title: NonExistence of Manifold structure on topological constructions

abstract:
  We show the non existence of manifold structure on certain topological
constructions. The main results of this paper are: (1) The wedge of manifold
with itself is not a manifold. (2) The cone of a manifold is not a manifold.
The main tools for the main result is homological algebra and algebraic
topology.


title: The Spanning Tree Model and the Assembly Kinetics of RNA Viruses

abstract:
  Single-stranded (ss) RNA viruses self-assemble spontaneously in solutions
that contain the viral RNA genome molecules and viral capsid proteins. The
self-assembly of empty capsids can be understood on the basis of free energy
minimization. However, during the self-assembly of complete viral particles in
the cytoplasm of an infected cell, the viral genome molecules must be selected
from a large pool of very similar host messenger RNA molecules and it is not
known whether this also can be understood by free energy minimization. We
address this question using a simple mathematical model recently proposed for
the assembly of small ssRNA viruses (submitted to PLOS Biocomputation). We
present a statistical physics analysis of the properties of the model finding
an effect kinetic RNA selection mechanism with selection taking place during
the formation of the nucleation complex. Surprisingly, kinetic selectivity is
greatly enhanced by a modest level of supersaturation and by reduced protein to
RNA concentration ratios. The mechanism is related to the Hopfield kinetic
proofreading scenario.


title:
CLIP on Wheels: Zero-Shot Object Navigation as Object Localization and
  Exploration

abstract:
  Households across the world contain arbitrary objects: from mate gourds and
coffee mugs to sitars and guitars. Considering this diversity, robot perception
must handle a large variety of semantic objects without additional fine-tuning
to be broadly applicable in homes. Recently, zero-shot models have demonstrated
impressive performance in image classification of arbitrary objects (i.e.,
classifying images at inference with categories not explicitly seen during
training). In this paper, we translate the success of zero-shot vision models
(e.g., CLIP) to the popular embodied AI task of object navigation. In our
setting, an agent must find an arbitrary goal object, specified via text, in
unseen environments coming from different datasets. Our key insight is to
modularize the task into zero-shot object localization and exploration.
Employing this philosophy, we design CLIP on Wheels (CoW) baselines for the
task and evaluate each zero-shot model in both Habitat and RoboTHOR simulators.
We find that a straightforward CoW, with CLIP-based object localization plus
classical exploration, and no additional training, often outperforms learnable
approaches in terms of success, efficiency, and robustness to dataset
distribution shift. This CoW achieves 6.3% SPL in Habitat and 10.0% SPL in
RoboTHOR, when tested zero-shot on all categories. On a subset of four RoboTHOR
categories considered in prior work, the same CoW shows a 16.1 percentage point
improvement in Success over the learnable state-of-the-art baseline.


title: Subspace Modeling for Fast Out-Of-Distribution and Anomaly Detection

abstract:
  This paper presents a fast, principled approach for detecting anomalous and
out-of-distribution (OOD) samples in deep neural networks (DNN). We propose the
application of linear statistical dimensionality reduction techniques on the
semantic features produced by a DNN, in order to capture the low-dimensional
subspace truly spanned by said features. We show that the "feature
reconstruction error" (FRE), which is the $\ell_2$-norm of the difference
between the original feature in the high-dimensional space and the pre-image of
its low-dimensional reduced embedding, is highly effective for OOD and anomaly
detection. To generalize to intermediate features produced at any given layer,
we extend the methodology by applying nonlinear kernel-based methods.
Experiments using standard image datasets and DNN architectures demonstrate
that our method meets or exceeds best-in-class quality performance, but at a
fraction of the computational and memory cost required by the state of the art.
It can be trained and run very efficiently, even on a traditional CPU.


title: A note on the distribution of pinned distance trees in dense sets

abstract:
  The recent breakthrough result of Guth, Iosevich, Ou, and Wang (2019) on the
Falconer distance problem states that for a compact set $A\subset
\mathbb{R}^2$, if the Hausdorff dimension of $A$ is greater than $\frac{5}{4}$,
then the distance set $\Delta(A)$ has positive Lebesgue measure. Ou and Taylor
(2021) recently generalized this result to the setting of (distance) trees. The
main purpose of this paper is to study the discrete version of their result
over both prime fields and arbitrary fields for small sets.


title:
MetaOnce: A Metaverse Framework Based on Multi-scene Relations and
  Entity-relation-event Game

abstract:
  Existing metaverse systems lack rich relation types between entities and
events. The challenge is that there is no portable framework to introduce rich
concepts, relations, events into the metaverse. This paper introduces a new
metaverse framework, MetaOnce. This framework proposes to build multi-scene
graphs. This framework not only describes rich relations in a single scene but
also combines multiple scene graphs into a complete graph for more
comprehensive analysis and inference. Prior social network systems mainly
describe friend relations. They ignore the effect of entity-relation-event
games on the metaverse system and existing rule constraints. We propose a rule
controller and impose constraints on the relations that allow the framework to
behave in a compliant manner. We build a metaverse system to test the features
of the framework, and experimental results show that our framework can build a
multi-scene metaverse with memory and rule constraints.


title:
A Study on Robustness to Perturbations for Representations of
  Environmental Sound

abstract:
  Audio applications involving environmental sound analysis increasingly use
general-purpose audio representations, also known as embeddings, for transfer
learning. Recently, Holistic Evaluation of Audio Representations (HEAR)
evaluated twenty-nine embedding models on nineteen diverse tasks. However, the
evaluation's effectiveness depends on the variation already captured within a
given dataset. Therefore, for a given data domain, it is unclear how the
representations would be affected by the variations caused by myriad
microphones' range and acoustic conditions -- commonly known as channel
effects. We aim to extend HEAR to evaluate invariance to channel effects in
this work. To accomplish this, we imitate channel effects by injecting
perturbations to the audio signal and measure the shift in the new (perturbed)
embeddings with three distance measures, making the evaluation domain-dependent
but not task-dependent. Combined with the downstream performance, it helps us
make a more informed prediction of how robust the embeddings are to the channel
effects. We evaluate two embeddings -- YAMNet, and OpenL$^3$ on monophonic
(UrbanSound8K) and polyphonic (SONYC UST) datasets. We show that one distance
measure does not suffice in such task-independent evaluation. Although
Fr\'echet Audio Distance (FAD) correlates with the trend of the performance
drop in the downstream task most accurately, we show that we need to study this
in conjunction with the other distances to get a clear understanding of the
overall effect of the perturbation. In terms of the embedding performance, we
find OpenL$^3$ to be more robust to YAMNet, which aligns with the HEAR
evaluation.


title:
STEMM: Self-learning with Speech-text Manifold Mixup for Speech
  Translation

abstract:
  How to learn a better speech representation for end-to-end speech-to-text
translation (ST) with limited labeled data? Existing techniques often attempt
to transfer powerful machine translation (MT) capabilities to ST, but neglect
the representation discrepancy across modalities. In this paper, we propose the
Speech-TExt Manifold Mixup (STEMM) method to calibrate such discrepancy.
Specifically, we mix up the representation sequences of different modalities,
and take both unimodal speech sequences and multimodal mixed sequences as input
to the translation model in parallel, and regularize their output predictions
with a self-learning framework. Experiments on MuST-C speech translation
benchmark and further analysis show that our method effectively alleviates the
cross-modal representation discrepancy, and achieves significant improvements
over a strong baseline on eight translation directions.


title:
A laser interferometer accelerometer for vibration sensitive cryogenic
  experiments

abstract:
  Monitoring motion originating from ultra low-temperature cooling systems like
cryocoolers is important for vibration sensitive cryogenic experiments like
KAGRA. Since no commercial cryogenic accelerometers are available, we developed
a compact self-calibrating accelerometer with a Michelson interferometer
readout for cryogenic use. Change in calibration factor and drop in
interferometer output originating from temperature drop were the main concerns
which were tackled. Sensitivity of $3.38\times10^{-11}$ m/$\sqrt{\mathrm{Hz}}$
at 1 Hz was achieved at 300 K. The accelerometer was tested inside the KAGRA
cryostat; showed stable operation down to 12 K in 0.1-100 Hz band with only 1%
visibility drop. Our accelerometer can be employed in low vibration cryogenic
environment for a multitude of applications.


title:
PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks
  with Pipelined Feature Communication

abstract:
  Graph Convolutional Networks (GCNs) is the state-of-the-art method for
learning graph-structured data, and training large-scale GCNs requires
distributed training across multiple accelerators such that each accelerator is
able to hold a partitioned subgraph. However, distributed GCN training incurs
prohibitive overhead of communicating node features and feature gradients among
partitions for every GCN layer during each training iteration, limiting the
achievable training efficiency and model scalability. To this end, we propose
PipeGCN, a simple yet effective scheme that hides the communication overhead by
pipelining inter-partition communication with intra-partition computation. It
is non-trivial to pipeline for efficient GCN training, as communicated node
features/gradients will become stale and thus can harm the convergence,
negating the pipeline benefit. Notably, little is known regarding the
convergence rate of GCN training with both stale features and stale feature
gradients. This work not only provides a theoretical convergence analysis but
also finds the convergence rate of PipeGCN to be close to that of the vanilla
distributed GCN training without any staleness. Furthermore, we develop a
smoothing method to further improve PipeGCN's convergence. Extensive
experiments show that PipeGCN can largely boost the training throughput
(1.7x~28.5x) while achieving the same accuracy as its vanilla counterpart and
existing full-graph training methods. The code is available at
https://github.com/RICE-EIC/PipeGCN.


title: Hermitian-Toeplitz determinants for certain univalent functions

abstract:
  Sharp upper and lower bounds for the second and third order
Hermitian-Toepilitz determinants are obtained for some generalized subclasses
of starlike and convex functions. Applications of these results are also
discussed for several widely known classes.


title:
g2pW: A Conditional Weighted Softmax BERT for Polyphone Disambiguation
  in Mandarin

abstract:
  Polyphone disambiguation is the most crucial task in Mandarin
grapheme-to-phoneme (g2p) conversion. Previous studies have approached this
problem using pre-trained language models, restricted output, and extra
information from Part-Of-Speech (POS) tagging. Inspired by these strategies, we
propose a novel approach, called g2pW, which adapts learnable softmax-weights
to condition the outputs of BERT with the polyphonic character of interest and
its POS tagging. Rather than using the hard mask as in previous works, our
experiments show that learning a soft-weighting function for the candidate
phonemes benefits performance. In addition, our proposed g2pW does not require
extra pre-trained POS tagging models while using POS tags as auxiliary features
since we train the POS tagging model simultaneously with the unified encoder.
Experimental results show that our g2pW outperforms existing methods on the
public CPP dataset. All codes, model weights, and a user-friendly package are
publicly available.


