title: Hate Me Not: Detecting Hate Inducing Memes in Code Switched Languages

abstract:
  The rise in the number of social media users has led to an increase in the
hateful content posted online. In countries like India, where multiple
languages are spoken, these abhorrent posts are from an unusual blend of
code-switched languages. This hate speech is depicted with the help of images
to form "Memes" which create a long-lasting impact on the human mind. In this
paper, we take up the task of hate and offense detection from multimodal data,
i.e. images (Memes) that contain text in code-switched languages. We firstly
present a novel triply annotated Indian political Memes (IPM) dataset, which
comprises memes from various Indian political events that have taken place
post-independence and are classified into three distinct categories. We also
propose a binary-channelled CNN cum LSTM based model to process the images
using the CNN model and text using the LSTM model to get state-of-the-art
results for this task.


title:
Improving Deep Learning Model Robustness Against Adversarial Attack by
  Increasing the Network Capacity

abstract:
  Nowadays, we are more and more reliant on Deep Learning (DL) models and thus
it is essential to safeguard the security of these systems. This paper explores
the security issues in Deep Learning and analyses, through the use of
experiments, the way forward to build more resilient models. Experiments are
conducted to identify the strengths and weaknesses of a new approach to improve
the robustness of DL models against adversarial attacks. The results show
improvements and new ideas that can be used as recommendations for researchers
and practitioners to create increasingly better DL algorithms.


title:
The relationship between age, metallicity, and abundances for disk stars
  in a simulated Milky Way galaxy

abstract:
  Observations of the Milky Way's low-$\alpha$ disk show that at fixed
metallicity, [Fe/H], several element abundance, [X/Fe], correlate with age,
with unique slopes and small scatters around the age-[X/Fe] relations. In this
study, we turn to simulations to explore the age-[X/Fe] relations for the
elements C, N, O, Mg, Si, S, and Ca that are traced in a FIRE-2 cosmological
zoom-in simulation of a Milky Way-like galaxy, m12i, and understand what
physical conditions give rise to the observed age-[X/Fe] trends. We first
explore the distributions of mono-age populations in their birth and current
locations, [Fe/H], and [X/Fe], and find evidence for inside-out radial growth
for stars with ages < 7 Gyr. We then examine the age-[X/Fe] relations across
m12i's disk and find that the direction of the trends agree with observations,
apart from C, O, and Ca, with remarkably small intrinsic scatters,
$\sigma_{int}$ (0.01-0.04 dex). This $\sigma_{int}$ measured in the simulations
is also metallicity-dependent, with $\sigma_{int}$ $\approx$ 0.025 dex at
[Fe/H]=-0.25 dex versus $\sigma_{int}$ $\approx$ 0.015 dex at [Fe/H]=0 dex, and
a similar metallicity dependence is seen in the GALAH survey for the elements
in common. Additionally, we find that $\sigma_{int}$ is higher in the inner
galaxy, where stars are older and formed in less chemically-homogeneous
environments. The age-[X/Fe] relations and the small scatter around them
indicate that simulations capture similar chemical enrichment variance as
observed in the Milky Way, arising from stars sharing similar element
abundances at a given birth place and time.


title:
Navier-Stokes equations: a new estimate of a possible gap related to the
  energy equality of a suitable weak solution

abstract:
  The paper is concerned with the IBVP of the Navier-Stokes equations. The
result of the paper is in the wake of analogous results obtained by the authors
in previous articles [4, 5]. The goal is to estimaste the possible gap between
the energy equality and the energy inequality deduced for a weak solution.


title: Monochromatic components with many edges

abstract:
  Given an $r$-edge-coloring of the complete graph $K_n$, what is the largest
number of edges in a monochromatic connected component? This natural question
has only recently received the attention it deserves, with work by two disjoint
subsets of the authors resolving it for the first two special cases, when $r =
2$ or $3$. Here we introduce a general framework for studying this problem and
apply it to fully resolve the $r = 4$ case, showing that such a coloring always
yields a monochromatic component with at least $\frac{1}{12}\binom{n}{2}$
edges, where the constant $\frac{1}{12}$ is optimal only when the coloring
matches a certain construction of Gy\'arf\'as.


title: Hypergraph matrix models and generating functions

abstract:
  Recently we introduced the hypergraph matrix model (HMM), a Hermitian matrix
model generalizing the classical Gaussian Unitary Ensemble (GUE). In this model
the Gaussians of the GUE, whose moments count partitions of finite sets into
pairs, are replaced by formal measures whose moments count set partitions into
parts of a fixed even size 2m >= 2. Just as the expectations of the trace
polynomials Tr X^{2r} in the GUE produce polynomials counting unicellular
orientable maps of different genera, in the HHM these expectations give
polynomials counting certain unicelled edge-ramified CW complexes with extra
data that we call (orientable CW) maps with instructions. In this paper we
describe generating functions for maps with instructions of fixed genus and
with the number of vertices arbitrary. Our results are motivated by work of
Wright. In particular Wright computed generating functions of connected graphs
of fixed first Betti number as rational functions in the rooted tree function T
(x), given as the solution to the functional relation x = T e^{-T}.


title: Error-correcting Identifying Codes

abstract:
  Assume that a graph $G$ models a detection system for a facility with a
possible "intruder," or a multiprocessor network with a possible malfunctioning
processor. We consider the problem of placing (the minimum number of) detectors
at a subset of vertices in $G$ to automatically determine if there is an
intruder, and if so, its precise location. In this research we explore a
fault-tolerant variant of identifying codes, known as error-correcting
identifying codes, which permit one false positive or negative and are
applicable to real-world systems. We present the proof of NP-completeness of
the problem of determining said minimum size in arbitrary graphs, and determine
bounds on the parameter in cubic graphs.


title: Survey on the metric SYZ conjecture and non-archimedean geometry

abstract:
  We survey the metric aspects of the Strominger-Yau-Zaslow conjecture on the
existence of special Lagrangian fibrations on Calabi-Yau manifolds near the
large complex structure limit. We will discuss the diverse motivations for the
conjectural picture, what the best hopes are, and a number of subtleties. The
bulk of the survey highlights the role of pluripotential theory, and
non-archimedean geometry in particular, with a list of open questions.


title: Information Theoretic Secure Aggregation with Uncoded Groupwise Keys

abstract:
  Secure aggregation, which is a core component of federated learning,
aggregates locally trained models from distributed users at a central server.
The "secure" nature of such aggregation consists of the fact that no
information about the local users' data must be leaked to the server except the
aggregated local models. In order to guarantee security, some keys may be
shared among the users (this is referred to as the key sharing phase). After
the key sharing phase, each user masks its trained model which is then sent to
the server (this is referred to as the model aggregation phase). This paper
follows the information theoretic secure aggregation problem originally
formulated by Zhao and Sun, with the objective to characterize the minimum
communication cost from the $K$ users in the model aggregation phase. Due to
user dropouts, the server may not receive all messages from the users. A secure
aggregation schemes should tolerate the dropouts of at most $K-U$ users, where
$U$ is a system parameter. The optimal communication cost is characterized by
Zhao and Sun, but with the assumption that the keys stored by the users could
be any random variables with arbitrary dependency. On the motivation that
uncoded groupwise keys are more convenient to be shared and could be used in
large range of applications besides federated learning, in this paper we add
one constraint into the above problem, that the key variables are mutually
independent and each key is shared by a group of at most $S$ users, where $S$
is another system parameter. To the best of our knowledge, all existing secure
aggregation schemes assign coded keys to the users. We show that if $S > K -
U$, a new secure aggregation scheme with uncoded groupwise keys can achieve the
same optimal communication cost as the best scheme with coded keys; if $S \leq
K - U$, uncoded groupwise key sharing is strictly sub-optimal.


title:
Singular structures in solutions to the Monge-Amp\`{e}re equation with
  point masses

abstract:
  We construct new examples of Monge-Amp\`{e}re metrics with polyhedral
singular structures, motivated by problems related to the optimal transport of
point masses and to mirror symmetry. We also analyze the stability of the
singular structures under small perturbations of the data given in the problem
under consideration.


title: Breather solution of non-linear Klein-Gordon equation

abstract:
  A technique for obtaining an approximate breather solution of the
Klein-Gordon equation is presented. A breather solution of the equation
describing the propagation of nonlinear waves in a graphene-based superlattice
is investigated.


title:
Ordered-logit pedestrian stress model for traffic flow with automated
  vehicles

abstract:
  An ordered-logit model is developed to study the effects of Automated
Vehicles (AVs) in the traffic mix on the average stress level of a pedestrian
when crossing an urban street at mid-block. Information collected from a
galvanic skin resistance sensor and virtual reality experiments are transformed
into a dataset with interpretable average stress levels (low, medium, and high)
and geometric, traffic, and environmental conditions. Modelling results
indicate a decrease in average stress level with the increase in the percentage
of AVs in the traffic mix.


title:
Enhancing the STIX Representation of MITRE ATT&CK for Group Filtering
  and Technique Prioritization

abstract:
  In this paper, we enhance the machine-readable representation of the ATT&CK
Groups knowledge base provided by MITRE in STIX 2.1 format to make available
and queryable additional types of contextual information. Such information
includes the motivations of activity groups, the countries they have originated
from, and the sectors and countries they have targeted. We demonstrate how to
utilize the enhanced model to construct intelligible queries to filter activity
groups of interest and retrieve relevant tactical intelligence.


title:
LTE Cellular Networks Packet Scheduling Algorithms in Downlink and
  Uplink Transmission, A Survey

abstract:
  This survey paper provides a detailed explanation of Long Term Evolution
cellular network packet scheduling algorithms in both downlink and uplink
directions. It starts by explaining the difference between Orthogonal Frequency
Division Multiple Access that is used in downlink transmission, and Single
Carrier Frequency Division Multiple Access is used in uplink. Then, it explains
the difference between the LTE scheduling process in the donwlink and uplink
through explaining the interaction between users and the scheduler. Then, it
explains the most commonly used downlink and uplink scheduling algorithms
through analyzing their formulas, characteristics, most suitable conditions for
them to work in, and the main differences among them. This explanation covers
the Max Carrier-to-Interference, Round Robin, Proportional Fair, Earliest
Deadline First, Modified EDF-PF, Modified-Largest Weighted Delay First,
Exponential Proportional Fairness, Token Queues Mechanism, Packet Loss Ratio,
Quality Guaranteed, Opportunistic Packet Loss Fair, Low Complexity, LC-Delay,
PF-Delay, Maximum Throughput, First Maximum Expansion, and Adaptive Resource
Allocation Based Packet Scheduling. Lastly, it provides some concluding
remarks.


