title: Non-reflective categories of some kinds of weakly sober spaces

abstract:
  Ern\'e weakened the concept of sobriety in order to extend the theory of
sober spaces and locally hypercompact spaces to situations where directed joins
were missing, and introduced and discussed three kinds of non-sober spaces: cut
spaces, weakly sober spaces, and quasisober spaces. Three other kinds of
non-sober spaces, namely $\mathsf{DC}$ space, $\mathsf{RD}$ space and
$\mathsf{WD}$ space, were introduced and investigated by Xu, Shen, Xi and Zhao.
All these six kinds of spaces are strictly weaker than sober spaces. In this
paper, it is shown that none of the category of all $\mathsf{DC}$ spaces, that
of all $\mathsf{RD}$ spaces, that of all $\mathsf{WD}$ spaces, that of all
quasisober spaces, that of all weakly spaces and that of all cut spaces is
reflective in the category of all $T_0$ spaces with continuous mappings.


title: To Simulate the Spread of Infectious Diseases by the Random Matrix

abstract:
  The main aim to build models capable of simulating the spreading of
infectious diseases is to control them. And along this way, the key to find the
optimal strategy for disease control is to obtain a large number of simulations
of disease transitions under different scenarios. Therefore, the models that
can simulate the spreading of diseases under scenarios closer to the reality
and are with high efficiency are preferred. In the realistic social networks,
the random contact, including contacts between people in the public places and
the public transits, becomes the important access for the spreading of
infectious diseases. In this paper, a model can efficiently simulate the
spreading of infectious diseases under random contacts is proposed. In this
approach, the random contact between people is characterized by the random
matrix with elements randomly generated and the spread of the diseases is
simulated by the Markov process. We report an interesting property of the
proposed model: the main indicators of the spreading of the diseases such as
the death rate are invariant of the size of the population. Therefore,
representative simulations can be conducted on models consist of small number
of populations. The main advantage of this model is that it can easily simulate
the spreading of diseases under more realistic scenarios and thus is able to
give a large number of simulations needed for the searching of the optimal
control strategy. Based on this work, the reinforcement learning will be
introduced to give the optimal control strategy in the following work.


title: Neural Topic Modeling of Psychotherapy Sessions

abstract:
  In this work, we compare different neural topic modeling methods in learning
the topical propensities of different psychiatric conditions from the
psychotherapy session transcripts parsed from speech recordings. We also
incorporate temporal modeling to put this additional interpretability to action
by parsing out topic similarities as a time series in a turn-level resolution.
We believe this topic modeling framework can offer interpretable insights for
the therapist to optimally decide his or her strategy and improve the
psychotherapy effectiveness.


title:
Investigating User Radicalization: A Novel Dataset for Identifying
  Fine-Grained Temporal Shifts in Opinion

abstract:
  There is an increasing need for the ability to model fine-grained opinion
shifts of social media users, as concerns about the potential polarizing social
effects increase. However, the lack of publicly available datasets that are
suitable for the task presents a major challenge. In this paper, we introduce
an innovative annotated dataset for modeling subtle opinion fluctuations and
detecting fine-grained stances. The dataset includes a sufficient amount of
stance polarity and intensity labels per user over time and within entire
conversational threads, thus making subtle opinion fluctuations detectable both
in long term and in short term. All posts are annotated by non-experts and a
significant portion of the data is also annotated by experts. We provide a
strategy for recruiting suitable non-experts. Our analysis of the
inter-annotator agreements shows that the resulting annotations obtained from
the majority vote of the non-experts are of comparable quality to the
annotations of the experts. We provide analyses of the stance evolution in
short term and long term levels, a comparison of language usage between users
with vacillating and resolute attitudes, and fine-grained stance detection
baselines.


title:
Alexa as an Active Listener: How Backchanneling Can Elicit
  Self-Disclosure and Promote User Experience

abstract:
  Active listening is a well-known skill applied in human communication to
build intimacy and elicit self-disclosure to support a wide variety of
cooperative tasks. When applied to conversational UIs, active listening from
machines can also elicit greater self-disclosure by signaling to the users that
they are being heard, which can have positive outcomes. However, it takes
considerable engineering effort and training to embed active listening skills
in machines at scale, given the need to personalize active-listening cues to
individual users and their specific utterances. A more generic solution is
needed given the increasing use of conversational agents, especially by the
growing number of socially isolated individuals. With this in mind, we
developed an Amazon Alexa skill that provides privacy-preserving and
pseudo-random backchanneling to indicate active listening. User study (N = 40)
data show that backchanneling improves perceived degree of active listening by
smart speakers. It also results in more emotional disclosure, with participants
using more positive words. Perception of smart speakers as active listeners is
positively associated with perceived emotional support. Interview data
corroborate the feasibility of using smart speakers to provide emotional
support. These findings have important implications for smart speaker
interaction design in several domains of cooperative work and social computing.


title: Residue-Based Natural Language Adversarial Attack Detection

abstract:
  Deep learning based systems are susceptible to adversarial attacks, where a
small, imperceptible change at the input alters the model prediction. However,
to date the majority of the approaches to detect these attacks have been
designed for image processing systems. Many popular image adversarial detection
approaches are able to identify adversarial examples from embedding feature
spaces, whilst in the NLP domain existing state of the art detection approaches
solely focus on input text features, without consideration of model embedding
spaces. This work examines what differences result when porting these image
designed strategies to Natural Language Processing (NLP) tasks - these
detectors are found to not port over well. This is expected as NLP systems have
a very different form of input: discrete and sequential in nature, rather than
the continuous and fixed size inputs for images. As an equivalent model-focused
NLP detection approach, this work proposes a simple sentence-embedding
"residue" based detector to identify adversarial examples. On many tasks, it
out-performs ported image domain detectors and recent state of the art NLP
specific detectors.


title:
Condition Monitoring of Transformer Bushings Using Computational
  Intelligence

abstract:
  Dissolved Gas-in-oil analysis (DGA) is used to monitor the condition of
bushings on large power transformers. There are different techniques used in
determining the conditions from the data collected, but in this work the
Artificial Intelligence techniques are investigated. This work investigates
which gases in DGA are related to each other and which ones are important for
making decisions. When the related and crucial gases are determined, the other
gases are discarded thereby reducing the number of attributes in DGA. Hence a
further investigation is done to see how these new datasets influence the
performance of the classifiers used to classify the DGA of full attributes. The
classifiers used in these experiments were Backpropagation Neural Networks
(BPNN) and Support Vector Machines (SVM) whereas the Principal Component
Analysis (PCA), Rough Set (RS), Incremental Granular Ranking (GR++) and
Decision Trees (DT) were used to reduce the attributes of the dataset. The
parameters used when training the BPNN and SVM classifiers are kept fixed to
create a controlled test environment when investigating the effects of reducing
the number of gases. This work further introduced a new classifier that can
handle high dimension dataset and noisy dataset, Rough Neural Network (RNN).


title:
Semantic Structure based Query Graph Prediction for Question Answering
  over Knowledge Graph

abstract:
  Building query graphs from natural language questions is an important step in
complex question answering over knowledge graph (Complex KGQA). In general, a
question can be correctly answered if its query graph is built correctly and
the right answer is then retrieved by issuing the query graph against the KG.
Therefore, this paper focuses on query graph generation from natural language
questions. Existing approaches for query graph generation ignore the semantic
structure of a question, resulting in a large number of noisy query graph
candidates that undermine prediction accuracies. In this paper, we define six
semantic structures from common questions in KGQA and develop a novel
Structure-BERT to predict the semantic structure of a question. By doing so, we
can first filter out noisy candidate query graphs by the predicted semantic
structures, and then rank the remaining candidates with a BERT-based ranking
model. Extensive experiments on two popular benchmarks MetaQA and
WebQuestionsSP (WSP) demonstrate the effectiveness of our method as compared to
state-of-the-arts.


title:
IIITDWD-ShankarB@ Dravidian-CodeMixi-HASOC2021: mBERT based model for
  identification of offensive content in south Indian languages

abstract:
  In recent years, there has been a lot of focus on offensive content. The
amount of offensive content generated by social media is increasing at an
alarming rate. This created a greater need to address this issue than ever
before. To address these issues, the organizers of "Dravidian-Code Mixed
HASOC-2020" have created two challenges. Task 1 involves identifying offensive
content in Malayalam data, whereas Task 2 includes Malayalam and Tamil Code
Mixed Sentences. Our team participated in Task 2. In our suggested model, we
experiment with multilingual BERT to extract features, and three different
classifiers are used on extracted features. Our model received a weighted F1
score of 0.70 for Malayalam data and was ranked fifth; we also received a
weighted F1 score of 0.573 for Tamil Code Mixed data and were ranked eleventh.


title: Multimodal Hate Speech Detection from Bengali Memes and Texts

abstract:
  Numerous works have been proposed to employ machine learning (ML) and deep
learning (DL) techniques to utilize textual data from social media for
anti-social behavior analysis such as cyberbullying, fake news propagation, and
hate speech mainly for highly resourced languages like English. However,
despite having a lot of diversity and millions of native speakers, some
languages such as Bengali are under-resourced, which is due to a lack of
computational resources for natural language processing (NLP). Like English,
Bengali social media content also includes images along with texts (e.g.,
multimodal contents are posted by embedding short texts into images on
Facebook), only the textual data is not enough to judge them (e.g., to
determine they are hate speech). In those cases, images might give extra
context to properly judge. This paper is about hate speech detection from
multimodal Bengali memes and texts. We prepared the only multimodal hate speech
detection dataset1 for a kind of problem for Bengali. We train several neural
architectures (i.e., neural networks like Bi-LSTM/Conv-LSTM with word
embeddings, EfficientNet + transformer architectures such as monolingual Bangla
BERT, multilingual BERT-cased/uncased, and XLM-RoBERTa) jointly analyze textual
and visual information for hate speech detection. The Conv-LSTM and XLM-RoBERTa
models performed best for texts, yielding F1 scores of 0.78 and 0.82,
respectively. As of memes, ResNet152 and DenseNet201 models yield F1 scores of
0.78 and 0.7, respectively. The multimodal fusion of mBERT-uncased +
EfficientNet-B1 performed the best, yielding an F1 score of 0.80. Our study
suggests that memes are moderately useful for hate speech detection in Bengali,
but none of the multimodal models outperform unimodal models analyzing only
textual data.


