title:
A Single Index Model for Longitudinal Outcomes to Optimize Individual
  Treatment Decision Rules

abstract:
  A pressing challenge in medical research is to identify optimal treatments
for individual patients. This is particularly challenging in mental health
settings where mean responses are often similar across multiple treatments. For
example, the mean longitudinal trajectories for patients treated with an active
drug and placebo may be very similar but different treatments may exhibit
distinctly different individual trajectory shapes. Most precision medicine
approaches using longitudinal data often ignore information from the
longitudinal data structure. This paper investigates a powerful precision
medicine approach by examining the impact of baseline covariates on
longitudinal outcome trajectories to guide treatment decisions instead of
traditional scalar outcome measures derived from longitudinal data, such as a
change score. We introduce a method of estimating "biosignatures" defined as
linear combinations of baseline characteristics (i.e., a single index) that
optimally separate longitudinal trajectories among different treatment groups.
The criterion used is to maximize the Kullback-Leibler Divergence between
different treatment outcome distributions. The approach is illustrated via
simulation studies and a depression clinical trial. The approach is also
contrasted with more traditional methods and compares performance in the
presence of missing data.


title:
Tuning the competition between superconductivity and charge order in
  kagome superconductor Cs(V1-xNbx)3Sb5

abstract:
  The recently discovered coexistence of superconductivity and charge density
wave order in the kagome systems AV3Sb5 (A = K, Rb, Cs) has stimulated enormous
interest. According to theory, a vanadium-based kagome system may host a flat
band, nontrivial linear dispersive Dirac surface states and electronic
correlation. Despite intensive investigations, it remains controversial about
the origin of the charge density wave (CDW) order, how does the
superconductivity relate to the CDW, and whether the anomalous Hall effect
(AHE) arises primarily from the kagome lattice or the CDW order. We report an
extensive investigation on Cs(V1-xNbx)3Sb5 samples with systematic Nb doping.
Our results show that the Nb doping induces apparent suppression of CDW order
and promotes superconductivity; meanwhile, the AHE and magnetoresistance (MR)
will be significantly weakened together with the CDW order. Combining with our
density functional calculations, we interpret these effects by an antiphase
shift of the Fermi energy with respect to the saddle points near M and the
Fermi surface centered around {\Gamma}. It is found that the former depletes
the filled states for the CDW instability and worsens the nesting condition for
CDW order; while the latter lifts the Fermi level upward and enlarges the Fermi
surface surrounding the {\Gamma} point, and thus promotes superconductivity.
Our results uncover a delicate but unusual competition between the CDW order
and superconductivity.


title: Complexity of Conjugacy Search in some Polycyclic and Matrix Groups

abstract:
  The most prominent algorithmic problem employed in the recently emerging
field of nonabelian group-based cryptography is the Conjugacy Search Problem
(CSP). While several methods of attacks on nonabelian protocols have been
devised, many of these are heuristic, protocol-specific, and focus on
retrieving the shared keys without solving the underlying CSP in the group. So
far, the true complexity of the CSP in different platform groups has not been
sufficiently investigated. In this paper, we study the complexity of various
versions of the CSP in polycyclic groups and matrix groups over finite fields.
In particular we show that in $\GL_n(\fq)$ and in polycyclic groups with two
generators, a CSP where conjugators are restricted to a cyclic subgroup is
reducible to a set of $\mathcal{O}(n^2)$ DLPs. As a consequence of our results
we also demonstrate the cryptanalysis of a few independently proposed
cryptosystems.


title: On the Conjugacy Search Problem in Extraspecial $p$-Groups

abstract:
  In the recently emerging field of group-based cryptography, the Conjugacy
Search Problem (CSP) has gained traction as a non-commutative replacement of
the Discrete Log Problem (DLP). The problem of finding a secure class of
nonabelian groups for use as platforms is open and a subject of active
research. This paper demonstrates a polynomial time solution of the CSP in an
important class of nonabelian groups, the extraspecial $p$-groups. For this
purpose, and as a further result, we provide a reduction of the CSP in certain
types of central products. The consequences of our results are practically
relevant for ruling out several groups as platforms, since several nonabelian
groups are constructed by combining smaller groups by taking direct and central
products.


title: G-stable rank of symmetric tensors and log canonical threshold

abstract:
  Shitov recently gave a counterexample to Comon's conjecture that the
symmetric tensor rank and tensor rank of a symmetric tensor are the same. In
this paper we show that an analog of Comon's conjecture for the G-stable rank
introduced by Derksen is true: the symmetric G-stable rank and G-stable rank of
a symmetric tensor are the same. We also show that the log-canonical threshold
of a singularity is bounded by the G-stable rank of the defining ideal.


title:
Blocked or Broken? Automatically Detecting When Privacy Interventions
  Break Websites

abstract:
  A core problem in the development and maintenance of crowd-sourced filter
lists is that their maintainers cannot confidently predict whether (and where)
a new filter list rule will break websites. This is a result of enormity of the
Web, which prevents filter list authors from broadly understanding the impact
of a new blocking rule before they ship it to millions of users. The inability
of filter list authors to evaluate the Web compatibility impact of a new rule
before shipping it severely reduces the benefits of filter-list-based content
blocking: filter lists are both overly-conservative (i.e. rules are tailored
narrowly to reduce the risk of breaking things) and error-prone (i.e. blocking
tools still break large numbers of sites). To scale to the size and scope of
the Web, filter list authors need an automated system to detect when a new
filter rule breaks websites, before that breakage has a chance to make it to
end users.
  In this work, we design and implement the first automated system for
predicting when a filter list rule breaks a website. We build a classifier,
trained on a dataset generated by a combination of compatibility data from the
EasyList project and novel browser instrumentation, and find it is accurate to
practical levels (AUC 0.88). Our open source system requires no human
interaction when assessing the compatibility risk of a proposed privacy
intervention. We also present the 40 page behaviors that most predict breakage
in observed websites.


title:
Spectroscopic and imaging observations of transient hot and cool loops
  by IRIS and SDO

abstract:
  Coronal loops are basic building blocks of solar atmosphere and are observed
on various length scales. However, their formation mechanism is still unclear.
In this paper, we present the spectroscopic and imaging observations of
small-scale transients and subsequent formation of transient loops. For the
purpose, we have utilized the multi-wavelength observations recorded by AIA and
IRIS-SJI, along with spectroscopic measurements provided by IRIS. For the
photospheric magnetic field data, we obtained line-of-sight magnetogram data
provided by HMI. Small-scale transients are simultaneously observed with
several EUV and UV passbands of AIA and IRIS-SJI. HMI magnetogram provides
evidence of negative flux cancellations beneath these transients. Differential
Emission Measure (DEM) analysis shows that one of the transient attains
temperature up to 8 MK whereas another one reaches only up to 0.4 MK. These
transients further lead to the formation of small-scale loops with similar
temperature distributions, and thus termed as hot and cool loops respectively.
During the course of events, IRIS slit was rastering the region and thus
provided spectroscopic measurements at both transients and associated loops.
This enabled us to perform in-depth investigations of hot and cool loops. Using
density sensitive O IV line pair, we obtained average electron densities along
the hot and cool loop to be $10^{11.2}$ and $10^{10.8}$ cm$^{-3}$ respectively.
Energy estimates suggest that flux cancellation can easily power the hot
transient whereas is insufficient for cool transient. Life time estimates and
magnetic field extrapolation suggest presence of small-scale and fine
structures within these loops. Results provide crucial ingredients on the
physics of loop formation and involved thermodynamics.


title: A geometric model for blocks of Frobenius kernels

abstract:
  Building on a geometric counterpart of Steinberg's tensor product formula for
simple representations of a connected reductive algebraic group $G$ over a
field of positive characteristic, and following an idea of
Arkhipov--Bezrukavnikov--Braverman--Gaitsgory--Mirkovi\'c, we define and
initiate the study of some categories of perverse sheaves on the affine
Grassmannian of the Langlands dual group to $G$ that should provide geometric
models for blocks of representations of the Frobenius kernel $G_1$ of $G$. In
particular, we show that these categories admit enough projective and injective
objects, which are closely related to some tilting perverse sheaves, and that
they are highest weight categories in an appropriate generalized sense.


title: Adding Level Structure to Supersingular Elliptic Curve Isogeny Graphs

abstract:
  Supersingular elliptic curve isogeny graphs are an interesting new direction
for post-quantum cryptography. In this paper, we add the information of level
structure to supersingular elliptic curves and study these objects with the
motivation of isogeny-based cryptography. Supersingular elliptic curves with
level structure map to Eichler orders in a quaternion algebra, just as
supersingular elliptic curves map to maximal orders in a quaternion algebra. We
study this map and the Eichler orders themselves. We also look at isogeny
graphs of supersingular elliptic curves with level structure, and how they
relate to supersingular isogeny graphs used in post-quantum cryptography.


title: E-detectors: a nonparametric framework for online changepoint detection

abstract:
  Sequential changepoint detection is a classical problem with a variety of
applications. However, the majority of prior work has been parametric, for
example, focusing on exponential families. We develop a fundamentally new and
general framework for changepoint detection when the pre- and post-change
distributions are nonparametrically specified (and thus composite). Our
procedures come with clean, nonasymptotic bounds on the average run length
(frequency of false alarms). In certain nonparametric cases (like sub-Gaussian
or sub-exponential), we also provide near-optimal bounds on the detection delay
following a changepoint. The primary technical tool that we introduce is called
an e-detector, which is composed of sums of e-processes -- a fundamental
generalization of nonnegative supermartingales -- that are started at
consecutive times. We first introduce simple Shiryaev-Roberts and CUSUM-style
e-detectors, and then show how to design their mixtures in order to achieve
both statistical and computational efficiency. We demonstrate their efficacy in
detecting changes in the mean of a bounded random variable without any i.i.d.
assumptions, with an application to tracking the performance of a sports team
over multiple seasons.


