# VCR: Video representation for Contextual Retrieval

Oron Nir  0000-0002-8443-5014 Reichman University and Microsoft
CorporationHerzeliyaIsrael ,  Idan Vidra  Reichman UniversityHerzeliyaIsrael ,
Avi Neeman  Microsoft CorporationHerzeliyaIsrael ,  Barak Kinarti  Microsoft
CorporationHerzeliyaIsrael and  Ariel Shamir  Reichman
UniversityHerzeliyaIsrael 0000-0001-7082-7845

(2024)

###### Abstract.

Streamlining content discovery within media archives requires integrating
advanced data representations and effective visualization techniques for clear
communication of video topics to users. The proposed system addresses the
challenge of efficiently navigating large video collections by exploiting a
fusion of visual, audio, and textual features to accurately index and
categorize video content through a text-based method. Additionally, semantic
embeddings are employed to provide contextually relevant information and
recommendations to users, resulting in an intuitive and engaging exploratory
experience over our topics ontology map using OpenAI GPT-4 (GitHub).

Video Representation, Archive Exploration, Media Search

††copyright: acmlicensed††journalyear: 2024††doi: XXXXXXX.XXXXXXX††conference:
; March 08–10 ,2024; Shanghai, China††journal: POMACS††journalvolume:
37††journalnumber: 4††article: CE202††publicationmonth: 8††submissionid:
CE202††ccs: Information systems Video search Figure 1. Video Archive Explorer
empowers media users in content discovery scenarios while searching for a
video with semantic similarity over multimodal insights. By browsing our
Topics-Map users can tune their search while looking for semantically similar
videos in the archive using OpenAI GPT4 and video insights embeddings.
Insights extracted from TED.com.

##  1\. Introduction

Managing vast amounts of unstructured media content at scale poses a
significant challenge for media and internet companies. Although content
categorization by topics is an intuitive approach that facilitates content
search, it is often deductive and may not appear explicitly in the video. For
example, a video about “Politics” may not mention the word “politics”, making
content categorization an even more complex problem to solve. Manual tagging
is a common approach, but it is costly, time-consuming, error-prone, requires
periodic curation, and does not scale well. The success of data visualization
user experiences lies not only in the design of the depiction but also often
in the underlying data representation. We propose an approach that employs
multimodal video _representation_ and a _visualization_ tool based on topic
detection and tracking in video (TDT) for a holistic cross-video semantic
similarity exploration.

Videos convey information through multiple perception channels, including
speech, visual text, actions with objects, scenery context, and individuals’
identities, which all influence the message conveyed in the video. Hence, to
properly address video indexing a multimodal approach is required. Multimodal
machine learning algorithms have been widely researched to capture such
patterns (Ngiam et al., 2011; Chivadshetti et al., 2015; Qian et al., 2015).
Multimodality is also the approach that humans naturally leverage to capture
the concepts in a video. However, videos are spread across various domains,
including web video, social media, entertainment, and more, and each type
delivers information in a different mixture of channels. For instance,
educational content and news broadcasts typically convey heavier visual text
information than other genres, while featured films and entertainment
typically feature famous people and conversational data.

In this paper, we focus on high-information density videos and generate a
semantic representation that captures their main themes. We targeted
organizational, educational, and entertainment media content types due to
their high information density levels in automatic speech recognition (ASR)
transcription, Optical Character Recognition (OCR), and contextual visual
captioning. We evaluate our proposed methodology on the TED repository by the
TED organization (TED, 2023) and using the MSR-VTT dataset (Xu et al., 2016).
Still, the proposed methodology could be easily extended with additional
sources to enable the recognition of new video types for different domains,
such as animal recognition for nature films. The TED dataset, we suggest, was
obtained from publicly available TED talks online. The model takes as an input
the video transcription, optical character recognition, and frame captioning
from both textual and visual information, and is evaluated on various tasks.

We propose an interactive media archive explorer user experience we call
Topics-Map. It is designed to guide the user through the concepts conveyed in
its media archive. We utilize topics ontology visualization and semantic
representation in 2D. The main search functionality is based on the set of
selected topics. Thus, the map enables an intuitive adjustment to the query
while refining the search to discover related videos.

Our content discovery approach divides into offline indexing and the online
querying mechanism - the Topics-Map. The offline step runs the aforementioned
models on the archive. To fuse different video insights together we decode all
details into a time-based interleaved textual format and encode it again using
Large Language Models (LLMs) into a latent semantic representation. At
inference time, the user specifies a set of topics, using the Topics-Map,
while the system leverages GPT-4 LLM to generate a comprehensive video
description that depicts the type of video the user would like to explore.
This textual description is embedded using LLM and efficiently ranks the
entire archive according to the query. This process yields a ranked list of
relevant videos (Figure 1).

The key contributions of this paper are: (1) The development of a novel
methodology for multimodal text-based video representation using a LLM with
SoTA results on unsupervised Text-Video Retrieval task over MSR-VTT, (2) A
visualization tool that allows effective content discovery on a large media
archive - using OpenAI GPT-4 and state-of-the-art text encoders, (3) the TED
multimodal text-based dataset and MSR-VTT (Xu et al., 2016) multimodal text-
based dataset. We provide a full evaluation of the proposed methodology on the
TED dataset, a user study, and the code. Overall, this paper provides a new
visual approach for exploring video archives that can help organizations
transform their media content into business value more efficiently and
effectively.

##  2\. Related work

Topic modeling (TM) is a well-established field in natural language processing
(NLP) that has been widely explored for TDT. TM dates back to the development
of pLSA/I (Hofmann, 1999) and LDA (Blei et al., 2003), which both took a
generative probabilistic approach for word-topic vs. topic-document
distributions. Other significant developments in this field include
Hierarchical Dirichlet Processes (Teh et al., 2006), Word2Vec (Mikolov et al.,
2013), and GloVe (Pennington et al., 2014), which paved the way for subsequent
neural network architectures. Multimodal TDT have been explored with various
methods for feature fusion including classification-based fusion, clustering-
based fusion, and online TDT feature fusion. E.g., Virtanen et al. (Virtanen
et al., 2012) proposed a hybrid approach that integrates LDA and canonical
correlation analysis to embed different modalities and classify topics
simultaneously. Harakawa et al. (Harakawa et al., 2018) applied a clustering-
based approach using graph clustering with Modularity Maximization. (Li et
al., 2016) developed an online-based TDT approach using a learning algorithm
that incrementally adapts to reveal new topics.

Recurrent Neural Networks (RNNs) have been used for document summarizing and
classification e.g. SummaRuNNer (Nallapati et al., 2017) which uses GRU (Chung
et al., 2014) to summarize texts. As RNNs have been challenged to avoid the
vanishing and exploding gradients problem over the years, even with advanced
works like ELMo (Peters et al., 2018), other architectures based on the
Attention mechanism have managed to significantly push the boundaries of LMs
with the Transformer (Vaswani et al., 2017) architecture. The BERT (Devlin et
al., 2018a) architecture leveraged the Transformer mechanism to encode
semantics. RoBERTa (Liu et al., 2019) has optimized the same architecture for
several tasks. BART (Lewis et al., 2019) is one of many strong incremental
works that followed it. Another important challenge towards our goal is long
texts classification. The LM’s capacity poses a challenge while working with
long texts like video ASR. This difficulty is addressed in many ways e.g.
Longformer (Beltagy et al., 2020), Big-Bird (Zaheer et al., 2020),
Hierarchical DocBERT (Adhikari et al., 2019) and others. Recent developments
by OpenAI have introduced the generative pre-trained transformer (GPT) family
of LLMs (Radford et al., 2019; OpenAI, 2023) which introduced ground breaking
advancement in language understanding and generation.

Another challenging aspect is the size of the label set. Chang et al. (Chang
et al., 2020) have analyzed the extreme multilabel text classification (XMC)
problem on product descriptions queries online for a retail website. They have
demonstrated their work, X-Transformer, on MNLI GLUE (Wang et al., 2018) with
$2K$ labels, on Wiki-500K (Varma et al., 2019) with $500K$ labels, as well as
on XMC with up to $1M$. The reported metrics were precision@$k$ and
recall@$k$: $k\in\\{1,3,5,10\\}$. X-Transformer’s architecture was designed to
avoid limiting the representation capacity by using prior adaptation methods
like ELMo (Matthew, 2018) and not LMs thus excluded from this analysis. One
powerful LM architecture that followed the aforementioned works is Decoding-
enhanced BERT with Disentangled Attention or DeBERTa (He et al., 2020). Its
followup paper V3 (He et al., 2021) has significantly improved its performance
using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing.

Large video archive retrieval is a basic functionality at the core of any
media archive explorer. TRECVID (Chaisorn et al., 2010) have suggested a
method and the known-item search (KIS) task for locating a single true answer
for a textual query over a video archive in a multimodal search. SEA was
suggested for a text-based search over multimodal set of features leveraging a
sentence encoder for Ad-hoc search (Li et al., 2020). The evaluation task was
Ad-hoc Video Search (AVS). The main difference between the two tasks lies in
the KIS assumption for a single correct answer while AVS accepts more. Text-
Video Retrieval is the formal KIS task performed today over datasets like (Xu
et al., 2016; Hendricks et al., 2018; Rohrbach and Park, 2019; Miech et al.,
2019; Huang et al., 2020; Bain et al., 2020; Soldan et al., 2022) with VAST
(Chen et al., 2023) being the SoTA on many of them. SoTA methods today use all
relevant modalities e.g., audio, video, and captions, while depending on an
over-fitting phase towards a dataset and a downstream task.

