title: Can Authoritative Governments Abuse the Right to Access?

abstract:
  The right to access is a great tool provided by the GDPR to empower data
subjects with their data. However, it needs to be implemented properly
otherwise it could turn subject access requests against the subjects privacy.
Indeed, recent works have shown that it is possible to abuse the right to
access using impersonation attacks. We propose to extend those impersonation
attacks by considering that the adversary has an access to governmental
resources. In this case, the adversary can forge official documents or exploit
copy of them. Our attack affects more people than one may expect. To defeat the
attacks from this kind of adversary, several solutions are available like
multi-factors or proof of aliveness. Our attacks highlight the need for strong
procedures to authenticate subject access requests.


title: Sim2Real Instance-Level Style Transfer for 6D Pose Estimation

abstract:
  In recent years, synthetic data has been widely used in the training of 6D
pose estimation networks, in part because it automatically provides perfect
annotation at low cost. However, there are still non-trivial domain gaps, such
as differences in textures/materials, between synthetic and real data. These
gaps have a measurable impact on performance. To solve this problem, we
introduce a simulation to reality (sim2real) instance-level style transfer for
6D pose estimation network training. Our approach transfers the style of target
objects individually, from synthetic to real, without human intervention. This
improves the quality of synthetic data for training pose estimation networks.
We also propose a complete pipeline from data collection to the training of a
pose estimation network and conduct extensive evaluation on a real-world
robotic platform. Our evaluation shows significant improvement achieved by our
method in both pose estimation performance and the realism of images adapted by
the style transfer.


title:
Computing zeta functions of algebraic curves using Harvey's trace
  formula

abstract:
  We present a new method for computing the zeta function of an algebraic curve
over a finite field. The algorithm relies on a trace formula of Harvey to count
points on a plane model of the curve. The zeta function of the curve is then
obtained by making corrections at singular points. We report on an
implementation and provide some examples in MAGMA which demonstrate an
improvement over Tuitman's algorithm.


title: Yield Stress and Compliance in Active Cell Monolayers

abstract:
  The rheology of biological tissue plays an important role in many processes,
from organ formation to cancer invasion. Here, we use a multi-phase field model
of motile cells to simulate active microrheology within a tissue monolayer.
When unperturbed, the tissue exhibits a transition between a solid-like state
and a fluid-like state tuned by cell motility and deformability - the ratio of
the energetic costs of steric cell-cell repulsion and cell surface tension.
When perturbed, solid tissues exhibit yield-stress behavior, with a threshold
force for the onset of motion of a probe particle that vanishes upon
approaching the solid-to-liquid transition. This onset of motion is
qualitatively different in the low and high deformability regimes. At high
deformability, the tissue is amorphous when solid, it responds compliantly to
deformations, and the probe transition to motion is smooth. At low
deformability, the monolayer is more ordered translationally and stiffer, and
the onset of motion appears discontinuous. Our results suggest that cellular or
nanoparticle transport in different types of tissues can be fundamentally
different, and point to ways in which it can be controlled.


title:
X2T: Training an X-to-Text Typing Interface with Online Learning from
  User Feedback

abstract:
  We aim to help users communicate their intent to machines using flexible,
adaptive interfaces that translate arbitrary user input into desired actions.
In this work, we focus on assistive typing applications in which a user cannot
operate a keyboard, but can instead supply other inputs, such as webcam images
that capture eye gaze or neural activity measured by a brain implant. Standard
methods train a model on a fixed dataset of user inputs, then deploy a static
interface that does not learn from its mistakes; in part, because extracting an
error signal from user behavior can be challenging. We investigate a simple
idea that would enable such interfaces to improve over time, with minimal
additional effort from the user: online learning from user feedback on the
accuracy of the interface's actions. In the typing domain, we leverage
backspaces as feedback that the interface did not perform the desired action.
We propose an algorithm called x-to-text (X2T) that trains a predictive model
of this feedback signal, and uses this model to fine-tune any existing, default
interface for translating user input into actions that select words or
characters. We evaluate X2T through a small-scale online user study with 12
participants who type sentences by gazing at their desired words, a large-scale
observational study on handwriting samples from 60 users, and a pilot study
with one participant using an electrocorticography-based brain-computer
interface. The results show that X2T learns to outperform a non-adaptive
default interface, stimulates user co-adaptation to the interface, personalizes
the interface to individual users, and can leverage offline data collected from
the default interface to improve its initial performance and accelerate online
learning.


title: Differentially Private Label Protection in Split Learning

abstract:
  Split learning is a distributed training framework that allows multiple
parties to jointly train a machine learning model over vertically partitioned
data (partitioned by attributes). The idea is that only intermediate
computation results, rather than private features and labels, are shared
between parties so that raw training data remains private. Nevertheless, recent
works showed that the plaintext implementation of split learning suffers from
severe privacy risks that a semi-honest adversary can easily reconstruct
labels. In this work, we propose \textsf{TPSL} (Transcript Private Split
Learning), a generic gradient perturbation based split learning framework that
provides provable differential privacy guarantee. Differential privacy is
enforced on not only the model weights, but also the communicated messages in
the distributed computation setting. Our experiments on large-scale real-world
datasets demonstrate the robustness and effectiveness of \textsf{TPSL} against
label leakage attacks. We also find that \textsf{TPSL} have a better
utility-privacy trade-off than baselines.


title:
Polar Codes-based Information Reconciliation Scheme with Frozen Bits
  Erasure Strategy for Quantum Key Distribution

abstract:
  Information reconciliation (IR) ensures the correctness of quantum key
distribution systems, by correcting the error bits existed in the sifted keys.
In this article, we propose a polar codes-based IR scheme with the frozen bits
erasure strategy, where an equivalent transmission of sifted keys is conducted,
so that the frozen bits in the decoding procedure is erased to 0. Thus, our IR
scheme can be implemented efficiently without the assumption of true random
numbers. Furthermore, we implement the proposed IR scheme with the fast
simplified successive cancellation list decoder and its throughput reaches to
0.88Mbps with the yield of 0.8333, where the decoder list size is 16, the block
size is 1Mb and the quantum bit error rate is 0.02.


title:
Active exterior cloaking for the 2D Helmholtz equation with complex
  wavenumbers and application to thermal cloaking

abstract:
  We design sources for the two-dimensional Helmholtz equation that can cloak
an object by cancelling out the incident field in a region, without the sources
completely surrounding the object to hide. As in previous work for real
positive wavenumbers, the sources are also determined by the Green identities.
The novelty is that we prove that the same approach works for complex
wavenumbers which makes it applicable to a variety of media, including media
with dispersion, loss and gain. Furthermore, by deriving bounds on Graf's
addition formulas with complex arguments, we obtain new estimates that allow to
quantify the quality of the cloaking effect. We illustrate our results by
applying them to achieve active exterior cloaking for the heat equation.


title: LaSDI: Parametric Latent Space Dynamics Identification

abstract:
  Enabling fast and accurate physical simulations with data has become an
important area of computational physics to aid in inverse problems,
design-optimization, uncertainty quantification, and other various
decision-making applications. This paper presents a data-driven framework for
parametric latent space dynamics identification procedure that enables fast and
accurate simulations. The parametric model is achieved by building a set of
local latent space model and designing an interaction among them. An individual
local latent space dynamics model achieves accurate solution in a trust region.
By letting the set of trust region to cover the whole parameter space, our
model shows an increase in accuracy with an increase in training data. We
introduce two different types of interaction mechanisms, i.e., point-wise and
region-based approach. Both linear and nonlinear data compression techniques
are used. We illustrate the framework of Latent Space Dynamics Identification
(LaSDI) enable a fast and accurate solution process on various partial
differential equations, i.e., Burgers' equations, radial advection problem, and
nonlinear heat conduction problem, achieving $O(100)$x speed-up and $O(1)\%$
relative error with respect to the corresponding full order models.