title: On elliptic curves with $p$-isogenies over quadratic fields

abstract:
  Let $E$ be an elliptic curve defined over a number field $K$. For which
primes $p$ does $E$ admit a $K$-rational $p$-isogeny? Although we have an
answer to this question over the rationals, extending this to other number
fields is a fundamental open problem in number theory. In this paper, we study
this question in the case that $K$ is a quadratic field, subject to the
assumption that $E$ is semistable at the primes of $K$ above $p$. We prove
results both for families of quadratic fields and for specific quadratic
fields.


title: Krylov complexity in saddle-dominated scrambling

abstract:
  In semi-classical systems, the exponential growth of the out-of-timeorder
correlator (OTOC) is believed to be the hallmark of quantum chaos. However,on
several occasions, it has been argued that, even in integrable systems, OTOC
can grow exponentially due to the presence of unstable saddle points in the
phase space. In this work, we probe such an integrable system exhibiting saddle
dominated scrambling through Krylov complexity and the associated Lanczos
coefficients. In the realm of the universal operator growth hypothesis, we
demonstrate that the Lanczos coefficients follow the linear growth, which
ensures the exponential behavior of Krylov complexity at early times. The
linear growth arises entirely due to the saddle, which dominates other
phase-space points even away from itself. Our results reveal that the
exponential growth of Krylov complexity can be observed in integrable systems
with saddle-dominated scrambling and thus need not be associated with the
presence of chaos.

categories: quant-ph cond-mat.stat-mech cond-mat.str-el hep-th


title: Influencing Long-Term Behavior in Multiagent Reinforcement Learning

abstract:
  The main challenge of multiagent reinforcement learning is the difficulty of
learning useful policies in the presence of other simultaneously learning
agents whose changing behaviors jointly affect the environment's transition and
reward dynamics. An effective approach that has recently emerged for addressing
this non-stationarity is for each agent to anticipate the learning of other
interacting agents and influence the evolution of their future policies towards
desirable behavior for its own benefit. Unfortunately, all previous approaches
for achieving this suffer from myopic evaluation, considering only a few or a
finite number of updates to the policies of other agents. In this paper, we
propose a principled framework for considering the limiting policies of other
agents as the time approaches infinity. Specifically, we develop a new
optimization objective that maximizes each agent's average reward by directly
accounting for the impact of its behavior on the limiting set of policies that
other agents will take on. Thanks to our farsighted evaluation, we demonstrate
better long-term performance than state-of-the-art baselines in various
domains, including the full spectrum of general-sum, competitive, and
cooperative settings.


title:
Stochastic gravitational-wave background searches and constraints on
  neutron-star ellipticity

abstract:
  Rotating neutron stars (NSs) are promising sources of gravitational waves
(GWs) in the frequency band of ground-based detectors. They are expected to
emit quasi-monochromatic, long-duration GW signals, called continuous waves
(CWs), due to their deviations from spherical symmetry. The degree of such
deformations, and hence the information about the internal structure of a NS,
is encoded in a dimensionless parameter $\varepsilon$ called ellipticity.
Searches for CW signals from isolated Galactic NSs have shown to be sensitive
to ellipticities as low as $\varepsilon \sim \mathcal{O}(10^{-9})$. These
searches are optimal for detecting and characterising GWs from individual NSs,
but they are not designed to measure the properties of NSs as population, such
as the average ellipticity $\varepsilon_{\mathrm{av}}$. These ensemble
properties can be determined by the measurement of the stochastic
gravitational-wave background (SGWB) arising from the superposition of GW
signals from individually-undetectable NSs. In this work, we perform a
cross-correlation search for such a SGWB using the data from the first three
observation runs of Advanced LIGO and Virgo. Finding no evidence for a SGWB
signal, we set upper limits on the dimensionless energy density parameter
$\Omega_{\mathrm{gw}}(f)$. Using these results, we also constrain the average
ellipticity of Galactic NSs and five NS ``hotspots'', as a function of the
number of NSs emitting GWs within the frequency band of the search
$N_{\mathrm{band}}$. We find $\varepsilon_{\mathrm{av}} \lesssim 1.8 \times
10^{-8}$, with $N_{\mathrm{band}}=1.6 \times 10^7$, for Galactic NSs, and
$\varepsilon_{\mathrm{av}} \lesssim [3.5-11.8]\times 10^{-7}$, with
$N_{\mathrm{band}}=1.6 \times 10^{10}$, for NS hotspots.


title:
The wave stability of solitary waves over a bump for the full Euler
  equations

abstract:
  In this work, we present a numerical study of the wave stability of steady
solitary waves over a localised topographic obstacle through the full Euler
equations. There are two branches of the solutions: one from the perturbed
uniform flow and the other from the perturbed solitary-wave flow. We find that
steady waves from the perturbed uniform flow are always stable with respect to
perturbations of its amplitude. Regarding the perturbed solitary-wave, when the
perturbed initial condition has smaller amplitude than the steady solution we
notice a certain type of stability. Yet, when the perturbed initial condition
has larger amplitude than the steady solution an onset of wave-breaking seem to
occur.


title:
AI-based Approach for Safety Signals Detection from Social Networks:
  Application to the Levothyrox Scandal in 2017 on Doctissimo Forum

abstract:
  Social media can be an important source of information facilitating the
detection of new safety signals in pharmacovigilance. Various approaches have
investigated the analysis of social media data using AI such as NLP techniques
for detecting adverse drug events. Existing approaches have focused on the
extraction and identification of Adverse Drug Reactions, Drug-Drug Interactions
and drug misuse. However, non of the works tackled the detection of potential
safety signals by taking into account the evolution in time of relevant
indicators. Moreover, despite the success of deep learning in various
healthcare applications, it was not explored for this task. We propose an
AI-based approach for the detection of potential pharmaceutical safety signals
from patients' reviews that can be used as part of the pharmacovigilance
surveillance process to flag the necessity of an in-depth pharmacovigilance
investigation. We focus on the Levothyrox case in France which triggered huge
attention from the media following the change of the medication formula,
leading to an increase in the frequency of adverse drug reactions normally
reported by patients. Our approach is two-fold. (1) We investigate various
NLP-based indicators extracted from patients' reviews including words and
n-grams frequency, semantic similarity, Adverse Drug Reactions mentions, and
sentiment analysis. (2) We propose a deep learning architecture, named Word
Cloud Convolutional Neural Network (WC-CNN) which trains a CNN on word clouds
extracted from the patients comments. We study the effect of different time
resolutions and different NLP pre-processing techniques on the model
performance. Our results show that the proposed indicators could be used in the
future to effectively detect new safety signals. The WC-CNN model trained on
word clouds extracted at monthly resolution outperforms the others with an
accuracy of 75%.


title:
One Objective for All Models -- Self-supervised Learning for Topic
  Models

abstract:
  Self-supervised learning has significantly improved the performance of many
NLP tasks. In this paper, we highlight a key advantage of self-supervised
learning -- when applied to data generated by topic models, self-supervised
learning can be oblivious to the specific model, and hence is less susceptible
to model misspecification. In particular, we prove that commonly used
self-supervised objectives based on reconstruction or contrastive samples can
both recover useful posterior information for general topic models.
Empirically, we show that the same objectives can perform competitively against
posterior inference using the correct model, while outperforming posterior
inference using misspecified model.


title:
GatorTron: A Large Clinical Language Model to Unlock Patient Information
  from Unstructured Electronic Health Records

abstract:
  Objective: To develop a large pretrained clinical language model from scratch
using transformer architecture; systematically examine how transformer models
of different sizes could help 5 clinical natural language processing (NLP)
tasks at different linguistic levels. Methods: We created a large corpus with
>90 billion words from clinical narratives (>82 billion words), scientific
literature (6 billion words), and general English text (2.5 billion words). We
developed GatorTron models from scratch using the BERT architecture of
different sizes including 345 million, 3.9 billion, and 8.9 billion parameters,
compared GatorTron with three existing transformer models in the clinical and
biomedical domain on 5 different clinical NLP tasks including clinical concept
extraction, relation extraction, semantic textual similarity, natural language
inference, and medical question answering, to examine how large transformer
models could help clinical NLP at different linguistic levels. Results and
Conclusion: GatorTron scaled up transformer-based clinical language models to a
size of 8.9 billion parameters and achieved state-of-the-art performance on 5
clinical NLP tasks of different linguistic levels targeting various healthcare
information documented in unstructured electronic health records (EHRs). The
proposed GatorTron models performed remarkably better in much complex clinical
NLP tasks such as natural language inference (9.6% and 7.5% improvements) and
question answering (9.5% and 7.77% improvements) compared with existing smaller
clinical transformer models (i.e., BioBERT and ClinicalBERT), demonstrating the
potential of large transformer-based clinical models for advanced medical
artificial intelligent (AI) applications such as question answering.


title:
Fairness for Text Classification Tasks with Identity Information Data
  Augmentation Methods

abstract:
  Counterfactual fairness methods address the question: How would the
prediction change if the sensitive identity attributes referenced in the text
instance were different? These methods are entirely based on generating
counterfactuals for the given training and test set instances. Counterfactual
instances are commonly prepared by replacing sensitive identity terms, i.e.,
the identity terms present in the instance are replaced with other identity
terms that fall under the same sensitive category. Therefore, the efficacy of
these methods depends heavily on the quality and comprehensiveness of identity
pairs. In this paper, we offer a two-step data augmentation process where (1)
the former stage consists of a novel method for preparing a comprehensive list
of identity pairs with word embeddings, and (2) the latter consists of
leveraging prepared identity pairs list to enhance the training instances by
applying three simple operations (namely identity pair replacement, identity
term blindness, and identity pair swap). We empirically show that the two-stage
augmentation process leads to diverse identity pairs and an enhanced training
set, with an improved counterfactual token-based fairness metric score on two
well-known text classification tasks.


title:
Fine-Tuning Approach for Arabic Offensive Language Detection System:
  BERT-Based Model

abstract:
  The problem of online offensive language limits the health and security of
online users. It is essential to apply the latest state-of-the-art techniques
in developing a system to detect online offensive language and to ensure social
justice to the online communities. Our study investigates the effects of
fine-tuning across several Arabic offensive language datasets. We develop
multiple classifiers that use four datasets individually and in combination in
order to gain knowledge about online Arabic offensive content and classify
users comments accordingly. Our results demonstrate the limited effects of
transfer learning on the classifiers performance, particularly for highly
dialectal comments.


