title:
Information criteria for detecting change-points in the Cox proportional
  hazards model

abstract:
  The Cox proportional hazards model, commonly used in clinical trials, assumes
proportional hazards. However, it does not hold when, for example, there is a
delayed onset of the treatment effect. In such a situation, an acute change in
the hazard ratio function is expected to exist. This paper considers the Cox
model with change-points and derives AIC-type information criteria for
detecting those change-points. The change-point model does not allow for
conventional statistical asymptotics due to its irregularity, thus a formal AIC
that penalizes twice the number of parameters would not be analytically
derived, and using it would clearly give overfitting analysis results.
Therefore, we will construct specific asymptotics using the partial likelihood
estimation method in the Cox model with change-points. Based on the original
derivation method for AIC, we propose information criteria that are
mathematically guaranteed. If the partial likelihood is used in the estimation,
information criteria with penalties much larger than twice the number of
parameters could be obtained in an explicit form. Numerical experiments confirm
that the proposed criterion is clearly superior in terms of the original
purpose of AIC, which is to provide an estimate that is close to the true
structure. We also apply the proposed criterion to actual clinical trial data
to indicate that it will easily lead to different results from the formal AIC.


title: Multi-scale Speaker Diarization with Dynamic Scale Weighting

abstract:
  Speaker diarization systems are challenged by a trade-off between the
temporal resolution and the fidelity of the speaker representation. By
obtaining a superior temporal resolution with an enhanced accuracy, a
multi-scale approach is a way to cope with such a trade-off. In this paper, we
propose a more advanced multi-scale diarization system based on a multi-scale
diarization decoder. There are two main contributions in this study that
significantly improve the diarization performance. First, we use multi-scale
clustering as an initialization to estimate the number of speakers and obtain
the average speaker representation vector for each speaker and each scale.
Next, we propose the use of 1-D convolutional neural networks that dynamically
determine the importance of each scale at each time step. To handle a variable
number of speakers and overlapping speech, the proposed system can estimate the
number of existing speakers. Our proposed system achieves a state-of-art
performance on the CALLHOME and AMI MixHeadset datasets, with 3.92% and 1.05%
diarization error rates, respectively.


title:
Device-Directed Speech Detection: Regularization via Distillation for
  Weakly-Supervised Models

abstract:
  We address the problem of detecting speech directed to a device that does not
contain a specific wake-word. Specifically, we focus on audio coming from a
touch-based invocation. Mitigating virtual assistants (VAs) activation due to
accidental button presses is critical for user experience. While the majority
of approaches to false trigger mitigation (FTM) are designed to detect the
presence of a target keyword, inferring user intent in absence of keyword is
difficult. This also poses a challenge when creating the training/evaluation
data for such systems due to inherent ambiguity in the user's data. To this
end, we propose a novel FTM approach that uses weakly-labeled training data
obtained with a newly introduced data sampling strategy. While this sampling
strategy reduces data annotation efforts, the data labels are noisy as the data
are not annotated manually. We use these data to train an acoustics-only model
for the FTM task by regularizing its loss function via knowledge distillation
from an ASR-based (LatticeRNN) model. This improves the model decisions,
resulting in 66% gain in accuracy, as measured by equal-error-rate (EER), over
the base acoustics-only model. We also show that the ensemble of the LatticeRNN
and acoustic-distilled models brings further accuracy improvement of 20%.


title:
Deterministic distribution of orbital angular momentum multiplexed
  continuous-variable entanglement and quantum steering

abstract:
  Orbital angular momentum (OAM) multiplexing provides an efficient method to
improve data-carrying capacity in various quantum communication protocols. It
is a precondition to distribute OAM multiplexed quantum resources in quantum
channels for implementing quantum communication. However, quantum steering of
OAM multiplexed optical fields and the effect of channel noise on OAM
multiplexed quantum resources remain unclear. Here, we generate OAM multiplexed
continuous-variable (CV) entangled states and distribute them in lossy or noisy
channels. We show that the decoherence property of entanglement and quantum
steering of the OAM multiplexed states carrying topological charges $l=1$ and
$l=2$ are the same as that of the Gaussian mode with $l=0$ in lossy and noisy
channels. The sudden death of entanglement and quantum steering of high-order
OAM multiplexed states is observed in the presence of excess noise. Our results
demonstrate the feasibility to realize high data-carrying capacity quantum
information processing by utilizing OAM multiplexed CV entangled states.


title:
Closed-Form Minkowski Sum Approximations for Efficient
  Optimization-Based Collision Avoidance

abstract:
  Motion planning methods for autonomous systems based on nonlinear programming
offer great flexibility in incorporating various dynamics, objectives, and
constraints. One limitation of such tools is the difficulty of efficiently
representing obstacle avoidance conditions for non-trivial shapes. For example,
it is possible to define collision avoidance constraints suitable for nonlinear
programming solvers in the canonical setting of a circular robot navigating
around M convex polytopes over N time steps. However, it requires introducing
(2+L)MN additional constraints and LMN additional variables, with L being the
number of halfplanes per polytope, leading to larger nonlinear programs with
slower and less reliable solving time. In this paper, we overcome this issue by
building closed-form representations of the collision avoidance conditions by
outer-approximating the Minkowski sum conditions for collision. Our solution
requires only MN constraints (and no additional variables), leading to a
smaller nonlinear program. On motion planning problems for an autonomous car
and quadcopter in cluttered environments, we achieve speedups of 4.8x and 8.7x
respectively with significantly less variance in solve times and negligible
impact on performance arising from the use of outer approximations.


title:
Snowmass 2021: Superconducting Sensor Fabrication Capabilities for HEP
  Science

abstract:
  Superconducting sensors are a key enabling technology for many HEP
experiments with advances in sensor capabilities leading directly to expanded
science reach. The unique materials and processes required for the fabrication
of these sensors makes commercial sourcing impractical in comparison with
semiconducting devices. Subsequently, the development and fabrication of new
sensors are often performed at academic cleanrooms supported through HEP basic
detector research and/or project funds. While this operational model has been
successful to date, we are at a turning point in the history of superconducting
electronics, as evidenced by the rapid growth in the field of quantum
computing, when scale and sophistication of these sensors can lead to
significant progress. In order to achieve this progress and meet the needs of
the next generations of HEP experiments, continued support of all stages of the
superconducting sensors development pipeline is necessary.


title:
A First Look at Duplicate and Near-duplicate Self-admitted Technical
  Debt Comments

abstract:
  Self-admitted technical debt (SATD) refers to technical debt that is
intentionally introduced by developers and explicitly documented in code
comments or other software artifacts (e.g., issue reports) to annotate
sub-optimal decisions made by developers in the software development process.
  In this work, we take the first look at the existence and characteristics of
duplicate and near-duplicate SATD comments in five popular Apache OSS projects,
i.e., JSPWiki, Helix, Jackrabbit, Archiva, and SystemML. We design a method to
automatically identify groups of duplicate and near-duplicate SATD comments and
track their evolution in the software system by mining the commit history of a
software project. Leveraging the proposed method, we identified 3,520 duplicate
and near-duplicate SATD comments from the target projects, which belong to
1,141 groups. We manually analyze the content and context of a sample of 1,505
SATD comments (by sampling 100 groups for each project) and identify if they
annotate the same root cause. We also investigate whether duplicate SATD
comments exist in code clones, whether they co-exist in the same file, and
whether they are introduced and removed simultaneously. Our preliminary study
reveals several surprising findings that would shed light on future studies
aiming to improve the management of duplicate SATD comments. For instance, only
48.5% duplicate SATD comment groups with the same root cause exist in regular
code clones, and only 33.9% of the duplicate SATD comment pairs are introduced
in the same commit.


title: DELTA: Dynamically Optimizing GPU Memory beyond Tensor Recomputation

abstract:
  The further development of deep neural networks is hampered by the limited
GPU memory resource. Therefore, the optimization of GPU memory resources is
highly demanded. Swapping and recomputation are commonly applied to make better
use of GPU memory in deep learning. However, as an emerging domain, several
challenges remain:1)The efficiency of recomputation is limited for both static
and dynamic methods. 2)Swapping requires offloading parameters manually, which
incurs a great time cost. 3) There is no such dynamic and fine-grained method
that involves tensor swapping together with tensor recomputation nowadays. To
remedy the above issues, we propose a novel scheduler manager named
DELTA(Dynamic tEnsor offLoad and recompuTAtion). To the best of our knowledge,
we are the first to make a reasonable dynamic runtime scheduler on the
combination of tensor swapping and tensor recomputation without user oversight.
In DELTA, we propose a filter algorithm to select the optimal tensors to be
released out of GPU memory and present a director algorithm to select a proper
action for each of these tensors. Furthermore, prefetching and overlapping are
deliberately considered to overcome the time cost caused by swapping and
recomputing tensors. Experimental results show that DELTA not only saves
40%-70% of GPU memory, surpassing the state-of-the-art method to a great extent
but also gets comparable convergence results as the baseline with acceptable
time delay. Also, DELTA gains 2.04$\times$ maximum batchsize when training
ResNet-50 and 2.25$\times$ when training ResNet-101 compared with the baseline.
Besides, comparisons between the swapping cost and recomputation cost in our
experiments demonstrate the importance of making a reasonable dynamic scheduler
on tensor swapping and tensor recomputation, which refutes the arguments in
some related work that swapping should be the first and best choice.


title: Spy in the GPU-box: Covert and Side Channel Attacks on Multi-GPU Systems

abstract:
  The deep learning revolution has been enabled in large part by GPUs, and more
recently accelerators, which make it possible to carry out computationally
demanding training and inference in acceptable times. As the size of machine
learning networks and workloads continues to increase, multi-GPU machines have
emerged as an important platform offered on High Performance Computing and
cloud data centers. As these machines are shared between multiple users, it
becomes increasingly important to protect applications against potential
attacks. In this paper, we explore the vulnerability of Nvidia's DGX multi-GPU
machines to covert and side channel attacks. These machines consist of a number
of discrete GPUs that are interconnected through a combination of custom
interconnect (NVLink) and PCIe connections. We reverse engineer the cache
hierarchy and show that it is possible for an attacker on one GPU to cause
contention on the L2 cache of another GPU. We use this observation to first
develop a covert channel attack across two GPUs, achieving the best bandwidth
of 3.95 MB/s. We also develop a prime and probe attack on a remote GPU allowing
an attacker to recover the cache hit and miss behavior of another workload.
This basic capability can be used in any number of side channel attacks: we
demonstrate a proof of concept attack that fingerprints the application running
on the remote GPU, with high accuracy. Our work establishes for the first time
the vulnerability of these machines to microarchitectural attacks, and we hope
that it guides future research to improve their security.