title:
Critical problems of energy frontier Muon Colliders: optics, magnets and
  radiation

abstract:
  This White Paper brings together our previous studies on a Muon Collider (MC)
and presents a design concept of the 6 TeV MC optics, the superconducting (SC)
magnets, and a preliminary analysis of the protection system to reduce magnet
radiation loads as well as particle backgrounds in the detector. The SC magnets
and detector protection considerations impose strict limitations on the lattice
choice, hence the design of the collider optics, magnets and Machine Detector
Interface (MDI) are closely intertwined. As a first approximation we use the
Interaction Region (IR) design with beta-star=3 mm, whereas for the arcs we
re-scale the arc cell design of the 3 TeV MC. Traditional cos-theta coil
geometry and Nb3Sn superconductor were used to provide field maps for the
analysis and optimization of the arc lattice and IR design, as well as for
studies of beam dynamics and magnet protection against radiation. The stress
management in the coil will be needed to avoid large degradation or even damage
of the brittle SC coils. In the assumed IR designs, the dipoles close to the
Interaction Point (IP) and tungsten masks in each IR (to protect magnets) help
reducing background particle fluxes in the detector by a substantial factor.
The tungsten nozzles in the 6 to 600 cm region from the IP, assisted by the
detector solenoid field, trap most of the decay electrons created close to the
IP as well as most of the incoherent electron-positron pairs generated in the
IP. With sophisticated tungsten, iron, concrete and borated polyethylene
shielding in the MDI region, the total reduction of background loads by more
than three orders of magnitude can be achieved.


title:
Interpretability of Fine-grained Classification of Sadness and
  Depression

abstract:
  While sadness is a human emotion that people experience at certain times
throughout their lives, inflicting them with emotional disappointment and pain,
depression is a longer term mental illness which impairs social, occupational,
and other vital regions of functioning making it a much more serious issue and
needs to be catered to at the earliest. NLP techniques can be utilized for the
detection and subsequent diagnosis of these emotions. Most of the open sourced
data on the web deal with sadness as a part of depression, as an emotion even
though the difference in severity of both is huge. Thus, we create our own
novel dataset illustrating the difference between the two. In this paper, we
aim to highlight the difference between the two and highlight how interpretable
our models are to distinctly label sadness and depression. Due to the sensitive
nature of such information, privacy measures need to be taken for handling and
training of such data. Hence, we also explore the effect of Federated Learning
(FL) on contextualised language models.


title: End-to-End Human-Gaze-Target Detection with Transformers

abstract:
  In this paper, we propose an effective and efficient method for
Human-Gaze-Target (HGT) detection, i.e., gaze following. Current approaches
decouple the HGT detection task into separate branches of salient object
detection and human gaze prediction, employing a two-stage framework where
human head locations must first be detected and then be fed into the next gaze
target prediction sub-network. In contrast, we redefine the HGT detection task
as detecting human head locations and their gaze targets, simultaneously. By
this way, our method, named Human-Gaze-Target detection TRansformer or HGTTR,
streamlines the HGT detection pipeline by eliminating all other additional
components. HGTTR reasons about the relations of salient objects and human gaze
from the global image context. Moreover, unlike existing two-stage methods that
require human head locations as input and can predict only one human's gaze
target at a time, HGTTR can directly predict the locations of all people and
their gaze targets at one time in an end-to-end manner. The effectiveness and
robustness of our proposed method are verified with extensive experiments on
the two standard benchmark datasets, GazeFollowing and VideoAttentionTarget.
Without bells and whistles, HGTTR outperforms existing state-of-the-art methods
by large margins (6.4 mAP gain on GazeFollowing and 10.3 mAP gain on
VideoAttentionTarget) with a much simpler architecture.


title:
A H\"older Stability Estimate for a 3D Coefficient Inverse Problem for a
  Hyperbolic Equation With a Plane Wave

abstract:
  A 3D coefficient inverse problem for a hyperbolic equation with
non-overdetermined data is considered. The forward problem is the Cauchy
problems with the initial condition the delta function concentrated at a single
plane (i.e. the plane wave). A certain associated operator is written in finite
differences with respect to two out of three spatial variables, i.e. "partial
finite differences". The grid step size is bounded from the below by a fixed
number. A Carleman estimate is applied to obtain, for the first time, a
H\"older stability estimates for this problem. Another new result is an
estimate from the below of the amplitude of the first term of the expansion of
the solution of the forward problem near the characteristic wedge.


abstract:
  Transformers exhibit great advantages in handling computer vision tasks. They
model image classification tasks by utilizing a multi-head attention mechanism
to process a series of patches consisting of split images. However, for complex
tasks, Transformer in computer vision not only requires inheriting a bit of
dynamic attention and global context, but also needs to introduce features
concerning noise reduction, shifting, and scaling invariance of objects.
Therefore, here we take a step forward to study the structural characteristics
of Transformer and convolution and propose an architecture search method-Vision
Transformer with Convolutions Architecture Search (VTCAS). The high-performance
backbone network searched by VTCAS introduces the desirable features of
convolutional neural networks into the Transformer architecture while
maintaining the benefits of the multi-head attention mechanism. The searched
block-based backbone network can extract feature maps at different scales.
These features are compatible with a wider range of visual tasks, such as image
classification (32 M parameters, 82.0% Top-1 accuracy on ImageNet-1K) and
object detection (50.4% mAP on COCO2017). The proposed topology based on the
multi-head attention mechanism and CNN adaptively associates relational
features of pixels with multi-scale features of objects. It enhances the
robustness of the neural network for object recognition, especially in the low
illumination indoor scene.


title: Refinements of strong multiplicity one for $\mathrm{GL}(2)$

abstract:
  For distinct unitary cuspidal automorphic representations $\pi_1$ and $\pi_2$
for $\mathrm{GL}(2)$ over a number field $F$ and any $\alpha\in\Bbb{R}$, let
$\mathcal{S}_{\alpha}$ be the set of primes $v$ of $F$ for which
$\lambda_{\pi_1}(v)\neq e^{i\alpha} \lambda_{\pi_2}(v)$, where
$\lambda_{\pi_i}(v)$ is the Fourier coefficient of $\pi_i$ at $v$. In this
article, we show that the lower Dirichlet density of $\mathcal{S}_\alpha$ is at
least $\frac{1}{16}$. Moreover, if $\pi_1$ and $\pi_2$ are not
twist-equivalent, we show that the lower Dirichlet densities of
$\mathcal{S}_\alpha$ and $ \cap_\alpha\mathcal{S}_\alpha$ are at least
$\frac{2}{13}$ and $\frac{1}{11}$, respectively. Furthermore, for
non-twist-equivalent $\pi_1$ and $\pi_2$, if each $\pi_i$ corresponds to a
non-CM newform of weight $k_i\ge 2$ and with trivial nebentypus, we obtain
various upper bounds for the number of primes $p\le x$ such that
$\lambda_{\pi_1}(p)^2 = \lambda_{\pi_2}(p)^2$. These present refinements of the
works of Murty-Pujahari, Murty-Rajan, Ramakrishnan, and Walji.


title: Dilaton-Axion Inflation with PBHs and GWs

abstract:
  We discuss two-stage dilaton-axion inflation models [1] and describe
$\alpha$-attractor models with either exponential or polynomial approach to the
plateau. We implement one of the models of primordial black hole production
proposed in [2] in the $\alpha$-attractor context, and develop its supergravity
version. The predictions of this model following from its polynomial attractor
properties are: $n_s$ and $r$ are $\alpha$-independent, $r$ depends on the mass
parameter $\mu$ defining the approach to the plateau. The tachyonic instability
at the transition point between the two stages of inflation is proportional to
the negative curvature of the hyperbolic space $\mathcal{R}_K=-2/3\alpha$.
Therefore the masses of primordial black holes (PBHs) and the frequencies of
small-scale gravitational waves (GWs) in this model show significant dependence
on $\alpha$.


title:
Improved lower bounds of analytic radius for the Benjamin-Bona-Mahony
  equation

abstract:
  This paper is devoted to the spatial analyticity of the solution of the BBM
equation on the real line with an analytic initial data. It is shown that the
analytic radius has a lower bound like $t^{-\frac{2}{3}}$ as time $t$ goes to
infinity, which is an improvement of previous results. The main new ingredient
is a higher order almost conservation law in analytic spaces. This is proved by
introducing an equivalent analytic norm with smooth symbol and establishing
some algebra identities of higher order polynomials.


title:
Search for new hadronic decays of $h_{c}$ and observation of $h_{c}\to
  p\bar{p}\eta$

abstract:
  A search for the hadronic decays of the $h_{c}$ meson to the final states
$p\bar{p}\pi^{+}\pi^{-}\pi^{0}$, $p\bar{p}\eta$, and $p\bar{p}\pi^0$ via the
process $\psi(3686) \to \pi^{0}{h_c}$ is performed using
$(4.48\pm0.03)\times10^{8}$ $\psi(3686)$ events collected with the BESIII
detector. The decay channel $h_{c}\to p\bar{p}\eta$ is observed for the first
time with a significance greater than $5\sigma$ and a branching fraction of
$\left( {6.41 \pm 1.74 \pm 0.53 \pm 1.00} \right) \times {10^{ -4}}$, where the
uncertainties are statistical, systematic, and that from the branching fraction
of $\psi(3686)\to\pi^{0}h_{c}$. Strong evidence for the decay ${h_c} \to
p\bar{p}{\pi^+}{\pi^-}{\pi^0}$ is found with a significance of $4.9\sigma$ and
a branching fraction of $\left( {3.84 \pm 0.83 \pm0.69} \pm 0.58 \right) \times
{10^{ - 3}}$. The significances include systematic uncertainties. No clear
signal of the decay $h_c\to p\bar{p}\pi^{0}$ is found, and an upper limit of
$6.59\times 10^{-4}$ on its branching fraction is set at the 90% confidence
level.