title:
RNN Transducers for Nested Named Entity Recognition with constraints on
  alignment for long sequences

abstract:
  Popular solutions to Named Entity Recognition (NER) include conditional
random fields, sequence-to-sequence models, or utilizing the question-answering
framework. However, they are not suitable for nested and overlapping spans with
large ontologies and for predicting the position of the entities. To fill this
gap, we introduce a new model for NER task -- an RNN transducer (RNN-T). These
models are trained using paired input and output sequences without explicitly
specifying the alignment between them, similar to other seq-to-seq models.
RNN-T models learn the alignment using a loss function that sums over all
alignments. In NER tasks, however, the alignment between words and target
labels are available from the human annotations. We propose a fixed alignment
RNN-T model that utilizes the given alignment, while preserving the benefits of
RNN-Ts such as modeling output dependencies. As a more general case, we also
propose a constrained alignment model where users can specify a relaxation of
the given input alignment and the model will learn an alignment within the
given constraints. In other words, we propose a family of seq-to-seq models
which can leverage alignments between input and target sequences when
available. Through empirical experiments on a challenging real-world medical
NER task with multiple nested ontologies, we demonstrate that our fixed
alignment model outperforms the standard RNN-T model, improving F1-score from
0.70 to 0.74.


title: Online Adaptable Bug Localization for Rapidly Evolving Software

abstract:
  Bug localization aims to reduce debugging time by recommending program
elements that are relevant for a specific bug report. To date, researchers have
primarily addressed this problem by applying different information retrieval
techniques that leverage similarities between a given bug report and source
code. However, with modern software development trending towards increased
speed of software change and continuous delivery to the user, the current
generation of bug localization techniques, which cannot quickly adapt to the
latest version of the software, is becoming inadequate. In this paper, we
propose a technique for online bug localization, which enables rapidly
updatable bug localization models. More specifically, we propose a streaming
bug localization technique, based on an ensemble of online topic models, that
is able to adapt to both specific (with explicit code mentions) and more
abstract bug reports. By using changesets (diffs) as the input instead of a
snapshot of the source code, the model naturally integrates defect prediction
and co-change information into its prediction. Initial results indicate that
the proposed approach improves bug localization performance for 42 out of 56
evaluation projects, with an average MAP improvement of 5.9%.


title: Double-Barreled Question Detection at Momentive

abstract:
  Momentive offers solutions in market research, customer experience, and
enterprise feedback. The technology is gleaned from the billions of real
responses to questions asked on the platform. However, people may create biased
questions. A double-barreled question (DBQ) is a common type of biased question
that asks two aspects in one question. For example, "Do you agree with the
statement: The food is yummy, and the service is great.". This DBQ confuses
survey respondents because there are two parts in a question. DBQs impact both
the survey respondents and the survey owners. Momentive aims to detect DBQs and
recommend survey creators to make a change towards gathering high quality
unbiased survey data. Previous research work has suggested detecting DBQs by
checking the existence of grammatical conjunction. While this is a simple
rule-based approach, this method is error-prone because conjunctions can also
exist in properly constructed questions. We present an end-to-end machine
learning approach for DBQ classification in this work. We handled this
imbalanced data using active learning, and compared state-of-the-art embedding
algorithms to transform text data into vectors. Furthermore, we proposed a
model interpretation technique propagating the vector-level SHAP values to a
SHAP value for each word in the questions. We concluded that the word2vec
subword embedding with maximum pooling is the optimal word embedding
representation in terms of precision and running time in the offline
experiments using the survey data at Momentive. The A/B test and production
metrics indicate that this model brings a positive change to the business. To
the best of our knowledge, this is the first machine learning framework for DBQ
detection, and it successfully differentiates Momentive from the competitors.
We hope our work sheds light on machine learning approaches for bias question
detection.


title:
LMN at SemEval-2022 Task 11: A Transformer-based System for English
  Named Entity Recognition

abstract:
  Processing complex and ambiguous named entities is a challenging research
problem, but it has not received sufficient attention from the natural language
processing community. In this short paper, we present our participation in the
English track of SemEval-2022 Task 11: Multilingual Complex Named Entity
Recognition. Inspired by the recent advances in pretrained Transformer language
models, we propose a simple yet effective Transformer-based baseline for the
task. Despite its simplicity, our proposed approach shows competitive results
in the leaderboard as we ranked 12 over 30 teams. Our system achieved a macro
F1 score of 72.50% on the held-out test set. We have also explored a data
augmentation approach using entity linking. While the approach does not improve
the final performance, we also discuss it in this paper.


title:
Assessment of contextualised representations in detecting outcome
  phrases in clinical trials

abstract:
  Automating the recognition of outcomes reported in clinical trials using
machine learning has a huge potential of speeding up access to evidence
necessary in healthcare decision-making. Prior research has however
acknowledged inadequate training corpora as a challenge for the Outcome
detection (OD) task. Additionally, several contextualized representations like
BERT and ELMO have achieved unparalleled success in detecting various diseases,
genes, proteins, and chemicals, however, the same cannot be emphatically stated
for outcomes, because these models have been relatively under-tested and
studied for the OD task. We introduce "EBM-COMET", a dataset in which 300
PubMed abstracts are expertly annotated for clinical outcomes. Unlike prior
related datasets that use arbitrary outcome classifications, we use labels from
a taxonomy recently published to standardize outcome classifications. To
extract outcomes, we fine-tune a variety of pre-trained contextualized
representations, additionally, we use frozen contextualized and
context-independent representations in our custom neural model augmented with
clinically informed Part-Of-Speech embeddings and a cost-sensitive loss
function. We adopt strict evaluation for the trained models by rewarding them
for correctly identifying full outcome phrases rather than words within the
entities i.e. given an outcome "systolic blood pressure", the models are
rewarded a classification score only when they predict all 3 words in sequence,
otherwise, they are not rewarded. We observe our best model (BioBERT) achieve
81.5\% F1, 81.3\% sensitivity and 98.0\% specificity. We reach a consensus on
which contextualized representations are best suited for detecting outcomes
from clinical-trial abstracts. Furthermore, our best model outperforms scores
published on the original EBM-NLP dataset leader-board scores.


title: Toxic Comments Hunter : Score Severity of Toxic Comments

abstract:
  The detection and identification of toxic comments are conducive to creating
a civilized and harmonious Internet environment. In this experiment, we
collected various data sets related to toxic comments. Because of the
characteristics of comment data, we perform data cleaning and feature
extraction operations on it from different angles to obtain different toxic
comment training sets. In terms of model construction, we used the training set
to train the models based on TFIDF and finetuned the Bert model separately.
Finally, we encapsulated the code into software to score toxic comments in
real-time.


title: Question-Answer Sentence Graph for Joint Modeling Answer Selection

abstract:
  This research studies graph-based approaches for Answer Sentence Selection
(AS2), an essential component for building retrieval-based Question Answering
systems. Given a question, our model creates a small-scale relevant training
graph to perform more accurate AS2. The nodes of the graphs are question-answer
pairs, where the answers are also sentences. We train and apply
state-of-the-art models for computing scores between question-question,
question-answer, and answer-answer pairs. We apply thresholding to the
relevance scores for creating edges between nodes. Finally, we apply Graph
Neural Networks to the obtained graph to perform joint learning and inference
for solving the AS2 task. The experiments on two well-known academic benchmarks
and a real-world dataset show that our approach consistently outperforms
state-of-the-art models.


title:
When BERT Meets Quantum Temporal Convolution Learning for Text
  Classification in Heterogeneous Computing

abstract:
  The rapid development of quantum computing has demonstrated many unique
characteristics of quantum advantages, such as richer feature representation
and more secured protection on model parameters. This work proposes a vertical
federated learning architecture based on variational quantum circuits to
demonstrate the competitive performance of a quantum-enhanced pre-trained BERT
model for text classification. In particular, our proposed hybrid
classical-quantum model consists of a novel random quantum temporal convolution
(QTC) learning framework replacing some layers in the BERT-based decoder. Our
experiments on intent classification show that our proposed BERT-QTC model
attains competitive experimental results in the Snips and ATIS spoken language
datasets. Particularly, the BERT-QTC boosts the performance of the existing
quantum circuit-based language model in two text classification datasets by
1.57% and 1.52% relative improvements. Furthermore, BERT-QTC can be feasibly
deployed on both existing commercial-accessible quantum computation hardware
and CPU-based interface for ensuring data isolation.


title:
Semi-supervised Nonnegative Matrix Factorization for Document
  Classification

abstract:
  We propose new semi-supervised nonnegative matrix factorization (SSNMF)
models for document classification and provide motivation for these models as
maximum likelihood estimators. The proposed SSNMF models simultaneously provide
both a topic model and a model for classification, thereby offering highly
interpretable classification results. We derive training methods using
multiplicative updates for each new model, and demonstrate the application of
these models to single-label and multi-label document classification, although
the models are flexible to other supervised learning tasks such as regression.
We illustrate the promise of these models and training methods on document
classification datasets (e.g., 20 Newsgroups, Reuters).


title: Automated Single-Label Patent Classification using Ensemble Classifiers

abstract:
  Many thousands of patent applications arrive at patent offices around the
world every day. One important subtask when a patent application is submitted
is to assign one or more classification codes from the complex and hierarchical
patent classification schemes that will enable routing of the patent
application to a patent examiner who is knowledgeable about the specific
technical field. This task is typically undertaken by patent professionals,
however due to the large number of applications and the potential complexity of
an invention, they are usually overwhelmed. Therefore, there is a need for this
code assignment manual task to be supported or even fully automated by
classification systems that will classify patent applications, hopefully with
an accuracy close to patent professionals. Like in many other text analysis
problems, in the last years, this intellectually demanding task has been
studied using word embeddings and deep learning techniques. In this paper we
shortly review these research efforts and experiment with similar deep learning
techniques using different feature representations on automatic patent
classification in the level of sub-classes. On top of that, we present an
innovative method of ensemble classifiers trained with different parts of the
patent document. To the best of our knowledge, this is the first time that an
ensemble method was proposed for the patent classification problem. Our first
results are quite promising showing that an ensemble architecture of
classifiers significantly outperforms current state-of-the-art techniques using
the same classifiers as standalone solutions.


