# Inter-particle adhesion induced strong mechanical memory in a dense granular
suspension

Sebanti Chattopadhyay and Sayantan Majumdar<EMAIL_ADDRESS>Raman
Research Institute, Bengaluru 560080, India

(August 30, 2024)

###### Abstract

Repeated/cyclic shearing can drive amorphous solids to a steady state encoding
a memory of the applied strain amplitude. However, recent experiments find
that the effect of such memory formation on mechanical properties of the bulk
material is rather weak. Here we study the memory effect in a yield stress
solid formed by a dense suspension of cornstarch particles in paraffin oil.
Under cyclic shear, the system evolves towards a steady state showing
training-induced strain stiffening and plasticity. A readout reveals that the
system encodes a strong memory of the training amplitude ($\gamma_{T}$) as
indicated by a large change in the differential shear modulus. We observe that
memory can be encoded for a wide range of $\gamma_{T}$ both above and below
the yielding, albeit, the strength of the memory decreases with increasing
$\gamma_{T}$. In-situ boundary imaging shows strain localization close to the
shearing boundaries, while the bulk of the sample moves like a solid plug. In
the steady state, the average particle velocity $\left\langle v\right\rangle$
inside the solid-like region slows down with respect to the moving plate as
$\gamma$ approaches $\gamma_{T}$, however, as the readout strain crosses
$\gamma_{T}$, $\left\langle v\right\rangle$ suddenly increases. We demonstrate
that inter-particle adhesive interaction is crucial for such strong memory
effect. Interestingly, our system can also remember more than one input only
if the training strain with smaller amplitude is applied last.

††preprint: AIP/123-QED

##  I Introduction

Many out of equilibrium materials encode memory of past perturbations. The
imprint of such perturbation history is stored in the material structure and
can be revealed using a suitable readout protocol even long after the
perturbation is removed. Cyclic perturbation protocols have been extensively
used to encode memories in many of these systems. Such perturbations can be in
the form of shear, temperature change, electrical/magnetic fields, etc. [1].
Few examples include dilute granular suspensions under cyclic shear [2, 3, 4],
aging and rejuvenation in glasses [5, 6], charge density wave conductors under
voltage pulses [7, 8].

Memory formation in amorphous solids have attracted significant recent
interests. Despite the diversity in microscopic details, presence of long-
range correlations and complex energy landscapes, these materials show very
similar localized rearrangements under stress. Each of such rearrangements can
be thought of as a transition between two local energy minima of the system
[9, 10]. Interestingly, these systems can also reach a steady state under
cyclic deformations encoding a memory of the deformation amplitude. The
approach to a steady state and memory formation in amorphous solids under
cyclic shear has been demonstrated in numerical simulations of glassy and
frictional granular systems [11, 12, 13, 14, 15, 16, 17]. Experimental studies
have explored memory formation in soft glassy systems in both 2-D [18, 19, 20,
21] and 3-D [22], colloidal gels [23], cross-linked biopolymer networks [24,
25] . Many of these systems also have the ability to remember multiple inputs
even in the absence of external noise.

The signature of the memory formed under cyclic shear is reflected as a sudden
increase in particle mean square displacement (MSD) as the readout strain
crosses the training strain amplitude marking an onset of irreversibility in
the system. However, the effect of such reversible-irreversible transition on
the mechanical properties of the bulk material has rarely been explored.
Experiments on dilute non-Brownian suspensions [4] and a soft glassy system of
2-D bubble raft [18] reports that encoding memory induces only a small change
in shear modulus of the system. These observations indicate a limitation of
widely tuning the material properties using an imposed training.

In this Communication, we report strong memory formation in an amorphous solid
formed by dense granular suspensions of cornstarch particles in paraffin oil.
We find that memory can be encoded for a wide range of strain amplitudes both
above and below yielding. Remarkably, such memory effect is directly reflected
as a sharp change in the differential shear modulus of the system. We observe
that in the case of consecutive training with the strain amplitudes
$\gamma_{1}\leq\gamma_{2}\leq...\leq\gamma_{n}$, only the memory of largest
amplitude $\gamma_{n}$ is retained. However, if the system is trained with a
smaller strain amplitude $\gamma_{i}(<\gamma_{n}$) at the end of the training
sequence, then the memory of $\gamma_{i}$ can also be encoded. We show that
such strong memory originates from a training-induced non-trivial coupling-
decoupling dynamics of the solid-like region inside bulk of the sample with
the shearing plate via high shear rate bands near the boundaries. We also
demonstrate the crucial role played by the inter-particle adhesion in forming
such strong mechanical memory.

Figure 1: (a) Triangular wave strain deformation applied to the system for 300
cycles with a strain amplitude $\gamma_{T}$ as indicated in the figure. The
magenta color indicates cycles 1-6 and the blue color indicates cycles 295-300
as shown. (b) and (c) show intra-cycle stress ($\sigma$) vs number of cycles
(N) corresponding to the regions mentioned in (a) for $\gamma_{T}=0.02$. The
dotted lines correspond to the peak stress ($\sigma_{peak}$) for N = 1 (panel
b) and N = 295 (panel c) showing that $\sigma_{peak}$ drops with increasing N
in the beginning of the training, but finally saturates. (d) Variation of
$\sigma_{peak}$ with N for different $\gamma_{T}$ values as indicated. The
data is averaged over at least 3 independent measurements and error bars are
the standard deviations. (e) Lissajous plots showing normalized intra-cycle
stress ($\sigma/\sigma_{peak}$) vs. strain ($\gamma$) for few discrete N
values. The arrow indicates increasing values of N.

##  II Materials and methods

For all our experiments, dense suspensions are prepared by dispersing
Cornstarch (CS) particles (Sigma Aldrich) in paraffin oil with volume fraction
($\phi$) = 0.4. The CS particles have a mean diameter of 15$\pm 5\mu$m. This
system shows yield stress resulting from a solvent-mediated adhesion [26]. We
use a MCR-702 stress controlled rheometer (Anton Paar) with a cone and plate
geometry (C-P) having rough sand-blasted surfaces for all our measurements. In
C-P, the shear-rate remains uniform everywhere inside the shearing geometry.
The diameter of both cone and plate = 50 mm and the cone angle $\approx$ 2°.
Our experiments are done in the separate motor transducer mode of the
rheometer with the bottom plate moving and the cone remaining stationary at
all times. We use an in-situ boundary imaging setup with a CCD camera
(Lumenera) with a 5X long working distance objective (Mitutoyo) to capture the
particle dynamics during the experiments. Images are captured at a rate of 4
Hz with a resolution of 1000 x 2000 pixels2 for all the measurements. For
varying the adhesive interaction in the system, we use the non-ionic
surfactant Span©60\. For more details about the system and the experimental
set-up please see S.I. Notes and also [27].

Figure 2: (a) Top: Training and readout protocol with $\gamma_{T}=0.02$ and
$\gamma_{R}=0.04$, respectively. Bottom: Protocol of applying a single readout
pulse ($\gamma_{R}=0.04$) without any training. (b) Top panel: Stress
($\sigma$) vs strain($\gamma$) plots obtained from the readouts shown in (a)
with training (in red) and without training (in black). Bottom panel: The
corresponding differential shear moduli $K=\frac{d\sigma}{d\gamma}$ vs.
$\gamma$ with same colour coding as the top panel. (c) Variation of $K$ vs.
$\gamma$ for a wide range of $\gamma_{T}$ values as indicated in the legend
with arrows indicating the position of the peak of $K$ for different
$\gamma_{T}$ values. Each data set is averaged over at least 4 independent
measurements. Error bars are the standard deviations. Figure 3: (a) A typical
boundary image of the sample with the X-axis along the direction of flow and
Y-axis along the gradient. Three points: A (on the plate), B and C (inside the
bulk of the sample) indicated by boxes denote the location of the bright
speckles for Kymograph analysis. The displacements of the bright speckles
during the course of training are contained in the corresponding dotted lines.
Corresponding Kymographs (for $\gamma_{T}$ = 0.07) are shown in panel (b) for
the first 10 (N: 1 - 10) and last 10 (N: 291 - 300) training cycles, as
indicated. (c) Kymographs corresponding to the last training (N = 300) and the
readout cycle for the point A (bottom panel) and B (top panel). Dashed lines
indicate a change in slope of the particle trajectory as the readout strain
crosses $\gamma_{T}$. Scale bar in (a) denotes 100 $\mu$m.

