title:
A smoothed particle hydrodynamics approach for phase field modeling of
  brittle fracture

abstract:
  Fracture is a very challenging and complicated problem with various
applications in engineering and physics. Although it has been extensively
studied within the context of mesh-based numerical techniques, such as the
finite element method (FEM), the research activity within the Smoothed Particle
Hydrodynamics (SPH) community remains scarce. SPH is a particle based method,
ideal to simulate fracture scenarios that involve extreme deformations.
However, to model fracture, SPH researchers have mostly relied on ad-hoc
empirical local damage models, cohesive zone approaches, or pseudo-spring
models, which come with a set of drawbacks and limitations. On the other hand,
phase field models of brittle fracture have recently gained popularity in
academic circles and provide significant improvements compared to previous
approaches. These improvements include the derivation from fundamental fracture
theories, the introduction of non-locality, and the ability to model multiple
crack initiation, propagation, branching, and coalescence, in situations where
no prior knowledge of the crack paths is available. Nevertheless, phase field
modeling has not been combined with SPH for fracture simulations. In this
proof-of-concept paper we develop and implement a phase field model of brittle
fracture within the context of SPH. Comprehensive mathematical and
implementation details are provided, and several challenging numerical examples
are computed and illustrate the proposed method's ability to accurately and
efficiently simulate complex fracture scenarios with the SPH framework.


title: Quantum Entanglement with Generalized Uncertainty Principle

abstract:
  We explore how the quantum entanglement is modified in the generalized
uncertainty principle (GUP)-corrected quantum mechanics by introducing the
coupled harmonic oscillator system. Constructing the ground state $\rho_0$ and
its reduced substate $\rho_A = \mbox{Tr}_B \rho_0$, we compute two entanglement
measures of $\rho_0$, i.e. ${\cal E}_{EoF} (\rho_0) = S_{von} (\rho_A)$ and
${\cal E}_{\gamma} (\rho_0) = S_{\gamma} (\rho_A)$, where $S_{von}$ and
$S_{\gamma}$ are the von Neumann and R\'{e}nyi entropies, up to the first order
of the GUP parameter $\alpha$. It is shown that ${\cal E}_{\gamma} (\rho_0)$
increases with increasing $\alpha$ when $\gamma = 2, 3, \cdots$. The remarkable
fact is that ${\cal E}_{EoF} (\rho_0)$ does not have first-order of $\alpha$.
Based on there results we conjecture that ${\cal E}_{\gamma} (\rho_0)$
increases or decreases with increasing $\alpha$ when $\gamma > 1$ or $\gamma <
1$ respectively for nonnegative real $\gamma$.


title:
AutoGPart: Intermediate Supervision Search for Generalizable 3D Part
  Segmentation

abstract:
  Training a generalizable 3D part segmentation network is quite challenging
but of great importance in real-world applications. To tackle this problem,
some works design task-specific solutions by translating human understanding of
the task to machine's learning process, which faces the risk of missing the
optimal strategy since machines do not necessarily understand in the exact
human way. Others try to use conventional task-agnostic approaches designed for
domain generalization problems with no task prior knowledge considered. To
solve the above issues, we propose AutoGPart, a generic method enabling
training generalizable 3D part segmentation networks with the task prior
considered. AutoGPart builds a supervision space with geometric prior knowledge
encoded, and lets the machine to search for the optimal supervisions from the
space for a specific segmentation task automatically. Extensive experiments on
three generalizable 3D part segmentation tasks are conducted to demonstrate the
effectiveness and versatility of AutoGPart. We demonstrate that the performance
of segmentation networks using simple backbones can be significantly improved
when trained with supervisions searched by our method.


title: A Brief Introduction to Redis

abstract:
  This note provides a brief introduction to Redis highlighting its usefulness
in multi-lingual statistical computing.


title:
Query-Efficient Black-box Adversarial Attacks Guided by a Transfer-based
  Prior

abstract:
  Adversarial attacks have been extensively studied in recent years since they
can identify the vulnerability of deep learning models before deployed. In this
paper, we consider the black-box adversarial setting, where the adversary needs
to craft adversarial examples without access to the gradients of a target
model. Previous methods attempted to approximate the true gradient either by
using the transfer gradient of a surrogate white-box model or based on the
feedback of model queries. However, the existing methods inevitably suffer from
low attack success rates or poor query efficiency since it is difficult to
estimate the gradient in a high-dimensional input space with limited
information. To address these problems and improve black-box attacks, we
propose two prior-guided random gradient-free (PRGF) algorithms based on biased
sampling and gradient averaging, respectively. Our methods can take the
advantage of a transfer-based prior given by the gradient of a surrogate model
and the query information simultaneously. Through theoretical analyses, the
transfer-based prior is appropriately integrated with model queries by an
optimal coefficient in each method. Extensive experiments demonstrate that, in
comparison with the alternative state-of-the-arts, both of our methods require
much fewer queries to attack black-box models with higher success rates.


title:
Quantum Dynamical Resource Theory under Resource Non-increasing
  Framework

abstract:
  We defne the resource non-increasing (RNI) framework to study the dynamical
resource theory. With such a defnition, we propose several potential
quantifcation candidates under various free operation sets. For explicit
demonstrations, we quantify the quantum dynamical coherence in the scenarios
with and without post-selective measurements. Correspondingly, we show that
maximally incoherent operations (MIO) and incoherent operations (IO) in the
static coherence resource theory are free in the sense of dynamical coherence.
We also provide operational meanings for the measures by the quantum
discrimination tasks. Moreover, for the dynamical total coherence, we also
present convenient measures and give the analytic calculation for the amplitude
damping channel.


title:
Snowmass 2021 White Paper Instrumentation Frontier 05 -- White Paper 1:
  MPGDs: Recent advances and current R&D

abstract:
  This paper will review the origins, development, and examples of new versions
of Micro-Pattern Gas Detectors. The goal for MPGD development was the creation
of detectors that could cost-effectively cover large areas while offering
excellent position and timing resolution, and the ability to operate at high
incident particle rates. The early MPGD developments culminated in the
formation of the RD51 collaboration which has become the critical organization
for the promotion of MPGDs and all aspects of their production,
characterization, simulation, and uses in an expanding array of experimental
configurations. For the Snowmass 2021 study, a number of Letters of Interest
were received that illustrate ongoing developments and expansion of the use of
MPGDs. In this paper, we highlight high precision timing, high rate
application, trigger capability expansion of the SRS readout system, and a
structure designed for low ion backflow.


title:
Phase transitions of Ising and Baxter-Wu model in two dimensional Finite
  Size Lattices

abstract:
  This article offers a detailed analysis of pseudo-phase transitions of Ising
and Baxter-Wu model in the two-dimensional finite size lattices. We carry out
Wang-Landau sampling to obtain the density of states. Using microcanonical
inflection-point analysis with the microcanonical entropy, we obtain the order
of the psuedo-phase transitions in the models. The microcanonical analyzing
results of second order transition for Ising model and the first order
transition for Baxter-Wu model are well consistent with the traditional
canonical results. In addition, the third-order transitions are found in both
models implying the universality of higher order phase transitions.


title: Ternary rings of operators arising from inverse semigroups

abstract:
  We are interested in properties, especially injectivity (in the sense of
category theory), of the ternary rings of operators generated by certain
subsets of an inverse semigroup via the regular representation. We determine
all subsets of the extended bicyclic semigroup which are closed under the
triple product $xy^*z$ (called semiheaps) and show that the weakly closed
ternary rings of operators generated by them (W*-TROs) are injective operator
spaces.


title:
Magnetic Flux Transport Identification of Active Reconnection: MMS
  Observations in Earth's Magnetosphere

abstract:
  Magnetic reconnection plays an important role in converting energy while
modifying field topology. This process takes place in varied plasma
environments in which the transport of magnetic flux is intrinsic. Identifying
active magnetic reconnection sites in in-situ observations is challenging. A
new technique, Magnetic Flux Transport (MFT) analysis, has been developed
recently and proven in numerical simulation for identifying active reconnection
efficiently and accurately. In this study, we examine the MFT process in 37
previously reported electron diffusion region (EDR)/reconnection-line crossing
events at the dayside magnetopause and in the magnetotail and turbulent
magnetosheath using Magnetospheric Multiscale measurements. The coexisting
inward and outward MFT flows at an X-point provides a signature that magnetic
field lines become disconnected and reconnected. The application of MFT
analysis to in-situ observations demonstrates that MFT can successfully
identify active reconnection sites under complex varied conditions, including
asymmetric and turbulent upstream conditions. It also provides a higher rate of
identification than plasma outflow jets alone. MFT can be applied to in situ
measurements from both single- and multi-spacecraft missions and laboratory
experiments.


title:
PromptChainer: Chaining Large Language Model Prompts through Visual
  Programming

abstract:
  While LLMs can effectively help prototype single ML functionalities, many
real-world applications involve complex tasks that cannot be easily handled via
a single run of an LLM. Recent work has found that chaining multiple LLM runs
together (with the output of one step being the input to the next) can help
users accomplish these more complex tasks, and in a way that is perceived to be
more transparent and controllable. However, it remains unknown what users need
when authoring their own LLM chains -- a key step for lowering the barriers for
non-AI-experts to prototype AI-infused applications. In this work, we explore
the LLM chain authoring process. We conclude from pilot studies find that
chaining requires careful scaffolding for transforming intermediate node
outputs, as well as debugging the chain at multiple granularities; to help with
these needs, we designed PromptChainer, an interactive interface for visually
programming chains. Through case studies with four people, we show that
PromptChainer supports building prototypes for a range of applications, and
conclude with open questions on scaling chains to complex tasks, and supporting
low-fi chain prototyping.