title:
Flexible and dependable manufacturing beyond xURLLC: A novel framework
  for communication-control co-design

abstract:
  Future Industrial 4.0 applications in the 6G era is calling for high
dependability that goes far beyond the current ultra-reliable low latency
communication (URLLC), and therewith proposed critical challenges to the
communication technology. Instead of struggling against the physical and
technical limits towards an extreme URLLC (xURLLC), communication-control
co-design (CoCoCo) appears a more promising solution. This work proposes a
novel framework of CoCoCo, which is not only enhancing the dependability of 6G
industrial applications such as remote control, but also exhibiting rich
potential in revolutionizing the future industry per openness and flexibility
of manufacturing systems.


title: Context-Aware Language Modeling for Goal-Oriented Dialogue Systems

abstract:
  Goal-oriented dialogue systems face a trade-off between fluent language
generation and task-specific control. While supervised learning with large
language models is capable of producing realistic text, how to steer such
responses towards completing a specific task without sacrificing language
quality remains an open question. In this work, we formulate goal-oriented
dialogue as a partially observed Markov decision process, interpreting the
language model as a representation of both the dynamics and the policy. This
view allows us to extend techniques from learning-based control, such as task
relabeling, to derive a simple and effective method to finetune language models
in a goal-aware way, leading to significantly improved task performance. We
additionally introduce a number of training strategies that serve to better
focus the model on the task at hand. We evaluate our method, Context-Aware
Language Models (CALM), on a practical flight-booking task using AirDialogue.
Empirically, CALM outperforms the state-of-the-art method by 7% in terms of
task success, matching human-level task performance.


title:
Near-Optimal Fidelity in Quantum Circuits through Incorporating
  Efficient Real-time Error Based Heuristics in Compiler Mappings

abstract:
  To run a quantum program in the real device, the compiler maps the logical
qubits to physical qubits. This is the most crucial step of compiling a quantum
circuit. Because the fidelity of a quantum circuit depends heavily on this
mapping process. However, this qubit mapping problem is NP-complete. Therefore,
we should resort to heuristics to find high-fidelity mappings. In this paper,
we focused on finding efficient heuristic techniques to incorporate real-time
error feedback and device connectivity information in order to achieve high
fidelity mapping of the quantum circuits. We performed extensive analysis and
experimental study based on two baseline algorithms. We performed our
experimentation on various combinations of different error rates and heuristic
techniques. Consequently, we designed very elegant techniques to consider both
all types of real-time error feedback and connectivity information. We showed
that our best heuristic approach performs \textbf{1.62x} ( on average) better
than one baseline and \textbf{1.934x} ( on average ) better than the other
baseline on random benchmarks. Finally, we compared our best heuristic ( CAES )
with the state-of-the-art heuristic-based mapping algorithm on representative
benchmarks. We found that CAES performed \textbf{1.7x} ( on average ) better
than the state of the art in terms of success rate.


title: An Exploratory Study on Code Attention in BERT

abstract:
  Many recent models in software engineering introduced deep neural models
based on the Transformer architecture or use transformer-based Pre-trained
Language Models (PLM) trained on code. Although these models achieve the state
of the arts results in many downstream tasks such as code summarization and bug
detection, they are based on Transformer and PLM, which are mainly studied in
the Natural Language Processing (NLP) field. The current studies rely on the
reasoning and practices from NLP for these models in code, despite the
differences between natural languages and programming languages. There is also
limited literature on explaining how code is modeled.
  Here, we investigate the attention behavior of PLM on code and compare it
with natural language. We pre-trained BERT, a Transformer based PLM, on code
and explored what kind of information it learns, both semantic and syntactic.
We run several experiments to analyze the attention values of code constructs
on each other and what BERT learns in each layer. Our analyses show that BERT
pays more attention to syntactic entities, specifically identifiers and
separators, in contrast to the most attended token [CLS] in NLP. This
observation motivated us to leverage identifiers to represent the code sequence
instead of the [CLS] token when used for code clone detection. Our results show
that employing embeddings from identifiers increases the performance of BERT by
605% and 4% F1-score in its lower layers and the upper layers, respectively.
When identifiers' embeddings are used in CodeBERT, a code-based PLM, the
performance is improved by 21-24% in the F1-score of clone detection. The
findings can benefit the research community by using code-specific
representations instead of applying the common embeddings used in NLP, and open
new directions for developing smaller models with similar performance.


title: Usage-based learning of grammatical categories

abstract:
  Human languages use a wide range of grammatical categories to constrain which
words or phrases can fill certain slots in grammatical patterns and to express
additional meanings, such as tense or aspect, through morpho-syntactic means.
These grammatical categories, which are most often language-specific and
changing over time, are difficult to define and learn. This paper raises the
question how these categories can be acquired and where they have come from. We
explore a usage-based approach. This means that categories and grammatical
constructions are selected and aligned by their success in language
interactions. We report on a multi-agent experiment in which agents are endowed
with mechanisms for understanding and producing utterances as well as
mechanisms for expanding their inventories using a meta-level learning process
based on pro- and anti-unification. We show that a categorial type network
which has scores based on the success in a language interaction leads to the
spontaneous formation of grammatical categories in tandem with the formation of
grammatical patterns.


title:
Unsupervised Numerical Reasoning to Extract Phenotypes from Clinical
  Text by Leveraging External Knowledge

abstract:
  Extracting phenotypes from clinical text has been shown to be useful for a
variety of clinical use cases such as identifying patients with rare diseases.
However, reasoning with numerical values remains challenging for phenotyping in
clinical text, for example, temperature 102F representing Fever. Current
state-of-the-art phenotyping models are able to detect general phenotypes, but
perform poorly when they detect phenotypes requiring numerical reasoning. We
present a novel unsupervised methodology leveraging external knowledge and
contextualized word embeddings from ClinicalBERT for numerical reasoning in a
variety of phenotypic contexts. Comparing against unsupervised benchmarks, it
shows a substantial performance improvement with absolute gains on generalized
Recall and F1 scores up to 79% and 71%, respectively. In the supervised
setting, it also surpasses the performance of alternative approaches with
absolute gains on generalized Recall and F1 scores up to 70% and 44%,
respectively.


title:
Maximizing the Influence of Bichromatic Reverse k Nearest Neighbors in
  Geo-Social Networks

abstract:
  Geo-social networks offer opportunities for the marketing and promotion of
geo-located services. In this setting, we explore a new problem, called
Maximizing the Influence of Bichromatic Reverse k Nearest Neighbors
(MaxInfBRkNN). The objective is to find a set of points of interest (POIs),
which are geo-textually and socially attractive to social influencers who are
expected to largely promote the POIs through online influence propagation. In
other words, the problem aims to detect an optimal set of POIs with the largest
word-of-mouth (WOM) marketing potential. This functionality is useful in
various real-life applications, including social advertising, location-based
viral marketing, and personalized POI recommendation. However, solving
MaxInfBRkNN with theoretical guarantees is challenging, because of the
prohibitive overheads on BRkNN retrieval in geo-social networks, and the NP and
#P-hardness in finding the optimal POI set. To achieve practical solutions, we
present a framework with carefully designed indexes, efficient batch BRkNN
processing algorithms, and alternative POI selection policies that support both
approximate and heuristic solutions. Extensive experiments on real and
synthetic datasets demonstrate the good performance of our proposed methods.


title: On the number of squares in a finite word

abstract:
  A {\em square} is a word of the form $uu$. In this paper we prove that for a
given finite word $w$, the number of distinct square factors of $w$ is bounded
by $|w|-|\Alphabet(w)|+1$, where $|w|$ denotes the length of $w$ and
$|\Alphabet(w)|$ denotes the number of distinct letters in $w$. This result
answers a conjecture of Fraenkel and Simpson stated in 1998.


title: Opus on conformal symmetry of the Nambu-Goto versus Polyakov strings

abstract:
  I investigate the Nambu-Goto and Polyakov strings, accounting for
higher-derivative terms in the induced action for the metric tensor which are
classically negligible for smooth metrics but revive quantumly. Using the
conformal field theory technique by KPZ-DDK, I compute in the one-loop
approximation the conformal dimension and the central charge which differs in
the two cases causing the difference between the Nambu-Goto and Polyakov
strings.


title:
Benchmarking Answer Verification Methods for Question Answering-Based
  Summarization Evaluation Metrics

abstract:
  Question answering-based summarization evaluation metrics must automatically
determine whether the QA model's prediction is correct or not, a task known as
answer verification. In this work, we benchmark the lexical answer verification
methods which have been used by current QA-based metrics as well as two more
sophisticated text comparison methods, BERTScore and LERC. We find that LERC
out-performs the other methods in some settings while remaining statistically
indistinguishable from lexical overlap in others. However, our experiments
reveal that improved verification performance does not necessarily translate to
overall QA-based metric quality: In some scenarios, using a worse verification
method -- or using none at all -- has comparable performance to using the best
verification method, a result that we attribute to properties of the datasets.


title:
Assessment of Natural Radioactivity Concentration and Radiological Risk
  in Tanjung Enim's Coal Mine, South Sumatra Indonesia

abstract:
  Environmental radioactivity analysis has been carried out to determine the
level of environmental radioactivity and the potential radiological hazards at
Tanjung Enims coal mine. Gamma spectroscopy method has been carried out to
identify radionuclides and their types of activity. The results of radionuclide
concentration are used to determine the radiological hazard index and become
input data for the Residual Radioactivity Onsite 7.2 application to determine
the dose rate and long-term cancer potential received by workers in coal mines.
The results obtained for the average concentration of radionuclide activity in
coal samples are 226Ra, 232Th, and 40K are 72.468 Bq/kg, 86.905 Bq/kg, and
1802.049 Bq/kg, respectively. While the soil samples 226Ra, 232Th, and 40K
respectively 79.205 Bq/kg, 100.209 Bq/kg, and 1443.275 Bq/kg. The radionuclide
concentrations of both samples exceeded the UNSCEAR and worldwide reported
averages for coal and soil. The average radiological hazard index for coal
samples, namely Raeq, Hin, and Hex, was 335.500 Bq/kg, 1.102, 0.906,
respectively. While the soil samples were 333.636 Bq/kg, 1.115, and 0.901,
respectively. The index parameter is already lower than the UNSCEAR
recommendation except for Hin, so there is a potential radiological hazard in
internal pathways such as respiratory and digestive organs for mining workers.
The total annual effective dose rate based on 5 RESRAD Onsite 7.2 pathways,
namely external gamma, inhalation, radon, soil ingestion, and drinking water,
is 1.675 mSv/year, exceeding the dose limits determined by ICRP, 1 mSv/year.
The ELCR is 6.625*10^-3 which exceeds the UNSCEAR recommendation, 2.4*10^-4.
Based on the results, it is necessary to intervene in the mining environment of
the Tanjung Enims coal mine.