Large scale media archive corpus visualization is an important area of
research that has been gaining more attention in recent years. With the ever-
growing amount of multimedia data available, it has become crucial to develop
effective methods for exploration and analysis. One of the recent efforts in
this direction was conducted by Wu et al. (Wu and Qu, 2018). John et al. (John
et al., 2019), who proposed a method based on topic inference and clustering
for visualizing multimedia news data. Renoust et al. (Renoust et al., 2021)
suggested a featured films archive browser using a multilayer network modeling
approach that was validated over the IMDb dataset. These studies demonstrate
the importance of large scale media archive corpus visualization for
exploration and analysis of multimedia and provide valuable insights into the
potential of different approaches towards this goal.

Several other studies have developed large-scale media archive exploration
visualization tools. Television broadcast archive visualization applications
(Michael et al., 2009; Haesen et al., 2013; Mühling et al., 2022). Others have
developed visual analysis systems for exploring large-scale multimedia news
archives using a combination of topic modeling and visualization techniques as
well as a facial recognition based politics TDT (Renoust et al., 2016; Ren et
al., 2019). Nir et al. (Nir et al., 2022) have suggested applications for
animation. Shahaf et al. (Shahaf et al., 2013) have structured storylines
visualization of metro-map narratives. A recent paper by Afzal et al. (Afzal
et al., 2023) conducts a wide survey on media corpus visualization. One
interesting work is VIAN (Halter et al., 2019) which suggested a novel
artistic color control through a user experience which helps post production
analysis and editing of color according to its semantics. VisTa (Fan et al.,
2019) was suggested for assisting in video tagging. Other related domains
included sports broadcast, image collages, surveillance footage visualization,
and more.

In recent years, online education through Massive Open Online Courses
platforms, like Coursera, have gained immense popularity (Shi et al., 2015).
Platforms that offer these courses are interested in analyzing engagement with
the course material through web access logs or clickstream data (Chen et al.,
2018). Visual analytics tools, such as PeakVizor (Chen et al., 2015), enabled
experts to gain insights from raw data. He et al. (He et al., 2018) developed
a visual analytics system, VUSphere, for the exploration and comparative
analysis of video utilization in courses, students, and distance-learning
centers. The system provided multiple interactive views of the video
utilization, such as video access and completion rate, to support the
identification of useful insights. We aim to empower users towards semantic
content discovery using a text-based video insights fusion and LLMs.

##  3\. Multimodality

Automatic speech recognition (ASR) takes a raw audio file and recognizes the
spoken text. This classic task have surpassed human parity with the work of
(Xiong et al., 2016) and therefore this is the service used to extract spoken
text with in this work. We believe that the conversations in the video are a
strong video signal for different media types e.g., in Education.

Optical character recognition (OCR) is a classic computer vision task that
extracts the visual text appeared in an image. OCR tracking in videos requires
higher level of recognition to match permutations of the same text over
multiple frames where it may vary due to recognition errors, occlusions, and
other effects. To overcome these challenges and yield a corrected result per
video, a consolidation algorithm was applied using multi-sequence alignment
(Wemhoener et al., 2013) to cluster and correct unique texts out of all
permutations in an unsupervised manner. The underlying hypothesis of the OCR
signal usage to represent video topics could be easily explained with examples
like a presentation’s deck, the titles on a news broadcast, the rolling
credits of films, or even social media content. All examples above describe a
natural information communication that we humans easily perceive hence, OCR
was selected as a source signal. The underlying method is based on Florence
(Yuan et al., 2021).

Frame Captioning has been an active research area in computer vision, and
recent advancements have shown promising results in generating accurate and
meaningful captions for images. Florence (Yuan et al., 2021), a SoTA image
captioning model, utilizes a transformer-based architecture to generate
captions that capture fine-grained details and contextual information.
However, the application of image captioning models to video frame description
poses unique challenges, as it requires the generation of temporally coherent
and contextually relevant captions that convey the narrative of the video. One
of the key challenges is to maintain the continuity of the context across
multiple frames, as the temporal relationships among the frames can be complex
and non-linear. Another challenge is to balance the trade-off between
generating detailed and accurate descriptions for individual frames vs.
producing a coherent and concise narrative for the entire video. To address
these challenges, we aim at leveraging a LLM to fuse the multimodal
information.

##  4\. Method

Figure 2. Our Text-based multimodal video representation with LLMs.

In this section, we first present our approach for content-based video
retrieval using Ad-hoc Video Search (AVS) (Li et al., 2020) on multiple AI
models and then describe the interface. Our method is designed to understand
conveyed information in media files per channel by leveraging machine vision
and audio analysis algorithms. We then aggregate these video insights into a
semantic vector representation (see Figure 2). Specifically, we utilize
different LLMs to embed the interleaved content prompts in a latent semantic
space which serves as the video index. To enable efficient content discovery,
we propose an application that allows users to search the video archive by
embedding an auto generated language query from selected topics on the Topics-
Map, which is then used to rank the search results with Cosine Similarity. We
compare both a supervised learning approach on language models such as BERT,
RoBERTa, and DeBERTa, as well as leveraging the popular GPT-based OpenAI
embeddings.

###  4.1. A supervised approach

To represent the recognized video insights semantically we first fine-tune a
LLM to classify these insights according to the video topics using Multilabel
sequence classification and Binary Cross Entropy. The hypothesis behind
selecting the topics of the video as the pretext task is since topics are by
definition the holistic subject of the video which makes a suitable trait
while trying to distinguish videos or look for content which is semantically
similar.

During training, a sampling method was used to fit the backbone LLM towards
representation. First, an initialization step, data indexing mapping labels to
documents. Next, a batch sampling method, sampling a label, a document, and a
$512$ token window. Using this data loader method, a text segment is sampled
in constant time and the window size is kept constraint to avoid long text
memory inefficiency. Its hypothesis is that any sampled window holds the same
global multilabel topics.

Our approach takes a textual representation of insights ordered by the time of
appearance and name the main topics of the video. We compare different
backbone architectures: BERT (Devlin et al., 2018b), RoBERTa (Liu et al.,
2019), and DeBERTa (He et al., 2021). Each LLM is trained on a random text
segment of 512 tokens and evaluated on the mean pooling of sliding windows of
the same size without intersection.

###  4.2. A learning-free approach

The recent development of GPT has shown significant advancements in NLP. GPT
is a LLM that has been pre-trained on a massive corpora of diverse texts,
making it capable of generating high-quality text outputs with coherent and
fluent language. Besides its capabilities in text generation, GPT can also be
used as an encoder to extract features from textual input. GPT-based encoders
have shown promising results in various NLP tasks such as text classification
and question-answering. GPT can also be used as an encoder-decoder
architecture for text generation. This approach have shown success in various
applications like chatbots, translation, and summarization (Radford et al.,
2019).

Like the supervised approach, we first run the aforementioned video AI models
on the entire archive. Then we split the extracted insights into semantically
coherent segments of up-to $4,096$ tokens using a scene segmentation
algorithm. Then, we serialize all insights to text, order it according to its
start time and tag it with the source type, e.g., “[OCR] ¡some visual text on
frame¿”. These interleaved segments are then encoded using the OpenAI encoder
and indexed in an index matrix of size $N\times M$ where $N$ is the number of
archive segments and $M=1,536$ is the embedding dimension.

###  4.3. Topics ontology query generation

The browsing experience described in details in the following section from the
user experience perspective. However, its level of abstraction is enabled by
auto generation of detailed textual queries using GPT-4 (OpenAI, 2023) with
prompt engineered request including a set of topics provided by the user. At
inference time, when the user is changing the set of selected topics, a new
query string is generated with the prompt below (see section 4.5). It is then
rephrased by GPT-4 as a description paragraph which is encoded and applied
with a Cosine Similarity on the index matrix while returning the K nearest
neighbors ranked by relevance.

###  4.4. The Topics-Map

The Topics-Map represents the different ontology topics on a 2D plane using
semantic representation of their names which resides concepts like “Law”
closer to “Policy” than topics like “Biology”. Its motivation comes from the
hypothesis that users prefer semantic order rather than lexicographical order
when exploring an ontology due to its natural association. The Topics-Map
positions are kept fixed to minimize the user’s orientation overload. Color
coding the ontology w.r.t. the set of selected topics is done using our LLM
for embedding both the auto-generated query against the entire ontology and
ranking using Cosine-Similarity.

###  4.5. Implementation Details

Training the LLMs was done by first training two epochs while freezing the
encoder and training only the classifier with a learning rate of $10^{-3}$,
later additional 38 epochs on the entire network using Nvidia A100 GPUs with a
batch of size 32 and learning rate: $10^{-5}$. We used AdamW optimizer. The
application of the model is done by taking the last linear layer before the
classifier. When the input is longer than 512 tokens, the model runs a sliding
window with no intersection and uses mean pooling for representation.

OpenAI embeddings was used with the “text-embedding-ada-002” engine. Long
input texts were invoked and aggregated with Mean Pooling. We use the
following prompt to inflate the set of selected topics as the seed for a
textual query using OpenAI’s GPT-4: “Write a full description for this TED
talk which discusses the following topics ¡selected topics¿.”. While the user
is required to pick a small set of concepts, the generative model fuses these
ideas together to form a detailed textual query.