title:
Deep Reinforcement Learning Using a Low-Dimensional Observation Filter
  for Visual Complex Video Game Playing

abstract:
  Deep Reinforcement Learning (DRL) has produced great achievements since it
was proposed, including the possibility of processing raw vision input data.
However, training an agent to perform tasks based on image feedback remains a
challenge. It requires the processing of large amounts of data from
high-dimensional observation spaces, frame by frame, and the agent's actions
are computed according to deep neural network policies, end-to-end. Image
pre-processing is an effective way of reducing these high dimensional spaces,
eliminating unnecessary information present in the scene, supporting the
extraction of features and their representations in the agent's neural network.
Modern video-games are examples of this type of challenge for DRL algorithms
because of their visual complexity. In this paper, we propose a low-dimensional
observation filter that allows a deep Q-network agent to successfully play in a
visually complex and modern video-game, called Neon Drive.


title: Learning Symmetric Embeddings for Equivariant World Models

abstract:
  Incorporating symmetries can lead to highly data-efficient and generalizable
models by defining equivalence classes of data samples related by
transformations. However, characterizing how transformations act on input data
is often difficult, limiting the applicability of equivariant models. We
propose learning symmetric embedding networks (SENs) that encode an input space
(e.g. images), where we do not know the effect of transformations (e.g.
rotations), to a feature space that transforms in a known manner under these
operations. This network can be trained end-to-end with an equivariant task
network to learn an explicitly symmetric representation. We validate this
approach in the context of equivariant transition models with 3 distinct forms
of symmetry. Our experiments demonstrate that SENs facilitate the application
of equivariant networks to data with complex symmetry representations.
Moreover, doing so can yield improvements in accuracy and generalization
relative to both fully-equivariant and non-equivariant baselines.


title: Noise-resilient Majorana Edge Modes on a Chain of Superconducting Qubits

abstract:
  Inherent symmetry of a quantum system may protect its otherwise fragile
states. Leveraging such protection requires testing its robustness against
uncontrolled environmental interactions. Using 47 superconducting qubits, we
implement the kicked Ising model which exhibits Majorana edge modes (MEMs)
protected by $\mathbb{Z}_2$ parity symmetry. Remarkably, we find that any
multi-qubit Pauli operator overlapping with the MEMs exhibits a uniform decay
rate comparable to single-qubit relaxation rates, irrespective of its size or
composition. This finding allows us to accurately reconstruct the exponentially
localized spatial profiles of the MEMs. Spectroscopic measurements further
indicate exponentially suppressed hybridization between the MEMs at larger
system sizes, which manifests as a strong resilience against low-frequency
noise. Our work elucidates the noise sensitivity of symmetry-protected edge
modes in a solid-state environment.


title:
Entity-Conditioned Question Generation for Robust Attention Distribution
  in Neural Information Retrieval

abstract:
  We show that supervised neural information retrieval (IR) models are prone to
learning sparse attention patterns over passage tokens, which can result in key
phrases including named entities receiving low attention weights, eventually
leading to model under-performance. Using a novel targeted synthetic data
generation method that identifies poorly attended entities and conditions the
generation episodes on those, we teach neural IR to attend more uniformly and
robustly to all entities in a given passage. On two public IR benchmarks, we
empirically show that the proposed method helps improve both the model's
attention patterns and retrieval performance, including in zero-shot settings.


title:
Stochastic Optimization Approaches for an Operating Room and
  Anesthesiologist Scheduling Problem

abstract:
  We propose combined allocation, assignment, sequencing, and scheduling
problems under uncertainty involving multiple operation rooms (ORs),
anesthesiologists, and surgeries, as well as methodologies for solving such
problems. Specifically, given sets of ORs, regular anesthesiologists, on-call
anesthesiologists, and surgeries, our methodologies solve the following
decision-making problems simultaneously: (1) an allocation problem that decides
which ORs to open and which on-call anesthesiologists to call in, (2) an
assignment problem that assigns an OR and an anesthesiologist to each surgery,
and (3) a sequencing and scheduling problem that determines the order of
surgeries and their scheduled start times in each OR. To address uncertainty of
each surgery's duration, we propose and analyze stochastic programming (SP) and
distributionally robust optimization (DRO) methodologies. We obtain
near-optimal solutions of our SP model using sample average approximation and
propose a computationally efficient column-and-constraint generation method to
solve our DRO model. In addition, we derive symmetry-breaking constraints that
improve the models' solvability. Using real-world, publicly available surgery
data and a case study from a health system in New York, we conduct extensive
computational experiments comparing the proposed methodologies empirically and
theoretically, demonstrating where significant performance improvements can be
gained. Additionally, we derive several managerial insights relevant to
practice.


title:
Numerical Computation of Partial Differential Equations by Hidden-Layer
  Concatenated Extreme Learning Machine

abstract:
  The extreme learning machine (ELM) method can yield highly accurate solutions
to linear/nonlinear partial differential equations (PDEs), but requires the
last hidden layer of the neural network to be wide to achieve a high accuracy.
If the last hidden layer is narrow, the accuracy of the existing ELM method
will be poor, irrespective of the rest of the network configuration. In this
paper we present a modified ELM method, termed HLConcELM (hidden-layer
concatenated ELM), to overcome the above drawback of the conventional ELM
method. The HLConcELM method can produce highly accurate solutions to
linear/nonlinear PDEs when the last hidden layer of the network is narrow and
when it is wide. The new method is based on a type of modified feedforward
neural networks (FNN), termed HLConcFNN (hidden-layer concatenated FNN), which
incorporates a logical concatenation of the hidden layers in the network and
exposes all the hidden nodes to the output-layer nodes. We show that HLConcFNNs
have the remarkable property that, given a network architecture, when
additional hidden layers are appended to the network or when extra nodes are
added to the existing hidden layers, the approximation capacity of the
HLConcFNN associated with the new architecture is guaranteed to be not smaller
than that of the original network architecture. We present ample benchmark
tests with linear/nonlinear PDEs to demonstrate the computational accuracy and
performance of the HLConcELM method and the superiority of this method to the
conventional ELM from previous works.

categories: math.NA cs.LG cs.NA physics.comp-ph physics.flu-dyn


title: Model Repair via Symmetry

abstract:
  The symmetry of a Kripke structure $\mathcal{M}$ has been exploited to
replace a model check of $\mathcal{M}$ by a model check of the potentially
smaller structure $\mathcal{N}$ obtained as the quotient of $\mathcal{M}$ by
its symmetry group $G$. We extend previous work to model repair: identify a
substructure that satisfies a given temporal logic formula. We show that the
substructures of $\mathcal{M}$ that are preserved by $G$ form a lattice that
maps to the substructure lattice of $\mathcal{N}$. We also show the existence
of a monotone Galois connection between the lattice of substructures of
$\mathcal{N}$ and the lattice of substructures of $\mathcal{M}$ that are
"maximal" w.r.t. an appropriately defined group action of $G$ on $\mathcal{M}$.
These results enable us to repair $\mathcal{N}$ and then to lift the repair to
$\mathcal{M}$. We can thus repair symmetric finite-state concurrent programs by
repairing the corresponding $\mathcal{N}$, thereby effecting program repair
while avoiding state-explosion.


title: Input-output theory with time-reversal

abstract:
  We incorporate unitary transformations to the photon wave packet propagating
between cascaded quantum systems in input-output theory. This is a basis for
quantum state transfer between different quantum systems (quantum
transduction), from system 1 to system 2. We present a transformation, $U$,
that time reverses, frequency translates, and stretches the photon wave packet.
$U$ can be tuned such that a wave packet emitted by system 1 is absorbed by
system 2. We find concomitant modifications to the standard input-output theory
that can best be understood in terms of a change to the state's time argument,
$\rho(t) = \rho_1(\tilde{t}) \otimes \rho_2(t)$, where $\tilde{t}$ is a
fictitious time for system 1, which runs backwards, as determined by $U$.


title:
KMT-2021-BLG-1898: Planetary microlensing event involved with binary
  source stars

abstract:
  The light curve of the microlensing event KMT-2021-BLG-1898 exhibits a
short-term central anomaly with double-bump features that cannot be explained
by the usual binary-lens or binary-source interpretations. With the aim of
interpreting the anomaly, we analyze the lensing light curve under various
sophisticated models. We find that the anomaly is explained by a model, in
which both the lens and source are binaries (2L2S model). For this
interpretation, the lens is a planetary system with a planet/host mass ratio of
$q\sim 1.5\times 10^{-3}$, and the source is a binary composed of a turn off or
a subgiant star and a mid K dwarf. The double-bump feature of the anomaly can
also be depicted by a triple-lens model (3L1S model), in which the lens is a
planetary system containing two planets. Among the two interpretations, the
2L2S model is favored over the 3L1S model not only because it yields a better
fit to the data, by $\Delta\chi^2=[14.3$--18.5], but also the Einstein radii
derived independently from the two stars of the binary source result in
consistent values. According to the 2L2S interpretation, KMT-2021-BLG-1898 is
the third planetary lensing event occurring on a binary stellar system,
following MOA-2010-BLG-117 and KMT-2018-BLG-1743. Under the 2L2S
interpretation, we identify two solutions resulting from the close-wide
degeneracy in determining the planet-host separation. From a Bayesian analysis,
we estimate that the planet has a mass of $\sim 0.7$--0.8~$M_{\rm J}$, and it
orbits an early M dwarf host with a mass of $\sim 0.5~M_\odot$. The projected
planet-host separation is $\sim 1.9$~AU and $\sim 3.0$~AU according to the
close and wide solutions, respectively.


title:
Theory of QED radiative corrections to neutrino scattering at
  accelerator energies

abstract:
  Control over quantum electrodynamics (QED) radiative corrections is critical
for precise determination of neutrino oscillation probabilities from observed
(anti)neutrino detection rates. It is particularly important to understand any
difference between such corrections for different flavors of (anti)neutrinos in
charged-current interactions. We provide theoretical foundations for
calculating these corrections. Using effective field theory, the corrections
are shown to factorize into soft, collinear, and hard functions. The soft and
collinear functions contain large logarithms in perturbation theory but are
computable from QED. The hard function parameterizes hadronic structure but is
free from large logarithms. Using a simple model for the hard function, we
investigate the numerical impact of QED corrections in charged-current
(anti)neutrino-nucleon elastic cross sections and cross-section ratios at GeV
energies. We consider the implications of mass singularity theorems that govern
the lepton-mass dependence of cross sections for sufficiently inclusive
observables, and demonstrate the cancellation of leading hadronic and nuclear
corrections in phenomenologically relevant observables.