##  III Results and discussion

To train the system, we apply 300 cycles of a triangular-wave strain
deformation with amplitude $\gamma_{T}$ at a constant strain rate of 0.01
$s^{-1}$, as shown in Fig. 1(a). At this shear rate, the Reynolds Number
$Re<<1$, indicating that inertial effects can be neglected. We use different
colors to indicate the cycles in the beginning (magenta) and in the end
(blue). In Fig. 1(b) and (c), we show the resulting intra-cycle stress
($\sigma$) as a function of number of cycles (N) for $\gamma_{T}=0.02$. We
notice that the peak stress ($\sigma_{peak}$) shows a systematic drop in the
beginning (Fig. 1(b)) whereas near the end, it saturates to a steady state
value of $\approx$ 15 Pa (Fig. 1(c)). Similar behaviour is also observed for
other $\gamma_{T}$ values (Fig. 1(d)). From Fig. 1(d) we also see that
$\sigma_{peak}$ for any N value decreases with increasing $\gamma_{T}$. This
is related to the strain softening behaviour of the system: shear moduli
$G^{\prime}$ and $G^{\prime\prime}$ decrease with increasing strain amplitude
as shown in Fig. S2. We note that the nature of the stress waveform also
evolves with N. This is more clearly observed from the variation of normalized
$\sigma$ vs. $\gamma$ (Lissajous plot) in Fig. 1(e). Out of total 300 cycles,
we show the Lissajous plots for only a few discrete N values. We observe from
Fig. 1(e) that starting from a quasi-linear visco-elastic response for small
values of N, the system gradually shows a highly non-linear response for
larger N values, approaching a steady state. Also, as we go to larger N
values, the slope of the Lissajous plots remains negligible for
$\gamma<<\gamma_{T}$ however, near $\gamma_{T}$, the slope increases sharply.
This indicates the development of plasticity and strain-stiffening behaviour
under training. Similar behaviour has also been observed for colloidal gels
and cross-linked biopolymer networks under cyclic shear [28, 24].

Now, to see if the system encodes memory of the training amplitude, we apply a
readout after 300 cycles of training. The readout is a triangular wave pulse
having the same strain rate of 0.01$s^{-1}$ but with an amplitude
$\gamma_{R}=2\gamma_{T}$ as shown in Fig. 2(a) (top panel) for
$\gamma_{T}=0.02$. We plot $\sigma$ vs $\gamma$ of the readout cycle for
$\gamma_{T}=0.02$ in Fig. 2(b) (top panel, red curve). We observe a sharp
change in the slope of $\sigma$ as we cross $\gamma=\gamma_{T}$. For a
comparison, we apply the same readout to an untrained sample (Fig. 2(a),
bottom panel) and plot the corresponding $\sigma$ vs. $\gamma$ in Fig. 2(b)
(top panel, black curve) where we do not observe any such change. This
difference is more clearly reflected in the differential shear modulus of the
system $K(=\frac{d\sigma}{d\gamma}$) vs. $\gamma$ as shown in Fig. 2(b)
(bottom panel): the trained sample shows a sharp peak in $K$ with value
$K_{peak}$ at $\gamma=\gamma_{T}$, indicating that the system encodes a strong
memory of the training amplitude which can change the differential shear
modulus of the system by a huge amount. In our case $K_{peak}>>K_{baseline}$
(with $K_{baseline}$ as the value of $K$ at strain values slightly away from
$\gamma_{T}$, where $K$ varies relatively slowly). Whereas, for earlier
studies [4, 18] $K_{peak}\sim K_{baseline}$, further highlighting that memory
formation is much stronger in our case. Additionally, we find that once the
system is trained at a particular $\gamma_{T}$, any memory of
$\gamma<\gamma_{T}$ gets erased (Fig. S3). We discuss the possibility of
encoding multiple memories in our system later in the manuscript. We also find
that the signature of encoded memory stays essentially the same, even when the
readout is taken after 1000 s, implying that the relaxation of the structures
supporting the memory is very slow. (not shown).
To inspect the effect of training amplitude on the strength of memory
formation, we encode and readout the memory for a range of $\gamma_{T}$
values. For each $\gamma_{T}$ we use a fresh loading of the sample. In Fig.
2(c), we plot average $K$ vs. $\gamma$ for various $\gamma_{T}$ values. We
find a steady drop in the strength of the memory (quantified by the peak
values of $K$) as $\gamma_{T}$ increases. In fact, beyond $\gamma_{T}$ = 0.1,
$K_{peak}$ is significantly lower, albeit still present. This value of
$\gamma_{T}$ is close to the fluidization/yielding onset of the system (Fig.
S2). Thus, in contrast to 2-D soft glassy system [18], we do not find any
enhancement of memory effect near the yield point.

So far we have established that the encoded memory gets reflected as a large
change in the value of differential shear modulus of the system. In order to
understand the mechanism behind this striking behaviour, we perform in-situ
optical imaging (in the flow(X)-gradient(Y) plane). Details of the set-up are
given in [27]. A typical image is shown in Fig. 3(a) where the bright speckles
correspond to CS particles which protrude out of the air-solvent interface.
Internal connectivity of the fractal network giving rise to solid-like yield
stress in the system stabilizes such protruding particles against the stress
due to surface tension. We map out the velocity profile across the shear-gap
using particle imaging velocimetry (PIV) during the training and readout
experiments as shown in Fig. S4. We observe that except for narrow regions of
high shear rate close to the shearing boundaries, the bulk of the sample moves
like a solid plug with negligible velocity gradient. We use Kymographs to map
out the space-time variation of the speckle distribution at different
locations inside the sample acquired during training and readout experiments
(Materials and Methods). We consider kymographs at three different locations:
1. on the moving plate (Region A), 2. inside the solid-like region close to
the moving plate (Region B), and 3. inside the solid-like region close to the
static cone (Region C). All these regions contain atleast one dominant bright
speckle whose trajectory can be tracked during the training and readout. We
show in Fig. 3(b) the kymographs corresponding to these regions for the first
10 (N = 1 - 10) and last 10 (N = 291-300) training cycles. We find that the
trajectory obtained for Region A (Fig. 3(b), bottom panel) precisely mimics
the motion of the shearing plate during training. The self-similarity of the
waveform establishes the robustness of the input shear. Interestingly,
Kymographs for Region 2 and 3 show more complex displacement waveforms
involving a gradual evolution in both amplitude and shape before reaching a
steady state during the course of training. However, except for the first
cycle (involving start-up transients) waveform in these regions remains very
similar, further confirming the solid plug-like motion of the bulk of the
sample.
Interestingly, the displacement waveform inside the solid-like region of the
sample indicates a complex coupling-decoupling dynamics between the moving
plate and the bulk of the sample through the high shear rate bands near the
boundaries. Clearly such dynamics is developed through training as it is
absent in the beginning (left panels of Fig. 3(b)). In the steady state,
particle trajectories in the solid-like region of the sample settle down to a
waveform which is very different from the input, demonstrating a slowing down
of the bulk as the intra-cycle input strain ($\gamma$) approaches the training
amplitude ($\gamma_{T}$). Now, we take a look at the readout by plotting
kymographs corresponding to the last training cycle (N = 300) and the readout
cycle in Fig. 3(c). For clarity, we show the data only for Region A (bottom
panel) and Region B (top panel) with expanded views. We find that the speckle-
displacement during readout follows the last training cycle up to
$\gamma=\gamma_{T}$. However, as $\gamma$ crosses $\gamma_{T}$, there is a
sudden increase in the slope of the trajectory (marked by dashed lines in Fig.
3(c), top panel). Similar change in slope is also observed for other speckle
trajectories inside the solid-like region (Fig. S1). This implies that beyond
$\gamma_{T}$, there is a sudden buildup of coupling between the solid-like
region and the moving plate. This sudden coupling will lead to an abrupt
increase in velocity of a large number of particles, which can explain the
origin of the sharp peak of $K$ around $\gamma=\gamma_{T}$. Such abrupt
increase in velocity of the solid-like region inside the bulk of the sample
also reflects in the velocity profile obtained from the PIV analysis (Fig.
S5). Difference images constructed through stroboscopic sampling indicate a
reversible to irreversible transition (RIT) in the sample beyond $\gamma_{T}$
(Fig. S6) similar to the earlier studies [4, 18, 12].

Figure 4: (a) Schematic indicating the gradual dispersion of fractal clusters
with increasing surfactant concentration ($c$). For a given sample, $c$
denotes the mass fraction of surfactant w.r.t. corn starch particles. (b)
Variation of the peak values of differential shear modulus $K_{peak}$ as a
function of $\gamma_{T}$ for a range of $c$ values indicated. Each data set is
averaged over at least 3 independent measurements with the error bars denoting
the standard deviations. Inset shows a log-log plot of the same. Figure 5: (a)
Intra-cycle stress ($\sigma$) vs strain ($\gamma$) obtained from the readout
for a sample trained at $\gamma_{T1}$ = 0.02 and $\gamma_{T2}$ = 0.04. (b)
Corresponding $K$ vs $\gamma$ showing the signature of multiple memory
formation.

