title: Do cities have a unique magnetic pulse?

abstract:
  We present a comparative analysis of urban magnetic fields between two
American cities: Berkeley (California) and Brooklyn Borough of New York City
(New York). Our analysis uses data taken over a four-week period during which
magnetic field data were continuously recorded using a fluxgate magnetometer of
70 pT/$\sqrt{\mathrm{Hz}}$ sensitivity. We identified significant differences
in the magnetic signatures. In particular, we noticed that Berkeley reaches a
near-zero magnetic field activity at night whereas magnetic activity in
Brooklyn continues during nighttime. We also present auxiliary measurements
acquired using magnetoresistive vector magnetometers (VMR), with sensitivity of
300 pT/$\sqrt{\mathrm{Hz}}$, and demonstrate how cross-correlation, and
frequency-domain analysis, combined with data filtering can be used to extract
urban magnetometry signals and study local anthropogenic activities. Finally,
we discuss the potential of using magnetometer networks to characterize the
global magnetic field of cities and give directions for future development.


title:
Ultra-fine Entity Typing with Indirect Supervision from Natural Language
  Inference

abstract:
  The task of ultra-fine entity typing (UFET) seeks to predict diverse and
free-form words or phrases that describe the appropriate types of entities
mentioned in sentences. A key challenge for this task lies in the large amount
of types and the scarcity of annotated data per type. Existing systems
formulate the task as a multi-way classification problem and train directly or
distantly supervised classifiers. This causes two issues: (i) the classifiers
do not capture the type semantics since types are often converted into indices;
(ii) systems developed in this way are limited to predicting within a
pre-defined type set, and often fall short of generalizing to types that are
rarely seen or unseen in training. This work presents LITE, a new approach that
formulates entity typing as a natural language inference (NLI) problem, making
use of (i) the indirect supervision from NLI to infer type information
meaningfully represented as textual hypotheses and alleviate the data scarcity
issue, as well as (ii) a learning-to-rank objective to avoid the pre-defining
of a type set. Experiments show that, with limited training data, LITE obtains
state-of-the-art performance on the UFET task. In addition, LITE demonstrates
its strong generalizability, by not only yielding best results on other
fine-grained entity typing benchmarks, more importantly, a pre-trained LITE
system works well on new data containing unseen types.


title:
A macroscopic version of the delayed-choice experiments using coherent
  photons via post-measurements of polarization bases

abstract:
  Quantum superposition is the cornerstone of quantum mechanics, where
interference fringes originate in the self-interference of a single photon via
random probability amplitudes of finding the photon in both paths. Wheeler
delayed-choice thought experiments have been extensively demonstrated over the
last decades to understand the complementarity between the wave-like and
particle-like behaviors of a single particle. Here, we propose and demonstrate
a macroscopic version of the delayed-choice experiments using coherent photons,
where the delayed choice is conducted via post-projection of polarization bases
onto a rotated polarizer. Unlike most delayed-choice schemes limited to the
complementarity of a quantum particle in an interferometric system, the present
scheme is for post-control of output photons in polarization bases, where
photon numbers are irrelevant to the observed violations of causality. Thus,
the present demonstrations should expand the scope of the quantum mystery into
a macroscopic realm.


title:
Interacting 1D Chiral Fermions with Pairing: Transition from Integrable
  to Chaotic

abstract:
  We study a generic one-dimensonal quantum model of two flavors (pseudospins)
chiral complex fermions by exact diagonalization, which can have local
interflavor interaction and superconducting pairings (with all irrelevant terms
ignored). Analytically, the model has two solvable (integrable) points in the
parameter space: it is a free fermion model when the fermion interaction is
zero, and is a free boson Luttinger liquid when there is a global
U(1)$^{(\uparrow)}\times$U(1)$^{(\downarrow)}$ symmetry (with nonzero
interaction). When the global symmetry of the interacting model is lowered by
turning on symmetry breaking parameters, the model undergoes a transition from
a quantum integrable model to a fully quantum chaotic model, as we demonstrate
by examining the level spacing statistics (LSS) of the many-body energy
spectrum. In particular, there is a possibly integrable regime with
intermediate global symmetries, where the model is neither free bosons nor free
fermions, but shows Poisson LSS in each global symmetry charge sector. This
implies the existence of hidden (quasi)local conserved quantities. When the
global symmetries are further lowered, the LSS in each charge sector becomes
Wigner-Dyson, implying quantum chaos.

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


title:
An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for
  Extended Domains applied to Multiphase Flow in Pipes

abstract:
  The modelling of multiphase flow in a pipe presents a significant challenge
for high-resolution computational fluid dynamics (CFD) models due to the high
aspect ratio (length over diameter) of the domain. In subsea applications, the
pipe length can be several hundreds of kilometres versus a pipe diameter of
just a few inches. In this paper, we present a new AI-based non-intrusive
reduced-order model within a domain decomposition framework (AI-DDNIROM) which
is capable of making predictions for domains significantly larger than the
domain used in training. This is achieved by using domain decomposition;
dimensionality reduction; training a neural network to make predictions for a
single subdomain; and by using an iteration-by-subdomain technique to converge
the solution over the whole domain. To find the low-dimensional space, we
explore several types of autoencoder networks, known for their ability to
compress information accurately and compactly. The performance of the
autoencoders is assessed on two advection-dominated problems: flow past a
cylinder and slug flow in a pipe. To make predictions in time, we exploit an
adversarial network which aims to learn the distribution of the training data,
in addition to learning the mapping between particular inputs and outputs. This
type of network has shown the potential to produce realistic outputs. The whole
framework is applied to multiphase slug flow in a horizontal pipe for which an
AI-DDNIROM is trained on high-fidelity CFD simulations of a pipe of length 10 m
with an aspect ratio of 13:1, and tested by simulating the flow for a pipe of
length 98 m with an aspect ratio of almost 130:1. Statistics of the flows
obtained from the CFD simulations are compared to those of the AI-DDNIROM
predictions to demonstrate the success of our approach.


title:
Interface properties of CsPbBr$_3$ /CsPbI$_3$ perovskite heterostructure
  for solar cell

abstract:
  We explore the interface properties of perovskite heterostructure
CsPbBr$_3$/CsPbI$_3$ through first-principles calculations. The structural
interface is formed by the bonding of Cs-Br and Cs-I with bond length of
$\sim$4.106 and 3.922 \AA. The upshift of Goldsmith tolerance factor in the
range $0.8<t<1$ from $t<0.8$ is revealed for the bi-layer interface, from bulk,
reflecting the structural rearrangement from anisotropy to isotropy in
confinement. The band gap arises mainly due to the energy difference of I-5p
orbital than that of Br-4p at the valence band and Pb-6p at the conduction
band. Heavier halide shows the red shift in the absorption spectra, for the
pristine monolayer component. For the bilayer geometry, iodine contribution is
more observed than that of bromine and the underlying interface properties may
be useful for solar cell devices application.


title:
Efficient Spatial Representation and Routing of Deformable
  One-Dimensional Objects for Manipulation

abstract:
  With the field of rigid-body robotics having matured in the last fifty years,
routing, planning, and manipulation of deformable objects have recently emerged
as a more untouched research area in many fields ranging from surgical robotics
to industrial assembly and construction. Routing approaches for deformable
objects which rely on learned implicit spatial representations (e.g.,
Learning-from-Demonstration methods) make them vulnerable to changes in the
environment and the specific setup. On the other hand, algorithms that entirely
separate the spatial representation of the deformable object from the routing
and manipulation, often using a representation approach independent of
planning, result in slow planning in high dimensional space.
  This paper proposes a novel approach to routing deformable one-dimensional
objects (e.g., wires, cables, ropes, sutures, threads). This approach utilizes
a compact representation for the object, allowing efficient and fast online
routing. The spatial representation is based on the geometrical decomposition
of the space into convex subspaces, resulting in a discrete coding of the
deformable object configuration as a sequence. With such a configuration, the
routing problem can be solved using a fast dynamic programming sequence
matching method that calculates the next routing move. The proposed method
couples the routing and efficient configuration for improved planning time. Our
simulation and real experiments show the method correctly computing the next
manipulation action in sub-millisecond time and accomplishing various routing
and manipulation tasks.


title: Exponentially generalized vortex

abstract:
  In this work, we propose an exponentially generalized Abelian model. We
investigated the presence of vortex structures in models coupled to Maxwell and
Chern-Simons fields. We chose to investigate the dynamics of the complex scalar
field in models coupled separately to the Maxwell term and the Chern-Simons
term. For this, we analyze the Bogomol'nyi equations in both cases to describe
the static field configurations. An interesting result appears when we note
that scalar field solutions generate degenerate minimum energy configurations
by a factor of $\nu^{2}$ in Maxwell's case. On the other hand, in the case of
Chern-Simons, the solutions in this sector are degenerate by a factor of
$\kappa\nu^{2}/a_{s}$. Finally, we solve the Bogomol'nyi equations numerically
and discuss our results.


title: Source-Free Progressive Graph Learning for Open-Set Domain Adaptation

abstract:
  Open-set domain adaptation (OSDA) has gained considerable attention in many
visual recognition tasks. However, most existing OSDA approaches are limited
due to three main reasons, including: (1) the lack of essential theoretical
analysis of generalization bound, (2) the reliance on the coexistence of source
and target data during adaptation, and (3) failing to accurately estimate the
uncertainty of model predictions. We propose a Progressive Graph Learning (PGL)
framework that decomposes the target hypothesis space into the shared and
unknown subspaces, and then progressively pseudo-labels the most confident
known samples from the target domain for hypothesis adaptation. Moreover, we
tackle a more realistic source-free open-set domain adaptation (SF-OSDA)
setting that makes no assumption about the coexistence of source and target
domains, and introduce a balanced pseudo-labeling (BP-L) strategy in a
two-stage framework, namely SF-PGL. Different from PGL that applies a
class-agnostic constant threshold for all target samples for pseudo-labeling,
the SF-PGL model uniformly selects the most confident target instances from
each category at a fixed ratio. The confidence thresholds in each class are
regarded as the 'uncertainty' of learning the semantic information, which are
then used to weigh the classification loss in the adaptation step. We conducted
unsupervised and semi-supervised OSDA and SF-OSDA experiments on the benchmark
image classification and action recognition datasets. Additionally, we find
that balanced pseudo-labeling plays a significant role in improving
calibration, which makes the trained model less prone to over-confident or
under-confident predictions on the target data. Source code is available at
https://github.com/Luoyadan/SF-PGL.