title: Helicity amplitudes without gauge cancellation for electroweak processes

abstract:
  We introduce 5-component representation of weak bosons, W and Z bosons of the
standard model. The first four components make a Lorentz four vector,
representing the transverse and longitudinal polarizations excluding the scalar
component of the weak bosons. Its fifth component corresponds to the Goldstone
boson. We show that this description can be extended to off-shell weak bosons,
with the $5\times5$ component propagators, and prove that exactly the same
scattering amplitudes are obtained by making use of the BRST
(Becchi-Rouet-Stora-Tyutin) identities among two sub-amplitudes connected by
one off-shell weak boson line in the unitary gauge. By replacing all weak boson
vertices with those among the 5-component wavefunctions, we arrive at the
expression of the electroweak scattering amplitudes, where the magnitude of
each Feynman amplitude has the correct on-shell limits for all internal
propagators, and hence with no artificial gauge cancellation among diagrams. We
implement the 5-component weak boson propagators and their vertices in the
numerical helicity amplitude calculation code HELAS (Helicity Amplitude
Subroutines), so that an automatic amplitude generation program such as
MadGraph can generate the scattering amplitudes without gauge cancellation. We
present results for several high-energy scattering processes where subtle
gauge-theory cancellation among diagrams takes place in all the other known
approaches.


title:
First-principles LCPAO Approach to Insulators Under Finite Electric
  Fields

abstract:
  We developed the linear-combination-of-pseudo-atomic-orbitals (LCPAO) scheme
of a finite electric field method based on the modern theory of polarization.
Our implementation successfully evaluated the dielectric constants and Born
effective charges of typical semiconducting and insulating materials. Our
formalism will become a powerful tool to expand subjects of researches on
materials under electric fields.


title:
Towards Structuring Real-World Data at Scale: Deep Learning for
  Extracting Key Oncology Information from Clinical Text with Patient-Level
  Supervision

abstract:
  Objective: The majority of detailed patient information in real-world data
(RWD) is only consistently available in free-text clinical documents. Manual
curation is expensive and time-consuming. Developing natural language
processing (NLP) methods for structuring RWD is thus essential for scaling
real-world evidence generation.
  Materials and Methods: Traditional rule-based systems are vulnerable to the
prevalent linguistic variations and ambiguities in clinical text, and prior
applications of machine-learning methods typically require sentence-level or
report-level labeled examples that are hard to produce at scale. We propose
leveraging patient-level supervision from medical registries, which are often
readily available and capture key patient information, for general RWD
applications. To combat the lack of sentence-level or report-level annotations,
we explore advanced deep-learning methods by combining domain-specific
pretraining, recurrent neural networks, and hierarchical attention.
  Results: We conduct an extensive study on 135,107 patients from the cancer
registry of a large integrated delivery network (IDN) comprising healthcare
systems in five western US states. Our deep learning methods attain test AUROC
of 94-99% for key tumor attributes and comparable performance on held-out data
from separate health systems and states.
  Discussion and Conclusion: Ablation results demonstrate clear superiority of
these advanced deep-learning methods over prior approaches. Error analysis
shows that our NLP system sometimes even corrects errors in registrar labels.
We also conduct a preliminary investigation in accelerating registry curation
and general RWD structuring via assisted curation for over 1.2 million cancer
patients in this healthcare network.


title:
Quantum Multi-Agent Reinforcement Learning via Variational Quantum
  Circuit Design

abstract:
  In recent years, quantum computing (QC) has been getting a lot of attention
from industry and academia. Especially, among various QC research topics,
variational quantum circuit (VQC) enables quantum deep reinforcement learning
(QRL). Many studies of QRL have shown that the QRL is superior to the classical
reinforcement learning (RL) methods under the constraints of the number of
training parameters. This paper extends and demonstrates the QRL to quantum
multi-agent RL (QMARL). However, the extension of QRL to QMARL is not
straightforward due to the challenge of the noise intermediate-scale quantum
(NISQ) and the non-stationary properties in classical multi-agent RL (MARL).
Therefore, this paper proposes the centralized training and decentralized
execution (CTDE) QMARL framework by designing novel VQCs for the framework to
cope with these issues. To corroborate the QMARL framework, this paper conducts
the QMARL demonstration in a single-hop environment where edge agents offload
packets to clouds. The extensive demonstration shows that the proposed QMARL
framework enhances 57.7% of total reward than classical frameworks.


title: VGSE: Visually-Grounded Semantic Embeddings for Zero-Shot Learning

abstract:
  Human-annotated attributes serve as powerful semantic embeddings in zero-shot
learning. However, their annotation process is labor-intensive and needs expert
supervision. Current unsupervised semantic embeddings, i.e., word embeddings,
enable knowledge transfer between classes. However, word embeddings do not
always reflect visual similarities and result in inferior zero-shot
performance. We propose to discover semantic embeddings containing
discriminative visual properties for zero-shot learning, without requiring any
human annotation. Our model visually divides a set of images from seen classes
into clusters of local image regions according to their visual similarity, and
further imposes their class discrimination and semantic relatedness. To
associate these clusters with previously unseen classes, we use external
knowledge, e.g., word embeddings and propose a novel class relation discovery
module. Through quantitative and qualitative evaluation, we demonstrate that
our model discovers semantic embeddings that model the visual properties of
both seen and unseen classes. Furthermore, we demonstrate on three benchmarks
that our visually-grounded semantic embeddings further improve performance over
word embeddings across various ZSL models by a large margin.


title: Smoothing Advantage Learning

abstract:
  Advantage learning (AL) aims to improve the robustness of value-based
reinforcement learning against estimation errors with action-gap-based
regularization. Unfortunately, the method tends to be unstable in the case of
function approximation. In this paper, we propose a simple variant of AL, named
smoothing advantage learning (SAL), to alleviate this problem. The key to our
method is to replace the original Bellman Optimal operator in AL with a smooth
one so as to obtain more reliable estimation of the temporal difference target.
We give a detailed account of the resulting action gap and the performance
bound for approximate SAL. Further theoretical analysis reveals that the
proposed value smoothing technique not only helps to stabilize the training
procedure of AL by controlling the trade-off between convergence rate and the
upper bound of the approximation errors, but is beneficial to increase the
action gap between the optimal and sub-optimal action value as well.


title: A 3D Molecule Generative Model for Structure-Based Drug Design

abstract:
  We study a fundamental problem in structure-based drug design -- generating
molecules that bind to specific protein binding sites. While we have witnessed
the great success of deep generative models in drug design, the existing
methods are mostly string-based or graph-based. They are limited by the lack of
spatial information and thus unable to be applied to structure-based design
tasks. Particularly, such models have no or little knowledge of how molecules
interact with their target proteins exactly in 3D space. In this paper, we
propose a 3D generative model that generates molecules given a designated 3D
protein binding site. Specifically, given a binding site as the 3D context, our
model estimates the probability density of atom's occurrences in 3D space --
positions that are more likely to have atoms will be assigned higher
probability. To generate 3D molecules, we propose an auto-regressive sampling
scheme -- atoms are sampled sequentially from the learned distribution until
there is no room for new atoms. Combined with this sampling scheme, our model
can generate valid and diverse molecules, which could be applicable to various
structure-based molecular design tasks such as molecule sampling and linker
design. Experimental results demonstrate that molecules sampled from our model
exhibit high binding affinity to specific targets and good drug properties such
as drug-likeness even if the model is not explicitly optimized for them.


title: Over-parameterization: A Necessary Condition for Models that Extrapolate

abstract:
  In this work, we study over-parameterization as a necessary condition for
having the ability for the models to extrapolate outside the convex hull of
training set. We specifically, consider classification models, e.g., image
classification and other applications of deep learning. Such models are
classification functions that partition their domain and assign a class to each
partition \cite{strang2019linear}. Partitions are defined by decision
boundaries and so is the classification model/function. Convex hull of training
set may occupy only a subset of the domain, but trained model may partition the
entire domain and not just the convex hull of training set. This is important
because many of the testing samples may be outside the convex hull of training
set and the way in which a model partitions its domain outside the convex hull
would be influential in its generalization. Using approximation theory, we
prove that over-parameterization is a necessary condition for having control
over the partitioning of the domain outside the convex hull of training set. We
also propose a more clear definition for the notion of over-parametrization
based on the learning task and the training set at hand. We present empirical
evidence about geometry of datasets, both image and non-image, to provide
insights about the extent of extrapolation performed by the models. We consider
a 64-dimensional feature space learned by a ResNet model and investigate the
geometric arrangements of convex hulls and decision boundaries in that space.
We also formalize the notion of extrapolation and relate it to the scope of the
model. Finally, we review the rich extrapolation literature in pure and applied
mathematics, e.g., the Whitney's Extension Problem, and place our theory in
that context.


title:
Well-posedness of initial-boundary value problem for time-fractional
  diffusion-wave equation with time-dependent coefficients

abstract:
  We consider the well-posedness of the initial-boundary value problem for a
time-fractional partial differential equation with the fractional order lying
in (1,2]. First, we show some results on the unique existence of solution to
time-fractional ordinary differential equations. Then the unique existence of
weak and strong solutions to a time-fractional partial differential equation is
derived by using the Galerkin method.


title: Poeschl-Teller quantum oscillator and its limiting cases

abstract:
  One of the most interesting models of the nonrelativistic quantum mechanics
(in one-dimensional case) introduced by G. Poeschl and E.Teller at 1933 is
considered. It is shown that this model possesses as its limiting cases two
most popular quantum models: the quasi-free quantum particle in the box as well
as the quantum harmonic oscillator introduced by F. Bloch at 1932.


title:
Asymptotic behaviour of Dirichlet eigenvalues for homogeneous
  H\"{o}rmander operators and algebraic geometry approach

abstract:
  We study the Dirichlet eigenvalue problem of homogeneous H\"{o}rmander
operators $\triangle_{X}=\sum_{j=1}^{m}X_{j}^{2}$ on a bounded connected open
domain containing the origin, where $X_{1}, X_{2},\ldots, X_{m}$ are linearly
independent smooth vector fields in $\mathbb{R}^n$ satisfying H\"{o}rmander's
condition and a suitable homogeneity property with respect to a family of
non-isotropic dilations. Suppose that $\Omega$ is a bounded connected open
domain in $\mathbb{R}^n$ containing the origin, and its boundary
$\partial\Omega$ is smooth and non-characteristic for $X$. Combining the
subelliptic heat kernel estimates, the resolution of singularities in algebraic
geometry and some refined analysis involving convex geometry, we establish the
explicit asymptotic behaviour $\lambda_k\approx k^{\frac{2}{Q_{0}}}(\ln
k)^{-\frac{2d_{0}}{Q_{0}}}$ as $k\to +\infty$, where $\lambda_k$ denotes the
$k$-th Dirichlet eigenvalue of $\triangle_{X}$ on $\Omega$, $Q_{0}$ is a
positive rational number, and $d_{0}$ is a non-negative integer.Furthermore, we
also give the optimal bounds of index $Q_{0}$, which depends on the homogeneous
dimension associated with vector fields $X_{1}, X_{2},\ldots, X_{m}$.