Next, we address the role of inter-particle adhesive interaction on the strong
memory formation in our system. Recently, we reported that addition of a small
amount of surfactant to the cornstarch-paraffin oil system can reduce the
adhesive interaction between the particles as quantified by the systematic
change in jamming volume fraction [27]. With sufficient amount of surfactant,
the system completely transforms into a steric-repulsive one. Physically, as
we increase the surfactant concentration ($c$), the large fractal clusters
initially stabilized by inter-particle adhesion gradually disintegrate and
finally form a well dispersed suspension as shown in the schematic Fig. 4(a).
We prepare samples with increasing $c$ and subject them to the similar
training and readout protocols mentioned earlier for different $\gamma_{T}$
values. In fig. 4(b), we plot the average peak values of $K$ for different
$\gamma_{T}$ values obtained from the readouts for a range of $c$ values. We
see that the strength of memory ($K_{peak}$) decreases with increasing $c$ and
the system loses ability to form strong memory beyond sufficiently high
concentration of surfactant ($c\geq$ 0.3 %). However, even at the highest
surfactant concentration, the memory signature is not totally absent, as can
be seen from the log-log plot (inset of Fig. 4b) and also Fig. S7. We find
that for the same range of $c$, the training induced strain stiffening also
becomes significantly weaker (Fig. S8). Thus, the memory formation in our
system is intimately related to the strain stiffening response of the system.
From the Kymograph analysis we do not find any discontinuity in the particle
trajectory across $\gamma_{T}$ in the readout for sufficiently high surfactant
concentrations (Fig. S9).

Lastly, we address the possibility of encoding multiple memories in our
system. As mentioned earlier, if the system is trained at $\gamma_{T}$, it
erases all memory of $\gamma<\gamma_{T}$. Interestingly, we find that if the
sample is trained with the smaller strain amplitude just before the readout,
it can retain the memory of the smaller $\gamma_{T}$ and more than one memory
can be encoded, as has also been reported earlier for other systems [4, 19].
We use the following protocol: we apply 1 cycle of the larger training strain
amplitude ($\gamma_{T2}$ = 0.04) followed by 20 cycles of the smaller training
strain amplitude ($\gamma_{T1}$ = 0.02). This whole sequence is repeated 15
times and then we apply a readout strain $\gamma_{R}$ = 0.06. The stress
($\sigma$) vs. strain ($\gamma$) plot for the readout shows sharp changes at
$\gamma$ = 0.02 and 0.04 (Fig. 5(a)), giving two prominent peaks for the
differential shear modulus $K$ (Fig. 5(b)). This indicates that the system has
the ability to encode memories of more than one strain amplitude under a
suitable training protocol.

##  IV Conclusion

We report strong memory formation in an adhesive dense particulate suspension
under cyclic shear. The Differential shear modulus of the bulk system show a
huge enhancement near the training strain amplitude ($\gamma_{T}$). A possible
explanation for such striking effect is the following: in the presence of
adhesion, the fractal particle clusters can form strand-like connected
structures. Under cyclic shear deformations such structures can reorganize and
develop a slack up to $\gamma_{T}$. The adhesive interaction maintains the
contact between the particles in these loose strand-like structures for strain
values $\gamma<\gamma_{T}$. Similar strand plasticity has been reported in
colloidal gels under cyclic shear [28]. For our case, such reorganization
dynamics predominantly takes place in the high shear rate bands close to the
shearing boundaries. Due to the slack in the particle strands, the plate can
easily move without significantly disturbing the solid-like bulk region inside
the sample for $\gamma<\gamma_{T}$. This gives rise to an apparent decoupling
between the plate and the bulk sample in the steady state. However, when
$\gamma$ crosses $\gamma_{T}$ during the readout, the loose strands suddenly
get stretched and the coupling between the moving plate and the solid-like
region builds up. This results in a strong stress response. Interestingly,
such picture can also shed some light on the reduction in the strength of
memory with increasing $\gamma_{T}$: for small $\gamma_{T}$ values, a large
number of clusters can contribute to strand formation, however for large
$\gamma_{T}$ values, many small clusters can get completely disintegrated and
only large connected structures will contribute. Although, at present we
cannot directly visualize the strand dynamics, the connection between the
strand formation/plasticity and the observed memory effect is further
supported by the role of adhesive interaction in the system. Once the inter-
particle adhesion gets sufficiently weakened by addition of surfactant, the
strand formation is no longer possible, as a result the memory effect gets
diminished. This weaker memory in principle is similar to that observed
earlier in dilute non-Brownian suspensions [4]. However, further work is
needed to understand the exact correspondence.

Memory effect reported here is reminiscent of similar phenomena in transiently
cross-linked biopolymer networks [24] and Mullin’s effect in filled-polymeric
systems [29]. Our system also shows a connection between the memory formation
and reversible-irreversible transition like many repulsive particulate
systems. Thus, adhesive particulate system can be thought of as an
intermediate between repulsive dense suspensions and polymeric materials.
Remarkably, our system presents a distinct advantage: since the particles are
very robust and the adhesive particle-bonds are reversible, training and
readout for a wide range of strain values does not cause any permanent damage
to the system. The sample can be reloaded and reprogrammed arbitrarily. On the
other hand, polymeric materials are prone to permanent damage due to breakage
of filaments or chemical bonds. Interestingly, the strand picture together
with the reversibility of adhesive bonds can explain the origin of multiple
memories in our case. After encoding a memory at a smaller strain amplitude,
application of a larger training strain can destroy the strands supporting the
memory at smaller amplitude. However, after this, if the system is again
trained at the smaller amplitude, the reversibility of the adhesive bonds
ensures that some local connectivity can build up once more (Fig. S10), thus
re-encoding the memory of smaller amplitude.

Due to the opaque nature of the particles, we can only probe the particle-
dynamics on the sample surface. However, as our system is very dense (Fig.
3(a)), it is hard to directly probe training induced changes in the nodes or
connectivity between the particles using boundary imaging alone. Mapping out
the system dynamics in 3-D using optical/acoustic techniques remains a future
challenge. Further theoretical insights regarding the mechanism of such strong
memory effect in adhesive systems, including the formation of multiple
memories can open up new avenues to explore. Our study can have important
implications in designing programmable materials.

##  V Supplementary Material

See supplementary material for information about sample preparation, data
analysis and additional measurements.

##  VI Authors Contributions

S.C. and S.M. designed the research, S.C. performed the experiments, S.C.and
S.M. analyzed the data and wrote the manuscript.

##  VII Conflict of interests

There is no conflict to declare.

##  VIII Acknowledgments

S.M. thanks SERB (under DST, Govt. of India) for a Ramanujan Fellowship. We
acknowledge Ivo Peters for developing the Matlab codes used for PIV analysis.
We thank Daniel Hexner for helpful discussion.