title: Iterative Deep Homography Estimation

abstract:
  We propose Iterative Homography Network, namely IHN, a new deep homography
estimation architecture. Different from previous works that achieve iterative
refinement by network cascading or untrainable IC-LK iterator, the iterator of
IHN has tied weights and is completely trainable. IHN achieves state-of-the-art
accuracy on several datasets including challenging scenes. We propose 2
versions of IHN: (1) IHN for static scenes, (2) IHN-mov for dynamic scenes with
moving objects. Both versions can be arranged in 1-scale for efficiency or
2-scale for accuracy. We show that the basic 1-scale IHN already outperforms
most of the existing methods. On a variety of datasets, the 2-scale IHN
outperforms all competitors by a large gap. We introduce IHN-mov by producing
an inlier mask to further improve the estimation accuracy of moving-objects
scenes. We experimentally show that the iterative framework of IHN can achieve
95% error reduction while considerably saving network parameters. When
processing sequential image pairs, IHN can achieve 32.7 fps, which is about 8x
the speed of IC-LK iterator. Source code is available at
https://github.com/imdumpl78/IHN.


title:
VI-IKD: High-Speed Accurate Off-Road Navigation using Learned
  Visual-Inertial Inverse Kinodynamics

abstract:
  One of the key challenges in high speed off road navigation on ground
vehicles is that the kinodynamics of the vehicle terrain interaction can differ
dramatically depending on the terrain. Previous approaches to addressing this
challenge have considered learning an inverse kinodynamics (IKD) model,
conditioned on inertial information of the vehicle to sense the kinodynamic
interactions. In this paper, we hypothesize that to enable accurate high-speed
off-road navigation using a learned IKD model, in addition to inertial
information from the past, one must also anticipate the kinodynamic
interactions of the vehicle with the terrain in the future. To this end, we
introduce Visual-Inertial Inverse Kinodynamics (VI-IKD), a novel learning based
IKD model that is conditioned on visual information from a terrain patch ahead
of the robot in addition to past inertial information, enabling it to
anticipate kinodynamic interactions in the future. We validate the
effectiveness of VI-IKD in accurate high-speed off-road navigation
experimentally on a scale 1/5 UT-AlphaTruck off-road autonomous vehicle in both
indoor and outdoor environments and show that compared to other
state-of-the-art approaches, VI-IKD enables more accurate and robust off-road
navigation on a variety of different terrains at speeds of up to 3.5 m/s.


title: Advanced RF Sources R&D for Economical Future Colliders

abstract:
  The high power RF system will be a significant budgetary driver for any
future collider. An order-of-magnitude improvement in cost/capability is
needed, and as a result, a robust R&D program in next-generation, economical RF
sources is essential. In this paper, we discuss the challenges and
opportunities that arise from advancing the state of the art in these devices.
Specifically, research initiatives in new circuit topologies, advanced
manufacturing techniques, and novel alternatives to conventional RF source
components are discussed.


title: Quantum trace map for 3-manifolds and a 'length conjecture'

abstract:
  We introduce a quantum trace map for an ideally triangulated hyperbolic knot
complement $S^3\backslash \mathcal{K}$. The map assigns a quantum operator to
each element of Kauffmann Skein module of the 3-manifold. The quantum operator
lives in a module generated by products of quantized edge parameters of the
ideal triangulation modulo some equivalence relations determined by gluing
equations. Combining the quantum map with a state-integral model of
$SL(2,\mathbb{C})$ Chern-Simons theory, one can define perturbative invariants
of knot $K$ in the knot complement whose leading part is determined by its
complex hyperbolic length. We then conjecture that the perturbative invariants
determine an asymptotic expansion of the Jones polynomial for a link composed
of $\mathcal{K}$ and $K$. We propose the explicit quantum trace map for
figure-eight knot complement and confirm the length conjecture up to the second
order in the asymptotic expansion both numerically and analytically.


title:
Cosmological constraints from the density gradient weighted correlation
  function

abstract:
  The mark weighted correlation function (MCF) $W(s,\mu)$ is a computationally
efficient statistical measure which can probe clustering information beyond
that of the conventional 2-point statistics. In this work, we extend the
traditional mark weighted statistics by using powers of the density field
gradient $|\nabla \rho/\rho|^\alpha$ as the weight, and use the angular
dependence of the scale-averaged MCFs to constrain cosmological parameters. The
analysis shows that the gradient based weighting scheme is statistically more
powerful than the density based weighting scheme, while combining the two
schemes together is more powerful than separately using either of them.
Utilising the density weighted or the gradient weighted MCFs with $\alpha=0.5,\
1$, we can strengthen the constraint on $\Omega_m$ by factors of 2 or 4,
respectively, compared with the standard 2-point correlation function, while
simultaneously using the MCFs of the two weighting schemes together can be
$1.25$ times more statistically powerful than using the gradient weighting
scheme alone. The mark weighted statistics may play an important role in
cosmological analysis of future large-scale surveys. Many issues, including the
possibility of using other types of weights, the influence of the bias on this
statistics, as well as the usage of MCFs in the tomographic Alcock-Paczynski
method, are worth further investigations.


title: Fine-Grained Object Classification via Self-Supervised Pose Alignment

abstract:
  Semantic patterns of fine-grained objects are determined by subtle appearance
difference of local parts, which thus inspires a number of part-based methods.
However, due to uncontrollable object poses in images, distinctive details
carried by local regions can be spatially distributed or even self-occluded,
leading to a large variation on object representation. For discounting pose
variations, this paper proposes to learn a novel graph based object
representation to reveal a global configuration of local parts for
self-supervised pose alignment across classes, which is employed as an
auxiliary feature regularization on a deep representation learning
network.Moreover, a coarse-to-fine supervision together with the proposed
pose-insensitive constraint on shallow-to-deep sub-networks encourages
discriminative features in a curriculum learning manner. We evaluate our method
on three popular fine-grained object classification benchmarks, consistently
achieving the state-of-the-art performance. Source codes are available at
https://github.com/yangxh11/P2P-Net.


title:
Does Configuration Encoding Matter in Learning Software Performance? An
  Empirical Study on Encoding Schemes

abstract:
  Learning and predicting the performance of a configurable software system
helps to provide better quality assurance. One important engineering decision
therein is how to encode the configuration into the model built. Despite the
presence of different encoding schemes, there is still little understanding of
which is better and under what circumstances, as the community often relies on
some general beliefs that inform the decision in an ad-hoc manner. To bridge
this gap, in this paper, we empirically compared the widely used encoding
schemes for software performance learning, namely label, scaled label, and
one-hot encoding. The study covers five systems, seven models, and three
encoding schemes, leading to 105 cases of investigation.
  Our key findings reveal that: (1) conducting trial-and-error to find the best
encoding scheme in a case by case manner can be rather expensive, requiring up
to 400+ hours on some models and systems; (2) the one-hot encoding often leads
to the most accurate results while the scaled label encoding is generally weak
on accuracy over different models; (3) conversely, the scaled label encoding
tends to result in the fastest training time across the models/systems while
the one-hot encoding is the slowest; (4) for all models studied, label and
scaled label encoding often lead to relatively less biased outcomes between
accuracy and training time, but the paired model varies according to the
system.
  We discuss the actionable suggestions derived from our findings, hoping to
provide a better understanding of this topic for the community. To promote open
science, the data and code of this work can be publicly accessed at
https://github.com/ideas-labo/MSR2022-encoding-study.


title:
Top-level Design and Simulated Performance of the First Portable CT-MR
  scanner

abstract:
  Multi-modality imaging hardware can be integrated in a single gantry to
collect diverse datasets for complementary information and spatiotemporal
correlation, with excellent examples including PET-CT and PET-MRI. However,
there is no CT-MRI prototype up to today due to technical challenges and
associated cost-benefit considerations. Thanks to the rapid development of
medical imaging, it becomes feasible now to integrate cost-effective CT and MRI
imagers together for portability, popularity, and point of care. In this paper,
we present the top-level design of the first portable CT-MRI system and
evaluate its imaging performance via realistic numerical simulations. In this
CT-MRI system, the magnet made of two NdFeB rings of about 40.0 cm radius forms
a magnetic field of about 57 mT at the isocenter and has a gap of 11.3 cm to
accommodate the rotating CT gantry. The targeted MR imaging field of view (FOV)
is a sphere of ~15 cm in diameter and that of CT is approximately 20 cm
diameter in axial direction and 5 cm in longitudinal direction. Our results
show a great potential of such a hybrid system. The proposed CT-MRI system will
be valuable in applications such as imaging in underdeveloped countries,
disaster scenes and battle fields.


title: pycefr: Python Competency Level through Code Analysis

abstract:
  Python is known to be a versatile language, well suited both for beginners
and advanced users. Some elements of the language are easier to understand than
others: some are found in any kind of code, while some others are used only by
experienced programmers. The use of these elements lead to different ways to
code, depending on the experience with the language and the knowledge of its
elements, the general programming competence and programming skills, etc. In
this paper, we present pycefr, a tool that detects the use of the different
elements of the Python language, effectively measuring the level of Python
proficiency required to comprehend and deal with a fragment of Python code.
Following the well-known Common European Framework of Reference for Languages
(CEFR), widely used for natural languages, pycefr categorizes Python code in
six levels, depending on the proficiency required to create and understand it.
We also discuss different use cases for pycefr: identifying code snippets that
can be understood by developers with a certain proficiency, labeling code
examples in online resources such as Stackoverflow and GitHub to suit them to a
certain level of competency, helping in the onboarding process of new
developers in Open Source Software projects, etc. A video shows availability
and usage of the tool: https://tinyurl.com/ypdt3fwe.