title:
Message Flow Analysis with Complex Causal Links for Distributed ROS 2
  Systems

abstract:
  Distributed robotic systems rely heavily on publish-subscribe frameworks such
as the Robot Operating System (ROS) to efficiently implement modular
computation graphs. The ROS 2 executor, a high-level task scheduler which
handles ROS 2 messages, is a performance bottleneck. We extend ros2_tracing, a
framework with instrumentation and tools for real-time tracing of ROS 2, with
the analysis and visualization of the flow of messages across distributed ROS 2
systems. Our method detects one-to-many and many-to-many causal links between
input and output messages, including indirect causal links through simple
user-level annotations. We validate our method on both synthetic and real
robotic systems, and demonstrate its low runtime overhead. Moreover, the
underlying intermediate execution representation database can be further
leveraged to extract additional metrics and high-level results. This can
provide valuable timing and scheduling information to further study and improve
the ROS 2 executor as well as optimize any ROS 2 system. The source code is
available at: https://github.com/christophebedard/ros2-message-flow-analysis.


title:
BTranspose: Bottleneck Transformers for Human Pose Estimation with
  Self-Supervised Pre-Training

abstract:
  The task of 2D human pose estimation is challenging as the number of
keypoints is typically large (~ 17) and this necessitates the use of robust
neural network architectures and training pipelines that can capture the
relevant features from the input image. These features are then aggregated to
make accurate heatmap predictions from which the final keypoints of human body
parts can be inferred. Many papers in literature use CNN-based architectures
for the backbone, and/or combine it with a transformer, after which the
features are aggregated to make the final keypoint predictions [1]. In this
paper, we consider the recently proposed Bottleneck Transformers [2], which
combine CNN and multi-head self attention (MHSA) layers effectively, and we
integrate it with a Transformer encoder and apply it to the task of 2D human
pose estimation. We consider different backbone architectures and pre-train
them using the DINO self-supervised learning method [3], this pre-training is
found to improve the overall prediction accuracy. We call our model BTranspose,
and experiments show that on the COCO validation set, our model achieves an AP
of 76.4, which is competitive with other methods such as [1] and has fewer
network parameters. Furthermore, we also present the dependencies of the final
predicted keypoints on both the MHSA block and the Transformer encoder layers,
providing clues on the image sub-regions the network attends to at the mid and
high levels.


title:
System-size dependence of the charged-particle pseudorapidity density at
  $\sqrt{s_{\rm NN}} = 5.02$ TeV for pp, p-Pb, and Pb-Pb collisions

abstract:
  We present and compare the charged-particle pseudorapidity densities for pp,
p-Pb, and Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV measured over a
wide pseudorapidity range (${-3.5 <\eta <5}$), using ALICE at the Large Hadron
Collider. The distributions for p-Pb and Pb-Pb collisions are determined as a
function of the centrality of the collisions, while results from pp collisions
are reported for inelastic events with at least one charged particle at
midrapidity. The charged-particle pseudorapidity densities are, under simple
and robust assumptions, transformed to charged-particle rapidity densities.
This allows for the calculation and the presentation of the evolution of the
width of the rapidity distributions and of a lower bound on the Bjorken energy
density, as a function of the number of participants in all three collision
systems. We find a decreasing width of the particle production, and roughly a
ten fold increase in the energy density, as the system size grows.


title:
SmartPortraits: Depth Powered Handheld Smartphone Dataset of Human
  Portraits for State Estimation, Reconstruction and Synthesis

abstract:
  We present a dataset of 1000 video sequences of human portraits recorded in
real and uncontrolled conditions by using a handheld smartphone accompanied by
an external high-quality depth camera. The collected dataset contains 200
people captured in different poses and locations and its main purpose is to
bridge the gap between raw measurements obtained from a smartphone and
downstream applications, such as state estimation, 3D reconstruction, view
synthesis, etc. The sensors employed in data collection are the smartphone's
camera and Inertial Measurement Unit (IMU), and an external Azure Kinect DK
depth camera software synchronized with sub-millisecond precision to the
smartphone system. During the recording, the smartphone flash is used to
provide a periodic secondary source of lightning. Accurate mask of the foremost
person is provided as well as its impact on the camera alignment accuracy. For
evaluation purposes, we compare multiple state-of-the-art camera alignment
methods by using a Motion Capture system. We provide a smartphone
visual-inertial benchmark for portrait capturing, where we report results for
multiple methods and motivate further use of the provided trajectories,
available in the dataset, in view synthesis and 3D reconstruction tasks.


title:
OCTOPUS -- optical coherence tomography plaque and stent analysis
  software

abstract:
  Compared with other imaging modalities, intravascular optical coherence
tomography (IVOCT) has significant advantages for guiding percutaneous coronary
interventions. To aid IVOCT research studies, we developed the Optical
Coherence TOmography PlaqUe and Stent (OCTOPUS) analysis software. To automate
image analysis results, the software includes several important algorithmic
steps: pre-processing, deep learning plaque segmentation, machine learning
identification of stent struts, and registration of pullbacks. Interactive
visualization and manual editing of segmentations were included in the
software. Quantifications include stent deployment characteristics (e.g., stent
strut malapposition), strut level analysis, calcium angle, and calcium
thickness measurements. Interactive visualizations include (x,y) anatomical, en
face, and longitudinal views with optional overlays. Underlying plaque
segmentation algorithm yielded excellent pixel-wise results (86.2% sensitivity
and 0.781 F1 score). Using OCTOPUS on 34 new pullbacks, we determined that
following automated segmentation, only 13% and 23% of frames needed any manual
touch up for detailed lumen and calcification labeling, respectively. Only up
to 3.8% of plaque pixels were modified, leading to an average editing time of
only 7.5 seconds/frame, an approximately 80% reduction compared to manual
analysis. Regarding stent analysis, sensitivity and precision were both greater
than 90%, and each strut was successfully classified as either covered or
uncovered with high sensitivity (94%) and specificity (90%). We introduced and
evaluated the clinical application of a highly automated software package,
OCTOPUS, for quantitative plaque and stent analysis in IVOCT images. The
software is currently used as an offline tool for research purposes; however,
the software's embedded algorithms may also be useful for real-time treatment
planning.


title:
Lexicographically maximal edges of dual hypergraphs and Nash-solvability
  of tight game forms

abstract:
  Let $\mathcal{A} = \{A_1, \ldots, A_m\}$ and $\mathcal{B} = \{B_1, \ldots,
B_n\}$ be a pair of dual multi-hypergraphs on the common ground set $O = \{o_1,
\ldots, o_k\}$. Note that each of them may have embedded or equal edges. An
edge is called containment minimal (or just minimal, for short) if it is not a
strict superset of another edge. Yet, equal minimal edges may exist. By
duality,
  (i) $A \cap B \neq \emptyset$ for every pair $A \in \mathcal{A}$ and $B \in
\mathcal{B}$;
  (ii) if $A$ is minimal then for every $o \in A$ there exists a $B \in
\mathcal{B}$ such that $A \cap B = \{o\}$.
  We will extend claim (ii) as follows. A linear order $\succ$ over $O$ defines
a unique lexicographic order $\succ_L$ over the $2^O$.
  Let $A$ be a lexicographically maximal (lexmax) edge of $\mathcal{A}$. Then,
  (iii) $A$ is minimal and for every $o \in A$ there exists a minimal $B \in
\mathcal{B}$ such that
  $A \cap B = \{o\}$ and $o \succeq o'$ for each $o' \in B$.
  This property has important applications in game theory implying
Nash-solvability of tight game forms as shown in the old (1975 and 1989) work
of the first author. Here we give a new, very short, proof of (iii).
  Edges $A$ and $B$ mentioned in (iii) can be found out in polynomial time.
This is trivial if $\mathcal{A}$ and $\mathcal{B}$ are given explicitly. Yet,
it is true even if only $\mathcal{A}$ is given, and not explicitly, but by a
polynomial containment oracle, which for a subset $O_A \subseteq O$ answers in
polynomial time whether $O_A$ contains an edge of $\mathcal{A}$.


title: Color decorations of Jackiw-Teitelboim gravity

abstract:
  We introduce the colored version of Jackiw-Teitelboim (JT) gravity which is
the two-dimensional dilaton gravity model with matrix-valued fields. It is
straightforwardly formulated in terms of BF action with $su(N,N)$ gauge algebra
so that the standard JT gravity is embedded as $su(1,1) \subset su(N,N)$
subsector. We also elaborate on the respective metric formulation which is
shown to involve the JT fields plus $su(N)$ non-Abelian fields as well as
$su(N)$-matrix valued metric and dilaton fields. Their interactions are
governed by minimal couplings and potential terms of cubic and quartic orders
involving derivatives.


title:
A Scale-Separated Dynamic Mode Decomposition From Observations of the
  Ionospheric Electron Density Profile

abstract:
  We present a method for modeling a time series of ionospheric electron
density profiles using modal decompositions. Our method is based on the Dynamic
Mode Decomposition (DMD), which provides a means of determining spatiotemporal
modes from measurements alone. DMD-derived models can be easily updated as new
data is recorded and do not require any physics to inform the dynamics.
However, in the case of ionospheric profiles, we find a wide range of
oscillations, including some far above the diurnal frequency. Therefore, we
propose nontrivial extensions to DMD using multiresolution analysis (MRA) via
wavelet decompositions. We call this method the Scale-Separated Dynamic Mode
Decomposition (SSDMD) since the MRA isolates fluctuations at different scales
within the time series into separated components. We show that this method
provides a stable reconstruction of the mean plasma density and can be used to
predict the state of the vertical profile at future time steps. We demonstrate
the SSDMD method on data sets covering periods of high and low solar activity.


title:
Re-Examining System-Level Correlations of Automatic Summarization
  Evaluation Metrics

abstract:
  How reliably an automatic summarization evaluation metric replicates human
judgments of summary quality is quantified by system-level correlations. We
identify two ways in which the definition of the system-level correlation is
inconsistent with how metrics are used to evaluate systems in practice and
propose changes to rectify this disconnect. First, we calculate the system
score for an automatic metric using the full test set instead of the subset of
summaries judged by humans, which is currently standard practice. We
demonstrate how this small change leads to more precise estimates of
system-level correlations. Second, we propose to calculate correlations only on
pairs of systems that are separated by small differences in automatic scores
which are commonly observed in practice. This allows us to demonstrate that our
best estimate of the correlation of ROUGE to human judgments is near 0 in
realistic scenarios. The results from the analyses point to the need to collect
more high-quality human judgments and to improve automatic metrics when
differences in system scores are small.


title: Adiabatic Approximation of Coarse Grained Second Order Response

abstract:
  We propose estimators for dynamic second order response theory in coarse
grained variables for driven out-of-equilibrium subsystems. The error is
controlled through the the notion of subsystem spectral gap for the convergence
of coarse grained observables.