##  5\. User Experience Design

For content discovery purposes, we propose an interactive UX which is built
out of three main components: (1) a set of selected topics from either a
closed set or custom terms, (2) the Topics-Map, and (3) the top results panel.
The flow begins with topics selection, either from the ontology or custom
topics, which is automatically augmented into a full video description and
embedded to color code semantic similarity as described in section 4.5. By
hovering the Topics-Map a tool-tip reveals the semantically mapped topics with
attributes like the topic’s relevance to the current query, the frequency of
the topic in the dataset (also coded as the size of the circle) and its name.
Any change to the set of selected topics yields a new top video results panel
as well as an updated color code of the Topics-Map. The location and the size
of the circles remain frozen. The semantic space is created either by a
supervised or a learning-free approach. It preserves local ontology
similarities using dimensionality reduction on the 2D plane. Each circle on
the scatter plot represents a topic, and users can either interact with the
plot to navigate the Topics-Map or to refine its topic set selection. The
underlying hypothesis is that interacting with locally related topic
suggestions on a 2D grid can assist the user in correcting a query to its true
intent. This interface provides an intuitive and effective method for content
discovery, leveraging the power of semantic embeddings for quick and efficient
exploration of large-scale video corpora. There are four underlying steps in
the application implementation. Initially the user is (1) adding topics which
are used to generate a prompt to GPT-4. (2) GPT-4 generates the video
description which is then (3) embedded and correlated with the entire ontology
and (4) updates the Topics-Map. This process is repeated indefinitely.

The proposed interactive UX has several advantages over traditional browsing
methods. First, the combination of recommendations and a color-coded 2D
Topics-Map provide a holistic approach to video discovery by allowing users to
explore content that they didn’t consider before. The map serves as a visual
representation of the video archive, allowing users to easily navigate through
different topics. Additionally, the incorporation of both exploration and
exploitation search strategies provide users with a more personalized browsing
experience. Users can switch between the two strategies based on their current
needs and preferences, leading to a more efficient and satisfying video
browsing experience. By focusing the user’s attention on the Topics-Map we
steer away from technical challenges like big data visualization as the user
interface requires to visualize the ontology and not the entire archive.
Projecting the ontology on the map is done with t-SNE (van der Maaten and
Hinton, 2008) reduction of topic names’ embeddings. Overall, this approach
offers an engaging and immersive user experience that encourages discovery and
personalization while emitting cognitive load through the Topics-Map
abstraction.

The Top Video Results panel displays relevant metadata on the ranked video
results. The visualized information include the video title, thumbnail,
author’s name, the event date, and the number of views and likes online.
Clicking the thumbnail opens a video player with the relevant TED talk. The
search interface allows users to update their search and provide near real-
time feedback to refine the query based on the video archive’s content.

Our user experience is designed for ongoing session with a user to
continuously refine the query and switch between exploration and exploitation
strategies trading off precision and recall during the search session.
Initially the map is grayed out until the first topic is selected. As the user
types the query significant topics are highlighted and the top results on
Figure 1 are updated.

##  6\. Evaluation

The method was evaluated in three steps. We first compared different
architectural backbones, then ASR-only vs. multimodal video insights, and
conclude with quantifying the impact of the method as a recommendation/search
engine like other known-item search (KIS) tasks. Three datasets were leveraged
in these experiments. The TED dataset, TDT2 English Audio Topics dataset
(Cieri et al., 1999), and MSR-VTT dataset (Xu et al., 2016).

TED Topics Dataset
We have introduced and open sourced the TED dataset for Multilabel Video
Topics classification. The dataset is based on TED.com website which assigns a
variable size of label-set per talk. These were considered as the target
variable of this task. The dataset consists of $5,439$ TED talks which were
originally represented by $345$ topics. The topics have been distributed in a
long-tail distribution having “Science” as the most popular topic with $1,215$
talks while “Hinduism” for instance, appears in a single talk. We’ve decided
to keep topics which have at least $10$ talks and lose TED specific topics
which are not generalize-able to other domains, e.g., “TEDx”. Splitting the
dataset into Training (80%), Validation (10%), and Test (10%) was done
according to a method for multilabel balance preserving stratified sampling
(Sechidis et al., 2011). The TED organization provides also the textual
descriptions of character length $\mu=369,max=1,391$. These descriptions are
short implicit characterization of the content. We used them later to mimic a
textual query by users for which the suggested method would try and recommend
from a large set of videos. We believe that its insinuated articulation makes
a good query which a user would have used to find some specific video.

Augmented TDT2 Topics Dataset
To finetune our model towards both multilabel classification and document
representation we leveraged TDT2 (Cieri et al., 1999). TDT refers to automatic
techniques for finding topically related material in streams of data such as
newswire and broadcast news. We have augmented this document dataset with
online articles from open forums and manually label them according to the IPTC
Media Topics ontology (International Press Telecommunications Council, sing).
The dataset contains over 200k documents and the topics ontology holds 4k
categories. This ontology deals with different concepts from Politics,
Commerce, Tech, Society, Healthcare, Education, and essentially top media-
related titles. The dataset was split into training (80%), validation (10%)
and test (10%) with the same method (Sechidis et al., 2011) and used in our
supervised approach.

Topics Representation
To evaluate the models’ performance on our test set we considered the video
topics multilabel classification task as our pretext task with standard
multilabel metrics like Precision, Recall, and F1 score in its “Micro” version
of the metrics as we analyzed the entirety of TED and maintain label ratios
between dataset splits. We trained and compared the backbones BERT (Devlin et
al., 2018a), RoBERTa (Liu et al., 2019), and DeBERTa-v3 (He et al., 2021)
through multilabel classification experiments on TED test set (see results on
Table 1). To compare apples-to-apples we used a “base” model version across
backbones i.e., $~{}86M$ parameters. The best F1 results were achieved using
DeBERTa-v3 using finetuning upon our augmented TDT2 Topics Dataset. We split
the result w.r.t. two windowing strategies, the first takes only the first
window while the later runs a sliding window with stride size equal window
size equal 512 tokens. Our model has out-performed vanilla DeBERTa-v3-base on
this task by a significant margin on all measures.

Backbone |  Window strategy |  F1 |  Precision |  Recall
---|---|---|---|---
BERT |  First |  17.7 |  36.4 |  13.0
RoBERTa |  First |  43.8 |  45.2 |  42.4
DeBERTa-v3 |  First |  41.0 |  48.4 |  35.5
Ours (refined Dv3) |  First |  60.4 |  72.9 |  51.6
DeBERTa-v3 |  Sliding |  40.6 |  56.9 |  31.5
Ours (refined Dv3) |  Sliding |  65.0 |  66.6 |  63.6
Table 1. Ablation study on TED Topics test set (N=542) using ASR input on
’base’ class backbones and window strategies.

Search Recommendations
To further measure the application’s recommendations the entire test set was
embedded. A ranked list was recommended per video description by its Cosine-
Similarity. The quality of recommendations were quantified using Mean
Reciprocal Rank (MRR) (Jurafsky and Martin, 2000) and $Recall@k$. The MRR is a
measure to evaluate search engines that returns a ranked list of answers to
queries. For a single query, the reciprocal rank is $\frac{1}{rank_{i}}$ where
$rank_{i}$ is the position of the first correct answer. If no correct answer
was returned in the query, then the reciprocal rank is 0. For multiple queries
Q, the Mean Reciprocal Rank is the mean of the Q reciprocal ranks:
$MRR=\frac{1}{|Q|}\displaystyle\sum_{n=1}^{|Q|}\frac{1}{rank_{i}}$. We used
$Q=N=542$ hence, the score was bounded to the range [$\frac{1}{542}$, 1] where
a value of $1$ indicates that the first recommendation is always correct. If
the right result is always ranked second its MRR is $\frac{1}{2}$ even for a
large $Q$ which is a strict penalty. Given a TED video description, a correct
answer was considered to be its corresponding video. Although no model was
trained on the descriptions, We report the MRR on the TED Test dataset. The
correlation matrix between all video descriptions and all video transcriptions
is illustrated in Figure 3. The main diagonal’s significance highlights how
our method successfully ranks the relevant videos first out of the TED Test
set (N=542). The resulting $MRR=0.645$ which means that the mean rank of the
correct video using our method is $1.55$ out of $542$ possibilities.

The same experiment was conducted using multimodal video insights mentioned
above. Our model has reached $63.9$%, $52.2$%, and $57.4$% Precision, Recall,
and F1 Micro respectively on the Multilabel Classification task. While
embedding the TED Test set along with its description the method has reached
an MRR of $0.727$ i.e., exceeding ASR-only performance by $13$%. The mean rank
of the correct answer according to our method is $1.37$ out of $542$.
$Recall@k$: <EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
Figure 3. Cosine-Similarities between TED descriptions and its corresponding
ASR transcripts. The bold main diagonal illustrates the significance of our
method in matching them.