## References

  * Keim  _et al._ [2019] N. C. Keim, J. D. Paulsen, Z. Zeravcic, S. Sastry, and S. R. Nagel, “Memory formation in matter,” Reviews of Modern Physics 91, 035002 (2019).
  * Corté  _et al._ [2008] L. Corté, P. M. Chaikin, J. P. Gollub, and D. J. Pine, “Random organization in periodically driven systems - Nature Physics,” Nat. Phys. 4, 420–424 (2008).
  * Keim and Nagel [2011] N. C. Keim and S. R. Nagel, “Generic Transient Memory Formation in Disordered Systems with Noise,” Phys. Rev. Lett. 107, 010603 (2011).
  * Paulsen, Keim, and Nagel [2014] J. D. Paulsen, N. C. Keim, and S. R. Nagel, “Multiple Transient Memories in Experiments on Sheared Non-Brownian Suspensions,” Phys. Rev. Lett. 113, 068301 (2014).
  * Jonason  _et al._ [1998] K. Jonason, E. Vincent, J. Hammann, J. Bouchaud, and P. Nordblad, “Memory and chaos effects in spin glasses,” Physical Review Letters 81, 3243 (1998).
  * Zou and Nagel [2010] L.-N. Zou and S. R. Nagel, “Glassy Dynamics in Thermally Activated List Sorting,” Phys. Rev. Lett. 104, 257201 (2010).
  * Fleming and Schneemeyer [1986] R. Fleming and L. Schneemeyer, “Observation of a pulse-duration memory effect in k 0.30 moo 3,” Physical Review B 33, 2930 (1986).
  * Coppersmith  _et al._ [1997] S. Coppersmith, T. Jones, L. Kadanoff, A. Levine, J. McCarten, S. Nagel, S. Venkataramani, and X. Wu, “Self-organized short-term memories,” Physical Review Letters 78, 3983 (1997).
  * Falk and Langer [2011] M. L. Falk and J. S. Langer, “Deformation and failure of amorphous, solidlike materials,” Annu. Rev. Condens. Matter Phys. 2, 353–373 (2011).
  * Cubuk  _et al._ [2017] E. D. Cubuk, R. Ivancic, S. S. Schoenholz, D. Strickland, A. Basu, Z. Davidson, J. Fontaine, J. L. Hor, Y.-R. Huang, Y. Jiang, _et al._ , “Structure-property relationships from universal signatures of plasticity in disordered solids,” Science 358, 1033–1037 (2017).
  * Fiocco, Foffi, and Sastry [2013] D. Fiocco, G. Foffi, and S. Sastry, “Oscillatory athermal quasistatic deformation of a model glass,” Phys. Rev. E 88, 020301 (2013).
  * Fiocco, Foffi, and Sastry [2014] D. Fiocco, G. Foffi, and S. Sastry, “Encoding of Memory in Sheared Amorphous Solids,” Phys. Rev. Lett. 112, 025702 (2014).
  * Adhikari and Sastry [2018] M. Adhikari and S. Sastry, “Memory formation in cyclically deformed amorphous solids and sphere assemblies,” Eur. Phys. J. E 41, 1–17 (2018).
  * Mungan  _et al._ [2019] M. Mungan, S. Sastry, K. Dahmen, and I. Regev, “Networks and hierarchies: How amorphous materials learn to remember,” Physical Review Letters 123, 178002 (2019).
  * Regev, Lookman, and Reichhardt [2013] I. Regev, T. Lookman, and C. Reichhardt, “Onset of irreversibility and chaos in amorphous solids under periodic shear,” Physical Review E 88, 062401 (2013).
  * Keim and Paulsen [2021] N. C. Keim and J. D. Paulsen, “Multiperiodic orbits from interacting soft spots in cyclically sheared amorphous solids,” Science Advances 7, eabg7685 (2021).
  * Royer and Chaikin [2015] J. R. Royer and P. M. Chaikin, “Precisely cyclic sand: Self-organization of periodically sheared frictional grains,” Proceedings of the National Academy of Sciences 112, 49–53 (2015).
  * Mukherji  _et al._ [2019] S. Mukherji, N. Kandula, A. K. Sood, and R. Ganapathy, “Strength of Mechanical Memories is Maximal at the Yield Point of a Soft Glass,” Phys. Rev. Lett. 122, 158001 (2019).
  * Keim  _et al._ [2020] N. C. Keim, J. Hass, B. Kroger, and D. Wieker, “Global memory from local hysteresis in an amorphous solid,” Physical Review Research 2, 012004 (2020).
  * Keim and Arratia [2013] N. C. Keim and P. E. Arratia, “Yielding and microstructure in a 2d jammed material under shear deformation,” Soft Matter 9, 6222–6225 (2013).
  * Keim and Arratia [2014] N. C. Keim and P. E. Arratia, “Mechanical and microscopic properties of the reversible plastic regime in a 2d jammed material,” Physical Review Letters 112, 028302 (2014).
  * Nagamanasa  _et al._ [2014] K. H. Nagamanasa, S. Gokhale, A. Sood, and R. Ganapathy, “Experimental signatures of a nonequilibrium phase transition governing the yielding of a soft glass,” Physical Review E 89, 062308 (2014).
  * Schwen  _et al._ [2020] E. M. Schwen, M. Ramaswamy, C.-M. Cheng, L. Jan, and I. Cohen, “Embedding orthogonal memories in a colloidal gel through oscillatory shear,” Soft Matter 16, 3746–3752 (2020).
  * Schmoller  _et al._ [2010] K. Schmoller, P. Fernandez, R. Arevalo, D. Blair, and A. Bausch, “Cyclic hardening in bundled actin networks,” Nature Communications 1, 1–8 (2010).
  * Majumdar  _et al._ [2018] S. Majumdar, L. C. Foucard, A. J. Levine, and M. L. Gardel, “Mechanical hysteresis in actin networks,” Soft Matter 14, 2052–2058 (2018).
  * Richards  _et al._ [2020] J. A. Richards, B. M. Guy, E. Blanco, M. Hermes, G. Poy, and W. C. K. Poon, “The role of friction in the yielding of adhesive non-Brownian suspensions,” J. Rheol. 64, 405 (2020).
  * Chattopadhyay, Nagaraja, and Majumdar [2022] S. Chattopadhyay, S. Nagaraja, and S. Majumdar, “Effect of adhesive interaction on strain stiffening and dissipation in granular gels undergoing yielding,” (2022), arXiv:2202.11421 [cond-mat.soft] .
  * van Doorn  _et al._ [2018] J. M. van Doorn, J. E. Verweij, J. Sprakel, and J. van der Gucht, “Strand Plasticity Governs Fatigue in Colloidal Gels,” Phys. Rev. Lett. 120, 208005 (2018).
  * Mullins [1948] L. Mullins, “Effect of stretching on the properties of rubber,” Rubber chemistry and technology 21, 281–300 (1948).

Supplementary Information

##  IX Supplementary note 1: Details of sample preparation and measurements

When dispersed in oil, the cornstarch (CS) particles, which are hydrophilic
due to the presence of -OH groups on their surface, experience a solvent-
mediated adhesion. This adhesive interaction gives rise to a yield stress
solid having system spanning fractal clusters for particle volume fraction
$\phi>0.23$. For all our measurements in this work, the particle volume
fraction is $\phi=0.4$. To homogenize the samples, we use a combination of
magnetic stirring and hand mixing. For making the samples with added
surfactant, we dry mix the surfactant with the CS particles in a mortar pestle
before adding oil to this mixture. We then homogenize the sample in a magnetic
stirrer. We perform triangular wave measurements as shown in Fig. 1 in the
main text. We do not pre-shear the samples after loading as the pre-shearing
can also encode some memory in the system. All our measurements are performed
at a shear rate of 0.01 s-1. We have also used higher shear rates for our
experiments, but we have not found any systematic dependence of the strength
of memory encoded on the shear rate over the range (0.01 - 0.1 s-1) probed.
Lower values shear rate lead to long experimental times, especially for larger
training amplitudes, which may lead to sample drift. For this reason we have
not explored shear rates lower than 0.01 s-1.

##  X Supplementary note 2: Kymograph analysis

We represent the space-time variation of surface intensity along a line
segment using a Kymograph. For this, we first consider a series of images
equally spaced in time. Then we choose a fixed line segment along the
direction of shear (X-direction) (see Fig. 3 in the main text). The velocity
gradient is along the Y-direction. Thus the segment represents a fixed value
of y = $y_{0}$. The 2-D representation of the intensity distribution
$I(x,y_{0},t)$ of the line segment as a function of space and time gives the
Kymograph. Interestingly, if a relatively bright speckle (representing a
protruding particle at the air-liquid interface) moves along the chosen fixed
line under shear and the line always contains the position of the speckle ,
then the Kymograph can directly represent the particle trajectory (Fig. S1).