title:
Location Intelligence Reveals the Extent, Timing, and Spatial Variation
  of Hurricane Preparedness

abstract:
  Improving hurricane preparedness is essential to reduce hurricane impacts.
Inherent in traditional methods for quantifying and monitoring hurricane
preparedness are significant lags. This study establishes a methodological
framework to quantify the extent, timing, and spatial variation of hurricane
preparedness at the CBG level using high-resolution location intelligence data.
Anonymized cell phone data on visits to POIs for each CBG before 2017 Hurricane
Harvey were used to examine hurricane preparedness. Four categories of POI,
grocery stores, gas stations, pharmacies and home improvement stores, were
identified as having close relationship with hurricane preparedness, and the
daily number of visits from each CBG to these four categories of POIs were
calculated during preparation period. Two metrics, extent of preparedness and
proactivity, were calculated based on the daily visit percentage change
compared to the baseline period. The results show that peak visits to
pharmacies often occurred in the early stage, whereas the peak of visits to gas
stations happened closer to landfall. The spatial and temporal patterns of
visits to grocery stores and home improvement stores were quite similar.
However, correlation analysis demonstrates that extent of preparedness and
proactivity are independent of each other. Combined with synchronous evacuation
data, CBGs were divided into four clusters in terms of extent of preparedness
and evacuation rate. The clusters with low preparedness and low evacuation rate
were identified as hotspots of vulnerability for shelter-in-place households
that would need urgent attention during response. The study advances
data-driven understanding of human protective actions and provide emergency
response managers with novel insights to proactively monitor disaster
preparedness, facilitating identifying under-prepared areas and better
allocating resources timely.


title:
Semidefinite programming bounds for binary codes from a split
  Terwilliger algebra

abstract:
  We study the upper bounds for $A(n,d)$, the maximum size of codewords with
length $n$ and Hamming distance at least $d$. Schrijver studied the Terwilliger
algebra of the Hamming scheme and proposed a semidefinite program to bound
$A(n, d)$. We derive more sophisticated matrix inequalities based on a split
Terwilliger algebra to improve Schrijver's semidefinite programming bounds on
$A(n, d)$. In particular, we improve the semidefinite programming bounds on
$A(18,4)$ and $A(19, 4)$ to $6551$ and $13087$, respectively.


title:
SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for
  Abstractive Summarization

abstract:
  Sequence-to-sequence neural networks have recently achieved great success in
abstractive summarization, especially through fine-tuning large pre-trained
language models on the downstream dataset. These models are typically decoded
with beam search to generate a unique summary. However, the search space is
very large, and with the exposure bias, such decoding is not optimal. In this
paper, we show that it is possible to directly train a second-stage model
performing re-ranking on a set of summary candidates. Our mixture-of-experts
SummaReranker learns to select a better candidate and consistently improves the
performance of the base model. With a base PEGASUS, we push ROUGE scores by
5.44% on CNN-DailyMail (47.16 ROUGE-1), 1.31% on XSum (48.12 ROUGE-1) and 9.34%
on Reddit TIFU (29.83 ROUGE-1), reaching a new state-of-the-art. Our code and
checkpoints will be available at https://github.com/ntunlp/SummaReranker.


title:
Model Inversion Attack against Transfer Learning: Inverting a Model
  without Accessing It

abstract:
  Transfer learning is an important approach that produces pre-trained teacher
models which can be used to quickly build specialized student models. However,
recent research on transfer learning has found that it is vulnerable to various
attacks, e.g., misclassification and backdoor attacks. However, it is still not
clear whether transfer learning is vulnerable to model inversion attacks.
Launching a model inversion attack against transfer learning scheme is
challenging. Not only does the student model hide its structural parameters,
but it is also inaccessible to the adversary. Hence, when targeting a student
model, both the white-box and black-box versions of existing model inversion
attacks fail. White-box attacks fail as they need the target model's
parameters. Black-box attacks fail as they depend on making repeated queries of
the target model. However, they may not mean that transfer learning models are
impervious to model inversion attacks. Hence, with this paper, we initiate
research into model inversion attacks against transfer learning schemes with
two novel attack methods. Both are black-box attacks, suiting different
situations, that do not rely on queries to the target student model. In the
first method, the adversary has the data samples that share the same
distribution as the training set of the teacher model. In the second method,
the adversary does not have any such samples. Experiments show that highly
recognizable data records can be recovered with both of these methods. This
means that even if a model is an inaccessible black-box, it can still be
inverted.


title: Higher order transversality in harmonic analysis

abstract:
  In differential topology two smooth submanifolds $S_1$ and $S_2$ of euclidean
space are said to be transverse if the tangent spaces at each common point
together form a spanning set. The purpose of this article is to explore a much
more general notion of transversality pertaining to a collection of
submanifolds of euclidean space. In particular, we show that three seemingly
different concepts of transversality arising naturally in harmonic analysis,
are in fact equivalent. This result is an amalgamation of several recent works
on variants of the Brascamp--Lieb inequality, and we take the opportunity here
to briefly survey this growing area. This is not intended to be an exhaustive
account, and the choices made reflect the particular perspectives of the
authors.


title: A comparison of the absolute and relative real analytic torsion forms

abstract:
  In this paper we establish a comparison formula of the absolute and relative
real analytic torsion forms over fibrations with boundaries. The key tool is a
gluing formula of analytic torsion forms proved by M. Puchol, Y. Zhang and the
author. As a consequence of the comparison formula, we prove another version of
the gluing formula of the analytic torsion forms conjectured originally by the
author.


title:
Homogeneity and Sub-homogeneity Pursuit: Iterative Complement Clustering
  PCA

abstract:
  Principal component analysis (PCA), the most popular dimension-reduction
technique, has been used to analyze high-dimensional data in many areas. It
discovers the homogeneity within the data and creates a reduced feature space
to capture as much information as possible from the original data. However, in
the presence of a group structure of the data, PCA often fails to identify the
group-specific pattern, which is known as sub-homogeneity in this study.
Group-specific information that is missed can result in an unsatisfactory
representation of the data from a particular group. It is important to capture
both homogeneity and sub-homogeneity in high-dimensional data analysis, but
this poses a great challenge. In this study, we propose a novel iterative
complement-clustering principal component analysis (CPCA) to iteratively
estimate the homogeneity and sub-homogeneity. A principal component regression
based clustering method is also introduced to provide reliable information
about clusters. Theoretically, this study shows that our proposed clustering
approach can correctly identify the cluster membership under certain
conditions. The simulation study and real analysis of the stock return data
confirm the superior performance of our proposed methods.


title: Worst Case Matters for Few-Shot Recognition

abstract:
  Few-shot recognition learns a recognition model with very few (e.g., 1 or 5)
images per category, and current few-shot learning methods focus on improving
the average accuracy over many episodes. We argue that in real-world
applications we may often only try one episode instead of many, and hence
maximizing the worst-case accuracy is more important than maximizing the
average accuracy. We empirically show that a high average accuracy not
necessarily means a high worst-case accuracy. Since this objective is not
accessible, we propose to reduce the standard deviation and increase the
average accuracy simultaneously. In turn, we devise two strategies from the
bias-variance tradeoff perspective to implicitly reach this goal: a simple yet
effective stability regularization (SR) loss together with model ensemble to
reduce variance during fine-tuning, and an adaptability calibration mechanism
to reduce the bias. Extensive experiments on benchmark datasets demonstrate the
effectiveness of the proposed strategies, which outperforms current
state-of-the-art methods with a significant margin in terms of not only
average, but also worst-case accuracy.


title: Revisiting the quasi-molecular mechanism of recombination

abstract:
  The quasi-molecular mechanism of recombination, recently suggested by
Kereselidze et al., is a non-standard process where an electron and two
neighboring protons in the early universe directly form an ionized hydrogen
molecule in a highly excited state, which then descends to lower levels or
dissociates. It has been suggested that the increased binding energy due to the
participation of a second proton may lead to an earlier cosmic recombination
that alleviates the Hubble tension. Revisiting the quasi-molecular channel of
recombination in more details, we find that the original work significantly
overestimated the probability of finding a pair of adjacent protons in the
relevant epoch ($z\sim $ a few thousand). Our new estimation suggests that the
quasi-molecular mechanism of recombination cannot be the primary cause of the
Hubble tension.

categories: astro-ph.CO gr-qc hep-ph hep-th physics.atom-ph


title: A global look into the world of interacting supernovae

abstract:
  Interacting supernovae (SNe) IIn and Ibn show narrow emission lines and have
always been a mysterious and unsolved genre in SNe physics. We present a
comprehensive analysis of the temporal and spectroscopic behaviour of a group
of interacting SNe~IIn and Ibn. We choose SNe~2012ab, 2020cui, 2020rc and
2019uo as representative members of these SN sub-types to probe the nature of
explosion. Our study reveals that SNe~IIn are heterogeneous, bright depicting
multi-staged temporal evolution while SNe~Ibn are moreover homogeneous,
comparatively fainter than SNe~IIn and short lived, but limited in sample to
firmly constrain the homogeneity. The spectroscopic features display a great
diversity in H$\alpha$ and He profiles for both SNe~IIn and Ibn. The
representative SN~Ibn also show flash ionisation signatures of CIII and NIII.
Modelling of H$\alpha$ reveals that SNe~IIn have in general an asymmetric CSM
which interacts with SN ejecta resulting in diversity in H$\alpha$ profiles.


title:
Quasiparticle Poisoning of Superconducting Qubits from Resonant
  Absorption of Pair-breaking Photons

abstract:
  The ideal superconductor provides a pristine environment for the delicate
states of a quantum computer: because there is an energy gap to excitations,
there are no spurious modes with which the qubits can interact, causing
irreversible decay of the quantum state. As a practical matter, however, there
exists a high density of excitations out of the superconducting ground state
even at ultralow temperature; these are known as quasiparticles. Observed
quasiparticle densities are of order 1~$\mu$m$^{-3}$, tens of orders of
magnitude larger than the equilibrium density expected from theory.
Nonequilibrium quasiparticles extract energy from the qubit mode and induce
discrete changes in qubit offset charge, a potential source of dephasing. Here
we show that a dominant mechanism for quasiparticle poisoning in
superconducting qubits is direct absorption of high-energy photons at the qubit
junction. We use a Josephson junction-based photon source to controllably dose
qubit circuits with millimeter-wave radiation, and we use an interferometric
quantum gate sequence to reconstruct the charge parity on the qubit island. We
find that the structure of the qubit itself acts as a resonant antenna for
millimeter-wave radiation, providing an efficient path for photons to generate
quasiparticle excitations. A deep understanding of this physics will pave the
way to realization of next-generation superconducting qubits that are robust
against quasiparticle poisoning and could enable a new class of quantum sensors
for dark matter detection.


