title:
Compatible group topologies on a locally quasi-convex abelian group and
  the Mackey group problem

abstract:
  For a locally quasi-convex (lqc) abelian group $G$, we give the first
description of all compatible group topologies on $G$ and apply this result to
the Mackey group problem for lqc groups. We characterize lqc abelian groups
which are Mackey groups or admit a Mackey group topology and provide a
characterization of two Mackey groups whose product is Mackey. We obtain the
first characterization of locally convex spaces which are Mackey groups.


title:
Deep neural network goes lighter: A case study of deep compression
  techniques on automatic RF modulation recognition for Beyond 5G networks

abstract:
  Automatic RF modulation recognition is a primary signal intelligence (SIGINT)
technique that serves as a physical layer authentication enabler and automated
signal processing scheme for the beyond 5G and military networks. Most existing
works rely on adopting deep neural network architectures to enable RF
modulation recognition. The application of deep compression for the wireless
domain, especially automatic RF modulation classification, is still in its
infancy. Lightweight neural networks are key to sustain edge computation
capability on resource-constrained platforms. In this letter, we provide an
in-depth view of the state-of-the-art deep compression and acceleration
techniques with an emphasis on edge deployment for beyond 5G networks. Finally,
we present an extensive analysis of the representative acceleration approaches
as a case study on automatic radar modulation classification and evaluate them
in terms of the computational metrics.


title:
MINER: Improving Out-of-Vocabulary Named Entity Recognition from an
  Information Theoretic Perspective

abstract:
  NER model has achieved promising performance on standard NER benchmarks.
However, recent studies show that previous approaches may over-rely on entity
mention information, resulting in poor performance on out-of-vocabulary (OOV)
entity recognition. In this work, we propose MINER, a novel NER learning
framework, to remedy this issue from an information-theoretic perspective. The
proposed approach contains two mutual information-based training objectives: i)
generalizing information maximization, which enhances representation via deep
understanding of context and entity surface forms; ii) superfluous information
minimization, which discourages representation from rote memorizing entity
names or exploiting biased cues in data. Experiments on various settings and
datasets demonstrate that it achieves better performance in predicting OOV
entities.


title: Contrastive Demonstration Tuning for Pre-trained Language Models

abstract:
  Pretrained language models can be effectively stimulated by textual prompts
or demonstrations, especially in low-data scenarios. Recent works have focused
on automatically searching discrete or continuous prompts or optimized
verbalizers, yet studies for the demonstration are still limited. Concretely,
the demonstration examples are crucial for an excellent final performance of
prompt-tuning. In this paper, we propose a novel pluggable, extensible, and
efficient approach named contrastive demonstration tuning, which is free of
demonstration sampling. Furthermore, the proposed approach can be: (i) Plugged
to any previous prompt-tuning approaches; (ii) Extended to widespread
classification tasks with a large number of categories. Experimental results on
16 datasets illustrate that our method integrated with previous approaches
LM-BFF and P-tuning can yield better performance. Code is available in
https://github.com/zjunlp/PromptKG/tree/main/research/Demo-Tuning.


title:
Star-Convex Constrained Optimization for Visibility Planning with
  Application to Aerial Inspection

abstract:
  The visible capability is critical in many robot applications, such as
inspection and surveillance, etc. Without the assurance of the visibility to
targets, some tasks end up not being complete or even failing. In this paper,
we propose a visibility guaranteed planner by star-convex constrained
optimization. The visible space is modeled as star convex polytope (SCP) by
nature and is generated by finding the visible points directly on point cloud.
By exploiting the properties of the SCP, the visibility constraint is
formulated for trajectory optimization. The trajectory is confined in the safe
and visible flight corridor which consists of convex polytopes and SCPs. We
further make a relaxation to the visibility constraints and transform the
constrained trajectory optimization problem into an unconstrained one that can
be reliably and efficiently solved. To validate the capability of the proposed
planner, we present the practical application in site inspection. The
experimental results show that the method is efficient, scalable, and
visibility guaranteed, presenting the prospect of application to various other
applications in the future.


title:
Generalised Mathematical Formulations for Non-Linear Optimized
  Scheduling

abstract:
  In practice, most of the optimization problems are non-linear requiring
certain interactive solutions and approaches to model. In 5G Advanced and
Beyond network slicing, mathematically modeling the users, type of service
distributions and its adaptive SLAs are complex due to several dependencies. To
facilitate the above, in this paper, we present novel Non-linear mathematical
formulations and results that will form the base to achieve Optimized
Scheduling.


title:
A Machine Learning-Based Method for Identifying Critical Distance Relays
  for Transient Stability Studies

abstract:
  Modeling protective relays is crucial for performing accurate stability
studies as they play a critical role in defining the dynamic responses of power
systems during disturbances. Nevertheless, due to the current limitations of
stability software and the challenges of keeping track of the changes in the
settings information of thousands of protective relays, modeling all the
protective relays in bulk power systems is a challenging task. Distance relays
are among the critical protection schemes, which are not properly modeled in
current practices of stability studies. This paper proposes a machine
learning-based method that uses the results of early-terminated stability
studies to identify the critical distance relays required to be modeled in
those studies. The algorithm used is the random forest (RF) classifier. GE
positive sequence load flow analysis (PSLF) software is used to perform
stability studies. The model is trained and tested on the Western Electricity
Coordinating Council (WECC) system data representing the 2018 summer peak load
under different operating conditions and topologies of the system. The results
show the great performance of the method in identifying the critical distance
relays. The results also show that only modeling the identified critical
distance relays suffices to perform accurate stability studies.


title:
Local Knowledge and Natural Resource Management in a Peasant Farming
  Community Facing Rapid Change: A Critical Examination

abstract:
  Environmental degradation is a major global problem. Its impacts are not just
environmental, but also economic, with degradation recognised as a key cause of
reduced agricultural productivity and rural poverty in the developing world.
The degradation literature typically emphasises common property or open access
natural resources, and how perverse incentives or missing institutions lead
optimising private actors to degrade them. By contrast, the present paper
considers degradation occurring on private farms in peasant communities. This
is a critical yet delicate issue, given the poverty of such areas and questions
about the role of farmers in either degrading or regenerating rural lands. The
paper examines natural resource management by peasant farmers in Tanzania. Its
key concern is how the local knowledge informing their management decisions
adapts to challenges associated with environmental degradation and market
liberalisation. Given their poverty, this question could have direct
implications for the capacity of households to meet their livelihood needs.
Based on fresh empirical data, the paper finds that differential farmer
knowledge helps explain the large differences in how households respond to the
degradation challenge. The implication is that some farmers adapt more
effectively to emerging challenges than others, despite all being rational,
optimising agents who follow the strategies they deem best. The paper thus
provides a critique of local knowledge, implying that some farmers experience
adaptation slippages while others race ahead with effective adaptations. The
paper speaks to the chronic poverty that plagues many rural communities in the
developing world. It helps explain the failure of proven sustainable
agriculture technologies to disseminate readily beyond early innovators. Its
key policy implication is to inform improved capacity building for such
communities.


title:
Denoising Neural Network for News Recommendation with Positive and
  Negative Implicit Feedback

abstract:
  News recommendation is different from movie or e-commercial recommendation as
people usually do not grade the news. Therefore, user feedback for news is
always implicit (click behavior, reading time, etc). Inevitably, there are
noises in implicit feedback. On one hand, the user may exit immediately after
clicking the news as he dislikes the news content, leaving the noise in his
positive implicit feedback; on the other hand, the user may be recommended
multiple interesting news at the same time and only click one of them,
producing the noise in his negative implicit feedback. Opposite implicit
feedback could construct more integrated user preferences and help each other
to minimize the noise influence. Previous works on news recommendation only
used positive implicit feedback and suffered from the noise impact. In this
paper, we propose a denoising neural network for news recommendation with
positive and negative implicit feedback, named DRPN. DRPN utilizes both
feedback for recommendation with a module to denoise both positive and negative
implicit feedback to further enhance the performance. Experiments on the
real-world large-scale dataset demonstrate the state-of-the-art performance of
DRPN.


title: Stable categories of spherical modules and torsionfree modules

abstract:
  Auslander and Bridger introduced the notions of n-spherical modules and
n-torsionfree modules. In this paper, we construct an equivalence between the
stable category of n-spherical modules and the category of modules of grade at
least n, and provide its Gorenstein analogue. As an application, we prove that
if R is a Gorenstein local ring of Krull dimension d>0, then there exists a
stable equivalence between the category of (d-1)-torsionfree R-modules and the
category of d-spherical modules relative to the local cohomology functor.


title:
Crime Patterns in Los Angeles County Before and After Covid19
  (2018-2021)

abstract:
  The objective of our research is to present the change in crime rates in Los
Angeles post-Covid19. Using data analysis with Geo-Mapping, bubbles, Marimekko,
and a time series charts, we can illustrate which areas have the largest crime
rate, and how it has changed. Through regression modeling, we can interpret
which locations may also have a correlation to crime versus income, race, type
of crime, and gender. The story will help to uncover whether the areas
associated with crime are due to demographic or income variance. In showing the
details of crimes in Los Angeles along with the factors at play we hope to see
a compelling relationship between crime rates and recent events from 2020 to
the present, along with changes in crime type trends during these periods. We
use Excel to clean the data for SAP SAC to model effectively, as well as
resources from other studies a comparison.


title: Deformations of log terminal and semi log canonical singularities

abstract:
  In this paper, we prove that klt singularities are invariant under a
deformation over a (not necessarily one-dimensional) smooth base if the generic
fiber is $\mathbb{Q}$-Gorenstein. We obtain a similar result for slc
singularities when the base is one-dimensional. These are generalizations of
results of Esnault-Viehweg and S. Ishii.


title: Quantum convolution inequalities on Frobenius von Neumann algebras

abstract:
  In this paper, we introduce Frobenius von Neumann algebras and study quantum
convolution inequalities. In this framework, we unify quantum Young's
inequality on quantum symmetries such as subfactors, and fusion bi-algebras
studied in quantum Fourier analysis. Moreover, we prove quantum entropic
convolution inequalities and characterize the extremizers in the subfactor
case. We also prove quantum smooth entropic convolution inequalities. We obtain
the positivity of comultiplications of subfactor planar algebras, which is
stronger than the quantum Schur product theorem. All these inequalities provide
analytic obstructions of unitary categorification of fusion rings stronger than
Schur product criterion.