title:
A Hamiltonian approach for point vortices on non-orientable surfaces II:
  the Klein bottle

abstract:
  This is the second of two companion papers dedicated to the investigation of
vortex motion on non-orientable surfaces. The first paper of the pair is
predominantly concerned with establishing the Hamiltonian approach to systems
of point vortices on non-orientable manifolds and investigating the limits of
the intrinsic (restricted to the non-orientable manifold) approach. In
addition, point vortex motion on the Mobius band is closely examined. In this
paper, we investigate dynamics of one and two point vortices on the Klein
bottle through establishing explicit forms of the Hamiltonian, equations of
motion on the charts, describing relative equilibria, etc.


title:
Detection of genuine multipartite entanglement based on principal basis
  matrix representations

abstract:
  We study the genuine multipartite entanglement in tripartite quantum systems
by using the principal basis matrix representations of density matrices. Using
the Schmidt decomposition and local unitary transformation, we first convert
the general states to simpler forms and then construct some special matrices
from the correlation tensors of the simplified density matrices. Based on the
different linear combinations of these matrices, necessary conditions are
presented to detect genuine multipartite entanglement of tripartite states.
Detailed examples show that our method can detect more entangled states than
previous ones.


title:
Semi-analytical pricing of barrier options in the time-dependent Heston
  model

abstract:
  We develop the general integral transforms (GIT) method for pricing barrier
options in the time-dependent Heston model (also with a time-dependent barrier)
where the option price is represented in a semi-analytical form as a
two-dimensional integral. This integral depends on yet unknown function
$\Phi(t,v)$ which is the gradient of the solution at the moving boundary $S =
L(t)$ and solves a linear mixed Volterra-Fredholm equation of the second kind
also derived in the paper. Thus, we generalize the one-dimensional GIT method,
developed in (Itkin, Lipton, Muravey, Generalized integral transforms in
mathematical finance, WS, 2021) and the corresponding papers, to the
two-dimensional case. In other words, we show that the GIT method can be
extended to stochastic volatility models (two drivers with inhomogeneous
correlation). As such, this 2D approach naturally inherits all advantages of
the corresponding 1D methods, in particular, their speed and accuracy. This
result is new and has various applications not just in finance but also in
physics. Numerical examples illustrate high speed and accuracy of the method as
compared with the finite-difference approach.


title:
Higher order GUP black hole based on COW experiment and Einstein-Bohr's
  photon box

abstract:
  In this work we have explored the effects of higher order generalized
uncertainty principle (GUP), inspired from the quantum gravity COW experiment
and the Einstein-Bohr's photon box thought experiment, on the properties of a
black hole. Two different GUP models, namely, GUP to all orders in the Planck
length model, and GUP with minimal length uncertainty and maximal momentum
model are considered for our study. For each model, we have investigated the
modified de Broglie formula, modification in gravitational phase shift,
Einstein-Bohr's photon box, and the modified Hawking temperature and the tidal
force of the GUP modified black hole.


title:
Weak Radiative Decays of Anti-triplet Bottomed Baryons in Light-Front
  Quark Model

abstract:
  We study the weak radiative decays of ${\bf B}_b \to {\bf B}_n \gamma$ with
${\bf B}_{b(n)} $ the anti-triplet-bottom (octet) baryons in the light front
quark model. We obtain that ${\cal B}(\Lambda_b \to \Lambda \gamma)= (7.1 \pm
0.3 )\times 10 ^{-6}$, which agrees well with the current experimental value of
$(7.1 \pm 1.7 )\times 10 ^{-6}$. We predict that ${\cal B}(\Xi_b^0 \to \Xi^0
\gamma)= (1.0 \pm 0.1 )\times 10 ^{-5}$ and ${\cal B}(\Xi_b^- \to \Xi^- \gamma)
= (1.1 \pm 0.1) \times 10^{-5}$, which are consistent with the latest upper
limits set by the LHCb collaboration. In addition, we find that the $SU(3)_F$
flavor symmetry breaking effects for the modes related to the $b\to d \gamma$
transition can be as large as $20\%$.


title:
Learning long-term music representations via hierarchical contextual
  constraints

abstract:
  Learning symbolic music representations, especially disentangled
representations with probabilistic interpretations, has been shown to benefit
both music understanding and generation. However, most models are only
applicable to short-term music, while learning long-term music representations
remains a challenging task. We have seen several studies attempting to learn
hierarchical representations directly in an end-to-end manner, but these models
have not been able to achieve the desired results and the training process is
not stable. In this paper, we propose a novel approach to learn long-term
symbolic music representations through contextual constraints. First, we use
contrastive learning to pre-train a long-term representation by constraining
its difference from the short-term representation (extracted by an
off-the-shelf model). Then, we fine-tune the long-term representation by a
hierarchical prediction model such that a good long-term representation (e.g.,
an 8-bar representation) can reconstruct the corresponding short-term ones
(e.g., the 2-bar representations within the 8-bar range). Experiments show that
our method stabilizes the training and the fine-tuning steps. In addition, the
designed contextual constraints benefit both reconstruction and
disentanglement, significantly outperforming the baselines.


title: Theory of kaon-nuclear systems

abstract:
  The strong interaction between an antikaon and a nucleon is at the origin of
various interesting phenomena in kaon-nuclear systems. In particular, the
interaction in the isospin $I=0$ channel is sufficiently attractive to generate
a quasi-bound state, the $\Lambda(1405)$ resonance, below the $\bar{K}N$
threshold. Based on this picture, it may be expected that the $\bar{K}N$
interaction also generates quasi-bound states in kaon-nuclear systems,
sometimes called kaonic nuclei. At the same time, the $\bar{K}N$ quasi-bound
picture of the $\Lambda(1405)$ is also related to the discussion of hadronic
molecules in hadron spectroscopy. Here an overview is presented of the
theoretical studies developed for kaon-nucleon and kaon-nuclear systems. We
start from the modern understanding of the $\Lambda(1405)$ resonance. We then
discuss the $\bar{K}N$ interaction and various aspects of few-body kaonic
nuclei. Heavier kaon-nuclear systems are examined from the viewpoint of nuclear
many-body physics, with focus on the properties of antikaons in nuclear matter.
Related topics, such as the $K^{-}p$ momentum correlation functions in
high-energy collisions and the studies of kaonic atoms, are also discussed.


title:
Estimation of symbiotic bacterial structure in a sustainable seagrass
  ecosystem on recycled management

abstract:
  Measures to address global warming are urgently needed, and blue carbon has
great potential in this regard. Seagrass fosters fish growth, pathogen
reduction, and carbon absorption. A survey of seagrass distribution in 52
coasts along the bay of Saiki city, Oita Prefecture in Japan demonstrated the
characteristic growth of seagrass Zostera around an aquaculture facility that
used recycled compost, which was fermented by thermophilic Bacillaceae from
unused marine resources, as a feed additive. Bacterial analyses of the sea
bottom sediment there showed a marked decrease in pathogen candidates, order
Flavobacteriales and family Fusobacteriaceae. Further, bacterial structural
equation analyses inferred that the sediment's order Clostridiales, family
Rhodobacteraceae, and genera Paracoccus and Phaeobacter were strongly
causalized with seagrass growth. These results emphasize that continuous
exposure to the thermophiles-fermented compost may evoke blue carbon with low
pathogenicity through changes in bacterial populations.


title: Video Game Project Management Anti-patterns

abstract:
  Project Management anti-patterns are well-documented in the
software-engineering literature, and studying them allows understanding their
impacts on teams and projects. The video game development industry is known for
its mismanagement practices, and therefore applying this knowledge would help
improving game developers' productivity and well-being. In this paper, we map
project management anti-patterns to anti-patterns reported by game developers
in the gray literature. We read 440 postmortems problems, identified
anti-pattern candidates, and related them with definitions from the
software-engineering literature. We discovered that most anti-pattern
candidates could be mapped to anti-patterns in the software-engineering
literature, except for Feature Creep, Feature Cuts, Working on Multiple
Projects, and Absent or Inadequate Tools. We discussed the impact of the
unmapped candidates on the development process while also drawing a parallel
between video games and traditional software development. Future works include
validating the definitions of the candidates via survey with practitioners and
also considering development anti-patterns.


title:
Breakdown of bulk-projected isotropy in surface electronic states of
  topological Kondo insulator SmB$_6$(001)

abstract:
  The topology and spin-orbital polarization of two-dimensional (2D) surface
electronic states have been extensively studied in this decade. One major
interest in them is their close relationship with the parities of the bulk (3D)
electronic states.In this context, the surface is often regarded as a simple
truncation of the bulk crystal. Here we show breakdown of the bulk-related
in-plane rotation symmetry in the topological surface states of the Kondo
insulator SmB$_6$. Angle-resolved photoelectron spectroscopy (ARPES) performed
on the vicinal SmB$_6$(001)-$p$(2$\times$2) surface showed that the topological
surface states are anisotropic and that the Fermi contour lacks the fourfold
rotation symmetry maintained in the bulk. This result emphasizes the important
role of the surface atomic structure even in the topological surface electronic
states. Moreover, the characteristic anisotropy of the SmB$_6$(001) surface
states provides a new pathway to interpret the exotic electronic phenomena in
the small Kondo gap of SmB$_6$, whose origin, from the 3D bulk or 2D surface,
is still controversial.


title: Tangent Flows of K\"ahler Metric Flows

abstract:
  We improve the description of $\mathbb{F}$-limits of noncollapsed Ricci flows
in the K\"ahler setting. In particular, the singular strata $\mathcal{S}^k$ of
such metric flows satisfy $\mathcal{S}^{2j}=\mathcal{S}^{2j+1}$. We also prove
an analogous result for quantitative strata, and show that any tangent flow
admits a nontrivial one-parameter action by isometries, which is locally free
on the cone link in the static case. The main results are established using
parabolic regularizations of conjugate heat kernel potential functions based at
almost-selfsimilar points, which may be of independent interest.