title: Symbolic Learning to Optimize: Towards Interpretability and Scalability

abstract:
  Recent studies on Learning to Optimize (L2O) suggest a promising path to
automating and accelerating the optimization procedure for complicated tasks.
Existing L2O models parameterize optimization rules by neural networks, and
learn those numerical rules via meta-training. However, they face two common
pitfalls: (1) scalability: the numerical rules represented by neural networks
create extra memory overhead for applying L2O models, and limit their
applicability to optimizing larger tasks; (2) interpretability: it is unclear
what an L2O model has learned in its black-box optimization rule, nor is it
straightforward to compare different L2O models in an explainable way. To avoid
both pitfalls, this paper proves the concept that we can "kill two birds by one
stone", by introducing the powerful tool of symbolic regression to L2O. In this
paper, we establish a holistic symbolic representation and analysis framework
for L2O, which yields a series of insights for learnable optimizers. Leveraging
our findings, we further propose a lightweight L2O model that can be
meta-trained on large-scale problems and outperformed human-designed and tuned
optimizers. Our work is set to supply a brand-new perspective to L2O research.
Codes are available at:
https://github.com/VITA-Group/Symbolic-Learning-To-Optimize.


title:
Spectral Graph Clustering for Intentional Islanding Operations in
  Resilient Hybrid Energy Systems

abstract:
  Establishing cleaner energy generation therefore improving the sustainability
of the power system is a crucial task in this century, and one of the key
strategies being pursued is to shift the dependence on fossil fuel to renewable
technologies such as wind, solar, and nuclear. However, with the increasing
number of heterogeneous components included, the complexity of the hybrid
energy system becomes more significant. And the complex system imposes a more
stringent requirement of the contingency plan to enhance the overall system
resilience. Among different strategies to ensure a reliable system, intentional
islanding is commonly applied in practical applications for power systems and
attracts abundant interest in the literature. In this study, we propose a
hierarchical spectral clustering-based intentional islanding strategy at the
transmission level with renewable generations. To incorporate the renewable
generation that relies on the inverter technology, the frequency measurements
are considered to represent the transient response. And it has been further
used as embedded information in the clustering algorithm along with other
important electrical information from the system to enrich the modeling
capability of the proposed framework. To demonstrate the effectiveness of the
islanding strategy, the modified IEEE-9 bus and IEEE-118 bus systems coupled
with wind farms are considered as the test cases.


title:
One Parameter Defense -- Defending against Data Inference Attacks via
  Differential Privacy

abstract:
  Machine learning models are vulnerable to data inference attacks, such as
membership inference and model inversion attacks. In these types of breaches,
an adversary attempts to infer a data record's membership in a dataset or even
reconstruct this data record using a confidence score vector predicted by the
target model. However, most existing defense methods only protect against
membership inference attacks. Methods that can combat both types of attacks
require a new model to be trained, which may not be time-efficient. In this
paper, we propose a differentially private defense method that handles both
types of attacks in a time-efficient manner by tuning only one parameter, the
privacy budget. The central idea is to modify and normalize the confidence
score vectors with a differential privacy mechanism which preserves privacy and
obscures membership and reconstructed data. Moreover, this method can guarantee
the order of scores in the vector to avoid any loss in classification accuracy.
The experimental results show the method to be an effective and timely defense
against both membership inference and model inversion attacks with no reduction
in accuracy.


title:
An implicitly extended Crank-Nicolson scheme for the heat equation on
  time-dependent domains

abstract:
  We consider a time-stepping scheme of Crank-Nicolson type for the heat
equation on a moving domain in Eulerian coordinates. As the spatial domain
varies between subsequent time steps, an extension of the solution at the
previous time step is required. Following Lehrenfeld \& Olskanskii [ESAIM:
M2AN, 53(2):\,585-614, 2019], we apply an implicit extension based on so-called
ghost-penalty terms. For spatial discretisation, a cut finite element method is
used. We derive a complete a priori error analysis in space and time, which
shows in particular second-order convergence in time under a parabolic CFL
condition. Finally, we present numerical results in two and three space
dimensions that confirm the analytical estimates.


title:
Calibration and performance of the readout system based on switched
  capacitor arrays for the Large-Sized Telescope of the Cherenkov Telescope
  Array

abstract:
  The Cherenkov Telescope Array (CTA) is the next-generation ground-based
very-high-energy gamma-ray observatory. The Large-Sized Telescope (LST) of CTA
is designed to detect gamma rays between 20 GeV and a few TeV with a 23-meter
diameter mirror. We have developed the focal plane camera of the first LST,
which has 1855 photomultiplier tubes (PMTs) and the readout system which
samples a PMT waveform at GHz with switched capacitor arrays, Domino Ring
Sampler ver4 (DRS4). To measure the precise pulse charge and arrival time of
Cherenkov signals, we developed a method to calibrate the output voltage of
DRS4 and the sampling time interval, as well as an analysis method to correct
the spike noise of DRS4. Since the first LST was inaugurated in 2018, we have
performed the commissioning tests and calibrated the camera. We characterised
the camera in terms of the charge pedestal under various conditions of the
night sky background, the charge resolution of each pixel, the charge
uniformity of the whole camera, and the time resolutions with a test pulse and
calibration laser.


title:
Bi-Sampling Approach to Classify Music Mood leveraging Raga-Rasa
  Association in Indian Classical Music

abstract:
  The impact of Music on the mood or emotion of the listener is a
well-researched area in human psychology and behavioral science. In Indian
classical music, ragas are the melodic structure that defines the various
styles and forms of the music. Each raga has been found to evoke a specific
emotion in the listener. With the advent of advanced capabilities of audio
signal processing and the application of machine learning, the demand for
intelligent music classifiers and recommenders has received increased
attention, especially in the 'Music as a service' cloud applications. This
paper explores a novel framework to leverage the raga-rasa association in
Indian classical Music to build an intelligent classifier and its application
in music recommendation system based on user's current mood and the mood they
aspire to be in.


title:
Investigating the Impact of COVID-19 on Education by Social Network
  Mining

abstract:
  The Covid-19 virus has been one of the most discussed topics on social
networks in 2020 and 2021 and has affected the classic educational paradigm,
worldwide. In this research, many tweets related to the Covid-19 virus and
education are considered and geo-tagged with the help of the GeoNames
geographic database, which contains a large number of place names. To detect
the feeling of users, sentiment analysis is performed using the RoBERTa
language-based model. Finally, we obtain the trends of frequency of total,
positive, and negative tweets for countries with a high number of Covid-19
confirmed cases. Investigating the results reveals a correlation between the
trends of tweet frequency and the official statistic of confirmed cases for
several countries.


title: CVFNet: Real-time 3D Object Detection by Learning Cross View Features

abstract:
  In recent years 3D object detection from LiDAR point clouds has made great
progress thanks to the development of deep learning technologies. Although
voxel or point based methods are popular in 3D object detection, they usually
involve time-consuming operations such as 3D convolutions on voxels or ball
query among points, making the resulting network inappropriate for time
critical applications. On the other hand, 2D view-based methods feature high
computing efficiency while usually obtaining inferior performance than the
voxel or point based methods. In this work, we present a real-time view-based
single stage 3D object detector, namely CVFNet to fulfill this task. To
strengthen the cross-view feature learning under the condition of demanding
efficiency, our framework extracts the features of different views and fuses
them in an efficient progressive way. We first propose a novel Point-Range
feature fusion module that deeply integrates point and range view features in
multiple stages. Then, a special Slice Pillar is designed to well maintain the
3D geometry when transforming the obtained deep point-view features into bird's
eye view. To better balance the ratio of samples, a sparse pillar detection
head is presented to focus the detection on the nonempty grids. We conduct
experiments on the popular KITTI and NuScenes benchmark, and state-of-the-art
performances are achieved in terms of both accuracy and speed.


title:
Phonon downconversion to suppress correlated errors in superconducting
  qubits

abstract:
  Quantum error correction can preserve quantum information in the presence of
local errors; however, errors that are correlated across a qubit array are
fatal. For superconducting qubits, high-energy particle impacts due to
background radioactivity or cosmic ray muons produce bursts of energetic
phonons that travel throughout the substrate and create excitations out of the
superconducting ground state, known as quasiparticles, which poison all qubits
on the chip. Here we use thick normal metal reservoirs on the back side of the
chip to promote rapid downconversion of phonons to sufficiently low energies
where they can no longer poison qubits. We introduce a pump-probe scheme
involving controlled injection of pair-breaking phonons into the qubit chips.
We examine quasiparticle poisoning on chips with and without backside
metallization and demonstrate a reduction in the flux of pair-breaking phonons
by more than a factor of 20. In addition, we use a Ramsey interferometer scheme
to simultaneously monitor quasiparticle parity on three qubits for each chip
and observe a two-order of magnitude reduction in correlated poisoning due to
ambient radiation. Our approach reduces correlated errors due to background
radiation below the level necessary for fault-tolerant operation of a
multiqubit array.


title:
Policy Learning for Robust Markov Decision Process with a Mismatched
  Generative Model

abstract:
  In high-stake scenarios like medical treatment and auto-piloting, it's risky
or even infeasible to collect online experimental data to train the agent.
Simulation-based training can alleviate this issue, but may suffer from its
inherent mismatches from the simulator and real environment. It is therefore
imperative to utilize the simulator to learn a robust policy for the real-world
deployment. In this work, we consider policy learning for Robust Markov
Decision Processes (RMDP), where the agent tries to seek a robust policy with
respect to unexpected perturbations on the environments. Specifically, we focus
on the setting where the training environment can be characterized as a
generative model and a constrained perturbation can be added to the model
during testing. Our goal is to identify a near-optimal robust policy for the
perturbed testing environment, which introduces additional technical
difficulties as we need to simultaneously estimate the training environment
uncertainty from samples and find the worst-case perturbation for testing. To
solve this issue, we propose a generic method which formalizes the perturbation
as an opponent to obtain a two-player zero-sum game, and further show that the
Nash Equilibrium corresponds to the robust policy. We prove that, with a
polynomial number of samples from the generative model, our algorithm can find
a near-optimal robust policy with a high probability. Our method is able to
deal with general perturbations under some mild assumptions and can also be
extended to more complex problems like robust partial observable Markov
decision process, thanks to the game-theoretical formulation.