title:
The rise of the primordial tensor spectrum from an early scalar-tensor
  epoch

abstract:
  Primordial gravitational waves (PGW) produced during inflation span a large
range of frequencies, carrying information on the dynamics of the primordial
universe. During an early scalar-tensor dominated epoch, the amplitude of the
PGW spectrum can be enhanced over a wide range of frequencies. To study this
phenomenon, we focus on a class of scalar-tensor theories, well motivated by
high energy theories of dark energy and dark matter, where the scalar is
conformally and disformally coupled to matter during the early cosmological
evolution. For a conformally dominated epoch, the PGW spectrum has a flat
step-like shape. More interestingly, a disformally dominated epoch is
characterised by a peaked spectrum with a broken power-law profile, with slopes
depending on the scalar-tensor theory considered. We introduce a graphical
tool, called broken power-law sensitivity curve, as a convenient visual
indicator for understanding whether a given broken power-law profile can be
detected by GW experiments. We then analyse the GW spectra for a variety of
representative conformal and disformal models, discussing their detectability
prospects with the Einstein Telescope (ET), Laser Interferometer Space Antenna
(LISA), DECi-hertz Interferometer Gravitational wave Observatory (DECIGO), and
Big Bang Observer (BBO).


title: Giant component of the soft random geometric graph

abstract:
  Consider a 2-dimensional soft random geometric graph $G(\lambda,s,\phi)$,
obtained by placing a Poisson($\lambda s^2$) number of vertices uniformly at
random in a square of side $s$, with edges placed between each pair $x,y$ of
vertices with probability $\phi(\|x-y\|)$, where $\phi: {\bf R}_+ \to [0,1]$ is
a finite-range connection function. This paper is concerned with the asymptotic
behaviour of the graph $G(\lambda,s,\phi)$ in the large-$s$ limit with
$(\lambda,\phi)$ fixed. We prove that the proportion of vertices in the largest
component converges in probability to the percolation probability for the
corresponding random connection model, which is a random graph defined
similarly for a Poisson process on the whole plane. We do not cover the case
where $\lambda$ equals the critical value $\lambda_c(\phi)$.


title:
Off-axis electron holography for the direct visualization of
  perpendicular shape anisotropy in nano-scale 3D magnetic random-access-memory
  devices

abstract:
  Perpendicular shape anisotropy (PSA) and double magnetic tunnel junctions
(DMTJ) offer practical solutions to downscale spin-transfer-torque Magnetic
Random-Access Memory (STT-MRAM) beyond 20 nm technology nodes, whilst retaining
their thermal stability and reducing critical currents applied. However, as
these modern devices become smaller and three-dimensionally (3D) complex, our
understanding of their functional magnetic behavior is often indirect, relying
on magnetoresistance measurements and micromagnetic modelling. In this paper,
we review recent work that was performed on these structures using a range of
advanced electron microscopy techniques, focusing on aspects specific to the 3D
and nanoscale nature of such elements. We present the methodology for the
systematic transfer of individual SST-MRAM nano-pillars from large-scale arrays
to image their magnetic configurations directly using off-axis electron
holography. We show that improved phase sensitivity through stacking of
electron holograms can be used to image subtle variations in DMTJs and the
thermal stability of < 20 nm PSA-STT-MRAM nano-pillars during in-situ heating.
The experimental practicalities, benefits and limits of using electron
holography for analysis of MRAM devices are discussed, unlocking practical
pathways for direct imaging of the functional magnetic performance of these
systems with high spatial resolution and sensitivity.


title: A Graphical Workflow Exploration Environment For Visual Analytics

abstract:
  Graphical history mechanisms have been widely utilized in many domains to
support humans' limited working memory, error recovery, collaboration, and
presentation in visual analysis. Yet, there are aspects that remain
under-explored in designing graphical history systems for visual analytics
systems to help analysts who have complicated workflows. In this paper we
report on our design study performed with domain experts, where we characterize
domain tasks and designed a visual graphical workflow management environment.
Our environment allows analysts to efficiently review, edit, navigate, and
explore their complex workflows with their colleagues. In order to evaluate the
environment, we present a case study and user study. In the case study, we
explore how two domain experts perform collaborative review, communication, and
training with our environment; while in the user study with the car data, we
reveal that how our environment helps users and how the history mechanism
affects users' visual problem-solving behaviors.


title:
Learning spatiotemporal features from incomplete data for traffic flow
  prediction using hybrid deep neural networks

abstract:
  Urban traffic flow prediction using data-driven models can play an important
role in route planning and preventing congestion on highways. These methods
utilize data collected from traffic recording stations at different timestamps
to predict the future status of traffic. Hence, data collection, transmission,
storage, and extraction techniques can have a significant impact on the
performance of the traffic flow model. On the other hand, a comprehensive
database can provide the opportunity for using complex, yet reliable predictive
models such as deep learning methods. However, most of these methods have
difficulties in handling missing values and outliers. This study focuses on
hybrid deep neural networks to predict traffic flow in the California Freeway
Performance Measurement System (PeMS) with missing values. The proposed
networks are based on a combination of recurrent neural networks (RNNs) to
consider the temporal dependencies in the data recorded in each station and
convolutional neural networks (CNNs) to take the spatial correlations in the
adjacent stations into account. Various architecture configurations with series
and parallel connections are considered based on RNNs and CNNs, and several
prevalent data imputation techniques are used to examine the robustness of the
hybrid networks to missing values. A comprehensive analysis performed on two
different datasets from PeMS indicates that the proposed series-parallel hybrid
network with the mean imputation technique achieves the lowest error in
predicting the traffic flow and is robust to missing values up until 21%
missing ratio in both complete and incomplete training data scenarios when
applied to an incomplete test data.


title:
A generating function associated with the alternating elements in the
  positive part of $U_q(\widehat{\mathfrak{sl}}_2)$

abstract:
  The positive part $U_q^+$ of $U_q(\widehat{\mathfrak{sl}}_2)$ admits an
embedding into a $q$-shuffle algebra. This embedding was introduced by M. Rosso
in 1995. In 2019, Terwilliger introduced the alternating elements
$\{W_{-n}\}_{n \in \mathbb{N}}$, $\{W_{n+1}\}_{n \in \mathbb{N}}$, $\{G_n\}_{n
\in \mathbb{N}}$, $\{\tilde{G}_n\}_{n \in \mathbb{N}}$ in $U_q^+$ using the
Rosso embedding. He showed that the alternating elements $\{W_{-n}\}_{n \in
\mathbb{N}}$, $\{W_{n+1}\}_{n \in \mathbb{N}}$, $\{\tilde{G}_{n+1}\}_{n \in
\mathbb{N}}$ form a PBW basis for $U_q^+$. He also studied the generating
function $\tilde{G}(t)$ for $\{\tilde{G}_n\}_{n \in \mathbb{N}}$ as well as the
multiplicative inverse $D(t)$ of $\tilde{G}(t)$. In this paper, we will express
the coefficients of $D(t)$ in closed form.


title:
The Proton Spin Structure Function $g_2$ and Generalized
  Polarizabilities in the Strong QCD Regime

abstract:
  The strong interaction is not well understood at low energy, or for
interactions with low momentum transfer $Q^2$, but one of the clearest insights
we have comes from Chiral Perturbation Theory ($\chi$PT). This effective
treatment gives testable predictions for the nucleonic generalized
polarizabilities -- fundamental quantities describing the nucleon's response to
an external field. We have measured the proton's generalized spin
polarizabilities in the region where $\chi$PT is expected to be valid. Our
results include the first ever data for the transverse-longitudinal spin
polarizability $\delta_{LT}$, and also extend the coverage of the
polarizability $\bar{d_2}$ to very low $Q^2$ for the first time. These results
were extracted from moments of the structure function $g_2$, a quantity which
characterizes the internal spin structure of the proton. Our experiment ran at
Jefferson Lab using a polarized electron beam and a polarized solid ammonia
(NH$_3$) target. The $\delta_{LT}$ polarizability has remained a challenging
quantity for $\chi$PT to reproduce, despite its reduced sensitivity to higher
resonance contributions; recent competing calculations still disagree with each
other and also diverge from the measured neutron data at very low $Q^2$. Our
proton results provide discriminating power between existing calculations, and
will help provide a better understanding of this strong QCD regime.


title:
Defects Evolution and Mg Segregation in Mg-implanted GaN with
  Ultra-High-Pressure Annealing

abstract:
  Annealing Mg-implanted homoepitaxial GaN at temperatures at or above 1400
{\deg}C eliminates the formation of inversion domains and leads to improved
dopant activation efficiency. Extended defects in the form of inversion domains
contain electrically inactive Mg after post-implantation annealing at
temperatures as high as 1300 {\deg}C (one GPa N2 overpressure), which results
in a low dopant activation efficiency. Triple axis X-ray data show that the
implant-induced strain is fully relieved after annealing at 1300 {\deg}C for 10
min, indicating that the strain-inducing point defects formed during
implantation have reconfigured. However, annealing at temperatures of 1400
{\deg}C to 1500 {\deg}C (also one GPa N2 overpressure) eliminates the presence
of the inversion domains. Annealing at these higher temperatures and for a
longer time does not have any further impact on the strain state. While
residual defects, such as dislocation loops, still exist after annealing at and
above 1400 {\deg}C, chemical analysis at the dislocation loops shows no sign of
Mg segregation. Meanwhile, an overall decreasing trend in the dislocation loop
size and density is observed after annealing at higher temperatures and longer
times. Earlier work [1] addressing electrical measurements of these types of
samples showed that annealing at 1400 {\deg}C leads to a dopant activation
efficiency that is an order of magnitude higher than that observed at 1300
{\deg}C. This work complements the earlier work by identifying the microscopic
defects (inversion domains) which incorporate Mg, and points to the benefits,
in terms of defect density and p-type dopant activation, of using higher
temperatures annealing cycles to activate Mg in GaN.


title: Tunneling phase diagrams in anisotropic Multi-Weyl semimetals

abstract:
  Motivated by the exciting prediction of Multi-Weyl topological semimetals
that are stabilized by point group symmetries [Phys. Rev. Lett. 108 (2012)
266802], we study tunneling phenomena for a class of anisotropic Multi-Weyl
semimetals. We find that a distant detector for different ranges of an
anisotropy parameter $\lambda$ and incident angle $\theta$ will measure a
different number of propagating transmitted modes. We present these findings in
terms of phase diagrams that is valid for an incoming wave with fixed
wavenumber $k$--energy is not fixed. To gain a deeper understanding of this
phenomenon we then focus on the simplest case of an anisotropic quadratic
Weyl-semimetal and analyze tunneling coefficients analytically and numerically
to confirm the observations from the phase diagram. Our results show
non-analytical behavior, which is the hallmark of a phase transition. This
serves as a motivation to make a formal analogy with phase transitions that are
known from statistical mechanics. Specifically, we argue that the long distance
limit in our tunneling problem takes the place of the thermodynamic limit in
statistical mechanics. More precisely, find a direct formal connection to the
recently developed formalism for dynamical phase transitions [Reports on
Progress in Physics 81 (5) (2018) 054001]. We propose that this analogy to
phase transitions can help classify transport properties in exotic semimetals.