title:
Unusual Magnetotransport from two dimensional Dirac Fermions in
  Pd$_{3}$Bi$_{2}$Se$_{2}$

abstract:
  Pd$_{3}$Bi$_{2}$Se$_{2}$ has been proposed to be topologically non-trivial in
nature. However, evidence of its non-trivial behavior is still unexplored. We
report the growth and magneto-transport study of Pd$_{3}$Bi$_{2}$Se$_{2}$ thin
films, revealing for the first time the contribution of two-dimensional (2D)
topological surface states. We observe exceptional non-saturated linear
magnetoresistance which results from Dirac fermions inhabiting the lowest
Landau level in the quantum limit. The transverse magnetoresistance changes
from a semi-classical weak-field $B^{2}$ dependence to a high-field $B$
dependence at a critical field $B^{\star}$. It is found that $B^{\star} \propto
T^2$, which is expected from the Landau level splitting of a linear energy
dispersion. In addition, the magnetoconductivity shows signatures of 2D weak
anti-localization (WAL). These novel magnetotransport signatures evince the
presence of 2D Dirac fermions in Pd$_{3}$Bi$_{2}$Se$_{2}$ thin films.


title:
Improve Generalization of Driving Policy at Signalized Intersections
  with Adversarial Learning

abstract:
  Intersections are quite challenging among various driving scenes wherein the
interaction of signal lights and distinct traffic actors poses great difficulty
to learn a wise and robust driving policy. Current research rarely considers
the diversity of intersections and stochastic behaviors of traffic
participants. For practical applications, the randomness usually leads to some
devastating events, which should be the focus of autonomous driving. This paper
introduces an adversarial learning paradigm to boost the intelligence and
robustness of driving policy for signalized intersections with dense traffic
flow. Firstly, we design a static path planner which is capable of generating
trackable candidate paths for multiple intersections with diversified topology.
Next, a constrained optimal control problem (COCP) is built based on these
candidate paths wherein the bounded uncertainty of dynamic models is considered
to capture the randomness of driving environment. We propose adversarial policy
gradient (APG) to solve the COCP wherein the adversarial policy is introduced
to provide disturbances by seeking the most severe uncertainty while the
driving policy learns to handle this situation by competition. Finally, a
comprehensive system is established to conduct training and testing wherein the
perception module is introduced and the human experience is incorporated to
solve the yellow light dilemma. Experiments indicate that the trained policy
can handle the signal lights flexibly meanwhile realizing the smooth and
efficient passing with a humanoid paradigm. Besides, APG enables a large-margin
improvement of the resistance to the abnormal behaviors and thus ensures a high
safety level for the autonomous vehicle.


title:
Directly Detecting the Envelopes of Low-mass Planets Embedded In
  Protoplanetary discs and The Case For TW Hydrae

abstract:
  Despite that many methods have been developed to find young massive planets
in protoplanetary discs, it is still challenging to directly detect low-mass
planets that are embedded in discs. On the other hand, the core-accretion
theory suggests that there could be a large population of embedded low-mass
young planets at the Kelvin-Helmholtz (KH) contraction phase. We adopt both 1-D
models and 3-D simulations to calculate the envelope structure around low-mass
cores (several to tens of $M_{\oplus}$) with different luminosities, and derive
their thermal fluxes at radio wavelengths. We find that, when the disc is
optically thin at these wavelengths, the observation can see through the disc
and probe the denser envelope within the planet's Hill sphere. When the
optically thin disc is observed with the resolution reaching one disc scale
height, the radio thermal flux from the planetary envelope around a 10
M$_{\oplus}$ core is more than 10\% higher than the flux from the background
disc. The emitting region can be extended and elongated. Finally, our model
suggests that the au-scale excess detected by ALMA at 52 au in TW Hydrae disc
is consistent with the envelope of an embedded 10-20 $M_{\oplus}$ planet, which
can explain the detected flux, the spectral index dip, and the tentative
spirals. The observation is also consistent with the planet undergoing pebble
accretion. Future ALMA and ngVLA observations may directly reveal more such
low-mass planets, enabling us to study core growth and even reconstruct the
planet formation history using the "protoplanet" population.


title: Finding the ET Signal from the Cosmic Noise

abstract:
  This paper highlights a methodological approach designed to enhance the
search for extraterrestrial intelligence (SETI) by hypothesizing that a
transmission technosignature would likely have two features: 1) be wideband in
the microwave or higher frequency range that originates from a hub within a
supposed ET interplanetary navigation/communication (nav/comm) network, and 2)
contain x-ray pulsar-based navigation (XNAV) metadata.
  Potential contributions to the field include improved accuracy in finding
transmission technosignatures and other technosignatures in the electromagnetic
spectrum, a common standard in reaching a Schelling Point (a mutual realization
of how we and ETs can find each other), and operationalizing models such as the
Drake Equation.


title:
Dispersionless version of the constrained Toda hierarchy and symmetric
  radial L\"owner equation

abstract:
  We study the dispersionless version of the recently introduced constrained
Toda hierarchy. Like the Toda lattice itself, it admits three equivalent
formulations: the formulation in terms of Lax equations, the formulation of the
Zakharov-Shabat type and the formulation through the generating equation for
the dispersionless limit of logarithm of the tau-function. We show that the
dispersionless constrained Toda hierarchy describes conformal maps of
reflection-symmetric planar domains to the exterior of the unit disc. We also
find finite-dimensional reductions of the hierarchy and show that they are
characterized by a differential equation of the L\"owner type which we call the
symmetric radial L\"owner equation. It is also shown that solutions to the
symmetric radial L\"owner equation are conformal maps of the exterior of the
unit circle with two symmetric slits to the exterior of the unit circle.


title: Characters, Commutators and Centers of Sylow Subgroups

abstract:
  The character table of a finite group G determines whether |P:P'|=p^2 and
whether |P:Z(P)|=p^2, where P is a Sylow p-subgroup of G. To prove the latter,
we give a detailed classification of those groups in terms of the generalized
Fitting subgroup.


title:
A Probabilistic Model-Based Robust Waveform Design for MIMO Radar
  Detection

abstract:
  This paper addresses robust waveform design for
multiple-input-multiple-output (MIMO) radar detection. A probabilistic model is
proposed to describe the target uncertainty. Considering that waveform design
based on maximizing the probability of detection is intractable, the relative
entropy between the distributions of the observations under two hypotheses
(viz., the target is present/absent) is employed as the design metric. To
tackle the resulting non-convex optimization problem, an efficient algorithm
based on minorization-maximization (MM) is derived. Numerical results
demonstrate that the waveform synthesized by the proposed algorithm is more
robust to model mismatches.


title: Convergence in conformal field theory

abstract:
  Convergence and analytic extension are of fundamental importance in the
mathematical construction and study of conformal field theory. We review some
main convergence results, conjectures and problems in the construction and
study of conformal field theories using the representation theory of vertex
operator algebras. We also review the related analytic extension results,
conjectures and problems. We discuss the convergence and analytic extensions of
products of intertwining operators (chiral conformal fields) and of $q$-traces
and pseudo-$q$-traces of products of intertwining operators. We also discuss
the convergence results related to the sewing operation and the determinant
line bundle and a higher-genus convergence result. We then explain conjectures
and problems on the convergence and analytic extensions in orbifold conformal
field theory and in the cohomology theory of vertex operator algebras.


title: Thermal and magnetic field structure of near equatorial coronal holes

abstract:
  We use full-disk, SOHO/EIT 195 $\AA$ calibrated images to measure latitudinal
and day to day variations of area and average photon fluxes of the near
equatorial coronal holes. In addition, energy emitted by the coronal holes with
their temperature and strength of magnetic field structures are estimated. By
analyzing data of 2001-2008, we find that variations of average area (A),
photon flux (F), radiative energy (E) and temperature (T) of coronal holes are
independent of latitude. Whereas inferred strength of magnetic field structure
of the coronal holes is dependent on the latitudes and varies from low near the
equator to high near both the poles. Typical average values of estimated
physical parameters are: $A \sim 3.8(\pm0.5)\times10^{20}~cm^{2}, F \sim
2.3(\pm0.2)\times10^{13}~photons\;cm^{-2}\;sec^{-1}, E \sim 2.32(\pm0.5)\times
10^{3}~ergscm^{-2}sec^{-1} \ and \ T \sim 0.94(\pm0.1)\times10^{6} ~$ K.
Average strength of magnetic field structure of coronal hole at the corona is
estimated to be $\sim$ $0.08 \pm 0.02$ Gauss. If coronal holes are anchored in
the convection zone, one would expect they should rotate differentially. Hence,
thermal wind balance and isorotation of coronal holes with the solar plasma
implies the temperature difference between the equator and both the poles.
Contrary to this fact, variation of thermal structure of near equatorial
coronal holes is independent of latitude leading to a conclusion that coronal
holes must rotate rigidly that are likely to be anchored initially below the
tachocline confirming our previous study (ApJ, 763, 137, 2013).


title:
Crossover in dynamics in the Kob-Andersen binary mixture glass-forming
  liquid

abstract:
  Glass-forming liquids are broadly classified as being fragile or strong,
depending on the deviation from Arrhenius behavior of their relaxation times. A
fragile to strong crossover is observed or inferred in liquids like water and
silica, and more recently also in metallic glasses and phase change alloys,
leading to the expectation that such a crossover is more widely realised among
glass formers. We investigate computationally the well-studied Kob-Andersen
model, accessing temperatures well below the mode coupling temperature
$T_{MCT}$. We find that relaxation times exhibit a crossover in dynamics around
$T_{MCT}$, and discuss whether it bears characteristics of the fragile to
strong crossover. Several aspects of dynamical heterogeneity exhibit behavior
mirroring the dynamical crossover, whereas thermodynamic quantities do not. In
particular, the Adam-Gibbs relation describing the relation between relaxation
times and configurational entropy continues to hold below the dynamical
crossover, when anharmonic corrections to the vibrational entropy are included.