title: User-Level Membership Inference Attack against Metric Embedding Learning

abstract:
  Membership inference (MI) determines if a sample was part of a victim model
training set. Recent development of MI attacks focus on record-level membership
inference which limits their application in many real-world scenarios. For
example, in the person re-identification task, the attacker (or investigator)
is interested in determining if a user's images have been used during training
or not. However, the exact training images might not be accessible to the
attacker. In this paper, we develop a user-level MI attack where the goal is to
find if any sample from the target user has been used during training even when
no exact training sample is available to the attacker. We focus on metric
embedding learning due to its dominance in person re-identification, where
user-level MI attack is more sensible. We conduct an extensive evaluation on
several datasets and show that our approach achieves high accuracy on
user-level MI task.


title:
CGN: A Capacity-Guaranteed Network Architecture for Future Ultra-Dense
  Wireless Systems

abstract:
  The sixth generation (6G) era is envisioned to be a fully intelligent and
autonomous era, with physical and digital lifestyles merged together. Future
wireless network architectures should provide a solid support for such new
lifestyles. A key problem thus arises that what kind of network architectures
are suitable for 6G. In this paper, we propose a capacity-guaranteed network
(CGN) architecture, which provides high capacity for wireless devices densely
distributed everywhere, and ensures a superior scalability with low signaling
overhead and computation complexity simultaneously. Our theorem proves that the
essence of a CGN architecture is to decompose the whole network into
non-overlapping clusters with equal cluster sum capacity. Simulation results
reveal that in terms of the minimum cluster sum capacity, the proposed CGN can
achieve at least 30% performance gain compared with existing base station
clustering (BS-clustering) architectures. In addition, our theorem is
sufficiently general and can be applied for networks with different
distributions of BSs and users.


title: Dynamic Backdoors with Global Average Pooling

abstract:
  Outsourced training and machine learning as a service have resulted in novel
attack vectors like backdoor attacks. Such attacks embed a secret functionality
in a neural network activated when the trigger is added to its input. In most
works in the literature, the trigger is static, both in terms of location and
pattern. The effectiveness of various detection mechanisms depends on this
property. It was recently shown that countermeasures in image classification,
like Neural Cleanse and ABS, could be bypassed with dynamic triggers that are
effective regardless of their pattern and location. Still, such backdoors are
demanding as they require a large percentage of poisoned training data. In this
work, we are the first to show that dynamic backdoor attacks could happen due
to a global average pooling layer without increasing the percentage of the
poisoned training data. Nevertheless, our experiments in sound classification,
text sentiment analysis, and image classification show this to be very
difficult in practice.


title: An Efficient Subpopulation-based Membership Inference Attack

abstract:
  Membership inference attacks allow a malicious entity to predict whether a
sample is used during training of a victim model or not. State-of-the-art
membership inference attacks have shown to achieve good accuracy which poses a
great privacy threat. However, majority of SOTA attacks require training dozens
to hundreds of shadow models to accurately infer membership. This huge
computation cost raises questions about practicality of these attacks on deep
models. In this paper, we introduce a fundamentally different MI attack
approach which obviates the need to train hundreds of shadow models. Simply
put, we compare the victim model output on the target sample versus the samples
from the same subpopulation (i.e., semantically similar samples), instead of
comparing it with the output of hundreds of shadow models. The intuition is
that the model response should not be significantly different between the
target sample and its subpopulation if it was not a training sample. In cases
where subpopulation samples are not available to the attacker, we show that
training only a single generative model can fulfill the requirement. Hence, we
achieve the state-of-the-art membership inference accuracy while significantly
reducing the training computation cost.


title:
Twisted-light-induced exciton wave packets in transition-metal
  dichalcogenide monolayers

abstract:
  We present a comprehensive theoretical investigation of the photo-generated
excitons in transition-metal dichalcogenide monolayers (TMD-ML's) by
Laguerre-Gaussian beams, a celebrated kind of twisted lights (TL's) carrying
quantized orbital angular momenta (OAM). We show that the photo-excitation of
TL incident to a TMD-ML leads to the formation of spatially localized exciton
wave packets, constituted by the superposition of finite-momentum exciton
states determined by the intriguing interplay between the multiple degrees of
freedom of the optical and excitonic subsystems. Consequently, the TL-induced
exciton wave packets yield profound directional photo-luminescences whose
polar-angle-dependences are encoded by the transferred optical OAM and
azimuthal angle part, despite OAM-irrelevant, optically resolves the
exchange-split longitudinal and transverse exciton bands. Interestingly, the
application of linearly polarized TL onto a valley-excitonic system mimics an
exciton multiplexer allowing for selectively detecting the individual
valley-mixed exciton bands, which are normally hardly measured spectrally.


title: Canonical bases of $q$-Brauer algebras and $\imath$Schur dualities

abstract:
  Expanding the classical work of Kazhdan-Lusztig, we construct a bar
involution and canonical bases on the $q$-Brauer algebra introduced by Wenzl.
We define explicit actions of the $q$-Brauer algebra on the tensor spaces, and
formulate $\imath$Schur dualities between the $q$-Brauer algebra and the
$\imath$quantum groups of type AI and AII respectively.


title: TransMUSE: Transferable Traffic Prediction in MUlti-Service EdgeNetworks

abstract:
  The Covid-19 pandemic has forced the workforce to switch to working from
home, which has put significant burdens on the management of broadband networks
and called for intelligent service-by-service resource optimization at the
network edge. In this context, network traffic prediction is crucial for
operators to provide reliable connectivity across large geographic regions.
Although recent advances in neural network design have demonstrated potential
to effectively tackle forecasting, in this work we reveal based on real-world
measurements that network traffic across different regions differs widely. As a
result, models trained on historical traffic data observed in one region can
hardly serve in making accurate predictions in other areas. Training bespoke
models for different regions is tempting, but that approach bears significant
measurement overhead, is computationally expensive, and does not scale.
Therefore, in this paper we propose TransMUSE, a novel deep learning framework
that clusters similar services, groups edge-nodes into cohorts by traffic
feature similarity, and employs a Transformer-based Multi-service Traffic
Prediction Network (TMTPN), which can be directly transferred within a cohort
without any customization. We demonstrate that TransMUSE exhibits imperceptible
performance degradation in terms of mean absolute error (MAE) when forecasting
traffic, compared with settings where a model is trained for each individual
edge node. Moreover, our proposed TMTPN architecture outperforms the
state-of-the-art, achieving up to 43.21% lower MAE in the multi-service traffic
prediction task. To the best of our knowledge, this is the first work that
jointly employs model transfer and multi-service traffic prediction to reduce
measurement overhead, while providing fine-grained accurate demand forecasts
for edge services provisioning.


title:
Robust Approximate Simulation for Hierarchical Control of Piecewise
  Affine Systems under Bounded Disturbances

abstract:
  Piecewise affine (PWA) systems are widely applied in many practical cases
such as the control of nonlinear systems and hybrid dynamics. However, most of
the existing PWA control methods have poor scalability with respect to the
number of modes and system dimensions and may not be robust to the disturbances
in performance. In this paper, we present a robust approximate simulation based
control method for PWA systems under bounded external disturbances. First, a
lower-dimensional linear system (abstraction) and an associated interface are
designed to enable the output of the PWA system (concrete system) to track the
output of the abstraction. Then, a Lyapunov-like simulation function is
designed to show the boundedness of the output errors between the two systems.
Furthermore, the results obtained for linear abstraction are extended to the
case that a simpler PWA system is the abstraction. To illustrate the
effectiveness of the proposed approach, simulation results are provided for two
design examples.