title: Compression of user generated content using denoised references

abstract:
  Video shared over the internet is commonly referred to as user generated
content (UGC). UGC video may have low quality due to various factors including
previous compression. UGC video is uploaded by users, and then it is re encoded
to be made available at various levels of quality and resolution. In a
traditional video coding pipeline the encoder parameters are optimized to
minimize a rate-distortion criteria, but when the input signal has low quality,
this results in sub-optimal coding parameters optimized to preserve undesirable
artifacts. In this paper we formulate the UGC compression problem as that of
compression of a noisy/corrupted source. The noisy source coding theorem
reveals that an optimal UGC compression system is comprised of optimal
denoising of the UGC signal, followed by compression of the denoised signal.
Since optimal denoising is unattainable and users may be against modification
of their content, we propose using denoised references to compute distortion,
so the encoding process can be guided towards perceptually better solutions. We
demonstrate the effectiveness of the proposed strategy for JPEG compression of
UGC images and videos.


title: Free Fock parafermions in the tight-binding model with dissipation

abstract:
  Parafermions that generalize (Majorana or usual) fermions appear as
interacting quasi-particles because of their nature. Although attempts to
develop models with free (non-interacting) parafermions have been undertaken,
existing proposals require unphysical conditions such as realizing purely
non-Hermitian systems. Here we present a way for the realization of free Fock
parafermions in the tight-binding model with controlled dissipation of a very
simple form. Introducing dissipation transforms an originally non-integrable
quantum model to an exactly solvable classical one.


title:
On realizing differential-algebraic equationsby rational dynamical
  systems

abstract:
  Real-world phenomena can often be conveniently described by dynamical systems
(that is, ODE systems in the state-space form). However, if one observes the
state of the system only partially, the observed quantities (outputs) and the
inputs of the system can typically be related by more complicated
differential-algebraic equations (DAEs). Therefore, a natural question
(referred to as the realizability problem) is: given a differential-algebraic
equation (say, fitted from data), does it come from a partially observed
dynamical system? A special case in which the functions involved in the
dynamical system are rational is of particular interest. For a single
differential-algebraic equation in a single output variable, Forsman has shown
that it is realizable by a rational dynamical system if and only if the
corresponding hypersurface is unirational, and he turned this into an algorithm
in the first-order case.
  In this paper, we study a more general case of single-input-single-output
equations. We show that if a realization by a rational dynamical system exists,
the system can be taken to have the dimension equal to the order of the DAE. We
provide a complete algorithm for first-order DAEs. We also show that the same
approach can be used for higher-order DAEs using several examples from the
literature.


title: Quantum Deep Learning for Mutant COVID-19 Strain Prediction

abstract:
  New COVID-19 epidemic strains like Delta and Omicron with increased
transmissibility and pathogenicity emerge and spread across the whole world
rapidly while causing high mortality during the pandemic period. Early
prediction of possible variants (especially spike protein) of COVID-19 epidemic
strains based on available mutated SARS-CoV-2 RNA sequences may lead to early
prevention and treatment. Here, combining the advantage of quantum and
quantum-inspired algorithms with the wide application of deep learning, we
propose a development tool named DeepQuantum, and use this software to realize
the goal of predicting spike protein variation structure of COVID-19 epidemic
strains. In addition, this hybrid quantum-classical model for the first time
achieves quantum-inspired blur convolution similar to classical depthwise
convolution and also successfully applies quantum progressive training with
quantum circuits, both of which guarantee that our model is the quantum
counterpart of the famous style-based GAN. The results state that the
fidelities of random generating spike protein variation structure are always
beyond 96% for Delta, 94% for Omicron. The training loss curve is more stable
and converges better with multiple loss functions compared with the
corresponding classical algorithm. At last, evidences that quantum-inspired
algorithms promote the classical deep learning and hybrid models effectively
predict the mutant strains are strong.


title:
OCR quality affects perceived usefulness of historical newspaper
  clippings -- a user study

abstract:
  Effects of Optical Character Recognition (OCR) quality on historical
information retrieval have so far been studied in data-oriented scenarios
regarding the effectiveness of retrieval results. Such studies have either
focused on the effects of artificially degraded OCR quality (see, e.g., [1-2])
or utilized test collections containing texts based on authentic low quality
OCR data (see, e.g., [3]). In this paper the effects of OCR quality are studied
in a user-oriented information retrieval setting. Thirty-two users evaluated
subjectively query results of six topics each (out of 30 topics) based on
pre-formulated queries using a simulated work task setting. To the best of our
knowledge our simulated work task experiment is the first one showing
empirically that users' subjective relevance assessments of retrieved documents
are affected by a change in the quality of optically read text. Users of
historical newspaper collections have so far commented effects of OCR'ed data
quality mainly in impressionistic ways, and controlled user environments for
studying effects of OCR quality on users' relevance assessments of the
retrieval results have so far been missing. To remedy this The National Library
of Finland (NLF) set up an experimental query environment for the contents of
one Finnish historical newspaper, Uusi Suometar 1869-1918, to be able to
compare users' evaluation of search results of two different OCR qualities for
digitized newspaper articles. The query interface was able to present the same
underlying document for the user based on two alternatives: either based on the
lower OCR quality, or based on the higher OCR quality, and the choice was
randomized. The users did not know about quality differences in the article
texts they evaluated. The main result of the study is that improved optical
character recognition quality affects perceived usefulness of historical
newspaper articles significantly. The mean average evaluation score for the
improved OCR results was 7.94% higher than the mean average evaluation score of
the old OCR results.


title:
Hands-free Telelocomotion of a Wheeled Humanoid toward Dynamic Mobile
  Manipulation via Teleoperation

abstract:
  Robotic systems that can dynamically combine manipulation and locomotion
could facilitate dangerous or physically demanding labor. For instance,
firefighter humanoid robots could leverage their body by leaning against
collapsed building rubble to push it aside. Here we introduce a teleoperation
system that targets the realization of these tasks using human whole-body motor
skills. We describe a new wheeled humanoid platform, SATYRR, and a novel
hands-free teleoperation architecture using a whole-body Human Machine
Interface (HMI). This system enables telelocomotion of the humanoid robot using
the operator body motion, freeing their arms for manipulation tasks. In this
study we evaluate the efficacy of the proposed system on hardware, and explore
the control of SATYRR using two teleoperation mappings that map the operators
body pitch and twist to the robot velocity or acceleration. Through experiments
and user feedback we showcase our preliminary findings of the pilot-system
response. Results suggest that the HMI is capable of effectively telelocomoting
SATYRR, that pilot preferences should dictate the appropriate motion mapping
and gains, and finally that the pilot can better learn to control the system
over time. This study represents a fundamental step towards the realization of
combined manipulation and locomotion via teleoperation.


title: Adaptive Security and Trust Management for Autonomous Messaging Systems

abstract:
  With society's increased dependence on information communication systems, the
need for dependable, trustable, robust, and secure adaptive systems becomes
ever more acute. Modern autonomic message-oriented middleware platforms have
stringent requirements for self-healing, adapting, evolving, fault-tolerance,
security, and active vulnerability assessment, especially when the internal
working model of a system and the environmental influences on the system are
uncertain during run-time. In this paper, we present an adaptive and evolving
security approach, and adaptive trust management approach to autonomous
messaging middleware systems. This approach learns, anticipates, evolves, and
adapts to a changing environment at run-time in the face of changing threats.
The approach combines adaptive risk-based security, trust-based security, and
security-based trust: the resultant supra-additive synergy improves and
increases the strength of security and the degree of trust in the system. The
approach also integrates different metrics, assessment tools, and observation
tools that improve and increase the assessability and verifiability of the
trustworthiness of the system. Validation of results is through industrial case
studies and end-user assessment.


title:
Targeted Data Poisoning Attack on News Recommendation System by Content
  Perturbation

abstract:
  News Recommendation System(NRS) has become a fundamental technology to many
online news services. Meanwhile, several studies show that recommendation
systems(RS) are vulnerable to data poisoning attacks, and the attackers have
the ability to mislead the system to perform as their desires. A widely studied
attack approach, injecting fake users, can be applied on the NRS when the NRS
is treated the same as the other systems whose items are fixed. However, in the
NRS, as each item (i.e. news) is more informative, we propose a novel approach
to poison the NRS, which is to perturb contents of some browsed news that
results in the manipulation of the rank of the target news. Intuitively, an
attack is useless if it is highly likely to be caught, i.e., exposed. To
address this, we introduce a notion of the exposure risk and propose a novel
problem of attacking a history news dataset by means of perturbations where the
goal is to maximize the manipulation of the target news rank while keeping the
risk of exposure under a given budget. We design a reinforcement learning
framework, called TDP-CP, which contains a two-stage hierarchical model to
reduce the searching space. Meanwhile, influence estimation is also applied to
save the time on retraining the NRS for rewards. We test the performance of
TDP-CP under three NRSs and on different target news. Our experiments show that
TDP-CP can increase the rank of the target news successfully with a limited
exposure budget.


title: Amorphous Boron Nitride: Ab initio Study of its vibrational properties

abstract:
  Boron nitride (BN) is a structurally versatile insulator since it can be
found in several crystalline structures with interesting mechanical and
electrical properties, making this material attractive for technological
applications. Seeking to improve its features, experimental and simulational
studies for the amorphous phase (a-BN) have been carried out by some groups,
focusing on the electrical and structural properties, pressure-induced phase
transformations, and hydrogenated a-BN. In this work two amorphous structures
are computationally generated and studied using ab initio Molecular Dynamics on
a 216-atom supercell with two different densities, 2.04 and 2.80 g cm$^{-3}$.
Our undermelt-quench approach is followed, since it has proven to give good
structures for disordered materials and their properties. The topology, the
vibrational density of states and some thermodynamic properties of the two
samples are reported and compared with existing experiment. Some computational
results are also revisited.