title:
Leaderless Swarm Formation Control: From Global Specifications to Local
  Control Laws

abstract:
  This paper introduces a distributed leaderless swarm formation control
framework to address the problem of collectively driving a swarm of robots to
track a time-varying formation. The swarm's formation is captured by the
trajectory of an abstract shape that circumscribes the convex hull of robots'
positions and is independent of the number of robots and their ordering in the
swarm. For each robot in the swarm, given global specifications in terms of the
trajectory of the abstract shape parameters, the proposed framework synthesizes
a control law that steers the swarm to track the desired formation using the
information available at the robot's local neighbors. For this purpose, we
generate a suitable local reference trajectory that the robot controller tracks
by solving the input-output linearization problem. Here, we select the swarm
output to be the parameters of the abstract shape. For this purpose, we design
a dynamic average consensus estimator to estimate the abstract shape
parameters. The abstract shape parameters are used as the swarm state feedback
to generate a suitable robot trajectory. We demonstrate the effectiveness and
robustness of the proposed control framework by providing the simulation of
coordinated collective navigation of a group of car-like robots in the presence
of robots and communication link failures.


title: PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization

abstract:
  Few-shot abstractive summarization has become a challenging task in natural
language generation. To support it, we designed a novel soft prompts
architecture coupled with a prompt pre-training plus fine-tuning paradigm that
is effective and tunes only extremely light parameters. The soft prompts
include continuous input embeddings across an encoder and a decoder to fit the
structure of the generation models. Importantly, a novel inner-prompt placed in
the text is introduced to capture document-level information. The aim is to
devote attention to understanding the document that better prompts the model to
generate document-related content. The first step in the summarization
procedure is to conduct prompt pre-training with self-supervised pseudo-data.
This teaches the model basic summarizing capabilities. The model is then
fine-tuned with few-shot examples. Experimental results on the CNN/DailyMail
and XSum datasets show that our method, with only 0.1% of the parameters,
outperforms full-model tuning where all model parameters are tuned. It also
surpasses Prompt Tuning by a large margin and delivers competitive results
against Prefix-Tuning with 3% of the parameters.


title: Lions' representation theorem and applications

abstract:
  The Representation Theorem of Lions (RTL) is a version of the Lax--Milgram
Theorem where completeness of one of the spaces is not complete. In this paper,
RTL is deduced from an operator-theoretical version on normed space. The main
point of the paper is a theory of derivations, based on RTL, for which
well-posedness is proved. One application concerns non-autonomous evolution
equations with a new initial-value and a periodic boundary condition for the
time variable.


title:
Robust Dynamic Average Consensus for a Network of Agents with
  Time-varying Reference Signals

abstract:
  This paper presents continuous dynamic average consensus (DAC) algorithms for
a group of agents to estimate the average of their time-varying reference
signals cooperatively. We propose consensus algorithms that are robust to
agents joining and leaving the network, at the same time, avoid the chattering
phenomena and guarantee zero steady-state consensus error. Our algorithms are
edge-based protocols with smooth functions in their internal structure to avoid
the chattering effect. Furthermore, each agent is only capable of performing
local computations and can only communicate with its local neighbors. For a
balanced and strongly connected underlying communication graph, we provide the
convergence analysis to determine the consensus design parameters that
guarantee the agents' estimate of their average to asymptotically converge to
the average of the time-varying reference signals of the agents. We provide
simulation results to validate the proposed consensus algorithms and to perform
a performance comparison of the proposed algorithms to existing algorithms in
the literature.


title: E^2TAD: An Energy-Efficient Tracking-based Action Detector

abstract:
  Video action detection (spatio-temporal action localization) is usually the
starting point for human-centric intelligent analysis of videos nowadays. It
has high practical impacts for many applications across robotics, security,
healthcare, etc. The two-stage paradigm of Faster R-CNN inspires a standard
paradigm of video action detection in object detection, i.e., firstly
generating person proposals and then classifying their actions. However, none
of the existing solutions could provide fine-grained action detection to the
"who-when-where-what" level. This paper presents a tracking-based solution to
accurately and efficiently localize predefined key actions spatially (by
predicting the associated target IDs and locations) and temporally (by
predicting the time in exact frame indices). This solution won first place in
the UAV-Video Track of 2021 Low-Power Computer Vision Challenge (LPCVC).


title: Gamma-Ray Bursts

abstract:
  Gamma-ray bursts (GRB) are short and intense bursts of $\sim$100 keV$-$1MeV
photons, usually followed by long-lasting decaying afterglow emission in a wide
range of electromagnetic wavelengths from radio to X-ray and, sometimes, even
to GeV gamma-rays. These emissions are believed to originate from a
relativistic jet, which is driven due to the collapse of special massive stars
and the mergers of compact binaries (i.e., double neutron stars or a neutron
star and a black hole). This chapter first briefly introduces the basic
observational facts of the GRB phenomena, including the prompt emission,
afterglow emission, and host galaxies. Secondly, a general theoretical
understanding of the GRB phenomena is described based on a relativistic jet's
overall dynamical evolution, including the acceleration, propagation, internal
dissipation, and deceleration phases. Here a long-lasting central engine of the
GRBs can substantially influence the dynamical evolution of the jet. In
addition, a supernova/kilonova emission can appear in the optical afterglow of
some nearby GRBs, which can provide an important probe to the nature of the GRB
progenitors. Finally, as luminous cosmological phenomena, it is expected to use
GRBs to probe the early universe and to constrain the cosmological parameters.


title: Energies of Two-State Systems

abstract:
  Energies of quantum states are given by the arguments of phase-evolution
exponentials. It follows then that an analysis of the energies of a two-state
system (TSS) can revolve around phase-emphasized description of states'
probability amplitudes in the Schrodinger picture. Here, studying energies of
TSSs semi-classically, we suggest an energy-revealing format in which the
time-dependence of the probability amplitudes is expressed by phase-evolution
factors only. With this fresh energy-studying approach, we first revisit
non-driven TSSs, write the conditions for setting a system (in general) in a
stationary state, and identify the associated (single) definite energy. Then,
more importantly, we revisit driven-TSSs, identify the two stationary states
and prove the existence of two quasi-energies associated with each stationary
state. Resulting from our phase-keeping framework, we display a
"breathing-spiral" type precession-mode of a strongly-driven spin one-half TSS.
Further, we exemplify our findings through a set of two coupling-probe computer
simulations. For specific examples, we compute and list the energies of several
concrete TSSs under typical operating conditions.


title:
Mapping Temporary Slums from Satellite Imagery using a Semi-Supervised
  Approach

abstract:
  One billion people worldwide are estimated to be living in slums, and
documenting and analyzing these regions is a challenging task. As compared to
regular slums; the small, scattered and temporary nature of temporary slums
makes data collection and labeling tedious and time-consuming. To tackle this
challenging problem of temporary slums detection, we present a semi-supervised
deep learning segmentation-based approach; with the strategy to detect initial
seed images in the zero-labeled data settings. A small set of seed samples (32
in our case) are automatically discovered by analyzing the temporal changes,
which are manually labeled to train a segmentation and representation learning
module. The segmentation module gathers high dimensional image representations,
and the representation learning module transforms image representations into
embedding vectors. After that, a scoring module uses the embedding vectors to
sample images from a large pool of unlabeled images and generates pseudo-labels
for the sampled images. These sampled images with their pseudo-labels are added
to the training set to update the segmentation and representation learning
modules iteratively. To analyze the effectiveness of our technique, we
construct a large geographically marked dataset of temporary slums. This
dataset constitutes more than 200 potential temporary slum locations (2.28
square kilometers) found by sieving sixty-eight thousand images from 12
metropolitan cities of Pakistan covering 8000 square kilometers. Furthermore,
our proposed method outperforms several competitive semi-supervised semantic
segmentation baselines on a similar setting. The code and the dataset will be
made publicly available.


title:
Investigating Deep Learning Benchmarks for Electrocardiography Signal
  Processing

abstract:
  In recent years, deep learning has witnessed its blossom in the field of
Electrocardiography (ECG) processing, outperforming traditional signal
processing methods in various tasks, for example, classification, QRS
detection, wave delineation. Although many neural architectures have been
proposed in the literature, there is a lack of systematic studies and
open-source libraries for ECG deep learning.
  In this paper, we propose a deep learning framework, named
\texttt{torch\_ecg}, which gathers a large number of neural networks, both from
literature and novel, for various ECG processing tasks. It establishes a
convenient and modular way for automatic building and flexible scaling of the
networks, as well as a neat and uniform way of organizing the preprocessing
procedures and augmentation techniques for preparing the input data for the
models. Besides, \texttt{torch\_ecg} provides benchmark studies using the
latest databases, illustrating the principles and pipelines for solving ECG
processing tasks and reproducing results from the literature.
\texttt{torch\_ecg} offers the ECG research community a powerful tool meeting
the growing demand for the application of deep learning techniques.


title: Unbiased Directed Object Attention Graph for Object Navigation

abstract:
  Object navigation tasks require agents to locate specific objects in unknown
environments based on visual information. Previously, graph convolutions were
used to implicitly explore the relationships between objects. However, due to
differences in visibility among objects, it is easy to generate biases in
object attention. Thus, in this paper, we propose a directed object attention
(DOA) graph to guide the agent in explicitly learning the attention
relationships between objects, thereby reducing the object attention bias. In
particular, we use the DOA graph to perform unbiased adaptive object attention
(UAOA) on the object features and unbiased adaptive image attention (UAIA) on
the raw images, respectively. To distinguish features in different branches, a
concise adaptive branch energy distribution (ABED) method is proposed. We
assess our methods on the AI2-Thor dataset. Compared with the state-of-the-art
(SOTA) method, our method reports 7.4%, 8.1% and 17.6% increase in success rate
(SR), success weighted by path length (SPL) and success weighted by action
efficiency (SAE), respectively.


title: Reduction ratio of the IS-algorithm: worst and random cases

abstract:
  We study the IS-algorithm, a well-known linear-time algorithm for computing
the suffix array of a word. This algorithm relies on transforming the input
word $w$ into another word, called the reduced word of $w$, that will be at
least twice shorter; then, the algorithm recursively computes the suffix array
of the reduced word. In this article, we study the reduction ratio of the
IS-algorithm, i.e., the ratio between the lengths of the input word and the
word obtained after reducing $k$ times the input word. We investigate both
worst cases, in which we find precise results, and random cases, where we prove
some strong convergence phenomena. Finally, we prove that, if the input word is
a randomly chosen word of length $n$, we should not expect much more than
$\log(\log(n))$ recursive function calls.