OpenAI Embeddings
To evaluate the OpenAI embeddings we leveraged the TED descriptions as queries
per talk again. We quantified the effectiveness of our method by reporting MRR
using the OpenAI embeddings, meaning without training. The OpenAI text
representation provides a vector of dimension $1,536$ which encodes the latent
information in the input text. By using these embeddings as the video
representation we’ve reached $MRR=0.950$ on the ASR-only input and $MRR=1.000$
on the multimodal video insights input respectively. Perfect score that
practically solves the task. See comparison in Table 3. Base models of $86M$
parameters are not comparable to GPT3.5 scale for which the true number of
parameters is kept unknown to this moment but estimated in $175B$ which is
$2k$ times more. In addition, its rich expressiveness ($1,536$) is
significantly larger than ours ($314$). The key takeaway from these
experiments is the contribution of multimodality to the video representation.
When evaluating the MRR with OpenAI’s embeddings on the entire TED catalog
(N=$5,439$) we reach $MRR=0.871$ on ASR-only and $MRR=0.999$ on the multimodal
version. To this day OpenAI did not share its training data hence we cannot
guarantee TED was not included in some way. In-fact, a simple interaction with
ChatGPT indicates that the system knows something about TED thus, confirms
some level of training set contamination. Yet, the text-based multimodal
superiority remains a significant finding for this task.

Embeddings/MRR[$\uparrow$] |  Ours (refined Dv3) |  OpenAI
---|---|---
Transcription |  0.645 |  0.950
Multimodal |  0.727 |  1.000

Table 2. An ablation study over the TED Topics test dataset (N=542) comparing
our model with OpenAI GPT Embeddings.

Model |  Signal |  MRR |  R@1 |  R@3 |  R@5 |  R@10
---|---|---|---|---|---|---
Dv3 (No FT) |  ASR |  0.189 |  12.7 |  18.9 |  23.8 |  31.6
Dv3 (No FT) |  MM |  0.229 |  14.6 |  24.6 |  31.5 |  40.1
OpenAI |  ASR |  0.467 |  41.0 |  48.0 |  51.5 |  56.2
OpenAI |  MM |  0.634 |  54.3 |  68.6 |  73.9 |  81.1
SoTA (Chen et al., 2023) |  MM |  NA |  63.9 |  NA |  68.3 |  73.9

Table 3. MSR-VTT MRR and Recall@k with no fine-tuning.

MSR-VTT: A learning-free evaluation
Our method was evaluated on the MSR-VTT (Xu et al., 2016) Test dataset (N=3k)
Text-Video retrieval task without finetuning. The domain gap is significant as
the average video is 80 times shorter than TED. The resulting metrics on Table
3 underlines the significant advantage of our multimodal approach which is on
par with SoTA with no training.

##  7\. User Study

As the Evaluation was focused at a KIS task of a single true video retrieval,
we focused the user study on AVS tasks to demonstrate the UX recall
orientation. The goal was to quantify the system’s effectiveness in enabling
content discovery and exploration. We considered $k=5$ to be a reasonable item
set to retrieve at a time. To cancel learning curve we provided the subjects
with a warm-up demonstration. To cancel order bias we alternated the order of
methods between our method and the baselines. The user study included ten
participants at ages 25–60 from various backgrounds which are familiar with
graphical user interface basic usage on a personal computer. No reward was
offered to volunteers. The interview was designed to first align on the
suggested method with the user’s expectations, conduct two content discovery
tasks and collect both performance metrics as well as qualitative evaluation
on the user experience. An overall interview duration took between 60 to 70
minutes. Each participant have taken six search sessions in total
corresponding to testing performance on our approach vs. the baselines in two
types of tasks.

The participants were asked to describe their experience with media archive
search and content discovery. Most of them described tutorial video search on
services like Google Search, Bing, YouTube, and Social Media platforms.
Participant#6 have mentioned internal corporate media repository tool named
Amazon—knet, which she uses for technical training. Some mentioned home use of
Netflix and other VoD streaming services, PC file explorers on Mac and Windows
as well as storage services like Dropbox, GDrive, and OneDrive. One
participant (P#2) have mentioned also CCTV footage browsing in his day job.
With 20 years of experience in security media analytics he is considered as
Expert1. Participant#8 manages the video library of a large university with
all Zoom course lectures using Kaltura. With over 30 years of experience as
the prior CTO of the National Library and COO in different communication
corporates, he’s considered as Expert2.

The TED website supports three ways to search. TED.com/topics is an alphabetic
ontology index in a lexicographic order. It supports a single topic selection.
TED.com/talks is a structured query interface which allows users to filter
talks by topic names up to two topics at a time. The third web page is the
TED.com/search component which runs a query over textual indexed set of
insights like title, speaker name, topics, transcript and more. Since the
later is the strongest option for content discovery we considered it to be the
TED baseline. youtube.com/@TED/videos is the TED YouTube channel which
provides a search box that works with free text search, we consider it as the
YouTube baseline. On each experiment the participants compared our method with
the two baselines and assigned a score for every combination of video from the
first $k=5$ results and a selected topic name. The scores were binned to
$s=0$: “The topic is not included in the video”, $s=1/2$: “The topic is
related with the video topics but not specific enough”, and $s=1$: “The topic
is central to the video”. The scores were average per participant, method, and
task and considered as the $PerceivedPrecision@k$.

Perceived P@k[$\uparrow$] |  Task1 |  Task2
---|---|---
TED/Search |  $0.47\pm(0.30)$ |  $0.21\pm(0.22)$
YouTube |  $0.61\pm(0.30)$ |  $0.18\pm(0.20)$
Ours (Topics-Map) |  $\textbf{0.82}\pm(0.14)$ |  $\textbf{0.67}\pm(0.21)$
Table 4. Perceived Precision@k over TED comparing our method with YouTube and
TED baselines.

Task #1: Search over the TED ontology
The first task addressed the challenge in a multi-dimensional semantic search
of multiple topics. The participants selected a set of 3-4 topics out of the
TED ontology and asked to search for $k$ video names with these topics. Users
have evaluated whether the results align with the search topic names through
its $PerceivedPrecision@k$.

We have found that our method significantly outperforms the baselines when it
comes to $Precision@5=82\%$ ($P_{val}=0.5\%$ in a single-factor Anova test).
See comparison in Table 4. When considered the top two methods, i.e., ours and
YouTube, ours was found significantly better on a Pairwise t-Test
($P_{val}=0.8\%$) even when applying the Bonferroni correction for multiple
hypothesis testing.

Task #2: Fuzzy Search of open-set topics
The second task considered topics that are not included in the TED ontology.
Participants were asked to search videos according to a pre-defined topics
set. We use the same baselines.

The Anova test have found a significant difference between methods in terms of
$Precision@k$ with $P_{val}=3\cdot 10^{-5}$. A followup pairwise t-Test also
revealed $Precision@k=67\%$ vs. $21\%$ ($P_{val}=0.02\%$) for our method over
the next best baseline (TED). Similarly, the Bonferroni correction holds.

User Experience Evaluation - Interviews
The purpose of this study was to evaluate the effectiveness of our media
archive browser, in supporting exploratory content discovery. To achieve this
goal, we designed a questionnaire that assessed users’ perceived value and
satisfaction with our UX. The interviews have taught us that 8 out of 10 users
voluntarily stated that the Topics-Map concept is intuitive and supports the
query tuning process. Expert2 suggested using a Lasso selector to
simultaneously include multiple locally related topic on the map. Two
participants took longer to gain confidence in the map and the concept of
semantic similarity on a 2D plane. A common feedback ($\frac{6}{10}$) was that
the fact we always return results, while sometimes the baselines doesn’t,
makes our system much more productive as its perceived precision is high.

Expert1 was interested in Action Recognition Topics-Map and sub-video micro
events for CCTV applications which identifies a domain gap from education
content discovery. Expert2 was asked how would the Topics-Map work for the
university. He mentioned COVID as a major driver to online services like Zoom
and says that although each course is self-contained, its true value should
surface by expanding the search to the faculty level, university level, or
even on a national scale of higher education. Quote: “Students studying
environmental science might not only be interested in lectures from their own
course but also related lectures in biology, geology, or public policy. The
content discovery system can help them easily navigate through and find
relevant lectures from other courses that might have a connection to their
primary area of study.”

##  8\. Discussion and Limitations

This paper presented a comprehensive solution for video archive browsing by
leveraging AI models for media understanding, latent semantic embeddings, and
a recommendation system to provide users with an interactive and efficient
content discovery UX. We offered a Realtime video archive search experience
that is scalable, robust, and easily adaptable to different domains and
ontologies.

Our method relies on archive batch processing to generate semantic embeddings
that are indexed to provide relevant search results efficiently. We
highlighted that different domains require specific AI models to generate
value from the data, and the choice of base-class networks ($86$M parameters)
vs. foundation models (over $10$ trillion parameters) networks can have a
significant impact on representation performance. We also showed that text-
based multimodality improves the representation quality for search purposes in
both network classes over two datasets — achieving SoTA results without
finetuning. Our Topics-Map concept was examined through a user study and
interviews. The productivity of our method was found to significantly
outperform both YouTube and the TED websites in two different AVS tasks:
content discovery using TED ontology and custom topics ($P_{val}=5\cdot
10^{-3}\%$ and $P_{val}=3\cdot 10^{-5}\%$ respectively). Overall, our proposed
solution provides an effective and scalable video archive browsing method
which is adaptable to various domains and applications.