title: The Sound of Bounding-Boxes

abstract:
  In the task of audio-visual sound source separation, which leverages visual
information for sound source separation, identifying objects in an image is a
crucial step prior to separating the sound source. However, existing methods
that assign sound on detected bounding boxes suffer from a problem that their
approach heavily relies on pre-trained object detectors. Specifically, when
using these existing methods, it is required to predetermine all the possible
categories of objects that can produce sound and use an object detector
applicable to all such categories. To tackle this problem, we propose a fully
unsupervised method that learns to detect objects in an image and separate
sound source simultaneously. As our method does not rely on any pre-trained
detector, our method is applicable to arbitrary categories without any
additional annotation. Furthermore, although being fully unsupervised, we found
that our method performs comparably in separation accuracy.


title:
Automorphism groups and uniqueness of holomorphic vertex operator
  algebras of central charge $24$

abstract:
  We describe the automorphism groups of all holomorphic vertex operator
algebras of central charge $24$ with non-trivial weight one Lie algebras by
using their constructions as simple current extensions. We also confirm a
conjecture of G. H\"ohn on the numbers of holomorphic vertex operator algebras
of central charge $24$ obtained as inequivalent simple current extensions of
certain vertex operator algebras, which gives another proof of the uniqueness
of holomorphic vertex operator algebras of central charge $24$ with non-trivial
weight one Lie algebras.


title: Improved Quantum Algorithms for Fidelity Estimation

abstract:
  Fidelity is a fundamental measure for the closeness of two quantum states,
which is important both from a theoretical and a practical point of view. Yet,
in general, it is difficult to give good estimates of fidelity, especially when
one works with mixed states over Hilbert spaces of very high dimension.
Although, there has been some progress on fidelity estimation, all prior work
either requires a large number of identical copies of the relevant states, or
relies on unproven heuristics. In this work, we improve on both of these
aspects by developing new and efficient quantum algorithms for fidelity
estimation with provable performance guarantees in case at least one of the
states is approximately low-rank. Our algorithms use advanced quantum linear
algebra techniques, such as the quantum singular value transformation, as well
as density matrix exponentiation and quantum spectral sampling. As a
complementary result, we prove that fidelity estimation to any non-trivial
constant additive accuracy is hard in general, by giving a sample complexity
lower bound that depends polynomially on the dimension. Moreover, if circuit
descriptions for the relevant states are provided, we show that the task is
hard for the complexity class called (honest verifier) quantum statistical zero
knowledge via a reduction to a closely related result by Watrous.


title: Optimal Learning

abstract:
  This paper studies the problem of learning an unknown function $f$ from given
data about $f$. The learning problem is to give an approximation $\hat f$ to
$f$ that predicts the values of $f$ away from the data. There are numerous
settings for this learning problem depending on (i) what additional information
we have about $f$ (known as a model class assumption), (ii) how we measure the
accuracy of how well $\hat f$ predicts $f$, (iii) what is known about the data
and data sites, (iv) whether the data observations are polluted by noise. A
mathematical description of the optimal performance possible (the smallest
possible error of recovery) is known in the presence of a model class
assumption. Under standard model class assumptions, it is shown in this paper
that a near optimal $\hat f$ can be found by solving a certain discrete
over-parameterized optimization problem with a penalty term. Here, near optimal
means that the error is bounded by a fixed constant times the optimal error.
This explains the advantage of over-parameterization which is commonly used in
modern machine learning. The main results of this paper prove that
over-parameterized learning with an appropriate loss function gives a near
optimal approximation $\hat f$ of the function $f$ from which the data is
collected. Quantitative bounds are given for how much over-parameterization
needs to be employed and how the penalization needs to be scaled in order to
guarantee a near optimal recovery of $f$. An extension of these results to the
case where the data is polluted by additive deterministic noise is also given.


title:
News and views regarding PSR J1757-1854, a highly-relativistic binary
  pulsar

abstract:
  We provide an update on the ongoing monitoring and study of the
highly-relativistic double neutron star binary system PSR J1757-1854, a 21.5-ms
pulsar in a highly eccentric, 4.4-hour orbit. The extreme nature of this
pulsar's orbit allows it to probe a parameter space largely unexplored by other
relativistic binary pulsars. For example, it displays one of the highest
gravitational wave (GW) luminosities of any known binary pulsar, as well as the
highest rate or orbital decay due to GW damping. PSR J1757-1854 is also notable
in that it is an excellent candidate for exploring new tests of General
Relativity and other gravitational theories, with possible measurements of both
Lense-Thirring precession and relativistic orbital deformation (through the
post-Keplerian parameter $\delta_\theta$) anticipated within the next 3-5
years.
  Here we present a summary of the latest interim results from the ongoing
monitoring of this pulsar as part of an international, multi-telescope
campaign. This includes an update of the pulsar's long-term timing and
post-Keplerian parameters, new constraints on the pulsar's proper motion and
corresponding Shklovskii kinematic correction, and new limits on the pulsar's
geodetic precession as determined by monitoring for secular changes in the
pulse profile. We also highlight prospects for future work, including an
updated timeline on new relativistic tests following the introduction of
MeerKAT observations.


title: TextPruner: A Model Pruning Toolkit for Pre-Trained Language Models

abstract:
  Pre-trained language models have been prevailed in natural language
processing and become the backbones of many NLP tasks, but the demands for
computational resources have limited their applications. In this paper, we
introduce TextPruner, an open-source model pruning toolkit designed for
pre-trained language models, targeting fast and easy model compression.
TextPruner offers structured post-training pruning methods, including
vocabulary pruning and transformer pruning, and can be applied to various
models and tasks. We also propose a self-supervised pruning method that can be
applied without the labeled data. Our experiments with several NLP tasks
demonstrate the ability of TextPruner to reduce the model size without
re-training the model.


title:
Quillen-type bundle and geometric prequantization on moduli space of the
  Seiberg-Witten equations on product of Riemann surfaces

abstract:
  We show the existence of a symplectic structure on the moduli space of the
Seiberg-Witten equations on $\Sigma \times \Sigma$ where $\Sigma$ is a compact
oriented Riemann surface. To prequantize the moduli space, we construct a
Quillen-type determinant line bundle on it and show its curvature is
proportional to the symplectic form.


title: On the algebraicity of polyquadratic plectic points

abstract:
  We establish direct evidence of the arithmetic significance of plectic
Stark-Heegner points for elliptic curves of arbitrarily large rank. The main
contribution is a proof of the algebraicity of plectic points associated to
polyquadratic CM extensions of totally real number fields. Moreover, we relate
the non-vanishing of plectic points to analytic and algebraic ranks of elliptic
curves.


title: Spatial distribution of HOCN around Sagittarius B2

abstract:
  HOCN and HNCO abundance ratio in molecular gas can tell us the information of
their formation mechanism. We performed high-sensitivity mapping observations
of HOCN, HNCO, and HNC$^{18}$O lines around Sagittarius B2 (Sgr B2) with IRAM
30m telescope at 3-mm wavelength. HNCO 4$_{04}$-3$_{03}$ and HOCN
4$_{04}$-3$_{03}$ are used to obtain the abundance ratio of HNCO to HOCN. The
ratio of HNCO 4$_{04}$-3$_{03}$ to HNC$^{18}$O 4$_{04}$-3$_{03}$ is used to
calculate the optical depth of HNCO 4$_{04}$-3$_{03}$. The abundance ratio of
HOCN and HNCO is observed to range from 0.4% to 0.7% toward most positions,
which agrees well with the gas-grain model. However, the relative abundance of
HOCN is observed to be enhanced toward the direction of Sgr B2 (S), with HOCN
to HNCO abundance ratio of $\sim$ 0.9%. The reason for that still needs further
investigation.Based on the intensity ratio of HNCO and HNC$^{18}$O lines, we
updated the isotopic ratio of $^{16}$O/$^{18}$O to be 296 $\pm$ 54 in Sgr B2.


title: StyleFool: Fooling Video Classification Systems via Style Transfer

abstract:
  Video classification systems are vulnerable to adversarial attacks, which can
create severe security problems in video verification. Current black-box
attacks need a large number of queries to succeed, resulting in high
computational overhead in the process of attack. On the other hand, attacks
with restricted perturbations are ineffective against defenses such as
denoising or adversarial training. In this paper, we focus on unrestricted
perturbations and propose StyleFool, a black-box video adversarial attack via
style transfer to fool the video classification system. StyleFool first
utilizes color theme proximity to select the best style image, which helps
avoid unnatural details in the stylized videos. Meanwhile, the target class
confidence is additionally considered in targeted attack to influence the
output distribution of the classifier by moving the stylized video closer to or
even across the decision boundary. A gradient-free method is then employed to
further optimize the adversarial perturbation. We carry out extensive
experiments to evaluate StyleFool on two standard datasets, UCF-101 and
HMDB-51. The experimental results suggest that StyleFool outperforms the
state-of-the-art adversarial attacks in terms of both number of queries and
robustness against existing defenses. We identify that 50% of the stylized
videos in untargeted attack do not need any query since they can already fool
the video classification model. Furthermore, we evaluate the
indistinguishability through a user study to show that the adversarial samples
of StyleFool look imperceptible to human eyes, despite unrestricted
perturbations.


title:
Meta-Sampler: Almost-Universal yet Task-Oriented Sampling for Point
  Clouds

abstract:
  Sampling is a key operation in point-cloud task and acts to increase
computational efficiency and tractability by discarding redundant points.
Universal sampling algorithms (e.g., Farthest Point Sampling) work without
modification across different tasks, models, and datasets, but by their very
nature are agnostic about the downstream task/model. As such, they have no
implicit knowledge about which points would be best to keep and which to
reject. Recent work has shown how task-specific point cloud sampling (e.g.,
SampleNet) can be used to outperform traditional sampling approaches by
learning which points are more informative. However, these learnable samplers
face two inherent issues: i) overfitting to a model rather than a task, and
\ii) requiring training of the sampling network from scratch, in addition to
the task network, somewhat countering the original objective of down-sampling
to increase efficiency. In this work, we propose an almost-universal sampler,
in our quest for a sampler that can learn to preserve the most useful points
for a particular task, yet be inexpensive to adapt to different tasks, models,
or datasets. We first demonstrate how training over multiple models for the
same task (e.g., shape reconstruction) significantly outperforms the vanilla
SampleNet in terms of accuracy by not overfitting the sample network to a
particular task network. Second, we show how we can train an almost-universal
meta-sampler across multiple tasks. This meta-sampler can then be rapidly
fine-tuned when applied to different datasets, networks, or even different
tasks, thus amortizing the initial cost of training.