title:
Revisiting the Effect of Branch Handling Strategies on Change
  Recommendation

abstract:
  Although literature has noted the effects of branch handling strategies on
change recommendation based on evolutionary coupling, they have been tested in
a limited experimental setting. Additionally, the branches characteristics that
lead to these effects have not been investigated. In this study, we revisited
the investigation conducted by Kovalenko et al. on the effect to change
recommendation using two different branch handling strategies: including
changesets from commits on a branch and excluding them. In addition to the
setting by Kovalenko et al., we introduced another setting to compare:
extracting a changeset for a branch from a merge commit at once. We compared
the change recommendation results and the similarity of the extracted
co-changes to those in the future obtained using two strategies through 30
open-source software systems. The results show that handling commits on a
branch separately is often more appropriate in change recommendation, although
the comparison in an additional setting resulted in a balanced performance
among the branch handling strategies. Additionally, we found that the merge
commit size and the branch length positively influence the change
recommendation results.


title:
Adaptive Differential Filters for Fast and Communication-Efficient
  Federated Learning

abstract:
  Federated learning (FL) scenarios inherently generate a large communication
overhead by frequently transmitting neural network updates between clients and
server. To minimize the communication cost, introducing sparsity in conjunction
with differential updates is a commonly used technique. However, sparse model
updates can slow down convergence speed or unintentionally skip certain update
aspects, e.g., learned features, if error accumulation is not properly
addressed. In this work, we propose a new scaling method operating at the
granularity of convolutional filters which 1) compensates for highly sparse
updates in FL processes, 2) adapts the local models to new data domains by
enhancing some features in the filter space while diminishing others and 3)
motivates extra sparsity in updates and thus achieves higher compression
ratios, i.e., savings in the overall data transfer. Compared to unscaled
updates and previous work, experimental results on different computer vision
tasks (Pascal VOC, CIFAR10, Chest X-Ray) and neural networks (ResNets,
MobileNets, VGGs) in uni-, bidirectional and partial update FL settings show
that the proposed method improves the performance of the central server model
while converging faster and reducing the total amount of transmitted data by up
to 377 times.


title:
Congruence Relations Connecting Tate-Shafarevich Groups with
  Bernoulli-Hurwitz Numbers by Elliptic Gauss Sums in Eisenstein Case

abstract:
  There are classical congruences between the class number of an imaginary
quadratic field and a Bernoulli number or an Euler number. Under the BSD
conjecture, Onishi obtained an elliptic generalization of these congruences,
which gives congruences between the order of the Tate-Shafarevich group of
certain elliptic curves with CM by the Gauss integers ring and Mordell-Weil
rank 0, and a coefficient of power series expansion of an elliptic function
associated to Gauss integers ring. In this paper, we provide Onishi's type
congruences for the Eisenstein integers case.


title:
Effects of zigzag edge states on the thermoelectric properties of finite
  graphene nanoribbons

abstract:
  Thermoelectric properties of finite graphene nanoribbons (GNRs) coupled to
metallic electrodes are theoretically studied in the framework of tight-binding
model and Green's function approach. When the zigzag sides are coupled to the
electrodes, the electron transport through the localized edge states can occur
only if the channel length between electrodes is smaller than the decay length
of these localized zigzag edge states. When the armchair edges are coupled to
the electrodes, there is an interesting thermoelectric behavior associated with
the mid-gap states when the GNR is in the semiconducting phase. Here we show
that the thermoelectric behavior of zigzag edge states of GNRs with armchair
sides connected to electrodes is similar to that of two parallel quantum dots
with similar orbital degeneracy. Furthermore, it is demonstrated that the
electrical conductance and power factor given by the zigzag edge states are
quite robust against the defect scattering.


title:
Probing the electroweak symmetry breaking history with Gravitational
  waves

abstract:
  We perform a three dimensional lattice simulation of the electroweak symmetry
breaking process through a two-step phase transition, where one of the two
steps is a first order phase transition. Our results show that: 1) when the
electroweak symmetry breaking is driven by the beyond Standard Model sector
around $\sim \mathcal{O}(10^{2-3})$ GeV, the gravitational wave spectra
produced from the phase transitions are of broken power-law double-peak shapes;
2) when the electroweak symmetry breaking is induced by a first-order phase
transition of a high-scale global U(1) theory, cosmic strings can form and then
disappear through particle radiation, and the yielded gravitational wave
spectra are of plateau shapes. The two scenarios can be distinguished through
probing gravitational wave spectra. Our study suggests that the stochastic
gravitational waves provide an alternative way to probe the beyond Standard
Model sector relevant to the electroweak symmetry breaking pattern in the early
Universe.


title:
ManiTrans: Entity-Level Text-Guided Image Manipulation via Token-wise
  Semantic Alignment and Generation

abstract:
  Existing text-guided image manipulation methods aim to modify the appearance
of the image or to edit a few objects in a virtual or simple scenario, which is
far from practical application. In this work, we study a novel task on
text-guided image manipulation on the entity level in the real world. The task
imposes three basic requirements, (1) to edit the entity consistent with the
text descriptions, (2) to preserve the text-irrelevant regions, and (3) to
merge the manipulated entity into the image naturally. To this end, we propose
a new transformer-based framework based on the two-stage image synthesis
method, namely \textbf{ManiTrans}, which can not only edit the appearance of
entities but also generate new entities corresponding to the text guidance. Our
framework incorporates a semantic alignment module to locate the image regions
to be manipulated, and a semantic loss to help align the relationship between
the vision and language. We conduct extensive experiments on the real datasets,
CUB, Oxford, and COCO datasets to verify that our method can distinguish the
relevant and irrelevant regions and achieve more precise and flexible
manipulation compared with baseline methods. The project homepage is
\url{https://jawang19.github.io/manitrans}.


title: What is measured when measuring a thermoelectric coefficient?

abstract:
  A thermal gradient generates an electric field in any solid hosting mobile
electrons. In presence of a finite magnetic field (or Berry curvature) this
electric field has a transverse component. These are known as Seebeck and
Nernst coefficients. As Callen argued, back in 1948, the Seebeck effect
quantifies the entropy carried by a flow of charged particles in absence of
thermal gradient. Similarly, the Nernst conductivity, $\alpha_{xy}$, quantifies
the entropy carried by a flow of magnetic flux in absence of thermal gradient.
The present paper summarizes a picture in which the rough amplitude of the
thermoelectric response is given by fundamental units and material-dependent
length scales. Therefore, knowledge of material-dependent length scales allows
predicting the amplitude of the signal measured by experiments. Specifically,
the Nernst conductivity scales with the square of the mean-free-path in metals.
Its anomalous component in magnets scales with the square of the fictitious
magnetic length. Ephemeral Cooper pairs in the normal state of a superconductor
generate a signal, which scales with the square of the superconducting
coherence length and smoothly evolves to the signal produced by mobile vortices
below the critical temperature.


title:
CMOS Circuit Implementation of Spiking Neural Network for Pattern
  Recognition Using On-chip Unsupervised STDP Learning

abstract:
  Computation on a large volume of data at high speed and low power requires
energy-efficient computing architectures. Spiking neural network (SNN) with
bio-inspired spike-timing-dependent plasticity learning (STDP) is a promising
solution for energy-efficient neuromorphic systems than conventional artificial
neural network (ANN). Previous works on SNN with STDP learning primarily uses
memristive devices which are difficult to fabricate. Some reported works on SNN
makes use of memristor macro models, which are software-based and cannot give
complete insight into circuit implementation challenges. This article presents
for the first time, a full circuit-level implementation of the SNN system
featuring on-chip unsupervised STDP learning in standard CMOS technology. It
does not involve the use of FPGAs, CPUs or GPUs for training the neural
network. We demonstrated the complete circuit-level design, implementation and
simulation of SNN with on-chip training and inference for pattern
classification using 180 nm CMOS technology. A comprehensive comparison of the
proposed SNN circuit with the previous related work is also presented. To
demonstrate the versatility of the CMOS synapse circuit for application
scenarios requiring rate-based learning, we have tuned the pair-based STDP
circuit to obtain Bienenstock-Cooper-Munro (BCM) characteristics and applied it
to heart rate classification.


title: A Spiking Neural Network Structure Implementing Reinforcement Learning

abstract:
  At present, implementation of learning mechanisms in spiking neural networks
(SNN) cannot be considered as a solved scientific problem despite plenty of SNN
learning algorithms proposed. It is also true for SNN implementation of
reinforcement learning (RL), while RL is especially important for SNNs because
of its close relationship to the domains most promising from the viewpoint of
SNN application such as robotics. In the present paper, I describe an SNN
structure which, seemingly, can be used in wide range of RL tasks. The
distinctive feature of my approach is usage of only the spike forms of all
signals involved - sensory input streams, output signals sent to actuators and
reward/punishment signals. Besides that, selecting the neuron/plasticity
models, I was guided by the requirement that they should be easily implemented
on modern neurochips. The SNN structure considered in the paper includes
spiking neurons described by a generalization of the LIFAT (leaky
integrate-and-fire neuron with adaptive threshold) model and a simple spike
timing dependent synaptic plasticity model (a generalization of
dopamine-modulated plasticity). My concept is based on very general assumptions
about RL task characteristics and has no visible limitations on its
applicability. To test it, I selected a simple but non-trivial task of training
the network to keep a chaotically moving light spot in the view field of an
emulated DVS camera. Successful solution of this RL problem by the SNN
described can be considered as evidence in favor of efficiency of my approach.


title: Testing Screening Mechanisms with Mass Profiles of Galaxy Clusters

abstract:
  We present \textsc{MG-MAMPOSSt}, a license-free code to constrain modified
gravity models by reconstructing the mass profile of galaxy clusters with the
kinematics of the cluster's member galaxies. We describe the main features of
the code and we show the capability of the method when the kinematic
information is combined with lensing data. We discuss recent results and
forecasts on two classes of models currently implemented in the code,
characterized by different screening mechanisms, namely, chameleon and
Vainshtein screening. We further explore the impact of possible systematics in
view of application to the data from upcoming surveys. This proceedings
summarizes the results presented at the ALTECOSMOFUN workshop in September
2021.