The limitations of our approach are dependent on some of the architectural
assumptions. Relying on archive batch indexing prior to any querying
capability requires indexing also new videos which otherwise won’t be found.
Applying our method on different domains like CCTV or nature films, different
set of AI models should be considered like action recognition and animal
classifier. Additionally, we assumed the availability of a topics ontology and
labeled dataset in the case of supervised approach. Lastly, we discussed the
practicality of using OpenAI as the representation method, as it may not
always be possible due to network isolation or data privacy concerns.
Therefore, applying a supervised approach with a pretrained model could be
effective and efficient, as the model size impact operational costs and
private data residency.

For future work, on the back-end side, we investigate generic architectures
that may skip the textual fusion step by sharing tensor-based fusion from
multiple AI models into a LLM decoder. On the other hand, from the front-end
perspective, we look into visualizations that could improve the abstraction of
the Topics-Map. For example, by defining a metric which is inspired by TD-IDF
to penalize on frequent topics like “Science”, in the case of TED, which is
too popular to be indicative or to run cluster analysis and unify related
topics to reduce cognitive load.

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The DarkSide Collaboration

# Search for dark matter particle interactions with electron final states with
DarkSide-50

P. Agnes  Department of Physics, Royal Holloway University of London, Egham
TW20 0EX, UK  I.F.M. Albuquerque  Instituto de Física, Universidade de São
Paulo, São Paulo 05508-090, Brazil  T. Alexander  Pacific Northwest National
Laboratory, Richland, WA 99352, USA  A.K. Alton  Physics Department, Augustana
University, Sioux Falls, SD 57197, USA  M. Ave  Instituto de Física,
Universidade de São Paulo, São Paulo 05508-090, Brazil  H.O. Back  Pacific
Northwest National Laboratory, Richland, WA 99352, USA  G. Batignani  INFN
Pisa, Pisa 56127, Italy  Physics Department, Università degli Studi di Pisa,
Pisa 56127, Italy  K. Biery  Fermi National Accelerator Laboratory, Batavia,
IL 60510, USA  V. Bocci  INFN Sezione di Roma, Roma 00185, Italy  W.M.
Bonivento  INFN Cagliari, Cagliari 09042, Italy  B. Bottino  Physics
Department, Università degli Studi di Genova, Genova 16146, Italy  INFN
Genova, Genova 16146, Italy  S. Bussino  INFN Roma Tre, Roma 00146, Italy
Mathematics and Physics Department, Università degli Studi Roma Tre, Roma
00146, Italy  M. Cadeddu  INFN Cagliari, Cagliari 09042, Italy  M. Cadoni
Physics Department, Università degli Studi di Cagliari, Cagliari 09042, Italy
INFN Cagliari, Cagliari 09042, Italy  F. Calaprice  Physics Department,
Princeton University, Princeton, NJ 08544, USA  A. Caminata  INFN Genova,
Genova 16146, Italy  M.D. Campos  Physics, Kings College London, Strand,
London WC2R 2LS, UK  N. Canci  INFN Laboratori Nazionali del Gran Sasso,
Assergi (AQ) 67100, Italy  M. Caravati  INFN Cagliari, Cagliari 09042, Italy
N. Cargioli  INFN Cagliari, Cagliari 09042, Italy  M. Cariello  INFN Genova,
Genova 16146, Italy  M. Carlini  INFN Laboratori Nazionali del Gran Sasso,
Assergi (AQ) 67100, Italy  Gran Sasso Science Institute, L’Aquila 67100, Italy
V. Cataudella  Physics Department, Università degli Studi “Federico II” di
Napoli, Napoli 80126, Italy  INFN Napoli, Napoli 80126, Italy  P. Cavalcante
Virginia Tech, Blacksburg, VA 24061, USA  INFN Laboratori Nazionali del Gran
Sasso, Assergi (AQ) 67100, Italy  S. Cavuoti  Physics Department, Università
degli Studi “Federico II” di Napoli, Napoli 80126, Italy  INFN Napoli, Napoli
80126, Italy  S. Chashin  Skobeltsyn Institute of Nuclear Physics, Lomonosov
Moscow State University, Moscow 119234, Russia  A. Chepurnov  Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow
119234, Russia  C. Cicalò  INFN Cagliari, Cagliari 09042, Italy  G. Covone
Physics Department, Università degli Studi “Federico II” di Napoli, Napoli
80126, Italy  INFN Napoli, Napoli 80126, Italy  D. D’Angelo  Physics
Department, Università degli Studi di Milano, Milano 20133, Italy  INFN
Milano, Milano 20133, Italy  S. Davini  INFN Genova, Genova 16146, Italy  A.
De Candia  Physics Department, Università degli Studi “Federico II” di Napoli,
Napoli 80126, Italy  INFN Napoli, Napoli 80126, Italy  S. De Cecco  INFN
Sezione di Roma, Roma 00185, Italy  Physics Department, Sapienza Università di
Roma, Roma 00185, Italy  G. De Filippis  Physics Department, Università degli
Studi “Federico II” di Napoli, Napoli 80126, Italy  INFN Napoli, Napoli 80126,
Italy  G. De Rosa  Physics Department, Università degli Studi “Federico II” di
Napoli, Napoli 80126, Italy  INFN Napoli, Napoli 80126, Italy  A.V. Derbin
Saint Petersburg Nuclear Physics Institute, Gatchina 188350, Russia  A. Devoto
Physics Department, Università degli Studi di Cagliari, Cagliari 09042, Italy
INFN Cagliari, Cagliari 09042, Italy  M. D’Incecco  INFN Laboratori Nazionali
del Gran Sasso, Assergi (AQ) 67100, Italy  C. Dionisi  INFN Sezione di Roma,
Roma 00185, Italy  Physics Department, Sapienza Università di Roma, Roma
00185, Italy  F. Dordei  INFN Cagliari, Cagliari 09042, Italy  M. Downing
Amherst Center for Fundamental Interactions and Physics Department, University
of Massachusetts, Amherst, MA 01003, USA  D. D’Urso  Chemistry and Pharmacy
Department, Università degli Studi di Sassari, Sassari 07100, Italy  INFN
Laboratori Nazionali del Sud, Catania 95123, Italy  G. Fiorillo  Physics
Department, Università degli Studi “Federico II” di Napoli, Napoli 80126,
Italy  INFN Napoli, Napoli 80126, Italy  D. Franco  APC, Université de Paris,
CNRS, Astroparticule et Cosmologie, Paris F-75013, France  F. Gabriele  INFN
Cagliari, Cagliari 09042, Italy  C. Galbiati  Physics Department, Princeton
University, Princeton, NJ 08544, USA  Gran Sasso Science Institute, L’Aquila
67100, Italy  INFN Laboratori Nazionali del Gran Sasso, Assergi (AQ) 67100,
Italy  C. Ghiano  INFN Laboratori Nazionali del Gran Sasso, Assergi (AQ)
67100, Italy  C. Giganti  LPNHE, CNRS/IN2P3, Sorbonne Université, Université
Paris Diderot, Paris 75252, France  G.K. Giovanetti  Physics Department,
Princeton University, Princeton, NJ 08544, USA  A.M. Goretti  INFN Laboratori
Nazionali del Gran Sasso, Assergi (AQ) 67100, Italy  G. Grilli di Cortona
INFN Laboratori Nazionali di Frascati, Frascati 00044, Italy  A. Grobov
National Research Centre Kurchatov Institute, Moscow 123182, Russia  National
Research Nuclear University MEPhI, Moscow 115409, Russia  M. Gromov
Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University,
Moscow 119234, Russia  Joint Institute for Nuclear Research, Dubna 141980,
Russia  M. Guan  Institute of High Energy Physics, Beijing 100049, China  M.
Gulino  Engineering and Architecture Faculty, Università di Enna Kore, Enna
94100, Italy  INFN Laboratori Nazionali del Sud, Catania 95123, Italy  B.R.
Hackett  Pacific Northwest National Laboratory, Richland, WA 99352, USA  K.
Herner  Fermi National Accelerator Laboratory, Batavia, IL 60510, USA  T.
Hessel  APC, Université de Paris, CNRS, Astroparticule et Cosmologie, Paris
F-75013, France  B. Hosseini  INFN Cagliari, Cagliari 09042, Italy  F. Hubaut
Centre de Physique des Particules de Marseille, Aix Marseille Univ,
CNRS/IN2P3, CPPM, Marseille, France  E.V. Hungerford  Department of Physics,
University of Houston, Houston, TX 77204, USA  An. Ianni  Physics Department,
Princeton University, Princeton, NJ 08544, USA  INFN Laboratori Nazionali del
Gran Sasso, Assergi (AQ) 67100, Italy  V. Ippolito  INFN Sezione di Roma, Roma
00185, Italy  K. Keeter  School of Natural Sciences, Black Hills State
University, Spearfish, SD 57799, USA  C.L. Kendziora  Fermi National
Accelerator Laboratory, Batavia, IL 60510, USA  M. Kimura  AstroCeNT, Nicolaus
Copernicus Astronomical Center, 00-614 Warsaw, Poland  I. Kochanek  INFN
Laboratori Nazionali del Gran Sasso, Assergi (AQ) 67100, Italy  D. Korablev
Joint Institute for Nuclear Research, Dubna 141980, Russia  G. Korga