title: On the Computation of Necessary and Sufficient Explanations

abstract:
  The complete reason behind a decision is a Boolean formula that characterizes
why the decision was made. This recently introduced notion has a number of
applications, which include generating explanations, detecting decision bias
and evaluating counterfactual queries. Prime implicants of the complete reason
are known as sufficient reasons for the decision and they correspond to what is
known as PI explanations and abductive explanations. In this paper, we refer to
the prime implicates of a complete reason as necessary reasons for the
decision. We justify this terminology semantically and show that necessary
reasons correspond to what is known as contrastive explanations. We also study
the computation of complete reasons for multi-class decision trees and graphs
with nominal and numeric features for which we derive efficient, closed-form
complete reasons. We further investigate the computation of shortest necessary
and sufficient reasons for a broad class of complete reasons, which include the
derived closed forms and the complete reasons for Sentential Decision Diagrams
(SDDs). We provide an algorithm which can enumerate their shortest necessary
reasons in output polynomial time. Enumerating shortest sufficient reasons for
this class of complete reasons is hard even for a single reason. For this
problem, we provide an algorithm that appears to be quite efficient as we show
empirically.


title: CrossBeam: Learning to Search in Bottom-Up Program Synthesis

abstract:
  Many approaches to program synthesis perform a search within an enormous
space of programs to find one that satisfies a given specification. Prior works
have used neural models to guide combinatorial search algorithms, but such
approaches still explore a huge portion of the search space and quickly become
intractable as the size of the desired program increases. To tame the search
space blowup, we propose training a neural model to learn a hands-on search
policy for bottom-up synthesis, instead of relying on a combinatorial search
algorithm. Our approach, called CrossBeam, uses the neural model to choose how
to combine previously-explored programs into new programs, taking into account
the search history and partial program executions. Motivated by work in
structured prediction on learning to search, CrossBeam is trained on-policy
using data extracted from its own bottom-up searches on training tasks. We
evaluate CrossBeam in two very different domains, string manipulation and logic
programming. We observe that CrossBeam learns to search efficiently, exploring
much smaller portions of the program space compared to the state-of-the-art.


title:
Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal
  Transport

abstract:
  Recently, the pretrain-finetuning paradigm has attracted tons of attention in
graph learning community due to its power of alleviating the lack of labels
problem in many real-world applications. Current studies use existing
techniques, such as weight constraint, representation constraint, which are
derived from images or text data, to transfer the invariant knowledge from the
pre-train stage to fine-tuning stage. However, these methods failed to preserve
invariances from graph structure and Graph Neural Network (GNN) style models.
In this paper, we present a novel optimal transport-based fine-tuning framework
called GTOT-Tuning, namely, Graph Topology induced Optimal Transport
fine-Tuning, for GNN style backbones. GTOT-Tuning is required to utilize the
property of graph data to enhance the preservation of representation produced
by fine-tuned networks. Toward this goal, we formulate graph local knowledge
transfer as an Optimal Transport (OT) problem with a structural prior and
construct the GTOT regularizer to constrain the fine-tuned model behaviors. By
using the adjacency relationship amongst nodes, the GTOT regularizer achieves
node-level optimal transport procedures and reduces redundant transport
procedures, resulting in efficient knowledge transfer from the pre-trained
models. We evaluate GTOT-Tuning on eight downstream tasks with various GNN
backbones and demonstrate that it achieves state-of-the-art fine-tuning
performance for GNNs.


title: Partitioning Image Representation in Contrastive Learning

abstract:
  In contrastive learning in the image domain, the anchor and positive samples
are forced to have as close representations as possible. However, forcing the
two samples to have the same representation could be misleading because the
data augmentation techniques make the two samples different. In this paper, we
introduce a new representation, partitioned representation, which can learn
both common and unique features of the anchor and positive samples in
contrastive learning. The partitioned representation consists of two parts: the
content part and the style part. The content part represents common features of
the class, and the style part represents the own features of each sample, which
can lead to the representation of the data augmentation method. We can achieve
the partitioned representation simply by decomposing a loss function of
contrastive learning into two terms on the two separate representations,
respectively. To evaluate our representation with two parts, we take two
framework models: Variational AutoEncoder (VAE) and BootstrapYour Own
Latent(BYOL) to show the separability of content and style, and to confirm the
generalization ability in classification, respectively. Based on the
experiments, we show that our approach can separate two types of information in
the VAE framework and outperforms the conventional BYOL in linear separability
and a few-shot learning task as downstream tasks.


title: Adversarial Mutual Leakage Network for Cell Image Segmentation

abstract:
  We propose three segmentation methods using GAN and information leakage
between generator and discriminator. First, we propose an Adversarial Training
Attention Module (ATA-Module) that uses an attention mechanism from the
discriminator to the generator to enhance and leak important information in the
discriminator. ATA-Module transmits important information to the generator from
the discriminator. Second, we propose a Top-Down Pixel-wise Difficulty
Attention Module (Top-Down PDA-Module) that leaks an attention map based on
pixel-wise difficulty in the generator to the discriminator. The generator
trains to focus on pixel-wise difficulty, and the discriminator uses the
difficulty information leaked from the generator for classification. Finally,
we propose an Adversarial Mutual Leakage Network (AML-Net) that mutually leaks
the information each other between the generator and the discriminator. By
using the information of the other network, it is able to train more
efficiently than ordinary segmentation models. Three proposed methods have been
evaluated on two datasets for cell image segmentation. The experimental results
show that the segmentation accuracy of AML-Net was much improved in comparison
with conventional methods.


title:
simCrossTrans: A Simple Cross-Modality Transfer Learning for Object
  Detection with ConvNets or Vision Transformers

abstract:
  Transfer learning is widely used in computer vision (CV), natural language
processing (NLP) and achieves great success. Most transfer learning systems are
based on the same modality (e.g. RGB image in CV and text in NLP). However, the
cross-modality transfer learning (CMTL) systems are scarce. In this work, we
study CMTL from 2D to 3D sensor to explore the upper bound performance of 3D
sensor only systems, which play critical roles in robotic navigation and
perform well in low light scenarios. While most CMTL pipelines from 2D to 3D
vision are complicated and based on Convolutional Neural Networks (ConvNets),
ours is easy to implement, expand and based on both ConvNets and Vision
transformers(ViTs): 1) By converting point clouds to pseudo-images, we can use
an almost identical network from pre-trained models based on 2D images. This
makes our system easy to implement and expand. 2) Recently ViTs have been
showing good performance and robustness to occlusions, one of the key reasons
for poor performance of 3D vision systems. We explored both ViT and ConvNet
with similar model sizes to investigate the performance difference. We name our
approach simCrossTrans: simple cross-modality transfer learning with ConvNets
or ViTs. Experiments on SUN RGB-D dataset show: with simCrossTrans we achieve
$13.2\%$ and $16.1\%$ absolute performance gain based on ConvNets and ViTs
separately. We also observed the ViTs based performs $9.7\%$ better than the
ConvNets one, showing the power of simCrossTrans with ViT. simCrossTrans with
ViTs surpasses the previous state-of-the-art (SOTA) by a large margin of
$+15.4\%$ mAP50. Compared with the previous 2D detection SOTA based RGB images,
our depth image only system only has a $1\%$ gap. The code, training/inference
logs and models are publicly available at
https://github.com/liketheflower/simCrossTrans


title: The start of things for Kamiokande: The Kamioka Nucleon Decay Experiment

abstract:
  In 1980, detecting the evidence of the so-called Grand Unified Theory (GUT)
became a major challenge for experimentalists. The GUT predicted that protons
would decay, and M. Koshiba proposed the Kamioka Nucleon Decay Experiment
(Kamiokande), which was originally conceived and designed for the detection of
proton decay signals. This article describes the story of the Kamiokande
experiment from the preparation stage to the observation of the first candidate
events for proton decays.


title:
TreatmentEstimatoR: a Dashboard for Estimating Treatment Effects from
  Observational Health Data

abstract:
  Observational health data can be leveraged to measure the real-world use and
potential benefits or risks of existing medical interventions. However, lack of
programming proficiency and advanced knowledge of causal inference methods
excludes some clinicians and non-computational researchers from performing such
analyses. Code-free dashboard tools provide accessible means to estimate and
visualize treatment effects from observational health data. We present
TreatmentEstimatoR, an R Shiny dashboard that facilitates the estimation of
treatment effects from observational data without any programming knowledge
required. The dashboard provides effect estimates from multiple algorithms
simultaneously and accommodates binary, continuous, and time-to-event outcomes.
TreatmentEstimatoR allows for flexible covariate selection for treatment and
outcome models, comprehensive model performance metrics, and an exploratory
data analysis tool. TreatmentEstimatoR is available at
https://github.com/CollinSakal/TreatmentEstimatoR. We provide full installation
instructions and detailed vignettes for how to best use the dashboard.


title: Getting more from your regression model: A free lunch?

abstract:
  We consider a simple approach for approximating detailed information about
the conditional distribution of a real-valued response variable, given values
for its covariates, using only the outputs from a standard regression model. We
validate this approach by assessing its performance in the context of quantile
regression; when applied to the outputs of linear, gradient boosted tree
ensemble and random forest models. We find that it compares favourably to the
standard approach for estimating quantile regression functions, especially for
commonly selected tail probabilities, and is highly competitive with the
quantile regression forest model, across a large collection of benchmark data
sets.


title:
Non-Markovian quantum correlations of two-qubit system in equilibrium
  and nonequilibrium bosonic environments

abstract:
  The quantum correlations of the two-qubit system are studied in different
bosonic reservoirs by the tensor network algorithm. Both equilibrium and
nonequilibrium scenarios are discussed. The non-Markov effect can improve the
survival time of the quantum correlations significantly and weaken the
decoherence effect. The non-Markovian dynamics with existing memory can lead to
the entanglement rebirth in certain scenarios in contrast to the eventual
entanglement decay or death under the memoryless Markovian case. In particular,
the system in the sub-Ohmic reservoirs can reach steady state the quickest. The
quantum correlations are more robust to the changes in the temperature or the
temperature difference in the super-Ohmic reservoirs. At fixed times, we find
that the memory can boost the correlations. However, more memory is not always
better. Too much memory may also cause the decoherence.