title: Hawking radiation as quantum mechanical reflection

abstract:
  In this article, we explore an alternative derivation of Hawking radiation.
Instead of the field-theoretic derivation, we have suggested a simpler
calculation based on quantum mechanical reflection from a one-dimensional
potential. The reflection coefficient shows an exponential fall in energy
which, in comparison with the Boltzmann probability distribution, yields a
temperature. The temperature is the same as Hawking temperature for spherically
symmetric black holes. The derivation gives an exact local calculation of
Hawking temperature that involves a region lying entirely outside the horizon.
This is a crucial difference from the tunneling calculation, where it is
necessary to involve a region inside the horizon.


title: AugShuffleNet: Improve ShuffleNetV2 via More Information Communication

abstract:
  Based on ShuffleNetV2, we build a more powerful and efficient model family,
termed as AugShuffleNets, by introducing higher frequency of cross-layer
information communication for better model performance. Evaluated on the
CIFAR-10 and CIFAR-100 datasets, AugShuffleNet consistently outperforms
ShuffleNetV2 in terms of accuracy, with less computational cost, fewer
parameter count.


title:
Applications of a duality between generalized trigonometric and
  hyperbolic functions II

abstract:
  Generalized trigonometric functions and generalized hyperbolic functions can
be converted to each other by the duality formulas previously discovered by the
authors. In this paper, we apply the duality formulas to prove dual pairs of
Wilker-type inequalities, Huygens-type inequalities, and (relaxed)
Cusa-Huygens-type inequalities for the generalized functions. In addition,
multiple- and double-angle formulas not previously obtained are also given.


title: ORDSIM: Ordinal Regression for E-Commerce Query Similarity Prediction

abstract:
  Query similarity prediction task is generally solved by regression based
models with square loss. Such a model is agnostic of absolute similarity values
and it penalizes the regression error at all ranges of similarity values at the
same scale. However, to boost e-commerce platform's monetization, it is
important to predict high-level similarity more accurately than low-level
similarity, as highly similar queries retrieves items according to
user-intents, whereas moderately similar item retrieves related items, which
may not lead to a purchase. Regression models fail to customize its loss
function to concentrate around the high-similarity band, resulting poor
performance in query similarity prediction task. We address the above challenge
by considering the query prediction as an ordinal regression problem, and
thereby propose a model, ORDSIM (ORDinal Regression for SIMilarity Prediction).
ORDSIM exploits variable-width buckets to model ordinal loss, which penalizes
errors in high-level similarity harshly, and thus enable the regression model
to obtain better prediction results for high similarity values. We evaluate
ORDSIM on a dataset of over 10 millions e-commerce queries from eBay platform
and show that ORDSIM achieves substantially smaller prediction error compared
to the competing regression methods on this dataset.


title:
Informative Causality Extraction from Medical Literature via
  Dependency-tree based Patterns

abstract:
  Extracting cause-effect entities from medical literature is an important task
in medical information retrieval. A solution for solving this task can be used
for compilation of various causality relations, such as, causality between
disease and symptoms, between medications and side effects, between genes and
diseases, etc. Existing solutions for extracting cause-effect entities work
well for sentences where the cause and the effect phrases are name entities,
single-word nouns, or noun phrases consisting of two to three words.
Unfortunately, in medical literature, cause and effect phrases in a sentence
are not simply nouns or noun phrases, rather they are complex phrases
consisting of several words, and existing methods fail to correctly extract the
cause and effect entities in such sentences. Partial extraction of cause and
effect entities conveys poor quality, non informative, and often, contradictory
facts, comparing to the one intended in the given sentence. In this work, we
solve this problem by designing an unsupervised method for cause and effect
phrase extraction, PatternCausality, which is specifically suitable for the
medical literature. Our proposed approach first uses a collection of
cause-effect dependency patterns as template to extract head words of cause and
effect phrases and then it uses a novel phrase extraction method to obtain
complete and meaningful cause and effect phrases from a sentence. Experiments
on a cause-effect dataset built from sentences from PubMed articles show that
for extracting cause and effect entities, PatternCausality is substantially
better than the existing methods with an order of magnitude improvement in the
F-score metric over the best of the existing methods.


title:
Controllable optical and magneto-optical properties of magnetic CrI3
  nanoribbons

abstract:
  A monolayer of CrI3 has an amazing ferromagnetic ground-state. In this work,
we calculate band structures and magnetic moments of tensile-strained and bent
zigzag CrI3 nanoribbons with density functional theory. The edge iodine atoms
form flat low-lying conduction bands and couple with chromium atoms
ferromagnetically, while the non-edge iodine atoms weakly couple
antiferromagnetically. CrI3 nanoribbons have a nearly equal preference for the
out-of-plane and in-plane magnetic moment configurations, slightly favoring the
in-plane one. We also calculate optical absorption with many-body perturbation
GW-BSE (Bethe-Salpeter equation) and investigate magneto-optical properties,
including magnetic dichroism, Faraday and magneto-optical Kerr effects. The
low-energy dark excitons are mainly from transitions between electrons and
holes with unlike spins and are non-Frenkel-like, while the bright excitons
have mixed spin configurations. Tensile strains and bending manifestly modulate
the absorption spectra and magneto-optical properties of CrI3 nanoribbons
within a technologically important photon energy-range of ~1.0-2.0 eV,
suggesting a potential application in tunable magnetic optoelectronics.


title: On join product and local antimagic chromatic number of regular graphs

abstract:
  Let $G = (V,E)$ be a connected simple graph of order $p$ and size $q$. A
graph $G$ is called local antimagic if $G$ admits a local antimagic labeling. A
bijection $f : E \to \{1,2,\ldots,q\}$ is called a local antimagic labeling of
$G$ if for any two adjacent vertices $u$ and $v$, we have $f^+(u) \ne f^+(v)$,
where $f^+(u) = \sum_{e\in E(u)} f(e)$, and $E(u)$ is the set of edges incident
to $u$. Thus, any local antimagic labeling induces a proper vertex coloring of
$G$ if vertex $v$ is assigned the color $f^+(v)$. The local antimagic chromatic
number, denoted $\chi_{la}(G)$, is the minimum number of induced colors taken
over local antimagic labeling of $G$. Let $G$ and $H$ be two vertex disjoint
graphs. The join graph of $G$ and $H$, denoted $G \vee H$, is the graph
$V(G\vee H) = V(G) \cup V(H)$ and $E(G\vee H) = E(G) \cup E(H) \cup \{uv \,|\,
u\in V(G), v \in V(H)\}$. In this paper, we show the existence of non-complete
regular graphs with arbitrarily large order, regularity and local antimagic
chromatic numbers.


title:
Heat Conduction in Polymer Chains: Effect of Substrate on the Thermal
  Conductance

abstract:
  In standard molecular junctions, a molecular structure is placed between and
connected to metal leads. Understanding how mechanical tuning in such molecular
junctions can change heat conductance has interesting applications in nanoscale
energy transport. In this work, we use nonequilibrium molecular dynamics
simulations to address the effect of stretching on the phononic contribution to
the heat conduction of molecular junctions consisting of single long-chain
alkanes and various metal leads such as Ag, Au, Cu, Ni, and Pt. The thermal
conductance of such junctions is found to be much smaller than the intrinsic
thermal conductance of the polymer and significantly depends on the nature of
metal leads as expressed by the metal-molecule coupling and metal vibrational
density of states. This behavior is expected and reflects the mismatch of
phonon spectra at the metal molecule interfaces. As a function of stretching,
we find a behavior similar to what was observed earlier [J. Chem. Phys. 153,
164903 (2020)] for pure polymeric structures. At relatively short electrode
distances, where the polyethylene chains are compressed, it is found that the
thermal conductances of the molecular junctions remain almost constant as one
stretches the polymer chains. At critical electrode distances, the thermal
conductances start to increase, reaching the values of the fully-extended
molecular junctions. Similar behaviors are observed for junctions in which
several long-chain alkanes are sandwiched between various metal leads.
  These findings indicate that this behavior under stretching is an intrinsic
property of the polymer chain and not significantly associated with the
interfacial structures.


title: Unification of Modal Logic via Topological Categories I

abstract:
  In this paper, we provide a unifying description of different types of
semantics for modal logic found in the literature, using the language of
topological categories (over Set). Just like in categorical logic, we will
provide a detailed study of the correspondence between the syntactical
structure of (various extensions of) the modal language on one hand, and
semantic structures of topological categories on the other hand, using this
framework. This will allow us to obtain a conceptual understanding of the
abstract structure of modal logic.


title: Distributed Subgraph Finding: Progress and Challenges

abstract:
  This is a survey of the exciting recent progress made in understanding the
complexity of distributed subgraph finding problems. It overviews the results
and techniques for assorted variants of subgraph finding problems in various
models of distributed computing, and states intriguing open questions. This
version contains some updates over the ICALP 2021 version, and I will try to
keep updating it as additional progress is made.


title: Secret-to-Image Reversible Transformation for Generative Steganography

abstract:
  Recently, generative steganography that transforms secret information to a
generated image has been a promising technique to resist steganalysis
detection. However, due to the inefficiency and irreversibility of the
secret-to-image transformation, it is hard to find a good trade-off between the
information hiding capacity and extraction accuracy. To address this issue, we
propose a secret-to-image reversible transformation (S2IRT) scheme for
generative steganography. The proposed S2IRT scheme is based on a generative
model, i.e., Glow model, which enables a bijective-mapping between latent space
with multivariate Gaussian distribution and image space with a complex
distribution. In the process of S2I transformation, guided by a given secret
message, we construct a latent vector and then map it to a generated image by
the Glow model, so that the secret message is finally transformed to the
generated image. Owing to good efficiency and reversibility of S2IRT scheme,
the proposed steganographic approach achieves both high hiding capacity and
accurate extraction of secret message from generated image. Furthermore, a
separate encoding-based S2IRT (SE-S2IRT) scheme is also proposed to improve the
robustness to common image attacks. The experiments demonstrate the proposed
steganographic approaches can achieve high hiding capacity (up to 4 bpp) and
accurate information extraction (almost 100% accuracy rate) simultaneously,
while maintaining desirable anti-detectability and imperceptibility.