title:
An Online Stochastic Optimization Approach for Insulin Intensification
  in Type 2 Diabetes with Attention to Pseudo-Hypoglycemia

abstract:
  Upon the initiation of insulin treatment for subjects with type 2 diabetes
(T2D), symptoms of hypoglycemia can be experienced by the subjects depending on
how quick their blood glucose concentrations are reduced. This phenomena is
known as relative hypoglycemia or pseudo-hypoglycemia (PHG) and it is different
from patient to patient. In this paper, we propose a model free strategy to
calculate insulin doses for T2D which mitigates PHG. The strategy makes use of
a feedback rating mechanism obtained from the subjects. The proposed strategy
tunes the parameters of a proposed control law by using an online stochastic
optimization approach for a defined cost function. The strategy uses gradient
estimates obtained by a Recursive Least Square (RLS) scheme in an adaptive
moment estimation based approach named AdaBelief. The performance of the
insulin calculation strategy is demonstrated and compared with current insulin
calculation strategies using simulations with three different models.


title:
Substrate-Dependence of Monolayer MoS$_2$ Thermal Conductivity and
  Thermal Boundary Conductance

abstract:
  The thermal properties of two-dimensional (2D) materials, like MoS$_2$, are
known to be affected by interactions with their environment, but this has
primarily been studied only with SiO$_2$ substrates. Here, we compare the
thermal conductivity (TC) and thermal boundary conductance (TBC) of monolayer
MoS$_2$ on amorphous (a-) and crystalline (c-) SiO$_2$, AlN, Al$_2$O$_3$, and
$\textit{h}$-BN monolayers using molecular dynamics. The room temperature TC of
MoS$_2$ is ~38 Wm$^{-1}$K$^{-1}$ on amorphous substrates and up to ~68
Wm$^{-1}$K$^{-1}$ on crystalline substrates, with most of the difference due to
substrate interactions with long-wavelength MoS$_2$ phonons (< 2 THz). An
$\textit{h}$-BN monolayer used as a buffer between MoS$_2$ and the substrate
causes the MoS$_2$ TC to increase by up to 50%. Length-dependent calculations
reveal TC size effects below ~2 $\mu$m and show that the MoS$_2$ TC is size-
but not substrate-limited below ~100 nm. We also find that the TBC of MoS$_2$
with c-Al$_2$O$_3$ is over twice that with c-AlN despite a similar MoS$_2$ TC
on both, indicating that the TC and TBC could be tuned independently. Finally,
we compare the thermal resistance of MoS$_2$ transistors on all substrates to
show that MoS$_2$ TBC is the most important parameter for heat removal for
long-channel (> 150 nm) devices, while TBC and TC are equally important for
short channels. This work provides important insights for electro-thermal
applications of 2D materials on various substrates.


title:
Real-time Speech Emotion Recognition Based on Syllable-Level Feature
  Extraction

abstract:
  Speech emotion recognition systems have high prediction latency because of
the high computational requirements for deep learning models and low
generalizability mainly because of the poor reliability of emotional
measurements across multiple corpora. To solve these problems, we present a
speech emotion recognition system based on a reductionist approach of
decomposing and analyzing syllable-level features. Mel-spectrogram of an audio
stream is decomposed into syllable-level components, which are then analyzed to
extract statistical features. The proposed method uses formant attention,
noise-gate filtering, and rolling normalization contexts to increase feature
processing speed and tolerance to adversity. A set of syllable-level formant
features is extracted and fed into a single hidden layer neural network that
makes predictions for each syllable as opposed to the conventional approach of
using a sophisticated deep learner to make sentence-wide predictions. The
syllable level predictions help to achieve the real-time latency and lower the
aggregated error in utterance level cross-corpus predictions. The experiments
on IEMOCAP (IE), MSP-Improv (MI), and RAVDESS (RA) databases show that the
method archives real-time latency while predicting with state-of-the-art
cross-corpus unweighted accuracy of 47.6% for IE to MI and 56.2% for MI to IE.


title: KMT-2021-BLG-0240: Microlensing event with a deformed planetary signal

abstract:
  The light curve of the microlensing event KMT-2021-BLG-0240 exhibits a
short-lasting anomaly with complex features near the peak at the 0.1~mag level
from a single-lens single-source model. We conducted modeling of the lensing
light curve under various interpretations to reveal the nature of the anomaly.
It is found that the anomaly cannot be explained with the usual model based on
a binary-lens (2L1S) or a binary-source (1L2S) interpretation. However, a 2L1S
model with a planet companion can describe part of the anomaly, suggesting that
the anomaly may be deformed by a tertiary lens component or a close companion
to the source. From the additional modeling, we find that all the features of
the anomaly can be explained with either a triple-lens (3L1S) model or a
binary-lens binary-source (2L2S) model obtained under the 3L1S interpretation.
However, it is difficult to validate the 2L2S model because the light curve
does not exhibit signatures induced by the source orbital motion and the
ellipsoidal variations expected by the close separation between the source
stars according to the model. We, therefore, conclude that the two
interpretations cannot be distinguished with the available data, and either can
be correct. According to the 3L1S solution, the lens is a planetary system with
two sub-Jovian-mass planets in which the planets have masses of
0.32--0.47~$M_{\rm J}$ and 0.44--0.93~$M_{\rm J}$, and they orbit an M dwarf
host. According to the 2L2S solution, on the other hand, the lens is a single
planet system with a mass of $\sim 0.21~M_{\rm J}$ orbiting a late K-dwarf
host, and the source is a binary composed of a primary of a subgiant or a
turnoff star and a secondary of a late G dwarf. The distance to the planetary
system varies depending on the solution: $\sim 7.0$~kpc according to the 3L1S
solution and $\sim 6.6$~kpc according to the 2L2S solution.

categories: astro-ph.EP astro-ph.GA astro-ph.IM astro-ph.SR


title: Stronger gravity in the early universe

abstract:
  Scalar-tensor theories of gravity that embrace conformal coupling to the
scalar curvature are the focal point of cosmology on discussions of inflation
and late-time accelerating universe. Although there exists a stringent
nucleo-synthesis constraint on conformal gravity, one can formulate how to
evade this difficulty by modifying the standard particle theory action
consistently with the principles of gauge invariant quantum field theory. It is
shown that stronger gravity at early epochs of cosmological evolution than
previously thought of is inevitable in a class of conformal gravity models.
This enhances discovery potentials of primordial gravitational wave emission
and primordial black hole formation. The strong gravity effect may be enormous
if massive clumps are energetically dominated by cold dark matter made of
inflaton field, and created black holes may become a major candidate of cold
dark matter.


title: DRT: A Lightweight Single Image Deraining Recursive Transformer

abstract:
  Over parameterization is a common technique in deep learning to help models
learn and generalize sufficiently to the given task; nonetheless, this often
leads to enormous network structures and consumes considerable computing
resources during training. Recent powerful transformer-based deep learning
models on vision tasks usually have heavy parameters and bear training
difficulty. However, many dense-prediction low-level computer vision tasks,
such as rain streak removing, often need to be executed on devices with limited
computing power and memory in practice. Hence, we introduce a recursive local
window-based self-attention structure with residual connections and propose
deraining a recursive transformer (DRT), which enjoys the superiority of the
transformer but requires a small amount of computing resources. In particular,
through recursive architecture, our proposed model uses only 1.3% of the number
of parameters of the current best performing model in deraining while exceeding
the state-of-the-art methods on the Rain100L benchmark by at least 0.33 dB.
Ablation studies also investigate the impact of recursions on derain outcomes.
Moreover, since the model contains no deliberate design for deraining, it can
also be applied to other image restoration tasks. Our experiment shows that it
can achieve competitive results on desnowing. The source code and pretrained
model can be found at https://github.com/YC-Liang/DRT.


title:
A classification of complete $3$-dimensional self-shrinkers in the
  Euclidean space $\mathbb R^{4}$

abstract:
  In this paper, we completely classify $3$-dimensional complete self-shrinkers
with constant norm $S$ of the second fundamental form and constant $f_{3}$ in
Euclidean space $\mathbb R^{4}$, where $h_{ij}$ are components of the second
fundamental form, $S=\sum_{i,j}h^{2}_{ij}$ and
$f_{3}=\sum_{i,j,k}h_{ij}h_{jk}h_{ki}$.


title:
Triangular Tetrablock-contractions, factorization of contractions,
  dilation and subvarieties

abstract:
  A commuting triple of Hilbert space operators $(A,B,P)$, for which the closed
tetrablock $\bar{\mathbb E}$ is a spectral set, is called a
\textit{tetrablock-contraction} or simply an $\mathbb E$-\textit{contraction},
where \[ \mathbb E=\{(a_{11},a_{22}, \det A):\, A=[a_{ij}]\in \mathcal
M_2(\mathbb C), \; \|A\| <1 \} \subset \mathbb C^3 \] is a polynomially convex
domain which is naturally associated with the $\mu$-synthesis problem. We
introduce triangular $\mathbb E$-contractions and prove that every pure
triangular $\mathbb E$-contraction dilates to a pure triangular $\mathbb
E$-isometry. We construct a functional model for a pure triangular $\mathbb
E$-isometry and apply that model to find a new proof for the famous
Berger-Coburn-Lebow Model Theorem for commuting isometries. Next we give an
alternative proof to the more generalized version of Berger-Coburn-Lebow Model,
namely the factorization of a pure contraction due to Das, Sarkar and Sarkar
(\textit{Adv. Math.} 322 (2017), 186 -- 200). We find a necessary and
sufficient condition for the existence of $\mathbb E$-unitary dilation of an
$\mathbb E$-contraction $(A,B,P)$ on the smallest dilation space and show that
it is equivalent to the existence of a distinguished variety in $\mathbb E$
when the defect space $D_{P^*}$ is finite dimensional.


title: Distributed quantum algorithm for Simon's problem

abstract:
  Limited by today's physical devices, quantum circuits are usually noisy and
difficult to be designed deeply. The novel computing architecture of
distributed quantum computing is expected to reduce the noise and depth of
quantum circuits. In this paper, we study the Simon's problem in distributed
scenarios and design a distributed quantum algorithm to solve the problem. The
algorithm proposed by us has the advantage of exponential acceleration compared
with the classical distributed computing, and has the advantage of square
acceleration compared with the best distributed quantum algorithm proposed
before. In particular, the previous distributed quantum algorithm for Simon's
problem can not be extended to the case of more than {\it two computing nodes}
(i.e. two subproblems), but our distributed quantum algorithm can be extended
to the case of {\it multiple computing nodes} (i.e. multiple subproblems) as
well.