title:
The Silent Problem -- Machine Learning Model Failure -- How to Diagnose
  and Fix Ailing Machine Learning Models

abstract:
  The COVID-19 pandemic has dramatically changed how healthcare is delivered to
patients, how patients interact with healthcare providers, and how healthcare
information is disseminated to both healthcare providers and patients.
Analytical models that were trained and tested pre-pandemic may no longer be
performing up to expectations, providing unreliable and irrelevant learning
(ML) models given that ML depends on the basic principle that what happened in
the past are likely to repeat in the future. ML faced to two important
degradation principles, concept drift, when the underlying properties and
characteristics of the variables change and data drift, when the data
distributions, probabilities, co-variates, and other variable relationships
change, both of which are prime culprits of model failure. Therefore, detecting
and diagnosing drift in existing models is something that has become an
imperative. And perhaps even more important is a shift in our mindset towards a
conscious recognition that drift is inevitable, and model building must
incorporate intentional resilience, the ability to offset and recover quickly
from failure, and proactive robustness, avoiding failure by developing models
that are less vulnerable to drift and disruption.


title: The NIST CTS Speaker Recognition Challenge

abstract:
  The US National Institute of Standards and Technology (NIST) has been
conducting a second iteration of the CTS challenge since August 2020. The
current iteration of the CTS Challenge is a leaderboard-style speaker
recognition evaluation using telephony data extracted from the unexposed
portions of the Call My Net 2 (CMN2) and Multi-Language Speech (MLS) corpora
collected by the LDC. The CTS Challenge is currently organized in a similar
manner to the SRE19 CTS Challenge, offering only an open training condition
using two evaluation subsets, namely Progress and Test. Unlike in the SRE19
Challenge, no training or development set was initially released, and NIST has
publicly released the leaderboards on both subsets for the CTS Challenge. Which
subset (i.e., Progress or Test) a trial belongs to is unknown to challenge
participants, and each system submission needs to contain outputs for all of
the trials. The CTS Challenge has also served, and will continue to do so, as a
prerequisite for entrance to the regular SREs (such as SRE21). Since August
2020, a total of 53 organizations (forming 33 teams) from academia and industry
have participated in the CTS Challenge and submitted more than 4400 valid
system outputs. This paper presents an overview of the evaluation and several
analyses of system performance for some primary conditions in the CTS
Challenge. The CTS Challenge results thus far indicate remarkable improvements
in performance due to 1) speaker embeddings extracted using large-scale and
complex neural network architectures such as ResNets along with angular margin
losses for speaker embedding extraction, 2) extensive data augmentation, 3) the
use of large amounts of in-house proprietary data from a large number of
labeled speakers, 4) long-duration fine-tuning.


title:
Hot-SVD: Higher-Order t-Singular Value Decomposition for Tensors based
  on Tensor-Tensor Product

abstract:
  This paper considers a way of generalizing the t-SVD of third-order tensors
(regarded as tubal matrices) to tensors of arbitrary order N (which can be
similarly regarded as tubal tensors of order (N-1)). \color{black}Such a
generalization is different from the t-SVD for tensors of order greater than
three [Martin, Shafer, Larue, SIAM J. Sci. Comput., 35 (2013), A474--A490]. The
decomposition is called Hot-SVD since it can be recognized as a tensor-tensor
product version of HOSVD. The existence of Hot-SVD is proved. To this end, a
new transpose for third-order tensors is introduced. This transpose is crucial
in the verification of Hot-SVD, since it serves as a bridge between tubal
tensors and their unfoldings. We establish some properties of Hot-SVD,
analogous to those of HOSVD, and in doing so we emphasize the perspective of
tubal tensors. The truncated and sequentially truncated Hot-SVD are then
introduced, whose error bounds are $\sqrt{N}$ for an $(N+1)$-th order tensor.
We provide numerical examples to validate Hot-SVD, truncated Hot-SVD, and
sequentially truncated Hot-SVD.


title:
Cross-Lingual Query-Based Summarization of Crisis-Related Social Media:
  An Abstractive Approach Using Transformers

abstract:
  Relevant and timely information collected from social media during crises can
be an invaluable resource for emergency management. However, extracting this
information remains a challenging task, particularly when dealing with social
media postings in multiple languages. This work proposes a cross-lingual method
for retrieving and summarizing crisis-relevant information from social media
postings. We describe a uniform way of expressing various information needs
through structured queries and a way of creating summaries answering those
information needs. The method is based on multilingual transformers embeddings.
Queries are written in one of the languages supported by the embeddings, and
the extracted sentences can be in any of the other languages supported.
Abstractive summaries are created by transformers. The evaluation, done by
crowdsourcing evaluators and emergency management experts, and carried out on
collections extracted from Twitter during five large-scale disasters spanning
ten languages, shows the flexibility of our approach. The generated summaries
are regarded as more focused, structured, and coherent than existing
state-of-the-art methods, and experts compare them favorably against summaries
created by existing, state-of-the-art methods.


title:
Handling Imbalanced Classification Problems With Support Vector Machines
  via Evolutionary Bilevel Optimization

abstract:
  Support vector machines (SVMs) are popular learning algorithms to deal with
binary classification problems. They traditionally assume equal
misclassification costs for each class; however, real-world problems may have
an uneven class distribution. This article introduces EBCS-SVM: evolutionary
bilevel cost-sensitive SVMs. EBCS-SVM handles imbalanced classification
problems by simultaneously learning the support vectors and optimizing the SVM
hyperparameters, which comprise the kernel parameter and misclassification
costs. The resulting optimization problem is a bilevel problem, where the lower
level determines the support vectors and the upper level the hyperparameters.
This optimization problem is solved using an evolutionary algorithm (EA) at the
upper level and sequential minimal optimization (SMO) at the lower level. These
two methods work in a nested fashion, that is, the optimal support vectors help
guide the search of the hyperparameters, and the lower level is initialized
based on previous successful solutions. The proposed method is assessed using
70 datasets of imbalanced classification and compared with several
state-of-the-art methods. The experimental results, supported by a Bayesian
test, provided evidence of the effectiveness of EBCS-SVM when working with
highly imbalanced datasets.


title:
LibDB: An Effective and Efficient Framework for Detecting Third-Party
  Libraries in Binaries

abstract:
  Third-party libraries (TPLs) are reused frequently in software applications
for reducing development cost. However, they could introduce security risks as
well. Many TPL detection methods have been proposed to detect TPL reuse in
Android bytecode or in source code. This paper focuses on detecting TPL reuse
in binary code, which is a more challenging task. For a detection target in
binary form, libraries may be compiled and linked to separate dynamic-link
files or built into a fused binary that contains multiple libraries and
project-specific code. This could result in fewer available code features and
lower the effectiveness of feature engineering. In this paper, we propose a
binary TPL reuse detection framework, LibDB, which can effectively and
efficiently detect imported TPLs even in stripped and fused binaries. In
addition to the basic and coarse-grained features (string literals and exported
function names), LibDB utilizes function contents as a new type of feature. It
embeds all functions in a binary file to low-dimensional representations with a
trained neural network. It further adopts a function call graph-based
comparison method to improve the accuracy of the detection. LibDB is able to
support version identification of TPLs contained in the detection target, which
is not considered by existing detection methods. To evaluate the performance of
LibDB, we construct three datasets for binary-based TPL reuse detection. Our
experimental results show that LibDB is more accurate and efficient than
state-of-the-art tools on the binary TPL detection task and the version
identification task. Our datasets and source code used in this work are
anonymously available at https://github.com/DeepSoftwareAnalytics/LibDB.


title: A Sandbox Tool to Bias(Stress)-Test Fairness Algorithms

abstract:
  Motivated by the growing importance of reducing unfairness in ML predictions,
Fair-ML researchers have presented an extensive suite of algorithmic
"fairness-enhancing" remedies. Most existing algorithms, however, are agnostic
to the sources of the observed unfairness. As a result, the literature
currently lacks guiding frameworks to specify conditions under which each
algorithmic intervention can potentially alleviate the underpinning cause of
unfairness. To close this gap, we scrutinize the underlying biases (e.g., in
the training data or design choices) that cause observational unfairness. We
present a bias-injection sandbox tool to investigate fairness consequences of
various biases and assess the effectiveness of algorithmic remedies in the
presence of specific types of bias. We call this process the
bias(stress)-testing of algorithmic interventions. Unlike existing toolkits,
ours provides a controlled environment to counterfactually inject biases in the
ML pipeline. This stylized setup offers the distinct capability of testing
fairness interventions beyond observational data and against an unbiased
benchmark. In particular, we can test whether a given remedy can alleviate the
injected bias by comparing the predictions resulting after the intervention in
the biased setting with true labels in the unbiased regime -- that is, before
any bias injection. We illustrate the utility of our toolkit via a
proof-of-concept case study on synthetic data. Our empirical analysis showcases
the type of insights that can be obtained through our simulations.


title: Topology-mediated approach to the design of quasisymmetric stellarators

abstract:
  Magnetic confinement fusion designs require careful consideration for them to
confine particles effectively. A possible way to do so is to impose
quasisymmetry. In practice, large-scale optimizations are performed to minimize
deviations from exact quasisymmetry. Unfortunately, the large number of
parameters and complexity of the optimization process make it challenging to
organize and interpret the designs thus obtained. In this Letter, we present a
novel topology-mediated description of configurations to describe the space of
configurations in a reduced model with a small number of parameters. We show
that this approach reproduces existing designs and proves powerful to find
potentially attractive new designs, an explicit example of which is presented.


title: Planes vs. Chairs: Category-guided 3D shape learning without any 3D cues

abstract:
  We present a novel 3D shape reconstruction method which learns to predict an
implicit 3D shape representation from a single RGB image. Our approach uses a
set of single-view images of multiple object categories without viewpoint
annotation, forcing the model to learn across multiple object categories
without 3D supervision. To facilitate learning with such minimal supervision,
we use category labels to guide shape learning with a novel categorical metric
learning approach. We also utilize adversarial and viewpoint regularization
techniques to further disentangle the effects of viewpoint and shape. We obtain
the first results for large-scale (more than 50 categories) single-viewpoint
shape prediction using a single model without any 3D cues. We are also the
first to examine and quantify the benefit of class information in single-view
supervised 3D shape reconstruction. Our method achieves superior performance
over state-of-the-art methods on ShapeNet-13, ShapeNet-55 and Pascal3D+.


