative memory is to retrieve a set of previously
memorized patterns from their noisy versions using a network of neurons.
an ideal network should have the ability to 1) learn a set of patterns as they
arrive, 2) retrieve the correct patterns from noisy queries, and 3) maximize
the pattern retrieval capacity while maintaining the reliability in responding
to queries. the majority of work on neural associative memories has focused
on designing networks capable of memorizing any set of randomly chosen
patterns at the expense of limiting the retrieval capacity.
in this paper, we show that if we target memorizing only those patterns
that have inherent redundancy (i.e., belong to a subspace), we can obtain all
the aforementioned properties. this is in sharp contrast with the previous
work that could only improve one or two aspects at the expense of the third.
more specifically, we propose framework based on a convolutional neural
| 9 |
abstract—in this paper, we propose a new approach of
network performance analysis, which is based on our previous
works on the deterministic network analysis using the gaussian approximation (dna-ga). first, we extend our previous
works to a signal-to-interference ratio (sir) analysis, which
makes our dna-ga analysis a formal microscopic analysis tool.
second, we show two approaches for upgrading the dna-ga
analysis to a macroscopic analysis tool. finally, we perform a
comparison between the proposed dna-ga analysis and the
existing macroscopic analysis based on stochastic geometry. our
results show that the dna-ga analysis possesses a few special
features: (i) shadow fading is naturally considered in the dnaga analysis; (ii) the dna-ga analysis can handle non-uniform
user distributions and any type of multi-path fading; (iii) the
shape and/or the size of cell coverage areas in the dna-ga
analysis can be made arbitrary for the treatment of hotspot
network scenarios. thus, dna-ga analysis is very useful for
the network performance analysis of the 5th generation (5g)
systems with general cell deployment and user distribution, both
on a microscopic level and on a macroscopic level. 1
| 7 |
abstract—one of the methods for stratifying different molecular classes of breast cancer is the nottingham prognostic index plus
(npi+) which uses breast cancer relevant biomarkers to stain tumour tissues prepared on tissue microarray (tma). to determine the
molecular class of the tumour, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a
semi-quantitative assessment method to assign a histochemical score (h-score) to each tma core. however, manually marking
positively stained nuclei is a time consuming, imprecise and subjective process which will lead to inter-observer and intra-observer
discrepancies. in this paper, we present an end-to-end deep learning system which directly predicts the h-score automatically. the
innovative characteristics of our method is that it is inspired by the h-scoring process of the pathologists where they count the total
number of cells, the number of tumour cells, and categorise the cells based on the intensity of their positive stains. our system imitates
the pathologists’ decision process and uses one fully convolutional network (fcn) to extract all nuclei region (tumour and non-tumour),
a second fcn to extract tumour nuclei region, and a multi-column convolutional neural network which takes the outputs of the first two
fcns and the stain intensity description image as input and acts as the high-level decision making mechanism to directly output the
h-score of the input tma image. in additional to developing the deep learning framework, we also present methods for constructing
positive stain intensity description image and for handling discrete scores with numerical gaps. whilst deep learning has been widely
applied in digital pathology image analysis, to the best of our knowledge, this is the first end-to-end system that takes a tma image as
input and directly outputs a clinical score. we will present experimental results which demonstrate that the h-scores predicted by our
model have very high and statistically significant correlation with experienced pathologists’ scores and that the h-scoring discrepancy
between our algorithm and the pathologits is on par with that between the pathologists. although it is still a long way from clinical use,
this work demonstrates the possibility of using deep learning techniques to automatically and directly predicting the clinical scores of
digital pathology images.
index terms—h-score, immunohistochemistry, diaminobenzidine, convolutional neural network, breast cancer
| 1 |
abstract
in this paper, we propose a new class of lattices constructed from polar codes, namely polar lattices, to achieve the
capacity
| 7 |
abstract. ashtiani et al. (nips 2016) introduced a semi-supervised
framework for clustering (ssac) where a learner is allowed to make samecluster queries. more specifically, in their model, there is a query oracle
that answers queries of the form “given any two vertices, do they belong
to the same optimal cluster?”. in many clustering contexts, this kind of
oracle queries are feasible. ashtiani et al. showed the usefulness of such a
query framework by giving a polynomial time algorithm for the k-means
clustering problem where the input dataset satisfies some separation
condition. ailon et al. extended the above work to the approximation
setting by giving an efficient (1+ε)-approximation algorithm for k-means
for any small ε > 0 and any dataset within the ssac framework. in
this work, we extend this line of study to the correlation clustering
problem. correlation clustering is a graph clustering problem where
pairwise similarity (or dissimilarity) information is given for every pair
of vertices and the objective is to partition the vertices into clusters that
minimise the disagreement (or maximises agreement) with the pairwise
information given as input. these problems are popularly known as
mindisagree and maxagree problems, and mindisagree[k] and maxagree[k]
are versions of these problems where the number of optimal clusters is at
most k. there exist polynomial time approximation schemes (ptas)
for mindisagree[k] and maxagree[k] where the approximation guarantee
is (1 + ε) for any small ε and the running time is polynomial in the input
parameters but exponential in k and 1/ε. we get a significant running
time improvement within the ssac framework at the cost of making a
small number of same-cluster queries. we obtain an (1+ε)-approximation
algorithm for any small ε with running time that is polynomial in the
input parameters and also in k and 1/ε. we also give non-trivial upper
and lower bounds on the number of same-cluster queries, the lower bound
being based on the exponential time hypothesis (eth). note that the
existence of an efficient algorithm for mindisagree[k] in the ssac setting
exhibits the power of same-cluster queries since such polynomial time
algorithm (polynomial even in k and 1/ε) is not possible in the classical
⋆
⋆⋆
⋆⋆⋆
†
‡
| 8 |
abstract
demands on the disaster response capacity of the european union are likely
to increase, as the impacts of disasters continue to grow both in size and
frequency. this has resulted in intensive research on issues concerning
spatially-explicit information and modelling and their multiple sources of
uncertainty. geospatial support is one of the forms of assistance frequently
required by emergency response centres along with hazard forecast and event
management assessment. robust modelling of natural hazards requires dynamic simulations under an array of multiple inputs from different sources.