title:
Conformal $r$-matrix-Nijenhuis structures, symplectic-Nijenhuis
  structures and $\mathcal{O} N$-structures

abstract:
  In this paper, we first study infinitesimal deformations of a Lie conformal
algebra and a Lie conformal algebra with a module (called an $\mathsf{LCMod}$
pair), which lead to the notions of Nijenhuis operator on the Lie conformal
algebra and Nijenhuis structure on the $\mathsf{LCMod}$ pair, respectively.
Then by adding compatibility conditions between Nijenhuis structures and
$\mathcal{O}$-operators, we introduce the notion of an $\mathcal{O}
N$-structure on an $\mathsf{LCMod}$ pair and show that an $\mathcal{O}
N$-structure gives rise to a hierarchy of pairwise compatible
$\mathcal{O}$-operators. In particular, we show that compatible
$\mathcal{O}$-operators on a Lie conformal algebra can be characterized by
Nijenhuis operators on Lie conformal algebras. Finally, we introduce the
notions of conformal $r$-matrix-Nijenhuis structure and symplectic-Nijenhuis
structure on the Lie conformal algebra and study their relations.


title: An immersed $S^{n}$ $\lambda$-hypersurface

abstract:
  In this paper, we construct an immersed, non-embedded $S^{n}$
$\lambda$-hypersurface in Euclidean spaces $\mathbb{R}^{n+1}$.


title: Minimal isometric dilations and operator models for the polydisc

abstract:
  For commuting contractions $T_1,\dots ,T_n$ acting on a Hilbert space
$\mathcal H$ with $T=\prod_{i=1}^n T_i$, we find a necessary and sufficient
condition under which $(T_1,\dots ,T_n)$ dilates to commuting isometries
$(V_1,\dots ,V_n)$ on the minimal isometric dilation space $T$, where
$V=\prod_{i=1}^nV_i$ is the minimal isometric dilation of $T$. We construct
both Schaffer and Sz. Nagy-Foias type isometric and unitary dilations for
$(T_1,\dots ,T_n)$ on the minimal dilation spaces of $T$. Also, a different
dilation is constructed when the product $T$ is a $C._0$ contraction, that is
${T^*}^n \rightarrow 0$ as $n \rightarrow \infty$. As a consequence of these
dilation theorems we obtain different functional models for $(T_1,\dots ,T_n)$
in terms of multiplication operators on vectorial Hardy spaces. One notable
fact about our models is that the multipliers are analytic functions in one
variable. The dilation when $T$ is $C._0$ leads to a conditional factorization
of a $T$. Several examples have been constructed.


title:
Multi-wavelength properties of 850-$\mu$m selected sources from the
  North Ecliptic Pole SCUBA-2 survey

abstract:
  We present the multi-wavelength counterparts of 850-$\mu$m selected
submillimetre sources over a 2-deg$^2$ field centred on the North Ecliptic
Pole. In order to overcome the large beam size (15 arcsec) of the 850-$\mu$m
images, deep optical to near-infrared (NIR) photometric data and
arcsecond-resolution 20-cm images are used to identify counterparts of
submillimetre sources. Among 647 sources, we identify 514 reliable counterparts
for 449 sources (69 per cent in number), based either on probabilities of
chance associations calculated from positional offsets or offsets combined with
the optical-to-NIR colours. In the radio imaging, the fraction of 850-$\mu$m
sources having multiple counterparts is 7 per cent. The photometric redshift,
infrared luminosity, stellar mass, star-formation rate (SFR), and the AGN
contribution to the total infrared luminosity of the identified counterparts
are investigated through spectral energy distribution fitting. The SMGs are
infrared-luminous galaxies at an average $\langle z\rangle=2.5$ with
$\mathrm{log}_{10} (L_\mathrm{IR}/\mathrm{L}_\odot)=11.5-13.5$, with a mean
stellar mass of $\mathrm{log}_{10} (M_\mathrm{star}/\mathrm{M}_\odot)=10.90$
and SFR of $\mathrm{log}_{10} (\mathrm{SFR/M_\odot\,yr^{-1}})=2.34$. The SMGs
show twice as large SFR as galaxies on the star-forming main sequence, and
about 40 per cent of the SMGs are classified as objects with bursty star
formation. At $z\ge4$, the contribution of AGN luminosity to total luminosity
for most SMGs is larger than 30 per cent. The FIR-to-radio correlation
coefficient of SMGs is consistent with that of main-sequence galaxies at
$z\simeq2$.


title:
A high-fidelity seismic intensity measure to assess dynamic liquefaction
  in tailings

abstract:
  Deformation analyses of tailings dams under dynamic conditions requires using
earthquake records as input loading. Moreover, these records must represent the
local seismicity, expressed by a set of ground motion power indicators
denominated intensity measures (IM). The ability and accuracy to describe the
characteristics of a seismic record play a fundamental role in earthquake
engineering and damage assessment of geotechnical facilities. None of the
existing IMs represents a robust enough predictor of a given seismic demand
(e.g., residual displacements) as different signals may generate a wide
spectrum of results, with diverse effects that could produce from insignificant
damage to global failure depending on the structure. To overcome this
limitation, usual engineering procedures select a huge number of records and
develop a large set of numerical simulations to bound the uncertainty of the
results, which becomes a time-consuming approach. In order to perform more
accurate ground motion {selection}, this paper presents a new high-fidelity
seismic IM, which captures the spectral properties of the record for the
frequency content that the dam does not filter. This IM represents a way to
estimate beforehand a seismic demand, expressed for instance in terms of
displacements. The proposed IM is applied to a finite element model for an
upstream tailings dam cross section, using a constitutive model capable to
capture dynamic liquefaction. The obtained results show that our proposal gives
highly reliable correlations with different selected demands. Comparisons with
classical IMs are also discussed, showing that our proposal emerges as a
practical solution to a large dated discussion within our community.


title: Financial data analysis application via multi-strategy text processing

abstract:
  Maintaining financial system stability is critical to economic development,
and early identification of risks and opportunities is essential. The financial
industry contains a wide variety of data, such as financial statements,
customer information, stock trading data, news, etc. Massive heterogeneous data
calls for intelligent algorithms for machines to process and understand. This
paper mainly focuses on the stock trading data and news about China A-share
companies. We present a financial data analysis application, Financial Quotient
Porter, designed to combine textual and numerical data by using a
multi-strategy data mining approach. Additionally, we present our efforts and
plans in deep learning financial text processing application scenarios using
natural language processing (NLP) and knowledge graph (KG) technologies. Based
on KG technology, risks and opportunities can be identified from heterogeneous
data. NLP technology can be used to extract entities, relations, and events
from unstructured text, and analyze market sentiment. Experimental results show
market sentiments towards a company and an industry, as well as news-level
associations between companies.


title:
Decomposition of enveloping algebras of simple Lie algebras and their
  related polynomial algebras

abstract:
  The decomposition problem of the enveloping algebra of a simple Lie algebra
is reconsidered combining both the analytical and the algebraic approach,
showing its relation with the internal labelling problem with respect to a
nilpotent subalgebra. A lower bound for the number of generators of the
commutant as well as the maximal Abelian subalgebra are obtained. The
decomposition problem for the exceptional Lie algebra $G_2$ is completely
solved.


title: Video Frame Interpolation Based on Deformable Kernel Region

abstract:
  Video frame interpolation task has recently become more and more prevalent in
the computer vision field. At present, a number of researches based on deep
learning have achieved great success. Most of them are either based on optical
flow information, or interpolation kernel, or a combination of these two
methods. However, these methods have ignored that there are grid restrictions
on the position of kernel region during synthesizing each target pixel. These
limitations result in that they cannot well adapt to the irregularity of object
shape and uncertainty of motion, which may lead to irrelevant reference pixels
used for interpolation. In order to solve this problem, we revisit the
deformable convolution for video interpolation, which can break the fixed grid
restrictions on the kernel region, making the distribution of reference points
more suitable for the shape of the object, and thus warp a more accurate
interpolation frame. Experiments are conducted on four datasets to demonstrate
the superior performance of the proposed model in comparison to the
state-of-the-art alternatives.


title: Tensorial tomographic differential phase-contrast microscopy

abstract:
  We report Tensorial Tomographic Differential Phase-Contrast microscopy
(T2DPC), a quantitative label-free tomographic imaging method for simultaneous
measurement of phase and anisotropy. T2DPC extends differential phase-contrast
microscopy, a quantitative phase imaging technique, to highlight the vectorial
nature of light. The method solves for permittivity tensor of anisotropic
samples from intensity measurements acquired with a standard microscope
equipped with an LED matrix, a circular polarizer, and a polarization-sensitive
camera. We demonstrate accurate volumetric reconstructions of refractive index,
birefringence, and orientation for various validation samples, and show that
the reconstructed polarization structures of a biological specimen are
predictive of pathology.


title: Scalar Diffraction Analysis Of Dispersion In Low-Index Thin Flat Lenses

abstract:
  We analyze the dispersion property of low-index thin lenses by using scalar
diffraction and finite difference time domain (FDTD) methods. We compare the
dispersion results obtained by using these methods with reported experimental
results, and the well-known analytical formula for focal length (f) of
diffractive lenses as a function of wavelength ({\lambda}),f({\lambda})=(f_0
{\lambda}_0)/{\lambda},where f_0 is the designed focal length for wavelength
{\lambda}_0. We show that when the analytical formula is applied to thin flat
lenses with low-refractive index, the results are accurate for small numerical
aperture (NA) up to 0.2. For larger NA, the error between the analytical
approximation and the FDTD analysis remains around 8% over a wide range of NA.


title: Efficient Neural Neighborhood Search for Pickup and Delivery Problems

abstract:
  We present an efficient Neural Neighborhood Search (N2S) approach for pickup
and delivery problems (PDPs). In specific, we design a powerful Synthesis
Attention that allows the vanilla self-attention to synthesize various types of
features regarding a route solution. We also exploit two customized decoders
that automatically learn to perform removal and reinsertion of a
pickup-delivery node pair to tackle the precedence constraint. Additionally, a
diversity enhancement scheme is leveraged to further ameliorate the
performance. Our N2S is generic, and extensive experiments on two canonical PDP
variants show that it can produce state-of-the-art results among existing
neural methods. Moreover, it even outstrips the well-known LKH3 solver on the
more constrained PDP variant. Our implementation for N2S is available online.