title: Thermalization dynamics of macroscopic weakly nonintegrable maps

abstract:
  We study thermalization of weakly nonintegrable nonlinear unitary lattice
dynamics. We identify two distinct thermalization regimes close to the
integrable limits of either linear dynamics or disconnected lattice dynamics.
For weak nonlinearity the almost conserved actions correspond to extended
observables which are coupled into a long-range network. For weakly connected
lattices the corresponding local observables are coupled into a short-range
network. We compute the evolution of the variance $\sigma^2(T)$ of finite time
average distributions for extended and local observables. We extract the
ergodization time scale $T_E$ which marks the onset of thermalization, and
determine the type of network through the subsequent decay of $\sigma^2(T)$. We
use the complementary analysis of Lyapunov spectra (arXiv:2109.01361) and
compare the Lyapunov time $T_{\Lambda}$ with $T_E$. We characterize the spatial
properties of the tangent vector and arrive at a complete classification
picture of weakly nonintegrable macroscopic thermalization dynamics.


title: Optical Flow for Video Super-Resolution: A Survey

abstract:
  Video super-resolution is currently one of the most active research topics in
computer vision as it plays an important role in many visual applications.
Generally, video super-resolution contains a significant component, i.e.,
motion compensation, which is used to estimate the displacement between
successive video frames for temporal alignment. Optical flow, which can supply
dense and sub-pixel motion between consecutive frames, is among the most common
ways for this task. To obtain a good understanding of the effect that optical
flow acts in video super-resolution, in this work, we conduct a comprehensive
review on this subject for the first time. This investigation covers the
following major topics: the function of super-resolution (i.e., why we require
super-resolution); the concept of video super-resolution (i.e., what is video
super-resolution); the description of evaluation metrics (i.e., how (video)
superresolution performs); the introduction of optical flow based video
super-resolution; the investigation of using optical flow to capture temporal
dependency for video super-resolution. Prominently, we give an in-depth study
of the deep learning based video super-resolution method, where some
representative algorithms are analyzed and compared. Additionally, we highlight
some promising research directions and open issues that should be further
addressed.


title:
Unidirectional Thin Adapter for Efficient Adaptation of Deep Neural
  Networks

abstract:
  In this paper, we propose a new adapter network for adapting a pre-trained
deep neural network to a target domain with minimal computation. The proposed
model, unidirectional thin adapter (UDTA), helps the classifier adapt to new
data by providing auxiliary features that complement the backbone network. UDTA
takes outputs from multiple layers of the backbone as input features but does
not transmit any feature to the backbone. As a result, UDTA can learn without
computing the gradient of the backbone, which saves computation for training
significantly. In addition, since UDTA learns the target task without modifying
the backbone, a single backbone can adapt to multiple tasks by learning only
UDTAs separately. In experiments on five fine-grained classification datasets
consisting of a small number of samples, UDTA significantly reduced computation
and training time required for backpropagation while showing comparable or even
improved accuracy compared with conventional adapter models.


title:
Concentrations for nonlinear Schrodinger equation with magnetic
  potentials and constant electric potentials

abstract:
  This paper studies the concentration phenomena to nonlinear Schrodinger
equations with magnetic potentials and constant electric potentials. We find
that the magnetic field plays an important role in the location of
concentrations if the electric potential is constant. This is a completely new
result compared with the case of non-constant electric potentials.


title:
Inspection-L: A Self-Supervised GNN-Based Money Laundering Detection
  System for Bitcoin

abstract:
  Criminals have become increasingly experienced in using cryptocurrencies,
such as Bitcoin, for money laundering. The use of cryptocurrencies can hide
criminal identities and transfer hundreds of millions of dollars of dirty funds
through their criminal digital wallets. However, this is considered a paradox
because cryptocurrencies are gold mines for open-source intelligence, allowing
law enforcement agencies to have more power in conducting forensic analyses.
This paper proposed Inspection-L, a graph neural network (GNN) framework based
on self-supervised Deep Graph Infomax (DGI), with supervised learning
algorithms, namely Random Forest (RF) to detect illicit transactions for AML.
To the best of our knowledge, our proposal is the first of applying
self-supervised GNNs to the problem of AML in Bitcoin. The proposed method has
been evaluated on the Elliptic dataset and shows that our approach outperforms
the baseline in terms of key classification metrics, which demonstrates the
potential of self-supervised GNN in cryptocurrency illicit transaction
detection.


title:
System design and calibration of SITARA -- a global 21 cm short spacing
  interferometer prototype

abstract:
  Global 21-cm experiments require exquisitely precise calibration of the
measurement systems in order to separate the weak 21-cm signal from Galactic
and extragalactic foregrounds as well as instrumental systematics. Hitherto,
experiments aiming to make this measurement have concentrated on measuring this
signal using the single element approach. However, an alternative approach
based on interferometers with short baselines is expected to alleviate some of
the difficulties associated with a single element approach such as precision
modelling of the receiver noise spectrum. Short spacing Interferometer
Telescope probing cosmic dAwn and epoch of ReionisAtion (SITARA) is a short
spacing interferometer deployed at the Murchison Radio-astronomy Observatory
(MRO). It is intended to be a prototype or a test-bed to gain a better
understanding of interferometry at short baselines, and develop tools to
perform observations and data calibration. In this paper, we provide a
description of the SITARA system and its deployment at the MRO, and discuss
strategies developed to calibrate SITARA. We touch upon certain systematics
seen in SITARA data and their modelling. We find that SITARA has sensitivity to
all sky signals as well as non-negligible noise coupling between the antennas.
It is seen that the coupled receiver noise has a spectral shape that broadly
matches the theoretical calculations reported in prior works. We also find that
when appropriately modified antenna radiation patterns taking into account the
effects of mutual coupling are used, the measured data are well modelled by the
standard visibility equation.


title:
A Kastler-Kalau-Walze type theorem for the J-twist D_J of the Dirac
  operator

abstract:
  In this paper, we give a Lichnerowicz type formula for the J-twist D_J of the
Dirac operator. And we prove a Kastler-Kalau-Walze type theorem for the J-twist
D_J of the Dirac operator on 3-dimensional and 4-dimensional almost product
Riemannian spin manifold with boundary.


title: Anomalous dimension and quasinormal modes of flavor branes

abstract:
  We study scalar quasinormal modes in a D3/D7 system holographically dual to a
quantum field theory with chiral symmetry breaking at finite temperature. We
find a pure imaginary mode for massless quarks which depends on the anomalous
dimension of quark condensate. This mode becomes tachyonic for massive quarks
and we show that there is a critical momentum above which the system is stable.
Then we turn on a pseudoscalar field and using a simple ansatz study its effect
on the quasinormal modes of the scalar field. By varying the parameter of the
non trivial dilaton profile in the model, we qualitatively study quasinormal
modes in walking theories.


title: Improving Randomization Tests under Interference Based on Power Analysis

abstract:
  In causal inference, we can consider a situation in which treatment on one
unit affects others, i.e., interference exists. In the presence of
interference, we cannot perform a classical randomization test directly because
a null hypothesis is not sharp. Instead, we need to perform the randomization
test restricted to a subset of units and assignments that makes the null
hypothesis sharp. A previous study constructed a useful testing method, a
biclique test, by reducing the selection of the appropriate subsets to
searching for bicliques in a bipartite graph. However, since the power depends
on the features of selected subsets, there is still room to improve the power
by refining the selection procedure. In this paper, we propose a method to
improve the biclique test based on a power evaluation of the randomization
test. We explicitly derived an expression for the power of the randomization
test under several assumptions and found that a certain quantity calculated
from a given assignment set characterizes the power. Based on this fact, we
propose a method to improve the power of the biclique test by modifying the
selection rule for subsets of units and assignments. Through a simulation with
a spatial interference setting, we confirm that the proposed method has higher
power than the existing method.


title:
EdgeMatrix: A Resources Redefined Edge-Cloud System for Prioritized
  Services

abstract:
  The edge-cloud system has the potential to combine the advantages of
heterogeneous devices and truly realize ubiquitous computing. However, for
service providers to guarantee the Service-Level-Agreement (SLA) priorities,
the complex networked environment brings inherent challenges such as
multi-resource heterogeneity, resource competition, and networked system
dynamics. In this paper, we design a framework for the edge-cloud system,
namely EdgeMatrix, to maximize the throughput while guaranteeing various SLA
priorities. First, EdgeMatrix introduces Networked Multi-agent Actor-Critic
(NMAC) algorithm to redefine physical resources as logically isolated resource
combinations, i.e., resource cells. Then, we use a clustering algorithm to
group the cells with similar characteristics into various sets, i.e., resource
channels, for different channels can offer different SLA guarantees. Besides,
we design a multi-task mechanism to solve the problem of joint service
orchestration and request dispatch (JSORD) among edge-cloud clusters,
significantly reducing the runtime than traditional methods. To ensure
stability, EdgeMatrix adopts a two-time-scale framework, i.e., coordinating
resources and services at the large time scale and dispatching requests at the
small time scale. The real trace-based experimental results verify that
EdgeMatrix can improve system throughput in complex networked environments,
reduce SLA violations, and significantly reduce the runtime than traditional
methods.


title:
Stratigraphic and Isotopic Evolution of the Martian Polar Caps from
  Paleo-Climate Models

abstract:
  Exposed scarps images and ice-penetrating radar measurements in the North
Polar Layered Deposits (NPLD) of Mars show alternating layers that provide an
archive of past climate oscillations, that are thought to be linked to orbital
variations, akin to Milankovitch cycles on Earth. We use the Laboratoire de
Meteorologie Dynamique (LMD) Martian Global Climate Model (GCM) to study
paleoclimate states to enable a better interpretation of the NPLD physical and
chemical stratigraphy. When a tropical ice reservoir is present, water vapor
transport from the tropics to the poles at low obliquity is modulated by the
intensity of summer. At times of low and relatively constant obliquity, the
flux still varies due to other orbital elements, promoting polar layer
formation. Ice migrates from the tropics towards the poles in two stages.
First, when surface ice is present in the tropics, and second, when the
equatorial deposit is exhausted, from ice that was previously deposited in
mid-high latitudes. The polar accumulation rate is significantly higher when
tropical ice is available, forming thicker layers per orbital cycle. However,
the majority of the NPLD is sourced from ice that temporary resided in the
mid-high latitudes and the layers become thinner as the source location moves
poleward. The migration stages imprint different D/H ratios in different
sections in the PLDs. The NPLD is isotopically depleted compared to the SPLD in
all simulations. Thus we predict the D/H ratio of the atmosphere in contact
with NPLD upper layers is biased relative to the average global ice reservoirs.