title:
Relativistic potential energy of a bound particle (Non-dissipative
  relativistic harmonic oscillator)

abstract:
  The well known relation of Einstein relativistic energy for a free particle
is extended to cover the total relativistic energy of a bound particle by
calculating the relativistic potential energy. A non dissipative harmonic
oscillator (NDHO) is a fundamental bound system. Therefore, the potential
energy of an NDHO is analytically extended from the non relativistic to the
relativistic regime for the first time. This study is essentially concerned
with the relativistic mass, where the Lorentz factor transforms the state of
the second-order differential equation of an NDHO from linear into nonlinear.
The results are finally confirmed by demonstrating energy conservation since
the sum of kinetic and potential energies remains constant throughout the non
relativistic and relativistic regimes.


title:
WPNAS: Neural Architecture Search by jointly using Weight Sharing and
  Predictor

abstract:
  Weight sharing based and predictor based methods are two major types of fast
neural architecture search methods. In this paper, we propose to jointly use
weight sharing and predictor in a unified framework. First, we construct a
SuperNet in a weight-sharing way and probabilisticly sample architectures from
the SuperNet. To increase the correctness of the evaluation of architectures,
besides direct evaluation using the inherited weights, we further apply a
few-shot predictor to assess the architecture on the other hand. The final
evaluation of the architecture is the combination of direct evaluation, the
prediction from the predictor and the cost of the architecture. We regard the
evaluation as a reward and apply a self-critical policy gradient approach to
update the architecture probabilities. To further reduce the side effects of
weight sharing, we propose a weakly weight sharing method by introducing
another HyperNet. We conduct experiments on datasets including CIFAR-10,
CIFAR-100 and ImageNet under NATS-Bench, DARTS and MobileNet search space. The
proposed WPNAS method achieves state-of-the-art performance on these datasets.


title:
Scaling K2. V. Statistical Validation of 60 New Exoplanets From K2
  Campaigns 2-18

abstract:
  The NASA K2 mission, salvaged from the hardware failures of the Kepler
telescope, has continued Kepler's planet-hunting success. It has revealed
nearly 500 transiting planets around the ecliptic plane, many of which are the
subject of further study, and over 1000 additional candidates. Here we present
the results of an ongoing project to follow-up and statistically validate new
K2 planets, in particular to identify promising new targets for further
characterization. By analyzing the reconnaissance spectra, high-resolution
imaging, centroid variations, and statistical likelihood of the signals of 91
candidates, we validate 60 new planets in 46 systems. These include: a number
of planets amenable to transmission spectroscopy (K2-384 f, K2-387 b, K2-390 b,
K2-403 b, and K2-398 c), emission spectroscopy (K2-371 b, K2-370 b, and K2-399
b), and both (K2-405 b and K2-406 b); several systems with planets in or close
to mean motion resonances (K2-381, K2-398) including a compact, TRAPPIST-1-like
system of five small planets orbiting a mid-M dwarf (K2-384); an ultra-short
period sub-Saturn in the hot Saturn desert (K2-399 b); and a super-Earth
orbiting a moderately bright (V=11.93), metal-poor ([Fe/H]=-0.579+/-0.080) host
star (K2-408 b). In total we validate planets around 4 F stars, 26 G stars, 13
K stars, and 3 M dwarfs. In addition, we provide a list of 37 vetted planet
candidates that should be prioritized for future follow-up observation in order
to be confirmed or validated.


title: Second-order Symmetric Non-negative Latent Factor Analysis

abstract:
  Precise representation of large-scale undirected network is the basis for
understanding relations within a massive entity set. The undirected network
representation task can be efficiently addressed by a symmetry non-negative
latent factor (SNLF) model, whose objective is clearly non-convex. However,
existing SNLF models commonly adopt a first-order optimizer that cannot well
handle the non-convex objective, thereby resulting in inaccurate representation
results. On the other hand, higher-order learning algorithms are expected to
make a breakthrough, but their computation efficiency are greatly limited due
to the direct manipulation of the Hessian matrix, which can be huge in
undirected network representation tasks. Aiming at addressing this issue, this
study proposes to incorporate an efficient second-order method into SNLF,
thereby establishing a second-order symmetric non-negative latent factor
analysis model for undirected network with two-fold ideas: a) incorporating a
mapping strategy into SNLF model to form an unconstrained model, and b)
training the unconstrained model with a specially designed second order method
to acquire a proper second-order step efficiently. Empirical studies indicate
that proposed model outperforms state-of-the-art models in representation
accuracy with affordable computational burden.


title:
A Robust Statistical Analysis of the Role of Hydropower on the System
  Electricity Price and Price Volatility

abstract:
  Hydroelectric power (hydropower) is unique in that it can function as both a
conventional source of electricity and as backup storage (pumped hydroelectric
storage) for providing energy in times of high demand on the grid. This study
examines the impact of hydropower on system electricity price and price
volatility in the region served by the New England Independent System Operator
(ISONE) from 2014 - 2020. We perform a robust holistic analysis of the mean and
quantile effects, as well as the marginal contributing effects of hydropower in
the presence of solar and wind resources. First, the price data is adjusted for
deterministic temporal trends, correcting for seasonal, weekend, and diurnal
effects that may obscure actual representative trends in the data. Using
multiple linear regression and quantile regression, we observe that hydropower
contributes to a reduction in the system electricity price and price
volatility. While hydropower has a weak impact on decreasing price and
volatility at the mean, it has greater impact at extreme quantiles (> 70th
percentile). At these higher percentiles, we find that hydropower provides a
stabilizing effect on price volatility in the presence of volatile resources
such as wind. We conclude with a discussion of the observed relationship
between hydropower and system electricity price and volatility.


title: Bayesian community detection for networks with covariates

abstract:
  The increasing prevalence of network data in a vast variety of fields and the
need to extract useful information out of them have spurred fast developments
in related models and algorithms. Among the various learning tasks with network
data, community detection, the discovery of node clusters or "communities," has
arguably received the most attention in the scientific community. In many
real-world applications, the network data often come with additional
information in the form of node or edge covariates that should ideally be
leveraged for inference. In this paper, we add to a limited literature on
community detection for networks with covariates by proposing a Bayesian
stochastic block model with a covariate-dependent random partition prior. Under
our prior, the covariates are explicitly expressed in specifying the prior
distribution on the cluster membership. Our model has the flexibility of
modeling uncertainties of all the parameter estimates including the community
membership. Importantly, and unlike the majority of existing methods, our model
has the ability to learn the number of the communities via posterior inference
without having to assume it to be known. Our model can be applied to community
detection in both dense and sparse networks, with both categorical and
continuous covariates, and our MCMC algorithm is very efficient with good
mixing properties. We demonstrate the superior performance of our model over
existing models in a comprehensive simulation study and an application to two
real datasets.


title: Teaching Robots to Span the Space of Functional Expressive Motion

abstract:
  Our goal is to enable robots to perform functional tasks in emotive ways, be
it in response to their users' emotional states, or expressive of their
confidence levels. Prior work has proposed learning independent cost functions
from user feedback for each target emotion, so that the robot may optimize it
alongside task and environment specific objectives for any situation it
encounters. However, this approach is inefficient when modeling multiple
emotions and unable to generalize to new ones. In this work, we leverage the
fact that emotions are not independent of each other: they are related through
a latent space of Valence-Arousal-Dominance (VAD). Our key idea is to learn a
model for how trajectories map onto VAD with user labels. Considering the
distance between a trajectory's mapping and a target VAD allows this single
model to represent cost functions for all emotions. As a result 1) all user
feedback can contribute to learning about every emotion; 2) the robot can
generate trajectories for any emotion in the space instead of only a few
predefined ones; and 3) the robot can respond emotively to user-generated
natural language by mapping it to a target VAD. We introduce a method that
interactively learns to map trajectories to this latent space and test it in
simulation and in a user study. In experiments, we use a simple vacuum robot as
well as the Cassie biped.