title: Offline Retrieval Evaluation Without Evaluation Metrics

abstract:
  Offline evaluation of information retrieval and recommendation has
traditionally focused on distilling the quality of a ranking into a scalar
metric such as average precision or normalized discounted cumulative gain. We
can use this metric to compare the performance of multiple systems for the same
request. Although evaluation metrics provide a convenient summary of system
performance, they also collapse subtle differences across users into a single
number and can carry assumptions about user behavior and utility not supported
across retrieval scenarios. We propose recall-paired preference (RPP), a
metric-free evaluation method based on directly computing a preference between
ranked lists. RPP simulates multiple user subpopulations per query and compares
systems across these pseudo-populations. Our results across multiple search and
recommendation tasks demonstrate that RPP substantially improves discriminative
power while correlating well with existing metrics and being equally robust to
incomplete data.


title: Gaps labeling theorem for the Bubble-diamond self-similar graphs

abstract:
  We present a detailed spectral analysis for a class of fractal-type diamond
graphs, referred to as bubble-diamond graphs, and provide a gap-labeling
theorem in the sense of Bellissard for the corresponding probabilistic graph
Laplacians using the technique of spectral decimation. The spectrum of the
natural Laplacian on limit graphs is shown (generically) to be pure point with
support a Cantor set. Labeling the gaps in the Cantor set by the normalized
eigenvalue counting function (also known as the integrated density of states)
we describe the gap labels as orbits of a second dynamical system that reflects
the branching parameter of the bubble construction and the decimation
structure.


title: PointInst3D: Segmenting 3D Instances by Points

abstract:
  The current state-of-the-art methods in 3D instance segmentation typically
involve a clustering step, despite the tendency towards heuristics, greedy
algorithms, and a lack of robustness to the changes in data statistics. In
contrast, we propose a fully-convolutional 3D point cloud instance segmentation
method that works in a per-point prediction fashion. In doing so it avoids the
challenges that clustering-based methods face: introducing dependencies among
different tasks of the model. We find the key to its success is assigning a
suitable target to each sampled point. Instead of the commonly used static or
distance-based assignment strategies, we propose to use an Optimal Transport
approach to optimally assign target masks to the sampled points according to
the dynamic matching costs. Our approach achieves promising results on both
ScanNet and S3DIS benchmarks. The proposed approach removes intertask
dependencies and thus represents a simpler and more flexible 3D instance
segmentation framework than other competing methods, while achieving improved
segmentation accuracy.


title: Back-ends Selection for Deep Speaker Embeddings

abstract:
  Probabilistic Linear Discriminant Analysis (PLDA) was the dominant and
necessary back-end for early speaker recognition approaches, like i-vector and
x-vector. However, with the development of neural networks and margin-based
loss functions, we can obtain deep speaker embeddings (DSEs), which have
advantages of increased inter-class separation and smaller intra-class
distances. In this case, PLDA seems unnecessary or even counterproductive for
the discriminative embeddings, and cosine similarity scoring (Cos) achieves
better performance than PLDA in some situations. Motivated by this, in this
paper, we systematically explore how to select back-ends (Cos or PLDA) for deep
speaker embeddings to achieve better performance in different situations. By
analyzing PLDA and the properties of DSEs extracted from models with different
numbers of segment-level layers, we make the conjecture that Cos is better in
same-domain situations and PLDA is better in cross-domain situations. We
conduct experiments on VoxCeleb and NIST SRE datasets in four application
situations, single-/multi-domain training and same-/cross-domain test, to
validate our conjecture and briefly explain why back-ends adaption algorithms
work.


title:
Simulation and performance analysis of quantum error correction with a
  rotated surface code under a realistic noise model

abstract:
  The demonstration of quantum error correction (QEC) is one of the most
important milestones in the realization of fully-fledged quantum computers.
Toward this, QEC experiments using the surface codes have recently been
actively conducted. However, it has not yet been realized to protect logical
quantum information beyond the physical coherence time. In this work, we
performed a full simulation of QEC for the rotated surface codes with a code
distance 5, which employs 49 qubits and is within reach of the current
state-of-the-art quantum computers. In particular, we evaluate the logical
error probability in a realistic noise model that incorporates not only
stochastic Pauli errors but also coherent errors due to a systematic control
error or unintended interactions. While a straightforward simulation of 49
qubits is not tractable within a reasonable computational time, we reduced the
number of qubits required to 26 qubits by delaying the syndrome measurement in
simulation. This and a fast quantum computer simulator, Qulacs, implemented on
GPU allows us to simulate full QEC with an arbitrary local noise within
reasonable simulation time. Based on the numerical results, we also construct
and verify an effective model to incorporate the effect of the coherent error
into a stochastic noise model. This allows us to understand what the effect
coherent error has on the logical error probability on a large scale without
full simulation based on the detailed full simulation of a small scale. The
present simulation framework and effective model, which can handle arbitrary
local noise, will play a vital role in clarifying the physical parameters that
future experimental QEC should target.


title:
Adaptive cognitive fit: Artificial intelligence augmented management of
  information facets and representations

abstract:
  Explosive growth in big data technologies and artificial intelligence [AI]
applications have led to increasing pervasiveness of information facets and a
rapidly growing array of information representations. Information facets, such
as equivocality and veracity, can dominate and significantly influence human
perceptions of information and consequently affect human performance. Extant
research in cognitive fit, which preceded the big data and AI era, focused on
the effects of aligning information representation and task on performance,
without sufficient consideration to information facets and attendant cognitive
challenges. Therefore, there is a compelling need to understand the interplay
of these dominant information facets with information representations and
tasks, and their influence on human performance. We suggest that artificially
intelligent technologies that can adapt information representations to overcome
cognitive limitations are necessary for these complex information environments.
To this end, we propose and test a novel *Adaptive Cognitive Fit* [ACF]
framework that explains the influence of information facets and AI-augmented
information representations on human performance. We draw on information
processing theory and cognitive dissonance theory to advance the ACF framework
and a set of propositions. We empirically validate the ACF propositions with an
economic experiment that demonstrates the influence of information facets, and
a machine learning simulation that establishes the viability of using AI to
improve human performance.


title:
Robust Self-Augmentation for Named Entity Recognition with Meta
  Reweighting

abstract:
  Self-augmentation has received increasing research interest recently to
improve named entity recognition (NER) performance in low-resource scenarios.
Token substitution and mixup are two feasible heterogeneous self-augmentation
techniques for NER that can achieve effective performance with certain
specialized efforts. Noticeably, self-augmentation may introduce potentially
noisy augmented data. Prior research has mainly resorted to heuristic
rule-based constraints to reduce the noise for specific self-augmentation
methods individually. In this paper, we revisit these two typical
self-augmentation methods for NER, and propose a unified meta-reweighting
strategy for them to achieve a natural integration. Our method is easily
extensible, imposing little effort on a specific self-augmentation method.
Experiments on different Chinese and English NER benchmarks show that our token
substitution and mixup method, as well as their integration, can achieve
effective performance improvement. Based on the meta-reweighting mechanism, we
can enhance the advantages of the self-augmentation techniques without much
extra effort.


title:
Accelerated Multiplicative Weights Update Avoids Saddle Points almost
  always

abstract:
  We consider non-convex optimization problems with constraint that is a
product of simplices. A commonly used algorithm in solving this type of problem
is the Multiplicative Weights Update (MWU), an algorithm that is widely used in
game theory, machine learning and multi-agent systems. Despite it has been
known that MWU avoids saddle points, there is a question that remains
unaddressed:"Is there an accelerated version of MWU that avoids saddle points
provably?" In this paper we provide a positive answer to above question. We
provide an accelerated MWU based on Riemannian Accelerated Gradient Descent,
and prove that the Riemannian Accelerated Gradient Descent, thus the
accelerated MWU, almost always avoid saddle points.


title: Headline Diagnosis: Manipulation of Content Farm Headlines

abstract:
  As technology grows faster, the news spreads through social media. In order
to attract more readers and acquire additional profit, some news agencies
reproduce massive news in a more appealing manner. Therefore, it is essential
to accurately predict whether a news article is from official news agencies.
This work develops a headline classification based on Convoluted Neural Network
to determine credibility of a news article. The model primarily focuses on
investigating key factors from headlines. These factors include word
segmentation, part-of-speech tags, and sentiment features. With integrating
these features into the proposed classification model, the demonstrated
evaluation achieves 93.99% for accuracy.


title: Dynamic Point Cloud Compression with Cross-Sectional Approach

abstract:
  The recent development of dynamic point clouds has introduced the possibility
of mimicking natural reality, and greatly assisting quality of life. However,
to broadcast successfully, the dynamic point clouds require higher compression
due to their huge volume of data compared to the traditional video. Recently,
MPEG finalized a Video-based Point Cloud Compression standard known as V-PCC.
However, V-PCC requires huge computational time due to expensive normal
calculation and segmentation, sacrifices some points to limit the number of 2D
patches, and cannot occupy all spaces in the 2D frame. The proposed method
addresses these limitations by using a novel cross-sectional approach. This
approach reduces expensive normal estimation and segmentation, retains more
points, and utilizes more spaces for 2D frame generation compared to the VPCC.
The experimental results using standard video sequences show that the proposed
technique can achieve better compression in both geometric and texture data
compared to the V-PCC standard.


title: Single Object Tracking Research: A Survey

abstract:
  Visual object tracking is an important task in computer vision, which has
many real-world applications, e.g., video surveillance, visual navigation.
Visual object tracking also has many challenges, e.g., object occlusion and
deformation. To solve above problems and track the target accurately and
efficiently, many tracking algorithms have emerged in recent years. This paper
presents the rationale and representative works of two most popular tracking
frameworks in past ten years, i.e., the corelation filter and Siamese network
for object tracking. Then we present some deep learning based tracking methods
categorized by different network structures. We also introduce some classical
strategies for handling the challenges in tracking problem. Further, this paper
detailedly present and compare the benchmarks and challenges for tracking, from
which we summarize the development history and development trend of visual
tracking. Focusing on the future development of object tracking, which we think
would be applied in real-world scenes before some problems to be addressed,
such as the problems in long-term tracking, low-power high-speed tracking and
attack-robust tracking. In the future, the integration of multimodal data,
e.g., the depth image, thermal image with traditional color image, will provide
more solutions for visual tracking. Moreover, tracking task will go together
with some other tasks, e.g., video object detection and segmentation.