title:
Search for lepton-flavor-violating decays of the tau lepton at a future
  muon collider

abstract:
  Tau leptons can have lepton-flavor-violating (LFV) couplings to a muon or an
electron and an Axion-Like Particle (ALP). ALPs are pseudo Nambu-Goldstone
bosons associated with spontaneously broken global U(1) symmetries. LFV ALPs
have been of a great interest in the last several decades as they can address
some of the SM long-lasting problems. Assuming a future muon collider proposed
by the Muon Accelerator Program (MAP), we search for LFV decays
$\tau\rightarrow\ell a$ ($\ell=e,\mu$) of one of the tau leptons produced in
the muon-anti muon annihilation. The ALP mass is assumed to be in the range 100
eV to 1 MeV and three different chiral structures are considered for the LFV
coupling. Using a multivariate technique and performing a parameterized
simulation based on the ideal target performance, we obtain expected 95$\%$
confidence level upper limits on the LFV couplings tau-electron-ALP and
tau-muon-ALP. Limits are computed assuming the center-of-mass energies of 126,
350 and 1500 GeV which the future muon collider is supposed to operate at. We
study the two cases of unpolarized and polarized muon beams and show that
taking advantage of tau polarization-induced effects, the main background
$\tau\rightarrow e/\mu + \nu\bar{\nu}$ can be significantly reduced. Results
indicate that current limits on the LFV couplings can be improved by roughly
one order of magnitude using the present analysis.


title:
On Turing-Turing bifurcation of partial functional differential
  equations and its induced superposition patterns

abstract:
  When two Turing modes interact, i.e., Turing-Turing bifurcation occurs,
superposition patterns revealing complex dynamical phenomena appear. In this
paper, superposition patterns resulting from Turing-Turing bifurcation are
investigated in theory. Firstly, the third-order normal form locally
topologically equivalent to original partial functional differential equations
(PFDEs) is derived. When selecting 1D domain and Neumann boundary conditions,
three normal forms describing different spatial patterns are deduced from
original third-order normal form. Also, formulas for computing coefficients of
these normal forms are given, which are expressed in explicit form of original
system parameters. With the aid of three normal forms, spatial patterns of a
diffusive predator-prey system with Crowley-Martin functional response near
Turing-Turing singularity are investigated. For one set of parameters,
diffusive system supports the coexistence of four stable steady states with
different single characteristic wavelengths, which demonstrates our previous
conjecture. For another set of parameters, superposition patterns, tri-stable
patterns that a pair of stable superposition steady states coexists with the
stable coexistence equilibrium or another stable steady state, as well as
quad-stable patterns that a pair of stable superposition steady states and
another pair of stable steady states coexist, arise. Finally, numerical
simulations are shown to support theory analysis.


title:
HSTR-Net: High Spatio-Temporal Resolution Video Generation For Wide Area
  Surveillance

abstract:
  Wide area surveillance has many applications and tracking of objects under
observation is an important task, which often needs high spatio-temporal
resolution (HSTR) video for better precision. This paper presents the usage of
multiple video feeds for the generation of HSTR video as an extension of
reference based super resolution (RefSR). One feed captures video at high
spatial resolution with low frame rate (HSLF) while the other captures low
spatial resolution and high frame rate (LSHF) video simultaneously for the same
scene. The main purpose is to create an HSTR video from the fusion of HSLF and
LSHF videos. In this paper we propose an end-to-end trainable deep network that
performs optical flow estimation and frame reconstruction by combining inputs
from both video feeds. The proposed architecture provides significant
improvement over existing video frame interpolation and RefSR techniques in
terms of objective PSNR and SSIM metrics.


title:
Stability and error guarantees for least squares approximation with
  noisy samples

abstract:
  Given $n$ samples of a function $f : D\to\mathbb C$ in random points drawn
with respect to a measure $\nu$ we develop theoretical analysis of the $L_2(D,
\mu)$-approximation error. We show that the weighted least squares method from
finite dimensional function spaces $V_m$, $\dim(V_m) = m < \infty$ is stable
and optimal up to a multiplicative constant when given exact samples with
logarithmic oversampling. Further, for noisy samples, our bounds describe the
bias-variance trade off depending on the dimension $m$ of the approximation
space $V_m$. All results hold with high probability. For demonstration, we
consider functions defined on the $d$-dimensional cube given in unifom random
samples. We analyze polynomials, the half-perid cosine, and a bounded
orthonormal basis of the non-periodic Sobolev space $H_{\text{mix}}^2$.
Overcoming numerical issues of this $H_{\text{mix}}^2$ basis, this gives a
novel stable approximation method with quadratic error decay. Numerical
experiments indicate the applicability of our results.


title: Modeling Multi-Granularity Hierarchical Features for Relation Extraction

abstract:
  Relation extraction is a key task in Natural Language Processing (NLP), which
aims to extract relations between entity pairs from given texts. Recently,
relation extraction (RE) has achieved remarkable progress with the development
of deep neural networks. Most existing research focuses on constructing
explicit structured features using external knowledge such as knowledge graph
and dependency tree. In this paper, we propose a novel method to extract
multi-granularity features based solely on the original input sentences. We
show that effective structured features can be attained even without external
knowledge. Three kinds of features based on the input sentences are fully
exploited, which are in entity mention level, segment level, and sentence
level. All the three are jointly and hierarchically modeled. We evaluate our
method on three public benchmarks: SemEval 2010 Task 8, Tacred, and Tacred
Revisited. To verify the effectiveness, we apply our method to different
encoders such as LSTM and BERT. Experimental results show that our method
significantly outperforms existing state-of-the-art models that even use
external knowledge. Extensive analyses demonstrate that the performance of our
model is contributed by the capture of multi-granularity features and the model
of their hierarchical structure. Code and data are available at
\url{https://github.com/xnliang98/sms}.


title:
Guided deep learning by subaperture decomposition: ocean patterns from
  SAR imagery

abstract:
  Spaceborne synthetic aperture radar can provide meters scale images of the
ocean surface roughness day or night in nearly all weather conditions. This
makes it a unique asset for many geophysical applications. Sentinel 1 SAR wave
mode vignettes have made possible to capture many important oceanic and
atmospheric phenomena since 2014. However, considering the amount of data
provided, expanding applications requires a strategy to automatically process
and extract geophysical parameters. In this study, we propose to apply
subaperture decomposition as a preprocessing stage for SAR deep learning
models. Our data centring approach surpassed the baseline by 0.7, obtaining
state of the art on the TenGeoPSARwv data set. In addition, we empirically
showed that subaperture decomposition could bring additional information over
the original vignette, by rising the number of clusters for an unsupervised
segmentation method. Overall, we encourage the development of data centring
approaches, showing that, data preprocessing could bring significant
performance improvements over existing deep learning models.


title: The skyrmion bags in an anisotropy gradient

abstract:
  Skyrmion bags as spin textures with arbitrary topological charge are expected
to be the carriers in racetrack memory. Here, we theoretically and numerically
investigated the dynamics of skyrmion bags in an anisotropy gradient. It is
found that, without the boundary potential, the dynamics of skyrmion bags are
dependent on the spin textures, and the velocity of skyrmionium with $Q = 0$ is
faster than other skyrmion bags. However, when the skyrmion bags move along the
boundary, the velocities of all skyrmion bags with different $Q$ are same. This
can be attributed to the same value of $u/\eta_{xx}$, where the $u$ and
$\eta_{xx}$ are the terms related to the magnetization distribution of skyrmion
bag. In addition, we theoretically derived the dynamics of skyrmion bags in the
two cases using the Thiele approach and discussed the scope of Thiele equation.
Within a certain range, the simulation results are in good agreement with the
analytically calculated results. Our findings provide an alternative way to
manipulate the racetrack memory based on the skyrmion bags.


title:
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair
  Neural Networks

abstract:
  We show that deep neural networks that satisfy demographic parity do so
through a form of race or gender awareness, and that the more we force a
network to be fair, the more accurately we can recover race or gender from the
internal state of the network. Based on this observation, we propose a simple
two-stage solution for enforcing fairness. First, we train a two-headed network
to predict the protected attribute (such as race or gender) alongside the
original task, and second, we enforce demographic parity by taking a weighted
sum of the heads. In the end, this approach creates a single-headed network
with the same backbone architecture as the original network. Our approach has
near identical performance compared to existing regularization-based or
preprocessing methods, but has greater stability and higher accuracy where near
exact demographic parity is required. To cement the relationship between these
two approaches, we show that an unfair and optimally accurate classifier can be
recovered by taking a weighted sum of a fair classifier and a classifier
predicting the protected attribute. We use this to argue that both the fairness
approaches and our explicit formulation demonstrate disparate treatment and
that, consequentially, they are likely to be unlawful in a wide range of
scenarios under the US law.


title:
Structural engineering of transition-metal nitrides for surface-enhanced
  Raman scattering chips

abstract:
  Noble-metal-free surface-enhanced Raman scattering (SERS) substrates have
attracted great attention for their abundant sources, good signal uniformity,
superior biocompatibility, and high chemical stability. However, the lack of
controllable synthesis and fabrication of noble-metal-free substrates with high
SERS activity impedes their practical applications. Herein,we propose a general
strategy to fabricate a series of planar transition-metal nitride (TMN) SERS
chips via an ambient temperature sputtering deposition route.These planar TMN
(tungsten nitride, tantalum nitride, and molybdenum nitride) chips show
remarkable Raman enhancement factors (EFs) with ~105 owing to efficient
photoinduced charge transfer process between TMN chips and probe molecules.
Further, structural engineering of these TMN chips is used to improve their
SERS activity. Benefiting from the synergistic effect of charge transfer
process and electric field enhancement by constructing nanocavity structure,
the Raman EF of WN nanocavity chips could be greatly improved to 1.29 * 107,
which is an order of magnitude higher than that of planar chips. Moreover, we
also design the WN/monolayer MoS2 heterostructure chips. With the increase of
surface electron density on the upper WN and more exciton resonance transitions
in the heterostructure, a 1.94 * 107 level EF and a 5 * 10-10 m level detention
limit could be achieved. Our results provide important guidance for the
structural design of ultrasensitive noble-metal-free SERS chips.


title: Strategy-Driven Limit Theorems Associated Bandit Problems

abstract:
  Motivated by the study of asymptotic behaviour of the bandit problems, we
obtain several strategy-driven limit theorems including the law of large
numbers, the large deviation principle, and the central limit theorem.
Different from the classical limit theorems, we develop sampling
strategy-driven limit theorems that generate the maximum or minimum average
reward. The law of large numbers identifies all possible limits that are
achievable under various strategies. The large deviation principle provides the
maximum decay probabilities for deviations from the limiting domain. To
describe the fluctuations around averages, we obtain strategy-driven central
limit theorems under optimal strategies. The limits in these theorem are
identified explicitly, and depend heavily on the structure of the events or the
integrating functions and strategies. This demonstrates the key signature of
the learning structure. Our results can be used to estimate the maximal
(minimal) rewards, and to identify the conditions of avoiding the Parrondo's
paradox in the two-armed bandit problem. It also lays the theoretical
foundation for statistical inference in determining the arm that offers the
higher mean reward.