title:
Federated Spatial Reuse Optimization in Next-Generation Decentralized
  IEEE 802.11 WLANs

abstract:
  As wireless standards evolve, more complex functionalities are introduced to
address the increasing requirements in terms of throughput, latency, security,
and efficiency. To unleash the potential of such new features, artificial
intelligence (AI) and machine learning (ML) are currently being exploited for
deriving models and protocols from data, rather than by hand-programming. In
this paper, we explore the feasibility of applying ML in next-generation
wireless local area networks (WLANs). More specifically, we focus on the IEEE
802.11ax spatial reuse (SR) problem and predict its performance through
federated learning (FL) models. The set of FL solutions overviewed in this work
is part of the 2021 International Telecommunication Union (ITU) AI for 5G
Challenge.


title: ECAPA-TDNN for Multi-speaker Text-to-speech Synthesis

abstract:
  In recent years, neural network based methods for multi-speaker
text-to-speech synthesis (TTS) have made significant progress. However, the
current speaker encoder models used in these methods still cannot capture
enough speaker information. In this paper, we focus on accurate speaker encoder
modeling and propose an end-to-end method that can generate high-quality speech
and better similarity for both seen and unseen speakers. The proposed
architecture consists of three separately trained components: a speaker encoder
based on the state-of-the-art ECAPA-TDNN model which is derived from speaker
verification task, a FastSpeech2 based synthesizer, and a HiFi-GAN vocoder. The
comparison among different speaker encoder models shows our proposed method can
achieve better naturalness and similarity. To efficiently evaluate our
synthesized speech, we are the first to adopt deep learning based automatic MOS
evaluation methods to assess our results, and these methods show great
potential in automatic speech quality assessment.


title: Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data

abstract:
  In portraits, eyeglasses may occlude facial regions and generate cast shadows
on faces, which degrades the performance of many techniques like face
verification and expression recognition. Portrait eyeglasses removal is
critical in handling these problems. However, completely removing the
eyeglasses is challenging because the lighting effects (e.g., cast shadows)
caused by them are often complex. In this paper, we propose a novel framework
to remove eyeglasses as well as their cast shadows from face images. The method
works in a detect-then-remove manner, in which eyeglasses and cast shadows are
both detected and then removed from images. Due to the lack of paired data for
supervised training, we present a new synthetic portrait dataset with both
intermediate and final supervisions for both the detection and removal tasks.
Furthermore, we apply a cross-domain technique to fill the gap between the
synthetic and real data. To the best of our knowledge, the proposed technique
is the first to remove eyeglasses and their cast shadows simultaneously. The
code and synthetic dataset are available at
https://github.com/StoryMY/take-off-eyeglasses.


title:
Anisotropy of Magnetic Field Spectra at Kinetic Scales of Solar Wind
  Turbulence as Revealed by Parker Solar Probe in the Inner Heliosphere

abstract:
  Using the Parker Solar Probe data taken in the inner heliosphere, we
investigate the power and spatial anisotropy of magnetic-field spectra at
kinetic scales (i.e., around sub-ion scales) in solar wind turbulence in the
inner heliosphere. We find that strong anisotropy of magnetic spectra occurs at
kinetic scales with the strongest power in the perpendicular direction with
respect to the local magnetic field (forming an angle theta_B with the mean
flow velocity). The spectral index of magnetic spectra varies from -3.2 to -5.8
when the angle theta_B changes from 90 to 180 (or 0) deg, indicating that
strong anisotropy of the spectral indices occurs at kinetic scales in the solar
wind turbulence. Using a diagnosis based on the magnetic helicity, we show that
the anisotropy of the spectral indices can be explained by the nature of the
plasma modes that carry the cascade at kinetic scales. We discuss our findings
in light of existing theories and current development in the field.


title: Revisiting the Design Patterns of Composite Visualizations

abstract:
  Composite visualization is a popular design strategy that represents complex
datasets by integrating multiple visualizations in a meaningful and aesthetic
layout, such as juxtaposition, overlay, and nesting. With this strategy,
numerous novel designs have been proposed in visualization publications to
accomplish various visual analytic tasks. These well-crafted composite
visualizations have formed a valuable collection for designers and researchers
to address real-world problems and inspire new research topics and designs.
However, there is a lack of understanding of design patterns of composite
visualization, thus failing to provide holistic design space and concrete
examples for practical use. In this paper, we opted to revisit the composite
visualizations in VIS publications and answered what and how visualizations of
different types are composed together. To achieve this, we first constructed a
corpus of composite visualizations from IEEE VIS publications and decomposed
them into a series of basic visualization types (e.g., bar chart, map, and
matrix). With this corpus, we studied the spatial (e.g., separated or
overlaying) and semantic relationships (e.g., with same types or shared axis)
between visualizations and proposed a taxonomy consisting of eight different
design patterns (e.g., repeated, stacked, accompanied, and nested).
Furthermore, we analyzed and discussed common practices of composite
visualizations, such as the distribution of different patterns and correlations
between visualization types. From the analysis and examples, we obtained
insights into different design patterns on the utilities, advantages, and
disadvantages. Finally, we developed an interactive system to help
visualization developers and researchers conveniently explore collected
examples and design patterns.


title: Relative Schwarz Method

abstract:
  This paper proposes Relative Schwarz Method, which is a new domain
decomposition method for wave equation.


title: The knot invariant associated to two-parameter quantum algebras

abstract:
  Using the skew-Hopf pairing, we obtain $\mathcal{R}$-matrix for the
two-parameter quantum algebra $U_{v,t}$. We further construct a strict monoidal
functor $\mathcal{T}$ from the tangle category $(\mathrm{OTa},\otimes,
\emptyset)$ to the category $(\mathrm{Mod},\otimes, \mathbb{Q}(v,t))$ of
$U_{v,t}$-modules . As a consequence, the quantum knot invariant of the tangle
$L$ of type $(n,n)$ is obtained by the action of $\mathcal{T}$ on the closure
$\tilde{L}$ of $L$.


title:
Optimizing Camera Placements for Overlapped Coverage with 3D Camera
  Projections

abstract:
  This paper proposes a method to compute camera 6Dof poses to achieve a user
defined coverage. The camera placement problem is modeled as a combinatorial
optimization where given the maximum number of cameras, a camera set is
selected from a larger pool of possible camera poses. We propose to minimize
the squared error between the desired and the achieved coverage, and formulate
the non-linear cost function as a mixed integer linear programming problem. A
camera lens model is utilized to project the cameras view on a 3D voxel map to
compute a coverage score which makes the optimization problem in real
environments tractable. Experimental results in two real retail store
environments demonstrate the better performance of the proposed formulation in
terms of coverage and overlap for triangulation compared to existing methods.


title:
Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A
  Survey

abstract:
  In recent years, the prevalent online services generate a sheer volume of
user activity data. Service providers collect these data in order to perform
client behavior analysis, and offer better and more customized services.
Majority of these data can be modeled and stored as graph, such as the social
graph in Facebook, user-video interaction graph in Youtube. These graphs need
to evolve over time to capture the dynamics in the real world, leading to the
invention of dynamic graphs. However, the temporal information embedded in the
dynamic graphs brings new challenges in analyzing and deploying them. Events
staleness, temporal information learning and explicit time dimension usage are
some example challenges in dynamic graph learning. In order to offer a
convenient reference to both the industry and academia, this survey presents
the Three Stages Recurrent Temporal Learning Framework based on dynamic graph
evolution theories, so as to interpret the learning of temporal information
with a generalized framework. Under this framework, this survey categories and
reviews different learnable encoder-decoder architectures for supervised
dynamic graph learning. We believe that this survey could supply useful
guidelines to researchers and engineers in finding suitable graph structures
for their dynamic learning tasks.


title:
Linear-Quadratic Large-Population Problem with Partial Information:
  Hamiltonian Approach and Riccati Approach

abstract:
  This paper studies a class of partial information linear-quadratic mean-field
game problems. A general stochastic large-population system is considered,
where the diffusion term of the dynamic of each agent can depend on the state
and control. We study both the control constrained case and unconstrained case.
In control constrained case, by using Hamiltonian approach and convex analysis,
the explicit decentralized strategies can be obtained through projection
operator. The corresponding Hamiltonian type consistency condition system is
derived, which turns out to be a nonlinear mean-field forward-backward
stochastic differential equation with projection operator. The well-posedness
of such kind of equations is proved by using discounting method. Moreover, the
corresponding $\varepsilon$-Nash equilibrium property is verified. In control
unconstrained case, the decentralized strategies can be further represented
explicitly as the feedback of filtered state through Riccati approach. The
existence and uniqueness of a solution to a new Riccati type consistency
condition system is also discussed. As an application, a general inter-bank
borrowing and lending problem is studied to illustrate that the effect of
partial information cannot be ignored.


title:
DEIM: An effective deep encoding and interaction model for sentence
  matching

abstract:
  Natural language sentence matching is the task of comparing two sentences and
identifying the relationship between them.It has a wide range of applications
in natural language processing tasks such as reading comprehension, question
and answer systems. The main approach is to compute the interaction between
text representations and sentence pairs through an attention mechanism, which
can extract the semantic information between sentence pairs well. However,this
kind of method can not gain satisfactory results when dealing with complex
semantic features. To solve this problem, we propose a sentence matching method
based on deep encoding and interaction to extract deep semantic information. In
the encoder layer,we refer to the information of another sentence in the
process of encoding a single sentence, and later use a heuristic algorithm to
fuse the information. In the interaction layer, we use a bidirectional
attention mechanism and a self-attention mechanism to obtain deep semantic
information.Finally, we perform a pooling operation and input it to the MLP for
classification. we evaluate our model on three tasks: recognizing textual
entailment, paraphrase recognition, and answer selection. We conducted
experiments on the SNLI and SciTail datasets for the recognizing textual
entailment task, the Quora dataset for the paraphrase recognition task, and the
WikiQA dataset for the answer selection task. The experimental results show
that the proposed algorithm can effectively extract deep semantic features that
verify the effectiveness of the algorithm on sentence matching tasks.