title: Road Traffic Law Adaptive Decision-making for Self-Driving Vehicles

abstract:
  Self-driving vehicles have their own intelligence to drive on open roads.
However, vehicle managers, e.g., government or industrial companies, still need
a way to tell these self-driving vehicles what behaviors are encouraged or
forbidden. Unlike human drivers, current self-driving vehicles cannot
understand the traffic laws, thus rely on the programmers manually writing the
corresponding principles into the driving systems. It would be less efficient
and hard to adapt some temporary traffic laws, especially when the vehicles use
data-driven decision-making algorithms. Besides, current self-driving vehicle
systems rarely take traffic law modification into consideration. This work aims
to design a road traffic law adaptive decision-making method. The
decision-making algorithm is designed based on reinforcement learning, in which
the traffic rules are usually implicitly coded in deep neural networks. The
main idea is to supply the adaptability to traffic laws of self-driving
vehicles by a law-adaptive backup policy. In this work, the natural
language-based traffic laws are first translated into a logical expression by
the Linear Temporal Logic method. Then, the system will try to monitor in
advance whether the self-driving vehicle may break the traffic laws by
designing a long-term RL action space. Finally, a sample-based planning method
will re-plan the trajectory when the vehicle may break the traffic rules. The
method is validated in a Beijing Winter Olympic Lane scenario and an overtaking
case, built in CARLA simulator. The results show that by adopting this method,
the self-driving vehicles can comply with new issued or updated traffic laws
effectively. This method helps self-driving vehicles governed by digital
traffic laws, which is necessary for the wide adoption of autonomous driving.


title:
Reliability and Latency Analysis of Sliding Network Coding With
  Re-Transmission

abstract:
  Future networks are expected to support various ultra-reliable low-latency
communications via wireless links. To avoid the loss of packets and keep the
low latency, sliding network coding (SNC) is an emerging technology by
generating redundant packets that are the linear combination of the original
data packets from the current block and some previous blocks. However, how to
take the advantage of re-transmission for SNC is still an open problem since
higher reliability could be achieved at the expense of large latency caused by
round-trip time (RTT). To deal with this issue, in this paper, we consider the
idea of adjusting the transmission phase and the number of the redundant
packets for SNC with re-transmission. Specifically, If RTT is large, most of
the redundant packets are sent at the first transmission, otherwise,
re-transmission will be used. We first derive a concise and tight lower bound
of the block error probability of SNC without re-transmission. Then, based on
the bound, the theoretical expressions of the proposed re-transmission schemes
are derived regarding the block error probability, the average code length, and
the average packet latency. Results show that the proposed SNC with
re-transmission improves block error probability and keeps the low latency.


title:
Chemical Evolution with Radial Mixing Redux: Extending beyond the Solar
  Neighborhood

abstract:
  We present a multi-zone galactic chemical evolution model for the Milky Way
that takes into account the updated yields of major nucleosynthesis channels.
It incorporates physical processes like radial flow of gas in the disk and
infall of fresh gas, along with stellar scattering processes like radial
migration. For the first time using a physically motivated model, we
qualitatively reproduce the observed $([{\rm Fe/H}], [\alpha/{\rm Fe}])$
distribution of stars at different Galactic locations, along with the
age-$[\alpha/{\rm Fe}]$ and age-${\rm [Fe/H]}$ relations. The model
successfully reproduces the bifurcated high- and low-$[\alpha/{\rm Fe}]$
sequences and sheds light on the origin of the thick disc. We analyse the
effect of physical processes individually on the observed properties of the
Galaxy. We show that radial flow of gas plays an important role in establishing
the radial gradient for both ${\rm [Fe/H]}$ and $[\alpha/{\rm Fe}]$. The
dichotomy in $[\alpha/{\rm Fe}]$ is primarily due to the sharp fall of
$[\alpha/{\rm Fe}]$ with time and influenced by star formation history. We
identify at least three independent factors that govern the rate of \alphafe{}
declining with time: the fraction of white dwarfs that form SNe Ia, the
timescale associated with the delay time distribution of Type Ia supernovae
(SNe Ia), and star formation efficiency. ${\rm [Fe/H]}$ and $[\alpha/{\rm Fe}]$
evolve rapidly at early times but change gradually for at least the last 6 Gyr
as they approach equilibrium values. The final equilibrium value of
$[\alpha/{\rm Fe}]$ is governed by the ratio of SNe Ia to core-collapse
supernovae (CCSN).


title:
ATLASGAL-selected massive clumps in the inner Galaxy. X. Observations of
  atomic carbon at 492 GHz

abstract:
  (Abridged) In this paper, we investigate the physical conditions of
[CI]-traced gas in high-mass star-forming regions by analyzing APEX [CI] 492
GHz single-pointing observations of the ATLASGAL Top100 sources along with
other multi-wavelength data. Our 98 sources are clearly detected in [CI] 492
GHz emission, and the observed integrated intensities and line widths tend to
increase toward evolved stages of star formation. In addition to these "main"
components that are associated with the Top100 sample, 41 emission and two
absorption features are identified by their velocities toward 28 and two lines
of sight respectively as "secondary" components. The secondary components have
systematically smaller integrated intensities and line widths than the main
components. We found that [CI] 492 GHz and 13CO(2-1) are well correlated with
the 13CO(2-1)-to-[CI] 492 GHz integrated intensity ratio varying from 0.2 to
5.3. In addition, we derived the H2-to-[CI] conversion factor, X(CI), by
dividing 870 micron-based H2 column densities by the observed [CI] 492 GHz
integrated intensities and found that X(CI) ranges from 2.3e20 to 1.3e22 with a
median of 1.7e21. In contrast to the strong correlation with 13CO(2-1), [CI]
492 GHz has a scattered relation with the 870 micron-traced molecular gas.
Finally, we performed LTE and non-LTE analyses of the [CI] 492 GHz and 809 GHz
data for a subset of the Top100 sample and inferred that [CI] emission likely
originates from warm (kinetic temperature > 60 K), optically thin (opacity <
0.5), and highly pressurized (thermal pressure ~ e5 to e8 K/cm3) regions.


title:
Abnormally weak intervalley electron scattering in MoS2 monolayer:
  insights from the matching between electron and phonon bands

abstract:
  It is known that carrier mobility in layered semiconductors generally
increases from two-dimension (2D) to three-dimension due to suppressed
scattering channels resulting from decreased densities of electron and phonon
states. In this work, we find an abnormal decrease of electron mobility from
monolayer to bulk MoS2. By carefully analyzing the scattering mechanisms, we
can attribute such abnormality to the stronger intravalley scattering in the
monolayer but weaker intervalley scattering caused by less intervalley
scattering channels and weaker corresponding electron-phonon couplings compared
to the bulk case. We show that, it is the matching between electronic band
structure and phonon spectrum rather than their densities of electronic and
phonon states that determines scattering channels. We propose, for the first
time, the phonon-energy-resolved matching function to identify the intra- and
inter-valley scattering channels. Furthermore, we show that multiple valleys do
not necessarily lead to strong intervalley scattering if: (1) the scattering
channels, which can be explicitly captured by the distribution of the matching
function, are few due to the small matching between the corresponding electron
and phonon bands; and/or (2) the multiple valleys are far apart in the
reciprocal space and composed of out-of-plane orbitals so that the
corresponding electron-phonon coupling strengths are weak. Consequently, the
searching scope of high-mobility 2D materials can be reasonably enlarged using
the matching function as useful guidance with the help of band edge orbital
analysis.


title:
The Zeeman and hyperfine interactions of a single $^{167}Er^{3+}$ ion in
  Si

abstract:
  Er-doped Si is a promising candidate for quantum information applications due
to its telecom wavelength optical transition and its compatibility with Si
nanofabrication technologies. Recent spectroscopic studies based on
photoluminescence excitation have shown multiple well-defined lattice sites
that Er occupies in Si. Here we report the first measurement of the Zeeman and
hyperfine tensors of a single 167Er3+ ion in Si. All the obtained tensors are
highly anisotropic with the largest value principal axes aligning in nearly the
same direction, and the trace of the lowest crystal field level g-tensor is
17.78$\pm$0.40. The results indicate that this specific Er site is likely to be
a distorted cubic site that exhibits monoclinic (C1) symmetry. Finally, zero
first-order-Zeeman (ZEFOZ) fields are identified for this site and could be
used to reduce decoherence of hyperfine spin states in future experiments.


title:
Uncoupled Learning Dynamics with $O(\log T)$ Swap Regret in Multiplayer
  Games

abstract:
  In this paper we establish efficient and \emph{uncoupled} learning dynamics
so that, when employed by all players in a general-sum multiplayer game, the
\emph{swap regret} of each player after $T$ repetitions of the game is bounded
by $O(\log T)$, improving over the prior best bounds of $O(\log^4 (T))$. At the
same time, we guarantee optimal $O(\sqrt{T})$ swap regret in the adversarial
regime as well. To obtain these results, our primary contribution is to show
that when all players follow our dynamics with a \emph{time-invariant} learning
rate, the \emph{second-order path lengths} of the dynamics up to time $T$ are
bounded by $O(\log T)$, a fundamental property which could have further
implications beyond near-optimally bounding the (swap) regret. Our proposed
learning dynamics combine in a novel way \emph{optimistic} regularized learning
with the use of \emph{self-concordant barriers}. Further, our analysis is
remarkably simple, bypassing the cumbersome framework of higher-order
smoothness recently developed by Daskalakis, Fishelson, and Golowich
(NeurIPS'21).


title: Riemannian Hamiltonian methods for min-max optimization on manifolds

abstract:
  In this paper, we study the min-max optimization problems on Riemannian
manifolds. We introduce a Riemannian Hamiltonian function, minimization of
which serves as a proxy for solving the original min-max problems. Under the
Riemannian Polyak--{\L}ojasiewicz (PL) condition on the Hamiltonian function,
its minimizer corresponds to the desired min-max saddle point. We also provide
cases where this condition is satisfied. To minimize the Hamiltonian function,
we propose Riemannian Hamiltonian methods (RHM) and present their convergence
analysis. We extend RHM to include a consensus regularization and to the
stochastic setting. We illustrate the efficacy of the proposed RHM in
applications such as subspace robust Wasserstein distance, robust training of
neural networks, and generative adversarial networks.