title: An Extreme Toughening Mechanism for Soft Materials

abstract:
  Soft yet tough materials are ubiquitous in nature and everyday life. The
ratio between fracture toughness and intrinsic fracture energy of a soft
material defines its toughness enhancement. Soft materials' toughness
enhancement has been long attributed to their bulk stress-stretch hysteresis
induced by dissipation mechanisms such as Mullins effect and viscoelasticity.
With a combination of experiments and theory, here we show that the bulk
dissipation mechanisms significantly underestimate the toughness enhancement of
soft tough materials. We propose a new mechanism and scaling law to account for
the extreme toughening of diverse soft materials. We show that the toughness
enhancement of soft materials relies on both bulk hysteric dissipation, and
near-crack dissipation due to mechanisms such as polymer-chain entanglement.
Unlike the bulk hysteric dissipation, the near-crack dissipation does not
necessarily induce large stress-stretch hysteresis of the bulk material. The
extreme toughening mechanism can be universally applied to various soft tough
materials, ranging from double-network hydrogels, interpenetrating-network
hydrogels, entangled-network hydrogels and slide-ring hydrogels, to unfilled
and filled rubbers.


title: On the Convergence of Clustered Federated Learning

abstract:
  In a federated learning system, the clients, e.g. mobile devices and
organization participants, usually have different personal preferences or
behavior patterns, namely Non-IID data problems across clients. Clustered
federated learning is to group users into different clusters that the clients
in the same group will share the same or similar behavior patterns that are to
satisfy the IID data assumption for most traditional machine learning
algorithms. Most of the existing clustering methods in FL treat every client
equally that ignores the different importance contributions among clients. This
paper proposes a novel weighted client-based clustered FL algorithm to leverage
the client's group and each client in a unified optimization framework.
Moreover, the paper proposes convergence analysis to the proposed clustered FL
method. The experimental analysis has demonstrated the effectiveness of the
proposed method.


title:
Testing the number of common factors by bootstrap in high-dimensional
  factor models

abstract:
  This paper proposes to test the number of common factors in high-dimensional
factor models by bootstrap. We provide asymptotic distributions for the
eigenvalues of bootstrapped sample covariance matrix under mild conditions. The
spiked eigenvalues converge weakly to Gaussian limits after proper scaling and
centralization. The limiting distribution of the largest non-spiked eigenvalue
is mainly determined by order statistics of bootstrap resampling weights, and
follows extreme value distribution. We propose two testing schemes based on the
disparate behavior of the spiked and non-spiked eigenvalues. The testing
procedures can perform reliably with weak factors, cross-sectionally and
serially correlated errors. Our technical proofs contribute to random matrix
theory with convexly decaying density and unbounded support, or with general
elliptical distributions.


title:
Kinetic Monte Carlo modelling of Helium Bubble Nucleation onto Oxides in
  the Fe-Ti-Y-O System

abstract:
  A Kinetic Monte Carlo (KMC) model was created to simulate the insertion of
transmutation He atoms into nanostructured ferritic alloys (NFAs) under neutron
irradiation. Interstitial He atoms migrate through the NFA until becoming
trapped in bubbles of other He atoms and vacancies created from irradiation.
The Y-Ti-O nano-oxides in the NFAs were found to be effective in capturing
these He atoms and preventing bubbles from forming at the grain boundary and
appear to replicate the characteristics (size and number density) observed in
other experiments. The bubbles were found to prefer the <111> oxide interface
as a nucleation site and the stable bubbles have a He/Vac ratio between 1.3 and
1.8 He/Vac. The influence of He bubbles on the segregation of solutes to the
grain boundaries or on the stability of the nano-oxides were negligible.


title:
Fast Algorithms of Bath Calculations in Simulations of Quantum
  System-Bath Dynamics

abstract:
  We present fast algorithms for the summation of Dyson series and the inchworm
Monte Carlo method for quantum systems that are coupled with harmonic baths.
The algorithms are based on evolving the integro-differential equations where
the most expensive part comes from the computation of bath influence
functionals. To accelerate the computation, we design fast algorithms based on
reusing the bath influence functionals computed in the previous time steps to
reduce the number of calculations. It is proven that the proposed fast
algorithms reduce the number of such calculations by a factor of $O(N)$, where
$N$ is the total number of time steps. Numerical experiments are carried out to
show the efficiency of the method and to verify the theoretical results.


title: Incentivizing Participation in Clinical Trials

abstract:
  The difficulty of recruiting patients is a well-known issue in clinical
trials which inhibits or sometimes precludes them in practice. We incentivize
participation in clinical trials by leveraging information asymmetry between
the trial and the patients. We obtain an optimal solution in terms of the
statistical performance of the trial, as expressed by an estimation error.
Namely, we provide an incentive-compatible mechanism with a particular
guarantee, and a nearly matching impossibility result for any
incentive-compatible mechanism.


title: A note on hamiltonian cycles in $4$-tough $(P_2\cup kP_1)$-free graphs

abstract:
  Let $t>0$ be a real number and $G$ be a graph. We say $G$ is $t$-tough if for
every cutset $S$ of $G$, the ratio of $|S|$ to the number of components of
$G-S$ is at least $t$. The Toughness Conjecture of Chv\'atal, stating that
there exists a constant $t_0$ such that every $t_0$-tough graph with at least
three vertices is hamiltonian, is still open in general. For any given integer
$k\ge 1$, a graph $G$ is $(P_2\cup kP_1)$ free if $G$ does not contain the
disjoint union of $P_2$ and $k$ isolated vertices as an induced subgraph.
  In this note, we show that every 4-tough and $2k$-connected $(P_2\cup
kP_1)$-free graph with at least three vertices is hamiltonian.
  This result in some sense is an "extension" of the classical
Chv\'{a}tal-Erd\H{o}s Theorem that every $\max\{2,k\}$-connected
  $(k+1)P_1$-free graph on at least three vertices is hamiltonian.


title:
Online Scheduling of Transmission and Processing for AoI Minimization
  with Edge Computing

abstract:
  Age of Information (AoI), which measures the time elapsed since the
generation of the last received packet at the destination, is a new metric for
real-time status update tracking applications. In this paper, we consider a
status-update system in which a source node samples updates and sends them to
an edge server over a delay channel. The received updates are processed by the
server with an infinite buffer and then delivered to a destination. The channel
can send only one update at a time, and the server can process one at a time as
well. The source node applies generate-at-will model according to the state of
the channel, the edge server, and the buffer. We aim to minimize the average
AoI with \emph{independent and identically distributed} transmission time and
processing time. We consider three online scheduling policies. The first one is
the optimal long wait policy, under which the source node only transmits a new
packet after the old one is delivered. Secondly, we propose a peak age
threshold policy, under which the source node determines the sending time based
on the estimated peak age of information (PAoI). Finally, we improve the peak
age threshold policy by considering a postponed plan to reduce the waiting time
in the buffer. The AoI performance under these policies is illustrated by
numerical results with different parameters.


title:
What is needed of a scalar field if it is to unify inflation and late
  time acceleration ?

abstract:
  Quintessential inflation refers to scenarios in which a single scalar field
is used to describe inflation and late-time acceleration. This review is
dedicated to the framework of quintessential inflation, with a focus on the
building blocks of formalism. Consistent unification of inflation and late time
acceleration using a single scalar field asks for a shallow field potential
initially followed by steep behaviour thereafter and shallow again around the
present epoch. The requirement of non-interference of scalar field with thermal
history dictates steep nature of potential in the post-inflationary era, with a
further restriction that late time physics be independent of initial
conditions. We describe, in detail the scaling and asymptotic scaling solutions
and the mechanism of exit from the scaling regime to late time acceleration.
Review includes a fresh look at scaling solutions which are central to the
theme of unification of inflation and late time acceleration. As for the exit
mechanism, special attention is paid to the coupling of massive neutrino matter
to the scalar field, which builds up dynamically and can give rise to late time
acceleration. We present a detailed analytical treatment of scalar field
dynamics in the presence of coupling. We briefly discuss the distinguished
feature of quintessential inflation, namely, the blue spectrum of gravity waves
produced during the transition from inflation to the kinetic regime.


title:
Ap\'ery-Type Series with Summation Indices of Mixed Parities and Colored
  Multiple Zeta Values, I

abstract:
  In this paper, we shall study A\'{e}ry-type series in which the central
binomial coefficient appears as part of the summand. Let
$b_n=4^n/\binom{2n}{n}$. Let $s_1,\dots,s_d$ be positive integers with $s_1\ge
2$. We consider the series \begin{align*} \sum_{n_1>\cdots>n_d>0}
\frac{b_{n_1}}{n_1^{s_1}\cdots n_d^{s_d}} \end{align*} and the variants with
some or all indices $n_j$ replaced by $2n_j\pm 1$ and some or all "$>$"
replaced by "$\ge$", provided the series are defined. We can also replace
$b_{n_1}$ by its square in the above series when $s_1\ge 3$. The main result is
that all such series are $\mathbb{Q}$-linear combinations of the real and/or
the imaginary parts of some colored multiple zeta values of level 4, i.e.,
multiple polylogarithms evaluated at 4th roots of unity.


title: Fairness-aware Configuration of Machine Learning Libraries

abstract:
  This paper investigates the parameter space of machine learning (ML)
algorithms in aggravating or mitigating fairness bugs. Data-driven software is
increasingly applied in social-critical applications where ensuring fairness is
of paramount importance. The existing approaches focus on addressing fairness
bugs by either modifying the input dataset or modifying the learning
algorithms. On the other hand, the selection of hyperparameters, which provide
finer controls of ML algorithms, may enable a less intrusive approach to
influence the fairness. Can hyperparameters amplify or suppress discrimination
present in the input dataset? How can we help programmers in detecting,
understanding, and exploiting the role of hyperparameters to improve the
fairness?
  We design three search-based software testing algorithms to uncover the
precision-fairness frontier of the hyperparameter space. We complement these
algorithms with statistical debugging to explain the role of these parameters
in improving fairness. We implement the proposed approaches in the tool
Parfait-ML (PARameter FAIrness Testing for ML Libraries) and show its
effectiveness and utility over five mature ML algorithms as used in six
social-critical applications. In these applications, our approach successfully
identified hyperparameters that significantly improve (vis-a-vis the
state-of-the-art techniques) the fairness without sacrificing precision.
Surprisingly, for some algorithms (e.g., random forest), our approach showed
that certain configuration of hyperparameters (e.g., restricting the search
space of attributes) can amplify biases across applications. Upon further
investigation, we found intuitive explanations of these phenomena, and the
results corroborate similar observations from the literature.