Figure S1: (a) A typical boundary image of the sample with lines depicting
different regions inside the sample at varying distance from the moving plate.
The scale bar represents 100 $\mu$m. (b) Corresponding Kymographs taken along
the lines shown in (a). For clarity, only the last three training cycles (N:
298-300) and the readout is considered for each region. The multiple particle
trajectories depicted in each Kymograph are the displacement waveforms for
various speckles on the same line (see Materials and methods). The similarity
of the waveforms, as well as, the discontinuity due to encoded memory
indicates the solid plug-like motion of the bulk of the sample as also
mentioned in the main text. Here $\gamma_{T}=0.07$. Figure S2: Variation of
Elastic ($G^{\prime}$) and Viscous ($G^{\prime\prime}$) moduli as a function
of applied oscillatory strain amplitude ($\gamma_{0}$). The dashed line
indicates the strain value for the crossover of $G^{\prime}$ and
$G^{\prime\prime}$ (yield strain), implying an onset of fluidization of the
system. Here, we have used a sinusoidal waveform with angular frequency
$\omega$ = 0.1 rad/s. We found that the value of yield strain remains
independent of $\omega$ (we have checked over the range 0.05 to 0.5 rad/s).
The yielding onset obtained from the normalized energy dissipation (see
Ref.[27] in the main text) using triangular wave is in the same range as the
yield strain obtained from the amplitude sweep data, as shown in this figure.
Thus, in our case the nature of the imposed waveform does not affect yielding
significantly (not shown). Figure S3: The system is first trained at
$\gamma_{T}$ = 0.07. Immediately after this, the system is continued to be
trained at $\gamma_{T}$ = 0.14 for a total of N = 20 cycles. We plot the
intra-cycle stress ($\sigma$) vs. strain ($\gamma$) obtained for N = 1 (orange
curve) and N = 20 (green curve) for $\gamma_{T}$ = 0.14 in (a). Corresponding
$K$ vs. $\gamma$ plots are shown in (b). We see that while N = 1 shows a
signature of memory at $\gamma_{T}$ = 0.07, there is no such signature for N =
20. Thus, the memory of the lower $\gamma_{T}$ = 0.07 gets completely erased
within just 20 cycles of training at the larger strain $\gamma_{T}$ = 0.14.
Dashed vertical lines indicate $\gamma$ = 0.07. Figure S4: (a) A typical
boundary image with superposed vector profile obtained from Particle Imaging
Velocimetry (PIV) analysis (Materials and methods). (b) Velocity profile
across the shear gap corresponding to the vector image shown in (a). Here, the
velocity for each $y$ value represents the average over all the velocity
vectors at the same $y$ coordinate but, varying $x$ coordinates. It is clear
from the vector representation and the velocity profile that the system has
high shear rate bands near the shearing boundaries, while the bulk of the
sample is moving like a solid plug with almost a constant velocity. The red
dot on the velocity axis indicates the calculated plate velocity. Figure S5:
(a) A schematic showing the last training cycle (N = 300) and the readout
cycle with dotted lines marking $\gamma_{T}$ and $\gamma_{R}$. Here
$\gamma_{T}$ = 0.07 and $\gamma_{R}$ = 0.14. Vertical lines indicate different
strain values (both above and below $\gamma_{T}$) during the readout for which
the instantaneous velocity profiles are shown in panel (b). (b) Corresponding
velocity profiles across the shear gap obtained through PIV analysis (see Fig.
S4). Solid lines are profiles just below $\gamma_{T}$ and dashed lines are
those just above $\gamma_{T}$. The colour coding is same as that in (a). We
see that as the readout strain just crosses $\gamma_{T}$, the velocity of the
solid plug-like region suddenly increases. This indicates a sudden buildup of
contact between the solid plug and the moving plate. The red dot on the
velocity axis indicates the calculated plate velocity. Figure S6: Stroboscopic
difference between images of the system taken at the start and at the end of
each cycle during the training and readout as shown in the schematic in the
top panel. All images are cropped till the edges (including roughness) of the
cone and plate. (a)-(e) Stroboscopic difference for different cycle number (N)
indicated in the figure obtained during training. The bright spots in the
difference images indicate the location of irreversible particle displacements
that take place over the course of the Nth cycle. We see that the particle
rearrangements systematically drop with increasing number of training cycles
and become almost negligible near the end of the training sequence indicating
essentially reversible particle trajectories. Panel (f) represents the
stroboscopic image corresponding to readout. The high intensity spots in (f)
indicates the irreversibility of the particle trajectories. Clearly, a
reversible to irreversible transition (RIT) happens as the system crosses
$\gamma_{T}$. The data shown here is for $\gamma_{T}=0.07$ and $\gamma_{R}$ =
0.14. Figure S7: Readout Lissajous plot for $\phi=0.4$ with c = 0.5$\%$ as
indicated in the figure. Dashed vertical line indicates the training strain
amplitude. Green dashed lines along the curve indicate the change in slope
across the training strain amplitude, showing that there is still a weak
signature of memory even in the limit of negligible adhesion in the system.
However, from the inset of main text figure 4(b), we see that the mean value
of $K_{peak}$ is very small with large error bars. This is because, for low
adhesion limit the absolute values of stress are quite low. This makes the the
differential shear modulus data quite noisy. Figure S8: (a) Intra-cycle stress
($\sigma$) vs. strain ($\gamma$) corresponding to the first (N = 1, panel(a))
and the last training cycle (N = 300, panel (b)) for samples with different
concentrations of surfactant ($c$) as indicated. The training induced strain
stiffening behaviour gradually disappears with increasing surfactant
concentration. Figure S9: Kymographs of the last few training cycles (N:
295-300) and the readout cycle for the samples with a surfactant concentration
$c=0.5\%$. Kymographs corresponding to a point on the moving plate (panel
(a)), at a point inside the bulk but near the moving plate (panel (b)), at a
point inside the bulk but near to the stationary cone (panel (c)). We see that
there is no kink in the particle displacement waveform as the readout strain
crosses the training strain amplitude (compare with Fig. 3(c) in the main
text). This implies the absence of memory in the system for this concentration
of surfactant. Here $\gamma_{T}$ = 0.07, and $\gamma_{R}$ = 0.14. Figure S10:
(a) The formation of strain stiffening with repeated application of strain
amplitude $\gamma_{T1}$=0.02 for N=20 cycles. The curves are plotted at
discrete values of N as indicated by the colorbar. (b) Following the 20 cycles
shown in (a), we apply one cycle of $\gamma_{T2}$=0.04 (see main text for
multiple memory protocol). After this, we apply the triangular wave with
$\gamma_{T1}$=0.02 for 20 cycles once again. We see that while the initial
curves show a very plastic behavior, by the 20th cycle, the system show some
strain stiffened once again. On applying the larger amplitude of
$\gamma_{T2}$=0.04, the encoded memory of $\gamma_{T1}$ gets destroyed as
evidenced by the initial few curves of (b). However, on repeatedly applying
$\gamma_{T1}$, the local structures are re-established and the system is able
to strain stiffen at this amplitude once again, thus re-encoding the memory at
$\gamma_{T1}$. This is possible because our system has inter-particle adhesive
contacts that are physical bonds between the particles and thus, are
reversible under breakage.


# Convening during COVID-19: Lessons learnt from organizing virtual workshops
in 2020

Mandana Samiei‡1 Caroline Weis‡2 Larissa Schiavo‡3
Tatjana Chavdarova4 Fariba Yousefi5
‡These authors contributed equally to this manuscript. 1 McGill University 2
ETH Zurich 3 formerly Open AI 4 EPFL 5 University of Sheffield
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
(August 2020)

###### Contents

  1. 1 Overview
    1. 1.1 Background
    2. 1.2 Un-conferences
  2. 2 Registration and participation
    1. 2.1 Call for Participation (CfP)
    2. 2.2 Sign-up
    3. 2.3 Participant submissions
  3. 3 Program
    1. 3.1 Program items
    2. 3.2 Timezones
  4. 4 Technology platforms
    1. 4.1 Considerations before choosing a technology platform
    2. 4.2 Potential broadcast platforms
    3. 4.3 Final choices
  5. 5 Dry runs
    1. 5.1 Breakout Session Leaders and Facilitators
    2. 5.2 Gather.town Poster Presenters
  6. 6 Day-of-Event
    1. 6.1 Schedule of activities
    2. 6.2 Volunteers
  7. 7 Communication internal and external
    1. 7.1 Planning stage
    2. 7.2 Day-of-event (DOE)
  8. 8 Evaluation and Postmortem
  9. 9 Diversity and inclusion
    1. 9.1 Funding
    2. 9.2 Safety as it pertains to special interest group workshops
  10. 10 Security and privacy matters
  11. 11 Common failure modes in Gather.town
  12. 12 Conclusions

##  1 Overview

This year, for the first time ever, Women in Machine Learning (WiML) decided
to host an “un-workshop” as its small event at ICML, instead of its
traditional meal-based event. Un-workshops focus on participant-driven
structured discussions on pre-selected topics. This format encourages
participant interactions, emphasizes attendee contributions, and provides
opportunities for future collaborations (see § 1.2). This event became a ICML
2020 Affinity Workshop held on Monday, July 13, 2020. Due to the global
COVID-19 pandemic, and ICML going virtual, the “WiML Un-Workshop” also went
virtual.

When confronted with the many challenges, restrictions, but also opportunities
associated with virtual events, many critical decisions have to be made with
limited data. In this manuscript, we – the organizers of the first WiML Un-
Workshop – share our experiences with the hope that it will help future event
organizers host successful virtual events under similar conditions. For
completeness, we list our main steps we took and decision-making processes,
some of which may apply to physical conferences too.

###  1.1 Background

This report is an account of the authors’ experiences as organizers of the
WiML “Un-Workshop” event at ICML 2020. For clarity, this event was different
from the “WiML Workshop”, which is usually co-located with NeurIPS. Women in
Machine Learning (WiML)’s mission is creating connections within a small
community of women working in machine learning, in order to encourage
mentorship, networking, and interchange of ideas and increase the impact of
women in the community. More information can be found on the official WiML
website (https://wimlworkshop.org/mission/).

###  1.2 Un-conferences

The “un-workshop” is based on the concept of an un-conference. Un-conferences
aspire to increase participants’ engagement by reducing formal elements of a
conference, such as talks and panel discussion, and encouraging informal
discussions between participants [2]. The format gained more traction in the
machine learning community with the first Self-Organizing Conference on
Machine Learning (SOCML), first organized in 2016, and repeated installments
in 2017 and 2018 [2].

WiML chose the un-conference format for its first ever virtual workshop after
the pandemic started. In virtual form, the format of a traditional conference
bears the risk of increased detachment between speaker and audience, and an
impersonal atmosphere. With four invited keynote speakers and 27 breakout
sessions organized and led by participants throughout the day [1], a large
focus was put on small-group discussion via video-conference chats. In our
experience this format allowed for un-workshop participants with different
scientific and cultural backgrounds to meet over the shared interest in a
specific breakout session topic, leading to a high-energy atmosphere with
lively discussions.

##  2 Registration and participation

###  2.1 Call for Participation (CfP)

Registration and call for participation were carried out using a Google form
designed by the organizers. This form should be designed very carefully, as it
will initiate the first communication between participants and organizers.
Failing to ask for necessary information in this step can make follow-up calls
and emails necessary. Moreover, ambiguously-phrased questions may lead to a
flood of emails asking for clarifications. With this in mind, we did several
rounds of redesigning this Google form. Organizers tested the form while
assuming the role of every level of participant (attending only, poster
presenter, etc.) to ensure that the consecutive questions are aligned even if
parts of the form were skipped.

After deciding on a rough format (invited talks, breakout sessions, and poster
session), we designed a form which contains the following sections:

  1. 1.

Personal information

  2. 2.

Breakout session proposal and leaders information

  3. 3.

Poster

  4. 4.

Facilitators and volunteers

  5. 5.

Registration fee funding

  6. 6.

Elaboration on status (exceptional circumstances, early career researcher,
etc.)