title:
Exceptionally high phonon-limited carrier mobility in BX (X = P, As, Sb)
  monolayers

abstract:
  Ideal two-dimensional (2D) semiconductors with high mobility comparable to
three-dimensional (3D) Si or GaAs are still lacking, hindering the development
of high-performance 2D devices. Here in this work, using first-principles
calculations and considering all the electron-phonon couplings, we show that
monolayer BX (X = P, As, Sb) with honeycomb lattices have intrinsic
phonon-limited carrier mobility reaching record-high values of 1200-14000
$cm^2V^{-1}s^{-1}$ at room temperature. Despite being polar and the band edges
located at the K point with multiple valleys, these three systems unusually
have small carrier scattering rates. Detailed analysis shows that, both the
intravalley scattering and the intervalley scattering between two equivalent K
points are weak, which can be understood from the large mismatch between the
electron bands and phonon spectrum and suppressed electron-phonon coupling
strength. Furthermore, we reveal the general trend of mobility increase from BP
to BAs and to BSb and conclude that: smaller effective masses, larger sound
velocities, higher optical phonon energies, heavy atomic masses, and
out-of-plane orbitals tend to result in small match between the electron and
phonon bands, small electron-phonon coupling strengths, and thus high mobility.
Our work demonstrates that 2D semiconductors can achieve comparable carrier
mobility to 3D GaAs, thus opening doors to 2D high-performance electronic
devices.


title:
Audio-Visual Scene Classification Using A Transfer Learning Based Joint
  Optimization Strategy

abstract:
  Recently, audio-visual scene classification (AVSC) has attracted increasing
attention from multidisciplinary communities. Previous studies tended to adopt
a pipeline training strategy, which uses well-trained visual and acoustic
encoders to extract high-level representations (embeddings) first, then
utilizes them to train the audio-visual classifier. In this way, the extracted
embeddings are well suited for uni-modal classifiers, but not necessarily
suited for multi-modal ones. In this paper, we propose a joint training
framework, using the acoustic features and raw images directly as inputs for
the AVSC task. Specifically, we retrieve the bottom layers of pre-trained image
models as visual encoder, and jointly optimize the scene classifier and 1D-CNN
based acoustic encoder during training. We evaluate the approach on the
development dataset of TAU Urban Audio-Visual Scenes 2021. The experimental
results show that our proposed approach achieves significant improvement over
the conventional pipeline training strategy. Moreover, our best single system
outperforms previous state-of-the-art methods, yielding a log loss of 0.1517
and accuracy of 94.59% on the official test fold.


title:
Long-run User Value Optimization in Recommender Systems through Content
  Creation Modeling

abstract:
  Content recommender systems are generally adept at maximizing immediate user
satisfaction but to optimize for the \textit{long-run} user value, we need more
statistically sophisticated solutions than off-the-shelf simple recommender
algorithms. In this paper we lay out such a solution to optimize
\textit{long-run} user value through discounted utility maximization and a
machine learning method we have developed for estimating it. Our method
estimates which content producers are most likely to create the highest
long-run user value if their content is shown more to users who enjoy it in the
present. We do this estimation with the help of an A/B test and heterogeneous
effects machine learning model. We have used such models in Facebook's feed
ranking system, and such a model can be used in other recommender systems.


title:
Concrete one complex dimensional moduli spaces of hyperbolic manifolds
  and orbifolds

abstract:
  The Riley slice is arguably the simplest example of a moduli space of
Kleinian groups; it is naturally embedded in $ \mathbb{C} $, and has a natural
coordinate system (introduced by Linda Keen and Caroline Series in the early
1990s) which reflects the geometry of the underlying 3-manifold deformations.
The Riley slice arises in the study of arithmetic Kleinian groups, the theory
of two-bridge knots, the theory of Schottky groups, and the theory of
hyperbolic 3-manifolds; because of its simplicity it provides an easy source of
examples and deep questions related to these subjects. We give an introduction
for the non-expert to the Riley slice and much of the related background
material, assuming only graduate level complex analysis and topology; we review
the history of and literature surrounding the Riley slice; and we announce some
results of our own, extending the work of Keen and Series to the one complex
dimensional moduli spaces of Kleinian groups isomorphic to
$\mathbb{Z}_p*\mathbb{Z}_q$ acting on the Riemann sphere, $2\leq p,q \leq
\infty$. The Riley slice is the case $p=q=\infty$ (i.e. two parabolic
generators).


title: Trusted Multi-View Classification with Dynamic Evidential Fusion

abstract:
  Existing multi-view classification algorithms focus on promoting accuracy by
exploiting different views, typically integrating them into common
representations for follow-up tasks. Although effective, it is also crucial to
ensure the reliability of both the multi-view integration and the final
decision, especially for noisy, corrupted and out-of-distribution data.
Dynamically assessing the trustworthiness of each view for different samples
could provide reliable integration. This can be achieved through uncertainty
estimation. With this in mind, we propose a novel multi-view classification
algorithm, termed trusted multi-view classification (TMC), providing a new
paradigm for multi-view learning by dynamically integrating different views at
an evidence level. The proposed TMC can promote classification reliability by
considering evidence from each view. Specifically, we introduce the variational
Dirichlet to characterize the distribution of the class probabilities,
parameterized with evidence from different views and integrated with the
Dempster-Shafer theory. The unified learning framework induces accurate
uncertainty and accordingly endows the model with both reliability and
robustness against possible noise or corruption. Both theoretical and
experimental results validate the effectiveness of the proposed model in
accuracy, robustness and trustworthiness.


title:
It Takes Two Flints to Make a Fire: Multitask Learning of Neural
  Relation and Explanation Classifiers

abstract:
  We propose an explainable approach for relation extraction that mitigates the
tension between generalization and explainability by jointly training for the
two goals. Our approach uses a multi-task learning architecture, which jointly
trains a classifier for relation extraction, and a sequence model that labels
words in the context of the relation that explain the decisions of the relation
classifier. We also convert the model outputs to rules to bring global
explanations to this approach. This sequence model is trained using a hybrid
strategy: supervised, when supervision from pre-existing patterns is available,
and semi-supervised otherwise. In the latter situation, we treat the sequence
model's labels as latent variables, and learn the best assignment that
maximizes the performance of the relation classifier. We evaluate the proposed
approach on the two datasets and show that the sequence model provides labels
that serve as accurate explanations for the relation classifier's decisions,
and, importantly, that the joint training generally improves the performance of
the relation classifier. We also evaluate the performance of the generated
rules and show that the new rules are great add-on to the manual rules and
bring the rule-based system much closer to the neural models.


title:
BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid
  Pix2pix

abstract:
  The evaluation of human epidermal growth factor receptor 2 (HER2) expression
is essential to formulate a precise treatment for breast cancer. The routine
evaluation of HER2 is conducted with immunohistochemical techniques (IHC),
which is very expensive. Therefore, for the first time, we propose a breast
cancer immunohistochemical (BCI) benchmark attempting to synthesize IHC data
directly with the paired hematoxylin and eosin (HE) stained images. The dataset
contains 4870 registered image pairs, covering a variety of HER2 expression
levels. Based on BCI, as a minor contribution, we further build a pyramid
pix2pix image generation method, which achieves better HE to IHC translation
results than the other current popular algorithms. Extensive experiments
demonstrate that BCI poses new challenges to the existing image translation
research. Besides, BCI also opens the door for future pathology studies in HER2
expression evaluation based on the synthesized IHC images. BCI dataset can be
downloaded from https://bupt-ai-cz.github.io/BCI.


title: A note on the singular set of the no-sign obstacle problem

abstract:
  In this note, we show that the structure of singular set of the no-sign
obstacle problem is very similar to that of the classical obstacle problem. In
particular, we prove the uniqueness of blowups at singular points of the
no-sign obstacle problem $\Delta u=\chi_{_{B_1\backslash \{u=|Du|=0\}}}\
\text{in}\ B_1,$ thus give a positive answer to a problem raised in \cite[Notes
of Chaper 7, page 149]{PSU12}. We also provide a new proof of the regularity of
the singular set of the no-sign obstacle problem. The proof may also work for
the case when the right hand side is Dini continuous.


title: Smoothed Analysis of the Koml\'os Conjecture

abstract:
  The well-known Koml\'os conjecture states that given $n$ vectors in
$\mathbb{R}^d$ with Euclidean norm at most one, there always exists a $\pm 1$
coloring such that the $\ell_{\infty}$ norm of the signed-sum vector is a
constant independent of $n$ and $d$. We prove this conjecture in a smoothed
analysis setting where the vectors are perturbed by adding a small Gaussian
noise and when the number of vectors $n =\omega(d\log d)$. The dependence of
$n$ on $d$ is the best possible even in a completely random setting.
  Our proof relies on a weighted second moment method, where instead of
considering uniformly randomly colorings we apply the second moment method on
an implicit distribution on colorings obtained by applying the Gram-Schmidt
walk algorithm to a suitable set of vectors. The main technical idea is to use
various properties of these colorings, including subgaussianity, to control the
second moment.


title: Personal Research Knowledge Graphs

abstract:
  Maintaining research-related information in an organized manner can be
challenging for a researcher. In this paper, we envision personal research
knowledge graphs (PRKGs) as a means to represent structured information about
the research activities of a researcher. PRKGs can be used to power intelligent
personal assistants, and personalize various applications. We explore what
entities and relations could be potentially included in a PRKG, how to extract
them from various sources, and how to share a PRKG within a research group.


title: A Dynamical Approach to Nonhomogenous Spectra

abstract:
  Let $\alpha>0$ and $0<\gamma<1$. Define $g_{\alpha,\gamma}\colon
\mathbb{N}\to\mathbb{N}_0$ by $g_{\alpha,\gamma}(n)=\lfloor n\alpha
+\gamma\rfloor$, where $\lfloor x \rfloor$ is the largest integer less than or
equal to $x$. The set
$g_{\alpha,\gamma}(\mathbb{N})=\{g_{\alpha,\gamma}(n)\colon n\in\mathbb{N}\}$
is called the $\gamma$-nonhomogeneous spectrum of $\alpha$. By extension, the
functions $g_{\alpha,\gamma}$ are referred to as spectra. In 1996, Bergelson,
Hindman and Kra showed that the functions $g_{\alpha,\gamma}$ preserve some
largeness of subsets of $\mathbb{N}$, that is, if a subset $A$ of $\mathbb{N}$
is an IP-set, a central set, an IP$^*$-set, or a central$^*$-set, then
$g_{\alpha,\gamma}(A)$ is the corresponding object for all $\alpha>0$ and
$0<\gamma<1$. In 2012, Hindman and Johnson extended this result to include
several other notions of largeness: C-sets, J-sets, strongly central sets, and
piecewise syndetic sets. We adopt a dynamical approach to this issue and build
a correspondence between the preservation of spectra and the lift property of
suspension. As an application, we give a unified proof of some known results
and also obtain some new.