Department of Physics, University of Houston, Houston, TX 77204, USA  INFN
Laboratori Nazionali del Gran Sasso, Assergi (AQ) 67100, Italy  A. Kubankin
Radiation Physics Laboratory, Belgorod National Research University, Belgorod
308007, Russia  M. Kuss  INFN Pisa, Pisa 56127, Italy  M. La Commara  Physics
Department, Università degli Studi “Federico II” di Napoli, Napoli 80126,
Italy  INFN Napoli, Napoli 80126, Italy  M. Lai  Physics Department,
Università degli Studi di Cagliari, Cagliari 09042, Italy  INFN Cagliari,
Cagliari 09042, Italy  X. Li  Physics Department, Princeton University,
Princeton, NJ 08544, USA  M. Lissia  INFN Cagliari, Cagliari 09042, Italy  G.
Longo  Physics Department, Università degli Studi “Federico II” di Napoli,
Napoli 80126, Italy  INFN Napoli, Napoli 80126, Italy  O. Lychagina  Joint
Institute for Nuclear Research, Dubna 141980, Russia  Skobeltsyn Institute of
Nuclear Physics, Lomonosov Moscow State University, Moscow 119234, Russia
I.N. Machulin  National Research Centre Kurchatov Institute, Moscow 123182,
Russia  National Research Nuclear University MEPhI, Moscow 115409, Russia
L.P. Mapelli  Physics and Astronomy Department, University of California, Los
Angeles, CA 90095, USA  S.M. Mari  INFN Roma Tre, Roma 00146, Italy
Mathematics and Physics Department, Università degli Studi Roma Tre, Roma
00146, Italy  J. Maricic  Department of Physics and Astronomy, University of
Hawai’i, Honolulu, HI 96822, USA  A. Messina  INFN Sezione di Roma, Roma
00185, Italy  Physics Department, Sapienza Università di Roma, Roma 00185,
Italy  R. Milincic  Department of Physics and Astronomy, University of
Hawai’i, Honolulu, HI 96822, USA  J. Monroe  Department of Physics, Royal
Holloway University of London, Egham TW20 0EX, UK  M. Morrocchi  INFN Pisa,
Pisa 56127, Italy  Physics Department, Università degli Studi di Pisa, Pisa
56127, Italy  X. Mougeot  Université Paris-Saclay, CEA, List, Laboratoire
National Henri Becquerel (LNE-LNHB), F-91120 Palaiseau, France  V.N. Muratova
Saint Petersburg Nuclear Physics Institute, Gatchina 188350, Russia  P. Musico
INFN Genova, Genova 16146, Italy  A.O. Nozdrina  National Research Centre
Kurchatov Institute, Moscow 123182, Russia  National Research Nuclear
University MEPhI, Moscow 115409, Russia  A. Oleinik  Radiation Physics
Laboratory, Belgorod National Research University, Belgorod 308007, Russia  F.
Ortica  Chemistry, Biology and Biotechnology Department, Università degli
Studi di Perugia, Perugia 06123, Italy  INFN Perugia, Perugia 06123, Italy  L.
Pagani  Department of Physics, University of California, Davis, CA 95616, USA
M. Pallavicini  Physics Department, Università degli Studi di Genova, Genova
16146, Italy  INFN Genova, Genova 16146, Italy  L. Pandola  INFN Laboratori
Nazionali del Sud, Catania 95123, Italy  E. Pantic  Department of Physics,
University of California, Davis, CA 95616, USA  E. Paoloni  INFN Pisa, Pisa
56127, Italy  Physics Department, Università degli Studi di Pisa, Pisa 56127,
Italy  K. Pelczar  INFN Laboratori Nazionali del Gran Sasso, Assergi (AQ)
67100, Italy  M. Smoluchowski Institute of Physics, Jagiellonian University,
30-348 Krakow, Poland  N. Pelliccia  Chemistry, Biology and Biotechnology
Department, Università degli Studi di Perugia, Perugia 06123, Italy  INFN
Perugia, Perugia 06123, Italy  S. Piacentini  INFN Sezione di Roma, Roma
00185, Italy  A. Pocar  Amherst Center for Fundamental Interactions and
Physics Department, University of Massachusetts, Amherst, MA 01003, USA  D.M.
Poehlmann  Department of Physics, University of California, Davis, CA 95616,
USA  S. Pordes  Fermi National Accelerator Laboratory, Batavia, IL 60510, USA
S.S. Poudel  Department of Physics, University of Houston, Houston, TX 77204,
USA  P. Pralavorio  Centre de Physique des Particules de Marseille, Aix
Marseille Univ, CNRS/IN2P3, CPPM, Marseille, France  D.D. Price  The
University of Manchester, Manchester M13 9PL, United Kingdom  F. Ragusa
Physics Department, Università degli Studi di Milano, Milano 20133, Italy
INFN Milano, Milano 20133, Italy  M. Razeti  INFN Cagliari, Cagliari 09042,
Italy  A. Razeto  INFN Laboratori Nazionali del Gran Sasso, Assergi (AQ)
67100, Italy  A.L. Renshaw  Department of Physics, University of Houston,
Houston, TX 77204, USA  M. Rescigno  INFN Sezione di Roma, Roma 00185, Italy
J. Rode  LPNHE, CNRS/IN2P3, Sorbonne Université, Université Paris Diderot,
Paris 75252, France  APC, Université de Paris, CNRS, Astroparticule et
Cosmologie, Paris F-75013, France  A. Romani  Chemistry, Biology and
Biotechnology Department, Università degli Studi di Perugia, Perugia 06123,
Italy  INFN Perugia, Perugia 06123, Italy  D. Sablone  Physics Department,
Princeton University, Princeton, NJ 08544, USA  INFN Laboratori Nazionali del
Gran Sasso, Assergi (AQ) 67100, Italy  O. Samoylov  Joint Institute for
Nuclear Research, Dubna 141980, Russia  W. Sands  Physics Department,
Princeton University, Princeton, NJ 08544, USA  S. Sanfilippo  Mathematics and
Physics Department, Università degli Studi Roma Tre, Roma 00146, Italy  INFN
Roma Tre, Roma 00146, Italy  E. Sandford  The University of Manchester,
Manchester M13 9PL, United Kingdom  C. Savarese  Physics Department, Princeton
University, Princeton, NJ 08544, USA  B. Schlitzer  Department of Physics,
University of California, Davis, CA 95616, USA  D.A. Semenov  Saint Petersburg
Nuclear Physics Institute, Gatchina 188350, Russia  A. Shchagin  Radiation
Physics Laboratory, Belgorod National Research University, Belgorod 308007,
Russia  A. Sheshukov  Joint Institute for Nuclear Research, Dubna 141980,
Russia  M.D. Skorokhvatov  National Research Centre Kurchatov Institute,
Moscow 123182, Russia  National Research Nuclear University MEPhI, Moscow
115409, Russia  O. Smirnov  Joint Institute for Nuclear Research, Dubna
141980, Russia  A. Sotnikov  Joint Institute for Nuclear Research, Dubna
141980, Russia  S. Stracka  INFN Pisa, Pisa 56127, Italy  Y. Suvorov  Physics
Department, Università degli Studi “Federico II” di Napoli, Napoli 80126,
Italy  INFN Napoli, Napoli 80126, Italy  National Research Centre Kurchatov
Institute, Moscow 123182, Russia  R. Tartaglia  INFN Laboratori Nazionali del
Gran Sasso, Assergi (AQ) 67100, Italy  G. Testera  INFN Genova, Genova 16146,
Italy  A. Tonazzo  APC, Université de Paris, CNRS, Astroparticule et
Cosmologie, Paris F-75013, France  E.V. Unzhakov  Saint Petersburg Nuclear
Physics Institute, Gatchina 188350, Russia  A. Vishneva  Joint Institute for
Nuclear Research, Dubna 141980, Russia  R.B. Vogelaar  Virginia Tech,
Blacksburg, VA 24061, USA  M. Wada  AstroCeNT, Nicolaus Copernicus
Astronomical Center, 00-614 Warsaw, Poland  Physics Department, Università
degli Studi di Cagliari, Cagliari 09042, Italy  H. Wang  Physics and Astronomy
Department, University of California, Los Angeles, CA 90095, USA  Y. Wang
Physics and Astronomy Department, University of California, Los Angeles, CA
90095, USA  Institute of High Energy Physics, Beijing 100049, China  S.
Westerdale  Physics Department, Princeton University, Princeton, NJ 08544, USA
INFN Cagliari, Cagliari 09042, Italy  M.M. Wojcik  M. Smoluchowski Institute
of Physics, Jagiellonian University, 30-348 Krakow, Poland  X. Xiao  Physics
and Astronomy Department, University of California, Los Angeles, CA 90095, USA
C. Yang  Institute of High Energy Physics, Beijing 100049, China  G. Zuzel  M.
Smoluchowski Institute of Physics, Jagiellonian University, 30-348 Krakow,
Poland

###### Abstract

We present a search for dark matter particles with
sub-$\mathrm{GeV}\text{/}{\mathrm{\mbox{$c$}}}^{2}$ masses whose interactions
have final state electrons using the DarkSide-50 experiment’s $12\,306\pm
184.\text{\,}\mathrm{kg}\text{\,}\mathrm{d}\text{/}$ low-radioactivity liquid
argon exposure. By analyzing the ionization signals, we exclude new parameter
space for the dark matter-electron cross section $\bar{\sigma}_{e}$, the
axioelectric coupling constant $g_{\mathrm{A}e}$, and the dark photon kinetic
mixing parameter $\kappa$. We also set the first dark matter direct-detection
constraints on the mixing angle $\left|U_{e4}\right|^{2}$ for
$\mathrm{keV}\text{/}{\mathrm{\mbox{$c$}}}^{2}$ sterile neutrinos.