title:
Gravitational Collapse of Massless Vector Field with Positive
  Cosmological Constant

abstract:
  We investigate the dynamics of homogeneous gravitational collapse of a
massless vector field in the presence of a positive cosmological constant
$\Lambda$. The corresponding density function $\rho (a)$ obtained for the
massless vector field is inversely proportional to the fourth power of the
scale factor $a (t)$. The variation of the scale factor shows that for $0\,
\le\Lambda < 1$, we obtain the gravitational collapse of the vector fields
leading singularity formation in a {\it finite} comoving time resulting in a
{\it Blackhole} such that with increasing $\Lambda$, the singularity formation
time, $t_s$ increases. For $\Lambda = 1$, we obtain $a(t) = 0$, thus limiting
the maximum value of $\Lambda$, (w.r.t the initial density $\rho_0$) for which
the system could collapse under gravity.


title:
Path-Tree Optimization in Partially Observable Environments using
  Rapidly-Exploring Belief-Space Graphs

abstract:
  Robots often need to solve path planning problems where essential and
discrete aspects of the environment are partially observable. This introduces a
multi-modality, where the robot must be able to observe and infer the state of
its environment. To tackle this problem, we introduce the Path-Tree
Optimization (PTO) algorithm which plans a path-tree in belief-space. A
path-tree is a tree-like motion with branching points where the robot receives
an observation leading to a belief-state update. The robot takes different
branches depending on the observation received. The algorithm has three main
steps. First, a rapidly-exploring random graph (RRG) on the state space is
grown. Second, the RRG is expanded to a belief-space graph by querying the
observation model. In a third step, dynamic programming is performed on the
belief-space graph to extract a path-tree. The resulting path-tree combines
exploration with exploitation i.e. it balances the need for gaining knowledge
about the environment with the need for reaching the goal. We demonstrate the
algorithm capabilities on navigation and mobile manipulation tasks, and show
its advantage over a baseline using a task and motion planning approach (TAMP)
both in terms of optimality and runtime.


title:
Bayesian evidence-driven diagnosis of instrumental systematics for
  sky-averaged 21-cm cosmology experiments

abstract:
  We demonstrate the effectiveness of a Bayesian evidence-based analysis for
diagnosing and disentangling the sky-averaged 21-cm signal from instrumental
systematic effects. As a case study, we consider a simulated REACH pipeline
with an injected systematic. We demonstrate that very poor performance or
erroneous signal recovery is achieved if the systematic remains unmodelled.
These effects include sky-averaged 21-cm posterior estimates resembling a very
deep or wide signal. However, when including parameterised models of the
systematic, the signal recovery is dramatically improved in performance. Most
importantly, a Bayesian evidence-based model comparison is capable of
determining whether or not such a systematic model is needed as the true
underlying generative model of an experimental dataset is in principle unknown.
We, therefore, advocate a pipeline capable of testing a variety of potential
systematic errors with the Bayesian evidence acting as the mechanism for
detecting their presence.


title: Northcott numbers for the weighted Weil heights

abstract:
  We answer the question of Vidaux and Videla about the distribution of the
Northcott numbers for the Weil height. We solve the same problem for the
weighted Weil heights. These heights generalize both the absolute and relative
Weil height. Our results also refine those of Pazuki, Technau, and Widmer.


title:
Stable topological phase transitions without symmetry indications in
  NaZnSb$_{1-x}$Bi$_x$

abstract:
  We study topological phase transitions in tetragonal NaZnSb$_{1-x}$Bi$_x$,
driven by the chemical composition $x$. Notably, we examine mirror Chern
numbers that change without symmetry indicators. First-principles calculations
are performed to show that NaZnSb$_{1-x}$Bi$_x$ experiences consecutive
topological phase transitions, diagnosed by the strong $\mathbb Z_{2}$
topological index $\nu_{0}$ and two mirror Chern numbers $\mu_{x}$ and
$\mu_{xy}$. As the chemical composition $x$ increases, the topological
invariants ($\mu_{x}\mu_{xy}\nu_{0}$) change from $(000)$, $(020)$, $(220)$, to
$(111)$ at $x$ = 0.15, 0.20, and 0.53, respectively. A simplified low-energy
effective model is developed to examine the mirror Chern number changes,
highlighting the central role of spectator Dirac fermions in avoiding symmetry
indicators. Our findings suggest that NaZnSb$_{1-x}$Bi$_{x}$ can be an exciting
testbed for the exploration of the interplay between the topology and symmetry.


title: Nilpotent left quasigroups

abstract:
  In this paper we investigate central congruence of left quasigroups in the
sense of Freese and McKenzie \cite{comm} and we extend some known results for
quandles. In particular, we can extend the characterization of finite nilpotent
latin quandles and the characterization of distributive varieties of quandles
to the setting of idempotent left quasigroups.


title: Parameter estimation with gravitational waves

abstract:
  The new era of gravitational wave astronomy truly began on September 14, 2015
with the detection of GW150914, the sensational first direct observation of
gravitational waves from the inspiral and merger of two black holes by the two
Advanced LIGO detectors. In the subsequent first three observing runs of the
LIGO/Virgo network, gravitational waves from $\sim 50$ compact binary mergers
have been announced, with more results to come. The events have mostly been
produced by binary black holes, but two binary neutron star mergers have so far
been observed, as well as the mergers of two neutron star - black hole systems.
Furthermore, gravitational waves emitted by core-collapse supernovae, pulsars
and the stochastic gravitational wave background are within the
LIGO/Virgo/KAGRA sensitivity band and are likely to be observed in future
observation runs. Beyond signal detection, a major challenge has been the
development of statistical and computational methodology for estimating the
physical waveform parameters and quantifying their uncertainties in order to
accurately characterise the emitting system. These methods depend on the
sources of the gravitational waves and the gravitational waveform model that is
used. This article reviews the main waveform models and parameter estimation
methods used to extract physical parameters from gravitational wave signals
detected to date by LIGO and Virgo and from those expected to be observed in
the future, which will include KAGRA, and how these methods interface with
various aspects of LIGO/Virgo/KAGRA science. Also presented are the statistical
methods used by LIGO and Virgo to estimate detector noise, test general
relativity, and draw conclusions about the rates of compact binary mergers in
the universe. Furthermore, a summary of major publicly available gravitational
wave parameter estimation software packages is given.


title: Distributed Evolution Strategies for Black-box Stochastic Optimization

abstract:
  This work concerns the evolutionary approaches to distributed stochastic
black-box optimization, in which each worker can individually solve an
approximation of the problem with nature-inspired algorithms. We propose a
distributed evolution strategy (DES) algorithm grounded on a proper
modification to evolution strategies, a family of classic evolutionary
algorithms, as well as a careful combination with existing distributed
frameworks. On smooth and nonconvex landscapes, DES has a convergence rate
competitive to existing zeroth-order methods, and can exploit the sparsity, if
applicable, to match the rate of first-order methods. The DES method uses a
Gaussian probability model to guide the search and avoids the numerical issue
resulted from finite-difference techniques in existing zeroth-order methods.
The DES method is also fully adaptive to the problem landscape, as its
convergence is guaranteed with any parameter setting. We further propose two
alternative sampling schemes which significantly improve the sampling
efficiency while leading to similar performance. Simulation studies on several
machine learning problems suggest that the proposed methods show much promise
in reducing the convergence time and improving the robustness to parameter
settings.


title: An Axially Symmetric Transitioning models with Observational Constraints

abstract:
  In this study, we have demonstrated the expansion history of an axially
symmetric Bianchi type-I model of the universe. Our model as of now presents an
accelerating universe, which had been in the decelerating phase in the past.
Roles of the two crucial Hubble~$H(z)$ and deceleration~$q(z)$ parameters are
examined. The energy parameters of the universe are estimated with the help of
the latest observational Hubble data (46-data points) and Pantheon data (the
latest compilation of SNIa with 40 binned in the redshift range $0.014 \leq z
\leq 1.62)$. We also discuss the stability analysis of the model by state
finder diagnosis. The analysis reveals that in late time, the model is a
quintessence type and points towards the $\Lambda$CDM model. Our developed
model agrees with observational findings in a proper way. We have discussed
some of the physical aspects of the model.


title:
Yes, Topology Matters in Decentralized Optimization: Refined Convergence
  and Topology Learning under Heterogeneous Data

abstract:
  One of the key challenges in federated and decentralized learning is to
design algorithms that efficiently deal with highly heterogeneous data
distributions across agents. In this paper, we revisit the analysis of
Decentralized Stochastic Gradient Descent algorithm (D-SGD), a popular
decentralized learning algorithm, under data heterogeneity. We exhibit the key
role played by a new quantity, that we call neighborhood heterogeneity, on the
convergence rate of D-SGD. Unlike prior work, neighborhood heterogeneity is
measured at the level of the neighborhood of an agent in the graph topology. By
coupling the topology and the heterogeneity of the agents' distributions, our
analysis sheds light on the poorly understood interplay between these two
concepts in decentralized learning. We then argue that neighborhood
heterogeneity provides a natural criterion to learn sparse data-dependent
topologies that reduce (and can even eliminate) the otherwise detrimental
effect of data heterogeneity on the convergence time of D-SGD. For the
important case of classification with label skew, we formulate the problem of
learning such a good topology as a tractable optimization problem that we solve
with a Frank-Wolfe algorithm. Our approach provides a principled way to design
a sparse topology that balances the number of iterations and the per-iteration
communication costs of D-SGD under data heterogeneity.