title: Entailment Relation Aware Paraphrase Generation

abstract:
  We introduce a new task of entailment relation aware paraphrase generation
which aims at generating a paraphrase conforming to a given entailment relation
(e.g. equivalent, forward entailing, or reverse entailing) with respect to a
given input. We propose a reinforcement learning-based weakly-supervised
paraphrasing system, ERAP, that can be trained using existing paraphrase and
natural language inference (NLI) corpora without an explicit task-specific
corpus. A combination of automated and human evaluations show that ERAP
generates paraphrases conforming to the specified entailment relation and are
of good quality as compared to the baselines and uncontrolled paraphrasing
systems. Using ERAP for augmenting training data for downstream textual
entailment task improves performance over an uncontrolled paraphrasing system,
and introduces fewer training artifacts, indicating the benefit of explicit
control during paraphrasing.


title: Hierarchical Inductive Transfer for Continual Dialogue Learning

abstract:
  Pre-trained models have achieved excellent performance on the dialogue task.
However, for the continual increase of online chit-chat scenarios, directly
fine-tuning these models for each of the new tasks not only explodes the
capacity of the dialogue system on the embedded devices but also causes
knowledge forgetting on pre-trained models and knowledge interference among
diverse dialogue tasks. In this work, we propose a hierarchical inductive
transfer framework to learn and deploy the dialogue skills continually and
efficiently. First, we introduce the adapter module into pre-trained models for
learning new dialogue tasks. As the only trainable module, it is beneficial for
the dialogue system on the embedded devices to acquire new dialogue skills with
negligible additional parameters. Then, for alleviating knowledge interference
between tasks yet benefiting the regularization between them, we further design
hierarchical inductive transfer that enables new tasks to use general knowledge
in the base adapter without being misled by diverse knowledge in task-specific
adapters. Empirical evaluation and analysis indicate that our framework obtains
comparable performance under deployment-friendly model capacity.


title:
Twisted Angle-Dependent Work Functions in CVD-Grown Twisted Bilayer
  Graphene by Kelvin Probe Force Microscopy

abstract:
  Tailoring the interlayer twist angle of bilayer graphene (BLG) has a
significant influence on its electronic properties, including
superconductivity, topological transitions, ferromagnetic states and correlated
insulating states. These exotic electronic properties are sensitively dependent
on the work functions of bilayer graphene samples. Here, the twisted
angle-dependent work functions of CVD-grown twisted bilayer graphene (tBLG) are
detailed investigated by Kelvin Probe Force Microscopy (KPFM) in combination
with Raman spectra. The thickness-dependent surface potentials of
Bernal-stacked multilayer graphene were measured. The AB-BLG and tBLG are
directly determined by KPFM due to their twist angle-specific surface
potentials. The detailed relationship of twist angles and surface potentials
are further obtained by the in-situ combination investigation of KPFM and Raman
spectra measurements. The thermal stability of tBLG was further explored
through controlled annealing process. Our work provides the twisted
angle-dependent surface potentials of tBLG and lays the foundation for further
exploring their twist-angle-dependent novel electronic properties.


title:
PIMDB: Understanding Bulk-Bitwise Processing In-Memory Through Database
  Analytics

abstract:
  Bulk-bitwise processing-in-memory (PIM), where large bitwise operations are
performed in parallel by the memory array itself, is an emerging form of
computation with the potential to mitigate the memory wall problem. This paper
examines the capabilities of bulk-bitwise PIM by constructing PIMDB, a
fully-digital system based on memristive stateful logic, utilizing and focusing
on in-memory bulk-bitwise operations, designed to accelerate a real-life
workload: analytical processing of relational databases. We introduce a host
processor programming model to support bulk-bitwise PIM in virtual memory,
develop techniques to efficiently perform in-memory filtering and aggregation
operations, and present the mapping of the application into the memory. When
tested against an equivalent in-memory database on the same host system, our
approach substantially lowers the number of required memory read operations,
thus accelerating TPC-H filter operations by 1.6$\times$--18$\times$ and full
queries by 56$\times$--608$\times$, while reducing the energy consumption by
1.7$\times$--18.6$\times$ and 0.81$\times$--12$\times$ for these benchmarks,
respectively. Our extensive evaluation uses the gem5 full-system simulation
environment.


title: The Main Sequence of star-forming galaxies across cosmic times

abstract:
  By compiling a comprehensive census of literature studies, we investigate the
evolution of the Main Sequence (MS) of star-forming galaxies (SFGs) in the
widest range of redshift (0 < z < 6) and stellar mass ($10^{8.5}-10^{11.5}$
$M_{\odot}$) ever probed. We convert all observations to a common calibration
and find a remarkable consensus on the variation of the MS shape and
normalization across cosmic time. The relation exhibits a curvature towards the
high stellar masses at all redshifts. The best functional form is governed by
two parameters: the evolution of the normalization and the turn-over mass
($M_0(t)$), which both evolve as a power law of the Universe age. The turn-over
mass determines the MS shape. It marginally evolves with time, making the MS
slightly steeper towards $z\sim4-6$. At stellar masses below $M_0(t)$, SFGs
have a constant specific SFR (sSFR), while above $M_0(t)$ the sSFR is
suppressed. We find that the MS is dominated by central galaxies. This allows
transforming $M_0(t)$ into the corresponding host halo mass. This evolves as
the halo mass threshold between cold and hot accretion regimes, as predicted by
the theory of accretion, where the central galaxy is fed or starved of cold gas
supply, respectively. We, thus, argue that the progressive MS bending as a
function of the Universe age is caused by the lower availability of cold gas in
halos entering the hot accretion phase, in addition to black hole feedback. We
also find qualitatively the same trend in the largest sample of star-forming
galaxies provided by the IllustrisTNG simulation, although we note still a
large discrepancy with respect to observations.


title: Inferring Articulated Rigid Body Dynamics from RGBD Video

abstract:
  Being able to reproduce physical phenomena ranging from light interaction to
contact mechanics, simulators are becoming increasingly useful in more and more
application domains where real-world interaction or labeled data are difficult
to obtain. Despite recent progress, significant human effort is needed to
configure simulators to accurately reproduce real-world behavior. We introduce
a pipeline that combines inverse rendering with differentiable simulation to
create digital twins of real-world articulated mechanisms from depth or RGB
videos. Our approach automatically discovers joint types and estimates their
kinematic parameters, while the dynamic properties of the overall mechanism are
tuned to attain physically accurate simulations. Control policies optimized in
our derived simulation transfer successfully back to the original system, as we
demonstrate on a simulated system. Further, our approach accurately
reconstructs the kinematic tree of an articulated mechanism being manipulated
by a robot, and highly nonlinear dynamics of a real-world coupled pendulum
mechanism.
  Website: https://eric-heiden.github.io/video2sim


title:
TVConv: Efficient Translation Variant Convolution for Layout-aware
  Visual Processing

abstract:
  As convolution has empowered many smart applications, dynamic convolution
further equips it with the ability to adapt to diverse inputs. However, the
static and dynamic convolutions are either layout-agnostic or
computation-heavy, making it inappropriate for layout-specific applications,
e.g., face recognition and medical image segmentation. We observe that these
applications naturally exhibit the characteristics of large intra-image
(spatial) variance and small cross-image variance. This observation motivates
our efficient translation variant convolution (TVConv) for layout-aware visual
processing. Technically, TVConv is composed of affinity maps and a
weight-generating block. While affinity maps depict pixel-paired relationships
gracefully, the weight-generating block can be explicitly overparameterized for
better training while maintaining efficient inference. Although conceptually
simple, TVConv significantly improves the efficiency of the convolution and can
be readily plugged into various network architectures. Extensive experiments on
face recognition show that TVConv reduces the computational cost by up to 3.1x
and improves the corresponding throughput by 2.3x while maintaining a high
accuracy compared to the depthwise convolution. Moreover, for the same
computation cost, we boost the mean accuracy by up to 4.21%. We also conduct
experiments on the optic disc/cup segmentation task and obtain better
generalization performance, which helps mitigate the critical data scarcity
issue. Code is available at https://github.com/JierunChen/TVConv.


title: The Shadow Formalism of Galilean CFT$_2$

abstract:
  In this work, we develop the shadow formalism for two-dimensional Galilean
conformal field theory (GCFT$_2$). We define the principal series
representation of Galilean conformal symmetry group and find its relation with
the Wigner classification, then we determine the shadow transform of local
operators. Using this formalism we derive the OPE blocks, Clebsch-Gordan
kernels, conformal blocks and conformal partial waves. A new feature is that
the conformal block admits additional branch points, which would destroy the
convergence of OPE for certain parameters. We establish another inversion
formula different from the previous one, but get the same result when
decomposing the four-point functions in the mean field theory (MFT). We also
construct a continuous series of bilocal actions of MFT, and an exceptional
series of local actions, one of which is the BMS free scalar model. We notice
that there is an outer automorphism of the Galilean conformal symmetry, and the
GCFT$_2$ can be regarded as null defect in higher dimensional CFTs.


title:
Identification of Coronal Holes on AIA/SDO images using unsupervised
  Machine Learning

abstract:
  Through its magnetic activity, the Sun governs the conditions in Earth's
vicinity, creating space weather events, which have drastic effects on our
space- and ground-based technology. One of the most important solar magnetic
features creating the space weather is the solar wind, that originates from the
coronal holes (CHs). The identification of the CHs on the Sun as one of the
source regions of the solar wind is therefore crucial to achieve predictive
capabilities. In this study, we used an unsupervised machine learning method,
$k$-means, to pixel-wise cluster the passband images of the Sun taken by the
Atmospheric Imaging Assembly on {\it the Solar Dynamics Observatory} (AIA/SDO)
in 171 \AA, 193 \AA\,, and 211 \AA\,in different combinations. Our results show
that the pixel-wise $k$-means clustering together with systematic pre- and
post-processing steps provides compatible results with those from complex
methods, such as CNNs. More importantly, our study shows that there is a need
for a CH database that a consensus about the CH boundaries are reached by
observers independently. This database then can be used as the "ground truth",
when using a supervised method or just to evaluate the goodness of the models.