title:
Potential of the MeerKAT telescope to detect the stimulated decay of
  axion-like particles

abstract:
  A prominent aspect of the hunt for cold dark matter is looking for light
scalar candidates such as axion-like particles (ALPs). The coupling between
ALPs and photons allows for the spontaneous decay of ALPs into pairs of
photons. It has been previously shown that stimulated ALP decay rates can
become significant on cosmic time scales. Furthermore, it has been claimed, in
several recent works, that ALPs can gravitationally thermalize and form
macroscopic condensates. Consequently, the photon occupation number of ambient
populations (like the cosmic microwave background) can receive Bose enhancement
in dense ALP clumps and grows exponentially. For cold dark matter ALPs, this
can lead to radio emissions produced from this process and could be observed by
the forthcoming radio telescopes. In this work, we investigate the
detectability of such a radio signature from some astrophysical targets using
the MeerKAT radio telescopes. The results show that the MeerKAT telescope is
able to probe the ALPs parameter space with limits reaching the current level
of the CAST experiment and the potential level of the IAXO experiment with an
arcminute visibility taper.


title:
Galaxy luminosity functions at redshifts 0.6 to 1.2 in the Chandra Deep
  Field South

abstract:
  We present the rest-frame Ultra-Violet (UV) galaxy luminosity function (LF)
and luminosity density (LD) measurements in the far-UV (150 nm) wavelength, in
the redshift range z = 0.6 - 1.2. The UV LF is derived using XMM-Newton Optical
Monitor (XMM-OM), ultraviolet (160 - 400 nm) observations of the Chandra Deep
Field South, over an area of 396 sq. arcmin. Using the deep UV imaging of the
CDFS, we identified more than 2500 galaxies in our sample with faint UVW1(AB)
limit 24.5 mag. This sample along with various other catalogues containing
redshift information, is used to calculate the binned representation of the
galaxy UV LF in the two redshift bins 0.6 - 0.8 and 0.8 - 1.2, having a wide
range of 150 nm rest-frame UV magnitudes, reaching 1 - 1.5 magnitudes fainter
than previous studies at similar redshifts. The binned LF is described well by
the Schechter function form. Using maximum-likelihood the Schechter function is
fitted to the unbinned data to obtain the best-fit values of the the UV galaxy
LF parameters. We find that characteristic magnitude brightens by 0.8 mag from
z = 0.7 to z = 1, implying an increase in the star formation activity between
these redshifts, as reported by past studies. Our estimate of the faint-end
slope -1.10+0.19-0.18 is on the shallower side compared with previous studies
at z = 0.7, whereas a value of -1.56+0.19-0.18 estimated for z = 1.0, agrees
with previous results given the uncertainties.


title: TIGGER: Scalable Generative Modelling for Temporal Interaction Graphs

abstract:
  There has been a recent surge in learning generative models for graphs. While
impressive progress has been made on static graphs, work on generative modeling
of temporal graphs is at a nascent stage with significant scope for
improvement. First, existing generative models do not scale with either the
time horizon or the number of nodes. Second, existing techniques are
transductive in nature and thus do not facilitate knowledge transfer. Finally,
due to relying on one-to-one node mapping from source to the generated graph,
existing models leak node identity information and do not allow
up-scaling/down-scaling the source graph size. In this paper, we bridge these
gaps with a novel generative model called TIGGER. TIGGER derives its power
through a combination of temporal point processes with auto-regressive modeling
enabling both transductive and inductive variants. Through extensive
experiments on real datasets, we establish TIGGER generates graphs of superior
fidelity, while also being up to 3 orders of magnitude faster than the
state-of-the-art.


title: Monetary policy and the racial wage gap

abstract:
  This paper aims to clarify the relationship between monetary policy shocks
and wage inequality. We emphasize the relevance of within and between wage
group inequalities in explaining total wage inequality in the United States.
Relying on the quarterly data for the period 2000-2020, our analysis shows that
racial disparities explain 12\% of observed total wage inequality.
Subsequently, we examine the role of monetary policy in wage inequality. We do
not find compelling evidence that shows that monetary policy plays a role in
exacerbating the racial wage gap. However, there is evidence that accommodative
monetary policy plays a role in magnifying between group wage inequalities but
the impact occurs after 2008.


title:
Recent developments in spectral theory of the focusing NLS soliton and
  breather gases: the thermodynamic limit of average densities, fluxes and
  certain meromorphic differentials; periodic gases

abstract:
  In this paper we consider soliton and breather gases for one dimensional
integrable focusing Nonlinear Schr\"{o}dinger Equation (fNLS). We derive
average densities and fluxes for such gases by studying the thermodynamic limit
of the fNLS finite gap solutions. Thermodynamic limits of quasimomentum,
quasienergy and their connections with the corresponding $g$-functions were
also established.
  We then introduce the notion of periodic fNLS gases and calculate for them
the average densities, fluxes and thermodynamic limits of meromorphic
differentials. Certain accuracy estimates of the obtained results are also
included.
  Our results constitute another step towards the mathematical foundation for
the spectral theory of fNLS soliton and breather gases that appeared in work of
G. El and A. Tovbis, Phys. Rev. E, 2020.


title: Improved Search of Relevant Points for Nearest-Neighbor Classification

abstract:
  Given a training set $P \subset \mathbb{R}^d$, the nearest-neighbor
classifier assigns any query point $q \in \mathbb{R}^d$ to the class of its
closest point in $P$. To answer these classification queries, some training
points are more relevant than others. We say a training point is relevant if
its omission from the training set could induce the misclassification of some
query point in $\mathbb{R}^d$. These relevant points are commonly known as
border points, as they define the boundaries of the Voronoi diagram of $P$ that
separate points of different classes. Being able to compute this set of points
efficiently is crucial to reduce the size of the training set without affecting
the accuracy of the nearest-neighbor classifier.
  Improving over a decades-long result by Clarkson, in a recent paper by
Eppstein an output-sensitive algorithm was proposed to find the set of border
points of $P$ in $O( n^2 + nk^2 )$ time, where $k$ is the size of such set. In
this paper, we improve this algorithm to have time complexity equal to $O( nk^2
)$ by proving that the first steps of their algorithm, which require $O( n^2 )$
time, are unnecessary.


title:
An Unsupervised Domain Adaptive Approach for Multimodal 2D Object
  Detection in Adverse Weather Conditions

abstract:
  Integrating different representations from complementary sensing modalities
is crucial for robust scene interpretation in autonomous driving. While deep
learning architectures that fuse vision and range data for 2D object detection
have thrived in recent years, the corresponding modalities can degrade in
adverse weather or lighting conditions, ultimately leading to a drop in
performance. Although domain adaptation methods attempt to bridge the domain
gap between source and target domains, they do not readily extend to
heterogeneous data distributions. In this work, we propose an unsupervised
domain adaptation framework, which adapts a 2D object detector for RGB and
lidar sensors to one or more target domains featuring adverse weather
conditions. Our proposed approach consists of three components. First, a data
augmentation scheme that simulates weather distortions is devised to add domain
confusion and prevent overfitting on the source data. Second, to promote
cross-domain foreground object alignment, we leverage the complementary
features of multiple modalities through a multi-scale entropy-weighted domain
discriminator. Finally, we use carefully designed pretext tasks to learn a more
robust representation of the target domain data. Experiments performed on the
DENSE dataset show that our method can substantially alleviate the domain gap
under the single-target domain adaptation (STDA) setting and the less explored
yet more general multi-target domain adaptation (MTDA) setting.


title: Skyrmion-electron bound states in a N\'eel antiferromagnet

abstract:
  We show that, in a N\'eel antiferromagnet with a particular location of
electron band extrema, a Skyrmion and an electron form a bound state with
energy of the order of the gap $\Delta$ in the electron spectrum. The bound
state turns the Skyrmion into a charged particle, that may be manipulated by
electric field. We identify a region in the space of coupling constants, where
the Skyrmion-electron bound state makes the (otherwise metastable) Skyrmion
genuinely stable.


title: Kubric: A scalable dataset generator

abstract:
  Data is the driving force of machine learning, with the amount and quality of
training data often being more important for the performance of a system than
architecture and training details. But collecting, processing and annotating
real data at scale is difficult, expensive, and frequently raises additional
privacy, fairness and legal concerns. Synthetic data is a powerful tool with
the potential to address these shortcomings: 1) it is cheap 2) supports rich
ground-truth annotations 3) offers full control over data and 4) can circumvent
or mitigate problems regarding bias, privacy and licensing. Unfortunately,
software tools for effective data generation are less mature than those for
architecture design and training, which leads to fragmented generation efforts.
To address these problems we introduce Kubric, an open-source Python framework
that interfaces with PyBullet and Blender to generate photo-realistic scenes,
with rich annotations, and seamlessly scales to large jobs distributed over
thousands of machines, and generating TBs of data. We demonstrate the
effectiveness of Kubric by presenting a series of 13 different generated
datasets for tasks ranging from studying 3D NeRF models to optical flow
estimation. We release Kubric, the used assets, all of the generation code, as
well as the rendered datasets for reuse and modification.


title: A Unified View on the Functorial Nerve Theorem and its Variations

abstract:
  The nerve theorem is a basic result of algebraic topology that plays a
central role in computational and applied aspects of the subject. In applied
topology, one often needs a nerve theorem that is functorial in an appropriate
sense, and furthermore one often needs a nerve theorem for closed covers, as
well as for open covers. While the techniques for proving such functorial nerve
theorems have long been available, there is unfortunately no general-purpose,
explicit treatment of this topic in the literature. We address this by proving
a variety of functorial nerve theorems. First, we show how one can use
relatively elementary techniques to prove nerve theorems for covers by compact,
convex sets in Euclidean space, and for covers of a simplicial complex by
subcomplexes. Then, we prove a more general, ``unified'' nerve theorem that
recovers both of these, using standard techniques from abstract homotopy
theory.


title: Universal rigid abelian tensor categories and Schur finiteness

abstract:
  We study the construction of arXiv:2111.11217 [math:AG] in more detail,
especially in the case of Schur-finite rigid $\otimes$-categories. This leads
to some groundwork on the ideal structure of rigid additive and abelian
$\otimes$-categories.