title:
Deep Lexical Hypothesis: Identifying personality structure in natural
  language

abstract:
  Recent advances in natural language processing (NLP) have produced general
models that can perform complex tasks such as summarizing long passages and
translating across languages. Here, we introduce a method to extract adjective
similarities from language models as done with survey-based ratings in
traditional psycholexical studies but using millions of times more text in a
natural setting. The correlational structure produced through this method is
highly similar to that of self- and other-ratings of 435 terms reported by
Saucier and Goldberg (1996a). The first three unrotated factors produced using
NLP are congruent with those in survey data, with coefficients of 0.89, 0.79,
and 0.79. This structure is robust to many modeling decisions: adjective set,
including those with 1,710 terms (Goldberg, 1982) and 18,000 terms (Allport &
Odbert, 1936); the query used to extract correlations; and language model.
Notably, Neuroticism and Openness are only weakly and inconsistently recovered.
This is a new source of signal that is closer to the original (semantic) vision
of the Lexical Hypothesis. The method can be applied where surveys cannot: in
dozens of languages simultaneously, with tens of thousands of items, on
historical text, and at extremely large scale for little cost. The code is made
public to facilitate reproduction and fast iteration in new directions of
research.


title: Multi-fidelity validation of variable-density turbulent mixing models

abstract:
  In this study, ensembles of experimental data are presented and utilized to
compare and validate two models used in the simulation of variable density,
compressible turbulent mixing. Though models of this kind (Reynolds Averaged
Navier Stokes and Large Eddy Simulations) have been validated extensively with
more canonical flows in previous studies, the present approach offers novelty
in the complexity of the geometry, the ensemble based validation, and the
uniformity of the computational framework on which the models are tested.
Moreover, all experimental and computational tasks were completed by the
authors which has led to a tightly coupled experimental configuration with its
digital twin. The experimental divergent shock tube facility and its data
acquisition methods are described and replicated in simulation space. A 2D
Euler model which neglects the turbulent mixing at the interface is optimized
to experimental data using a Gaussian process. This model then serves as the
basis for both the 2D RANS and 3D LES studies that make comparisons to the
mixing layer data from the experiment. RANS is shown to produce good agreement
with experimental data only at late flow development times. The LES ensembles
generally show good agreement with experimental data, but display sensitivity
to the characterization of initial conditions. Resolution dependent behavior is
also observed for certain higher-order statistics of interest. Overall, the LES
model successfully captures the effects of divergent geometry, compressibility,
and combined non-linear instabilities inherent to the problem. The successful
prediction of mixing width and its growth rate highlight the existence of three
distinct regimes in the development of the instability, each with similarities
to previously studied instabilities.


title:
LiteTransformerSearch: Training-free On-device Search for Efficient
  Autoregressive Language Models

abstract:
  The transformer architecture is ubiquitously used as the building block of
most large-scale language models. However, it remains a painstaking guessing
game of trial and error to set its myriad of architectural hyperparameters,
e.g., number of layers, number of attention heads, and inner size of the feed
forward network, and find architectures with the optimal trade-off between task
performance like perplexity and compute constraints like memory and latency.
This challenge is further exacerbated by the proliferation of various hardware.
In this work, we leverage the somewhat surprising empirical observation that
the number of non-embedding parameters in autoregressive transformers has a
high rank correlation with task performance, irrespective of the architectural
hyperparameters. Since architectural hyperparameters affect the latency and
memory footprint in a hardware-dependent manner, the above observation
organically induces a simple search algorithm that can be directly run on
target devices. We rigorously show that the latency and perplexity
pareto-frontier can be found without need for any model training, using
non-embedding parameters as a proxy for perplexity. We evaluate our method,
dubbed Lightweight Transformer Search (LTS), on diverse devices from ARM CPUs
to Nvidia GPUs and show that the perplexity of Transformer-XL can be achieved
with up to 2x lower latency. LTS extracts the pareto-frontier in less than 3
hours while running on a commodity laptop. We effectively remove the carbon
footprint of training for hundreds of GPU hours, offering a strong simple
baseline for future NAS methods in autoregressive language modeling.


title:
Contrastive Graph Convolutional Networks for Hardware Trojan Detection
  in Third Party IP Cores

abstract:
  The availability of wide-ranging third-party intellectual property (3PIP)
cores enables integrated circuit (IC) designers to focus on designing
high-level features in ASICs/SoCs. The massive proliferation of ICs brings with
it an increased number of bad actors seeking to exploit those circuits for
various nefarious reasons. This is not surprising as integrated circuits affect
every aspect of society. Thus, malicious logic (Hardware Trojans, HT) being
surreptitiously injected by untrusted vendors into 3PIP cores used in IC design
is an ever present threat. In this paper, we explore methods for identification
of trigger-based HT in designs containing synthesizable IP cores without a
golden model. Specifically, we develop methods to detect hardware trojans by
detecting triggers embedded in ICs purely based on netlists acquired from the
vendor. We propose GATE-Net, a deep learning model based on graph-convolutional
networks (GCN) trained using supervised contrastive learning, for flagging
designs containing randomly-inserted triggers using only the corresponding
netlist. Our proposed architecture achieves significant improvements over
state-of-the-art learning models yielding an average 46.99% improvement in
detection performance for combinatorial triggers and 21.91% improvement for
sequential triggers across a variety of circuit types. Through rigorous
experimentation, qualitative and quantitative performance evaluations, we
demonstrate effectiveness of GATE-Net and the supervised contrastive training
of GATE-Net for HT detection.


title:
Portability for GPU-accelerated molecular docking applications for cloud
  and HPC: can portable compiler directives provide performance across all
  platforms?

abstract:
  High-throughput structure-based screening of drug-like molecules has become a
common tool in biomedical research. Recently, acceleration with graphics
processing units (GPUs) has provided a large performance boost for molecular
docking programs. Both cloud and high-performance computing (HPC) resources
have been used for large screens with molecular docking programs; while NVIDIA
GPUs have dominated cloud and HPC resources, new vendors such as AMD and Intel
are now entering the field, creating the problem of software portability across
different GPUs. Ideally, software productivity could be maximized with portable
programming models that are able to maintain high performance across
architectures. While in many cases compiler directives have been used as an
easy way to offload parallel regions of a CPU-based program to a GPU
accelerator, they may also be an attractive programming model for providing
portability across different GPU vendors, in which case the porting process may
proceed in the reverse direction: from low-level, architecture-specific code to
higher-level directive-based abstractions. MiniMDock is a new mini-application
(miniapp) designed to capture the essential computational kernels found in
molecular docking calculations, such as are used in pharmaceutical drug
discovery efforts, in order to test different solutions for porting across GPU
architectures. Here we extend MiniMDock to GPU offloading with OpenMP
directives, and compare to performance of kernels using CUDA, and HIP on both
NVIDIA and AMD GPUs, as well as across different compilers, exploring
performance bottlenecks. We document this reverse-porting process, from highly
optimized device code to a higher-level version using directives, compare code
structure, and describe barriers that were overcome in this effort.


title:
The Endomorphism Rings of Supersingular Elliptic Curves over
  $\mathbb{F}_p$ and the Binary Quadratic Forms

abstract:
  It is well known that there is a one-to-one correspondence between the
supersingular $j$-invariants up to the action of
$\text{Gal}(\mathbb{F}_{p^2}/\mathbb{F}_p)$ and the isomorphism classes of
maximal orders in $B_{p,\infty}$ by Deuring's theorem. Interestingly, we
establish a one-to-one correspondence between the $\mathbb{F}_p$-isomorphism
classes of supersingular elliptic curves and the primitive reduced binary
quadratic forms with discriminant $-p$ or $-16p$. Due to this correspondence
and the fact that the isogenies between elliptic curves over $\mathbb{F}_p$
could be represented by quadratic forms, we show that operations of these
isogenies on elliptic curves over $\mathbb{F}_p$ are compatible with
compositions of corresponding quadratic forms. Based on these results, we could
reduce the security of the CSIDH cryptosystem to the computation of this
correspondence explicitly.