uncertainty is associated with meteorological forecast and calibration of the
model parameters. software uncertainty also derives from the data transformation models (d-tm) needed for predicting hazard behaviour and its
consequences. on the other hand, social contributions have recently been
recognized as valuable in raw-data collection and mapping efforts traditionally dominated by professional organizations. here an architecture overview
is proposed for adaptive and robust modelling of natural hazards, following
the semantic array programming paradigm to also include the distributed
array of social contributors called citizen sensor in a semantically-enhanced
strategy for d-tm modelling. the modelling architecture proposes a multicriteria approach for assessing the array of potential impacts with qualitative rapid assessment methods based on a partial open loop feedback
control (polfc) schema and complementing more traditional and accurate a-posteriori assessment. we discuss the computational aspect of environmental risk modelling using array-based parallel paradigms on high
performance computing (hpc) platforms, in order for the implications of
urgency to be introduced into the systems (urgent-hpc).
keywords: geospatial, integrated natural resources modelling and management,
semantic array programming, warning system, remote sensing, parallel application, high performance computing, partial open loop feedback control
1
| 5 |
abstract
the use of programming languages such as java and c in open
source software (oss) has been well studied. however, many
other popular languages such as xsl or xml have received minor
attention. in this paper, we discuss some trends in oss
development that we observed when considering multiple
programming language evolution of oss. based on the revision
data of 22 oss projects, we tracked the evolution of language usage
and other artefacts such as documentation files, binaries and
graphics files. in these systems several different languages and
artefact types including c/c++, java, xml, xsl, makefile,
groovy, html, shell scripts, css, graphics files, javascript, jsp,
ruby, phyton, xquery, opendocument files, php, etc. have been
used. we found that the amount of code written in different
languages differs substantially. some of our findings can be
summarized as follows: (1) javascript and css files most often coevolve with xsl; (2) most java developers but only every second
c/c++ developer work with xml; (3) and more generally, we
observed a significant increase of usage of xml and xsl during
recent years and found that java or c are hardly ever the only
language used by a developer. in fact, a developer works with more
than 5 different artefact types (or 4 different languages) in a project
on average.
| 6 |
abstract—augmented reality is an emerging technology in
many application domains. among them is the beauty industry,
where live virtual try-on of beauty products is of great
importance. in this paper, we address the problem of live
hair color augmentation. to achieve this goal, hair needs to be
segmented quickly and accurately. we show how a modified
mobilenet cnn architecture can be used to segment the hair in
real-time. instead of training this network using large amounts
of accurate segmentation data, which is difficult to obtain, we
use crowd sourced hair segmentation data. while such data
is much simpler to obtain, the segmentations there are noisy
and coarse. despite this, we show how our system can produce
accurate and fine-detailed hair mattes, while running at over
30 fps on an ipad pro tablet.
keywords-hair segmentation; matting;augmented reality;
deep learning; neural networks
| 1 |
abstract
ensemble discriminative tracking utilizes a committee of
classifiers, to label data samples, which are in turn, used for
retraining the tracker to localize the target using the collective knowledge of the committee. committee members could
vary in their features, memory update schemes, or training
data, however, it is inevitable to have committee members
that excessively agree because of large overlaps in their
version space. to remove this redundancy and have an effective ensemble learning, it is critical for the committee to
include consistent hypotheses that differ from one-another,
covering the version space with minimum overlaps. in this
study, we propose an online ensemble tracker that directly
generates a diverse committee by generating an efficient set
of artificial training. the artificial data is sampled from the
empirical distribution of the samples taken from both target and background, whereas the process is governed by
query-by-committee to shrink the overlap between classifiers. the experimental results demonstrate that the proposed scheme outperforms conventional ensemble trackers
on public benchmarks.
| 1 |
abstract
we study the well-known problem of estimating a sparse n-dimensional
unknown mean vector θ = (θ1 , ..., θn ) with entries corrupted by gaussian white noise. in the bayesian framework, continuous shrinkage
priors which can be expressed as scale-mixture normal densities are
popular for obtaining sparse estimates of θ. in this article, we introduce a new fully bayesian scale-mixture prior known as the inverse
gamma-gamma (igg) prior. we prove that the posterior distribution
contracts around the true θ at (near) minimax rate under very mild
conditions. in the process, we prove that the sufficient conditions for
minimax posterior contraction given by van der pas et al. [25] are not
necessary for optimal posterior contraction. we further show that the
igg posterior density concentrates at a rate faster than those of the
horseshoe or the horseshoe+ in the kullback-leibler (k-l) sense. to
classify true signals (θi 6= 0), we also propose a hypothesis test based
on thresholding the posterior mean. taking the loss function to be the
expected number of misclassified tests, we show that our test procedure asymptotically attains the optimal bayes risk exactly. we illustrate through simulations and data analysis that the igg has excellent
finite sample performance for both estimation and classification.
∗
keywords and phrases: normal means problem, sparsity, nearly black vectors, posterior contraction, multiple hypothesis testing, heavy tail, shrinkage estimation
†
malay ghosh (email: [email protected]) is distinguished professor, department of
statistics, university of florida. ray bai (email: [email protected]) is graduate student,
department of statistics, university of florida.
| 10 |
abstract
| 1 |
abstract. all solutions sat (allsat for short) is a variant of
propositional satisfiability problem. despite its significance, allsat has been relatively unexplored compared to other variants.
we thus survey and discuss major techniques of allsat solvers.
we faithfully implement them and conduct comprehensive experiments using a large number of instances and various types of solvers
including one of the few public softwares. the experiments reveal
solver’s characteristics. our implemented solvers are made publicly
available so that other researchers can easily develop their solver
by modifying our codes and compare it with existing methods.
| 8 |
abstract
in the euclidean tsp with neighborhoods (tspn), we are given a collection of n regions
(neighborhoods) and we seek a shortest tour that visits each region. as a generalization of the
classical euclidean tsp, tspn is also np-hard. in this paper, we present new approximation
results for the tspn, including (1) a constant-factor approximation algorithm for the case of
arbitrary connected neighborhoods having comparable diameters; and (2) a ptas for the important special case of disjoint unit disk neighborhoods (or nearly disjoint, nearly-unit disks).