The very nature of dark matter (DM) remains unknown despite cosmological and
astronomical observations collecting evidence of its existence over the last
several decades [1, 2, 3, 4, 5]. Traditionally, DM particles with masses
ranging from a $\mathrm{GeV}\text{/}{\mathrm{\mbox{$c$}}}^{2}$ to few
$\mathrm{TeV}\text{/}{\mathrm{\mbox{$c$}}}^{2}$ have been extensively searched
for by experiments located in underground laboratories by detecting their
interactions with baryonic matter via elastic scattering off atomic nuclei [6,
7, 8, 9, 10, 11, 12, 13] – usually called nuclear recoils (NRs). Heavy DM can
also scatter off electrons, but the energy of such interactions – called
electron recoils (ERs) – is suppressed due to the small electron mass. The
lack of concrete evidence of direct DM detection motivates the search for
other candidates and their possible interactions via scattering off, or
absorption by, shell electrons, which may subsequently produce sufficiently
large ionization signals in the detector [14].

This Letter reports on the analysis of the $653.1\text{\,}\mathrm{\mbox{live-
days}}\text{/}$ of data collected with the DarkSide-50 experiment (DS-50) to
probe DM interactions in the form of light DM-electron scattering, absorption
of bosonic DM (axion-like particles and dark photons), and sterile neutrino-
electron scattering. This analysis uses a more accurate calibration of the
detector response [15], improved background modeling and determination of its
systematic uncertainties [16], and a larger data-set compared to the previous
study [17]. The same analysis approach was also applied to improve existing
limits on spin-independent WIMP-nucleon interactions for WIMP masses down to
1.2 GeV/c2 [16] and down to 40 MeV/c2 when including the Migdal effect [18].

DS-50 is a dual-phase time projection chamber (TPC) housed in Italy’s INFN
Laboratori Nazionali del Gran Sasso (LNGS). The active volume consists of low-
radioactivity underground liquid argon (LAr). Construction and performance
details regarding the DS-50 detector are described in Ref. [19]. Two
measurable signals can be observed: the light from scintillation in the liquid
(S1) and ionization electrons, which are drifted using a
$200\text{\,}\mathrm{V}\text{/}\mathrm{cm}$ electric field in the LAr volume
and extracted by a $2.8\text{\,}\mathrm{kV}\text{/}\mathrm{cm}$ electric field
into the gas phase, producing electroluminescence photons (S2) under
acceleration by a $4.2\text{\,}\mathrm{kV}\text{/}\mathrm{cm}$ electric field
in the GAr volume. Two arrays of 19 3-in photomultiplier tubes (PMTs), one
above the anode and one below the cathode, detect photons. The DS-50 TPC,
enclosed in a stainless steel double-walled, vacuum-insulated cryostat, lies
inside a $30\text{\,}\mathrm{t}\text{/}$ boron-loaded liquid scintillator veto
instrumented by 110 8-in PMTs – to actively reject neutrons in situ –
surrounded by a $1\text{\,}\mathrm{kt}\text{/}$ water Cerenkov veto with 80
PMTs – to actively tag cosmic muons and act as a passive shield against
external backgrounds.

The data selection criteria for this analysis aim to identify single-scatter,
low-energy events in the form of paired S1 and S2 or S2-only pulses
uncorrelated to any previous event. Various quality and selection cuts based
on the ratio of S1 and S2, S2 signal time profile, and S2 distribution across
the PMT arrays are implemented [16]. These cuts remove pileup pulses, surface
$\alpha$ events inducing electrons from the cathode, and spurious trapped
ionization electrons released up to $20\text{\,}\mathrm{ms}\text{/}$ after the
previous event. Moreover, only events reconstructed in the fiducial volume are
selected, defined by the seven central top PMTs [16]. Veto detector signals
are not used in the data selection since S2 triggers are delayed with respect
to the veto by the electron drift time in the TPC.

Fig. 1 shows the final ionization spectrum obtained after all cuts described
in [16], resulting in a fiducial mass of (19.4 $\pm$ 0.3) kg and exposure of
(12306 $\pm$ 184) kg d. This analysis is performed in the energy interval
$4170\text{\,}\mathrm{\mbox{$e^{-}$}}\text{/}$
($0.0621\text{\,}\mathrm{\mbox{keV${}_{\rm er}$}}\text{/}$), up to the range
of validity of the detector energy response calibration [16]. The low
threshold is introduced to avoid the region dominated by spurious electrons.

The background model accounts for the natural radioactivity present in the LAr
bulk due to 39Ar and 85Kr contamination, and $\gamma$s and X-rays from
detector components like the PMTs, the TPC structure, and stainless-steel
cryostat whose specific activities were determined via a comprehensive
material screening campaign. For each, the ionization spectra with associated
uncertainties were obtained via a detailed Monte Carlo simulation of DS-50
[16, 20]. The red curve in Fig. 1 shows the background model fitted to data.

In this Letter, we search for several DM candidates using the DS-50 data-set
and background model. The candidates are assumed to be non-relativistic and
comprise all of the galactic DM. While additional local sources for dark
matter may be present (e.g. solar production of dark photons [21]), we set
constraints using the interaction rates for the candidate only according to
the Standard Halo Model. Following the recommendations in Ref. [22], we assume
a local DM density ($\rho_{\mathrm{DM}}$) of
$0.3\text{\,}\mathrm{GeV}\text{/}\text{(}{\mathrm{\mbox{$c$}}}^{2}\text{\,}{\mathrm{cm}}^{3}\text{)}$,
a standard isothermal Maxwellian velocity distribution ($f(v)$ where $v$ the
DM’s velocity) with an escape velocity of
$544\text{\,}\mathrm{km}\text{/}\mathrm{s}$, and a local standard of rest
velocity of $238\text{\,}\mathrm{km}\text{/}\mathrm{s}$. Moreover, the
predicted ionization rates per unit mass ($R$) are expressed as a function of
the outgoing electron’s recoil energy $E_{\mathrm{er}}$. Using the argon
ionization response, the spectra are expressed in number of electrons
($\mathrm{\mbox{$N_{e}$}}\text{/}$). The ionization response is obtained from
the 39Ar $\beta$-decay sample from an atmospheric argon campaign and from the
low-energy 37Ar peaks. 37Ar was present in the first few months of DS-50 data
and decayed away before the present data-set [15]. The detector response model
[20] is applied to obtain the ionization spectra shown in Fig. 1.

Figure 1: Data (black) and background model (red) compared to expected
ionization spectra of illustrative mass values for DM candidates: LDM heavy
(blue and orange solid lines) and light (blue and orange dashed lines)
mediators, ALPs (purple), dark photons (yellow), and sterile neutrinos
(green). The region shaded blue is below threshold. Details on the background
model and its fit can be found in Ref. [16].

Fermion or scalar boson light dark matter (LDM) candidates, with masses below
a $\mathrm{GeV}\text{/}{\mathrm{\mbox{$c$}}}^{2}$, can interact with bound
electrons via a vector mediator, resulting in the ionization of argon atoms.
The LDM-electron interaction’s dependence on the momentum-transfer $q$ is
encapsulated by a dark matter form factor $F_{\mathrm{DM}}(q)$. The ionization
rate for a LDM candidate of mass $m_{\chi}$ is parametrized by a reference
cross section $\bar{\sigma}_{e}$ as [23, 14, 24]:

$\frac{dR}{d\ln{E_{\mathrm{er}}}}=N_{T}\frac{\rho_{\mathrm{DM}}}{m_{\chi}}\times\frac{\bar{\sigma}_{e}}{8\mu^{2}_{\chi
e}}\times\\\
\sum_{n\ell}\int\left|f^{n\ell}_{\mathrm{ion}}(k^{\prime},q)\right|^{2}\,\left|F_{\mathrm{DM}}(q)\right|^{2}\,\eta(v_{\mathrm{min}})\,q\,dq$
(1)

where $N_{T}$ is the number of target atoms per unit mass, $\mu_{\chi e}$ is
the DM-electron reduced mass, $f^{n\ell}_{\mathrm{ion}}(k^{\prime},q)$ is the
ionization form factor modeling the effects of the bound-electron in the
$(n,\ell)$ shell and outgoing final state,
$k^{\prime}=\sqrt{2m_{e}E_{\mathrm{er}}}$ is the electron recoil momentum, and
$\eta(v_{\mathrm{min}})=\int\frac{1}{v}f(v)\Theta(v-v_{\mathrm{min}})\,dv$ is
the inverse mean speed function that encodes the DM velocity profile for the
minimum DM velocity ($v_{\mathrm{min}}$) required to eject an electron with
$E_{\mathrm{er}}$ given $q$ [23]. Two benchmark interaction models are
considered: a heavy mediator (mass $\gg\alpha m_{e}$) with $F_{\mathrm{DM}}=1$
and a light mediator (mass $\ll\alpha m_{e}$) with $F_{\mathrm{DM}}=(\alpha
m_{e}/q)^{2}$, where $\alpha$ is the fine structure constant and $m_{e}$ is
the electron mass.