title:
Exclusive semileptonic $B_c$-meson decays to radially excited charmonium
  and charm meson states

abstract:
  In this work, we study exclusive semileptonic $B_c$-meson decays to radially
excited charmonium and charm meson states in the framework of the relativistic
independent quark(RIQ) model based on an average flavor independent confining
potential in equally mixed scalar-vector harmonic form. We calculate the
invariant form factors representing decay amplitudes from the overlapping
integrals of meson wave functions derivable in the RIQ model. To evaluate the
lepton mass effects in the semileptonic decays, we study the physical
quantities of interest for the decay processes in their $e^-$ and $\tau^-$ mode
separately. We predict the decay rates/ branching fractions, forward-backward
asymmetry, and the asymmetry parameter in reasonable agreement with other model
predictions, which can hopefully be tested in future experiments at the
Tevatron and LHC. We find that the decay channels $B_c\to \eta_c/\psi(nS)$ have
branching ratios $\sim(10^{-2}-10^{-4})$ and that for $B_c\to D^*(nS)$ in their
$e^-$ mode is $10^{-4}$, which are within the detection accuracy of current
experiments. Our predictions on the observable ${\cal R}$, which corresponds to
the ratio of branching fractions, for $B_c$ decays to radially excited
charmonium states are comparable to other standard models (SM) predictions,
which highlight the failure of the lepton flavor universality hinting at new
physics beyond SM. In the absence of predicted data from established model
approaches in the literature for $B_c$ decays to radially excited charm meson
states, our predictions can be useful to identify the $B_c$-channels
characterized by clear experimental signature.


title:
Bifurcation analysis of strongly nonlinear injection locked spin torque
  oscillators

abstract:
  We investigate the dynamics of an injection locked in-plane uniform spin
torque oscillator for several forcing configurations at large driving
amplitudes. For the analysis, the spin wave amplitude equation is used to
reduce the dynamics to a general oscillator equation in which the forcing is a
complex valued function $F(p,{\psi})\propto{\epsilon}_1
(p)cos({\psi})+i{\epsilon}_2 (p)sin({\psi})$. Assuming that the oscillator is
strongly nonisochronous and/or forced by a power forcing
$(|{\nu}{\epsilon}_1/{\epsilon}_2 |\gg 1)$, we show that the parameters
${\epsilon}_{1,2} (p)$ govern the main bifurcation features of the Arnold
tongue diagram : (i) the locking range asymmetry is mainly controlled by
$d{\epsilon}_1 (p)/dp$, (ii) the Taken-Bogdanov bifurcation occurs for a power
threshold depending on ${\epsilon}_{1,2} (p)$ and (iii) the frequency
hysteretic range is related to the transient regime through the resonant
frequency at zero mismatch frequency. Then, the model is compared with the
macrospin simulation for driving amplitudes as large as $10^0-10^3 A/m$ for the
magnetic field and $10^{10}-10^{12} A/m^2$ for the current density. As
predicted by the model, the forcing configuration (nature of the driving
signal, applied direction, the harmonic orders) affects substantially the
oscillator dynamic. However, some discrepancies are observed. In particular,
the prediction of the frequency and power locking range boundaries may be
misestimated if the hysteretic boundaries are of same magnitude order.
Moreover, the misestimation can be of two different types according if the
bifurcation is Saddle node or Taken Bogdanov. These effects are a further
manifestation of the complexity of the dynamics in nonisochronous
auto-oscillators.


title: Noise-based Enhancement for Foveated Rendering

abstract:
  Human visual sensitivity to spatial details declines towards the periphery.
Novel image synthesis techniques, so-called foveated rendering, exploit this
observation and reduce the spatial resolution of synthesized images for the
periphery, avoiding the synthesis of high-spatial-frequency details that are
costly to generate but not perceived by a viewer. However, contemporary
techniques do not make a clear distinction between the range of spatial
frequencies that must be reproduced and those that can be omitted. For a given
eccentricity, there is a range of frequencies that are detectable but not
resolvable. While the accurate reproduction of these frequencies is not
required, an observer can detect their absence if completely omitted. We use
this observation to improve the performance of existing foveated rendering
techniques. We demonstrate that this specific range of frequencies can be
efficiently replaced with procedural noise whose parameters are carefully tuned
to image content and human perception. Consequently, these frequencies do not
have to be synthesized during rendering, allowing more aggressive foveation,
and they can be replaced by noise generated in a less expensive post-processing
step, leading to improved performance of the rendering system. Our main
contribution is a perceptually-inspired technique for deriving the parameters
of the noise required for the enhancement and its calibration. The method
operates on rendering output and runs at rates exceeding 200FPS at 4K
resolution, making it suitable for integration with real-time foveated
rendering systems for VR and AR devices. We validate our results and compare
them to the existing contrast enhancement technique in user experiments.


title:
Approximation-free control based on the bioinspired reference model for
  suspension systems with uncertainty and unknown nonlinearity

abstract:
  Uncertainty and unknown nonlinearity are often inevitable in the suspension
systems, which were often solved using fuzzy logic system (FLS) or neural
networks (NNs). However, these methods are restricted by the structural
complexity of the controller and the huge computing cost. Meanwhile, the
estimation error of such approximators is affected by adopted adaptive laws and
learning gains. Thus, in view of the above problem, this paper proposes the
approximation-free control based on the bioinspired reference model for a class
of uncertain suspension systems with unknown nonlinearity. The proposed method
integrates the superior vibration suppression of the bioinspired reference
model and the structural advantage of the prescribed performance function (PPF)
in approximation-free control. Then, the vibration suppression performance is
improved, the calculation burden is relieved, and the transient performance is
improved, which is analyzed theoretically in this paper. Finally, the
simulation results validate the approach, and the comparisons show the
advantages of the proposed control method in terms of good vibration
suppression, fast convergence, and less calculation burden.


title: Refining time-space traffic diagrams: A multiple linear regression model

abstract:
  A time-space traffic (TS) diagram, which presents traffic states in
time-space cells with color, is an important traffic analysis and visualization
tool. Despite its importance for transportation research and engineering, most
TS diagrams that have already existed or are being produced are too coarse to
exhibit detailed traffic dynamics due to the limitations of existing
information technology and traffic infrastructure investment. To increase the
resolution of a TS diagram and enable it to present ample traffic details, this
paper introduces the TS diagram refinement problem and proposes a multiple
linear regression-based model to solve the problem. Two tests, which attempt to
increase the resolution of a TS diagram 4 and 16 times, are carried out to
evaluate the performance of the proposed model. Data collected at different
times, in different locations and even in different countries are employed to
thoroughly evaluate the accuracy and transferability of the proposed model.
Strict tests with diverse data show that the proposed model, despite its
simplicity, is able to refine a TS diagram with promising accuracy and reliable
transferability. The proposed refinement model will "save" widely existing TS
diagrams from their blurry "faces" and enable TS diagrams to show more traffic
details.


title:
Understanding, Detecting, and Separating Out-of-Distribution Samples and
  Adversarial Samples in Text Classification

abstract:
  In this paper, we study the differences and commonalities between
statistically out-of-distribution (OOD) samples and adversarial (Adv) samples,
both of which hurting a text classification model's performance. We conduct
analyses to compare the two types of anomalies (OOD and Adv samples) with the
in-distribution (ID) ones from three aspects: the input features, the hidden
representations in each layer of the model, and the output probability
distributions of the classifier. We find that OOD samples expose their
aberration starting from the first layer, while the abnormalities of Adv
samples do not emerge until the deeper layers of the model. We also illustrate
that the models' output probabilities for Adv samples tend to be more
unconfident. Based on our observations, we propose a simple method to separate
ID, OOD, and Adv samples using the hidden representations and output
probabilities of the model. On multiple combinations of ID, OOD datasets, and
Adv attacks, our proposed method shows exceptional results on distinguishing
ID, OOD, and Adv samples.


title:
The Variance of the Sum of Two Squares over Intervals in $\mathbb{F}_q
  [T]$: I

abstract:
  For $B \in \mathbb{F}_q [T]$ of degree $2n \geq 2$, consider the number of
ways of writing $B=E^2 + \gamma F^2$, where $\gamma \in \mathbb{F}_q^*$ is
fixed, and $E,F \in \mathbb{F}_q [T]$ with $\mathrm{deg} \hspace{0.25em} E = n$
and $\mathrm{deg} \hspace{0.25em} F = m < n$. We denote this by $S_{\gamma ; m}
(B)$. We obtain an exact formula for the variance of $S_{\gamma ; m} (B)$ over
intervals in $\mathbb{F}_q [T]$. We use the method of additive characters and
Hankel matrices that the author previously used for the variance and
correlations of the divisor function. In Section 2, we give a short overview of
our approach; and we briefly discuss the possible extension of our result to
the number of ways of writing $B=E^2 + T F^2$.


title:
Packing measure and dimension of the limit sets of IFSs of generalized
  complex continued fractions

abstract:
  We consider a family of conformal iterated function systems (for short,
CIFSs) of generalized complex continued fractions which is a generalization of
the CIFS of complex continued fractions. We show the packing dimension and the
Hausdorff dimension of the limit set of each CIFS in the family are equal and
the packing measure of the limit set with respect to the packing dimension of
the limit set is finite in order to present new and interesting examples of
infinite CIFSs. Note that the Hausdorff measure of the limit set with respect
to the Hausdorff dimension is zero. To prove the above results, we consider
three cases (essentially two cases) and define a `nice' subset of the index set
of the CIFS in each case. In addition, we estimate the cardinality of the
`nice' subsets and the conformal measure of the CIFSs.


title:
Modeling and mechanical perturbations reveal how spatially regulated
  anchorage gives rise to spatially distinct mechanics across the mammalian
  spindle

abstract:
  During cell division, the spindle generates force to move chromosomes. In
mammals, microtubule bundles called kinetochore-fibers (k-fibers) attach to and
segregate chromosomes. To do so, k-fibers must be robustly anchored to the
dynamic spindle. We previously developed microneedle manipulation to
mechanically challenge k-fiber anchorage, and observed spatially distinct
response features revealing the presence of heterogeneous anchorage (Suresh et
al. 2020). How anchorage is precisely spatially regulated, and what forces are
necessary and sufficient to recapitulate the k-fiber's response to force remain
unclear. Here, we develop a coarse-grained k-fiber model and combine with
manipulation experiments to infer underlying anchorage using shape analysis. By
systematically testing different anchorage schemes, we find that forces solely
at k-fiber ends are sufficient to recapitulate unmanipulated k-fiber shapes,
but not manipulated ones for which lateral anchorage over a 3 $\mu$m length
scale near chromosomes is also essential. Such anchorage robustly preserves
k-fiber orientation near chromosomes while allowing pivoting around poles.
Anchorage over a shorter length scale cannot robustly restrict pivoting near
chromosomes, while anchorage throughout the spindle obstructs pivoting at
poles. Together, this work reveals how spatially regulated anchorage gives rise
to spatially distinct mechanics in the mammalian spindle, which we propose are
key for function.