our methods also yield improved approximation ratios for various special classes of neighborhoods, which have previously been studied. further, we give a linear-time o(1)-approximation
algorithm for the case of neighborhoods that are (infinite) straight lines.
| 8 |
abstract
the growing importance of massive datasets with
the advent of deep learning makes robustness to
label noise a critical property for classifiers to
have. sources of label noise include automatic labeling for large datasets, non-expert labeling, and
label corruption by data poisoning adversaries. in
the latter case, corruptions may be arbitrarily bad,
even so bad that a classifier predicts the wrong labels with high confidence. to protect against such
sources of noise, we leverage the fact that a small
set of clean labels is often easy to procure. we
demonstrate that robustness to label noise up to
severe strengths can be achieved by using a set of
trusted data with clean labels, and propose a loss
correction that utilizes trusted examples in a dataefficient manner to mitigate the effects of label
noise on deep neural network classifiers. across
vision and natural language processing tasks, we
experiment with various label noises at several
strengths, and show that our method significantly
outperforms existing methods.
| 9 |
abstract. we reduce the openness conjecture of demailly and kollár on
the singularities of plurisubharmonic functions to a purely algebraic statement.
| 0 |
abstract
we present a general method to compute a presentation for any cusped hyperbolic lattice γ, applying a
classical result of macbeath to a suitable γ-invariant horoball cover of the corresponding symmetric space.
as applications we compute presentations for the picard modular groups pu(2, 1, od ) for d = 1, 3, 7 and the
quaternionic lattice pu(2, 1, h) with entries in the hurwitz integer ring h.
| 4 |
abstract—information in neural networks is represented as
weighted connections, or synapses, between neurons. this poses
a problem as the primary computational bottleneck for neural
networks is the vector-matrix multiply when inputs are multiplied
by the neural network weights. conventional processing architectures are not well suited for simulating neural networks, often requiring large amounts of energy and time. additionally, synapses
in biological neural networks are not binary connections, but
exhibit a nonlinear response function as neurotransmitters are
emitted and diffuse between neurons. inspired by neuroscience
principles, we present a digital neuromorphic architecture, the
spiking temporal processing unit (stpu), capable of modeling
arbitrary complex synaptic response functions without requiring
additional hardware components. we consider the paradigm of
spiking neurons with temporally coded information as opposed to
non-spiking rate coded neurons used in most neural networks. in
this paradigm we examine liquid state machines applied to speech
recognition and show how a liquid state machine with temporal
dynamics maps onto the stpu—demonstrating the flexibility and
efficiency of the stpu for instantiating neural algorithms.
| 9 |
abstract— the most commonly used weighted least square state
estimator in power industry is nonlinear and formulated by using
conventional measurements such as line flow and injection
measurements. pmus (phasor measurement units) are gradually
adding them to improve the state estimation process. in this
paper the way of corporation the pmu data to the conventional
measurements and a linear formulation of the state estimation
using only pmu measured data are investigated. six cases are
tested while gradually increasing the number of pmus which are
added to the measurement set and the effect of pmus on the
accuracy of variables are illustrated and compared by applying
them on ieee 14, 30 test systems.
keywords-conventional state estimation; hybrid
estimation; linear formulation; phasor measurement unit
| 3 |
abstract. when does a noetherian commutative ring r have uniform symbolic topologies on
primes–read, when does there exist an integer d > 0 such that the symbolic power p (dr) ⊆ p r for
all prime ideals p ⊆ r and all r > 0? groundbreaking work of ein-lazarsfeld-smith, as extended
by hochster and huneke, and by ma and schwede in turn, provides a beautiful answer in the setting
of finite-dimensional excellent regular rings. it is natural to then sleuth for analogues where the ring
r is non-regular, or where the above ideal containments can be improved using a linear function
whose growth rate is slower. this manuscript falls under the overlap of these research directions.
working with a prescribed type of prime ideal q inside of tensor products of domains of finite type
over an algebraically closed field f, we present binomial- and multinomial expansion criteria for
containments of type q(er) ⊆ qr , or even better, of type q(e(r−1)+1) ⊆ qr for all r > 0. the final
section consolidates remarks on how often we can utilize these criteria, presenting an example.
| 0 |
abstract
we present a quasi-analytic perturbation expansion for multivariate n dimensional gaussian integrals. the perturbation expansion is an infinite series
of lower-dimensional integrals (one-dimensional in the simplest approximation).
this perturbative idea can also be applied to multivariate student-t integrals. we
evaluate the perturbation expansion explicitly through 2nd order, and discuss the
convergence, including enhancement using padé approximants. brief comments
on potential applications in finance are given, including options, models for
credit risk and derivatives, and correlation sensitivities.
| 5 |
abstract
an important class of physical systems that are of interest in practice are inputoutput open quantum systems that can be described by quantum stochastic differential equations and defined on an infinite-dimensional underlying hilbert space.
most commonly, these systems involve coupling to a quantum harmonic oscillator
as a system component. this paper is concerned with error bounds in the finitedimensional approximations of input-output open quantum systems defined on an
infinite-dimensional hilbert space. we develop a framework for developing error
bounds between the time evolution of the state of a class of infinite-dimensional
quantum systems and its approximation on a finite-dimensional subspace of the original, when both are initialized in the latter subspace. this framework is then applied
to two approaches for obtaining finite-dimensional approximations: subspace truncation and adiabatic elimination. applications of the bounds to some physical examples
drawn from the literature are provided to illustrate our results.
| 3 |
abstract—hardware impairments, such as phase noise, quantization errors, non-linearities, and noise amplification, have
baneful effects on wireless communications. in this paper, we
investigate the effect of hardware impairments on multipair
massive multiple-input multiple-output (mimo) two-way fullduplex relay systems with amplify-and-forward scheme. more
specifically, novel closed-form approximate expressions for the
spectral efficiency are derived to obtain some important insights
into the practical design of the considered system. when the
number of relay antennas n increases without bound, we propose
a hardware scaling law, which reveals that the level of hardware
impairments that can be tolerated is roughly proportional to
√
n . this new result inspires us to design low-cost and practical
multipair massive mimo two-way full-duplex relay systems.