title: Universal Segmentation of 33 Anatomies

abstract:
  In the paper, we present an approach for learning a single model that
universally segments 33 anatomical structures, including vertebrae, pelvic
bones, and abdominal organs. Our model building has to address the following
challenges. Firstly, while it is ideal to learn such a model from a
large-scale, fully-annotated dataset, it is practically hard to curate such a
dataset. Thus, we resort to learn from a union of multiple datasets, with each
dataset containing the images that are partially labeled. Secondly, along the
line of partial labelling, we contribute an open-source, large-scale vertebra
segmentation dataset for the benefit of spine analysis community, CTSpine1K,
boasting over 1,000 3D volumes and over 11K annotated vertebrae. Thirdly, in a
3D medical image segmentation task, due to the limitation of GPU memory, we
always train a model using cropped patches as inputs instead a whole 3D volume,
which limits the amount of contextual information to be learned. To this, we
propose a cross-patch transformer module to fuse more information in adjacent
patches, which enlarges the aggregated receptive field for improved
segmentation performance. This is especially important for segmenting, say, the
elongated spine. Based on 7 partially labeled datasets that collectively
contain about 2,800 3D volumes, we successfully learn such a universal model.
Finally, we evaluate the universal model on multiple open-source datasets,
proving that our model has a good generalization performance and can
potentially serve as a solid foundation for downstream tasks.


title:
The Existence of Barren Plateaus in The Detection of Quantum
  Entanglement

abstract:
  Many entanglement measures are first defined for pure states of a bipartite
Hilbert space, and then extended to mixed states via the convex roof extension.
In this article we alter the convex roof extension of an entanglement measure,
to produce a sequence of extensions that we call $f$-$d$ extensions, for $d \in
\mathbb{N}$, where $f:[0,1]\to [0, \infty)$ is a fixed continuous function
which vanishes only at zero. We prove that for any such function $f$, and any
continuous, faithful, non-negative function, (such as an entanglement measure),
$\mu$ on the set of pure states of a finite dimensional bipartite Hilbert
space, the collection of $f$-$d$ extensions of $\mu$ detects entanglement, i.e.
a mixed state $\rho$ on a finite dimensional bipartite Hilbert space is
separable, if and only if there exists $d \in \mathbb{N}$ such that the $f$-$d$
extension of $\mu$ applied to $\rho$ is equal to zero. We introduce a quantum
variational algorithm which aims to approximate the $f$-$d$ extensions of
entanglement measures defined on pure states. However, the algorithm does have
its drawbacks. We show that this algorithm exhibits barren plateaus when used
to approximate the family of $f$-$d$ extensions of the Tsalis entanglement
entropy for a certain function $f$.


title: Learning Incrementally to Segment Multiple Organs in a CT Image

abstract:
  There exists a large number of datasets for organ segmentation, which are
partially annotated and sequentially constructed. A typical dataset is
constructed at a certain time by curating medical images and annotating the
organs of interest. In other words, new datasets with annotations of new organ
categories are built over time. To unleash the potential behind these partially
labeled, sequentially-constructed datasets, we propose to incrementally learn a
multi-organ segmentation model. In each incremental learning (IL) stage, we
lose the access to previous data and annotations, whose knowledge is assumingly
captured by the current model, and gain the access to a new dataset with
annotations of new organ categories, from which we learn to update the organ
segmentation model to include the new organs. While IL is notorious for its
`catastrophic forgetting' weakness in the context of natural image analysis, we
experimentally discover that such a weakness mostly disappears for CT
multi-organ segmentation. To further stabilize the model performance across the
IL stages, we introduce a light memory module and some loss functions to
restrain the representation of different categories in feature space,
aggregating feature representation of the same class and separating feature
representation of different classes. Extensive experiments on five open-sourced
datasets are conducted to illustrate the effectiveness of our method.


title: Chern insulator in a hyperbolic lattice

abstract:
  Motivated by the recent experimental realizations of hyperbolic lattices in
circuit quantum electrodynamics and the research interest in the non-Euclidean
generalization of topological phenomena, we investigate the Chern insulator
phases in a hyperbolic $\{8,3\}$ lattice, which is made from regular octagons
($8$-gons) such that the coordination number of each lattice site is $3$. Based
on the conformal projection of the hyperbolic lattice into the Euclidean plane,
i.e., the Poincar\'{e} disk model, by calculating the Bott index ($B$) and the
two-terminal conductance, we reveal two Chern insulator phases (with $B=1$ and
$B=-1$, respectively) accompanied with quantized conductance plateaus in the
hyperbolic $\{8,3\}$ lattice. The numerical calculation results of the
nonequilibrium local current distribution further confirm that the quantized
conductance plateau originates from the chiral edge states and the two Chern
insulator phases exhibit opposite chirality. Moreover, we explore the effect of
disorder on topological phases in the hyperbolic lattice. It is demonstrated
that the chiral edge states of Chern insulators are robust against weak
disorder in the hyperbolic lattice. More fascinating is the discovery of
disorder-induced topological non-trivial phases exhibiting chiral edge states
in the hyperbolic lattice, realizing a non-Euclidean analog of topological
Anderson insulator. Our work provides a route for the exploration of
topological non-trivial states in hyperbolic geometric systems.


title:
BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition
  Tool System

abstract:
  Stable and accurate electroencephalogram (EEG) signal acquisition is
fundamental in non-invasive brain-computer interface (BCI) technology. Commonly
used EEG acquisition system's hardware and software are usually closed-source.
Its inability to flexible expansion and secondary development is a major
obstacle to real-time BCI research. This paper presents an open-source,
high-precision, multi-channel EEG Acquisition Tool System developed by Beijing
University of Posts and Telecommunications named BEATS. It implements a
comprehensive system from hardware to software, composes of analog front-end,
microprocessor, and software platform. BEATS is capable of collecting
multi-channel micro-volt EEG signals up to 4000 $Hz$ with wireless
transmission. And it adopts a pluggable structure and easy-to-access materials,
which can easily support rapid prototyping, portability, and scalability. Some
underlying techniques like direct memory access, interrupt, first in first out
are used to ensure the precision and stability of the program at the
microsecond level. Compared to state-of-the-art systems, BEATS maintains a
relatively high channel number when acquiring data at a high sampling rate,
while being quick to set up and use, making it ideal for a wide range of BCI
scenarios or long-term daily monitoring. Schematics, source code, and other
materials of BEATS are available at https://github.com/bingzant/BEATS.


title:
Partially discontinuous nodal finite elements for $H(\mathrm{curl})$ and
  $H(\mathrm{div})$

abstract:
  We investigate discretization of $H(\mathrm{curl})$ and $H(\mathrm{div})$ in
two and three space dimensions by partially discontinuous nodal finite
elements, i.e., vector-valued Lagrange finite elements with discontinuity in
certain directions. These spaces can be implemented as a combination of
continuous and discontinuous Lagrange elements and fit in de~Rham complexes. We
construct well-conditioned nodal bases.