title: Generalized Mathisson-Papapetrou-Tulczyjew-Dixon Equations

abstract:
  We derive two generalizations of Mathisson-Papapetrou-Tulczyjew-Dixon
equations from Casalbuoni-Brink-Schwarz type pseudoclassical Lagrangians of
Majorana spinors on a Riemann-Cartan spacetime. One has a "color" freedom,
which makes the equations of motion also be a generalization of Wong equations.
The other is a spinor model coupled with a Rarita-Schwinger field to preserve
the supersymmetry. The coupling to the torsion is modified due to the existence
of the Rarita-Schwinger field. In both extensions, the Tulczyjew condition is
automatically satisfied.


title: Hardware Trojan Detection using Graph Neural Networks

abstract:
  The globalization of the Integrated Circuit (IC) supply chain has moved most
of the design, fabrication, and testing process from a single trusted entity to
various untrusted third-party entities around the world. The risk of using
untrusted third-Party Intellectual Property (3PIP) is the possibility for
adversaries to insert malicious modifications known as Hardware Trojans (HTs).
These HTs can compromise the integrity, deteriorate the performance, and deny
the functionality of the intended design. Various HT detection methods have
been proposed in the literature; however, many fall short due to their reliance
on a golden reference circuit, a limited detection scope, the need for manual
code review, or the inability to scale with large modern designs. We propose a
novel golden reference-free HT detection method for both Register Transfer
Level (RTL) and gate-level netlists by leveraging Graph Neural Networks (GNNs)
to learn the behavior of the circuit through a Data Flow Graph (DFG)
representation of the hardware design. We evaluate our model on a custom
dataset by expanding the Trusthub HT benchmarks \cite{trusthub1}. The results
demonstrate that our approach detects unknown HTs with 97% recall (true
positive rate) very fast in 21.1ms for RTL and 84% recall in 13.42s for
Gate-Level Netlist.


title:
Unsupervised Hierarchical Semantic Segmentation with Multiview
  Cosegmentation and Clustering Transformers

abstract:
  Unsupervised semantic segmentation aims to discover groupings within and
across images that capture object and view-invariance of a category without
external supervision. Grouping naturally has levels of granularity, creating
ambiguity in unsupervised segmentation. Existing methods avoid this ambiguity
and treat it as a factor outside modeling, whereas we embrace it and desire
hierarchical grouping consistency for unsupervised segmentation.
  We approach unsupervised segmentation as a pixel-wise feature learning
problem. Our idea is that a good representation shall reveal not just a
particular level of grouping, but any level of grouping in a consistent and
predictable manner. We enforce spatial consistency of grouping and bootstrap
feature learning with co-segmentation among multiple views of the same image,
and enforce semantic consistency across the grouping hierarchy with clustering
transformers between coarse- and fine-grained features.
  We deliver the first data-driven unsupervised hierarchical semantic
segmentation method called Hierarchical Segment Grouping (HSG). Capturing
visual similarity and statistical co-occurrences, HSG also outperforms existing
unsupervised segmentation methods by a large margin on five major object- and
scene-centric benchmarks. Our code is publicly available at
https://github.com/twke18/HSG .


title:
Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG
  Images with Curriculum Learning

abstract:
  We explore the potential of CNN-based models for gallbladder cancer (GBC)
detection from ultrasound (USG) images as no prior study is known. USG is the
most common diagnostic modality for GB diseases due to its low cost and
accessibility. However, USG images are challenging to analyze due to low image
quality, noise, and varying viewpoints due to the handheld nature of the
sensor. Our exhaustive study of state-of-the-art (SOTA) image classification
techniques for the problem reveals that they often fail to learn the salient GB
region due to the presence of shadows in the USG images. SOTA object detection
techniques also achieve low accuracy because of spurious textures due to noise
or adjacent organs. We propose GBCNet to tackle the challenges in our problem.
GBCNet first extracts the regions of interest (ROIs) by detecting the GB (and
not the cancer), and then uses a new multi-scale, second-order pooling
architecture specializing in classifying GBC. To effectively handle spurious
textures, we propose a curriculum inspired by human visual acuity, which
reduces the texture biases in GBCNet. Experimental results demonstrate that
GBCNet significantly outperforms SOTA CNN models, as well as the expert
radiologists. Our technical innovations are generic to other USG image analysis
tasks as well. Hence, as a validation, we also show the efficacy of GBCNet in
detecting breast cancer from USG images. Project page with source code, trained
models, and data is available at https://gbc-iitd.github.io/gbcnet


title:
Joint Feature Distribution Alignment Learning for NIR-VIS and VIS-VIS
  Face Recognition

abstract:
  Face recognition for visible light (VIS) images achieve high accuracy thanks
to the recent development of deep learning. However, heterogeneous face
recognition (HFR), which is a face matching in different domains, is still a
difficult task due to the domain discrepancy and lack of large HFR dataset.
Several methods have attempted to reduce the domain discrepancy by means of
fine-tuning, which causes significant degradation of the performance in the VIS
domain because it loses the highly discriminative VIS representation. To
overcome this problem, we propose joint feature distribution alignment learning
(JFDAL) which is a joint learning approach utilizing knowledge distillation. It
enables us to achieve high HFR performance with retaining the original
performance for the VIS domain. Extensive experiments demonstrate that our
proposed method delivers statistically significantly better performances
compared with the conventional fine-tuning approach on a public HFR dataset
Oulu-CASIA NIR&VIS and popular verification datasets in VIS domain such as FLW,
CFP, AgeDB. Furthermore, comparative experiments with existing state-of-the-art
HFR methods show that our method achieves a comparable HFR performance on the
Oulu-CASIA NIR&VIS dataset with less degradation of VIS performance.


title:
Influence of Salt on the Formation and Separation of Droplet Interface
  Bilayers

abstract:
  Phospholipid bilayers are a major component of the cell membrane that is in
contact with physiological electrolyte solutions including salt ions. The
effect of salt on the phospholipid bilayer mechanics is an active research area
due to its implications for cellular function and viability. In this manuscript
we utilize droplet interface bilayers(DIBs), a bilayer formed artificially
between two aqueous droplets, to unravel the bilayer formation and separation
mechanics with a combination of experiments and numerical modelling under the
effects of K$^+$, Na$^+$, Li$^+$, Ca$^{2+}$ and Mg$^{2+}$. Initially, we
measured the interfacial tension and the interfacial complex viscosity of lipid
monolayers at a flat oil-aqueous interface and show that both properties are
sensitive to salt concentration, ion size and valency. Subsequently, we
measured DIB formation rates and show that the characteristic bilayer formation
velocity scales with the ratio of the interfacial tension to the interfacial
viscosity. Next, we subjected the system to a step strain by separating the
drops in a stepwise manner. By tracking the evolution of the bilayer contact
angle and radius, we show that salt influences the bilayer separation mechanics
including the decay of the contact angle, the decay of the bilayer radius and
the corresponding relaxation time. Finally, we explain the salt effect on the
observed bilayer separation by means of a mathematical model comprising of the
Young-Laplace equation and an evolution equation.


title:
SwinFuse: A Residual Swin Transformer Fusion Network for Infrared and
  Visible Images

abstract:
  The existing deep learning fusion methods mainly concentrate on the
convolutional neural networks, and few attempts are made with transformer.
Meanwhile, the convolutional operation is a content-independent interaction
between the image and convolution kernel, which may lose some important
contexts and further limit fusion performance. Towards this end, we present a
simple and strong fusion baseline for infrared and visible images,
namely\textit{ Residual Swin Transformer Fusion Network}, termed as SwinFuse.
Our SwinFuse includes three parts: the global feature extraction, fusion layer
and feature reconstruction. In particular, we build a fully attentional feature
encoding backbone to model the long-range dependency, which is a pure
transformer network and has a stronger representation ability compared with the
convolutional neural networks. Moreover, we design a novel feature fusion
strategy based on $L_{1}$-norm for sequence matrices, and measure the
corresponding activity levels from row and column vector dimensions, which can
well retain competitive infrared brightness and distinct visible details.
Finally, we testify our SwinFuse with nine state-of-the-art traditional and
deep learning methods on three different datasets through subjective
observations and objective comparisons, and the experimental results manifest
that the proposed SwinFuse obtains surprising fusion performance with strong
generalization ability and competitive computational efficiency. The code will
be available at https://github.com/Zhishe-Wang/SwinFuse.


title: Understanding Audio Features via Trainable Basis Functions

abstract:
  In this paper we explore the possibility of maximizing the information
represented in spectrograms by making the spectrogram basis functions
trainable. We experiment with two different tasks, namely keyword spotting
(KWS) and automatic speech recognition (ASR). For most neural network models,
the architecture and hyperparameters are typically fine-tuned and optimized in
experiments. Input features, however, are often treated as fixed. In the case
of audio, signals can be mainly expressed in two main ways: raw waveforms
(time-domain) or spectrograms (time-frequency-domain). In addition, different
spectrogram types are often used and tailored to fit different applications. In
our experiments, we allow for this tailoring directly as part of the network.
  Our experimental results show that using trainable basis functions can boost
the accuracy of Keyword Spotting (KWS) by 14.2 percentage points, and lower the
Phone Error Rate (PER) by 9.5 percentage points. Although models using
trainable basis functions become less effective as the model complexity
increases, the trained filter shapes could still provide us with insights on
which frequency bins are important for that specific task. From our
experiments, we can conclude that trainable basis functions are a useful tool
to boost the performance when the model complexity is limited.


title: Joint mixability and negative orthant dependence

abstract:
  A joint mix is a random vector with a constant component-wise sum. It is
known to represent the minimizing dependence structure of some common
objectives, and it is usually regarded as a concept of extremal negative
dependence. In this paper, we explore the connection between the joint mix
structure and one of the most popular notions of negative dependence in
statistics, called negative orthant dependence (NOD). We show that a joint mix
does not always have NOD, but some natural classes of joint mixes have. In
particular, the Gaussian class is characterized as the only elliptical class
which supports NOD joint mixes of arbitrary dimension. For Gaussian margins, we
also derive a necessary and sufficient condition for the existence of an NOD
joint mix. Finally, for identical marginal distributions, we show that an NOD
Gaussian joint mix solves a multi-marginal optimal transport problem under
uncertainty on the number of components. Analysis of this optimal transport
problem with heterogeneous marginals reveals a trade-off between NOD and the
joint mix structure.


title: Algebraic Approximation of Cohen-Macaulay Algebras

abstract:
  This paper shows that Cohen-Macaulay algebras can be algebraically
approximated in such a way that their Cohen-Macaulayness and minimal Betti
numbers are preserved. This is achieved by showing that finitely generated
modules over power series rings can be algebraically approximated in a manner
that preserves their diagrams of initial exponents and their minimal Betti
numbers. These results are also applied to obtain an approximation result for
flat homomorphisms from rings of power series to Cohen-Macaulay algebras.