title:
The distribution of B-site in the perovskite for a d5-d3 superexchange
  system studied with Molecular field theory and Monte Carlo simulation

abstract:
  The B-site disorder in the d5 - d3 system of perovskites has been analyzed
with molecular field theory and Monte Carlo method. The model is applicable to
RFe1-pCrpO3 at any p value. When the saturation magnetization MS and phase
transition temperature TP are known, a model can be built to calculate the
order or disorder distribution of lattice B-sites. We analyze the case that the
Fe-Cr superexchange is antiferromagnetic and ferromagnetic coupling
respectively. The simulation result shows that the theoretical calculation
formula is suitable for the calculation of different B-site distribution.
Through the simulation, we find that when the x and y are large, the system
will appear obvious long-range order. The DM interaction has a certain
influence on the saturation magnetization. Via calculation, we found that the
distribution states of Fe and Cr do not always conform to the uniform
distribution but rather exhibit an effect that reduces the Fe-Fe clustering.
The establishment of this model offers an explanation for several previously
contentious issues, e.g., what is the phase transition temperature range of
double perovskite, and why the different phase transition temperatures with the
same doping proportion. It provides theoretical guidance for the design of
functional materials with an arbitrary phase transition temperature.


title:
Spectral Modification Based Data Augmentation For Improving End-to-End
  ASR For Children's Speech

abstract:
  Training a robust Automatic Speech Recognition (ASR) system for children's
speech recognition is a challenging task due to inherent differences in
acoustic attributes of adult and child speech and scarcity of publicly
available children's speech dataset. In this paper, a novel segmental spectrum
warping and perturbations in formant energy are introduced, to generate a
children-like speech spectrum from that of an adult's speech spectrum. Then,
this modified adult spectrum is used as augmented data to improve end-to-end
ASR systems for children's speech recognition. The proposed data augmentation
methods give 6.5% and 6.1% relative reduction in WER on children dev and test
sets respectively, compared to the vocal tract length perturbation (VTLP)
baseline system trained on Librispeech 100 hours adult speech dataset. When
children's speech data is added in training with Librispeech set, it gives a
3.7 % and 5.1% relative reduction in WER, compared to the VTLP baseline system.


title: Dynamics on higher-order networks: A review

abstract:
  Network science has evolved into an indispensable platform for studying
complex systems. But recent research has identified limits of classical
networks, where links connect pairs of nodes, to comprehensively describe group
interactions. Higher-order networks, where a link can connect more than two
nodes, have therefore emerged as a new frontier in network science. Since group
interactions are common in social, biological, and technological systems,
higher-order networks have recently led to important new discoveries across
many fields of research. We here review these works, focusing in particular on
the novel aspects of the dynamics that emerges on higher-order networks. We
cover a variety of dynamical processes that have thus far been studied,
including different synchronization phenomena, contagion processes, the
evolution of cooperation, and consensus formation. We also outline open
challenges and promising directions for future research.


title:
Nanofluid Filled Enclosures: Potential Photo-thermal Energy Conversion
  and Sensible Heat Storage Devices

abstract:
  In the present work we propose "nanofluid filled enclosures" as potential
photo-thermal energy conversion and sensible heat storage devices. Herein, the
optical charging of the nanofluid has been modeled as "solar radiant energy -
nanoparticles" interaction. The subsequent energy redistribution has been
modeled as coupled transport phenomena involving mass, momentum and energy
transport. In particular, nanofluid filled enclosure with adiabatic, and
isothermal (through convective) boundaries have been analyzed to decipher the
fundamental limits of sensible heat storage and thermal discharging capacities
respectively. Furthermore, the effect of nanoparticles volume fraction on the
photo-thermal energy conversion mechanisms and its redistribution thereof has
been critically investigated. Detailed analysis reveals that under similar
operating conditions, in volumetric absorption mode (i.e., at low nanoparticles
volume fraction) nanofluid filled enclosure has higher sensible heat storage
(7% - 27% higher) and thermal discharging (16% - 47% higher) capacities than in
the corresponding surface absorption mode (i.e., at high nanoparticles volume
fraction). Overall, "nanofluid filled enclosures", particularly in volumetric
absorption mode, could be deployed for efficient solar thermal conversion and
storage.


title: Sobolev algebras on Lie groups and noncommutative geometry

abstract:
  We show that there exists a quantum compact metric space which underlies the
setting of each Sobolev algebra associated to a subelliptic laplacian
$\Delta=X_1^2+\cdots+X_m^2$ on a compact connected Lie group $G$ if $p$ is
large enough, more precisely under the (sharp) condition $p > \frac{d}{\alpha}$
where $d$ is the local dimension of $(G,X)$ and where $0 < \alpha \leq 1$. We
also provide locally compact variants of this result and generalizations for
real second order subelliptic operators. We also introduce a compact spectral
triple (=noncommutative manifold) canonically associated to each subelliptic
laplacian on a compact group. In addition, we show that its spectral dimension
is equal to the local dimension of $(G,X)$ observing that the local
Coulhon-Varopoulos dimension of semigroups can be used to determine the
spectral dimensions of suitable spectral triples. Finally, we prove that the
Connes spectral pseudo-metric allows us to recover the Carnot-Carath\'eodory
distance.


title: Masked Autoencoders for Point Cloud Self-supervised Learning

abstract:
  As a promising scheme of self-supervised learning, masked autoencoding has
significantly advanced natural language processing and computer vision.
Inspired by this, we propose a neat scheme of masked autoencoders for point
cloud self-supervised learning, addressing the challenges posed by point
cloud's properties, including leakage of location information and uneven
information density. Concretely, we divide the input point cloud into irregular
point patches and randomly mask them at a high ratio. Then, a standard
Transformer based autoencoder, with an asymmetric design and a shifting mask
tokens operation, learns high-level latent features from unmasked point
patches, aiming to reconstruct the masked point patches. Extensive experiments
show that our approach is efficient during pre-training and generalizes well on
various downstream tasks. Specifically, our pre-trained models achieve 85.18%
accuracy on ScanObjectNN and 94.04% accuracy on ModelNet40, outperforming all
the other self-supervised learning methods. We show with our scheme, a simple
architecture entirely based on standard Transformers can surpass dedicated
Transformer models from supervised learning. Our approach also advances
state-of-the-art accuracies by 1.5%-2.3% in the few-shot object classification.
Furthermore, our work inspires the feasibility of applying unified
architectures from languages and images to the point cloud.


title:
Depth-Aware Generative Adversarial Network for Talking Head Video
  Generation

abstract:
  Talking head video generation aims to produce a synthetic human face video
that contains the identity and pose information respectively from a given
source image and a driving video.Existing works for this task heavily rely on
2D representations (e.g. appearance and motion) learned from the input images.
However, dense 3D facial geometry (e.g. pixel-wise depth) is extremely
important for this task as it is particularly beneficial for us to essentially
generate accurate 3D face structures and distinguish noisy information from the
possibly cluttered background. Nevertheless, dense 3D geometry annotations are
prohibitively costly for videos and are typically not available for this video
generation task. In this paper, we first introduce a self-supervised geometry
learning method to automatically recover the dense 3D geometry (i.e.depth) from
the face videos without the requirement of any expensive 3D annotation data.
Based on the learned dense depth maps, we further propose to leverage them to
estimate sparse facial keypoints that capture the critical movement of the
human head. In a more dense way, the depth is also utilized to learn 3D-aware
cross-modal (i.e. appearance and depth) attention to guide the generation of
motion fields for warping source image representations. All these contributions
compose a novel depth-aware generative adversarial network (DaGAN) for talking
head generation. Extensive experiments conducted demonstrate that our proposed
method can generate highly realistic faces, and achieve significant results on
the unseen human faces.


title:
Deep Learning for 1-Bit Compressed Sensing-based Superimposed CSI
  Feedback

abstract:
  In frequency-division duplexing (FDD) massive multiple-input multiple-output
(MIMO) systems, 1-bit compressed sensing (CS)-based superimposed channel state
information (CSI) feedback has shown many advantages, while still faces many
challenges, such as low accuracy of the downlink CSI recovery and large
processing delays. To overcome these drawbacks, this paper proposes a deep
learning (DL) scheme to improve the 1-bit compressed sensing-based superimposed
CSI feedback. On the user side, the downlink CSI is compressed with the 1-bit
CS technique, superimposed on the uplink user data sequences (UL-US), and then
sent back to the base station (BS). At the BS, based on the model-driven
approach and assisted by the superimposition-interference cancellation
technology, a multi-task detection network is first constructed for detecting
both the UL-US and downlink CSI. In particular, this detection network is
jointly trained to detect the UL-US and downlink CSI simultaneously, capturing
a globally optimized network parameter. Then, with the recovered bits for the
downlink CSI, a lightweight reconstruction scheme, which consists of an initial
feature extraction of the downlink CSI with the simplified traditional method
and a single hidden layer network, is utilized to reconstruct the downlink CSI
with low processing delay. Compared with the 1-bit CS-based superimposed CSI
feedback scheme, the proposed scheme improves the recovery accuracy of the
UL-US and downlink CSI with lower processing delay and possesses robustness
against parameter variations.


title:
A Systematic Study and Analysis of Bengali Folklore with Natural
  Language Processing Systems

abstract:
  Folklore, a solid branch of folk literature, is the hallmark of any nation or
any society. Such as oral tradition; as proverbs or jokes, it also includes
material culture as well as traditional folk beliefs, and various customs.
Bengali folklore is as rich in-depth as it is amazing. Nevertheless, in the
womb of time, it is determined to sustain its existence. Therefore, our aim in
this study is to make our rich folklore more comprehensible to everyone in a
more sophisticated computational way. Some studies concluded various aspects of
the Bengali language with NLP. Our proposed model is to be specific for Bengali
folklore. Technically, it will be the first step towards Bengali natural
language processing for studying and analyzing the folklore of Bengal.