title: Interactive Image Synthesis with Panoptic Layout Generation

abstract:
  Interactive image synthesis from user-guided input is a challenging task when
users wish to control the scene structure of a generated image with
ease.Although remarkable progress has been made on layout-based image synthesis
approaches, in order to get realistic fake image in interactive scene, existing
methods require high-precision inputs, which probably need adjustment several
times and are unfriendly to novice users. When placement of bounding boxes is
subject to perturbation, layout-based models suffer from "missing regions" in
the constructed semantic layouts and hence undesirable artifacts in the
generated images. In this work, we propose Panoptic Layout Generative
Adversarial Networks (PLGAN) to address this challenge. The PLGAN employs
panoptic theory which distinguishes object categories between "stuff" with
amorphous boundaries and "things" with well-defined shapes, such that stuff and
instance layouts are constructed through separate branches and later fused into
panoptic layouts. In particular, the stuff layouts can take amorphous shapes
and fill up the missing regions left out by the instance layouts. We
experimentally compare our PLGAN with state-of-the-art layout-based models on
the COCO-Stuff, Visual Genome, and Landscape datasets. The advantages of PLGAN
are not only visually demonstrated but quantitatively verified in terms of
inception score, Fr\'echet inception distance, classification accuracy score,
and coverage.


title:
Grid-Forming Inverter-based Wind Turbine Generators: Comprehensive
  Review, Comparative Analysis, and Recommendations

abstract:
  High penetration of wind power with conventional grid following controls for
inverter-based wind turbine generators (WTGs) reduces grid inertia and weakens
the power grid, challenging the power system stability. Grid-forming (GFM)
controls are emerging technologies that can address such stability issues.
Numerous methodologies of GFM inverters have been presented in literature;
however, their applications for WTGs have not been thoroughly explored. As WTGs
need to incorporate multiple control functions to operate reliably in different
operational regions, the GFM control should be appropriately developed for the
WTGs. This paper presents a review of GFM controls for WTGs, which covers the
latest developments in GFM controls, including multi-loop and single-loop GFM,
virtual synchronous machine-based GFM, and virtual inertia control-based GFM. A
comparison study for these GFM-based WTGs is then illustrated. In addition, the
challenges of applying these GFM controls to wind turbines are discussed,
including the impact of DC-link voltage and the current saturation algorithm on
the GFM control performance. Finally, recommendations and future developments
of GFM-based wind turbines to increase the power system reliability are
presented.


title:
Scribble-Supervised Medical Image Segmentation via Dual-Branch Network
  and Dynamically Mixed Pseudo Labels Supervision

abstract:
  Medical image segmentation plays an irreplaceable role in computer-assisted
diagnosis, treatment planning, and following-up. Collecting and annotating a
large-scale dataset is crucial to training a powerful segmentation model, but
producing high-quality segmentation masks is an expensive and time-consuming
procedure. Recently, weakly-supervised learning that uses sparse annotations
(points, scribbles, bounding boxes) for network training has achieved
encouraging performance and shown the potential for annotation cost reduction.
However, due to the limited supervision signal of sparse annotations, it is
still challenging to employ them for networks training directly. In this work,
we propose a simple yet efficient scribble-supervised image segmentation method
and apply it to cardiac MRI segmentation. Specifically, we employ a dual-branch
network with one encoder and two slightly different decoders for image
segmentation and dynamically mix the two decoders' predictions to generate
pseudo labels for auxiliary supervision. By combining the scribble supervision
and auxiliary pseudo labels supervision, the dual-branch network can
efficiently learn from scribble annotations end-to-end. Experiments on the
public ACDC dataset show that our method performs better than current
scribble-supervised segmentation methods and also outperforms several
semi-supervised segmentation methods.


title:
Learning Category-Level Generalizable Object Manipulation Policy via
  Generative Adversarial Self-Imitation Learning from Demonstrations

abstract:
  Generalizable object manipulation skills are critical for intelligent and
multi-functional robots to work in real-world complex scenes. Despite the
recent progress in reinforcement learning, it is still very challenging to
learn a generalizable manipulation policy that can handle a category of
geometrically diverse articulated objects. In this work, we tackle this
category-level object manipulation policy learning problem via imitation
learning in a task-agnostic manner, where we assume no handcrafted dense
rewards but only a terminal reward. Given this novel and challenging
generalizable policy learning problem, we identify several key issues that can
fail the previous imitation learning algorithms and hinder the generalization
to unseen instances. We then propose several general but critical techniques,
including generative adversarial self-imitation learning from demonstrations,
progressive growing of discriminator, and instance-balancing for expert buffer,
that accurately pinpoints and tackles these issues and can benefit
category-level manipulation policy learning regardless of the tasks. Our
experiments on ManiSkill benchmarks demonstrate a remarkable improvement on all
tasks and our ablation studies further validate the contribution of each
proposed technique.


title: Continual Horizontal Federated Learning for Heterogeneous Data

abstract:
  Federated learning is a promising machine learning technique that enables
multiple clients to collaboratively build a model without revealing the raw
data to each other. Among various types of federated learning methods,
horizontal federated learning (HFL) is the best-studied category and handles
homogeneous feature spaces. However, in the case of heterogeneous feature
spaces, HFL uses only common features and leaves client-specific features
unutilized. In this paper, we propose a HFL method using neural networks named
continual horizontal federated learning (CHFL), a continual learning approach
to improve the performance of HFL by taking advantage of unique features of
each client. CHFL splits the network into two columns corresponding to common
features and unique features, respectively. It jointly trains the first column
by using common features through vanilla HFL and locally trains the second
column by using unique features and leveraging the knowledge of the first one
via lateral connections without interfering with the federated training of it.
We conduct experiments on various real world datasets and show that CHFL
greatly outperforms vanilla HFL that only uses common features and local
learning that uses all features that each client has.


title:
Revisiting Linearized Bregman Iterations under Lipschitz-like Convexity
  Condition

abstract:
  The linearized Bregman iterations (LBreI) and its variants have received
considerable attention in signal/image processing and compressed sensing.
Recently, LBreI has been extended to a larger class of nonconvex functions,
along with several theoretical issues left for further investigation. In
particular, the gradient Lipschitz continuity assumption precludes its use in
many practical applications. In this study, we propose a generalized
algorithmic framework to unify LBreI-type methods. Our main discovery is that
the gradient Lipschitz continuity assumption can be replaced by a
Lipschitz-like convexity condition in both convex and nonconvex cases. The
proposed framework and theory are then applied to linear/quadratic inverse
problems.


title:
FairPrune: Achieving Fairness Through Pruning for Dermatological Disease
  Diagnosis

abstract:
  Many works have shown that deep learning-based medical image classification
models can exhibit bias toward certain demographic attributes like race,
gender, and age. Existing bias mitigation methods primarily focus on learning
debiased models, which may not necessarily guarantee all sensitive information
can be removed and usually comes with considerable accuracy degradation on both
privileged and unprivileged groups. To tackle this issue, we propose a method,
FairPrune, that achieves fairness by pruning. Conventionally, pruning is used
to reduce the model size for efficient inference. However, we show that pruning
can also be a powerful tool to achieve fairness. Our observation is that during
pruning, each parameter in the model has different importance for different
groups' accuracy. By pruning the parameters based on this importance
difference, we can reduce the accuracy difference between the privileged group
and the unprivileged group to improve fairness without a large accuracy drop.
To this end, we use the second derivative of the parameters of a pre-trained
model to quantify the importance of each parameter with respect to the model
accuracy for each group. Experiments on two skin lesion diagnosis datasets over
multiple sensitive attributes demonstrate that our method can greatly improve
fairness while keeping the average accuracy of both groups as high as possible.


title: Controllability of Multilayer Networked Sampled-data Systems

abstract:
  This paper explores the state controllability of multilayer networked
sampled-data systems with inter-layer couplings, where zero-order holders
(ZOHs) are on the control and transmission channels. The effects of both
single- and multi-rate sampling on controllability of multilayer networked
linear time-invariant (LTI) systems are analyzed, with some sufficient and/or
necessary controllability conditions derived. Under specific conditions, the
pathological sampling of single node systems could be eliminated by the network
structure and inner couplings among different nodes and different layers. The
representative drive-response inter-layer coupling mode is studied, and it
reveals that the whole system could be controllable due to the inter-layer
couplings even if the response layer is uncontrollable itself. Moreover,
simulated examples show that the modification of sampling rate on local
channels could lay a positive or negative effect on the controllability of the
whole system. All the results indicate that the controllability of the
multilayer networked sampled-data system is collectively affected by mutually
coupled factors.