title:
A3CLNN: Spatial, Spectral and Multiscale Attention ConvLSTM Neural
  Network for Multisource Remote Sensing Data Classification

abstract:
  The problem of effectively exploiting the information multiple data sources
has become a relevant but challenging research topic in remote sensing. In this
paper, we propose a new approach to exploit the complementarity of two data
sources: hyperspectral images (HSIs) and light detection and ranging (LiDAR)
data. Specifically, we develop a new dual-channel spatial, spectral and
multiscale attention convolutional long short-term memory neural network
(called dual-channel A3CLNN) for feature extraction and classification of
multisource remote sensing data. Spatial, spectral and multiscale attention
mechanisms are first designed for HSI and LiDAR data in order to learn
spectral- and spatial-enhanced feature representations, and to represent
multiscale information for different classes. In the designed fusion network, a
novel composite attention learning mechanism (combined with a three-level
fusion strategy) is used to fully integrate the features in these two data
sources. Finally, inspired by the idea of transfer learning, a novel stepwise
training strategy is designed to yield a final classification result. Our
experimental results, conducted on several multisource remote sensing data
sets, demonstrate that the newly proposed dual-channel A3CLNN exhibits better
feature representation ability (leading to more competitive classification
performance) than other state-of-the-art methods.


title:
A Family of Lanthanide Noncentrosymmetric Superconductors La$_4$$TX$
  ($T$ = Ru, Rh, Ir; $X$ = Al, In)

abstract:
  We report the discovery of superconductivity in a series of
noncentrosymmetric compounds La$_4$$TX$ ($T$ = Ru, Rh, Ir; $X$ = Al, In), which
have a cubic crystal structure with space group $F\bar{4}3m$. La$_4$RuAl,
La$_4$RhAl, La$_4$IrAl, La$_4$RuIn and La$_4$IrIn exhibit bulk superconducting
transitions with critical temperatures $T_c$ of 1.77 K, 3.05 K, 1.54 K, 0.58 K
and 0.93 K, respectively. The specific heat of the La$_4$$T$Al compounds are
consistent with an $s$-wave model with a fully open superconducting gap. In all
cases, the upper critical fields are well described by the
Werthamer-Helfand-Hohenberg model, and the values are well below the Pauli
limit, indicating that orbital limiting is the dominant pair-breaking
mechanism. Density functional theory (DFT) calculations reveal that the degree
of band splitting by the antisymmetric spin-orbit coupling (ASOC) shows
considerable variation between the different compounds. This indicates that the
strength of the ASOC is highly tunable across this series of superconductors,
suggesting that these are good candidates for examining the relationship
between the ASOC and superconducting properties in noncentrosymmetric
superconductors.


title: Multichannel Speech Separation with Narrow-band Conformer

abstract:
  This work proposes a multichannel speech separation method with narrow-band
Conformer (named NBC). The network is trained to learn to automatically exploit
narrow-band speech separation information, such as spatial vector clustering of
multiple speakers. Specifically, in the short-time Fourier transform (STFT)
domain, the network processes each frequency independently, and is shared by
all frequencies. For one frequency, the network inputs the STFT coefficients of
multichannel mixture signals, and predicts the STFT coefficients of separated
speech signals. Clustering of spatial vectors shares a similar principle with
the self-attention mechanism in the sense of computing the similarity of
vectors and then aggregating similar vectors. Therefore, Conformer would be
especially suitable for the present problem. Experiments show that the proposed
narrow-band Conformer achieves better speech separation performance than other
state-of-the-art methods by a large margin.


title:
Localization of moving sources: uniqueness, stability, and Bayesian
  inference

abstract:
  We consider the subsonic moving point source problem for the scalar wave
equation in $\pmb{R}^3$, proving a regularity result for the direct problem,
and uniqueness and stability results for the inverse problem. We then present
and investigate numerically a Bayesian framework for the inference of the
source trajectory and intensity from wave field measurements. The framework
employs Gaussian process priors, the pre-conditioned Crank-Nicholson scheme
with Markov Chain Monte Carlo sampling, and conditioning on functionals to
include prior information on the source trajectory.


title: Ultrasound Signal Processing: From Models to Deep Learning

abstract:
  Medical ultrasound imaging relies heavily on high-quality signal processing
algorithms to provide reliable and interpretable image reconstructions.
Hand-crafted reconstruction methods, often based on approximations of the
underlying measurement model, are useful in practice, but notoriously fall
behind in terms of image quality. More sophisticated solutions, based on
statistical modelling, careful parameter tuning, or through increased model
complexity, can be sensitive to different environments. Recently, deep learning
based methods have gained popularity, which are optimized in a data-driven
fashion. These model-agnostic methods often rely on generic model structures,
and require vast training data to converge to a robust solution. A relatively
new paradigm combines the power of the two: leveraging data-driven deep
learning, as well as exploiting domain knowledge. These model-based solutions
yield high robustness, and require less trainable parameters and training data
than conventional neural networks. In this work we provide an overview of these
methods from the recent literature, and discuss a wide variety of ultrasound
applications. We aim to inspire the reader to further research in this area,
and to address the opportunities within the field of ultrasound signal
processing. We conclude with a future perspective on these model-based deep
learning techniques for medical ultrasound applications.


title:
Bayesian parameter-estimation of Galactic binaries in LISA data with
  Gaussian Process Regression

abstract:
  The Laser Interferometer Space Antenna (LISA), which is currently under
construction, is designed to measure gravitational wave signals in the
milli-Hertz frequency band. It is expected that tens of millions of Galactic
binaries will be the dominant sources of observed gravitational waves. The
Galactic binaries producing signals at mHz frequency range emit quasi
monochromatic gravitational waves, which will be constantly measured by LISA.
To resolve as many Galactic binaries as possible is a central challenge of the
upcoming LISA data set analysis. Although it is estimated that tens of
thousands of these overlapping gravitational wave signals are resolvable, and
the rest blurs into a galactic foreground noise; extracting tens of thousands
of signals using Bayesian approaches is still computationally expensive. We
developed a new end-to-end pipeline using Gaussian Process Regression to model
the log-likelihood function in order to rapidly compute Bayesian posterior
distributions. Using the pipeline we are able to solve the Lisa Data Challange
(LDC) 1-3 consisting of noisy data as well as additional challenges with
overlapping signals and particularly faint signals.


title:
Simultaneously Enhanced Tenacity, Rupture Work, and Thermal Conductivity
  of Carbon Nanotubes Fibers by Increasing the Effective Tube Contribution

abstract:
  Although individual carbon nanotubes (CNTs) are superior as constituents to
polymer chains, the mechanical and thermal properties of CNT fibers (CNTFs)
remain inferior to commercial synthetic fibers due to the lack of synthesis
methods to embed CNTs effectively in superstructures. The application of
conventional techniques for mechanical enhancement resulted in a mild
improvement of target properties while achieving parity at best on others. In
this work, a Double-Drawing technique is developed to deform continuously grown
CNTFs and rearrange the constituent CNTs in both mesoscale and nanoscale
morphology. Consequently, the mechanical and thermal properties of the
resulting CNTFs can be jointly improved, and simultaneously reach their highest
performances with specific strength (tenacity) $\rm\sim3.30\,N\,tex^{-1}$, work
of rupture $\rm\sim70\,J\,g^{-1}$, and thermal conductivity
$\rm\sim354\,W\,m^{-1}\,K^{-1}$, despite starting from commercial
low-crystallinity materials ($I{\rm_G}:I{\rm_D}\sim5$). The processed CNTFs are
more versatile than comparable carbon fiber, Zylon, Dyneema, and Kevlar.
Furthermore, based on evidence of load transfer efficiency on individual CNTs
measured with In-Situ Stretching Raman, we find the main contributors to
property enhancements are (1) the increased proportion of load-bearing CNT
bundles and (2) the extension of effective length of tubes attached on these
bundles.


title: Modified Heider Balance on Sparse Random Networks

abstract:
  The lack of signed random networks in standard balance studies has prompted
us to extend the Hamiltonian of the standard balance model. Random networks
with tunable parameters are suitable for better understanding the behavior of
standard balance as an underlying dynamics. Moreover, the standard balance
model in its original form does not allow preserving tensed triads in the
network. Therefore, the thermal behavior of the balance model has been
investigated on a fully connected signed network recently. It has been shown
that the model undergoes an abrupt phase transition with temperature.
Considering these two issues together, we examine the thermal behavior of the
structural balance model defined on Erd\H{o}s-R\'enyi random networks. We
provide a Mean-Field solution for the model. We observe a first-order phase
transition with temperature, for both the sparse and densely connected
networks. We detect two transition temperatures, $T_{cold}$ and $T_{hot}$,
characterizing a hysteresis loop. We find that with increasing the network
sparsity, both $T_{cold}$ and $T_{hot}$ decrease. But the slope of decreasing
$T_{hot}$ with sparsity is larger than the slope of decreasing $T_{cold}$.
Hence, the hysteresis region gets narrower, until, in a certain sparsity, it
disappears. We provide a phase diagram in the temperature-tie density plane to
observe the meta-stable/coexistence region behavior more accurately. Then we
justify our Mean-Field results with a series of Monte-Carlo simulations.


title:
Thermodynamics of the Reissner-Nordstr\"om-de Sitter Spacetime with
  Quintessence

abstract:
  For Anti-de Sitte (AdS) black holes, the isochoric heat capacity of system is
vanished, while the isobaric heat capacity is not. However, this situation does
not hold on for de Sitter (dS) black holes. In this work, by introducing the
interaction between the black hole horizon and the cosmological horizon of the
Reissner-Nordstr\"om-de Sitter (RNdS) spacetime with quintessence, we discuss
the phase transition of this system. The results show that the spacetime not
only has the similar phase transition behavior to that of Van der Waals (VdW)
system, and the non-vanishing isochoric heat capacity fulfills the whole
thermodynamics system. Through the discussion of the entropic force between two
horizons, we find out the role of entropic force in the evolution of spacetime.
In addition, we also study the influence of various parameters on the phase
transition and entropic force, which will provide a new method for exploring
the interaction among black hole molecules from a micro perspective.