title: Web-Based File Clustering and Indexing for Mindoro State University

abstract:
  The Web Based File Clustering and Indexing for Mindoro State University aim
to organize data circulated over the Web into groups or collections to
facilitate data availability and access and at the same time meet user
preferences. The main benefits include increasing Web information
accessibility, understanding users navigation behavior, improving information
retrieval and content delivery on the Web. Web based file clustering could help
in reaching the required documents that the user is searching for. In this
paper a novel approach has been introduced for search results clustering that
is based on the semantics of the retrieved documents rather than the syntax of
the terms in those documents. Data clustering was used to improve the
information retrieval from the collection of documents. Data were processed and
analyzed using SPSS where the instrument was evaluated to test the reliability
and validity of the measures used. Evaluation was based on a Likert scale of
Excellent, Good, Fair, and Poor as described for the selected quality
characteristics.


title: Lip movements information disentanglement for lip sync

abstract:
  The lip movements information is critical for many audio-visual tasks.
However, extracting lip movements information from videos is challenging, as it
can be easily perturbed by factors like personal identities and head poses.
This paper proposes utilizing the parametric 3D face model to disentangle lip
movements information explicitly. Building on top of the recent 3D face
reconstruction advances, we firstly offer a method that can consistently
disentangle expression information, where the lip movements information lies.
Then we demonstrate that once the influences of perturbing factors are
alleviated by synthesizing faces with the disentangled lip movements
information, the lip-sync task can be done better with much fewer data.
Finally, we show its effectiveness in the wild by testing it on an unseen
dataset for the active speaker detection task and achieving competitive
performance.


title:
Panoramic mapping of phonon transport from ultrafast electron
  diffraction and machine learning

abstract:
  One central challenge in understanding phonon thermal transport is a lack of
experimental tools to investigate mode-based transport information. Although
recent advances in computation lead to mode-based information, it is hindered
by unknown defects in bulk region and at interfaces. Here we present a
framework that can reveal microscopic phonon transport information in
heterostructures, integrating state-of-the-art ultrafast electron diffraction
(UED) with advanced scientific machine learning. Taking advantage of the dual
temporal and reciprocal-space resolution in UED, we are able to reliably
recover the frequency-dependent interfacial transmittance with possible
extension to frequency-dependent relaxation times of the heterostructure. This
enables a direct reconstruction of real-space, real-time, frequency-resolved
phonon dynamics across an interface. Our work provides a new pathway to
experimentally probe phonon transport mechanisms with unprecedented details.


title:
Improving Graph Collaborative Filtering with Neighborhood-enriched
  Contrastive Learning

abstract:
  Recently, graph collaborative filtering methods have been proposed as an
effective recommendation approach, which can capture users' preference over
items by modeling the user-item interaction graphs. In order to reduce the
influence of data sparsity, contrastive learning is adopted in graph
collaborative filtering for enhancing the performance. However, these methods
typically construct the contrastive pairs by random sampling, which neglect the
neighboring relations among users (or items) and fail to fully exploit the
potential of contrastive learning for recommendation. To tackle the above
issue, we propose a novel contrastive learning approach, named
Neighborhood-enriched Contrastive Learning, named NCL, which explicitly
incorporates the potential neighbors into contrastive pairs. Specifically, we
introduce the neighbors of a user (or an item) from graph structure and
semantic space respectively. For the structural neighbors on the interaction
graph, we develop a novel structure-contrastive objective that regards users
(or items) and their structural neighbors as positive contrastive pairs. In
implementation, the representations of users (or items) and neighbors
correspond to the outputs of different GNN layers. Furthermore, to excavate the
potential neighbor relation in semantic space, we assume that users with
similar representations are within the semantic neighborhood, and incorporate
these semantic neighbors into the prototype-contrastive objective. The proposed
NCL can be optimized with EM algorithm and generalized to apply to graph
collaborative filtering methods. Extensive experiments on five public datasets
demonstrate the effectiveness of the proposed NCL, notably with 26% and 17%
performance gain over a competitive graph collaborative filtering base model on
the Yelp and Amazon-book datasets respectively. Our code is available at:
https://github.com/RUCAIBox/NCL.


title:
Unsupervised Disentanglement with Tensor Product Representations on the
  Torus

abstract:
  The current methods for learning representations with auto-encoders almost
exclusively employ vectors as the latent representations. In this work, we
propose to employ a tensor product structure for this purpose. This way, the
obtained representations are naturally disentangled. In contrast to the
conventional variations methods, which are targeted toward normally distributed
features, the latent space in our representation is distributed uniformly over
a set of unit circles. We argue that the torus structure of the latent space
captures the generative factors effectively. We employ recent tools for
measuring unsupervised disentanglement, and in an extensive set of experiments
demonstrate the advantage of our method in terms of disentanglement,
completeness, and informativeness. The code for our proposed method is
available at https://github.com/rotmanmi/Unsupervised-Disentanglement-Torus.


title:
Fast readout and reset of a superconducting qubit coupled to a resonator
  with an intrinsic Purcell filter

abstract:
  Coupling a resonator to a superconducting qubit enables various operations on
the qubit, including dispersive readout and unconditional reset. The speed of
these operations is limited by the external decay rate of the resonator.
However, increasing the decay rate also increases the rate of qubit decay via
the resonator, limiting the qubit lifetime. Here, we demonstrate that the
resonator-mediated qubit decay can be suppressed by utilizing the
distributed-element, multi-mode nature of the resonator. We show that the
suppression exceeds two orders of magnitude over a bandwidth of 600 MHz. We use
this "intrinsic Purcell filter" to demonstrate a 40-ns readout with 99.1%
fidelity and a 100-ns reset with residual excitation of less than 1.7%.


title:
Temperedness of $L^2(\Gamma\backslash G)$ and positive eigenfunctions in
  higher rank

abstract:
  Let $G=\operatorname{SO}^\circ(n,1) \times \operatorname{SO}^\circ(n,1)$ and
$X={\mathbb H}^{n}\times {\mathbb H}^{n}$ for $n\ge 2$. For a pair $(\pi_1,
\pi_2)$ of non-elementary convex cocompact representations of a finitely
generated group $\Sigma$ into $\operatorname{SO}^\circ(n,1)$, let
$\Gamma=(\pi_1\times \pi_2)(\Sigma)$. Denoting the bottom of the $L^2$-spectrum
of the negative Laplacian on $\Gamma\backslash X$ by $\lambda_0$, we show:
  (1) $L^2(\Gamma\backslash G)$ is tempered and $\lambda_0=\frac{1}{2}(n-1)^2$;
  (2) There exists no positive Laplace eigenfunction in $L^2(\Gamma \backslash
X)$.
  In fact, analogues of (1)-(2) hold for any Anosov subgroup $\Gamma$ in the
product of at least two simple algebraic groups of rank one as well as for
Hitchin subgroups $\Gamma<\operatorname{PSL}_d(\mathbb R)$, $d\ge 3$. Moreover,
if $G$ is a semisimple real algebraic group of rank at least $2$ and has the
trivial opposition involution, then (2) holds for any Anosov subgroup $\Gamma$
of $G$.


title:
Suppression of reconnection in polarized, thin magnetotail current
  sheets: 2D simulations and implications

abstract:
  Many in-situ spacecraft observations have demonstrated that magnetic
reconnection in Earth's magnetotail is largely controlled by the
prereconnection current sheet configuration. One of the most important thin
current sheet characteristics is the preponderance of electron currents driven
by strong polarized electric fields, which are commonly observed in the Earth's
magnetotail and may significantly alter current sheet reconnection. We use
particle-in-cell simulations to investigate magnetic reconnection under such
conditions, including the presence of a finite magnetic field component normal
to the current sheet. Under the same external driving conditions, reconnection
in a polarized current sheet is shown to occur at a lower rate than in a
nonpolarized current sheet. The reconnection rate in a polarized current sheet
decreases linearly as the electron current's contribution to the cross-tail
current increases. Lower background temperatures reduce the scale length of the
reconnection region across the current sheet and thus increase the reconnection
rate. We demonstrate that after reconnection in such a polarized current sheet,
the outflow energy flux is mostly in the form of ion enthalpy flux, followed by
electron enthalpy flux, Poynting flux, ion kinetic energy flux and electron
kinetic energy flux. These findings are consistent with spacecraft
observations. Because current sheet polarization is not uniform along the
magnetotail, our results suggest that it may slow down reconnection in the most
polarized near-Earth magnetotail and thereby move the location of reconnection
onset downtail.


title:
StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child
  Interactive Storytelling with Flexible Parental Involvement

abstract:
  Despite its benefits for children's skill development and parent-child
bonding, many parents do not often engage in interactive storytelling by having
story-related dialogues with their child due to limited availability or
challenges in coming up with appropriate questions. While recent advances made
AI generation of questions from stories possible, the fully-automated approach
excludes parent involvement, disregards educational goals, and underoptimizes
for child engagement. Informed by need-finding interviews and participatory
design (PD) results, we developed StoryBuddy, an AI-enabled system for parents
to create interactive storytelling experiences. StoryBuddy's design highlighted
the need for accommodating dynamic user needs between the desire for parent
involvement and parent-child bonding and the goal of minimizing parent
intervention when busy. The PD revealed varied assessment and educational goals
of parents, which StoryBuddy addressed by supporting configuring question types
and tracking child progress. A user study validated StoryBuddy's usability and
suggested design insights for future parent-AI collaboration systems.


title:
Fluctuation scaling in L\'evy-stable recruitment of marine fishes in
  randomly varying environments

abstract:
  This paper studies the scaling properties of recruitment fluctuations in
randomly varying environments, for abundant marine species with extreme
reproductive behavior. Fisheries stock-recruitment data from the North Atlantic
display fluctuation scaling, a proportionality between the standard deviation
and the average recruitment among stocks. The proportionality covers over five
orders of magnitude in the range studied. A linear-scaling behavior can be a
sign of a universal distribution of the normalized data across stocks. In light
of this conjecture, it is demonstrated that the L\'evy-stable model offers a
better effective description of the recruitment distribution than the
log-normal model. Care is devoted to the problem of random sums of random
variables. Recruitment is calculated by summing random offspring numbers with
infinite variance, where the number of summands (i.e. spawning population size)
is also a random process with infinite variance.


title:
Fundamental Performance of Integrated Sensing and Communications (ISAC)
  Systems

abstract:
  This letter analyzes the fundamental performance of integrated sensing and
communications (ISAC) systems. For downlink and uplink ISAC, the diversity
orders are analyzed to evaluate the communication rate (CR) and the high
signal-to-noise ratio (SNR) slopes are unveiled for the CR as well as the
sensing rate (SR). Furthermore, the achievable downlink and uplink CR-SR
regions are characterized. It is shown that ISAC can provide more degrees of
freedom for both the CR and the SR than conventional frequency-division sensing
and communications systems where isolated frequency bands are used for sensing
and communications, respectively.