title: Cosmic-void observations reconciled with primordial magnetogenesis

abstract:
  It has been suggested that the weak magnetic field hosted by the
intergalactic medium (IGM) in voids could be a relic from the early Universe.
However, accepted models of turbulent magnetohydrodynamic (MHD) decay predict
that the present-day strength of fields generated at the electroweak phase
transition (EWPT) should be too low to explain the observed scattering of
$\gamma$-rays from TeV blazars. Here, we apply recent theoretical developments
-- namely, the discovery of the Saffman helicity invariant and of the role of
magnetic reconnection in setting the decay rate -- to show that the accepted
models greatly underpredict the present-day strength of relic fields. Our
estimates restore the consistency of the EWPT-relic hypothesis with
observational constraints; moreover, we find that efficient magnetogenesis at
the EWPT could produce relics with the strength that is believed sufficient to
resolve the Hubble tension and explain galaxy-cluster magnetic fields without
invoking dynamo amplification.


title:
On the degeneracy between $f\sigma_8$ tension and its Gaussian process
  forecasting

abstract:
  In this Letter we reconstruct the growth and evolution of the cosmic
structure of the Universe using Markov chain Monte Carlo algorithms for
Gaussian processes. We estimate the difference between the reconstructions that
are calculated through a maximization of the kernel hyperparameters and those
that are obtained with a complete exploration of the parameter space. We find
that the difference between these two approaches is of the order of $1\%$-$5\%$
depending on the selected kernel. Furthermore, we compare our results with
those obtained by Planck Collaboration 2018 assuming a $\Lambda$CDM model and
we do not find a statistically significant difference in the redshift range
were the reconstructions of $f\sigma_{8}$ have been made in the literature,
which will impact seriously the reported cosmological tensions.


title: Quantum Computing for Software Engineering: Prospects

abstract:
  Quantum computers (QCs) are maturing. When QCs are powerful enough, they will
be able to handle problems in chemistry, physics, and finance. However, the
applicability of quantum algorithms to speed up Software Engineering (SE) tasks
has not been explored. We examine eight groups of algorithms that may
accelerate SE tasks across the different phases of SE and sketch potential
opportunities and challenges.


title:
Domain Adaptation of Automated Treatment Planning from Computed
  Tomography to Magnetic Resonance

abstract:
  Objective: Machine learning (ML) based radiation treatment (RT) planning
addresses the iterative and time-consuming nature of conventional inverse
planning. Given the rising importance of Magnetic resonance (MR) only treatment
planning workflows, we sought to determine if an ML based treatment planning
model, trained on computed tomography (CT) imaging, could be applied to MR
through domain adaptation. Methods: In this study, MR and CT imaging was
collected from 55 prostate cancer patients treated on an MR linear accelerator.
ML based plans were generated for each patient on both CT and MR imaging using
a commercially available model in RayStation 8B. The dose distributions and
acceptance rates of MR and CT based plans were compared using institutional
dose-volume evaluation criteria. The dosimetric differences between MR and CT
plans were further decomposed into setup, cohort, and imaging domain
components. Results: MR plans were highly acceptable, meeting 93.1% of all
evaluation criteria compared to 96.3% of CT plans, with dose equivalence for
all evaluation criteria except for the bladder wall, penile bulb, small and
large bowel, and one rectum wall criteria (p<0.05). Changing the input imaging
modality (domain component) only accounted for about half of the dosimetric
differences observed between MR and CT plans. Anatomical differences between
the ML training set and the MR linac cohort (cohort component) were also a
significant contributor. Significance: We were able to create highly acceptable
MR based treatment plans using a CT-trained ML model for treatment planning,
although clinically significant dose deviations from the CT based plans were
observed.


title: Data-driven Hi2Lo for Coarse-grid System Thermal Hydraulic Modeling

abstract:
  Traditional 1D system thermal hydraulic analysis has been widely applied in
nuclear industry for licensing purposes due to its numerical efficiency.
However, such codes are inherently deficient for modeling of multiscale
multidimensional flows. For such scenarios coarse-grid 3D simulations are
useful due to the balance between the cost and the amount of information a
modeler can extract from the results. At the same time, coarse grids do not
allow to accurately resolve and capture turbulent mixing in reactor enclosures,
while existing turbulence models (closures for the Reynolds stresses or
turbulent viscosity) have large model form uncertainties. Thus, there is an
interest in the improvement of such solvers. In this work two data-driven
high-to-low methodologies to reduce mesh and model-induced errors are explored
using a case study based on the Texas A&M upper plenum of high-temperature
gas-cooled reactor facility. The first approach relies on the usage of
turbulence closure for eddy viscosity with higher resolution/fidelity in a
coarse grid solver. The second methodology employs artificial neural networks
to map low-fidelity input features with errors in quantities of interest
(velocity fields). Both methods have shown potential to improve the coarse grid
simulation results by using data with higher fidelity (Reynolds-averaged
Navier-Stokes and large eddy simulations), though, influence of the
mesh-induced (discretization) error is quite complex and requires further
investigations.


title: Unsupervised Quantum Circuit Learning in High Energy Physics

abstract:
  Unsupervised training of generative models is a machine learning task that
has many applications in scientific computing. In this work we evaluate the
efficacy of using quantum circuit-based generative models to generate synthetic
data of high energy physics processes. We use non-adversarial, gradient-based
training of quantum circuit Born machines to generate joint distributions over
2 and 3 variables.


title: Lambda number of zero-divisor graphs of finite commutative rings

abstract:
  Let $\G = (\V, \E)$ be a simple graph, an $L(2,1)$-labelling of $\G$ is
assignment of labels from non-negative integers to the vertices of $\G$ such
that adjacent vertices gets labels which at least differ by two and vertices
which are at distance two from each other get different labels. The
$\lambda$-number of $\G$, denoted by $\lambda(\G)$ is the smallest positive
integer $\ell$ such that $\G$ has a $L(2,1)$-labelling with all the labels are
members of the set $\{ 0, 1, \cdots, \ell \}$. The zero-divisor graph denoted
by $\Gamma(R)$, of a finite commutative ring $R$ with unity is a simple graph
with vertices as non-zero zero divisors of $R$. Two vertices $u$ and $v$ are
adjacent in $\Gamma(R)$ if and only if $uv = 0$ in $R$. In this paper, we
investigate $L(2,1)$-labelling in zero-divisor graphs. We study the
\textit{partite truncation}, a graph operation that reduces a $n$-partite graph
of higher order to a graph of lower order. We establish the relation between
$\lambda$-numbers of two graphs. We make use of the operation \textit{partite
truncation} to contract the zero-divisor graph of a reduced ring to the
zero-divisor graph of a Boolean ring. We compute the exact value of
$\lambda$-numbers of zero-divisor graphs of some classes of local and mixed
rings such as $\Z_{p^n}, \Z_{p^n} \times\Z_{q^m},$ and
$\mathbb{F}_{q}\times\Z_{p^n}$.


title: The Unsurprising Effectiveness of Pre-Trained Vision Models for Control

abstract:
  Recent years have seen the emergence of pre-trained representations as a
powerful abstraction for AI applications in computer vision, natural language,
and speech. However, policy learning for control is still dominated by a
tabula-rasa learning paradigm, with visuo-motor policies often trained from
scratch using data from deployment environments. In this context, we revisit
and study the role of pre-trained visual representations for control, and in
particular representations trained on large-scale computer vision datasets.
Through extensive empirical evaluation in diverse control domains (Habitat,
DeepMind Control, Adroit, Franka Kitchen), we isolate and study the importance
of different representation training methods, data augmentations, and feature
hierarchies. Overall, we find that pre-trained visual representations can be
competitive or even better than ground-truth state representations to train
control policies. This is in spite of using only out-of-domain data from
standard vision datasets, without any in-domain data from the deployment
environments. Additional details and source code is available at
https://sites.google.com/view/pvr-control


title: Good quantum LDPC codes with linear time decoder from lossless expanders

abstract:
  Quantum low-density parity-check (qLDPC) codes are quantum stabilizer codes
where each stabilizer acts on a constant number of qubits and each qubit is
acted on by a constant number of stabilizers. We study qLDPC codes constructed
from balanced products and lossless expanders. We found that assuming the
existence of 2-sided lossless expander graphs with free group action, the
resulting qLDPC codes have constant rate, linear distance, and linear time
decoders.


title:
Improving CTC-based speech recognition via knowledge transferring from
  pre-trained language models

abstract:
  Recently, end-to-end automatic speech recognition models based on
connectionist temporal classification (CTC) have achieved impressive results,
especially when fine-tuned from wav2vec2.0 models. Due to the conditional
independence assumption, CTC-based models are always weaker than
attention-based encoder-decoder models and require the assistance of external
language models (LMs). To solve this issue, we propose two knowledge
transferring methods that leverage pre-trained LMs, such as BERT and GPT2, to
improve CTC-based models. The first method is based on representation learning,
in which the CTC-based models use the representation produced by BERT as an
auxiliary learning target. The second method is based on joint classification
learning, which combines GPT2 for text modeling with a hybrid CTC/attention
architecture. Experiment on AISHELL-1 corpus yields a character error rate
(CER) of 4.2% on the test set. When compared to the vanilla CTC-based models
fine-tuned from the wav2vec2.0 models, our knowledge transferring method
reduces CER by 16.1% relatively without external LMs.


title: Korean Tokenization for Beam Search Rescoring in Speech Recognition

abstract:
  The performance of automatic speech recognition (ASR) models can be greatly
improved by proper beam-search decoding with external language model (LM).
There has been an increasing interest in Korean speech recognition, but not
many studies have been focused on the decoding procedure. In this paper, we
propose a Korean tokenization method for neural network-based LM used for
Korean ASR. Although the common approach is to use the same tokenization method
for external LM as the ASR model, we show that it may not be the best choice
for Korean. We propose a new tokenization method that inserts a special token,
SkipTC, when there is no trailing consonant in a Korean syllable. By utilizing
the proposed SkipTC token, the input sequence for LM becomes very regularly
patterned so that the LM can better learn the linguistic characteristics. Our
experiments show that the proposed approach achieves a lower word error rate
compared to the same LM model without SkipTC. In addition, we are the first to
report the ASR performance for the recently introduced large-scale 7,600h
Korean speech dataset.