Figure 2: Exclusion limits at 90% C.L. on DM particle interactions with
electron final states. The x-axis shows the mass of the candidate while the
y-axis shows the model parameter. Limits set by this work are shown as solid
red lines while the $-1\sigma$ expected limits are dotted red lines, and newly
excluded parameter space is shaded red. Limits from laboratory experiments,
shown as solid lines, are set by $\beta$ spectrum analyses [25, 26, 27], DS-50
[23], PandaX-II [28, 29], SENSEI [30], SuperCDMS Soudan [31], XENON10 [24,
32], and XENON1T [33, 34, 35], with previously excluded parameter space shaded
gray. Astrophysical constraints (dashed lines) are set by Ref. [36, 37, 38,
39]. For sterile neutrinos, the limits set by NuSTAR [39] extend downwards to
$\left|U_{e4}\right|^{2}=10^{-13}$ at
$20\text{\,}\mathrm{keV}\text{/}{\mathrm{\mbox{$c$}}}^{2}$. All limits using
the Standard Halo Model are scaled to a local dark matter density
$\rho_{\mathrm{DM}}$ of
$0.3\text{\,}\mathrm{GeV}\text{/}\text{(}{\mathrm{\mbox{$c$}}}^{2}\text{\,}{\mathrm{cm}}^{3}\text{)}$.

DS-50 is also sensitive to pseudo-scalar DM such as axion-like particles
(ALPs) [40, 41, 29, 42] or vector-boson DM like dark photons [43] through
absorption by argon shell electrons. Absorption of either candidate would
result in a monoenergetic signal at the particle’s rest mass, $m_{\mathrm{A}}$
for an ALP or $m_{\mathrm{A}^{\prime}}$ for a dark photon. The absorption rate
per unit mass of galactic ALPs depends on the axion-electron coupling strength
$g_{\mathrm{A}e}$ [42, 43],

$R=N_{T}\frac{\rho_{\mathrm{DM}}}{m_{\mathrm{A}}}\times\frac{3m_{\mathrm{A}}^{2}g_{\mathrm{A}e}^{2}}{16\pi\alpha
m_{e}^{2}}\sigma_{\mathrm{pe}}(m_{\mathrm{A}}c^{2})c$ (2)

while that of dark photons depends on the strength of the kinetic mixing
$\kappa$ between the photon and dark photon [43],

$R=N_{T}\frac{\rho_{\mathrm{DM}}}{m_{\mathrm{A}^{\prime}}}\times\kappa^{2}\sigma_{\mathrm{pe}}(m_{\mathrm{A}^{\prime}}c^{2})c$
(3)

where $\sigma_{\mathrm{pe}}$ is argon’s photoelectric cross section [44]
evaluated at the particle’s rest energy.

A sterile neutrino $\nu_{s}$ with a mass between
$7\text{\,}\mathrm{keV}\text{/}{\mathrm{\mbox{$c$}}}^{2}$ and
$36\text{\,}\mathrm{keV}\text{/}{\mathrm{\mbox{$c$}}}^{2}$ can be a viable DM
candidate [45, 46]. Sterile neutrinos interact via
$\nu_{s}+e\rightarrow\nu_{e}+e$ (and
$\bar{\nu}_{s}+e\rightarrow\bar{\nu}_{e}+e$) [47], where a $\nu_{s}$ mixing
with an active state – parameterized by the mixing angle
$\left|U_{e4}\right|^{2}$ – inelastically scatters off a bound electron in the
detector. The ionization rate is governed by the cross section
$\sigma_{n\ell}$ between $\nu_{s}$ and an electron in a given orbital
$(n,\ell)$ [47]:

$\frac{dR}{dE_{\mathrm{er}}}=N_{T}\frac{\rho_{\mathrm{DM}}}{m_{\nu}}\times\\\
\sum_{n\ell}2(2\ell+1)\int\frac{d\sigma_{n\ell}}{dE_{\mathrm{er}}}(v,m_{\nu},\left|U_{e4}\right|^{2})~{}f(v)\,v\,dv$
(4)

where $m_{\nu}$ is the sterile neutrino’s mass. We note that Eq. (4) does not
include the effects of the ion on the outgoing electron, unlike the treatment
of LDM-electron scattering in Eq. (1). Additionally, we note that the
scattering rates calculated in Ref. [47] fail to evaluate the DM velocity
distribution in the lab frame; however, this is corrected in our work.

This analysis employs a binned Profile Likelihood Ratio (PLR) approach [48,
49] to determine exclusion limits for each DM candidate. The PLR includes a
set of nuisance parameters representing the nominal values and uncertainties
on the exposure, materials screening, theoretical energy spectra shape, and
ionization energy scale. Correlations among the different components are
encoded in the likelihood definition. See Ref. [16] for a complete description
of the above. We also verified that the expected limits have negligible
dependence on ER fluctuations by changing the Gaussian model described in Ref.
[16] to a binomial one.

Fig. 2 shows the 90% C.L. exclusion limits placed on each candidate via the
PLR method. The observed limits (solid red lines) are shown alongside the
expected limits at $-1\sigma$ (dotted red lines) for ALPs, dark photons, and
sterile neutrinos to show regions where observed limits are driven by under-
fluctuations of data.

We have established the best direct-detection limits on dark matter-electron
scattering in the mass range of
$16\text{\,}\mathrm{MeV}\text{/}{\mathrm{\mbox{$c$}}}^{2}56\text{\,}\mathrm{MeV}\text{/}{\mathrm{\mbox{$c$}}}^{2}$
for a heavy mediator and above
$80\text{\,}\mathrm{MeV}\text{/}{\mathrm{\mbox{$c$}}}^{2}$ for a light
mediator, with newly excluded parameter space shaded red. These new DS-50
results on LDM-electron scatters improve upon those previously obtained in
2018 [23] primarily due to the refined data selection criterion which
suppresses correlated events between
$4\text{\,}\mathrm{\mbox{$e^{-}$}}\text{/}$ and
$7\text{\,}\mathrm{\mbox{$e^{-}$}}\text{/}$ [16]. Additional sensitivity gain
comes from improved data selection, a more accurate detector calibration,
improved background modeling, and a larger data-set.

We have also placed the first constraints on galactic axion-like particles and
dark photons with an argon target. Stronger direct-detection limits are placed
on both $g_{\mathrm{A}e}$ and $\kappa$ for masses between
$0.03\text{\,}\mathrm{keV}\text{/}{\mathrm{\mbox{$c$}}}^{2}$ and
$0.2\text{\,}\mathrm{keV}\text{/}{\mathrm{\mbox{$c$}}}^{2}$.

DS-50 is the first DM direct-detection experiment to set limits on the sterile
neutrino mixing angle $\left|U_{e4}\right|^{2}$. Under the Standard Halo Model
assumption, our results improve upon existing direct limits set by a high-
precision measurement of the 63Ni $\beta$ spectrum [25]. However, these are
well above the indirect detection limits set by the NuSTAR experiment [39],
which looks for anomalous X-ray lines from radiative sterile neutrino DM
decays.

The upcoming DarkSide-20k experiment has a planned exposure almost four orders
of magnitude larger than DS-50 and will provide more sensitive searches for
each DM model considered here.

###### Acknowledgements.

The DarkSide Collaboration offers its profound gratitude to the LNGS and its
staff for their invaluable technical and logistical support. We also thank the
Fermilab Particle Physics, Scientific, and Core Computing Divisions.
Construction and operation of the DarkSide-50 detector was supported by the
U.S. National Science Foundation (NSF) (Grants No. PHY-0919363, No.
PHY-1004072, No. PHY-1004054, No. PHY-1242585, No. PHY-1314483, No.
PHY-1314501, No. PHY-1314507, No. PHY-1352795, No. PHY-1622415, and associated
collaborative grants No. PHY-1211308 and No. PHY-1455351), the Italian
Istituto Nazionale di Fisica Nucleare, the U.S. Department of Energy
(Contracts No. DE-FG02-91ER40671, No. DEAC02-07CH11359, and No. DE-
AC05-76RL01830), the Polish NCN (Grant No. UMO-2019/33/B/ST2/02884) and the
Polish Ministry for Education and Science (Grant No. 6811/IA/SP/2018). We also
acknowledge financial support from the French Institut National de Physique
Nucléaire et de Physique des Particules (IN2P3), the IN2P3-COPIN consortium
(Grant No. 20-152), and the UnivEarthS LabEx program (Grants No.
ANR-10-LABX-0023 and No. ANR-18-IDEX-0001), from the São Paulo Research
Foundation (FAPESP) (Grant No. 2016/09084-0), from the Interdisciplinary
Scientific and Educational School of Moscow University “Fundamental and
Applied Space Research”, from the Program of the Ministry of Education and
Science of the Russian Federation for higher education establishments, project
No. FZWG-2020-0032 (2019-1569), from IRAP AstroCeNT funded by FNP from ERDF,
and from the Science and Technology Facilities Council, United Kingdom. I.
Albuquerque is partially supported by the Brazilian Research Council (CNPq).
This project has received funding from the European Union’s Horizon 2020
research and innovation program under grant agreement No 952480. Isotopes used
in this research were supplied by the United States Department of Energy
Office of Science by the Isotope Program in the Office of Nuclear Physics.

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