title:
Metric Learning-enhanced Optimal Transport for Biochemical Regression
  Domain Adaptation

abstract:
  Generalizing knowledge beyond source domains is a crucial prerequisite for
many biomedical applications such as drug design and molecular property
prediction. To meet this challenge, researchers have used optimal transport
(OT) to perform representation alignment between the source and target domains.
Yet existing OT algorithms are mainly designed for classification tasks.
Accordingly, we consider regression tasks in the unsupervised and
semi-supervised settings in this paper. To exploit continuous labels, we
propose novel metrics to measure domain distances and introduce a posterior
variance regularizer on the transport plan. Further, while computationally
appealing, OT suffers from ambiguous decision boundaries and biased local data
distributions brought by the mini-batch training. To address those issues, we
propose to couple OT with metric learning to yield more robust boundaries and
reduce bias. Specifically, we present a dynamic hierarchical triplet loss to
describe the global data distribution, where the cluster centroids are
progressively adjusted among consecutive iterations. We evaluate our method on
both unsupervised and semi-supervised learning tasks in biochemistry.
Experiments show the proposed method significantly outperforms state-of-the-art
baselines across various benchmark datasets of small molecules and material
crystals.


title: Optomechanical crystal with bound states in the continuum

abstract:
  Chipscale micro- and nano-optomechanical systems, hinging on the intangible
radiation-pressure force, have shown their unique strength in sensing, signal
transduction, and exploration of quantum physics with mechanical resonators.
Optomechanical crystals, as one of the leading device platforms, enable
simultaneous molding of the band structure of optical photons and microwave
phonons with strong optomechanical coupling. Here, we demonstrate a new breed
of optomechanical crystals in two-dimensional slab-on-substrate structures
empowered by mechanical bound states in the continuum (BICs) at 8 GHz. We show
symmetry-induced BIC emergence with optomechanical couplings up to
$g/2\pi\approx 2.5$ MHz per unit cell, on par with low-dimensional
optomechanical crystals. Our work paves the way towards exploration of
photon-phonon interaction beyond suspended microcavities, which might lead to
new applications of optomechanics from phonon sensing to quantum transduction.


title: A gauge theoretic aspects of parabolic bundles over Klein surfaces

abstract:
  In this article, we study the gauge theoretic aspects of real and
quaternionic parabolic bundles over a real curve $(X, \sigma_X)$, where X is a
compact Riemann surface and {\sigma}X is an anti-holomorphic involution. For a
fixed real or quaternionic structure on a smooth parabolic bundle, we examine
the orbits space of real or quaternionic connection under the appropriate gauge
group. The corresponding gauge-theoretic quotients sit inside the real points
of the moduli of holomorphic parabolic bundles having a fixed parabolic type on
a compact Riemann surface $X$.


title:
Tac3D: A Novel Vision-based Tactile Sensor for Measuring Forces
  Distribution and Estimating Friction Coefficient Distribution

abstract:
  The importance of force perception in interacting with the environment was
proven years ago. However, it is still a challenge to measure the contact force
distribution accurately in real-time. In order to break through this
predicament, we propose a new vision-based tactile sensor, the Tac3D sensor,
for measuring the three-dimensional contact surface shape and contact force
distribution. In this work, virtual binocular vision is first applied to the
tactile sensor, which allows the Tac3D sensor to measure the three-dimensional
tactile information in a simple and efficient way and has the advantages of
simple structure, low computational costs, and inexpensive. Then, we used
contact surface shape and force distribution to estimate the friction
coefficient distribution in contact region. Further, combined with the global
position of the tactile sensor, the 3D model of the object with friction
coefficient distribution is reconstructed. These reconstruction experiments not
only demonstrate the excellent performance of the Tac3D sensor but also imply
the possibility to optimize the action planning in grasping based on the
friction coefficient distribution of the object.


title:
Uni-Retriever: Towards Learning The Unified Embedding Based Retriever in
  Bing Sponsored Search

abstract:
  Embedding based retrieval (EBR) is a fundamental building block in many web
applications. However, EBR in sponsored search is distinguished from other
generic scenarios and technically challenging due to the need of serving
multiple retrieval purposes: firstly, it has to retrieve high-relevance ads,
which may exactly serve user's search intent; secondly, it needs to retrieve
high-CTR ads so as to maximize the overall user clicks. In this paper, we
present a novel representation learning framework Uni-Retriever developed for
Bing Search, which unifies two different training modes knowledge distillation
and contrastive learning to realize both required objectives. On one hand, the
capability of making high-relevance retrieval is established by distilling
knowledge from the ``relevance teacher model''. On the other hand, the
capability of making high-CTR retrieval is optimized by learning to
discriminate user's clicked ads from the entire corpus. The two training modes
are jointly performed as a multi-objective learning process, such that the ads
of high relevance and CTR can be favored by the generated embeddings. Besides
the learning strategy, we also elaborate our solution for EBR serving pipeline
built upon the substantially optimized DiskANN, where massive-scale EBR can be
performed with competitive time and memory efficiency, and accomplished in
high-quality. We make comprehensive offline and online experiments to evaluate
the proposed techniques, whose findings may provide useful insights for the
future development of EBR systems. Uni-Retriever has been mainstreamed as the
major retrieval path in Bing's production thanks to the notable improvements on
the representation and EBR serving quality.


title:
Ticketing System: A Descriptive Research on the Use of Ticketing System
  for Project Management and Issue Tracking in IT Companies

abstract:
  IT companies are popular in the present time as technology arises in the
entire world. Incidents and Requests for either clients or company internal are
hard to manually track as it is a day-to-day transaction. To keep all the logs,
IT Companies are using a ticketing system to track all the records they are
gathering. One of the most popular ticketing systems is the JIRA. Either the
support team or company internal is benefitting from the use of JIRA as it
easily documents all the different transactions and keeps track of different
incidents and requests. One of the cons of JIRA is its limited features. The
researchers conducted research where it aims to determine the effectiveness of
JIRA as a ticketing system despite its limited features. The researchers used a
descriptive method to analyze the pros and cons of this ticketing system. It is
concluded that this JIRA Ticketing system is helpful for both support groups
and internal members of the company as they track all the incidents and request
tickets for their clients despite the limited features of JIRA. The researchers
strongly recommend to venture out and try other analytic models and start with
the ones mentioned in this study for a good foundation and idea. This is can
provide the research a new set of results that can be useful to both
established and startup companies.


title: On equivariant Lie-Yamaguti algebras and related structures

abstract:
  In this paper, we first discuss cohomology and a one-parameter formal
deformation theory of Lie-Yamaguti algebras. Next, we study finite group
actions on Lie-Yamaguti algebras and introduce equivariant cohomology for
Lie-Yamaguti algebras equipped with group actions. Finally, we study an
equivariant one-parameter formal deformation theory and show that our
equivariant cohomology is the suitable deformation cohomology.


title: Time quasi-periodic vortex patches

abstract:
  We prove the existence of small amplitude time quasi-periodic vortex patch
solutions of the 2D-Euler equations close to Kirchhoff elliptical vortices.
These solutions exist for all ellipse aspect ratios belonging to a Borel set of
asymptotically full Lebesgue measure. A major new difficulty is the the
presence of resonances related to the conservation of the angular momentum.


title:
Constraining the Cosmic Strings Gravitational Wave Spectra in No Scale
  Inflation with Viable Gravitino Dark matter and Non Thermal Leptogenesis

abstract:
  We revisit the hybrid inflation model gauged by $U(1)_{B-L}$ extension of the
Minimal Supersymmetric Standard Model (MSSM) in a no-scale background.
Considering a single predictive framework, we study inflation, leptogenesis,
gavitino cosmology, and the stochastic background of gravitational waves
produced by metastable cosmic strings. The spontaneous breaking of $U(1)_{B-L}$
at the end of inflation produces a network of metastable cosmic strings while,
the interaction between $U(1)_{B-L}$ Higgs field and the neutrinos generate
heavy Majorana masses for the right-handed neutrinos. The heavy Majorana masses
explain the tiny neutrino masses via the seesaw mechanism, a realistic scenario
for reheating and non-thermal leptogenesis. We show that a successful
non-thermal leptogenesis and a stable gravitino as a dark matter candidate can
be achieved for a wide range of reheating temperatures and $U(1)_{B-L}$
symmetry breaking scales. The possibility of realizing metastable cosmic
strings in a grand unified theory (GUT) setup is briefly discussed. We find
that a successful reheating with non-thermal leptogenesis and gravitino dark
matter restrict the allowed values of string tension to a narrow range $10^{-9}
\lesssim G\mu_{CS} \lesssim 8 \times 10^{-6}$, predicting a stochastic
gravitational-wave background that lies within the 1-$\sigma$ bounds of the
recent NANOGrav 12.5-yr data, as well as within the sensitivity bounds of
future GW experiments.


title:
The double contravariant powerset monad in the Goguen category of fuzzy
  sets

abstract:
  A monad is constructed in the Goguen category of fuzzy sets valued in a
unital quantale, which is an analog of the double contravariant powerset monad
in the category of sets. With help of this monad it is proved that the Goguen
category of fuzzy sets is dually monadic over itself.


title: Emotion Based Hate Speech Detection using Multimodal Learning

abstract:
  In recent years, monitoring hate speech and offensive language on social
media platforms has become paramount due to its widespread usage among all age
groups, races, and ethnicities. Consequently, there have been substantial
research efforts towards automated detection of such content using Natural
Language Processing (NLP). While successfully filtering textual data, no
research has focused on detecting hateful content in multimedia data. With
increased ease of data storage and the exponential growth of social media
platforms, multimedia content proliferates the internet as much as text data.
Nevertheless, it escapes the automatic filtering systems. Hate speech and
offensiveness can be detected in multimedia primarily via three modalities,
i.e., visual, acoustic, and verbal. Our preliminary study concluded that the
most essential features in classifying hate speech would be the speaker's
emotional state and its influence on the spoken words, therefore limiting our
current research to these modalities. This paper proposes the first multimodal
deep learning framework to combine the auditory features representing emotion
and the semantic features to detect hateful content. Our results demonstrate
that incorporating emotional attributes leads to significant improvement over
text-based models in detecting hateful multimedia content. This paper also
presents a new Hate Speech Detection Video Dataset (HSDVD) collected for the
purpose of multimodal learning as no such dataset exists today.