title:
Fast high-fidelity single-shot readout of spins in silicon using a
  single-electron box

abstract:
  Three key metrics for readout systems in quantum processors are measurement
speed, fidelity and footprint. Fast high-fidelity readout enables mid-circuit
measurements, a necessary feature for many dynamic algorithms and quantum error
correction, while a small footprint facilitates the design of scalable,
highly-connected architectures with the associated increase in computing
performance. Here, we present two complementary demonstrations of fast
high-fidelity single-shot readout of spins in silicon quantum dots using a
compact, dispersive charge sensor: a radio-frequency single-electron box. The
sensor, despite requiring fewer electrodes than conventional detectors,
performs at the state-of-the-art achieving spin read-out fidelity of 99.2% in
less than 6 $\mu$s. We demonstrate that low-loss high-impedance resonators,
highly coupled to the sensing dot, in conjunction with Josephson parametric
amplification are instrumental in achieving optimal performance. We quantify
the benefit of Pauli spin blockade over spin-dependent tunneling to a
reservoir, as the spin-to-charge conversion mechanism in these readout schemes.
Our results place dispersive charge sensing at the forefront of readout
methodologies for scalable semiconductor spin-based quantum processors.


title: Earthquakes on the once-punctured torus

abstract:
  We study earthquake deformations on Teichm\"uller space associated with
simple closed curves of the once-punctured torus. We describe two methods to
get an explicit form of the earthquake deformation for any simple closed curve.
The first method is rooted in linear recurrence relations, the second in
hyperbolic geometry. The two methods align, providing both an algebraic and
geometric interpretation of the earthquake deformations. Two families of curves
are used as examples. Examining the limiting behaviour of each gives insight
into earthquakes about measured geodesic laminations, of which simple closed
curves are a special case.


title: Context-LSTM: a robust classifier for video detection on UCF101

abstract:
  Video detection and human action recognition may be computationally
expensive, and need a long time to train models. In this paper, we were
intended to reduce the training time and the GPU memory usage of video
detection, and achieved a competitive detection accuracy. Other research works
such as Two-stream, C3D, TSN have shown excellent performance on UCF101. Here,
we used a LSTM structure simply for video detection. We used a simple structure
to perform a competitive top-1 accuracy on the entire validation dataset of
UCF101. The LSTM structure is named Context-LSTM, since it may process the deep
temporal features. The Context-LSTM may simulate the human recognition system.
We cascaded the LSTM blocks in PyTorch and connected the cell state flow and
hidden output flow. At the connection of the blocks, we used ReLU, Batch
Normalization, and MaxPooling functions. The Context-LSTM could reduce the
training time and the GPU memory usage, while keeping a state-of-the-art top-1
accuracy on UCF101 entire validation dataset, show a robust performance on
video action detection.


title:
Proposal for room-temperature quantum repeaters with nitrogen-vacancy
  centers and optomechanics

abstract:
  We propose a quantum repeater architecture that can operate under ambient
conditions. Our proposal builds on recent progress towards non-cryogenic
spin-photon interfaces based on nitrogen-vacancy centers, which have excellent
spin coherence times even at room temperature, and optomechanics, which allows
to avoid phonon-related decoherence and also allows the emitted photons to be
in the telecom band. We apply the photon number decomposition method to
quantify the fidelity and the efficiency of entanglement established between
two remote electron spins. We describe how the entanglement can be stored in
nuclear spins and extended to long distances via quasi-deterministic
entanglement swapping operations involving the electron and nuclear spins. We
furthermore propose schemes to achieve high-fidelity readout of the spin states
at room temperature using the spin-optomechanics interface. Our work shows that
long-distance quantum networks made of solid-state components that operate at
room temperature are within reach of current technological capabilities.


title:
A Single Correspondence Is Enough: Robust Global Registration to Avoid
  Degeneracy in Urban Environments

abstract:
  Global registration using 3D point clouds is a crucial technology for mobile
platforms to achieve localization or manage loop-closing situations. In recent
years, numerous researchers have proposed global registration methods to
address a large number of outlier correspondences. Unfortunately, the
degeneracy problem, which represents the phenomenon in which the number of
estimated inliers becomes lower than three, is still potentially inevitable. To
tackle the problem, a degeneracy-robust decoupling-based global registration
method is proposed, called Quatro. In particular, our method employs
quasi-SO(3) estimation by leveraging the Atlanta world assumption in urban
environments to avoid degeneracy in rotation estimation. Thus, the minimum
degree of freedom (DoF) of our method is reduced from three to one. As verified
in indoor and outdoor 3D LiDAR datasets, our proposed method yields robust
global registration performance compared with other global registration
methods, even for distant point cloud pairs. Furthermore, the experimental
results confirm the applicability of our method as a coarse alignment. Our code
is available: https://github.com/url-kaist/quatro.


title:
A Context-Aware Readout System for Sparse Touch Sensing Array Using
  Ultra-low-power Always-on Event Detection

abstract:
  Increasing demand for larger touch screen panels (TSPs) places more energy
burden to mobile systems with conventional sensing methods. To mitigate this
problem, taking advantage of the touch event sparsity, this paper proposes a
novel TSP readout system that can obtain huge energy saving by turning off the
readout circuits when none of the sensors are activated. To this end, a novel
ultra-low-power always-on event and region of interest detection based on
lightweight compressed sensing is proposed. Exploiting the proposed event
detector, the context-aware TSP readout system, which can improve the energy
efficiency by up to 40x, is presented.


title:
Simulations of thermal surface waves in a protoplanetary disk using 1+1D
  approximation

abstract:
  Heating by the central star is one of the key factors determining the
physical structure of protoplanetary disks. Due to the large optical thickness
in the radial direction, disk midplane regions are heated by the infrared
radiation from the disk surface (atmosphere), which in turn is directly heated
by the star. It was previously shown that interception of the stellar radiation
by inhomogeneities on the disk surface can cause perturbations that propagate
towards the star. In this work, we investigate the occurrence of such waves
within a detailed 1+1D numerical model of the protoplanetary disk. We confirm
the previous findings that in the disk, that is optically thick to its own
radiation, the surface perturbations indeed occur and propagate towards the
star. However, contrary to some analytical predictions, the thermal waves in
sufficiently massive disks affect only the upper layers without significant
fluctuations of temperature in the midplane. Our results indicate the need to
study this instability within more consistent hydrodynamic models.


title: Learning Maximum Margin Channel Decoders

abstract:
  The problem of learning a channel decoder is considered for two channel
models. The first model is an additive noise channel whose noise distribution
is unknown and nonparametric. The learner is provided with a fixed codebook and
a dataset comprised of independent samples of the noise, and is required to
select a precision matrix for a nearest neighbor decoder in terms of the
Mahalanobis distance. The second model is a non-linear channel with additive
white Gaussian noise and unknown channel transformation. The learner is
provided with a fixed codebook and a dataset comprised of independent
input-output samples of the channel, and is required to select a matrix for a
nearest neighbor decoder with a linear kernel. For both models, the objective
of maximizing the margin of the decoder is addressed. Accordingly, for each
channel model, a regularized loss minimization problem with a codebook-related
regularization term and hinge-like loss function is developed, which is
inspired by the support vector machine paradigm for classification problems.
Expected generalization error bounds for the error probability loss function
are provided for both models, under optimal choice of the regularization
parameter. For the additive noise channel, a theoretical guidance for choosing
the training signal-to-noise ratio is proposed based on this bound. In
addition, for the non-linear channel, a high probability uniform generalization
error bound is provided for the hypothesis class. For each channel, a
stochastic sub-gradient descent algorithm for solving the regularized loss
minimization problem is proposed, and an optimization error bound is stated.
The performance of the proposed algorithms is demonstrated through several
examples.


title: LAS-AT: Adversarial Training with Learnable Attack Strategy

abstract:
  Adversarial training (AT) is always formulated as a minimax problem, of which
the performance depends on the inner optimization that involves the generation
of adversarial examples (AEs). Most previous methods adopt Projected Gradient
Decent (PGD) with manually specifying attack parameters for AE generation. A
combination of the attack parameters can be referred to as an attack strategy.
Several works have revealed that using a fixed attack strategy to generate AEs
during the whole training phase limits the model robustness and propose to
exploit different attack strategies at different training stages to improve
robustness. But those multi-stage hand-crafted attack strategies need much
domain expertise, and the robustness improvement is limited. In this paper, we
propose a novel framework for adversarial training by introducing the concept
of "learnable attack strategy", dubbed LAS-AT, which learns to automatically
produce attack strategies to improve the model robustness. Our framework is
composed of a target network that uses AEs for training to improve robustness
and a strategy network that produces attack strategies to control the AE
generation. Experimental evaluations on three benchmark databases demonstrate
the superiority of the proposed method. The code is released at
https://github.com/jiaxiaojunQAQ/LAS-AT.


title: Median of Means Principle for Bayesian Inference

abstract:
  The topic of robustness is experiencing a resurgence of interest in the
statistical and machine learning communities. In particular, robust algorithms
making use of the so-called median of means estimator were shown to satisfy
strong performance guarantees for many problems, including estimation of the
mean, covariance structure as well as linear regression. In this work, we
propose an extension of the median of means principle to the Bayesian
framework, leading to the notion of the robust posterior distribution. In
particular, we (a) quantify robustness of this posterior to outliers, (b) show
that it satisfies a version of the Bernstein-von Mises theorem that connects
Bayesian credible sets to the traditional confidence intervals, and (c)
demonstrate that our approach performs well in applications.


title:
ALDI++: Automatic and parameter-less discord and outlier detection for
  building energy load profiles

abstract:
  Data-driven building energy prediction is an integral part of the process for
measurement and verification, building benchmarking, and building-to-grid
interaction. The ASHRAE Great Energy Predictor III (GEPIII) machine learning
competition used an extensive meter data set to crowdsource the most accurate
machine learning workflow for whole building energy prediction. A significant
component of the winning solutions was the pre-processing phase to remove
anomalous training data. Contemporary pre-processing methods focus on filtering
statistical threshold values or deep learning methods requiring training data
and multiple hyper-parameters. A recent method named ALDI (Automated Load
profile Discord Identification) managed to identify these discords using matrix
profile, but the technique still requires user-defined parameters. We develop
ALDI++, a method based on the previous work that bypasses user-defined
parameters and takes advantage of discord similarity. We evaluate ALDI++
against a statistical threshold, variational auto-encoder, and the original
ALDI as baselines in classifying discords and energy forecasting scenarios. Our
results demonstrate that while the classification performance improvement over
the original method is marginal, ALDI++ helps achieve the best forecasting
error improving 6% over the winning's team approach with six times less
computation time.


title: The isoperimetric inequalities of minimal hypersurfaces in spheres

abstract:
  Let $M^n$ be a closed hypersurface immersed in the unit sphere
$\mathbb{S}^{n+1}$, some special isoperimetric inequalities of $M^n$ will be
given in this paper, which reflect some rigid influences of external space $
\mathbb{S}^{n+1}$ on $M^n$.