title:
A Fast and Robust Method for Predicting the Phase Stability of
  Refractory Complex Concentrated Alloys using Pairwise Mixing Enthalpy

abstract:
  The ability to predict the composition- and temperature-dependent stability
of refractory complex concentrated alloys (RCCAs) is vital to the design of
high-temperature structural alloys. Here, we present a model based on
first-principles calculations to predict the thermodynamic stability of
multicomponent equimolar solid solutions in a high-throughput manner and apply
it to screen over 20,000 compositions. We develop a database that contains
pairwise mixing enthalpy of 17 refractory metals using density-functional
theory (DFT)-based total energy calculations. To these, we fit thermodynamic
solution models that can accurately capture the mixing enthalpy of
multicomponent BCC solid solutions. By comparing their energy with
DFT-calculated enthalpy of intermetallics from the Materials Project database
and using convex hull analyses, we identify the stable phase of any RCCA as a
function of temperature. The predicted stability of NbTiZr, NbTiZrV, and
NbTiZrVM (M = Mo,Ta,Cr) systems as a function of temperature agree well with
prior experimental observations. We apply our model to predict the phase
evolution in NbVZr-Tix (0 < x < 1), which is confirmed experimentally using a
high-throughput, laser deposition-based synthesis technique. This method
provides a fast and accurate way to estimate the phase stability of new RCCAs
to expedite their experimental discovery.


title:
Thermal Modulation of Gigahertz Surface Acoustic Waves on Lithium
  Niobate

abstract:
  Surface acoustic wave (SAW) devices have wide range of applications in
microwave signal processing. Microwave SAW components benefit from higher
quality factors and much smaller crosstalk when compared to their
electromagnetic counterparts. Efficient routing and modulation of SAWs are
essential for building large-scale and versatile acoustic-wave circuits. Here,
we demonstrate integrated thermo-acoustic modulators using two SAW platforms:
bulk lithium niobate and thin-film lithium niobate on sapphire. In both
approaches, the gigahertz-frequency SAWs are routed by integrated acoustic
waveguides while on-chip microheaters are used to locally change the
temperature and thus control the phase of SAW. Using this approach, we achieved
phase changes of over 720 degrees with the responsibility of 2.6 deg/mW for
bulk lithium niobate and 0.52 deg/mW for lithium niobate on sapphire.
Furthermore, we demonstrated amplitude modulation of SAWs using acoustic Mach
Zehnder interferometers. Our thermo-acoustic modulators can enable
reconfigurable acoustic signal processing for next generation wireless
communications and microwave systems.


title: Theory of Acceleration of Decision Making by Correlated Time Sequences

abstract:
  Photonic accelerators have been intensively studied to provide enhanced
information processing capability to benefit from the unique attributes of
physical processes. Recently, it has been reported that chaotically oscillating
ultrafast time series from a laser, called laser chaos, provides the ability to
solve multi-armed bandit (MAB) problems or decision-making problems at GHz
order. Furthermore, it has been confirmed that the negatively correlated
time-domain structure of laser chaos contributes to the acceleration of
decision-making. However, the underlying mechanism of why decision-making is
accelerated by correlated time series is unknown. In this paper, we demonstrate
a theoretical model to account for the acceleration of decision-making by
correlated time sequence. We first confirm the effectiveness of the negative
autocorrelation inherent in time series for solving two-armed bandit problems
using Fourier transform surrogate methods. We propose a theoretical model that
concerns the correlated time series subjected to the decision-making system and
the internal status of the system therein in a unified manner, inspired by
correlated random walks. We demonstrate that the performance derived
analytically by the theory agrees well with the numerical simulations, which
confirms the validity of the proposed model and leads to optimal system design.
The present study paves the new way for the effectiveness of correlated time
series for decision-making, impacting artificial intelligence and other
applications.


title:
Deep Joint Source-Channel Coding for CSI Feedback: An End-to-End
  Approach

abstract:
  The increased throughput brought by MIMO technology relies on the knowledge
of channel state information (CSI) acquired in the base station (BS). To make
the CSI feedback overhead affordable for the evolution of MIMO technology
(e.g., massive MIMO and ultra-massive MIMO), deep learning (DL) is introduced
to deal with the CSI compression task. Based on the separation principle in
existing communication systems, DL based CSI compression is used as source
coding. However, this separate source-channel coding (SSCC) scheme is inferior
to the joint source-channel coding (JSCC) scheme in the finite blocklength
regime. In this paper, we propose a deep joint source-channel coding (DJSCC)
based framework for the CSI feedback task. In particular, the proposed method
can simultaneously learn from the CSI source and the wireless channel. Instead
of truncating CSI via Fourier transform in the delay domain in existing
methods, we apply non-linear transform networks to compress the CSI.
Furthermore, we adopt an SNR adaption mechanism to deal with the wireless
channel variations. The extensive experiments demonstrate the validity,
adaptability, and generality of the proposed framework.


title:
Prognosis of Rotor Parts Fly-off Based on Cascade Classification and
  Online Prediction Ability Index

abstract:
  Large rotating machines, e.g., compressors, steam turbines, gas turbines, are
critical equipment in many process industries such as energy, chemical, and
power generation. Due to high rotating speed and tremendous momentum of the
rotor, the centrifugal force may lead to flying apart of the rotor parts, which
brings a great threat to the operation safety. Early detection and prediction
of potential failures could prevent the catastrophic plant downtime and
economic loss. In this paper, we divide the operational states of a rotating
machine into normal, risky, and high-risk ones based on the time to the moment
of failure. Then a cascade classifying algorithm is proposed to predict the
states in two steps, first we judge whether the machine is in normal or
abnormal condition; for time periods which are predicted as abnormal we further
classify them into risky or high-risk states. Moreover, traditional
classification model evaluation metrics, such as confusion matrix, true-false
accuracy, are static and neglect the online prediction dynamics and uneven
wrong-prediction prices. An Online Prediction Ability Index (OPAI) is proposed
to select prediction models with consistent online predictions and smaller
close-to-downtime prediction errors. Real-world data sets and computational
experiments are used to verify the effectiveness of proposed methods.


title: Multi-Target Filter and Detector for Speaker Diarization

abstract:
  A good representation of a target speaker usually helps to extract important
information about the speaker and detect the corresponding temporal regions in
a multi-speaker conversation. In this paper, we propose a neural architecture
that simultaneously extracts speaker embeddings consistent with the speaker
diarization objective and detects the presence of each speaker frame by frame,
regardless of the number of speakers in the conversation. To this end, a
residual network (ResNet) and a dual-path recurrent neural network (DPRNN) are
integrated into a unified structure. When tested on the 2-speaker CALLHOME
corpus, our proposed model outperforms most methods published so far. Evaluated
in a more challenging case of concurrent speakers ranging from two to seven,
our system also achieves relative diarization error rate reductions of 26.35%
and 6.4% over two typical baselines, namely the traditional x-vector clustering
system and the attention-based system.


title:
Atomistic modeling of spin and electron dynamics in two-dimensional
  magnets switched by two-dimensional topological insulators

abstract:
  To design fast memory devices, we need material combinations which can
facilitate fast read and write operation. We present a heterostructure
comprising a two-dimensional (2D) magnet and a 2D topological insulator (TI) as
a viable option for designing fast memory devices. We theoretically model
spin-charge dynamics between the 2D magnets and 2D TIs. Using the adiabatic
approximation, we combine the non-equilibrium Green's function method for
spin-dependent electron transport, and time-quantified Monte-Carlo for
simulating magnetization dynamics. We show that it is possible to switch the
magnetic domain of a ferromagnet using spin-torque from spin-polarized edge
states of 2D TI. We further show that the switching between TIs and 2D magnets
is strongly dependent on the interface exchange ($J_{\mathrm{int}}$), and an
optimal interface exchange depending on the exchange interaction within the
magnet is required for efficient switching. Finally, we compare the
experimentally grown Cr-compounds and show that Cr-compounds with higher
anisotropy (such as $\rm CrI_3$) results in lower switching speed but more
stable magnetic order.


title:
Towards Collaborative Intelligence: Routability Estimation based on
  Decentralized Private Data

abstract:
  Applying machine learning (ML) in design flow is a popular trend in EDA with
various applications from design quality predictions to optimizations. Despite
its promise, which has been demonstrated in both academic researches and
industrial tools, its effectiveness largely hinges on the availability of a
large amount of high-quality training data. In reality, EDA developers have
very limited access to the latest design data, which is owned by design
companies and mostly confidential. Although one can commission ML model
training to a design company, the data of a single company might be still
inadequate or biased, especially for small companies. Such data availability
problem is becoming the limiting constraint on future growth of ML for chip
design. In this work, we propose an Federated-Learning based approach for
well-studied ML applications in EDA. Our approach allows an ML model to be
collaboratively trained with data from multiple clients but without explicit
access to the data for respecting their data privacy. To further strengthen the
results, we co-design a customized ML model FLNet and its personalization under
the decentralized training scenario. Experiments on a comprehensive dataset
show that collaborative training improves accuracy by 11% compared with
individual local models, and our customized model FLNet significantly
outperforms the best of previous routability estimators in this collaborative
training flow.


title: Resonance induced by higher-order coupling diversity

abstract:
  The studies of collective oscillations induced by higher-order interactions
point out the necessity of group effect in coupling modelization. As yet the
related advances are mainly concentrated on nonlinear coupling patterns and
cannot be straightforwardly extended to the linear ones. In present work, we
introduce the standard deviation of dynamic behavior for the interacting group
to complement the higher-order effect that beyond pairwise in diffusive
coupling. By doing so, the higher-order effect can be flexibly extended to the
linearly coupled system. We leverage this modelization to embrace the influence
of heterogeneous higher-order coupling, including promoting and inhibiting
effects, on the signal response for two conventional models, the globally
coupled overdamped bistable oscillators and excitable FitzHugh-Nagumo neurons.
Particularly, we numerically and analytically reveal that the optimal signal
response can be obtained by an intermediate degree of higher-order coupling
diversity for both systems. This resonant signal response stems from the
competition between dispersion and aggregation induced by heterogeneous
higher-order and positive pairwise couplings, respectively. Our results
contribute to a better understanding of the signal propagation in linearly
coupled systems.