title: PQuAD: A Persian Question Answering Dataset

abstract:
  We present Persian Question Answering Dataset (PQuAD), a crowdsourced reading
comprehension dataset on Persian Wikipedia articles. It includes 80,000
questions along with their answers, with 25% of the questions being
adversarially unanswerable. We examine various properties of the dataset to
show the diversity and the level of its difficulty as an MRC benchmark. By
releasing this dataset, we aim to ease research on Persian reading
comprehension and development of Persian question answering systems. Our
experiments on different state-of-the-art pre-trained contextualized language
models show 74.8% Exact Match (EM) and 87.6% F1-score that can be used as the
baseline results for further research on Persian QA.


title: Factorization of Shapovalov elements

abstract:
  Shapovalov elements $\theta_{\beta,m}$ are special elements in a Borel
subalgebra of a classical or quantum universal enveloping algebra parameterized
by a positive root $\beta$ and a positive integer $m$. They relate the
canonical generator of a reducible Verma module with highest vectors of its
Verma submodules. For $m=1$, they can be explicitly obtained as matrix elements
of the inverse Shapovalov form. We extend this approach to $m>1$ for all
$\beta$ but three roots in $\mathfrak{g}_2$, $\mathfrak{f}_4$, and
$\mathfrak{e}_8$, presenting $\theta_{\beta,m}$ as a product of matrix elements
of weight $\beta$.


title:
Supporting Serendipitous Discovery and Balanced Analysis of Online
  Product Reviews with Interaction-Driven Metrics and Bias-Mitigating
  Suggestions

abstract:
  In this study, we investigate how supporting serendipitous discovery and
analysis of online product reviews can encourage readers to explore reviews
more comprehensively prior to making purchase decisions. We propose two
interventions -- Exploration Metrics that can help readers understand and track
their exploration patterns through visual indicators and a Bias Mitigation
Model that intends to maximize knowledge discovery by suggesting sentiment and
semantically diverse reviews. We designed, developed, and evaluated a text
analytics system called Serendyze, where we integrated these interventions. We
asked 100 crowd workers to use Serendyze to make purchase decisions based on
product reviews. Our evaluation suggests that exploration metrics enabled
readers to efficiently cover more reviews in a balanced way, and suggestions
from the bias mitigation model influenced readers to make confident data-driven
decisions. We discuss the role of user agency and trust in text-level analysis
systems and their applicability in domains beyond review exploration.


title:
Angular distribution of $\gamma$ rays from the p-wave resonance of
  $^{118}$Sn

abstract:
  The neutron energy-dependent angular distribution of $\gamma$ rays from
$^{117}{\rm Sn}(n,\gamma)$ reaction was measured with germanium detectors and a
pulsed neutron beam. The angular distribution was clearly observed in
$\gamma$-ray emissions with an energy of 9327 keV which corresponds to the
transition from a neutron resonance of $^{117}{\rm Sn}+n$ to the ground state
of $^{118}{\rm Sn}$. The angular distribution causes an angular-dependent
asymmetric resonance shape. An asymmetry $A_{\rm LH}$ was defined as $(N_{\rm
L}-N_{\rm H})/(N_{\rm L}+N_{\rm H})$, where $N_{\rm L}$ and $N_{\rm H}$ are
integrated values for lower- and higher-energy regions of a neutron resonance,
respectively. We found that the $A_{\rm LH}$ has the angular dependence of $(A
\cos \theta_\gamma +B)$, where $\theta_\gamma$ is the $\gamma$-ray emission
angle with respect to the incident neutron momentum, with $A=0.394 \pm 0.073$
and $B = 0.118 \pm 0.029$ in the 1.33 eV p-wave resonance.


title:
Novel Superposed Kink and Pulse Solutions for $\phi^4$, MKdV, NLS and
  Other Nonlinear Equations

abstract:
  We show that a number of nonlinear equations including symmetric as well as
asymmetric $\phi^4$, modified Korteweg de Vries (MKdV), mixed KdV-MKdV,
nonlinear Schr\"odinger (NLS), quadratic-cubic NLS as well as higher order
neutral scalar field theories, higher order KdV-MKdV and higher order
quadratic-cubic NLS admit superposed periodic kink and pulse solutions and some
of them also admit superposed hyperbolic kink solutions.


title: Generating the Level 2 Subgroup by Involutions

abstract:
  We obtain a minimal generating set of involutions for the level 2 subgroup of
the mapping class group of a closed nonorientable surface.


title: Circle actions and Suspension operations on Smooth manifolds

abstract:
  Let $M$ be a smooth manifold with $\dim M\geq 3$ and a base point $x_{0}$.
Surgeries along the oriented circle $S^{1}\times \{x_{0}\}$ on the product $
S^{1}\times M$ yields two manifolds $\Sigma _{0}M$ and $\Sigma _{1}M$, called
the suspensions of $M$.
  The suspension operations $\Sigma _{i}$ play a basic role in the construction
and classification of the smooth manifolds which admit free $ S^{1}$-actions.
We illustrate this by a number of applications.


title: Feature Construction and Selection for PV Solar Power Modeling

abstract:
  Using solar power in the process industry can reduce greenhouse gas emissions
and make the production process more sustainable. However, the intermittent
nature of solar power renders its usage challenging. Building a model to
predict photovoltaic (PV) power generation allows decision-makers to hedge
energy shortages and further design proper operations. The solar power output
is time-series data dependent on many factors, such as irradiance and weather.
A machine learning framework for 1-hour ahead solar power prediction is
developed in this paper based on the historical data. Our method extends the
input dataset into higher dimensional Chebyshev polynomial space. Then, a
feature selection scheme is developed with constrained linear regression to
construct the predictor for different weather types. Several tests show that
the proposed approach yields lower mean squared error than classical machine
learning methods, such as support vector machine (SVM), random forest (RF), and
gradient boosting decision tree (GBDT).


title:
Evidence for quasar fast outflows being accelerated at the scale of tens
  of parsecs

abstract:
  Quasar outflows may play a crucial role in regulating the host galaxy,
although the spatial scale of quasar outflows remain a major enigma, with their
acceleration mechanism poorly understood. The kinematic information of outflow
is the key to understanding its origin and acceleration mechanism. Here, we
report the galactocentric distances of different outflow components for both a
sample and an individual quasar. We find that the outflow distance increases
with velocity, with a typical value from several parsecs to more than one
hundred parsecs, providing direct evidence for an acceleration happening at a
scale of the order of 10 parsecs. These outflows carry ~1% of the total quasar
energy, while their kinematics are consistent with a dust driven model with a
launching radius comparable to the scale of a dusty torus, indicating that the
coupling between dust and quasar radiation may produce powerful feedback that
is crucial to galaxy evolution.


title: Robust Deepfake On Unrestricted Media: Generation And Detection

abstract:
  Recent advances in deep learning have led to substantial improvements in
deepfake generation, resulting in fake media with a more realistic appearance.
Although deepfake media have potential application in a wide range of areas and
are drawing much attention from both the academic and industrial communities,
it also leads to serious social and criminal concerns. This chapter explores
the evolution of and challenges in deepfake generation and detection. It also
discusses possible ways to improve the robustness of deepfake detection for a
wide variety of media (e.g., in-the-wild images and videos). Finally, it
suggests a focus for future fake media research.


title:
Vital Node Identification in Complex Networks Using a Machine
  Learning-Based Approach

abstract:
  Vital node identification is the problem of finding nodes of highest
importance in complex networks. This problem has crucial applications in
various contexts such as viral marketing or controlling the propagation of
virus or rumours in real-world networks. Existing approaches for vital node
identification mainly focus on capturing the importance of a node through a
mathematical expression which directly relates structural properties of the
node to its vitality. Although these heuristic approaches have achieved good
performance in practice, they have weak adaptability, and their performance is
limited to specific settings and certain dynamics. Inspired by the power of
machine learning models for efficiently capturing different types of patterns
and relations, we propose a machine learning-based, data driven approach for
vital node identification. The main idea is to train the model with a small
portion of the graph, say 0.5% of the nodes, and do the prediction on the rest
of the nodes. The ground-truth vitality for the train data is computed by
simulating the SIR diffusion method starting from the train nodes. We use
collective feature engineering where each node in the network is represented by
incorporating elements of its connectivity, degree and extended coreness.
Several machine learning models are trained on the node representations, but
the best results are achieved by a Support Vector Regression machine with RBF
kernel. The empirical results confirms that the proposed model outperforms
state-of-the-art models on a selection of datasets, while it also shows more
adaptability to changes in the dynamics parameters.


title: Accumulation points on 3-fold canonical thresholds

abstract:
  We obtain that the nonzero accumulation points of the set of 3-fold canonical
thresholds $\ct(X,S)$ are precisely $1/k$ where $k\ge 2$ is an integer and $S$
is an effective integral divisor of a projective 3-fold $X$ with only terminal
singularities. Moreover, we generalize the ascending chain condition for the
set of 3-fold canonical thresholds to pair.


title:
Performance Analysis of Multi-Reconfigurable Intelligent
  Surface-Empowered THz Wireless Systems

abstract:
  In this paper, we introduce a theoretical framework for analyzing the
performance of multi-reconfigurable intelligence surface (RIS) empowered
terahertz (THz) wireless systems subject to turbulence and stochastic beam
misalignment. In more detail, we extract a closed-form expression for the
outage probability that quantifies the joint impact of turbulence and
misalignment as well as the effect of transceivers' hardware imperfections. Our
results highlight the importance of accurately modeling both turbulence and
misalignment when assessing the performance of multi-RIS-empowered THz wireless
systems.


title: Understanding Natural Gradient in Sobolev Spaces

abstract:
  While natural gradients have been widely studied from both theoretical and
empirical perspectives, we argue that a fundamental theoretical issue regarding
the existence of gradients in infinite dimensional function spaces remains
underexplored. We therefore study the natural gradient induced by Sobolev
metrics and develop several rigorous results. Our results also establish new
connections between natural gradients and RKHS theory, and specifically to the
Neural Tangent Kernel (NTK). We develop computational techniques for the
efficient approximation of the proposed Sobolev Natural Gradient. Preliminary
experimental results reveal the potential of this new natural gradient variant.