moreover, the optimal number of relay antennas is derived to
maximize the energy efficiency. finally, motor-carlo simulation
results are provided to validate our analytical results.
| 7 |
abstract. we introduce a new native code compiler for curry codenamed
sprite. sprite is based on the fair scheme, a compilation strategy that provides
instructions for transforming declarative, non-deterministic programs of a certain
class into imperative, deterministic code. we outline salient features of sprite,
discuss its implementation of curry programs, and present benchmarking results. sprite is the first-to-date operationally complete implementation of curry.
preliminary results show that ensuring this property does not incur a significant
penalty.
| 6 |
abstract. several well-known open questions (such as: are all
groups sofic/hyperlinear?) have a common form: can all groups be
approximated by asymptotic homomorphisms into the symmetric
groups sym(n) (in the sofic case) or the finite dimensional unitary groups u(n) (in the hyperlinear case)? in the case of u(n),
the question can be asked with respect to different metrics and
norms. this paper answers, for the first time, one of these versions, showing that there exist fintely presented groups which are
not approximated
by u(n) with respect to the frobenius norm
√ n
∥t ∥frob = ∑i,j=1 ∣tij ∣2 , t = [tij ]ni,j=1 ∈ mn (c). our strategy is
to show that some higher dimensional cohomology vanishing phenomena implies stability, that is, every frobenius-approximate homomorphism into finite-dimensional unitary groups is close to an
actual homomorphism. this is combined with existence results of
certain non-residually finite central extensions of lattices in some
simple p-adic lie groups. these groups act on high rank bruhattits buildings and satisfy the needed vanishing cohomology phenomenon and are thus stable and not frobenius-approximated.
| 4 |
abstract—graphs form a natural model for relationships and
interactions between entities, for example, between people in
social and cooperation networks, servers in computer networks,
or tags and words in documents and tweets. but, which of these
relationships or interactions are the most lasting ones? in this
paper, we study the following problem: given a set of graph
snapshots, which may correspond to the state of an evolving
graph at different time instances, identify the set of nodes that
are the most densely connected in all snapshots. we call this
problem the best friends for ever (b ff) problem. we provide
definitions for density over multiple graph snapshots, that capture
different semantics of connectedness over time, and we study the
corresponding variants of the b ff problem. we then look at the
on-off b ff (o2 b ff) problem that relaxes the requirement of
nodes being connected in all snapshots, and asks for the densest
set of nodes in at least k of a given set of graph snapshots.
we show that this problem is np-complete for all definitions of
density, and we propose a set of efficient algorithms. finally, we
present experiments with synthetic and real datasets that show
both the efficiency of our algorithms and the usefulness of the
b ff and the o2 b ff problems.
| 8 |
abstract. we introduce quasi-homomorphisms of cluster algebras, a flexible notion
of a map between cluster algebras of the same type (but with different coefficients).
the definition is given in terms of seed orbits, the smallest equivalence classes of seeds
on which the mutation rules for non-normalized seeds are unambiguous. we present
examples of quasi-homomorphisms involving familiar cluster algebras, such as cluster
structures on grassmannians, and those associated with marked surfaces with boundary.
we explore the related notion of a quasi-automorphism, and compare the resulting group
with other groups of symmetries of cluster structures. for cluster algebras from surfaces,
we determine the subgroup of quasi-automorphisms inside the tagged mapping class
group of the surface.
| 0 |
abstract. we compute the zeta functions enumerating graded ideals in the graded
lie rings associated with the free d-generator lie rings fc,d of nilpotency class c for all
c ď 2 and for pc, dq p tp3, 3q, p3, 2q, p4, 2qu. we apply our computations to obtain information about p-adic, reduced, and topological zeta functions, in particular pertaining
to their degrees and some special values.
| 4 |
abstract
we consider the problem of designing and analyzing differentially private algorithms that
can be implemented on discrete models of computation in strict polynomial time, motivated
by known attacks on floating point implementations of real-arithmetic differentially private
algorithms (mironov, ccs 2012) and the potential for timing attacks on expected polynomialtime algorithms. we use a case study the basic problem of approximating the histogram of
a categorical dataset over a possibly large data universe x . the classic laplace mechanism
(dwork, mcsherry, nissim, smith, tcc 2006 and j. privacy & confidentiality 2017) does not
satisfy our requirements, as it is based on real arithmetic, and natural discrete analogues, such
as the geometric mechanism (ghosh, roughgarden, sundarajan, stoc 2009 and sicomp
2012), take time at least linear in |x |, which can be exponential in the bit length of the input.
in this paper, we provide strict polynomial-time discrete algorithms for approximate histograms whose simultaneous accuracy (the maximum error over all bins) matches that of the
laplace mechanism up to constant factors, while retaining the same (pure) differential privacy
guarantee. one of our algorithms produces a sparse histogram as output. its “per-bin accuracy” (the error on individual bins) is worse than that of the laplace mechanism by a factor
of log |x |, but we prove a lower bound showing that this is necessary for any algorithm that
produces a sparse histogram. a second algorithm avoids this lower bound, and matches the
per-bin accuracy of the laplace mechanism, by producing a compact and efficiently computable
representation of a dense histogram; it is based on an (n + 1)-wise independent implementation
of an appropriately clamped version of the discrete geometric mechanism.
| 8 |
abstract
building automation systems (bas) are exemplars of cyber-physical
systems (cps), incorporating digital control architectures over underlying
continuous physical processes. we provide a modular model library for
bas drawn from expertise developed on a real bas setup. the library
allows to build models comprising of either physical quantities or digital
control modules.the structure, operation, and dynamics of the model
can be complex, incorporating (i) stochasticity, (ii) non-linearities, (iii)
numerous continuous variables or discrete states, (iv) various input and
output signals, and (v) a large number of possible discrete configurations.
the modular composition of bas components can generate useful cps
benchmarks. we display this use by means of three realistic case studies,
where corresponding models are built and engaged with different analysis
goals. the benchmarks, the model library and data collected from the
bas setup at the university of oxford, are kept on-line at https://
github.com/natchi92/basbenchmarks.