title: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving

abstract:
  Pseudo-LiDAR 3D detectors have made remarkable progress in monocular 3D
detection by enhancing the capability of perceiving depth with depth estimation
networks, and using LiDAR-based 3D detection architectures. The advanced stereo
3D detectors can also accurately localize 3D objects. The gap in image-to-image
generation for stereo views is much smaller than that in image-to-LiDAR
generation. Motivated by this, we propose a Pseudo-Stereo 3D detection
framework with three novel virtual view generation methods, including
image-level generation, feature-level generation, and feature-clone, for
detecting 3D objects from a single image. Our analysis of depth-aware learning
shows that the depth loss is effective in only feature-level virtual view
generation and the estimated depth map is effective in both image-level and
feature-level in our framework. We propose a disparity-wise dynamic convolution
with dynamic kernels sampled from the disparity feature map to filter the
features adaptively from a single image for generating virtual image features,
which eases the feature degradation caused by the depth estimation errors. Till
submission (November 18, 2021), our Pseudo-Stereo 3D detection framework ranks
1st on car, pedestrian, and cyclist among the monocular 3D detectors with
publications on the KITTI-3D benchmark. The code is released at
https://github.com/revisitq/Pseudo-Stereo-3D.


title:
FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in
  Context

abstract:
  We advance sketch research to scenes with the first dataset of freehand scene
sketches, FS-COCO. With practical applications in mind, we collect sketches
that convey well scene content but can be sketched within a few minutes by a
person with any sketching skills. Our dataset comprises 10,000 freehand scene
vector sketches with per point space-time information by 100 non-expert
individuals, offering both object- and scene-level abstraction. Each sketch is
augmented with its text description. Using our dataset, we study for the first
time the problem of the fine-grained image retrieval from freehand scene
sketches and sketch captions. We draw insights on (i) Scene salience encoded in
sketches with strokes temporal order; (ii) The retrieval performance accuracy
from scene sketches against image captions; (iii) Complementarity of
information in sketches and image captions, as well as the potential benefit of
combining the two modalities. In addition, we propose new solutions enabled by
our dataset (i) We adopt meta-learning to show how the retrieval model can be
fine-tuned to a new user style given just a small set of sketches, (ii) We
extend a popular vector sketch LSTM-based encoder to handle sketches with
larger complexity than was supported by previous work. Namely, we propose a
hierarchical sketch decoder, which we leverage at a sketch-specific "pretext"
task. Our dataset enables for the first time research on freehand scene sketch
understanding and its practical applications.


title: MixCL: Pixel label matters to contrastive learning

abstract:
  Contrastive learning and self-supervised techniques have gained prevalence in
computer vision for the past few years. It is essential for medical image
analysis, which is often notorious for its lack of annotations. Most existing
self-supervised methods applied in natural imaging tasks focus on designing
proxy tasks for unlabeled data. For example, contrastive learning is often
based on the fact that an image and its transformed version share the same
identity. However, pixel annotations contain much valuable information for
medical image segmentation, which is largely ignored in contrastive learning.
In this work, we propose a novel pre-training framework called Mixed
Contrastive Learning (MixCL) that leverages both image identities and pixel
labels for better modeling by maintaining identity consistency, label
consistency, and reconstruction consistency together. Consequently, thus
pre-trained model has more robust representations that characterize medical
images. Extensive experiments demonstrate the effectiveness of the proposed
method, improving the baseline by 5.28% and 14.12% in Dice coefficient when 5%
labeled data of Spleen and 15% of BTVC are used in fine-tuning, respectively.


title: Towards Benchmarking and Evaluating Deepfake Detection

abstract:
  Deepfake detection automatically recognizes the manipulated medias through
the analysis of the difference between manipulated and non-altered videos. It
is natural to ask which are the top performers among the existing deepfake
detection approaches to identify promising research directions and provide
practical guidance. Unfortunately, it's difficult to conduct a sound
benchmarking comparison of existing detection approaches using the results in
the literature because evaluation conditions are inconsistent across studies.
Our objective is to establish a comprehensive and consistent benchmark, to
develop a repeatable evaluation procedure, and to measure the performance of a
range of detection approaches so that the results can be compared soundly. A
challenging dataset consisting of the manipulated samples generated by more
than 13 different methods has been collected, and 11 popular detection
approaches (9 algorithms) from the existing literature have been implemented
and evaluated with 6 fair-minded and practical evaluation metrics. Finally, 92
models have been trained and 644 experiments have been performed for the
evaluation. The results along with the shared data and evaluation methodology
constitute a benchmark for comparing deepfake detection approaches and
measuring progress.


title:
In the Service of Online Order: Tackling Cyber-Bullying with Machine
  Learning and Affect Analysis

abstract:
  One of the burning problems lately in Japan has been cyber-bullying, or
slandering and bullying people online. The problem has been especially noticed
on unofficial Web sites of Japanese schools. Volunteers consisting of school
personnel and PTA (Parent-Teacher Association) members have started Online
Patrol to spot malicious contents within Web forums and blogs. In practise,
Online Patrol assumes reading through the whole Web contents, which is a task
difficult to perform manually. With this paper we introduce a research intended
to help PTA members perform Online Patrol more efficiently. We aim to develop a
set of tools that can automatically detect malicious entries and report them to
PTA members. First, we collected cyber-bullying data from unofficial school Web
sites. Then we performed analysis of this data in two ways. Firstly, we
analysed the entries with a multifaceted affect analysis system in order to
find distinctive features for cyber-bullying and apply them to a machine
learning classifier. Secondly, we applied a SVM based machine learning method
to train a classifier for detection of cyber-bullying. The system was able to
classify cyber-bullying entries with 88.2% of balanced F-score.


title: Quaternionic metamonogenic functions in the unit disk

abstract:
  We construct a set of quaternionic metamonogenic functions (that is, in
$\mbox{Ker}(D+\lambda)$ for diverse $\lambda$) in the unit disk, such that
every metamonogenic function is approximable in the quaternionic Hilbert module
$L^2$ of the disk. The set is orthogonal except for the small subspace of
elements of orders zero and one. These functions are used to express
time-dependent solutions of the imaginary-time wave equation in the polar
coordinate system.


title: OmniWheg: An Omnidirectional Wheel-Leg Transformable Robot

abstract:
  This paper presents the design, analysis, and performance evaluation of an
omnidirectional transformable wheel-leg robot called OmniWheg. We design a
novel mechanism consisting of a separable Omni-wheel and 4-bar linkages,
allowing the robot to transform between Omni-wheeled and legged modes smoothly.
On wheeled mode, the robot can move in all directions and efficiently adjust
the relative position of its wheels, while it can overcome common obstacles in
legged mode, such as stairs and steps. Unlike other articles studying whegs,
this implementation with omnidirectional wheels allows the correction of
misalignments between right and left wheels before traversing obstacles, which
effectively improves the success rate and simplifies the preparation process
before the wheel-leg transformation. We describe the design concept, mechanism,
and the dynamic characteristic of the wheel-leg structure. We then evaluate its
performance in various scenarios, including passing obstacles, climbing steps
of different heights, and turning/moving omnidirectionally. Our results confirm
that this mobile platform can overcome common indoor obstacles and move
flexibly on the flat ground with the new transformable wheel-leg mechanism,
while keeping a high degree of stability.