title:
On the Origins of Life's Homochirality: Inducing Enantiomeric Excess
  with Spin-Polarized Electrons

abstract:
  Life as we know it is homochiral, but the origins of biological homochirality
on early Earth remain elusive. Shallow closed-basin lakes are a plausible
prebiotic environment on early Earth, and most are expected to have significant
sedimentary magnetite deposits. We hypothesize that UV (200-300nm) irradiation
of magnetite deposits could generate hydrated spin-polarized electrons
sufficient to induce chirally selective prebiotic chemistry. Such electrons are
potent reducing agents that drive reduction reactions where the spin
polarization direction can alter enantioselectively the reaction kinetics. Our
estimate of this chiral bias is based on the strong effective spin-orbit
coupling observed in the chiral-induced spin selectivity (CISS) effect, as
applied to energy differences in reduction reactions for different isomers. In
the original CISS experiments, spin selective electron transmission through a
monolayer of dsDNA molecules is observed at room temperature - indicating a
strong coupling between molecular chirality and electron spin. We propose that
the chiral symmetry breaking due to the CISS effect, when applied to reduction
chemistry, can induce enantioselective synthesis on the prebiotic Earth and
thus facilitate the homochiral assembly of life's building blocks.


title: Entanglement area law for 1D gauge theories and bosonic systems

abstract:
  We prove an entanglement area law for a class of 1D quantum systems involving
infinite-dimensional local Hilbert spaces. This class of quantum systems
include bosonic models such as the Hubbard-Holstein model, and both U(1) and
SU(2) lattice gauge theories in one spatial dimension. Our proof relies on new
results concerning the robustness of the ground state and spectral gap to the
truncation of Hilbert space, applied within the approximate ground state
projector (AGSP) framework from previous work. In establishing this area law,
we develop a system-size independent bound on the expectation value of local
observables for Hamiltonians without translation symmetry, which may be of
separate interest. Our result provides theoretical justification for using
tensor network methods to study the ground state properties of quantum systems
with infinite local degrees of freedom.


title: Enhanced Grey Box Fuzzing For Intel Media Driver

abstract:
  Grey box fuzzing is one of the most successful methods for automatic
vulnerability detection. However,conventional Grey box Fuzzers like AFL can
open perform fuzzing against the whole input and spend more time on smaller
seeds with lower execution time, which significantly impact fuzzing efficiency
for complicated input types. In this work, we introduce one intelligent grey
box fuzzing for Intel Media driver, MediaFuzzer, which can perform effective
fuzzing based on selective fields of complicated input. Also, with one novel
calling depth-based power schedule biased toward seed corpus which can lead to
deeper calling chain, it dramatically improves the vulnerability exposures
(~6.6 times more issues exposed) and fuzzing efficiency (~2.7 times more
efficient) against the baseline AFL for Intel media driver with almost
negligible overhead.


title: ESNI: Domestic Robots Design for Elderly and Disabled People

abstract:
  Our paper focuses on the research of the possibility for speech recognition
intelligent agents to assist the elderly and disabled people's lives, to
improve their life quality by utilizing cutting-edge technologies. After
researching the attitude of elderly and disabled people toward the household
agent, we propose a design framework: ESNI(Exploration, Segmentation,
Navigation, Instruction) that apply to mobile agent, achieve some
functionalities such as processing human commands, picking up a specified
object, and moving an object to another location. The agent starts the
exploration in an unseen environment, stores each item's information in the
grid cells to his memory and analyzes the corresponding features for each
section. We divided our indoor environment into 6 sections: Kitchen, Living
room, Bedroom, Studio, Bathroom, Balcony. The agent uses algorithms to assign
sections for each grid cell then generates a navigation trajectory base on the
section segmentation. When the user gives a command to the agent, feature words
will be extracted and processed into a sequence of sub-tasks.


title: ITTR: Unpaired Image-to-Image Translation with Transformers

abstract:
  Unpaired image-to-image translation is to translate an image from a source
domain to a target domain without paired training data. By utilizing CNN in
extracting local semantics, various techniques have been developed to improve
the translation performance. However, CNN-based generators lack the ability to
capture long-range dependency to well exploit global semantics. Recently,
Vision Transformers have been widely investigated for recognition tasks. Though
appealing, it is inappropriate to simply transfer a recognition-based vision
transformer to image-to-image translation due to the generation difficulty and
the computation limitation. In this paper, we propose an effective and
efficient architecture for unpaired Image-to-Image Translation with
Transformers (ITTR). It has two main designs: 1) hybrid perception block (HPB)
for token mixing from different receptive fields to utilize global semantics;
2) dual pruned self-attention (DPSA) to sharply reduce the computational
complexity. Our ITTR outperforms the state-of-the-arts for unpaired
image-to-image translation on six benchmark datasets.


title: Global asymptotics of the sixth Painlev\'e equation in Okamoto's space

abstract:
  We study dynamics of solutions in the initial value space of the sixth
Painlev\'e equation as the independent variable approaches zero. Our main
results describe the repeller set, show that the number of poles and zeroes of
general solutions is unbounded, and that the complex limit set of each solution
exists and is compact and connected.


title:
Quasi-two-dimensional bacterial swimming around pillars: enhanced
  trapping efficiency and curvature dependence

abstract:
  Microswimmers exhibit more diverse behavior in quasi-two dimensions than in
three dimensions. Such behavior remains elusive due to the analytical
difficulty of dealing with two parallel solid boundaries. The existence of
additional obstacles in quasi-two-dimensional systems further complicates the
analysis. We fabricated microscopic pillars in quasi-two dimensions by etching
glass coverslips and observed the interactions between swimming bacteria and
the pillars. Bacteria got trapped around the circular pillars and the trapping
efficiency increased as the quasi-two-dimensionality was increased or as the
curvature of the pillars was decreased. Experiments with elliptic pillars
demonstrated the possibility that the local curvature of the obstacle controls
the trapping efficiency.

categories: cond-mat.soft cond-mat.stat-mech physics.bio-ph physics.flu-dyn


title:
Plasmoids formation during simulations of coaxial helicity injection in
  the National Spherical Torus Experiment

abstract:
  Formation of an elongated Sweet-Parker current sheet and a transition to
plasmoid instability has for the first time been predicted by simulations in a
large-scale toroidal fusion plasma in the absence of any pre-existing
instability. Plasmoid instability is demonstrated through resistive MHD
simulations of transient Coaxial Helicity Injection (CHI) experiments in the
National Spherical Torus Experiment (NSTX).Consistent with the theory,
fundamental characteristics of the plasmoid instability, including fast
reconnection rate, have been observed in these realistic simulations. Motivated
by the simulations, experimental camera images have been revisited and suggest
the existence of reconnecting plasmoids in NSTX. Global, system-size plasmoid
formation observed here should also have strong implications for astrophysical
reconnection, such as rapid eruptive solar events.


title:
Geometry of transit orbits in the periodically-perturbed restricted
  three-body problem

abstract:
  In the circular restricted three-body problem, low energy transit orbits are
revealed by linearizing the governing differential equations about the
collinear Lagrange points. This procedure fails when time-periodic
perturbations are considered, such as perturbation due to the sun (i.e., the
bicircular problem) or orbital eccentricity of the primaries. For the case of a
time-periodic perturbation, the Lagrange point is replaced by a periodic orbit,
equivalently viewed as a hyperbolic-elliptic fixed point of a symplectic map
(the stroboscopic Poincar\'e map). Transit and non-transit orbits can be
identified in the discrete map about the fixed point, in analogy with the
geometric construction of Conley and McGehee about the index-1 saddle
equilibrium point in the continuous dynamical system. Furthermore, though the
continuous time system does not conserve the Hamiltonian energy (which is
time-varying), the linearized map locally conserves a time-independent
effective Hamiltonian function. We demonstrate that the phase space geometry of
transit and non-transit orbits is preserved in going from the unperturbed to a
periodically-perturbed situation, which carries over to the full nonlinear
equations.


title:
Shell DFT-1/2 method towards engineering accuracy for semiconductors:
  GGA versus LDA

abstract:
  The Kohn-Sham gaps of density functional theory (DFT) obtained in terms of
local density approximation (LDA) or generalized gradient approximation (GGA)
cannot be directly linked to the fundamental gaps of semiconductors, but in
engineering there is a strong demand to match them through certain
rectification methods. Shell DFT-1/2 (shDFT-1/2), as a variant of DFT-1/2, is a
potential candidate to yield much improved band gaps for covalent
semiconductors, but its accuracy depends on the LDA/GGA ground state, including
optimized lattice parameters, basic Kohn-Sham gap before self-energy correction
and the amount of self-energy correction that is specific to the
exchange-correlation (XC) functional. In this work, we test the LDA/GGA as well
as shDFT-1/2 results of six technically important covalent semiconductors Si,
Ge, GaN, GaP, GaAs and GaSb, with an additional ionic insulator LiF for
comparison. The impact of XC flavor (LDA, PBEsol, PBE and RPBE), either
directly on the gap value, or indirectly through the optimized lattice
constant, is examined comprehensively. Moreover, we test the impact of XC
flavor on LDA/GGA and shDFT-1/2 gaps under the condition of fixed experimental
lattice constants. In-depth analysis reveals the rule of reaching the best
accuracy in calculating the electronic band structures of typical covalent
semiconductors. Relevant parameters like lattice constant, self-consistency in
shDFT-1/2 runs, as well as the exchange enhancement factor of GGA, are
discussed in details.


title: Nonholomorphic superpotentials in heterotic Landau-Ginzburg models

abstract:
  The aim of this talk is to derive two constraints imposed by supersymmetry
for a class of heterotic Landau-Ginzburg models with nonholomorphic
superpotentials. One of these constraints relates the nonholomorphic parameters
of the superpotential to the Hermitian curvature. Various special cases of this
constraint have been used to establish properties of Mathai-Quillen form
analogues which arise in the corresponding heterotic Landau-Ginzburg models.
The other constraint was not anticipated from studies of Mathai-Quillen form
analogues.