title: The average size of maximal matchings in graphs

abstract:
  We investigate the ratio $I(G)$ of the average size of a maximal matching to
the size of a maximum matching in a graph $G$. If many maximal matchings have a
size close to $\mu(G)$, this graph invariant has a value close to 1.
Conversely, if many maximal matchings have a small size, $I(G)$ approaches
$\frac{1}{2}$.
  We propose a general technique to determine the asymptotic behavior of
$\avM(G)$ for various classes of graphs. To illustrate the use of this
technique, we first show how it makes it possible to find known asymptotic
values of $I(G)$ which were typically obtained using generating functions, and
we then determine the asymptotic value of $I(G)$ for other families of graphs,
highlighting the spectrum of possible values of this graph invariant between
$\frac{1}{2}$ and $1$.


title: On bundles of matrix pencils under strict equivalence

abstract:
  Bundles of matrix pencils (under strict equivalence) are sets of pencils
having the same Kronecker canonical form, up to the eigenvalues (namely, they
are an infinite union of orbits under strict equivalence). The notion of bundle
for matrix pencils was introduced in the 1990's, following the same notion for
matrices under similarity, introduced by Arnold in 1971, and it has been
extensively used since then. Despite the amount of literature devoted to
describing the topology of bundles of matrix pencils, some relevant questions
remain still open in this context. For example, the following two: (a) provide
a characterization for the inclusion relation between the closures (in the
standard topology) of bundles; and (b) are the bundles open in their closure?
The main goal of this paper is providing an explicit answer to these two
questions. In order to get this answer, we also review and/or formalize some
notions and results already existing in the literature. We also prove that
bundles of matrices under similarity, as well as bundles of matrix polynomials
(defined as the set of $m\times n$ matrix polynomials of the same grade having
the same spectral information, up to the eigenvalues) are open in their
closure.


title:
HEATGait: Hop-Extracted Adjacency Technique in Graph Convolution based
  Gait Recognition

abstract:
  Biometric authentication using gait has become a promising field due to its
unobtrusive nature. Recent approaches in model-based gait recognition
techniques utilize spatio-temporal graphs for the elegant extraction of gait
features. However, existing methods often rely on multi-scale operators for
extracting long-range relationships among joints resulting in biased weighting.
In this paper, we present HEATGait, a gait recognition system that improves the
existing multi-scale graph convolution by efficient hop-extraction technique to
alleviate the issue. Combined with preprocessing and augmentation techniques,
we propose a powerful feature extractor that utilizes ResGCN to achieve
state-of-the-art performance in model-based gait recognition on the CASIA-B
gait dataset.


title:
Linear-quadratic stochastic Volterra controls II: Optimal strategies and
  Riccati--Volterra equations

abstract:
  In this paper, we study linear-quadratic control problems for stochastic
Volterra integral equations with singular and non-convolution-type
coefficients. The weighting matrices in the cost functional are not assumed to
be non-negative definite. From a new viewpoint, we formulate a framework of
causal feedback strategies. The existence and the uniqueness of a causal
feedback optimal strategy are characterized by means of the corresponding
Riccati--Volterra equation.


title:
Elliptic flow of charged particles at midrapidity relative to the
  spectator plane in Pb-Pb and Xe-Xe collisions

abstract:
  Measurements of the elliptic flow coefficient relative to the collision plane
defined by the spectator neutrons $v_2${$\Psi_{\rm SP}$} in collisions of Pb
ions at center-of-mass energy per nucleon-nucleon pair $\sqrt{s_{\rm NN}}$=2.76
TeV and Xe ions at $\sqrt{s_{\rm NN}}$=5.44 TeV are reported. The results are
presented for charged particles produced at midrapidity as a function of
centrality and transverse momentum. The ratio between $v_2${$\Psi_{\rm SP}$}
and the elliptic flow coefficient relative to the participant plane $v_2${4},
estimated using four-particle correlations, deviates by up to 20% from unity
depending on centrality. This observation differs strongly from the magnitude
of the corresponding eccentricity ratios predicted by the TRENTo and the
elliptic power models of initial state fluctuations that are tuned to describe
the participant plane anisotropies. The differences can be interpreted as a
decorrelation of the neutron spectator plane and the reaction plane because of
fragmentation of the remnants from the colliding nuclei, which points to an
incompleteness of current models of initial state fluctuations. A significant
transverse momentum dependence of the ratio $v_2${$\Psi_{\rm SP}$}/$v_2${4} is
observed in all but the most central collisions, which may help to understand
whether momentum anisotropies at low and intermediate transverse momentum have
a common origin in initial state fluctuations. The ratios of $v_2${$\Psi_{\rm
SP}$} and $v_2${4} to the corresponding initial state eccentricities for Xe-Xe
and Pb-Pb collisions at similar initial entropy density show a difference of
$(7.0 \pm 0.9)$% with an additional variation of +1.8% when including RHIC data
in the TRENTo parameter extraction. These observations provide new experimental
constraints for viscous effects in the hydrodynamic modeling of the expanding
quark-gluon plasma.


title: On Nash-Solvability of Finite Two-Person Tight Vector Game Forms

abstract:
  We consider finite two-person normal form games. The following four
properties of their game forms are equivalent: (i) Nash-solvability, (ii)
zero-sum-solvability, (iii) win-lose-solvability, and (iv) tightness. For (ii,
iii, iv) this was shown by Edmonds and Fulkerson in 1970. Then, in 1975, (i)
was added to this list and it was also shown that these results cannot be
generalized for $n$-person case with $n > 2$. In 1990, tightness was extended
to vector game forms ($v$-forms) and it was shown that such $v$-tightness and
zero-sum-solvability are still equivalent, yet, do not imply Nash-solvability.
These results are applicable to several classes of stochastic games with
perfect information. Here we suggest one more extension of tightness
introducing $v^+$-tight vector game forms ($v^+$-forms). We show that such
$v^+$-tightness and Nash-solvability are equivalent in case of weakly
rectangular game forms and positive cost functions. This result allows us to
reduce the so-called bi-shortest path conjecture to $v^+$-tightness of
$v^+$-forms. However, both (equivalent) statements remain open.


title: The 2021 NIST Speaker Recognition Evaluation

abstract:
  The 2021 Speaker Recognition Evaluation (SRE21) was the latest cycle of the
ongoing evaluation series conducted by the U.S. National Institute of Standards
and Technology (NIST) since 1996. It was the second large-scale multimodal
speaker/person recognition evaluation organized by NIST (the first one being
SRE19). Similar to SRE19, it featured two core evaluation tracks, namely audio
and audio-visual, as well as an optional visual track. In addition to offering
fixed and open training conditions, it also introduced new challenges for the
community, thanks to a new multimodal (i.e., audio, video, and selfie images)
and multilingual (i.e., with multilingual speakers) corpus, termed WeCanTalk,
collected outside North America by the Linguistic Data Consortium (LDC). These
challenges included: 1) trials (target and non-target) with enrollment and test
segments originating from different domains (i.e., telephony versus video), and
2) trials (target and non-target) with enrollment and test segments spoken in
different languages (i.e., cross-lingual trials). This paper presents an
overview of SRE21 including the tasks, performance metric, data, evaluation
protocol, results and system performance analyses. A total of 23 organizations
(forming 15 teams) from academia and industry participated in SRE21 and
submitted 158 valid system outputs. Evaluation results indicate: audio-visual
fusion produce substantial gains in performance over audio-only or visual-only
systems; top performing speaker and face recognition systems exhibited
comparable performance under the matched domain conditions present in this
evaluation; and, the use of complex neural network architectures (e.g., ResNet)
along with angular losses with margin, data augmentation, as well as long
duration fine-tuning contributed to notable performance improvements for the
audio-only speaker recognition task.


title: How rarity shapes the NFT market

abstract:
  We quantify Non Fungible Token (NFT) rarity and investigate how it impacts
market behaviour by analysing a dataset of 3.7M transactions collected between
January 2018 and March 2022, involving 1.3M NFTs distributed across 410
collections. First, we consider the rarity of an NFT based on the set of
human-readable attributes it possesses and show that most collections present
heterogeneous rarity patterns, with few rare NFTs and a large number of more
common ones. Then, we analyse market performance and show that, on average,
rarer NFTs: (i) sell for higher prices, (ii) are traded less frequently, (iii)
guarantee higher returns on investment (ROIs), and (iv) are less risky, i.e.,
less prone to yield negative returns. We anticipate that these findings will be
of interest to researchers as well as NFT creators, collectors, and traders.


title:
MOMO V. Effelsberg, Swift and Fermi study of the blazar and supermassive
  binary black hole candidate OJ 287 in a period of high activity

abstract:
  We report results from our ongoing project MOMO (Multiwavelength Observations
and Modelling of OJ 287). In this latest publication of a sequence, we combine
our Swift UVOT--XRT and Effelsberg radio data (2.6-44 GHz) between 2019 and
2022.04 with public SMA data and gamma-ray data from the Fermi satellite. The
observational epoch covers OJ 287 in a high state of activity from radio to
X-rays. The epoch also covers two major events predicted by the binary
supermassive black hole (SMBH) model of OJ 287. Spectral and timing analyses
clearly establish: a new UV-optical minimum state in 2021 December at an epoch
where the secondary SMBH is predicted to cross the disk surrounding the primary
SMBH; an overall low level of gamma-ray activity in comparison to pre-2017
epochs; the presence of a remarkable, long-lasting UV--optical flare event of
intermediate amplitude in 2020--2021; a high level of activity in the radio
band with multiple flares; and particularly a bright, ongoing radio flare
peaking in 2021 November that may be associated with a gamma-ray flare, the
strongest in 6 years. Several explanations for the UV--optical minimum state
are explored, including the possibility that a secondary SMBH launches a
temporary jet, but the observations are best explained by variability
associated with the main jet.