title: The Sample Complexity of One-Hidden-Layer Neural Networks

abstract:
  We study norm-based uniform convergence bounds for neural networks, aiming at
a tight understanding of how these are affected by the architecture and type of
norm constraint, for the simple class of scalar-valued one-hidden-layer
networks, and inputs bounded in Euclidean norm. We begin by proving that in
general, controlling the spectral norm of the hidden layer weight matrix is
insufficient to get uniform convergence guarantees (independent of the network
width), while a stronger Frobenius norm control is sufficient, extending and
improving on previous work. Motivated by the proof constructions, we identify
and analyze two important settings where a mere spectral norm control turns out
to be sufficient: First, when the network's activation functions are
sufficiently smooth (with the result extending to deeper networks); and second,
for certain types of convolutional networks. In the latter setting, we study
how the sample complexity is additionally affected by parameters such as the
amount of overlap between patches and the overall number of patches.


title: Enumerating solutions to grid based puzzles with a fixed number of rows

abstract:
  In this paper we demonstrate a method for counting the number of solutions to
various logic puzzles. Specifically, we remove all of the "clues" from the
puzzle which help the solver to a unique solution, and instead start from an
empty grid. We then count the number of ways to fill in this empty grid to a
valid solution. We fix the number of rows $r$, vary the number of columns $k$,
and then compute the sequence $A_r(k)$, which gives the number of solutions on
an empty grid of size $r \times k$.


title: Tamagawa numbers of elliptic curves with torsion points

abstract:
  Let $K$ be a global field and let $E/K$ be an elliptic curve with a
$K$-rational point of prime order $p$. In this paper we are interested in how
often the (global) Tamagawa number $c(E/K)$ of $E/K$ is divisible by $p$. This
is a natural question to consider in view of the fact that the fraction
$c(E/K)/ |E(K)_{\text{tors}}|$ appears in the second part of the Birch and
Swinnerton-Dyer Conjecture. We focus on elliptic curves defined over global
fields, but we also prove a result for higher dimensional abelian varieties
defined over $\mathbb{Q}$.


title:
Efficient Natural Gradient Descent Methods for Large-Scale Optimization
  Problems

abstract:
  We propose an efficient numerical method for computing natural gradient
descent directions with respect to a generic metric in the state space. Our
technique relies on representing the natural gradient direction as a solution
to a standard least-squares problem. Hence, instead of calculating, storing, or
inverting the information matrix directly, we apply efficient methods from
numerical linear algebra to solve this least-squares problem. We treat both
scenarios where the derivative of the state variable with respect to the
parameter is either explicitly known or implicitly given through constraints.
We apply the QR decomposition to solve the least-squares problem in the former
case and utilize the adjoint-state method to compute the natural gradient
descent direction in the latter case. As a result, we can reliably compute
several natural gradient descents, including the Wasserstein natural gradient,
for a large-scale parameter space with thousands of dimensions, which was
believed to be out of reach. Finally, our numerical results shed light on the
qualitative differences among the standard gradient descent method and various
natural gradient descent methods based on different metric spaces in
large-scale nonconvex optimization problems.


title:
Codes and Designs in Johnson Graphs From Symplectic Actions on Quadratic
  Forms

abstract:
  The Johnson graph $J(v, k)$ has as vertices the $k$-subsets of
$\mathcal{V}=\{1,\ldots, v\}$, and two vertices are joined by an edge if their
intersection has size $k-1$. An \emph{$X$-strongly incidence-transitive code}
in $J (v, k)$ is a proper vertex subset $\Gamma$ such that the subgroup $X$ of
graph automorphisms leaving $\Gamma$ invariant is transitive on the set
$\Gamma$ of `codewords', and for each codeword $\Delta$, the setwise stabiliser
$X_\Delta$ is transitive on $\Delta \times (\mathcal{V}\setminus \Delta)$. We
classify the \emph{$X$-strongly incidence-transitive codes} in $J(v,k)$ for
which $X$ is the symplectic group $\mathrm{Sp}_{2n}(2)$ acting as a
$2$-transitive permutation group of degree $2^{2n-1}\pm 2^{n-1}$, where the
stabiliser $X_\Delta$ of a codeword $\Delta$ is contained in a \emph{geometric}
maximal subgroup of $X$. In particular, we construct two new infinite families
of strongly incidence-transitive codes associated with the reducible maximal
subgroups of $\mathrm{Sp}_{2n}(2)$.


title:
Multimodal Depression Classification Using Articulatory Coordination
  Features And Hierarchical Attention Based Text Embeddings

abstract:
  Multimodal depression classification has gained immense popularity over the
recent years. We develop a multimodal depression classification system using
articulatory coordination features extracted from vocal tract variables and
text transcriptions obtained from an automatic speech recognition tool that
yields improvements of area under the receiver operating characteristics curve
compared to uni-modal classifiers (7.5% and 13.7% for audio and text
respectively). We show that in the case of limited training data, a
segment-level classifier can first be trained to then obtain a session-wise
prediction without hindering the performance, using a multi-stage convolutional
recurrent neural network. A text model is trained using a Hierarchical
Attention Network (HAN). The multimodal system is developed by combining
embeddings from the session-level audio model and the HAN text model


title: Supported Policy Optimization for Offline Reinforcement Learning

abstract:
  Policy constraint methods to offline reinforcement learning (RL) typically
utilize parameterization or regularization that constrains the policy to
perform actions within the support set of the behavior policy. The elaborative
designs of parameterization methods usually intrude into the policy networks,
which may bring extra inference cost and cannot take full advantage of
well-established online methods. Regularization methods reduce the divergence
between the learned policy and the behavior policy, which may mismatch the
inherent density-based definition of support set thereby failing to avoid the
out-of-distribution actions effectively. This paper presents Supported Policy
OpTimization (SPOT), which is directly derived from the theoretical
formalization of the density-based support constraint. SPOT adopts a VAE-based
density estimator to explicitly model the support set of behavior policy and
presents a simple but effective density-based regularization term, which can be
plugged non-intrusively into off-the-shelf off-policy RL algorithms. On the
standard benchmarks for offline RL, SPOT substantially outperforms
state-of-the-art offline RL methods. Benefiting from the pluggable design, the
offline pretrained models from SPOT can also be applied to perform online
fine-tuning seamlessly.


title: FairStyle: Debiasing StyleGAN2 with Style Channel Manipulations

abstract:
  Recent advances in generative adversarial networks have shown that it is
possible to generate high-resolution and hyperrealistic images. However, the
images produced by GANs are only as fair and representative as the datasets on
which they are trained. In this paper, we propose a method for directly
modifying a pre-trained StyleGAN2 model that can be used to generate a balanced
set of images with respect to one (e.g., eyeglasses) or more attributes (e.g.,
gender and eyeglasses). Our method takes advantage of the style space of the
StyleGAN2 model to perform disentangled control of the target attributes to be
debiased. Our method does not require training additional models and directly
debiases the GAN model, paving the way for its use in various downstream
applications. Our experiments show that our method successfully debiases the
GAN model within a few minutes without compromising the quality of the
generated images. To promote fair generative models, we share the code and
debiased models at http://catlab-team.github.io/fairstyle.


title: Geometric Graph Representation Learning via Maximizing Rate Reduction

abstract:
  Learning discriminative node representations benefits various downstream
tasks in graph analysis such as community detection and node classification.
Existing graph representation learning methods (e.g., based on random walk and
contrastive learning) are limited to maximizing the local similarity of
connected nodes. Such pair-wise learning schemes could fail to capture the
global distribution of representations, since it has no explicit constraints on
the global geometric properties of representation space. To this end, we
propose Geometric Graph Representation Learning (G2R) to learn node
representations in an unsupervised manner via maximizing rate reduction. In
this way, G2R maps nodes in distinct groups (implicitly stored in the adjacency
matrix) into different subspaces, while each subspace is compact and different
subspaces are dispersedly distributed. G2R adopts a graph neural network as the
encoder and maximizes the rate reduction with the adjacency matrix.
Furthermore, we theoretically and empirically demonstrate that rate reduction
maximization is equivalent to maximizing the principal angles between different
subspaces. Experiments on real-world datasets show that G2R outperforms various
baselines on node classification and community detection tasks.


title:
Beyond NaN: Resiliency of Optimization Layers in The Face of
  Infeasibility

abstract:
  Prior work has successfully incorporated optimization layers as the last
layer in neural networks for various problems, thereby allowing joint learning
and planning in one neural network forward pass. In this work, we identify a
weakness in such a set-up where inputs to the optimization layer lead to
undefined output of the neural network. Such undefined decision outputs can
lead to possible catastrophic outcomes in critical real time applications. We
show that an adversary can cause such failures by forcing rank deficiency on
the matrix fed to the optimization layer which results in the optimization
failing to produce a solution. We provide a defense for the failure cases by
controlling the condition number of the input matrix. We study the problem in
the settings of synthetic data, Jigsaw Sudoku, and in speed planning for
autonomous driving, building on top of prior frameworks in end-to-end learning
and optimization. We show that our proposed defense effectively prevents the
framework from failing with undefined output. Finally, we surface a number of
edge cases which lead to serious bugs in popular equation and optimization
solvers which can be abused as well.


title: The Lieb-Schultz-Mattis Theorem: A Topological Point of View

abstract:
  We review the Lieb-Schultz-Mattis theorem and its variants, which are no-go
theorems that state that a quantum many-body system with certain conditions
cannot have a locally-unique gapped ground state. We restrict ourselves to
one-dimensional quantum spin systems and discuss both the generalized
Lieb-Schultz-Mattis theorem for models with U(1) symmetry and the extended
Lieb-Schultz-Mattis theorem for models with discrete symmetry. We also discuss
the implication of the same arguments to systems on the infinite cylinder, both
with the periodic boundary conditions and with the spiral boundary conditions.
  For models with U(1) symmetry, we here present a rearranged version of the
original proof of Lieb, Schultz, and Mattis based on the twist operator. As the
title suggests we take a modern topological point of view and prove the
generalized Lieb-Schultz-Mattis theorem by making use of a topological index
(which coincides with the filling factor). By a topological index, we mean an
index that characterizes a locally-unique gapped ground state and is invariant
under continuous (or smooth) modification of the ground state.
  For models with discrete symmetry, we describe the basic idea of the most
general proof based on the topological index introduced in the context of
symmetry-protected topological phases. We start from background materials such
as the classification of projective representations of the symmetry group.
  We also review the notion that we call a locally-unique gapped ground state
of a quantum spin system on an infinite lattice and present basic theorems.
This notion turns out to be natural and useful from the physicists' point of
view.
  We have tried to make the present article readable and almost self-contained.
We only assume basic knowledge about quantum spin systems.