title: Emergence of classical spacetimes in canonical tensor model

abstract:
  We study the wave function of a tensor model in the canonical formalism by
Hamiltonian Monte Carlo method for Lie group symmetric or nearby values for the
argument of the wave function, and show that there emerge Lie-group symmetric
semi-classical spacetimes. More precisely, we consider some $SO(n+1)\
(n=1,2,3)$ symmetric values for the tensor argument of the wave function, and
show that there emerge discrete $n$-dimensional spheres. A key fact is that
there exist two phases, the classical phase and the quantum phase, depending on
the values of the argument of the wave function, and emergence of classical
spaces above occurs in the former phase, while fluctuations of configurations
are too large for such emergence in the latter phase. The transition between
the two phases has similarity with the Gross-Witten-Wadia transition, or that
between the one-cut and the two-cut solutions in the matrix model. Based on the
results, we give some speculations on how spacetimes evolve in the tensor
model.


title: Strain engineering and the hidden role of magnetism in monolayer VTe$_2$

abstract:
  Two-dimensional transition metal dichalcogenides have attracted great
attention recently. Motivated by a recent study of crystalline bulk VTe$_2$, we
theoretically investigated the spin-charge-lattice interplay in monolayer
VTe$_2$. To understand the controversial experimental reports on several
different charge density wave ground states, we paid special attention to the
'hidden' role of antiferromagnetism as its direct experimental detection may be
challenging. Our first-principles calculations show that the 4$\times$1 charge
density wave and the corresponding lattice deformation are accompanied by the
'double-stripe' antiferromagnetic spin order in its ground state. This phase
has not only the lowest total energy but also the dynamical phonon stability,
which supports a group of previous experiments. Interestingly enough, this
ground state is stabilized only by assuming the underlying spin order. By
noticing this intriguing and previously unknown interplay between magnetism and
other degrees of freedom, we further suggest a possible strain engineering. By
applying tensile strain, monolayer VTe$_2$ exhibits phase transition first to a
different charge density wave phase and then eventually to a ferromagnetically
ordered one.


title: Longitudinal Fairness with Censorship

abstract:
  Recent works in artificial intelligence fairness attempt to mitigate
discrimination by proposing constrained optimization programs that achieve
parity for some fairness statistic. Most assume availability of the class
label, which is impractical in many real-world applications such as precision
medicine, actuarial analysis and recidivism prediction. Here we consider
fairness in longitudinal right-censored environments, where the time to event
might be unknown, resulting in censorship of the class label and
inapplicability of existing fairness studies. We devise applicable fairness
measures, propose a debiasing algorithm, and provide necessary theoretical
constructs to bridge fairness with and without censorship for these important
and socially-sensitive tasks. Our experiments on four censored datasets confirm
the utility of our approach.


title:
Multiple Narrow-band signals Direction Finding with TMLA by Nonuniform
  Period Modulation

abstract:
  A new array signal reconstruction and signal-channel DOA estimation method
based on TMLA by nonuniform period modulation are proposed. By using
non-uniform period modulation, the harmonic component produced by different
elements could be separated. Therefore, the conventional snapshot could be
reconstructed by analyzing the spectrum of the combined signal. Then spatial
spectrum estimation method is used to implement DOA estimation. Numerical
simulations are provided to verify the feasibility and accuracy of the proposed
method. Since the duration of the signal in the frequency domain analysis
processed in a single time is very short, this method is also applicable to
narrowband signals. Another highlight is that this method can simultaneously
measure the number of the elements-1 angle of incident signals.


title: Remarks on some results of G. Pisier and P. Saab on convolutions

abstract:
  A result of G. Pisier says that a convolution operator $\star f : M(G) \to
C(G),$ where $G$ is a compact Abelian group, can be factored through a Hilbert
space if and only if $f$ has the absolutely summable set of Fourier
coefficients. P. Saab (2010) generalized this result in some directions in the
vector-valued cases. We give some further generalizations of the results of G.
Pisier and P. Saab, considering, in particular, the factorizations of the
operators through the operators of Schatten classes in Hilbert spaces. Also,
some related theorem on the factorization of operators through the operators of
the Lorentz-Schatten classes are obtained.


title:
Clozer: Adaptable Data Augmentation for Cloze-style Reading
  Comprehension

abstract:
  Task-adaptive pre-training (TAPT) alleviates the lack of labelled data and
provides performance lift by adapting unlabelled data to downstream task.
Unfortunately, existing adaptations mainly involve deterministic rules that
cannot generalize well. Here, we propose Clozer, a sequence-tagging based cloze
answer extraction method used in TAPT that is extendable for adaptation on any
cloze-style machine reading comprehension (MRC) downstream tasks. We experiment
on multiple-choice cloze-style MRC tasks, and show that Clozer performs
significantly better compared to the oracle and state-of-the-art in escalating
TAPT effectiveness in lifting model performance, and prove that Clozer is able
to recognize the gold answers independently of any heuristics.


title:
Span Classification with Structured Information for Disfluency Detection
  in Spoken Utterances

abstract:
  Existing approaches in disfluency detection focus on solving a token-level
classification task for identifying and removing disfluencies in text.
Moreover, most works focus on leveraging only contextual information captured
by the linear sequences in text, thus ignoring the structured information in
text which is efficiently captured by dependency trees. In this paper, building
on the span classification paradigm of entity recognition, we propose a novel
architecture for detecting disfluencies in transcripts from spoken utterances,
incorporating both contextual information through transformers and
long-distance structured information captured by dependency trees, through
graph convolutional networks (GCNs). Experimental results show that our
proposed model achieves state-of-the-art results on the widely used English
Switchboard for disfluency detection and outperforms prior-art by a significant
margin. We make all our codes publicly available on GitHub
(https://github.com/Sreyan88/Disfluency-Detection-with-Span-Classification)


title:
ReplaceBlock: An improved regularization method based on background
  information

abstract:
  Attention mechanism, being frequently used to train networks for better
feature representations, can effectively disentangle the target object from
irrelevant objects in the background. Given an arbitrary image, we find that
the background's irrelevant objects are most likely to occlude/block the target
object. We propose, based on this finding, a ReplaceBlock to simulate the
situations when the target object is partially occluded by the objects that are
deemed as background. Specifically, ReplaceBlock erases the target object in
the image, and then generates a feature map with only irrelevant objects and
background by the model. Finally, some regions in the background feature map
are used to replace some regions of the target object in the original image
feature map. In this way, ReplaceBlock can effectively simulate the feature map
of the occluded image. The experimental results show that ReplaceBlock works
better than DropBlock in regularizing convolutional networks.


title: Bhabha scattering in the gauge-Higgs unification

abstract:
  We examine effects of $Z'$ bosons in gauge-Higgs unification (GHU) models at
$\mathrm{e}^{+}\mathrm{e}^{-}\to \mathrm{e}^{+}\mathrm{e}^{-}$ Bhabha
scatterings. We evaluate differential cross sections in Bhabha scatterings
including $Z'$ bosons in two types of $\mathrm{SO}(5) \times \mathrm{U}(1)
\times \mathrm{SU}(3)$ GHU models. We find that deviations of differential
cross sections in the GHU models from those in the SM can be seen at $\sqrt{s}
= 250\,\mathrm{GeV}$. With $80\%$-longitudinally polarized electron and
$30\%$-longitudinally polarized positron beams, the left-right asymmetries in
the GHU A- and B-models are resolved at more than $3\,\sigma$ at $L_{\rm int}
=250\,\mathrm{fb}^{-1}$. We also show that Bhabha scattering with scattering
angle less than 100 mrad can be safely used as luminosity measurements in
$\mathrm{e}^{+}\mathrm{e}^{-}$ colliders since the effects of $Z'$ bosons are
well suppressed for small scattering angle. We propose a new observable which
can be measured at future TeV-scale $\mathrm{e}^{+} \mathrm{e}^{-}$ linear
colliders.


title:
How Deep is Your Art: An Experimental Study on the Limits of Artistic
  Understanding in a Single-Task, Single-Modality Neural Network

abstract:
  Mathematical modeling and aesthetic rule extraction of works of art are
complex activities. This is because art is a multidimensional, subjective
discipline. Perception and interpretation of art are, to many extents, relative
and open-ended rather than measurable. Following the explainable Artificial
Intelligence paradigm, this paper investigated in a human-understandable
fashion the limits to which a single-task, single-modality benchmark computer
vision model performs in classifying contemporary 2D visual arts. It is
important to point out that this work does not introduce an interpreting method
to open the black box of Deep Neural Networks, instead it uses existing
evaluating metrics derived from the confusion matrix to try to uncover the
mechanism with which Deep Neural Networks understand art. To achieve so,
VGG-11, pre-trained on ImageNet and discriminatively fine-tuned, was used on
handcrafted small-data datasets designed from real-world photography gallery
shows. We demonstrated that the artwork's Exhibited Properties or formal
factors such as shape and color, rather than Non-Exhibited Properties or
content factors such as history and intention, have much higher potential to be
the determinant when art pieces have very similar Exhibited Properties. We also
showed that a single-task and single-modality model's understanding of art is
inadequate as it largely ignores Non-Exhibited Properties.


title:
ConferencingSpeech 2022 Challenge: Non-intrusive Objective Speech
  Quality Assessment (NISQA) Challenge for Online Conferencing Applications

abstract:
  With the advances in speech communication systems such as online conferencing
applications, we can seamlessly work with people regardless of where they are.
However, during online meetings, speech quality can be significantly affected
by background noise, reverberation, packet loss, network jitter, etc. Because
of its nature, speech quality is traditionally assessed in subjective tests in
laboratories and lately also in crowdsourcing following the international
standards from ITU-T Rec. P.800 series. However, those approaches are costly
and cannot be applied to customer data. Therefore, an effective objective
assessment approach is needed to evaluate or monitor the speech quality of the
ongoing conversation. The ConferencingSpeech 2022 challenge targets the
non-intrusive deep neural network models for the speech quality assessment
task. We open-sourced a training corpus with more than 86K speech clips in
different languages, with a wide range of synthesized and live degradations and
their corresponding subjective quality scores through crowdsourcing. 18 teams
submitted their models for evaluation in this challenge. The blind test sets
included about 4300 clips from wide ranges of degradations. This paper
describes the challenge, the datasets, and the evaluation methods and reports
the final results.