We asked all breakout session leaders and poster presenters to specify their
area of interests and this helped us to organize the sessions and talks along
with the program. We prepared the Call for Participation (CfP) and the
registration Google form almost in parallel. At this point the event was 8
weeks away. In the CfP, we introduced various ways of participating in the un-
workshop, presenting posters, leading a breakout session, moderating sessions,
and volunteering for technical issues.

###  2.2 Sign-up

The sign-up form was based on given templates from previous WiML workshops.
Due to the significant change in format and the virtual nature of the event,
it was difficult to predict the number of attendees based on past events. In
the initial phase, it was important to foresee different outcomes, such as the
requirement of volunteers, so as to include such questions in our sign-up
form.

The form also asked for participants to indicate a willingness to help as a
facilitator or a volunteer, to have a sufficient number of potential
volunteers to contact later on. Due to worldwide participation, we also
collected information on the preferred timezone of each participant, which was
later used to decide on the time and the span of the program, see § 3.2.

###  2.3 Participant submissions

Participants were offered two ways of submitting their own work/ideas to the
un-workshop, proposals for a breakout session, or submitting a poster.

#### Breakout session proposal.

A breakout session is a one hour free-form discussion overseen by 1-3 leaders
and with assistance from 1-2 facilitators to take notes and encourage
participant interactions. The submission included a one-page blind proposal
for which we provided a template in the CfP form. The names and the
biographies of leaders were submitted separately in the application form.

#### Poster submission.

We accepted submission either as an abstract in free text or posters in PDF
through the Google Form (see Section 4). As the format was virtual and there
was no physical limit to the number of posters, we wanted to give everyone who
wanted to present a poster the opportunity to do so. To review the large
number of submissions, in hindsight it would have been easier to settle on one
submission file format to find the content of each poster submission easily.
To speed up the review process, we recommend that organizers only ask for an
early abstract submission in the application form. Once the acceptance
notification was released, a final PDF version will be asked closer to the day
of the event.

#### CMT, EasyChair vs. Google Form.

We used a Google Form since CMT did not support PNGs at that time, however
PNGs are the only applicable format for posters in the Gather.town platform.
For the future, we strongly encourage Gather.town to allow for files in PDF
format in their interface. This way, organizers can consider CMT or EasyChair
over Google Form while in the first two platforms it is more convenient to
organize and evaluate proposals and posters.

##  3 Program

In this section, we will briefly outline how we decided on our un-workshop
program and discuss things needed to be considered. We chose a completely
different format of WiML workshop by introducing the concept of breakout
sessions. We minimized the number of talks to 4 and increased the number of
breakout sessions to 27 to encourage a dynamic spirit during the whole event.
This was a great opportunity for junior and early-career researchers to
communicate and network with leading people in the field.

###  3.1 Program items

  * •

Invited Talks

  * •

Breakout Sessions

  * •

Affinity Group Joint Poster Session

  * •

Panel

  * •

Socials (Welcome session and coffee breaks)

###  3.2 Timezones

Our goal was to provide an interesting program to participants from all
timezones, thus we needed relevant program points, such as invited speakers,
in the morning and in the evening. From the sign-up form, we knew in advance
to expect 70-80% of un-workshop participants to be based in North America and
Europe, leading us to schedule more breakout sessions in the second part of
our un-workshop. According to our exit survey, 70% of our participants were
satisfied with the proposed timeline.

##  4 Technology platforms

###  4.1 Considerations before choosing a technology platform

#### Robustness.

One of the primary concerns is robustness. With online events of an even
moderately large scale, a lot of platforms can cut out or fail, and getting
attendees to rejoin a meeting once that has happened can be difficult. A good
platform should be fairly robust against calls cutting out, network glitches,
etc. (assuming the host’s internet connection is stable, has high throughput
in Mbps, and is consistent).

#### Ease of use.

Ensure that the platform chosen for your event is accessible and easy to
understand for the participants, while avoiding the need for overly-difficult
software drivers, or proprietary hardware, etc. An important aspect to
consider is if it is likely that the participants are familiar with the
potential platform to use (e.g. if the same platform was used in previous
conferences).

#### Type of the event (panel, pre-recorded presentations, live Q&A, poster
session).

All our invited talks were prerecorded using SlidesLive (see § 4.2) and each
was followed by 5 minute live question/answering. The transition between pre-
recorded and live Q&A was smooth thanks to the integration between SlidesLive
and Zoom. Thanks to platforms like Zoom, streaming video as part of a live
event has gotten dramatically easier, but we strongly recommend virtual
conference organizers to consider a live component in their program to boost
attendee engagement. To encourage more interaction, we included 27 breakout
sessions which were held via Zoom and were fully live. These sections were
viewed as extremely helpful by participants, according to our exit survey.
Additionally, we also provided a 2-hour live poster session in Gather.town
where poster presenters and attendees could meet and talk around their
posters; this has been highlighted as the most fun part of our un-workshop and
got a lot of attention.

#### Length (hours, days).

We decided to hold the event on the first day of the conference, similar to
previous years, which also helps junior researchers to find a conference-buddy
during the event as in in-person conferences. The un-workshop lasted for 17
hours on July 13th. We spread the 10-hour program to 17 hours to cover almost
all time zones around the world.

#### Sessions in parallel.

We had 27 breakout sessions in total that were spread across 4 time slots. We
assigned 4 and 10 parallel streams in different sessions.

#### Number of attendees.

Almost 400 participants filled in our pre-event participation form. However,
SlidesLive informed us that our our un-workshop received around 2000 views
during the day of the event.

#### Cost.

While choosing the platform, make sure of the plans and pricing you need to
accommodate participants. For example, the standard plan of Gather.Town only
covers up to 500 people at the time whereas we needed 1000. So, we had to
subscribe for the premium plan. Also, for breakout sessions we had to enable
the breakout function in Zoom meetings and this was only designed for more
expensive plans. Therefore, we strongly recommend that organizers estimate
their needs, check the prices of different online platforms from available
companies, budget their available funding, and then make a purchase. Zoom
enterprise plan hosts up to 500 participants which can be extended to 1000 by
using optional add-on plans for large meetings.

###  4.2 Potential broadcast platforms

Some of the most popular broadcast platforms are discussed in the following.

#### Zoom.

This works well for webinars or breakout rooms. There are several options that
have differing costs, number of allowable attendees, and tools (chat, video
for attendees, poll). Overall, this seems to be a more robust option that can
handle a large number of participants. It can accommodate screen sharing,
either by the host or by a designated participant. The whiteboard feature is
generally useful, and can allow for multiple participants to contribute at the
same time, but there is some lag and drawing and note taking features are very
basic at times.

Zoom has the distinct advantage of being the “lingua franca” of remote
conferencing - many people are familiar with it, have the drivers and
additional software installed already, etc. We were able to ask breakout
session organizers to use their own personal Zoom links if they happened to
have the paid version of Zoom (often provided by universities and employers).
This saved a lot of headaches, as we were able to outsource some of the “stage
management” to the breakout session leads.

#### SlidesLive.

This was provided to us by the ICML conference organizers. We were required to
upload pre-recorded videos well in advance of the event to allow time for
video quality conversion and quality control, which was provided by
SlidesLive.

#### Sli.do

Sli.do is principally a platform for audience members to ask questions to a
panel. It allows for upvoting of questions by other audience members. This is
a very smooth interface, and provides some advantages over “traditional” in-
person conference queues for asking questions, or working off of questions
asked in a chat interface on, e.g., Zoom. It can be much less intimidating to
propose a question via Sli.do, watch others +1 your question, and then have
the question posed to the panel by a moderator, as compared to asking
panelists directly. The upvote feature also allows for more democratic
prioritization of questions. Sli.do offers a polling option as well to
increase participant engagement, which seems promising, though we didn’t
thoroughly test it.

#### Gather.town.

Gather.town is a virtual gathering space where you can host an online
conference, create your customized virtual office, hold a social with your
friends and watch a movie together etc. Check their website
(https://gather.town/) for more information

#### Crowdcast.

An in-browser platform that is inexpensive, accommodates a very large
audience, is easy to handle, allows for parallel sessions with only one link,
and makes recordings directly available. The “Ask a question box” is pretty
useful. It is also accessible via Zoom webinar – which makes the Q&A sessions
easier to handle and more productive than with a chat only. It also offers a
green room, which allows organizers and invited speakers to check the setup
before going live or while broadcasting live.

#### YouTube Live/Facebook Live.

While it is a familiar platform that can be paired with Zoom, this is better
suited for more casual presentations. It might be better suited to situations
where audience interaction can be less information-dense (e.g. general
reactions as are available on Facebook), questions can be queued more easily
and are much more ephemeral - generally, the chat feature does not allow for
deep connection between attendees. There is also limited user-verification -
who’s to say, e.g., YoshuaBengio on YouTube is actually the Yoshua Bengio?
Similarly, many use their personal email accounts for YouTube and do not have
their professional emails linked to YouTube - maybe a prominent researchers’
YouTube handle is * SuperCoolFortniteStreamer * .