categories: cond-mat.stat-mech math-ph math.MP quant-ph


title:
Pressure-induced superconductivity reentrant in transition metal
  dichalcogenide TiSe2

abstract:
  Through either elements intercalation or application of pressure, transition
metal dichalcogenide 1T-TiSe2 exhibits superconductivity in proximity to a
charge density wave (CDW) quantum critical point (QCP), thus providing an ideal
avenue to study the correlation between the two symmetry-breaking exotic
quantum electronic states. We report herein that, in addition to the well-known
superconducting dome that emerges within the low pressure range of 2 - 4 GPa
and peaks with the maximal Tc of about 1.8 K, the pressure induces another
separate superconducting transition starting around 15 GPa with a substantially
higher Tc that reaches 5.6 K at about 21.5 GPa. The high-pressure X-ray
diffraction and Raman spectroscopy measurements unveil that the
superconductivity reentrant is caused by a first-order structural phase
transition (from P-3m1 space group to Pnma space group), which is also
supported by the density functional theory calculation. A comparative
theoretical calculation also reveals that the conventional phonon-mediated
mechanism can account for the superconductivity of 1T-TiSe2 under low pressure,
while the electron-phonon coupling of 4O-TiSe2 under high pressure is too weak
to induce the superconductivity with a Tc as high as 5.6 K. This implies that
the emergent superconductivity in the 4O-TiSe2 may have an unconventional
origin. Our finding would open a new window toward the discovery of more exotic
quantum states in transition metal dichalcogenides via high pressure.


title: Reduced-Form Allocations with Complementarity: A 2-Person Case

abstract:
  We investigate the implementation of reduced-form allocation probabilities in
a two-person bargaining problem without side payments, where the agents have to
select one alternative from a finite set of social alternatives. We provide a
necessary and sufficient condition for the implementability. We find that the
implementability condition in bargaining has some new feature compared to
Border's theorem. Our results have applications in compromise problems and
package exchange problems where the agents barter indivisible objects and the
agents value the objects as complements.


title:
Exponential inequalities for the number of triangles in the
  Erd\"{o}s-R\'{e}nyi random graph

abstract:
  Upper exponential inequalities for the tail probabilities of the centered and
normalized number of triangles in the Erd\"{o}s-R\'{e}nyi graph are obtained,
where the probability of every edge is fixed. The result is formulated in terms
of random fields.


title: ORBGRAND Is Almost Capacity-Achieving

abstract:
  Decoding via sequentially guessing the error pattern in a received noisy
sequence has received attention recently, and the ORBGRAND has been proposed as
one such decoding algorithm that is capable of utilizing the soft information
embedded in the received noisy sequence. An information theoretic study is
conducted for the ORBGRAND, and it is shown that the achievable rate of the
ORBGRAND almost coincides with the channel capacity, for an additive white
Gaussian noise channel under antipodal input. For finite-length codes, improved
guessing schemes motivated by the information theoretic study are proposed that
attain lower error rates than the ORBGRAND, especially in the high
signal-to-noise ratio regime.


title:
A Tech Hybrid-Recommendation Engine and Personalized Notification: An
  integrated tool to assist users through Recommendations (Project ATHENA)

abstract:
  Project ATHENA aims to develop an application to address information
overload, primarily focused on Recommendation Systems (RSs) with the
personalization and user experience design of a modern system. Two machine
learning (ML) algorithms were used: (1) TF-IDF for Content-based filtering
(CBF); (2) Classification with Matrix Factorization- Singular Value
Decomposition(SVD) applied with Collaborative filtering (CF) and mean
(normalization) for prediction accuracy of the CF. Data sampling in academic
Research and Development of Philippine Council for Agriculture, Aquatic, and
Natural Resources Research and Development (PCAARRD) e-Library and Project
SARAI publications plus simulated data used as training sets to generate a
recommendation of items that uses the three RS filtering (CF, CBF, and
personalized version of item recommendations). Series of Testing and TAM
performed and discussed. Findings allow users to engage in online information
and quickly evaluate retrieved items produced by the application.
Compatibility-testing (CoT) shows the application is compatible with all major
browsers and mobile-friendly. Performance-testing (PT) recommended v-parameter
specs and TAM evaluations results indicate strongly associated with overall
positive feedback, thoroughly enough to address the information-overload
problem as the core of the paper. A modular architecture presented addressing
the information overload, primarily focused on RSs with the personalization and
design of modern systems. Developers utilized Two ML algorithms and prototyped
a simplified version of the architecture. Series of testing (CoT and PT) and
evaluations with TAM were performed and discussed. Project ATHENA added a UX
feature design of a modern system.


title: Extremal graphs for edge blow-up of lollipops

abstract:
  Given a graph $H$ and an integer $p$ ($p\geq 2$), the edge blow-up $H^{p+1}$
of $H$ is the graph obtained from replacing each edge in $H$ by a clique of
order $(p+1)$, where the new vertices of the cliques are all distinct. The
Tur\'{a}n numbers for edge blow-up of matchings were first studied by Erd\H{o}s
and Moon. Very recently some substantial progress of the extremal graphs for
$H^{p+1}$ of larger $p$ has been made by Yuan. The range of Tur\'{a}n numbers
for edge blow-up of all bipartite graphs when $p\geq 3$ and the exact Tur\'{a}n
numbers for edge blow-up of all non-bipartite graphs when $p\geq \chi(H) +1$
has been determined by Yuan (2022), where $\chi(H)$ is the chromatic number of
$H$. A lollipop $C_{k,\;\ell}$ is the graph obtained from a cycle $C_k$ by
appending a path $P_{\ell+1}$ to one of its vertices. In this paper, we
consider the extremal graphs for $C_{k,\;\ell}^{p+1}$ of the rest cases $p=2$
and $p=3$.


title: Privacy protection based on mask template

abstract:
  Powerful recognition algorithms are widely used in the Internet or important
medical systems, which poses a serious threat to personal privacy. Although the
law provides for diversity protection, e.g. The General Data Protection
Regulation (GDPR) in Europe and Articles 1032 to 1039 of the civil code in
China. However, as an important privacy disclosure event, biometric data is
often hidden, which is difficult for the owner to detect and trace to the
source. Human biometrics generally exist in images. In order to avoid the
disclosure of personal privacy, we should prevent unauthorized recognition
algorithms from acquiring the real features of the original image.


title:
Topological nature of higher-order hinge states revealed by spin
  transport

abstract:
  One-dimensional (1D) gapless hinge states are predicated in the
three-dimensional (3D) higher-order topological insulators and topological
semimetals, because of the higher-order bulk-boundary correspondence.
Nevertheless, the topologically protected property of the hinge states is still
not demonstrated so far, because it is not accessible by conventional methods,
such as spectroscopy experiments and quantum oscillations. Here, we reveal the
topological nature of hinge states in the higher-order topological semimetal
Cd3As2 nanoplate through spin potentiometric measurements. The results of
current induced spin polarization indicate that the spin-momentum locking of
the higher-order hinge state is similar to that of the quantum spin Hall state,
showing the helical characteristics. The spin-polarized hinge states are robust
up to room temperature and can nonlocally diffuse a long distance larger than 5
{\mu}m, further indicating their immunity protected by topology. Our work
deepens the understanding of transport properties of the higher-order
topological materials and should be valuable for future electronic and
spintronic applications.


title: Application of Color Block Code in Image Scaling

abstract:
  Aiming at the high cost of embedding annotation watermark in a narrow small
area and the information distortion caused by the change of annotation
watermark image resolution, this paper proposes a color block code technology,
which uses location information and color code to form recognizable graphics,
which can not only simplify the annotation graphics, but also ensure the
recognition efficiency. First, the constituent elements of color block code are
designed, and then the coding and decoding method of color block code is
proposed. Experiments show that color block code has high anti-scaling and
anti-interference, and can be widely used in the labeling of small object
surface and low resolution image.


title:
Autonomous Drone Swarm Navigation and Multi-target Tracking in 3D
  Environments with Dynamic Obstacles

abstract:
  Autonomous modeling of artificial swarms is necessary because manual creation
is a time intensive and complicated procedure which makes it impractical. An
autonomous approach employing deep reinforcement learning is presented in this
study for swarm navigation. In this approach, complex 3D environments with
static and dynamic obstacles and resistive forces (like linear drag, angular
drag, and gravity) are modeled to track multiple dynamic targets. Moreover,
reward functions for robust swarm formation and target tracking are devised for
learning complex swarm behaviors. Since the number of agents is not fixed and
has only the partial observance of the environment, swarm formation and
navigation become challenging. In this regard, the proposed strategy consists
of three main phases to tackle the aforementioned challenges: 1) A methodology
for dynamic swarm management, 2) Avoiding obstacles, Finding the shortest path
towards the targets, 3) Tracking the targets and Island modeling. The dynamic
swarm management phase translates basic sensory input to high level commands to
enhance swarm navigation and decentralized setup while maintaining the swarms
size fluctuations. While, in the island modeling, the swarm can split into
individual subswarms according to the number of targets, conversely, these
subswarms may join to form a single huge swarm, giving the swarm ability to
track multiple targets. Customized state of the art policy based deep
reinforcement learning algorithms are employed to achieve significant results.
The promising results show that our proposed strategy enhances swarm navigation
and can track multiple static and dynamic targets in complex dynamic
environments.


title:
Majorana corner states in an attractive quantum spin Hall insulator with
  opposite in-plane Zeeman energy at two sublattice sites

abstract:
  Higher-order topological superconductors and superfluids host
lower-dimensional Majorana corner and hinge states since novel topology
exhibitions on boundaries. While such topological nontrivial phases have been
explored extensively, more possible schemes are necessary for engineering
Majorana states. In this paper we propose Majorana corner states could be
realized in a two-dimensional attractive quantum spin-Hall insulator with
opposite in-plane Zeeman energy at two sublattice sites. The appropriate Zeeman
field leads to the opposite Dirac mass for adjacent edges of a square sample,
and naturally induce Majorana corner states. This topological phase can be
characterized by Majorana edge polarizations, and it is robust against
perturbations on random potentials as long as the edge gap remains open. Our
work provides a new possibility to realize a second-order topological
superfluid in two dimensions and engineer Majorana corner states.