title: Gauge symmetry of unimodular gravity in Hamiltonian formalism

abstract:
  We work out the description of the gauge symmetry of unimodular gravity in
the constrained Hamiltonian formalism. In particular, we demonstrate how the
transversality conditions restricting the diffeomorphism parameters emerge from
the algebra of the Hamiltonian constraints. The alternative form is long known
of parameterizing the volume preserving diffeomorphisms by unrestricted
two-forms instead of the transverse vector fields. This gauge symmetry is
reducible. We work out the Hamiltonian description of this form of UG gauge
symmetry. BFV-BRST Hamiltonian formalism is constructed for both forms of the
UG gauge symmetry. These two BRST complexes have a subtle inequivalence: their
BRST cohomology groups are not isomorphic. In particular, for the first
complex, which is related to the restricted gauge parameters, the cosmological
constant does not correspond to any non-trivial BRST co-cycle, while for the
alternative complex it does. In the wording of physics, this means $\Lambda$ is
a fixed parameter defined by the field asymptotics rather than the physical
observable from the standpoint of the first complex. The second formalism views
$\Lambda$ as the observable with unrestricted initial data.


title: Extended lattice Gelfand-Dickey hierarchy

abstract:
  The lattice Gelfand-Dickey hierarchy is a lattice version of the
Gelfand-Dickey hierarchy. A special case is the lattice KdV hierarchy. Inspired
by recent work of Buryak and Rossi, we propose an extension of the lattice
Gelfand-Dickey hierarchy. The extended system has an infinite number of
logarithmic flows alongside the usual flows. We present the Lax, Sato and
Hirota equations and a factorization problem of difference operators that
captures the whole set of solutions. The construction of this system resembles
the extended 1D and bigraded Toda hierarchy, but exhibits several novel
features as well.


title: Multi-Bracket High Dynamic Range Imaging with Event Cameras

abstract:
  Modern high dynamic range (HDR) imaging pipelines align and fuse multiple low
dynamic range (LDR) images captured at different exposure times. While these
methods work well in static scenes, dynamic scenes remain a challenge since the
LDR images still suffer from saturation and noise. In such scenarios, event
cameras would be a valid complement, thanks to their higher temporal resolution
and dynamic range. In this paper, we propose the first multi-bracket HDR
pipeline combining a standard camera with an event camera. Our results show
better overall robustness when using events, with improvements in PSNR by up to
5dB on synthetic data and up to 0.7dB on real-world data. We also introduce a
new dataset containing bracketed LDR images with aligned events and HDR ground
truth.


title:
Pseudo-Differential Equations with Weak Degeneration for Radial
  Functions of $p$-adic Argument

abstract:
  In earlier papers (A. N. Kochubei, Pacif. J. Math., 269 (2014), 355-369; J.
Math. Anal. Appl.483 (2020), Article 123609), one of the authors developed a
theory of pseudo-differential equations for radial real-valued functions on a
non-Archimedean local field, with some features resembling those of classical
ordinary differential equations. Here we consider equations of this kind, but
with a weak degeneration. Under various assumptions, we prove the local
existence and uniqueness of mild solutions, existence of their global
extensions and a regularity property.


title: Quantitative Molecular Simulations

abstract:
  All-atom simulations can provide molecular-level insights into the dynamics
of gas-phase, condensed-phase and surface processes. One important requirement
is a sufficiently realistic and detailed description of the underlying
intermolecular interactions. The present perspective provides an overview of
the present status of quantitative atomistic simulations from colleagues' and
our own efforts for gas- and solution-phase processes and for the dynamics on
surfaces. Particular attention is paid to direct comparison with experiment. An
outlook discusses present challenges and future extensions to bring such
dynamics simulations even closer to reality.


title: The graphs of non-degenerate linear codes

abstract:
  We consider the Grassmann graph of $k$-dimensional subspaces of an
$n$-dimensional vector space over the $q$-element field and its subgraph
$\Gamma(n,k)_q$ formed by non-degenerate linear $[n,k]_q$ codes. We assume that
$1<k<n-1$. It is well-known that every automorphism of the Grassmann graph is
induced by a semilinear automorphism of the corresponding vector space or a
semilinear isomorphism to the dual vector space; the second possibility is
realized only if $n=2k$. Our results are the following: if $q\ge 3$ or $k\ne
2$, then every isomorphism of $\Gamma(n,k)_{q}$ to a subgraph of the Grassmann
graph can be uniquely extended to an automorphism of the Grassmann graph; in
the case when $q=k=2$, there are subgraphs of the Grassmann graph isomorphic to
$\Gamma(n,k)_{q}$ and such that isomorphisms between these subgraphs and
$\Gamma(n,k)_{q}$ cannot be extended to automorphisms of the Grassmann graph.


title: A stochastic control approach to Sine Gordon EQFT

abstract:
  We study the Sine-Gordon model for $\beta^{2}< 4 \pi$ in infinite volume. We
give a variatonal characterization of it's laplace transform, and deduce from
this large deviations. Along the way we obtain estimates which are strong
enough to obtain a proof of the Osterwalder-Schrader axioms including
exponential decay of correlations as a byproduct. Our method is based on the
Boue-Dupuis formula with an emphasis on the stochastic control structure of the
problem.


title:
Convergence of Numerical Solution of The Tamed Milstein Method for
  NSDDEs

abstract:
  In this paper, we apply the tamed technique to the Milstein numerical scheme
to investigate Neutral Stochastic Delay Differential Equations(NSDDEs) with
highly nonlinear coefficients. Under the local Lipschitz condition and
Khasminskii condition, the tamed Milstein numerical solution converges strongly
to the exact solution.


title:
Helicity-dependent distribution of strange quarks in the proton from
  nonlocal chiral effective theory

abstract:
  The helicity-dependent strange quark distribution in the proton, $\Delta s$,
is calculated in a nonlocal chiral SU(3) effective field theory. The hadronic
proton to meson plus octet or decuplet baryon splitting functions are derived
at the one-loop level, with loop integrals rendered finite by correlation
functions introduced in the nonlocal Lagrangian. Within the convolution
framework, the proton strange helicity distribution is obtained using
spin-flavor symmetry to constrain the input valence quark distributions in the
hadronic intermediate states. The polarized strange quark distribution is found
to be quite small, with the lowest moment of $\Delta s$ negative, but
consistent with recent global QCD analyses.


title:
Note on large-$p$ limit of $(2,2p+1)$ minimal Liouville gravity and
  moduli space volumes

abstract:
  In this note we report on some properties of correlation numbers for
2-dimensional Liouville gravity coupled with $(2,2p+1)$ minimal model at large
$p$. In the limit $p \to \infty$, for some explicitly known examples in a
particular region of parameter space correlation numbers are shown to reduce to
Weil-Petersson volumes, analytically continued to imaginary geodesic lengths.
This marks another connection of this limit with JT-gravity. We also comment on
supposed geometric meaning of the obtained answers outside of this region, in
particular, the meaning of the minimal model fusion rules. Another observation
is the proportionality of correlation number to the number of conformal blocks
when $p$ is big enough compared to parameters of the correlator. This
proportionality is valid even without taking the limit.


title: Maximum cut on interval graphs of interval count two is NP-complete

abstract:
  We show that the Max-Cut problem is NP-complete on interval graphs of
interval count two.


title: A ROS Architecture for Personalised HRI with a Bartender Social Robot

abstract:
  BRILLO (Bartending Robot for Interactive Long-Lasting Operations) project has
the overall goal of creating an autonomous robotic bartender that can interact
with customers while accomplishing its bartending tasks. In such a scenario,
people's novelty effect connected to the use of an attractive technology is
destined to wear off and, consequently, it negatively affects the success of
the service robotics application. For this reason, providing personalised
natural interaction while accessing its services is of paramount importance for
increasing users' engagement and, consequently, their loyalty. In this paper,
we present the developed three-layers ROS architecture integrating a perception
layer managing the processing of different social signals, a decision-making
layer for handling multi-party interactions, and an execution layer controlling
the behaviour of a complex robot composed of arms and a face. Finally, user
modelling through a beliefs layer allows for personalised interaction.


title: Engineering entanglement between resonators by hot environment

abstract:
  Autonomous quantum thermal machines do not require an external coherent drive
or work input to perform the desired tasks, which makes them a promising
candidate for thermal management in quantum systems. Here, we propose an
autonomous quantum thermal machine in which two uncoupled macroscopic
mechanical resonators or microwave resonators achieve considerable entanglement
via a hot thermal bath. This becomes possible by coupling the resonators to a
common two-level system or third harmonic oscillator and driving it by the hot
incoherent thermal bath. The critical step to make the entanglement involves
suitable engineering of the hot bath, realized by bath spectrum filtering. Our
results suggest that the bath spectrum filtering can be an alternative to
typical non-autonomous reservoir engineering schemes to create exotic quantum
states.


title: A square root velocity framework for curves of bounded variation

abstract:
  The square root velocity transform is a powerful tool for the efficient
computation of distances between curves. Also, after factoring out
reparametrisations, it defines a distance between shapes that only depends on
their intrinsic geometry but not the concrete parametrisation. Though
originally formulated for smooth curves, the square root velocity transform and
the resulting shape distance have been thoroughly analysed for the setting of
absolutely continuous curves using a relaxed notion of reparametrisations. In
this paper, we will generalise the square root velocity distance even further
to a class of discontinuous curves. We will provide an explicit formula for the
natural extension of this distance to curves of bounded variation and analyse
the resulting quotient distance on the space of unparametrised curves. In
particular, we will discuss the existence of optimal reparametrisations for
which the minimal distance on the quotient space is realised.