keywords 1 cyber-physical systems, building automation systems, thermal modelling, hybrid models, simulation, reachability analysis, probabilistic safety, control synthesis
| 3 |
abstract
we explicitly compute the exit law of a certain hypoelliptic brownian motion on a solvable lie group. the underlying random variable
can be seen as a multidimensional exponential functional of brownian
motion. as a consequence, we obtain hidden identities in law between
gamma random variables as the probabilistic manifestation of braid
relations. the classical beta-gamma algebra identity corresponds to
the only braid move in a root system of type a2 . the other ones seem
new.
a key ingredient is a conditional representation theorem. it relates
our hypoelliptic brownian motion conditioned on exiting at a fixed
point to a certain deterministic transform of brownian motion.
the identities in law between gamma variables tropicalize to identities between exponential random variables. these are continuous
versions of identities between geometric random variables related to
changes of parametrizations in lusztig’s canonical basis. hence, we
see that the exit law of our hypoelliptic brownian motion is the geometric analogue of a simple natural measure on lusztig’s canonical
basis.
| 4 |
abstract. we present guarded dependent type theory, gdtt, an extensional dependent type theory with a ‘later’ modality and clock quantifiers
for programming and proving with guarded recursive and coinductive
types. the later modality is used to ensure the productivity of recursive
definitions in a modular, type based, way. clock quantifiers are used for
controlled elimination of the later modality and for encoding coinductive
types using guarded recursive types. key to the development of gdtt
are novel type and term formers involving what we call ‘delayed substitutions’. these generalise the applicative functor rules for the later
modality considered in earlier work, and are crucial for programming
and proving with dependent types. we show soundness of the type theory with respect to a denotational model.
this is the technical report version of a paper to appear in the proceedings
of fossacs 2016.
| 6 |
abstract
| 6 |
abstract
this paper investigates a joint source-channel secrecy problem for the shannon cipher broadcast system. we
suppose list secrecy is applied, i.e., a wiretapper is allowed to produce a list of reconstruction sequences and the
secrecy is measured by the minimum distortion over the entire list. for discrete communication cases, we propose a
permutation-based uncoded scheme, which cascades a random permutation with a symbol-by-symbol mapping. using
this scheme, we derive an inner bound for the admissible region of secret key rate, list rate, wiretapper distortion,
and distortions of legitimate users. for the converse part, we easily obtain an outer bound for the admissible region
from an existing result. comparing the outer bound with the inner bound shows that the proposed scheme is optimal
under certain conditions. besides, we extend the proposed scheme to the scalar and vector gaussian communication
scenarios, and characterize the corresponding performance as well. for these two cases, we also propose another
uncoded scheme, orthogonal-transform-based scheme, which achieves the same performance as the permutationbased scheme. interestingly, by introducing the random permutation or the random orthogonal transform into the
traditional uncoded scheme, the proposed uncoded schemes, on one hand, provide a certain level of secrecy, and on
the other hand, do not lose any performance in terms of the distortions for legitimate users.
index terms
uncoded scheme, secrecy, permutation, orthogonal transform, shannon cipher system.
| 7 |
abstract
we study the sample complexity of learning neural networks, by providing new bounds on their
rademacher complexity assuming norm constraints on the parameter matrix of each layer. compared
to previous work, these complexity bounds have improved dependence on the network depth, and under
some additional assumptions, are fully independent of the network size (both depth and width). these
results are derived using some novel techniques, which may be of independent interest.
| 9 |
abstract
| 2 |
abstract
the semantics of concurrent data structures is usually given by a sequential specification and a
consistency condition. linearizability is the most popular consistency condition due to its simplicity and general applicability. nevertheless, for applications that do not require all guarantees
offered by linearizability, recent research has focused on improving performance and scalability
of concurrent data structures by relaxing their semantics.
in this paper, we present local linearizability, a relaxed consistency condition that is applicable
to container-type concurrent data structures like pools, queues, and stacks. while linearizability
requires that the effect of each operation is observed by all threads at the same time, local
linearizability only requires that for each thread t, the effects of its local insertion operations and
the effects of those removal operations that remove values inserted by t are observed by all threads
at the same time. we investigate theoretical and practical properties of local linearizability and
its relationship to many existing consistency conditions. we present a generic implementation
method for locally linearizable data structures that uses existing linearizable data structures as
building blocks. our implementations show performance and scalability improvements over the
original building blocks and outperform the fastest existing container-type implementations.
1998 acm subject classification d.3.1 [programming languages]: formal definitions and
theory—semantics; e.1 [data structures]: lists, stacks, and queues; d.1.3 [software]: programming techniques—concurrent programming
keywords and phrases (concurrent) data structures, relaxed semantics, linearizability
| 6 |
abstract. it is widely accepted that the immune system undergoes age-related
changes correlating with increased disease in the elderly. t cell subsets have been
implicated. the aim of this work is firstly to implement and validate a simulation
of t regulatory cell (treg) dynamics throughout the lifetime, based on a model by
baltcheva. we show that our initial simulation produces an inversion between
precursor and mature treys at around 20 years of age, though the output differs
significantly from the original laboratory dataset. secondly, this report discusses
development of the model to incorporate new data from a cross-sectional study of
healthy blood donors addressing balance between treys and th17 cells with novel
markers for treg. the potential for simulation to add insight into immune aging is
discussed.
| 5 |
abstract. this paper is devoted to the analysis of the uniform null controllability for a family of nonlinear reaction-diffusion systems approximating a parabolic-elliptic system which models the electrical
activity of the heart. the uniform, with respect to the degenerating parameter, null controllability of
the approximating system by means of a single control is shown. the proof is based on the combination
of carleman estimates and weighted energy inequalities.
| 3 |
abstract
computational techniques have shown much promise in the field of finance, owing to their ability to extract sense out of
dauntingly complex systems. this paper reviews the most promising of these techniques, from traditional computational
intelligence methods to their machine learning siblings, with particular view to their application in optimising the
management of a portfolio of financial instruments. the current state of the art is assessed, and prospective further work
is assessed and recommended.