title:
SpaceE: Knowledge Graph Embedding by Relational Linear Transformation in
  the Entity Space

abstract:
  Translation distance based knowledge graph embedding (KGE) methods, such as
TransE and RotatE, model the relation in knowledge graphs as translation or
rotation in the vector space. Both translation and rotation are injective; that
is, the translation or rotation of different vectors results in different
results. In knowledge graphs, different entities may have a relation with the
same entity; for example, many actors starred in one movie. Such a
non-injective relation pattern cannot be well modeled by the translation or
rotation operations in existing translation distance based KGE methods. To
tackle the challenge, we propose a translation distance-based KGE method called
SpaceE to model relations as linear transformations. The proposed SpaceE embeds
both entities and relations in knowledge graphs as matrices and SpaceE
naturally models non-injective relations with singular linear transformations.
We theoretically demonstrate that SpaceE is a fully expressive model with the
ability to infer multiple desired relation patterns, including symmetry,
skew-symmetry, inversion, Abelian composition, and non-Abelian composition.
Experimental results on link prediction datasets illustrate that SpaceE
substantially outperforms many previous translation distance based knowledge
graph embedding methods, especially on datasets with many non-injective
relations. The code is available based on the PaddlePaddle deep learning
platform https://www.paddlepaddle.org.cn.


title:
Measurements of the groomed jet radius and momentum splitting fraction
  with the soft drop and dynamical grooming algorithms in pp collisions at
  $\sqrt{s}=5.02$ TeV

abstract:
  This article presents measurements of the groomed jet radius and momentum
splitting fraction in pp collisions at $\sqrt{s}=5.02$ TeV with the ALICE
detector at the Large Hadron Collider. Inclusive charged-particle jets are
reconstructed at midrapidity using the anti-$k_{\rm{T}}$ algorithm for
transverse momentum $60< p_{\mathrm{T}}^{\rm{ch\; jet}}<80$ GeV/$c$. We report
results using two different grooming algorithms: soft drop and, for the first
time, dynamical grooming. For each grooming algorithm, a variety of grooming
settings are used in order to explore the impact of collinear radiation on
these jet substructure observables. These results are compared to perturbative
calculations that include resummation of large logarithms at all orders in the
strong coupling constant. We find good agreement of the theoretical predictions
with the data for all grooming settings considered.


title: Stability and cohomology of kernel bundles on projective space

abstract:
  In this paper, we study the cohomology of vector bundles on projective space
defined as kernels or cokernels of general maps $V_1 \to V_2$, where the $V_i$
are direct sums of line bundles or certain exceptional bundles. We prove an
asymptotic cohomology vanishing theorem. We characterize the stability of
general Steiner bundles on projective space. We also give a criterion for a
Steiner bundle to be ample.


title: Hearing the shape of a quantum boundary condition

abstract:
  We study the isospectrality problem for a free quantum particle confined in a
ring with a junction, analyzing all the self-adjoint realizations of the
corresponding Hamiltonian in terms of a boundary condition at the junction. In
particular, by characterizing the energy spectrum in terms of a spectral
function, we classify the self-adjoint realizations in two classes, identifying
all the families of isospectral Hamiltonians. These two classes turn out to be
discerned by the action of parity (i.e. space reflection), which plays a
central role in our discussion.


title: Celestial Amplitudes as AdS-Witten Diagrams

abstract:
  Both celestial and momentum space amplitudes in four dimensions are beset by
divergences resulting from spacetime translation and sometimes scale
invariance. In this paper we consider a (linearized) marginal deformation of
the celestial CFT for Yang-Mills theory which preserves 2D conformal invariance
but breaks both spacetime translation and scale invariance and involves a
chirally coupled massive scalar. The resulting celestial amplitudes are
completely finite (apart from the usual soft and collinear divergences) and
take the canonical CFT form. Moreover, we show they can be simply rewritten in
terms of AdS$_3$-Witten contact diagrams which evaluate to the well-known
$D$-functions, thereby forging a direct connection between flat and AdS
holography.


title:
Lipschitz (non-)equivalence of the Gromov$\unicode{x2013}$Hausdorff
  distances, including on ultrametric spaces

abstract:
  The Gromov$\unicode{x2013}$Hausdorff distance measures the difference in
shape between compact metric spaces. While even approximating the distance up
to any practical factor poses an NP-hard problem, its relaxations have proven
useful for the problems in geometric data analysis, including on point clouds,
manifolds, and graphs. We investigate the modified
Gromov$\unicode{x2013}$Hausdorff distance, a relaxation of the standard
distance that retains many of its theoretical properties, which includes their
topological equivalence on a rich set of families of metric spaces. We show
that the two distances are Lipschitz-equivalent on any family of metric spaces
of uniformly bounded size, but that the equivalence does not hold in general,
not even when the distances are restricted to ultrametric spaces. We
additionally prove that the standard and the modified
Gromov$\unicode{x2013}$Hausdorff distances are either equal or within a factor
of 2 from each other when taken to a regular simplex, which connects the
relaxation to some well-known problems in discrete geometry.


title: Layered Complex Networks as Fluctuation Amplifiers

abstract:
  In complex networked systems theory, an important question is how to evaluate
the system robustness to external perturbations. With this task in mind, I
investigate the propagation of noise in a multi-layer networked systems. I find
that, for a two layer network, noise originally injected in one layer can be
strongly amplified in the other layer, depending on how well-connected are the
complex networks in each layer and on how much the eigenmodes of their
Laplacian matrices overlap. These results allow to predict potentially harmful
conditions for the system and its sub-networks, where the level of fluctuations
is important, and how to avoid them.


title:
Accelerating Convergence of Proximal Methods for Compressed Sensing
  using Polynomials with Application to MRI

abstract:
  This work aims to accelerate the convergence of iterative proximal methods
when applied to linear inverse problems that arise in compressed sensing
applications by designing a preconditioner using polynomials. By leveraging
polynomials, the preconditioner targets the eigenvalue spectrum of the normal
operator derived from the linear measurement operator in a manner that does not
assume any explicit structure, and can thus be applied various applications of
interest. The efficacy of the preconditioner is validated on four varied MRI
applications, where it seen to achieve faster convergence while achieving
similar reconstruction quality.


title:
Measurement of $\psi$(2S) production as a function of charged-particle
  pseudorapidity density in pp collisions at $\sqrt{s}$ = 13 TeV and p-Pb
  collisions at $\sqrt{s_{\rm NN}}$ = 8.16 TeV with ALICE at the LHC

abstract:
  Charmonium production in pp collisions at center-of-mass energy of $\sqrt{s}$
= 13 TeV and p-Pb collisions at center-of-mass energy per nucleon pair of
$\sqrt{s_{\rm NN}}$ = 8.16 TeV is studied as a function of charged-particle
pseudorapidity density with ALICE. Ground and excited charmonium states
(J/$\psi$, $\psi$(2S)) are measured from their dimuon decays in the interval of
rapidity in the center-of-mass frame $2.5 < y_{\rm cms} < 4.0$ for pp
collisions, and $2.03 < y_{\rm cms} < 3.53$ and $-4.46 < y_{\rm cms} < -2.96$
for p-Pb collisions. The charged-particle pseudorapidity density is measured
around midrapidity ($|\eta|<1.0$). In pp collisions, the measured
charged-particle multiplicity extends to about six times the average value,
while in p-Pb collisions at forward (backward) rapidity a multiplicity
corresponding to about three (four) times the average is reached. The
$\psi$(2S) yield increases with the charged-particle pseudorapidity density.
The ratio of $\psi$(2S) over J/$\psi$ yield does not show a significant
multiplicity dependence in either colliding system, suggesting a similar
behavior of J/$\psi$ and $\psi$(2S) yields with respect to charged-particle
pseudorapidity density. The results are also compared with model calculations.


title:
From Who You Know to What You Read: Augmenting Scientific
  Recommendations with Implicit Social Networks

abstract:
  The ever-increasing pace of scientific publication necessitates methods for
quickly identifying relevant papers. While neural recommenders trained on user
interests can help, they still result in long, monotonous lists of suggested
papers. To improve the discovery experience we introduce multiple new methods
for \em augmenting recommendations with textual relevance messages that
highlight knowledge-graph connections between recommended papers and a user's
publication and interaction history. We explore associations mediated by author
entities and those using citations alone. In a large-scale, real-world study,
we show how our approach significantly increases engagement -- and future
engagement when mediated by authors -- without introducing bias towards
highly-cited authors. To expand message coverage for users with less
publication or interaction history, we develop a novel method that highlights
connections with proxy authors of interest to users and evaluate it in a
controlled lab study. Finally, we synthesize design implications for future
graph-based messages.


title:
Quadriexcitons and excitonic condensate in a symmetric electron-hole
  bilayer with valley degeneracy

abstract:
  Using quantum Monte Carlo simulations we have mapped out the zero temperature
phase diagram of a symmetric electron-hole bilayer with twofold valley
degeneracy, as function of the interlayer distance $d$ and in-layer density
$n$. We find that the effect of the valley degeneracy is to shrink the region
of stability of the excitonic condensate, in favor of quadriexcitons at small
$d$ and of the four-component plasma at large $d$, with minor effects on the
value of the excitonic condensate fraction. The enclosure of the condensate in
a density window possibly explains why anomalous tunnelling conductivity,
interpreted as signature of condensation, is observed only between two finite
values of carrier density in graphene bilayers. Our phase diagram may provide
directions to select device parameters for future experiments.


title:
Revisiting Gaussian mixture critics in off-policy reinforcement
  learning: a sample-based approach

abstract:
  Actor-critic algorithms that make use of distributional policy evaluation
have frequently been shown to outperform their non-distributional counterparts
on many challenging control tasks. Examples of this behavior include the D4PG
and DMPO algorithms as compared to DDPG and MPO, respectively [Barth-Maron et
al., 2018; Hoffman et al., 2020]. However, both agents rely on the C51 critic
for value estimation.One major drawback of the C51 approach is its requirement
of prior knowledge about the minimum andmaximum values a policy can attain as
well as the number of bins used, which fixes the resolution ofthe
distributional estimate. While the DeepMind control suite of tasks utilizes
standardized rewards and episode lengths, thus enabling the entire suite to be
solved with a single setting of these hyperparameters, this is often not the
case. This paper revisits a natural alternative that removes this requirement,
namelya mixture of Gaussians, and a simple sample-based loss function to train
it in an off-policy regime. We empirically evaluate its performance on a broad
range of continuous control tasks and demonstrate that it eliminates the need
for these distributional hyperparameters and achieves state-of-the-art
performance on a variety of challenging tasks (e.g. the humanoid, dog,
quadruped, and manipulator domains). Finallywe provide an implementation in the
Acme agent repository.


title: Fourier restriction to smooth enough curves

abstract:
  We prove Fourier restriction estimates to arbitrary compact $C^{N}$ curves
for any $N > d$ in the (sharp) Drury range, using a power of the affine
arclength measure as a mitigating factor. In particular, we make no
nondegeneracy assumption on the curve.