title:
Adaptive Bit Rate Control in Semantic Communication with Incremental
  Knowledge-based HARQ

abstract:
  With the development of natural language processing (NLP) and deep learning
(DL), great progress has been made in the field of semantic communication.
Although existing semantic communication technologies can effectively reduce
errors in semantic interpretation, most of these solutions still adopt a fixed
bit rate structure, which is inefficient and inflexible for sentences with
different meanings and signal-to-noise ratio (SNR) conditions. In this paper,
we explore the impact of adaptive bit rates on semantic coding (SC) under
various channel conditions. First, we propose two progressive semantic hybrid
automatic repeat request (HARQ) schemes, both of which exploit the incremental
knowledge (IK) obtained from the retransmission, to further reduce the semantic
error in the transmission process. We then design a novel semantic encoding
solution with multi-bit rate selection. In this solution, the transmitter
employs a policy network to decide the appropriate coding rate, so as to ensure
the correct information delivery at the cost of minimal bits. Besides, we
design two specific denoisers to reduce the semantic errors encountered in the
transmission process according to the semantic characteristics of context and
SNR. Finally, a novel end-to-end semantic communication framework is proposed
by effectively combining the aforementioned methods. Extensive simulation
results have been conducted to verify the effectiveness of the proposed
solution.


title:
Interrelation of nonclassicality features in higher-dimensional systems
  through logical operators

abstract:
  In this work, we show that via the approach of logical qudits in higher
$d$--dimensional systems and continuous-level systems, conditions for coherence
themselves give rise to conditions for nonlocality and entanglement. As we
increase the number of parties in a logical qudit, conditions for coherence map
to those for entanglement and then, for nonlocality. We illustrate it with
examples of SLK inequality and a condition for entanglement in
continuous-variable systems. Furthermore, with the approach of logical
continuous-variable systems, we show a recently introduced form of correlation
can also be explained. Finally, we show how the condition for imaginarity maps
to partial-positive transpose criterion in two-qubit systems. This shows that a
single nonclassicality condition detects different forms of nonclassicalities
in different physical systems. Thereby, it reflects the interrelations of
different nonclassical features of states belonging to Hilbert spaces of
nonidentical dimensions.


title: Mixed excitonic nature in water-oxidized BiVO$_4$ surfaces with defects

abstract:
  BiVO$_4$ is a promising photocatalyst for efficient water oxidation, with
surface reactivity determined by the structure of active catalytic sites.
Surface oxidation in the presence of oxygen vacancies induces electron
localization, suggesting an atomistic route to improve the charge transfer
efficiency within the catalytic cycle. In this work, we study the effect of
oxygen vacancies on the electronic and optical properties at BiVO$_4$ surfaces
upon water oxidation. We use density functional theory and many-body
perturbation theory to explore the change in the electronic and quasiparticle
energy levels and to evaluate the electron-hole coupling as a function of the
underlying structure. We show that while the presence of defects alters the
atomic structure and largely modifies the wavefunction nature, leading to
defect-localized states at the quasipatricle gap region, the optical
excitations remain largely unchanged due to substantial hybridization of defect
and non-defect electron-hole transitions. Our findings suggest that
defect-induced surface oxidation supports improved electron transport, both
through bound and tunable electronic states and via a mixed nature of the
optical transitions, expected to reduce electron-hole defect trapping.


title: FFT based evaluation of microlensing magnification with extended source

abstract:
  The extended source effect on microlensing magnification is non-negligible
and must be taken into account for in an analysis of microlensing. However, the
evaluation of the extended source magnification is numerically expensive
because it includes the two-dimensional integral over source profile. Various
studies have developed methods to reduce this integral down to the
one-dimensional-integral or integral-free form, which adopt some approximations
or depend on the exact form of the source profile, e.g. disk, linear/quadratic
limb-darkening profile. In this paper, we develop a new method to evaluate the
extended source magnification based on fast Fourier transformation (FFT), which
does not adopt any approximations and is applicable to any source profiles. Our
implementation of the FFT based method enables the fast evaluation of the
extended source magnification as fast as $\sim1$ msec (CPU time on a laptop)
and guarantees an accuracy better than 0.3%. The FFT based method can be used
for the template fitting to a huge data set of light curves from the existing
and upcoming surveys.


title: Scaling the Wild: Decentralizing Hogwild!-style Shared-memory SGD

abstract:
  Powered by the simplicity of lock-free asynchrony, Hogwilld! is a go-to
approach to parallelize SGD over a shared-memory setting. Despite its
popularity and concomitant extensions, such as PASSM+ wherein concurrent
processes update a shared model with partitioned gradients, scaling it to
decentralized workers has surprisingly been relatively unexplored. To our
knowledge, there is no convergence theory of such methods, nor systematic
numerical comparisons evaluating speed-up.
  In this paper, we propose an algorithm incorporating decentralized
distributed memory computing architecture with each node running
multiprocessing parallel shared-memory SGD itself. Our scheme is based on the
following algorithmic tools and features: (a) asynchronous local gradient
updates on the shared-memory of workers, (b) partial backpropagation, and (c)
non-blocking in-place averaging of the local models. We prove that our method
guarantees ergodic convergence rates for non-convex objectives. On the
practical side, we show that the proposed method exhibits improved throughput
and competitive accuracy for standard image classification benchmarks on the
CIFAR-10, CIFAR-100, and Imagenet datasets. Our code is available at
https://github.com/bapi/LPP-SGD.


title:
Revisiting Deep Semi-supervised Learning: An Empirical Distribution
  Alignment Framework and Its Generalization Bound

abstract:
  In this work, we revisit the semi-supervised learning (SSL) problem from a
new perspective of explicitly reducing empirical distribution mismatch between
labeled and unlabeled samples. Benefited from this new perspective, we first
propose a new deep semi-supervised learning framework called Semi-supervised
Learning by Empirical Distribution Alignment (SLEDA), in which existing
technologies from the domain adaptation community can be readily used to
address the semi-supervised learning problem through reducing the empirical
distribution distance between labeled and unlabeled data. Based on this
framework, we also develop a new theoretical generalization bound for the
research community to better understand the semi-supervised learning problem,
in which we show the generalization error of semi-supervised learning can be
effectively bounded by minimizing the training error on labeled data and the
empirical distribution distance between labeled and unlabeled data. Building
upon our new framework and the theoretical bound, we develop a simple and
effective deep semi-supervised learning method called Augmented Distribution
Alignment Network (ADA-Net) by simultaneously adopting the well-established
adversarial training strategy from the domain adaptation community and a simple
sample interpolation strategy for data augmentation. Additionally, we
incorporate both strategies in our ADA-Net into two exiting SSL methods to
further improve their generalization capability, which indicates that our new
framework provides a complementary solution for solving the SSL problem. Our
comprehensive experimental results on two benchmark datasets SVHN and CIFAR-10
for the semi-supervised image recognition task and another two benchmark
datasets ModelNet40 and ShapeNet55 for the semi-supervised point cloud
recognition task demonstrate the effectiveness of our proposed framework for
SSL.


title:
Measuring anomalies in cigarette sales by using official data from
  Spanish provinces: Are there only the anomalies detected by the Empty Pack
  Surveys (EPS) used by Transnational Tobacco Companies (TTCs)?

abstract:
  There is literature that questions the veracity of the studies commissioned
by the transnational tobacco companies (TTC) to measure the illicit tobacco
trade. Furthermore, there are studies that indicate that the Empty Pack Surveys
(EPS) ordered by the TTCs are oversized. The novelty of this study is that, in
addition to detecting the anomalies analyzed in the EPSs, there are provinces
in which cigarette sales are higher than reasonable values, something that the
TTCs ignore. This study analyzed simultaneously, firstly, if the EPSs
established in each of the 47 Spanish provinces were fulfilled. Second,
anomalies observed in provinces where sales exceed expected values are
measured. To achieve the objective of the paper, provincial data on cigarette
sales, price and GDP per capita are used. These data are modeled with machine
learning techniques widely used to detect anomalies in other areas. The results
reveal that the provinces in which sales below reasonable values are observed
(as detected by the EPSs) present a clear geographical pattern. Furthermore,
the values provided by the EPSs in Spain, as indicated in the previous
literature, are slightly oversized. Finally, there are regions bordering other
countries or with a high tourist influence in which the observed sales are
higher than the expected values.


title:
Exploring Customer Price Preference and Product Profit Role in
  Recommender Systems

abstract:
  Most of the research in the recommender systems domain is focused on the
optimization of the metrics based on historical data such as Mean Average
Precision (MAP) or Recall. However, there is a gap between the research and
industry since the leading Key Performance Indicators (KPIs) for businesses are
revenue and profit. In this paper, we explore the impact of manipulating the
profit awareness of a recommender system. An average e-commerce business does
not usually use a complicated recommender algorithm. We propose an adjustment
of a predicted ranking for score-based recommender systems and explore the
effect of the profit and customers' price preferences on two industry datasets
from the fashion domain. In the experiments, we show the ability to improve
both the precision and the generated recommendations' profit. Such an outcome
represents a win-win situation when e-commerce increases the profit and
customers get more valuable recommendations.


title: Cluster Assignment in Multi-Agent Systems

abstract:
  We study cluster assignment in multi-agent networks. We consider homogeneous
diffusive networks, and focus on design of the graph that ensures the system
will converge to a prescribed cluster configuration, i.e., specifying the
number of clusters and agents within each cluster. Leveraging recent results
from cluster synthesis, we show that it is possible to design an oriented graph
such that the action of the automorphism group of the graph has orbits of
predetermined sizes, guaranteeing that the network will converge to the
prescribed cluster configuration. We provide upper and lower bounds on the
number of edges that are needed to construct these graphs along with a
constructive approach for generating these graphs. We support our analysis with
some numerical examples.

categories: eess.SY cs.DM cs.MA cs.SY math.CO math.OC


title: Constant-roll inflation driven by holographic dark energy

abstract:
  The scenario of constant-roll inflation is investigated assuming that it is
caused by holographic dark energy with energy density $3M_p^2 c^2 / L^2$. The
energy density with varying $c$ parameter and Hubble radius as infrared cutoff
provides a proper description for the late time acceleration expansion. This
motivates us to study the role of the same holographic dark energy in the
scenario of constant-roll inflation. One of the main features of constant-roll
inflation is that the second slow-roll parameter is constant which is not
necessarily small. This feature brings a differential equation for the
parameter $c$. After finding an analytical solution for the parameter, the
observable parameters are obtained at the time of inflation, and by comparing
the model with data, the constants of the model are determined. The energy
scale of inflation is estimated which is about $10^{16} \; {\rm GeV}$. Finally,
a correspondence between the HDE and canonical scalar field is considered and
the behavior of the constructed potential and its magnitude is discussed.