keywords: reinforcement, learning, temporal, difference, neural, network, portfolio optimisation, genetic algorithm,
genetic programming, markowitz portfolio theory, black-scholes, investment theory.
| 5 |
abstract
the generalization error of deep neural networks via their classification margin is studied in this
work. our approach is based on the jacobian matrix of a deep neural network and can be applied to
networks with arbitrary non-linearities and pooling layers, and to networks with different architectures
such as feed forward networks and residual networks. our analysis leads to the conclusion that a bounded
spectral norm of the network’s jacobian matrix in the neighbourhood of the training samples is crucial for
a deep neural network of arbitrary depth and width to generalize well. this is a significant improvement
over the current bounds in the literature, which imply that the generalization error grows with either the
width or the depth of the network. moreover, it shows that the recently proposed batch normalization
and weight normalization re-parametrizations enjoy good generalization properties, and leads to a novel
network regularizer based on the network’s jacobian matrix. the analysis is supported with experimental
results on the mnist, cifar-10, lared and imagenet datasets.
| 9 |
abstract
we propose a new way to self-adjust the mutation rate in population-based
evolutionary algorithms in discrete search spaces. roughly speaking, it consists of
creating half the offspring with a mutation rate that is twice the current mutation
rate and the other half with half the current rate. the mutation rate is then updated
to the rate used in that subpopulation which contains the best offspring.
we analyze how the (1+λ) evolutionary algorithm with this self-adjusting mutation rate optimizes the onemax test function. we prove that this dynamic version
of the (1+λ) ea finds the optimum in an expected optimization time (number of fitness evaluations) of o(nλ/log λ + n log n). this time is asymptotically smaller than
the optimization time of the classic (1 + λ) ea. previous work shows that this performance is best-possible among all λ-parallel mutation-based unbiased black-box
algorithms.
this result shows that the new way of adjusting the mutation rate can find
optimal dynamic parameter values on the fly. since our adjustment mechanism is
simpler than the ones previously used for adjusting the mutation rate and does not
have parameters itself, we are optimistic that it will find other applications.
| 9 |
abstract
√
we give a lower bound of ω̃( n) for the degree-4 sum-of-squares sdp relaxation for the
planted clique problem. specifically, we show that on an erdös-rényi graph g(n, 12 ), with high
probability there is a feasible
point for the degree-4 sos relaxation of the clique problem with
√
an objective value of ω̃( n), so that the program cannot
distinguish between a random graph
√
and a random graph with a planted clique of size õ( n). this bound is tight.
we build on the works of deshpande and montanari and meka et al., who give lower bounds
of ω̃(n1/3 ) and ω̃(n1/4 ) respectively. we improve on their results by making a perturbation to
the sdp solution proposed in their work, then showing that this perturbation remains psd as
the objective value approaches ω̃(n1/2 ).
in an independent work, hopkins, kothari and potechin [hkp15] have obtained a similar
lower bound for the degree-4 sos relaxation.
| 8 |
abstract
the motivation of this work is the detection of cerebrovascular accidents by
microwave tomographic imaging. this requires the solution of an inverse
problem relying on a minimization algorithm (for example, gradient-based),
where successive iterations consist in repeated solutions of a direct problem. the reconstruction algorithm is extremely computationally intensive
and makes use of efficient parallel algorithms and high-performance computing. the feasibility of this type of imaging is conditioned on one hand
by an accurate reconstruction of the material properties of the propagation
medium and on the other hand by a considerable reduction in simulation
time. fulfilling these two requirements will enable a very rapid and accurate
diagnosis. from the mathematical and numerical point of view, this means
solving maxwell’s equations in time-harmonic regime by appropriate domain
decomposition methods, which are naturally adapted to parallel architectures.
keywords: inverse problem, scalable preconditioners, maxwell’s equations,
microwave imaging
preprint submitted to parallel computing
| 5 |
abstract data stream mining problem has caused widely concerns in the area of machine learning
and data mining. in some recent studies, ensemble classification has been widely used in concept drift
detection, however, most of them regard classification accuracy as a criterion for judging whether
concept drift happening or not. information entropy is an important and effective method for
measuring uncertainty. based on the information entropy theory, a new algorithm using information
entropy to evaluate a classification result is developed. it uses ensemble classification techniques,
and the weight of each classifier is decided through the entropy of the result produced by an
ensemble classifiers system. when the concept in data streams changing, the classifiers’ weight below
a threshold value will be abandoned to adapt to a new concept in one time. in the experimental
analysis section, six databases and four proposed algorithms are executed. the results show that the
proposed method can not only handle concept drift effectively, but also have a better classification
accuracy and time performance than the contrastive algorithms.
j. wang
1. school of computer and information technology, shanxi university, taiyuan, china
2. key laboratory of computational intelligence and chinese information processing, ministry of education,
taiyuan, china
tel.: +86 0351-7010566
e-mail: [email protected]
s. xu
school of computer and information technology, shanxi university, taiyuan, china
e-mail: [email protected]
b. duan
school of computer and information technology, shanxi university, taiyuan, china
e-mail: [email protected]
c. liu
school of computer science and technology, faculty of electronic information and electrical engineering, dalian
university of technology, dalian, china
e-mail: [email protected]
j. liang
key laboratory of computational intelligence and chinese information processing, ministry of education, taiyuan,
china
e-mail: [email protected]
| 8 |
abstract. we determine the product structure on hochschild cohomology
of commutative algebras in low degrees, obtaining the answer in all degrees
for complete intersection algebras. as applications, we consider cyclic extension algebras as well as hochschild and ordinary cohomology of finite abelian
groups.