title: Counter Hate Speech in Social Media: A Survey

abstract:
  With the high prevalence of offensive language against minorities in social
media, counter-hate speeches (CHS) generation is considered an automatic way of
tackling this challenge. The CHS is supposed to appear as a third voice to
educate people and keep the social [red lines bold] without limiting the
principles of freedom of speech. In this paper, we review the most important
research in the past and present with a main focus on methodologies, collected
datasets and statistical analysis CHS's impact on social media. The CHS
generation is based on the optimistic assumption that any attempt to intervene
the hate speech in social media can play a positive role in this context.
Beyond that, previous works ignored the investigation of the sequence of
comments before and after the CHS. However, the positive impact is not
guaranteed, as shown in some previous works. To the best of our knowledge, no
attempt has been made to survey the related work to compare the past research
in terms of CHS's impact on social media. We take the first step in this
direction by providing a comprehensive review on related works and categorizing
them based on different factors including impact, methodology, data source,
etc.


title:
A quantitative assessment of the VO line list: Inaccuracies hamper
  high-resolution VO detections in exoplanet atmospheres

abstract:
  Metal hydrides and oxides are important species in hot-Jupiters since they
can affect their energy budgets and the thermal structure of their atmospheres.
One such species is VO, which is prominent in stellar M-dwarf spectra. Evidence
for VO has been found in the low-resolution transmission spectrum of WASP-121b,
but this has not been confirmed at high resolution. It has been suggested that
this is due to inaccuracies in its line list. In this paper, we quantitatively
evaluate the VO line list and assess whether inaccuracies are responsible for
the non-detections at high resolution in WASP-121b. Furthermore, we investigate
whether the detectability can be improved by selecting only those lines
associated with the most accurate quantum transitions. A cross-correlation
analysis was applied to archival HARPS and CARMENES spectra of several M
dwarfs. VO cross-correlation signals from the spectra were compared with those
in which synthetic VO models were injected, providing an estimate of the ratio
between the potential strength (in case of a perfect model) and the observed
strength of the signal. This was repeated for the reduced model covering the
most accurate quantum transitions. The findings were fed into injection and
recovery tests of VO in a UVES transmission spectrum of WASP-121b. We find that
inaccuracies cause VO cross-correlation signals in M-dwarf spectra to be
suppressed by about a factor 2.1 and 1.1 for the complete and reduced line
lists, respectively, corresponding to a reduced observing efficiency of a
factor 4.3 and 1.2. The reduced line list outperforms the complete line list in
recovering the actual VO signal in the M-dwarf spectra by about a factor of
1.8. Neither line list results in a VO detection in WASP-121b. Injection tests
show that with the reduced efficiency of the line lists, the potential signal
as seen at low resolution is not detectable in these data.


title:
A photonic integrated chip platform for interlayer exciton valley
  routing

abstract:
  Interlayer excitons in two dimensional semiconductor heterostructures show
suppressed electron-hole overlap resulting in longer radiative lifetimes as
compared to intralyer excitons. Such tightly bound interlayer excitons are
relevant for important optoelectronic applications including light storage and
quantum communication. Their optical accessibility is, however, limited due to
their out-of-plane transition dipole moment. In this work, we design a CMOS
compatible photonic integrated chip platform for enhanced near field coupling
of these interlayer excitons with the whispering gallery modes of a
microresonator, exploiting the high confinement of light in a small modal
volume and high quality factor of the system. Our platform allows for highly
selective emission routing via engineering an asymmetric light transmission
which facilitates efficient readout and channeling of the excitonic valley
state from such systems.


title:
On the pitfalls of entropy-based uncertainty for multi-class
  semi-supervised segmentation

abstract:
  Semi-supervised learning has emerged as an appealing strategy to train deep
models with limited supervision. Most prior literature under this learning
paradigm resorts to dual-based architectures, typically composed of a
teacher-student duple. To drive the learning of the student, many of these
models leverage the aleatoric uncertainty derived from the entropy of the
predictions. While this has shown to work well in a binary scenario, we
demonstrate in this work that this strategy leads to suboptimal results in a
multi-class context, a more realistic and challenging setting. We argue,
indeed, that these approaches underperform due to the erroneous uncertainty
approximations in the presence of inter-class overlap. Furthermore, we propose
an alternative solution to compute the uncertainty in a multi-class setting,
based on divergence distances and which account for inter-class overlap. We
evaluate the proposed solution on a challenging multi-class segmentation
dataset and in two well-known uncertainty-based segmentation methods. The
reported results demonstrate that by simply replacing the mechanism used to
compute the uncertainty, our proposed solution brings substantial improvement
on tested setups.


title: Functional Clustering of Neuronal Signals with FMM Mixture Models

abstract:
  The identification of unlabelled neuronal electric signals is one of the most
challenging open problems in neuroscience, widely known as Spike Sorting.
Motivated to solve this problem, we propose a model-based approach within the
mixture modeling framework for clustering oscillatory functional data called
MixFMM. The core of the approach is the FMM waves, which are non-linear
parametric time functions, flexible enough to describe different oscillatory
patterns and simple enough to be estimated efficiently. In particular, specific
model parameters describe the waveforms' phase, amplitude, and shape. A mixture
model is defined using FMM waves as basic functions and gaussian errors, and an
EM algorithm is proposed for estimating the parameters. In addition, the
approach includes a method for the number of clusters selection. Spike Sorting
has received considerable attention in the literature, and different functional
clustering approaches have traditionally been considered. We compare those
approaches with the MixFMM in a broad collection of datasets, including
benchmarking simulated and real data. The MixFMM approach achieves outstanding
results in a selection of indexes across datasets, and the significant
improvements attained in specific scenarios motivate interesting neuronal
insights.


title: Euler-Kronecker constants for cyclotomic fields

abstract:
  The Euler-Mascheroni constant $\gamma=0.5772\dots\!$ is the $K=\mathbb{Q}$
example of an Euler-Kronecker constant $\gamma_K$ of a number field $K.$ In
this note we consider the size of the $\gamma_q=\gamma_{K_q}$ for cyclotomic
fields $K_q:=\mathbb{Q}(\zeta_q).$ Assuming the Elliott-Halberstam Conjecture
(EH), we prove uniformly in $Q$ that $$\frac{1}{Q}\sum_{Q<q\le 2Q} \left
|\gamma_q - \log q\right |= o(\log Q).$$ In other words, under EH the $\gamma_q
/ \log q$ in these ranges converge to the one point distribution at $1$. This
theorem refines and extends a previous result of Ford, Luca, and Moree for
prime $q.$


title: Manoeuvre detection in Low Earth Orbit with Radar Data

abstract:
  This work outlines and assesses several methods for the detection of
manoeuvres in Low Earth Orbit (LEO) from surveillance radar data. To be able to
detect manoeuvres, the main starting assumption is that the object under
analysis has an orbit known with a sufficient degree of precision. Based on the
precise (a posteriori) orbit and radar data, several manoeuvre detection
methods are presented; one is based on unscented Kalman filtering, whereas two
others algorithms are based on reachability analysis of the state, which
correlates its prediction set with the next track from the radar. The filtering
algorithm can be extended for several radar tracks, whereas the
reachability-based methods are more precise in detecting manoeuvres. Then, to
inherit the best properties of both classes of algorithms, a manoeuvre
detection filter that combines both concepts is finally presented. Manoeuvre
detection results are presented first for simulated scenarios -- for validation
and calibration purposes -- and later for real data. Radar information comes
from the Spanish Space Surveillance Radar (S3TSR), with real manoeuvre
information and high-quality ephemerides. The results show promise, taking into
account that a single surveillance radar is the only source of data, obtaining
manoeuvre detection rates of more than 50% and false positive rates of less
than 10%.


title: Quantum Local Differential Privacy and Quantum Statistical Query Model

abstract:
  The problem of private learning has been extensively studied in classical
computer science. Notably, a striking equivalence between local differentially
private learning and statistical query learning has been shown. In addition,
the statistical query model has been recently extended to quantum computation.
In this work, we give a formal definition of quantum local differential privacy
and we extend the aforementioned result to quantum computation.


title: The color-dual fate of N=4 supergravity

abstract:
  We conjecture that the massless gauge theory of Yang-Mills deformed by a
higher-derivative Tr(F^3) operator will not be tree-level color-dual without an
infinite number of additional counterterms. The requirement of color-dual
kinematics and consistent factorization between four- and five-points induces a
tower of increasingly higher-dimensional operators. We find through explicit
calculation that their amplitudes are consistent with the alpha' expansion of
those generated by the (DF)^2+YM theory, a known color-dual theory where the
Tr(F^2) term has been given a mass squared proportional to 1/alpha'.
Considering consistent double-copy construction as a physical principle would
suggest that a Tr(F^3)-based attempt at a color-dual resolution of the UV
divergence of N=4 supergravity may be able to tame the bad ultraviolet behavior
of the theory, but at the cost of field-theoretic locality.


title: Values of the length function for nonassociative algebras

abstract:
  We study realizable values of the length function for unital possibly
nonassociative algebras of a given dimension. To do this we apply the method of
characteristic sequences and establish sufficient conditions of realisability
for a given value of length. The proposed conditions are based on binary
decompositions of the value and algebraic constructions that allow to modify
length function of an algebra. Additionally we provide a classification of
unital algebras of maximal possible length in terms of their basis.


title:
Continual and Sliding Window Release for Private Empirical Risk
  Minimization

abstract:
  It is difficult to continually update private machine learning models with
new data while maintaining privacy. Data incur increasing privacy loss -- as
measured by differential privacy -- when they are used in repeated
computations. In this paper, we describe regularized empirical risk
minimization algorithms that continually release models for a recent window of
data. One version of the algorithm uses the entire data history to improve the
model for the recent window. The second version uses a sliding window of
constant size to improve the model, ensuring more relevant models in case of
evolving data. The algorithms operate in the framework of stochastic gradient
descent. We prove that even with releasing a model at each time-step over an
infinite time horizon, the privacy cost of any data point is bounded by a
constant $\epsilon$ differential privacy, and the accuracy of the output models
are close to optimal. Experiments on MNIST and Arxiv publications data show
results consistent with the theory.