title:
Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for
  Real-Time Full-Band Speech Enhancement

abstract:
  Due to the high computational complexity to model more frequency bands, it is
still intractable to conduct real-time full-band speech enhancement based on
deep neural networks. Recent studies typically utilize the compressed
perceptually motivated features with relatively low frequency resolution to
filter the full-band spectrum by one-stage networks, leading to limited speech
quality improvements. In this paper, we propose a coordinated sub-band fusion
network for full-band speech enhancement, which aims to recover the low- (0-8
kHz), middle- (8-16 kHz), and high-band (16-24 kHz) in a step-wise manner.
Specifically, a dual-stream network is first pretrained to recover the low-band
complex spectrum, and another two sub-networks are designed as the middle- and
high-band noise suppressors in the magnitude-only domain. To fully capitalize
on the information intercommunication, we employ a sub-band interaction module
to provide external knowledge guidance across different frequency bands.
Extensive experiments show that the proposed method yields consistent
performance advantages over state-of-the-art full-band baselines.


title: Monitored Distillation for Positive Congruent Depth Completion

abstract:
  We propose a method to infer a dense depth map from a single image, its
calibration, and the associated sparse point cloud. In order to leverage
existing models that produce putative depth maps (teacher models), we propose
an adaptive knowledge distillation approach that yields a positive congruent
training process, where a student model avoids learning the error modes of the
teachers. We consider the scenario of a blind ensemble where we do not have
access to ground truth for model selection nor training. The crux of our
method, termed Monitored Distillation, lies in a validation criterion that
allows us to learn from teachers by choosing predictions that best minimize the
photometric reprojection error for a given image. The result of which is a
distilled depth map and a confidence map, or "monitor", for how well a
prediction from a particular teacher fits the observed image. The monitor
adaptively weights the distilled depth where, if all of the teachers exhibit
high residuals, the standard unsupervised image reconstruction loss takes over
as the supervisory signal. On indoor scenes (VOID), we outperform blind
ensembling baselines by 13.3% and unsupervised methods by 20.3%; we boast a 79%
model size reduction while maintaining comparable performance to the best
supervised method. For outdoors (KITTI), we tie for 5th overall on the
benchmark despite not using ground truth.


title:
Constraint energy minimizing generalized multiscale finite element
  method for convection diffusion equation

abstract:
  In this paper we present and analyze a constraint energy minimizing
generalized multiscale finite element method for convection diffusion equation.
To define the multiscale basis functions, we first build an auxiliary
multiscale space by solving local spectral problems motivated by analysis. Then
constraint energy minimization performed in oversampling domains is exploited
to construct the multiscale space. The resulting multiscale basis functions
have a good decay property even for high contrast diffusion and convection
coefficients. Furthermore, if the number of oversampling layer is chosen
properly, we can prove that the convergence rate is proportional to the coarse
mesh size. Our analysis also indicates that the size of the oversampling domain
weakly depends on the contrast of the heterogeneous coefficients. Several
numerical experiments are presented illustrating the performances of our
method.


title:
A posteriori error estimates for an optimal control problem with a
  bilinear state equation

abstract:
  We propose and analyze a posteriori error estimators for an optimal control
problem that involves an elliptic partial differential equation as state
equation and a control variable that enters the state equation as a
coefficient; pointwise constraints on the control variable are considered as
well. We consider two different strategies to approximate optimal variables: a
fully discrete scheme in which the admissible control set is discretized with
piecewise constant functions and a semi-discrete scheme where the admissible
control set is not discretized; the latter scheme being based on the so-called
variational discretization approach. We design, for each solution technique, an
a posteriori error estimator and show, in two and three dimensional Lipschitz
polygonal/polyhedral domains (not necessarily convex), that the proposed error
estimator is reliable and efficient. We design, based on the devised
estimators, adaptive strategies that deliver optimal experimental rates of
convergence for the performed numerical examples.


title:
Enhancing Zero-Shot Many to Many Voice Conversion with Self-Attention
  VAE

abstract:
  Variational auto-encoder(VAE) is an effective neural network architecture to
disentangle a speech utterance into speaker identity and linguistic content
latent embeddings, then generate an utterance for a target speaker from that of
a source speaker. This is possible by concatenating the identity embedding of
the target speaker and the content embedding of the source speaker uttering a
desired sentence. In this work, we found a suitable location of VAE's decoder
to add a self-attention layer for incorporating non-local information in
generating a converted utterance and hiding the source speaker's identity. In
experiments of zero-shot many-to-many voice conversion task on VCTK data set,
the self-attention layer enhances speaker classification accuracy on unseen
speakers by 27\% while increasing the decoder parameter size by 12\%. The voice
quality of converted utterance degrades by merely 3\% measured by the MOSNet
scores. To reduce over-fitting and generalization error, we further applied a
relaxed group-wise splitting method in network training and achieved a gain of
speaker classification accuracy on unseen speakers by 46\% while maintaining
the conversion voice quality in terms of MOSNet scores. Our encouraging
findings point to future research on integrating more variety of attention
structures in VAE framework for advancing zero-shot many-to-many voice
conversions.


title: Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels

abstract:
  Establishing dense correspondences across semantically similar images remains
a challenging task due to the significant intra-class variations and background
clutters. Traditionally, a supervised learning was used for training the
models, which required tremendous manually-labeled data, while some methods
suggested a self-supervised or weakly-supervised learning to mitigate the
reliance on the labeled data, but with limited performance. In this paper, we
present a simple, but effective solution for semantic correspondence that
learns the networks in a semi-supervised manner by supplementing few
ground-truth correspondences via utilization of a large amount of confident
correspondences as pseudo-labels, called SemiMatch. Specifically, our framework
generates the pseudo-labels using the model's prediction itself between source
and weakly-augmented target, and uses pseudo-labels to learn the model again
between source and strongly-augmented target, which improves the robustness of
the model. We also present a novel confidence measure for pseudo-labels and
data augmentation tailored for semantic correspondence. In experiments,
SemiMatch achieves state-of-the-art performance on various benchmarks,
especially on PF-Willow by a large margin.


title:
Asymptotic behavior of class groups and cyclotomic Iwasawa theory of
  elliptic curves

abstract:
  In this article, we study a relation between certain quotients of ideal class
groups and the cyclotomic Iwasawa module $X_\infty$ of the Pontrjagin dual of
the fine Selmer group of an elliptic curve $E$ defined over $\mathbb{Q}$. We
consider the Galois extension field $K^E_n$ of $\mathbb{Q}$ generated by
coordinates of all $p^n$-torsion points of $E$, and introduce a quotient
$A^E_n$ of the $p$-sylow subgroup of the ideal class group of $K^E_n$ cut out
by the modulo $p^n$ Galois representation $E[p^n]$. We describe the asymptotic
behavior of $A^E_n$ by using the Iwasawa module $X_\infty$. In particular,
under certain conditions, we obtain an asymptotic formula as Iwasawa's class
number formula on the order of $A^E_n$ by using Iwasawa's invariants of
$X_\infty$.


title:
Disentangling the Impacts of Language and Channel Variability on Speech
  Separation Networks

abstract:
  Because the performance of speech separation is excellent for speech in which
two speakers completely overlap, research attention has been shifted to dealing
with more realistic scenarios. However, domain mismatch between training/test
situations due to factors, such as speaker, content, channel, and environment,
remains a severe problem for speech separation. Speaker and environment
mismatches have been studied in the existing literature. Nevertheless, there
are few studies on speech content and channel mismatches. Moreover, the impacts
of language and channel in these studies are mostly tangled. In this study, we
create several datasets for various experiments. The results show that the
impacts of different languages are small enough to be ignored compared to the
impacts of different channels. In our experiments, training on data recorded by
Android phones leads to the best generalizability. Moreover, we provide a new
solution for channel mismatch by evaluating projection, where the channel
similarity can be measured and used to effectively select additional training
data to improve the performance of in-the-wild test data.


title: An Iterative Co-Training Transductive Framework for Zero Shot Learning

abstract:
  In zero-shot learning (ZSL) community, it is generally recognized that
transductive learning performs better than inductive one as the unseen-class
samples are also used in its training stage. How to generate pseudo labels for
unseen-class samples and how to use such usually noisy pseudo labels are two
critical issues in transductive learning. In this work, we introduce an
iterative co-training framework which contains two different base ZSL models
and an exchanging module. At each iteration, the two different ZSL models are
co-trained to separately predict pseudo labels for the unseen-class samples,
and the exchanging module exchanges the predicted pseudo labels, then the
exchanged pseudo-labeled samples are added into the training sets for the next
iteration. By such, our framework can gradually boost the ZSL performance by
fully exploiting the potential complementarity of the two models'
classification capabilities. In addition, our co-training framework is also
applied to the generalized ZSL (GZSL), in which a semantic-guided OOD detector
is proposed to pick out the most likely unseen-class samples before class-level
classification to alleviate the bias problem in GZSL. Extensive experiments on
three benchmarks show that our proposed methods could significantly outperform
about $31$ state-of-the-art ones.


title:
Remote Co-teaching in Rural Classroom: Current Practices, Impacts, and
  Challenges

abstract:
  The shortage of high-quality teachers is one of the biggest educational
problems faced by underdeveloped areas. With the development of information and
communication technologies (ICTs), China has begun a remote co-teaching
intervention program using ICTs for rural classes, forming a unique co-teaching
classroom. We conducted semi-structured interviews with nine remote urban
teachers and twelve local rural teachers. We identified the remote co-teaching
classes standard practices and co-teachers collaborative work process. We also
found that remote teachers high-quality class directly impacted local teachers
and students. Furthermore, interestingly, local teachers were also actively
involved in making indirect impacts on their students by deeply coordinating
with remote teachers and adapting the resources offered by the remote teachers.
We conclude by summarizing and discussing the challenges faced by teachers,
lessons learned from the current program, and related design implications to
achieve a more adaptive and sustainable ICT4D program design.


title:
Integration of Radar Sensing into Communications with Asynchronous
  Transceivers

abstract:
  Clock asynchronism is a central problem in integrating radar sensing into
communication networks. It can cause ranging ambiguity and prevent coherent
processing of dis-continuous measurements in integration with asynchronous
transceivers. Should it be resolved, sensing can be efficiently realized in
communication networks, requiring little network infrastructure and hardware
changes. This article provides a systematic overview of existing and potential
new techniques for tackling this critical problem. We first review existing
solutions, including using a fine-tuned global reference clock, and
single-node-based and network-based techniques. We then examine open problems
and research opportunities, offering insights into what may be better realized
in each of the three solutions areas.