###  4.3 Final choices

#### Breakout sessions.

For the breakout sessions, we used Zoom meetings and enabled the breakout room
functionality. An alternative to Zoom could be Hopin. It is much easier than
Zoom since the organizers do not require to collect the Zoom links and
passwords. They can just create an event in Hopin and create multiple parallel
sessions. Also, Zoom provides whiteboard functionality which is pretty helpful
for breakout sessions. If collecting Zoom links is not an issue, we would
still suggest using that.

#### Poster session.

For poster sessions, we used the Gather.town platform. Gather.town was a very
joyful environment for both presenters and attendees. It was very similar to
in-person poster sessions and having real human interactions. However, we had
some issues in uploading posters and links to their GUI. Also, many
participants reported that they couldn’t get inside the poster session even
though we had capacity for more attendees. Poster sessions on Twitter can also
be interesting as it encourages engagement between people who are not
registered for the conference.

#### Panel.

For our panel session, we relied on the broadcasting/streaming infrastructure
provided via SlidesLive paired with the Zoom conference call functionality we
were well versed with. All participants in the panel were able to hop on
pretty easily and frictionlessly.

#### Invited Talks and Q&A.

For our invited talks, we used the Sli.do platform. We had a moderator on-call
to screen the questions and select the most popular ones and re-direct them to
an organizer to ask the speaker.

##  5 Dry runs

We held two different types of dry run, one for breakout session
leaders/facilitators over Zoom and another one for poster presenters at
Gather.town platform.

###  5.1 Breakout Session Leaders and Facilitators

We recommend all leaders and facilitators arrive in the Zoom link at least 15
minutes before the breakout session starts, then the leader who is host of the
Zoom link can make all other leaders and facilitators co-hosts. Leaders and
facilitators can use this time to get familiar with the whiteboard, breakout
rooms, security toolbar, etc. We also recommend leaders and facilitators of a
breakout session stay in contact via a private side channel (e.g. group
message), turn off desktop notifications during the breakout session and mute
when they are not speaking.

###  5.2 Gather.town Poster Presenters

We asked all the presenters to:

  1. 1.

Select the right avatar for poster presenters

  2. 2.

Test Gather.town experience – walk around, view someone else’s poster, etc.

  3. 3.

Test Gather.town controls – how to change video quality.

  4. 4.

Test the video, audio, internet connection (see Gather.town Technology
Requirements to troubleshoot).

  5. 5.

Test the interaction distance (see Control toolbar) to find an interaction
distance that is comfortable for you.

#### Here is the general dry run checklist for all speakers and participants

  1. 1.

Internet speed

  2. 2.

Audio quality and video quality (including position of camera and lighting)

  3. 3.

Familiarity with the platform used, i.e. sharing screen etc.

##  6 Day-of-Event

###  6.1 Schedule of activities

As previously mentioned, we arranged the conference so that there were two
different time blocks - one block of talks and breakout sessions that were
compatible with participants in Europe, Africa, and parts of Asia, a
significantly long break, and a second time block with better compatibility
for those in North America. The organizers worked in shifts to serve as
organizer-on-call. The organizer-on-call for a given shift was tasked with
hosting the main Zoom room (where the conference talks were taking place),
serve as point of contact for supervolunteers, and to act as a liason for the
live panelists when necessary.

###  6.2 Volunteers

#### Volunteer structure

Due to the wide range of time zones that our event covered, we implemented a
structure of shifts where “supervolunteers” (those who have volunteered for
WiML in the past, or have a particularly deep knowledge of Zoom), who reported
to an organizer, supervised all other volunteers. This was to allow reasonable
waking hours and increase the available bandwidth for the organizers - for
example, the first shift began at 9AM CET, which is very early in the morning
for those in, e.g., PST timezones. Generally, we divided up the
responsibilities among the different volunteers based on tasks:

  1. 1.

_Moderators_ : Check all communication channels, including Slack, Zoom, and
Rocket.chat, for violations of the Code of Conduct. Moderator volunteers were
asked to familiarize themselves with the Code of Conduct.

  2. 2.

_Tech Support_ : Help with issues related to getting Zoom / Slack /
Rocket.chat / Gather.town up and running, troubleshoot minor technical
details, etc. Mainly answered questions posed in a dedicated Rocket.chat
channel that was accessible to all attendees. These volunteers were referred
to a handful of internal documents to use as guides during their shifts.

  3. 3.

_General Infodesk_ : Guide participants regarding general organizational
questions that would not fall under the jurisdiction of “tech support”.
Mainly, they answered questions posed via a dedicated public Slack channel.
These volunteers were asked to make themselves very familiar with the schedule
of the conference.

We requested that volunteers check in with their supervolunteer supervisor in
their team’s dedicated private Slack channel (within the Slack team intended
for all attendees) that was only visible to volunteers and supervolunteers of
the same assignment (e.g. #ModeratorVolunteers, #TechSupportVolunteers, etc).
We provided volunteers with access to a handful of documents designed to cover
anticipated questions, and asked that they familiarize themselves with these
documents and use them as reference during their shifts. Creating volunteer
documentation in an FAQ format resulted in surprisingly few questions from
confused volunteers on the day of the event. While participants were
encouraged to use Rocket.chat to ask technical support questions, we saw a lot
of participants use the general-information Slack channel to ask technical
support questions regardless. Other organizers should perhaps anticipate that
participants who need help at an online event might ask in channels outside of
a designated “help line”.

##  7 Communication internal and external

As in any collaborative project, communication is the most vital part to
ensuring effective teamwork. This is particularly true when organizing a
virtual event over a relatively short period of time.

###  7.1 Planning stage

In the introductory meeting with all the organizers, a weekly meeting was
fixed that worked with everyone’s schedule. Particular care should be taken
with considering the timezone of all members. Meeting times early in the
morning of one member might result in that member not being up to speed with
the latest developments on the project; meeting times late at night for
another member can prevent said member from participating in tasks that is
decided on during the meeting and are easy to solve right after the meeting,
with the whole team being updated and together. With members from central
Europe, West- and East-coast North America, all of our meetings had to be held
between 5pm and 10pm GMT/UTC. Each organizer had a task domain, so each task
could always easily be assigned to one person responsible overseeing the task.

#### Weekly meetings

The meeting schedule consisted of weekly Zoom meetings including all five
organizing members. Approaching the day-of-event, additional meetings were
called for subteams to work on specific tasks. From our experiences we want to
emphasize the importance of “overcommunicating” during these meetings. With
members spread out over several timezones, it can easily occur that member
have asynchronous levels of information, the task assignment is not clear etc.
Short recaps of what the current state is and next steps are in every tasks
can prevent misunderstandings and delays.

We and the other organizers of the un-workshop established a Slack channel to
communicate with each other and with members of the WiML Board of Directors.

All files, spreadsheets, drafts of publications (emails$/$Twitter$/$website)
were shared in a shared Google Drive accessible to all organizers and WiML
board members overseeing the organizers. Having a shared drive is highly
recommended for projects with a high level of bureaucracy, shared spreadsheets
etc., to avoid these documents being cluttered on a Slack channel and hard to
manage at a later point in time.

###  7.2 Day-of-event (DOE)

On the day-of-event, we anticipated numerous inquiries from participants
regarding e.g. problems with the technology platforms used and questions
regarding the program and organization of the event. In order to keep the
organizers free to handle any emergencies during the event, we implemented a
hierarchical volunteer structure to address incoming questions from attendees.
In this structure, incoming questions are directed at volunteers who worked in
1-hour shifts and were recruited from the attendees who indicated interest in
their sign-up form. We paid attention to the volunteers being familiar with
the technology platforms used. Above them, nine supervolunteers were
responsible for handling questions from volunteers and organizing check-in and
-out of volunteers for their shift. The supervolunteers were recruited from
attendees experienced in organizing similar events. Only if supervolunteers
could not solve a particular request, it was directed to the un-workshop
organizers.

The platform used for particular communications were:

#### Attendees - Organizers/Volunteers.

Attendees who watched the live stream online could use the Rocket.chat feature
on the _icml.cc_ website to give immediate feedback on the event or ask
questions to volunteers.

#### Volunteers - Supervolunteers/Organizers.

For the DOE we opened an additional Slack (external) workspace to coordinate
volunteers and provide a channel where volunteers’ questions could be answered
by supervolunteers, volunteers could check-in and check-out in private
channels, and supervolunteers and un-workshop organizers could communicate
with one another. This Slack workspace was also available to general
attendees, albeit with restricted access to volunteer-only channels.

#### Organizers - Organizers.

For internal communication in the organizing team, the internal Slack channel
was used and phone numbers were exchanged for urgent matters.

##  8 Evaluation and Postmortem

We sent a survey to all participants who signed up for the un-workshop to
gather feedback on their experience. Specifically, we asked for feedback on
each program item, and if it should be repeated in the way it was held.