title: Steady growth of length function and Malcev algebras

abstract:
  We introduce and investigate the algebras of steadily growing length, that is
the class of algebras, where the length is bounded by a linear function of the
dimension. In particular we show that Malcev algebras belong to this class and
establish the exact upper bound for its length.


title:
Algebra of local symmetric operators and braided fusion $n$-category --
  symmetry is a shadow of topological order

abstract:
  Symmetry is usually defined via transformations described by a (higher)
group. But a symmetry really corresponds to an algebra of local symmetric
operators, which directly constrains the properties of the system. In
particular, isomorphic operator algebras correspond to equivalent symmetries.
In this paper, we pointed out that the algebra of local symmetry operators
actually contains extended string-like, membrane-like, {\it etc} operators. The
algebra of those extended operators in $n$-dimensional space gives rise to a
non-degenerate braided fusion $n$-category, which happens to describe a
topological order in one higher dimension. This allows us to show that the
equivalent classes of finite symmetries actually correspond to topological
orders in one higher dimension. Such a holographic theory not only describes
(higher) symmetries, it also describes anomalous (higher) symmetries,
non-invertible (higher) symmetries (also known as algebraic higher symmetries),
and (invertible and non-invertible) gravitational anomalies, in a unified and
systematic way. We demonstrate this unified holographic framework via some
simple examples of (higher and/or anomalous) symmetries, as well as a symmetry
that is neither anomalous nor anomaly-free. We also show the equivalence
between $\mathbb{Z}_2\times \mathbb{Z}_2$ symmetry with mixed anomaly and
$\mathbb{Z}_4$ symmetry, as well as between many other symmetries, in
1-dimensional space.


title:
Fast rates for noisy interpolation require rethinking the effects of
  inductive bias

abstract:
  Good generalization performance on high-dimensional data crucially hinges on
a simple structure of the ground truth and a corresponding strong inductive
bias of the estimator. Even though this intuition is valid for regularized
models, in this paper we caution against a strong inductive bias for
interpolation in the presence of noise: Our results suggest that, while a
stronger inductive bias encourages a simpler structure that is more aligned
with the ground truth, it also increases the detrimental effect of noise.
Specifically, for both linear regression and classification with a sparse
ground truth, we prove that minimum $\ell_p$-norm and maximum $\ell_p$-margin
interpolators achieve fast polynomial rates up to order $1/n$ for $p > 1$
compared to a logarithmic rate for $p = 1$. Finally, we provide experimental
evidence that this trade-off may also play a crucial role in understanding
non-linear interpolating models used in practice.


title:
Audio-visual Generalised Zero-shot Learning with Cross-modal Attention
  and Language

abstract:
  Learning to classify video data from classes not included in the training
data, i.e. video-based zero-shot learning, is challenging. We conjecture that
the natural alignment between the audio and visual modalities in video data
provides a rich training signal for learning discriminative multi-modal
representations. Focusing on the relatively underexplored task of audio-visual
zero-shot learning, we propose to learn multi-modal representations from
audio-visual data using cross-modal attention and exploit textual label
embeddings for transferring knowledge from seen classes to unseen classes.
Taking this one step further, in our generalised audio-visual zero-shot
learning setting, we include all the training classes in the test-time search
space which act as distractors and increase the difficulty while making the
setting more realistic. Due to the lack of a unified benchmark in this domain,
we introduce a (generalised) zero-shot learning benchmark on three audio-visual
datasets of varying sizes and difficulty, VGGSound, UCF, and ActivityNet,
ensuring that the unseen test classes do not appear in the dataset used for
supervised training of the backbone deep models. Comparing multiple relevant
and recent methods, we demonstrate that our proposed AVCA model achieves
state-of-the-art performance on all three datasets. Code and data are available
at \url{https://github.com/ExplainableML/AVCA-GZSL}.


title: Ground state hyperfine structure of light muon-electron ions

abstract:
  The ground state hyperfine splitting of light muon-electron ions of lithium,
beryllium, boron and helium is calculated on the basis of analytical
perturbation theory in terms of small parameters of the fine structure constant
and electron-muon mass ratio. The corrections of vacuum polarization, nuclear
structure and recoil effects and electron vertex corrections are taken into
account. The dependence of the corrections on the nucleus charge Z is studied.
The obtained total values of hyperfine splitting intervals can be used for
comparison with future experimental data.


title:
Exponentially faster fixed-parameter algorithms for high-multiplicity
  scheduling

abstract:
  We consider so-called $N$-fold integer programs (IPs) of the form $\max\{c^T
x : Ax = b, \ell \leq x \leq u, x \in \mathbb Z^{nt}\}, where $A \in \mathbb
Z^{(r+sn)\times nt} consists of $n$ arbitrary matrices $A^{(i)} \in \mathbb
Z^{r\times t}$ on a horizontal, and $n$ arbitrary matrices $B^{(j)} \in \mathbb
Z^{s\times t} on a diagonal line. Several recent works design fixed-parameter
algorithms for $N$-fold IPs by taking as parameters the numbers of rows and
columns of the $A$- and $B$-matrices, together with the largest absolute value
$\Delta$ over their entries. These advances provide fast algorithms for several
well-studied combinatorial optimization problems on strings, on graphs, and in
machine scheduling. In this work, we extend this research by proposing
algorithms that additionally harness a partition structure of submatrices
$A^{(i)}$ and $B^{(j)}$, where row indices of non-zero entries do not overlap
between any two sets in the partition. Our main result is an algorithm for
solving any $N$-fold IP in time $nt
log(nt)L^2(S_A)^{O(r+s)}(p_Ap_B\Delta)^{O(rp_Ap_B+sp_Ap_B)}$, where $p_A$ and
$p_B$ are the size of the largest set in such a partition of $A^{(i)}$ and
$B^{(j)}$, respectively, $S_A$ is the number of parts in the partition of $A =
(A^{(1)},..., A^{(n)}), and $L = (log(||u - \ell||_\infty)\cdot
(log(max_{x:\ell \leq x \leq u} |c^Tx|))$ is a measure of the input. We show
that these new structural parameters are naturally small in high-multiplicity
scheduling problems, such as makespan minimization on related and unrelated
machines, with and without release times, the Santa Claus objective, and the
weighted sum of completion times. In essence, we obtain algorithms that are
exponentially faster than previous works by Knop et al. (ESA 2017) and
Eisenbrand et al./Kouteck{\'y} et al. (ICALP 2018) in terms of the number of
job types.


title: Creating Speech-to-Speech Corpus from Dubbed Series

abstract:
  Dubbed series are gaining a lot of popularity in recent years with strong
support from major media service providers. Such popularity is fueled by
studies that showed that dubbed versions of TV shows are more popular than
their subtitled equivalents. We propose an unsupervised approach to construct
speech-to-speech corpus, aligned on short segment levels, to produce a parallel
speech corpus in the source- and target- languages. Our methodology exploits
video frames, speech recognition, machine translation, and noisy frames removal
algorithms to match segments in both languages. To verify the performance of
the proposed method, we apply it on long and short dubbed clips. Out of 36
hours TR-AR dubbed series, our pipeline was able to generate 17 hours of paired
segments, which is about 47% of the corpus. We applied our method on another
language pair, EN-AR, to ensure it is robust enough and not tuned for a
specific language or a specific corpus. Regardless of the language pairs, the
accuracy of the paired segments was around 70% when evaluated using human
subjective evaluation. The corpus will be freely available for the research
community.


title: Using Timeliness in Tracking Infections

abstract:
  We consider real-time timely tracking of infection status (e.g., covid-19) of
individuals in a population. In this work, a health care provider wants to
detect infected people as well as people who have recovered from the disease as
quickly as possible. In order to measure the timeliness of the tracking
process, we use the long-term average difference between the actual infection
status of the people and their real-time estimate by the health care provider
based on the most recent test results. We first find an analytical expression
for this average difference for given test rates, infection rates and recovery
rates of people. Next, we propose an alternating minimization based algorithm
to find the test rates that minimize the average difference. We observe that if
the total test rate is limited, instead of testing all members of the
population equally, only a portion of the population may be tested in unequal
rates calculated based on their infection and recovery rates. Next, we
characterize the average difference when the test measurements are erroneous
(i.e., noisy). Further, we consider the case where the infection status of
individuals may be dependent, which happens when an infected person spreads the
disease to another person if they are not detected and isolated by the health
care provider. Then, we consider an age of incorrect information based error
metric where the staleness metric increases linearly over time as long as the
health care provider does not detect the changes in the infection status of the
people. In numerical results, we observe that an increased population size
increases diversity of people with different infection and recovery rates which
may be exploited to spend testing capacity more efficiently. Depending on the
health care provider's preferences, test rate allocation can be adjusted to
detect either the infected people or the recovered people more quickly.


title:
Surface Eigenvalues with Lattice-Based Approximation In comparison with
  analytical solution

abstract:
  In this paper, we propose a meshless method of computing eigenvalues and
eigenfunctions of a given surface embedded in $\mathbb R^3$. We use point cloud
data as input and generate the lattice approximation for some neighborhood of
the surface. We compute the eigenvalues and eigenvectors of the cubic lattice
graph as an approximation of the eigenvalues and eigenfunctions of the
Laplace-Beltrami operator on the surface. We perform extensive numerical
experiments on surfaces with various topology and compare our computed
eigenvalues from point cloud surface with exact solutions and standard finite
element methods using triangle mesh.


title:
Differential Privacy Amplification in Quantum and Quantum-inspired
  Algorithms

abstract:
  Differential privacy provides a theoretical framework for processing a
dataset about $n$ users, in a way that the output reveals a minimal information
about any single user. Such notion of privacy is usually ensured by
noise-adding mechanisms and amplified by several processes, including
subsampling, shuffling, iteration, mixing and diffusion. In this work, we
provide privacy amplification bounds for quantum and quantum-inspired
algorithms. In particular, we show for the first time, that algorithms running
on quantum encoding of a classical dataset or the outcomes of quantum-inspired
classical sampling, amplify differential privacy. Moreover, we prove that a
quantum version of differential privacy is amplified by the composition of
quantum channels, provided that they satisfy some mixing conditions.