title:
mpiQulacs: A Distributed Quantum Computer Simulator for A64FX-based
  Cluster Systems

abstract:
  Quantum computer simulators running on classical computers are essential for
developing real quantum computers and emerging quantum applications. In
particular, state vector simulators, which store a full state vector in memory
and update it in every quantum operation, are available to simulate an
arbitrary form of quantum circuits, debug quantum applications, and validate
future quantum computers. However, the time and space complexity grows
exponentially with the number of qubits and easily exceeds the capability of a
single machine.
  Therefore, we develop a distributed state vector simulator, $mpiQulacs$, that
is optimized for large-scale simulation on A64FX-based cluster systems. A64FX
is an ARM-based CPU that is also equipped in the world's top Fugaku
supercomputer. We evaluate weak and strong scaling of mpiQulacs with up to 36
qubits on a new 64-node A64FX-based cluster system named $Todoroki$. By
comparing mpiQulacs with existing distributed state vector simulators, we show
that mpiQulacs achieves the highest performance for large-scale simulation on
tens of nodes while sustaining a nearly ideal scalability. Besides, we define a
new metric, $quantum B/F ratio$, and use it to demonstrate that mpiQulacs
running on Todoroki fits the requirements of distributed state vector
simulation rather than the existing simulators running on general purpose
CPU-based or GPU-based cluster systems.


title:
Threshold Matters in WSSS: Manipulating the Activation for the Robust
  and Accurate Segmentation Model Against Thresholds

abstract:
  Weakly-supervised semantic segmentation (WSSS) has recently gained much
attention for its promise to train segmentation models only with image-level
labels. Existing WSSS methods commonly argue that the sparse coverage of CAM
incurs the performance bottleneck of WSSS. This paper provides analytical and
empirical evidence that the actual bottleneck may not be sparse coverage but a
global thresholding scheme applied after CAM. Then, we show that this issue can
be mitigated by satisfying two conditions; 1) reducing the imbalance in the
foreground activation and 2) increasing the gap between the foreground and the
background activation. Based on these findings, we propose a novel activation
manipulation network with a per-pixel classification loss and a label
conditioning module. Per-pixel classification naturally induces two-level
activation in activation maps, which can penalize the most discriminative
parts, promote the less discriminative parts, and deactivate the background
regions. Label conditioning imposes that the output label of pseudo-masks
should be any of true image-level labels; it penalizes the wrong activation
assigned to non-target classes. Based on extensive analysis and evaluations, we
demonstrate that each component helps produce accurate pseudo-masks, achieving
the robustness against the choice of the global threshold. Finally, our model
achieves state-of-the-art records on both PASCAL VOC 2012 and MS COCO 2014
datasets.


title:
Infrared-Shielding of Plasmonic Random Metasurface Constructed by
  Cesium-Doped Tungsten Bronze

abstract:
  The heat-shielding properties of random metasurface, composed of spherical or
spheroidal nanoparticles with random displacements and/or random deformation,
were theoretically investigated using the finite difference time domain method.
The effective coverage was defined using the total area of nanoparticles in the
metasurface, and the robustness of the near-infrared light reflection against
randomness was investigated. When the effective coverage was high, the
near-infrared light reflection was reduced by at least 20% in both nanoparticle
arrangement and shape randomness compared to the hexagonal close-packed perfect
metasurface. In contrast, when effective coverage was low, the randomness of
the nanoparticle arrangement had almost no effect on the near-infrared light
reflection. Furthermore, the near-infrared light reflection performance was
improved by the randomness of the nanoparticle shape.


title: $q$-Rational Reduction and $q$-Analogues of Series for $\pi$

abstract:
  In this paper, we present a $q$-analogue of the polynomial reduction which
was originally developed for hypergeometric terms. Using the $q$-Gosper
representation, we describe the structure of rational functions that are
summable when multiplied with a given $q$-hypergeometric term. The structure
theorem enables us to generalize the $q$-polynomail reduction to the rational
case, which can be used in the automatic proof and discovery of $q$-identities.
As applications, several $q$-analogues of series for $\pi$ are presented.


title:
Nonequilibrium Statistical Mechanics and Optimal Prediction of
  Partially-Observed Complex Systems

abstract:
  Only a subset of degrees of freedom are typically accessible or measurable in
real-world systems. As a consequence, the proper setting for empirical modeling
is that of partially-observed systems. Notably, data-driven models consistently
outperform physics-based models for systems with few observable degrees of
freedom; e.g., hydrological systems. Here, we provide an operator-theoretic
explanation for this empirical success. To predict a partially-observed
system's future behavior with physics-based models, the missing degrees of
freedom must be explicitly accounted for using data assimilation and model
parametrization. Data-driven models, in contrast, employ delay-coordinate
embeddings and their evolution under the Koopman operator to implicitly model
the effects of the missing degrees of freedom. We describe in detail the
statistical physics of partial observations underlying data-driven models using
novel Maximum Entropy and Maximum Caliber measures. The resulting
nonequilibrium Wiener projections applied to the Mori-Zwanzig formalism reveal
how data-driven models may converge to the true dynamics of the observable
degrees of freedom. Additionally, this framework shows how data-driven models
infer the effects of unobserved degrees of freedom implicitly, in much the same
way that physics models infer the effects explicitly. This provides a unified
implicit-explicit modeling framework for predicting partially-observed systems,
with hybrid physics-informed machine learning methods combining implicit and
explicit aspects.


title: Compact hyperbolic Coxeter five-dimensional polytopes with nine facets

abstract:
  In this paper, we obtain a complete classification of compact hyperbolic
Coxeter five-dimensional polytopes with nine facets.


title: The restriction problem on the ellipsoid

abstract:
  Following a restriction argument in the Euclidean space, we derive a
geometric invariant formula for a possible viscosity operator for an
incompressible fluid flow on an ellipsoid embedded in $\mathbb R^3$. We also
give an asymptotic expansion of the formula in terms of the eccentricity
associated with the ellipsoid.


title:
Progressively Generating Better Initial Guesses Towards Next Stages for
  High-Quality Human Motion Prediction

abstract:
  This paper presents a high-quality human motion prediction method that
accurately predicts future human poses given observed ones. Our method is based
on the observation that a good initial guess of the future poses is very
helpful in improving the forecasting accuracy. This motivates us to propose a
novel two-stage prediction framework, including an init-prediction network that
just computes the good guess and then a formal-prediction network that predicts
the target future poses based on the guess. More importantly, we extend this
idea further and design a multi-stage prediction framework where each stage
predicts initial guess for the next stage, which brings more performance gain.
To fulfill the prediction task at each stage, we propose a network comprising
Spatial Dense Graph Convolutional Networks (S-DGCN) and Temporal Dense Graph
Convolutional Networks (T-DGCN). Alternatively executing the two networks helps
extract spatiotemporal features over the global receptive field of the whole
pose sequence. All the above design choices cooperating together make our
method outperform previous approaches by large margins: 6%-7% on Human3.6M,
5%-10% on CMU-MoCap, and 13%-16% on 3DPW.


title: Review of Twisted Poincare Symmetry

abstract:
  This article reviews the construction and some applications of twisted
Poincare-covariant quantum fields on the Moyal plane. The Drinfeld twist, which
plays a key mathematical role in this construction, is then applied to the case
of discrete groups, with a view to applications to geons in quantum gravity.
The Poincare-twisted fields can also be applied to study the CMB anisotropies,
and corrections to the power spectrum are used to put constraints on spacetime
noncommutativity. The article also addresses the issue of the difference
between Moyal and Voros quantum fields. Finally, it is pointed out that the
Euclidean functional integrals of QFTs on the Moyal plane do not, in general,
obey reflection positivity.


title: Matrix Multiplication with Less Arithmetic Complexity and IO Complexity

abstract:
  After Strassen presented the first sub-cubic matrix multiplication algorithm,
many Strassen-like algorithms are presented. Most of them with low asymptotic
cost have large hidden leading coefficient which are thus impractical. To
reduce the leading coefficient, Cenk and Hasan give a general approach reducing
the leading coefficient of $<2,2,2;7>$-algorithm to $5$ but increasing IO
complexity. In 2017, Karstadt and Schwartz also reduce the leading coefficient
of $<2,2,2;7>$-algorithm to $5$ by the Alternative Basis Matrix Multiplication
method. Meanwhile, their method reduces the IO complexity and low-order
monomials in arithmetic complexity. In 2019, Beniamini and Schwartz generalize
Alternative Basis Matrix Multiplication method reducing leading coefficient in
arithmetic complexity but increasing IO complexity. In this paper, we propose a
new matrix multiplication algorithm which reduces leading coefficient both in
arithmetic complexity and IO complexity. We apply our method to Strassen-like
algorithms improving arithmetic complexity and IO complexity (the comparison
with previous results are shown in Tables 1 and 2). Surprisingly, our IO
complexity of $<3,3,3;23>$-algorithm is $14n^{\log_323}M^{-\frac{1}{2}} +
o(n^{\log_323})$ which breaks Ballard's IO complexity low bound
($\Omega(n^{\log_323}M^{1-\frac{\log_323}{2}})$) for recursive Strassen-like
algorithms.


title:
Coarse-to-Fine Recursive Speech Separation for Unknown Number of
  Speakers

abstract:
  The vast majority of speech separation methods assume that the number of
speakers is known in advance, hence they are specific to the number of
speakers. By contrast, a more realistic and challenging task is to separate a
mixture in which the number of speakers is unknown. This paper formulates the
speech separation with the unknown number of speakers as a multi-pass source
extraction problem and proposes a coarse-to-fine recursive speech separation
method. This method comprises two stages, namely, recursive cue extraction and
target speaker extraction. The recursive cue extraction stage determines how
many computational iterations need to be performed and outputs a coarse cue
speech by monitoring statistics in the mixture. As the number of recursive
iterations increases, the accumulation of distortion eventually comes into the
extracted speech and reminder. Therefore, in the second stage, we use a target
speaker extraction network to extract a fine speech based on the coarse target
cue and the original distortionless mixture. Experiments show that the proposed
method archived state-of-the-art performance on the WSJ0 dataset with a
different number of speakers. Furthermore, it generalizes well to an unseen
large number of speakers.