| 0 |
abstract
recently, multilayer bootstrap network (mbn) has demonstrated promising performance
in unsupervised dimensionality reduction. it can learn compact representations in standard data sets, i.e. mnist and rcv1. however, as a bootstrap method, the prediction
complexity of mbn is high. in this paper, we propose an unsupervised model compression
framework for this general problem of unsupervised bootstrap methods. the framework
compresses a large unsupervised bootstrap model into a small model by taking the bootstrap model and its application together as a black box and learning a mapping function
from the input of the bootstrap model to the output of the application by a supervised
learner. to specialize the framework, we propose a new technique, named compressive
mbn. it takes mbn as the unsupervised bootstrap model and deep neural network (dnn)
as the supervised learner. our initial result on mnist showed that compressive mbn
not only maintains the high prediction accuracy of mbn but also is over thousands of
times faster than mbn at the prediction stage. our result suggests that the new technique integrates the effectiveness of mbn on unsupervised learning and the effectiveness
and efficiency of dnn on supervised learning together for the effectiveness and efficiency
of compressive mbn on unsupervised learning.
keywords: model compression, multilayer bootstrap networks, unsupervised learning.
| 9 |
abstract
we study the problem of approximating the largest root of a real-rooted polynomial of degree
n using its top k coefficients and give nearly matching upper and lower bounds. we present
algorithms with running time polynomial in k that use the top k coefficients to approximate
the maximum root within a factor of n1/k and 1 + o( logk n )2 when k ≤ log n and k > log n
respectively. we also prove corresponding information-theoretic lower bounds of nω(1/k) and
2n 2
log k
, and show strong lower bounds for noisy version of the problem in which one is
1+ω
k
given access to approximate coefficients.
this problem has applications in the context of the method of interlacing families of polynomials, which was used for proving the existence of ramanujan graphs of all degrees, the solution
of the kadison-singer problem, and bounding the integrality gap of the asymmetric traveling
salesman problem. all of these involve computing the maximum root of certain real-rooted
polynomials for which the top few coefficients are√accessible in subexponential time. our results
3
yield an algorithm with the running time of 2õ( n) for all of them.
| 8 |
abstract.
we examine the issue of stability of probability in reasoning about complex systems with
uncertainty in structure. normally, propositions are viewed as probability functions on an
abstract random graph where it is implicitly assumed that the nodes of the graph have stable
properties. but what if some of the nodes change their characteristics? this is a situation that
cannot be covered by abstractions of either static or dynamic sets when these changes take
place at regular intervals. we propose the use of sets with elements that change, and modular
forms are proposed to account for one type of such change. an expression for the dependence
of the mean on the probability of the switching elements has been determined. the system is
also analyzed from the perspective of decision between different hypotheses. such sets are
likely to be of use in complex system queries and in analysis of surveys.
| 2 |
abstract
in this work, a strategy to estimate the information transfer between the elements
of a complex system, from the time series associated to the evolution of this elements,
is presented. by using the nearest neighbors of each state, the local approaches of the
deterministic dynamical rule generating the data and the probability density functions,
both marginals and conditionals, necessaries to calculate some measures of information
transfer, are estimated.
the performance of the method using numerically simulated data and real signals is
exposed.
| 7 |
abstract
we discuss from a practical point of view a number of issues involved in writing distributed
internet and www applications using lp/clp systems. we describe pillow, a publicdomain internet and www programming library for lp/clp systems that we have
designed in order to simplify the process of writing such applications. pillow provides
facilities for accessing documents and code on the www; parsing, manipulating and
generating html and xml structured documents and data; producing html forms;
writing form handlers and cgi-scripts; and processing html/xml templates. an important contribution of pillow is to model html/xml code (and, thus, the content
of www pages) as terms. the pillow library has been developed in the context of the
ciao prolog system, but it has been adapted to a number of popular lp/clp systems,
supporting most of its functionality. we also describe the use of concurrency and a highlevel model of client-server interaction, ciao prolog’s active modules, in the context of
www programming. we propose a solution for client-side downloading and execution of
prolog code, using generic browsers. finally, we also provide an overview of related work
on the topic.
keywords: www, html, xml, cgi, http, distributed execution, (constraint)
logic programming.
| 6 |
abstract. we show that call-by-need is observationally equivalent to
weak-head needed reduction. the proof of this result uses a semantical
argument based on a (non-idempotent) intersection type system called
v. interestingly, system v also allows to syntactically identify all the
weak-head needed redexes of a term.
| 6 |
abstract
pooling is a ubiquitous operation in image processing algorithms that allows for
higher-level processes to collect relevant low-level features from a region of interest.
currently, max-pooling is one of the most commonly used operators in the computational
literature. however, it can lack robustness to outliers due to the fact that it relies merely
on the peak of a function. pooling mechanisms are also present in the primate visual cortex where neurons of higher cortical areas pool signals from lower ones. the receptive
fields of these neurons have been shown to vary according to the contrast by aggregating
signals over a larger region in the presence of low contrast stimuli. we hypothesise that
this contrast-variant-pooling mechanism can address some of the shortcomings of maxpooling. we modelled this contrast variation through a histogram clipping in which the
percentage of pooled signal is inversely proportional to the local contrast of an image.
we tested our hypothesis by applying it to the phenomenon of colour constancy where a
number of popular algorithms utilise a max-pooling step (e.g. white-patch, grey-edge
and double-opponency). for each of these methods, we investigated the consequences
of replacing their original max-pooling by the proposed contrast-variant-pooling. our
experiments on three colour constancy benchmark datasets suggest that previous results
can significantly improve by adopting a contrast-variant-pooling mechanism.
| 1 |
abstract
a central question in the era of ’big data’ is what to do with the enormous amount of information. one possibility
is to characterize it through statistics, e.g., averages, or classify it using machine learning, in order to understand
the general structure of the overall data. the perspective in this paper is the opposite, namely that most of the value
in the information in some applications is in the parts that deviate from the average, that are unusual, atypical. we
define what we mean by ’atypical’ in an axiomatic way as data that can be encoded with fewer bits in itself rather
than using the code for the typical data. we show that this definition has good theoretical properties. we then develop
an implementation based on universal source coding, and apply this to a number of real world data sets.
index terms
big data, atypicality, minimum description length, data discovery, anomaly.
| 7 |
abstract
we present a confidence-based single-layer feed-forward learning algorithm s pi ral (spike regularized adaptive learning) relying on an encoding of activation
spikes. we adaptively update a weight vector relying on confidence estimates and
activation offsets relative to previous activity. we regularize updates proportionally
to item-level confidence and weight-specific support, loosely inspired by the observation from neurophysiology that high spike rates are sometimes accompanied
by low temporal precision. our experiments suggest that the new learning algorithm s piral is more robust and less prone to overfitting than both the averaged
perceptron and a row.