#### Exit survey results.

The talks and panel discussion received good ratings and – unsurprisingly, as
their format was most similar to the usual in-person conference format – were
liked by attendees as-is. For the Poster Session, several people reported that
they did not attend, presumably due to the late timing in Europe or the
technical issues with the platform, but feedback was positive from the
participants who attended. Breakout sessions received very positive feedback,
with more than 95% responses recommending to repeat them as they were held or
only with minor changes. The feedback for the split schedule was positive,
with three-quarters of responses indicating that the timezone worked for them.

#### Potential improvements.

We came up with several potential improvements for future virtual events.
Firstly, we used many different platforms to provide the optimal technology
for each program item. This lead to some confusion as how to access the
different channels, i.e. breakout session Zoom rooms, livestream on _icml.cc_
, pre-recorded videos of posters etc., One improvement could be to provide a
guide, ideally as a video, to demonstrate the navigation through the different
program points and how to access each program element.

Other improvements could be to check for capacity issues within the chosen
platform(s) for activities with many simultaneous interactions of
participants, i.e. the poster session. Scheduling different groups to
interact, or having several poster sessions at different timepoints could
prevent issues regarding capacity.

##  9 Diversity and inclusion

#### A global conference.

One of the unintended benefits of ICML switching to a virtual conference
format was the increase in participants who might have otherwise been unable
to attend. This had ramifications for how our scholarship funding was used and
the composition of our workshop attendees, among other factors.

###  9.1 Funding

This year, due to the virtual form of events, WiML could offer registration
fee funding (to every person who applied, and met the criteria for receiving
funding). Online conferences tend to be many times cheaper than in-person
conferences, both in terms of covering registration as well as in terms of not
having to pay for travel/lodging.

Reimbursement directly through ICML required an arrangement between WiML and
the ICML Organizers. Other conference organizers might need to negotiate this
payment arrangement. This is also significant as we received a lot of
applications for funding coverage from people in developing economies, where
the registration fee in virtual conferences is considered a large amount of
money that would require a loan or credit line to be extended, etc. Thus,
having a registration fee funding system where attendees pay the fee, and are
then later reimbursed for the cost might result in some scholarship recipients
not attending at all. Other online conference organizers might want to try to
negotiate reimbursement before the conference taking place, to avoid the
requirement of attendees paying the fee upfront.

#### Passlist.

In addition to “approving” attendees through a passlist to avoid unwelcome
interlopers, one might also want to check the list of words that are blocked
from use or are otherwise penalized. For example, some often-misused (but in
this case, necessary and applicable) words were banned on one of the platforms
we used during our joint poster session with other affinity groups, Queer in
AI and Latinx in AI.

###  9.2 Safety as it pertains to special interest group workshops

A virtual environment brings with it a number of challenges related to
anonymity and appropriate behavior, some intentional, some unintentional.

#### Code of Conduct (CoC).

We recommend using a registration page as an excellent opportunity to ensure
that your attendees have read the Code of Conduct for your conference, if your
organization has a code of conduct published. We required the WiML Code of
Conduct (https://wimlworkshop.org/conduct/) to be followed by all
participants, organizers, speakers, presenters, and sponsors of WiML ICML Un-
Workshop; confirmation that a participant would adhere to the code was
required in order to register for the event. Organizers of a conference have
to decide on the code of conduct statements early on in the planning process,
as this is a very important aspect of virtual events and allows safety and
privacy to be preserved during the online events. Inappropriate or
unprofessional behavior that interfered with another participants’ full
participation was not to be tolerated at the event. Any violation of the CoC
was to be reported through email to the organizers and board of directors.
Fortunately, we encountered no CoC infractions.

#### Avoiding Zoombombing.

With the advent of more and more remote conferences relying on Zoom, a handful
of issues have come to the forefront. Due to these issues, some companies
avoided using Zoom to avoid “zoombombing”. The threat of malicious attacks is
especially heightened given that attacks of this type in an online environment
often disproportionately target women and people of color (see Gamergate,
Anima Anandkumar’s tweet regarding getting zoom-bombed at the Knowledge
Discovery in Databases (KDD) conference) and what to do in case that happens
(see Wiebke Toussaint’s tweet).

#### Suggested Actions To Take.

As an organizer, make sure to keep Zoom links private and difficult to guess,
perhaps accessible only behind a password-protection wall. Also, explicitly
communicate to attendees that Zoom links should not be shared with those
outside of the conference and emphasize that they should use password
protected Zoom links.

##  10 Security and privacy matters

#### Full Name Policy.

Across all platforms, we encouraged participants to use their real full name
rather than their nicknames, Twitter handles, etc. This was to deter bad
actors from attending the event while pretending to be someone else, and then
possibly breaking the code of conduct. Implementing a “full name” policy might
also have advantages in follow-ups after the conference - it is easier to find
and contact someone afterwards by searching according to name versus once-used
Slack handle.

#### Chatham House Rules.

While typically more common in policy conversations, it may be helpful to
consider applying Chatham House Rules to your breakout sessions. This
effectively means that what is said can be on record, however, it will not be
attributed to a specific attendee. This may be helpful for accommodating
people who refuse to be on record due to privacy concerns.

#### Security considerations for Gather.town.

To ensure all poster presenters and participants are identified, we asked
everyone to only use their real full name in all platforms, including those
used for the poster session, namely Zoom and Gather.town. We defined a visual
code for particular roles in Gather.town, e.g. all volunteers in Gather.town
should have chosen an avatar with a red t-shirt, organizers were identified by
their avatars wearing a green t-shirt, and diversity and inclusion (D & I)
chairs’ avatars wore blue ones. Access to Gather.town was gated to only ICML
registered attendees. We did encounter some issues with those who registered
at the last minute for ICML. If there is some way to dynamically update a
passlist on Gather.town in the future, this would be a major improvement.

##  11 Common failure modes in Gather.town

Before the un-workshop, we communicated the following to participants so that
they could be prepared in case of any issues during the event.

#### Permission problem to access video and microphone.

Upon entering Gather.town, you will be asked to give Gather.town permission to
access your video and microphone. If you do not provide permission or block
the pop-up, you will see this message: “We need your video or audio permission
for Gather.town to run properly”. Read Gather.town instructions
(https://gather.town/video-issues) for how to fix this.

#### Audio/Video issues.

There are several reasons this could be happening. Each user can see a maximum
of eight other users’ videos at once, other users can use audio-only (can only
be heard, not seen). If a user cannot see more than a few videos, this might
be one reason. Another possible reason is that another application is
capturing the microphone or camera. For example, Zoom on Windows takes full
control of the camera. Close any other apps that might use the microphone or
camera. Yet another reason is that if the internet connection is not very
good, video may be cut automatically, leaving only audio. You can lower your
Gather.town video quality and close any other programs that might be using
Internet bandwidth. Also, consider moving to a better location in your room to
see if you can get better WiFi.

#### Physically blocked, cannot move.

If you are blocked on all four sides and cannot move, a pop-up should appear
asking if you want to move away. Press the key for “yes” so that your avatar
gets “transported”. If you are blocked and a pop-up does not show up, ask for
help in the public chat.

#### Wrong camera/microphone input.

Read Gather.town instructions (https://gather.town/video-issues) on how to
switch your camera/microphone input.

##  12 Conclusions

Despite the organizational challenges, the un-workshop program was carried out
with no disruptions, and most importantly, with great engagement and
participation from our attendees. Our attendees had the opportunity to: (i)
learn and discuss scientific and technical developments in machine learning,
(ii) meet and discuss with talented researchers, and (iii) promote some of the
work done by women and non-binary persons working in our field. The virtual
setting, reduced price of the ICML conference, and WiML funding to fund ICML
conference registration fees for un-workshop attendees all likely contributed
to high attendance at the un-workshop. Based on our post-event surveys, for
many of our attendees, this was their first-ever machine learning conference,
and a good opportunity to meet with colleagues interested in similar topics,
as well as promote their work. We are heartened by this success.

## Acknowledgements

The authors would like to thank Amy Zhang, Sarah Tan, Jennifer Healey, Ti
John, and Nils Murrugarra for their support and feedback on this or earlier
versions of the report. The WiML Virtual Un-Workshop could not have happened
without contributions from sponsors, volunteers, and many others as
acknowledged in https://wimlworkshop.org/icml2020/committee/, as well as
collaboration with affinity groups such as Queer in AI and LatinX in AI.

## Disclaimer

The views expressed in this document are solely those of the authors and do
not represent the opinions of any entity whatsoever with which they ever have
been, are now or in the future will be affiliated. In particular, this
document does not express the opinion of WiML, WiML Board of Directors, WiML
sponsors, or ICML. The authors of this document were recruited to help
organize the WiML event at ICML, which this year was held as an ICML workshop
titled _"WiML Un-Workshop”_. For the purposes of disambiguation, this event
was not _“WiML Workshop”_ which is a separate event co-located with NeurIPS.

## References

  * wim [2020] WiML Un-Workshop 2020 at ICML program.  https://wimlworkshop.org/icml2020/program/, 2020.
  * Goodfellow [2019] I. Goodfellow.  A quiet year for SOCML.  http://www.iangoodfellow.com/socml2019.html, 2019.