title:
SimAN: Exploring Self-Supervised Representation Learning of Scene Text
  via Similarity-Aware Normalization

abstract:
  Recently self-supervised representation learning has drawn considerable
attention from the scene text recognition community. Different from previous
studies using contrastive learning, we tackle the issue from an alternative
perspective, i.e., by formulating the representation learning scheme in a
generative manner. Typically, the neighboring image patches among one text line
tend to have similar styles, including the strokes, textures, colors, etc.
Motivated by this common sense, we augment one image patch and use its
neighboring patch as guidance to recover itself. Specifically, we propose a
Similarity-Aware Normalization (SimAN) module to identify the different
patterns and align the corresponding styles from the guiding patch. In this
way, the network gains representation capability for distinguishing complex
patterns such as messy strokes and cluttered backgrounds. Experiments show that
the proposed SimAN significantly improves the representation quality and
achieves promising performance. Moreover, we surprisingly find that our
self-supervised generative network has impressive potential for data synthesis,
text image editing, and font interpolation, which suggests that the proposed
SimAN has a wide range of practical applications.


title:
Depth Estimation by Combining Binocular Stereo and Monocular
  Structured-Light

abstract:
  It is well known that the passive stereo system cannot adapt well to weak
texture objects, e.g., white walls. However, these weak texture targets are
very common in indoor environments. In this paper, we present a novel stereo
system, which consists of two cameras (an RGB camera and an IR camera) and an
IR speckle projector. The RGB camera is used both for depth estimation and
texture acquisition. The IR camera and the speckle projector can form a
monocular structured-light (MSL) subsystem, while the two cameras can form a
binocular stereo subsystem. The depth map generated by the MSL subsystem can
provide external guidance for the stereo matching networks, which can improve
the matching accuracy significantly. In order to verify the effectiveness of
the proposed system, we build a prototype and collect a test dataset in indoor
scenes. The evaluation results show that the Bad 2.0 error of the proposed
system is 28.2% of the passive stereo system when the network RAFT is used. The
dataset and trained models are available at
https://github.com/YuhuaXu/MonoStereoFusion.


title: MicroRacer: a didactic environment for Deep Reinforcement Learning

abstract:
  MicroRacer is a simple, open source environment inspired by car racing
especially meant for the didactics of Deep Reinforcement Learning. The
complexity of the environment has been explicitly calibrated to allow users to
experiment with many different methods, networks and hyperparameters settings
without requiring sophisticated software or the need of exceedingly long
training times. Baseline agents for major learning algorithms such as DDPG,
PPO, SAC, TD2 and DSAC are provided too, along with a preliminary comparison in
terms of training time and performance.


title:
On a characterization of exponential and double exponential
  distributions

abstract:
  Recently, G.~Yanev obtained a characterization of the exponential family of
distributions in terms of a functional equation for certain mixture densities.
The purpose of this note is twofold: we extend Yanev's theorem by relaxing a
restriction on the sign of mixture coefficients and, in addition, obtain a
similar characterization for the Laplace family of distributions.


title:
NeuralReshaper: Single-image Human-body Retouching with Deep Neural
  Networks

abstract:
  In this paper, we present NeuralReshaper, a novel method for semantic
reshaping of human bodies in single images using deep generative networks. To
achieve globally coherent reshaping effects, our approach follows a
fit-then-reshape pipeline, which first fits a parametric 3D human model to a
source human image and then reshapes the fitted 3D model with respect to
user-specified semantic attributes. Previous methods rely on image warping to
transfer 3D reshaping effects to the entire image domain and thus often cause
distortions in both foreground and background. In contrast, we resort to
generative adversarial nets conditioned on the source image and a 2D warping
field induced by the reshaped 3D model, to achieve more realistic reshaping
results. Specifically, we separately encode the foreground and background
information in the source image using a two-headed UNet-like generator, and
guide the information flow from the foreground branch to the background branch
via feature space warping. Furthermore, to deal with the lack-of-data problem
that no paired data exist (i.e., the same human bodies in varying shapes), we
introduce a novel self-supervised strategy to train our network. Unlike
previous methods that often require manual efforts to correct undesirable
artifacts caused by incorrect body-to-image fitting, our method is fully
automatic. Extensive experiments on both indoor and outdoor datasets
demonstrate the superiority of our method over previous approaches.


title: Fundamental Trackability Problems for Iterative Learning Control

abstract:
  Generally, the classic iterative learning control (ILC) methods focus on
finding design conditions for repetitive systems to achieve the perfect
tracking of any specified trajectory, whereas they ignore a fundamental problem
of ILC: whether the specified trajectory is trackable, or equivalently, whether
there exist some inputs for the repetitive systems under consideration to
generate the specified trajectory? The current paper contributes to dealing
with this problem. Not only is a concept of trackability introduced formally
for any specified trajectory in ILC, but also some related trackability
criteria are established. Further, the relation between the trackability and
the perfect tracking tasks for ILC is bridged, based on which a new convergence
analysis approach is developed for ILC by leveraging properties of a functional
Cauchy sequence (FCS). Simulation examples are given to verify the
effectiveness of the presented trackability criteria and FCS-induced
convergence analysis method for ILC.


title: Generating Task-specific Robotic Grasps

abstract:
  This paper describes a method for generating robot grasps by jointly
considering stability and other task and object-specific constraints. We
introduce a three-level representation that is acquired for each object class
from a small number of exemplars of objects, tasks, and relevant grasps. The
representation encodes task-specific knowledge for each object class as a
relationship between a keypoint skeleton and suitable grasp points that is
preserved despite intra-class variations in scale and orientation. The learned
models are queried at run time by a simple sampling-based method to guide the
generation of grasps that balance task and stability constraints. We ground and
evaluate our method in the context of a Franka Emika Panda robot assisting a
human in picking tabletop objects for which the robot does not have prior CAD
models. Experimental results demonstrate that in comparison with a baseline
method that only focuses on stability, our method is able to provide suitable
grasps for different tasks.


title:
Anisotropic and tunable optical conductivity of a two-dimensional
  semi-Dirac system in the presence of elliptically polarized radiation

abstract:
  We investigate the effect of ellipticity ratio of the polarized radiation
field on optoelectronic properties of a two-dimensional (2D) semi-Dirac (SD)
system. The optical conductivity is calculated within the energy balance
equation approach derived from the semiclassical Boltzmann equation. We find
that there exists the anisotropic optical absorption induced via both the
intra- and interband electronic transition channels in the perpendicular $xx$
and $yy$ directions. Furthermore, we examine the effects of the ellipticity
ratio, the temperature, the carrier density, and the band-gap parameter on the
optical conductivity of the 2D SD system placed in transverse and vertical
directions, respectively. It is shown that the ellipticity ratio, temperature,
carrier density, and band-gap parameter can play the important roles in tuning
the strength, peak position, and shape of the optical conductivity spectrum.
The results obtained from this study indicate that the 2D SD system can be a
promising anisotropic and tunable optical and optoelectronic material for
applications in innovative 2D optical and optoelectronic devices, which are
active in the infrared and terahertz bandwidths.


title:
Some examples of equivalent rearrangement-invariant quasi-norms defined
  via f* or f**

abstract:
  We consider Lorentz-Karamata spaces, small and grand Lorentz-Karamata spaces,
and the so-called $\mathcal{L}$, $\mathcal{R}$, $\mathcal{LL}$, $\mathcal{RL}$,
$\mathcal{RL}$, and $\mathcal{RR}$ spaces. The quasi-norms for a function $f$
in each of these spaces can be defined via the non-increasing rearrangement
$f^*$ or via the maximal function $f^{**}$. We investigate when these
quasi-norms are equivalent. Most of the proofs are based on Hardy-type
inequalities. As application we demonstrate how our general results can be used
to establish interpolation formulae for the grand and small Lorentz-Karamata
spaces.


title:
Quantum Fluctuation-Response Inequality and Its Application in Quantum
  Hypothesis Testing

abstract:
  We uncover the quantum fluctuation-response inequality, which, in the most
general setting, establishes a bound for the mean difference of an observable
at two different quantum states, in terms of the quantum relative entropy. When
the spectrum of the observable is bounded, the sub-Gaussian property is used to
further our result by explicitly linking the bound with the sub-Gaussian norm
of the observable, based on which we derive a novel bound for the sum of
statistical errors in quantum hypothesis testing. This error bound holds
nonasymptotically and is stronger and more informative than that based on
quantum Pinsker's inequality. We also show the versatility of our results by
their applications in problems like thermodynamic inference and speed limit.


title: Adversarial Parameter Attack on Deep Neural Networks

abstract:
  In this paper, a new parameter perturbation attack on DNNs, called
adversarial parameter attack, is proposed, in which small perturbations to the
parameters of the DNN are made such that the accuracy of the attacked DNN does
not decrease much, but its robustness becomes much lower. The adversarial
parameter attack is stronger than previous parameter perturbation attacks in
that the attack is more difficult to be recognized by users and the attacked
DNN gives a wrong label for any modified sample input with high probability.
The existence of adversarial parameters is proved. For a DNN $F_{\Theta}$ with
the parameter set $\Theta$ satisfying certain conditions, it is shown that if
the depth of the DNN is sufficiently large, then there exists an adversarial
parameter set $\Theta_a$ for $\Theta$ such that the accuracy of $F_{\Theta_a}$
is equal to that of $F_{\Theta}$, but the robustness measure of $F_{\Theta_a}$
is smaller than any given bound. An effective training algorithm is given to
compute adversarial parameters and numerical experiments are used to
demonstrate that the algorithms are effective to produce high quality
adversarial parameters.


title: On wall-crossing invariance of certain sums of Welschinger numbers

abstract:
  We continue our quest for real enumerative invariants not sensitive to
changing the real structure and extend the construction we uncovered previously
for counting curves of anti-canonical degree $\leqslant 2$ on del Pezzo
surfaces with $K^2=1$ to curves of any anti-canonical degree and on any del
Pezzo surfaces of degree $K^2\leqslant 3$.


title: Note on a Fibonacci Parity Sequence

abstract:
  Let ftm = 0111010010001... be the analogue of the Thue-Morse sequence in
Fibonacci representation. In this note we show how, using the Walnut
theorem-prover, to obtain a measure of its complexity, previously studied by
Jamet, Popoli, and Stoll. We strengthen one of their theorems and disprove one
of their conjectures.