title:
First measurement of the $\Lambda$-$\Xi$ interaction in proton-proton
  collisions at the LHC

abstract:
  The first experimental information on the strong interaction between
$\Lambda$ and $\Xi^-$ strange baryons is presented in this Letter. The
correlation function of $\Lambda-\Xi^-$ and
$\overline{\Lambda}-\overline{\Xi}^{+}$ pairs produced in high-multiplicity
proton-proton (pp) collisions at $\sqrt{s}$ = 13 TeV at the LHC is measured as
a function of the relative momentum of the pair. The femtoscopy method is used
to calculate the correlation function, which is then compared with theoretical
expectations obtained using a meson exchange model, chiral effective field
theory, and Lattice QCD calculations close to the physical point. Data support
predictions of small scattering parameters while discarding versions with large
ones, thus suggesting a weak $\Lambda-\Xi^{-}$ interaction. The limited
statistical significance of the data does not yet allow one to constrain the
effects of coupled channels like $\Sigma-\Xi$ and N$-\Omega$.


title: Pattern avoidance and K-orbit closures

abstract:
  We review the various pattern avoidance criteria that have been developed for
smoothness and rational smoothness of symmetric subvarieties of flag varieties
in the classical cases, including some proofs and giving references for other
results.


title:
Steady states of an Elo-type rating model for players of varying
  strength

abstract:
  In this paper we study the long-time behaviour of a kinetic formulation of an
Elo-type rating model for a large number of interacting players with variable
strength. The model results in a non-linear mean-field Fokker-Planck equation
and we show the existence of steady states via a Schauder fixed point argument.
Our proof relies on the study of a related linear equation using hypocoercivity
techniques.


title:
The HD 260655 system: Two rocky worlds transiting a bright M dwarf at 10
  pc

abstract:
  We report the discovery of a multi-planetary system transiting the M0 V dwarf
HD 260655 (GJ 239, TOI-4599). The system consists of at least two transiting
planets, namely HD 260655 b, with a period of 2.77 d, a radius of R$_b$ =
1.240$\pm$0.023 R$_\oplus$, a mass of M$_b$ = 2.14$\pm$0.34 M$_\oplus$, and a
bulk density of $\rho_b$ = 6.2$\pm$1.0 g cm$^{-3}$, and HD 260655 c, with a
period of 5.71 d, a radius of R$_c$ = 1.533$^{+0.051}_{-0.046}$ R$_\oplus$, a
mass of M$_c$ = 3.09$\pm$0.48 M$_\oplus$, and a bulk density of $\rho_c$ =
4.7$^{+0.9}_{-0.8}$ g cm$^{-3}$. The planets were detected in transit by the
TESS mission and confirmed independently with archival and new precise radial
velocities obtained with the HIRES and CARMENES instruments since 1998 and
2016, respectively. At a distance of 10 pc, HD 260655 becomes the fourth
closest known multi-transiting planet system after HD 219134, LTT 1445 A, and
AU Mic. Due to the apparent brightness of the host star (J = 6.7 mag), both
planets are among the most suitable rocky worlds known today for atmospheric
studies with the JWST, both in transmission and emission.


title: An Examination of Bias of Facial Analysis based BMI Prediction Models

abstract:
  Obesity is one of the most important public health problems that the world is
facing today. A recent trend is in the development of intervention tools that
predict BMI using facial images for weight monitoring and management to combat
obesity. Most of these studies used BMI annotated facial image datasets that
mainly consisted of Caucasian subjects. Research on bias evaluation of
face-based gender-, age-classification, and face recognition systems suggest
that these technologies perform poorly for women, dark-skinned people, and
older adults. The bias of facial analysis-based BMI prediction tools has not
been studied until now. This paper evaluates the bias of facial-analysis-based
BMI prediction models across Caucasian and African-American Males and Females.
Experimental investigations on the gender, race, and BMI balanced version of
the modified MORPH-II dataset suggested that the error rate in BMI prediction
was least for Black Males and highest for White Females. Further, the
psychology-related facial features correlated with weight suggested that as the
BMI increases, the changes in the facial region are more prominent for Black
Males and the least for White Females. This is the reason for the least error
rate of the facial analysis-based BMI prediction tool for Black Males and
highest for White Females.


title:
Multiplicity and rapidity dependence of ${\rm K}^*(892)^0$ and
  $\phi(1020)$ production in p-Pb collisions at $\sqrt{s_{\rm NN}}$ = 5.02 TeV

abstract:
  The transverse-momentum ($p_{\rm T}$) spectra of ${\rm K}^*(892)^0$ and
$\phi(1020)$ measured with the ALICE detector up to $p_{\rm T}$ = 16 GeV/$c$ in
the rapidity range $-1.2 < y < 0.3$, in p-Pb collisions at center-of-mass
energy per nucleon-nucleon collision $\sqrt{s_{\rm NN}}$ = 5.02 TeV are
presented as a function of charged particle multiplicity and rapidity. The
measured $p_{\rm T}$ distributions show a dependence on both multiplicity and
rapidity at low $p_{\rm T}$ whereas no significant dependence is observed at
high $p_{\rm T}$. A rapidity dependence is observed in the $p_{\rm
T}$-integrated yield (d$N$/d$y$), whereas the mean transverse momentum ($\left<
p_{\rm T} \right>$) shows a flat behavior as a function of rapidity. The
rapidity asymmetry ($Y_{\rm asym}$) at low $p_{\rm T}$ ( < 5 GeV/$c$) is more
significant for higher multiplicity classes. At high $p_{\rm T}$, no
significant rapidity asymmetry is observed in none of the multiplicity classes.
Both ${\rm K}^*(892)^0$ and $\phi(1020)$ show similar $Y_{\rm asym}$. The
nuclear modification factor ($Q_{\rm CP}$) as a function of $p_{\rm T}$ shows a
Cronin-like enhancement at intermediate $p_{\rm T}$, which is more prominent at
higher rapidities (Pb-going direction) and in higher multiplicity classes. At
high $p_{\rm T}$ (> 5 GeV/$c$), the $Q_{\rm CP}$ values are greater than unity
and no significant rapidity dependence is observed.


title:
A Real-time Calculus Approach for Integrating Sporadic Events in
  Time-triggered Systems

abstract:
  In time-triggered systems, where the schedule table is predefined and
statically configured at design time, sporadic event-triggered (ET) tasks can
only be handled within specially dedicated slots or when time-triggered (TT)
tasks finish their execution early. We introduce a new paradigm for
synthesizing TT schedules that guarantee the correct temporal behavior of TT
tasks and the schedulability of sporadic ET tasks with arbitrary deadlines. The
approach first expresses a constraint for the TT task schedule in the form of a
maximal affine envelope that guarantees that as long as the schedule generation
respects this envelope, all sporadic ET tasks meet their deadline. The second
step consists of modeling this envelope as a burst limiting constraint and
building the TT schedule via simulating a modified Least-Laxity-First (LLF)
scheduler. Using this novel technique, we show that we achieve equal or better
schedulability and a faster schedule generation for most use-cases compared to
other approaches inspired by, e.g., hierarchical scheduling. Moreover, we
present an extension to our method that finds the most favourable schedule for
TT tasks with respect to ET schedulability, thus increasing the probability of
the computed TT schedule remaining feasible when ET tasks are later added or
changed.


title: Experimentally distinguishable origin for electroweak symmetry breaking

abstract:
  We consider a classically conformal $U(1)$ gauge extension of the Standard
Model (SM), in which the $U(1)$ gauge symmetry is radiatively broken by the
Coleman-Weinberg mechanism. This breaking triggers the electroweak (EW)
symmetry breaking through a mixed quartic coupling between the $U(1)$ Higgs
field and the SM Higgs doublet. For two Higgs boson mass eigenstates after the
symmetry breaking, $h_1$ (SM-like Higgs boson) and $h_2$ (SM singlet-like Higgs
boson), we calculate the Higgs boson trilinear coupling ($g_{h_{1} h_{2}
h_{2}}$) in the model by setting the Higgs boson mass spectrum to be $M_{h_1} >
2 M_{h_2}$. For a common Higgs mass spectrum and mixing angle between two Higgs
fields, we find that $g_{h_{1} h_{2} h_{2}}$ in the classically conformal model
is highly suppressed compared to that calculated for the conventional Higgs
potential, where the $U(1)$ and EW symmetry breaking originate from the
negative squared masses for the Higgs fields at the tree-level. Thus, this
coupling suppression is a striking nature of the radiative origin of EW
symmetry breaking. We then consider how to distinguish this origin at the
proposed International Linear Collider (ILC) via precise measurements of
anomalous SM Higgs boson couplings and the search for anomalous SM Higgs boson
decay $h_1 \rightarrow h_2 h_2$ followed by $h_2 \to b \bar{b}$. We conclude
that once the anomalous couplings are measured at the ILC, the observation of
the anomalous Higgs boson decay is promising in the conventional Higgs
potential, while this decay process is highly suppressed and undetectable for
the classically conformal model.


title:
DooDLeNet: Double DeepLab Enhanced Feature Fusion for Thermal-color
  Semantic Segmentation

abstract:
  In this paper we present a new approach for feature fusion between RGB and
LWIR Thermal images for the task of semantic segmentation for driving
perception. We propose DooDLeNet, a double DeepLab architecture with
specialized encoder-decoders for thermal and color modalities and a shared
decoder for final segmentation. We combine two strategies for feature fusion:
confidence weighting and correlation weighting. We report state-of-the-art mean
IoU results on the MF dataset.


title:
J-matrix method of scattering for inverse-square singular potentials
  with supercritical coupling II. Regularization

abstract:
  This paper is a continuation of the previous one [Journal xx, xxxxx (2022)].
Here, we reformulate the same J-matrix theory by regularizing the inverse
square singular potential. The objective is to restore rapid convergence of the
calculation in the theory and recover the conventional tridiagonal
representation. Partial success is achieved.


title: Out-of-distribution generalization for learning quantum dynamics

abstract:
  Generalization bounds are a critical tool to assess the training data
requirements of Quantum Machine Learning (QML). Recent work has established
guarantees for in-distribution generalization of quantum neural networks
(QNNs), where training and testing data are assumed to be drawn from the same
data distribution. However, there are currently no results on
out-of-distribution generalization in QML, where we require a trained model to
perform well even on data drawn from a distribution different from the training
distribution. In this work, we prove out-of-distribution generalization for the
task of learning an unknown unitary using a QNN and for a broad class of
training and testing distributions. In particular, we show that one can learn
the action of a unitary on entangled states using only product state training
data. We numerically illustrate this by showing that the evolution of a
Heisenberg spin chain can be learned using only product training states. Since
product states can be prepared using only single-qubit gates, this advances the
prospects of learning quantum dynamics using near term quantum computers and
quantum experiments, and further opens up new methods for both the classical
and quantum compilation of quantum circuits.