| 9 |
abstract
this essay discusses whether computers, using artificial intelligence (ai), could create art. the first part concerns ai-based tools for assisting with art making. the history
of technologies that automated aspects of art is covered, including photography and animation. in each case, we see initial fears and denial of the technology, followed by a
blossoming of new creative and professional opportunities for artists. the hype and reality
of artificial intelligence (ai) tools for art making is discussed, together with predictions
about how ai tools will be used. the second part speculates about whether it could ever
happen that ai systems could conceive of artwork, and be credited with authorship of an
artwork. it is theorized that art is something created by social agents, and so computers
cannot be credited with authorship of art in our current understanding. a few ways that
this could change are also hypothesized.
| 2 |
abstract
in this paper, we further develop the approach, originating in [13], to “computation-friendly”
hypothesis testing via convex programming. most of the existing results on hypothesis testing
aim to quantify in a closed analytic form separation between sets of distributions allowing for
reliable decision in precisely stated observation models. in contrast to this descriptive (and highly
instructive) traditional framework, the approach we promote here can be qualified as operational –
the testing routines and their risks are yielded by an efficient computation. all we know in advance
is that, under favorable circumstances, specified in [13], the risk of such test, whether high or low,
is provably near-optimal under the circumstances. as a compensation for the lack of “explanatory
power,” this approach is applicable to a much wider family of observation schemes and hypotheses
to be tested than those where “closed form descriptive analysis” is possible.
in the present paper our primary emphasis is on computation: we make a step further in
extending the principal tool developed in [13] – testing routines based on affine detectors – to a
large variety of testing problems. the price of this development is the loss of blanket near-optimality
of the proposed procedures (though it is still preserved in the observation schemes studied in [13],
which now become particular cases of the general setting considered here).
| 10 |
abstract. let r be a commutative noetherian local ring and let a be a proper
ideal of r. in this paper, as a main result, it is shown that if m is a gorenstein
r-module with c = ht m a, then hia (m ) = 0 for all i 6= c is completely encoded in
homological properties of hca (m ), in particular in its bass numbers. notice that,
this result provides a generalization of a result of hellus and schenzel which has
been proved before, as a main result, in the case where m = r.
| 0 |
abstract
the discrete-time distributed bayesian filtering (dbf) algorithm is presented for the problem of tracking a target dynamic
model using a time-varying network of heterogeneous sensing agents. in the dbf algorithm, the sensing agents combine their
normalized likelihood functions in a distributed manner using the logarithmic opinion pool and the dynamic average consensus
algorithm. we show that each agent’s estimated likelihood function globally exponentially converges to an error ball centered
on the joint likelihood function of the centralized multi-sensor bayesian filtering algorithm. we rigorously characterize the
convergence, stability, and robustness properties of the dbf algorithm. moreover, we provide an explicit bound on the time
step size of the dbf algorithm that depends on the time-scale of the target dynamics, the desired convergence error bound,
and the modeling and communication error bounds. furthermore, the dbf algorithm for linear-gaussian models is cast into
a modified form of the kalman information filter. the performance and robust properties of the dbf algorithm are validated
using numerical simulations.
key words: bayesian filtering, distributed estimation, sensor network, data fusion, logarithmic opinion pool.
| 7 |
abstract
in this article, we discuss a flow–sensitive analysis of equality relationships
for imperative programs. we describe its semantic domains, general purpose operations over abstract computational states (term evaluation and identification,
semantic completion, widening operator, etc.) and semantic transformers corresponding to program constructs. we summarize our experiences from the last
few years concerning this analysis and give attention to applications of analysis of
automatically generated code. among other illustrating examples, we consider a
program for which the analysis diverges without a widening operator and results
of analyzing residual programs produced by some automatic partial evaluator. an
example of analysis of a program generated by this evaluator is given.
keywords: abstract interpretation, value numbering, equality relationships for
program terms, formal grammars, semantic transformers, widening operator, automatically generated programs.
| 2 |
abstract
in this thesis we present a new algorithm for the vehicle routing problem called the enhanced bees algorithm. it is adapted
from a fairly recent algorithm, the bees algorithm, which was
developed for continuous optimisation problems. we show that
the results obtained by the enhanced bees algorithm are competitive with the best meta-heuristics available for the vehicle
routing problem—it is able to achieve results that are within
0.5% of the optimal solution on a commonly used set of test
instances. we show that the algorithm has good runtime performance, producing results within 2% of the optimal solution
within 60 seconds, making it suitable for use within real world
dispatch scenarios. additionally, we provide a short history of
well known results from the literature along with a detailed description of the foundational methods developed to solve the
vehicle routing problem.
| 9 |
abstract
fuel efficient homogeneous charge compression ignition (hcci) engine combustion timing predictions must contend
with non-linear chemistry, non-linear physics, period doubling bifurcation(s), turbulent mixing, model parameters that
can drift day-to-day, and air-fuel mixture state information that cannot typically be resolved on a cycle-to-cycle basis,
especially during transients. in previous work, an abstract cycle-to-cycle mapping function coupled with 𝜖-support
vector regression was shown to predict experimentally observed cycle-to-cycle combustion timing over a wide range of
engine conditions, despite some of the aforementioned difficulties. the main limitation of the previous approach was
that a partially acausual randomly sampled training dataset was used to train proof of concept offline predictions. the
objective of this paper is to address this limitation by proposing a new online adaptive extreme learning machine (elm)
extension named weighted ring-elm. this extension enables fully causal combustion timing predictions at randomly
chosen engine set points, and is shown to achieve results that are as good as or better than the previous offline method.
the broader objective of this approach is to enable a new class of real-time model predictive control strategies for high
variability hcci and, ultimately, to bring hcci’s low engine-out nox and reduced co2 emissions to production engines.
keywords: non-linear, non-stationary, time series, chaos theory, dynamical system, adaptive extreme learning machine
| 5 |
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