title: GraspARL: Dynamic Grasping via Adversarial Reinforcement Learning

abstract:
  Grasping moving objects, such as goods on a belt or living animals, is an
important but challenging task in robotics. Conventional approaches rely on a
set of manually defined object motion patterns for training, resulting in poor
generalization to unseen object trajectories. In this work, we introduce an
adversarial reinforcement learning framework for dynamic grasping, namely
GraspARL. To be specific. we formulate the dynamic grasping problem as a
'move-and-grasp' game, where the robot is to pick up the object on the mover
and the adversarial mover is to find a path to escape it. Hence, the two agents
play a min-max game and are trained by reinforcement learning. In this way, the
mover can auto-generate diverse moving trajectories while training. And the
robot trained with the adversarial trajectories can generalize to various
motion patterns. Empirical results on the simulator and real-world scenario
demonstrate the effectiveness of each and good generalization of our method.


title:
Truncation Error Analysis for an Accurate Nonlocal Manifold Poisson
  Model with Dirichlet Boundary

abstract:
  In this work, we introduced a class of nonlocal models to accurately
approximate the Poisson model on manifolds that are embedded in high
dimensional Euclid spaces with Dirichlet boundary. In comparison to the
existing nonlocal Poisson models, instead of utilizing volumetric boundary
constraint to reduce the truncation error to its local counterpart, we rely on
the Poisson equation itself along the boundary to explicitly express the second
order normal derivative by some geometry-based terms, so that to create a new
model with $\mathcal{O}(\delta)$ truncation error along the $2\delta-$boundary
layer and $\mathcal{O}(\delta^2)$ at interior, with $\delta$ be the nonlocal
interaction horizon. Our concentration is on the construction and the
truncation error analysis of such nonlocal model. The control on the truncation
error is currently optimal among all nonlocal models, and is sufficient to
attain second order localization rate that will be derived in our subsequent
work.


title: Adversarial Patterns: Building Robust Android Malware Classifiers

abstract:
  Deep learning-based classifiers have substantially improved recognition of
malware samples. However, these classifiers can be vulnerable to adversarial
input perturbations. Any vulnerability in malware classifiers poses significant
threats to the platforms they defend. Therefore, to create stronger defense
models against malware, we must understand the patterns in input perturbations
caused by an adversary. This survey paper presents a comprehensive study on
adversarial machine learning for android malware classifiers. We first present
an extensive background in building a machine learning classifier for android
malware, covering both image-based and text-based feature extraction
approaches. Then, we examine the pattern and advancements in the
state-of-the-art research in evasion attacks and defenses. Finally, we present
guidelines for designing robust malware classifiers and enlist research
directions for the future.


title:
Two-dimensional ionic crystals: The cases of IA-VII alkali halides and
  IA-IB CsAu

abstract:
  The alkali halides, known as ionic crystals, have the NaCl-type or CsCl-type
structure as the ground state. We study the structural, vibrational, and
electronic properties of two-dimensional (2D) ionic crystals from
first-principles. Two potential structures that are hexagonal and tetragonal
are investigated as structural templates. Through phonon dispersion
calculations, 8 and 16 out of 20 alkali halides in the hexagonal and tetragonal
structures are dynamically stable, respectively. The electron energy gaps range
from 6.8 eV for LiF to 3.9 eV for RbI and CsI in the tetragonal structure
within the generalized gradient approximation. By considering the Madelung
energy and the core-core repulsion, we propose a hard sphere model that
accounts for the nearest-neighbor bond length and the cohesive energy of 2D
alkali halides. The 2D CsAu in the tetragonal structure is also predicted to be
stable as an ionic crystal including only metallic elements, showing a band gap
of 2.6 eV that is higher than that of the 3D counterparts.


title:
Detecting Offensive Language on Social Networks: An End-to-end Detection
  Method based on Graph Attention Networks

abstract:
  The pervasiveness of offensive language on the social network has caused
adverse effects on society, such as abusive behavior online. It is urgent to
detect offensive language and curb its spread. Existing research shows that
methods with community structure features effectively improve the performance
of offensive language detection. However, the existing models deal with
community structure independently, which seriously affects the effectiveness of
detection models. In this paper, we propose an end-to-end method based on
community structure and text features for offensive language detection
(CT-OLD). Specifically, the community structure features are directly captured
by the graph attention network layer, and the text embeddings are taken from
the last hidden layer of BERT. Attention mechanisms and position encoding are
used to fuse these features. Meanwhile, we add user opinion to the community
structure for representing user features. The user opinion is represented by
user historical behavior information, which outperforms that represented by
text information. Besides the above point, the distribution of users and tweets
is unbalanced in the popular datasets, which limits the generalization ability
of the model. To address this issue, we construct and release a dataset with
reasonable user distribution. Our method outperforms baselines with the F1
score of 89.94%. The results show that the end-to-end model effectively learns
the potential information of community structure and text, and user historical
behavior information is more suitable for user opinion representation.


title:
Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware
  Machine Learning

abstract:
  This work presents a fraud and abuse detection framework for streaming
services by modeling user streaming behavior. The goal is to discover anomalous
and suspicious incidents and scale the investigation efforts by creating models
that characterize the user behavior. We study the use of semi-supervised as
well as supervised approaches for anomaly detection. In the semi-supervised
approach, by leveraging only a set of authenticated anomaly-free data samples,
we show the use of one-class classification algorithms as well as autoencoder
deep neural networks for anomaly detection. In the supervised anomaly detection
task, we present a so-called heuristic-aware data labeling strategy for
creating labeled data samples. We carry out binary classification as well as
multi-class multi-label classification tasks for not only detecting the
anomalous samples but also identifying the underlying anomaly behavior(s)
associated with each one. Finally, using a systematic feature importance study
we provide insights into the underlying set of features that characterize
different streaming fraud categories. To the best of our knowledge, this is the
first paper to use machine learning methods for fraud and abuse detection in
real-world scale streaming services.


title:
Pressure-induced structural phase transition of vanadium: A revisit from
  the perspective of ensemble theory

abstract:
  For realistic crystals, the free energy strictly formulated in ensemble
theory can hardly be obtained because of the difficulty in solving the
high-dimension integral of the partition function, the dilemma of which makes
it even a doubt if the rigorous ensemble theory is applicable to phase
transitions of condensed matters. In the present work, the partition function
of crystal vanadium under compression up to $320$ GPa at room temperature is
solved by an approach developed very recently, and the derived equation of
state is in a good agreement with all the experimental measurements, especially
the latest one covering the widest pressure range up to $300$ GPa. Furthermore,
the derived Gibbs free energy proves the very argument to understand most of
the experiments reported in the past decade on the pressure-induced phase
transition, and, especially, a novel phase transition sequence concerning three
different phases observed very recently and the measured angles of two phases
agree with our theoretical results excellently.


title:
Ultrasound-Guided Assistive Robots for Scoliosis Assessment with
  Optimization-based Control and Variable Impedance

abstract:
  Assistive robots for healthcare have seen a growing demand due to the great
potential of relieving medical practitioners from routine jobs. In this paper,
we investigate the development of an optimization-based control framework for
an ultrasound-guided assistive robot to perform scoliosis assessment. A
conventional procedure for scoliosis assessment with ultrasound imaging
typically requires a medical practitioner to slide an ultrasound probe along a
patient's back. To automate this type of procedure, we need to consider
multiple objectives, such as contact force, position, orientation, energy,
posture, etc. To address the aforementioned components, we propose to formulate
the control framework design as a quadratic programming problem with each
objective weighed by its task priority subject to a set of equality and
inequality constraints. In addition, as the robot needs to establish constant
contact with the patient during spine scanning, we incorporate variable
impedance regulation of the end-effector position and orientation in the
control architecture to enhance safety and stability during the physical
human-robot interaction. Wherein, the variable impedance gains are retrieved by
learning from the medical expert's demonstrations. The proposed methodology is
evaluated by conducting real-world experiments of autonomous scoliosis
assessment with a robot manipulator xArm. The effectiveness is verified by the
obtained coronal spinal images of both a phantom and a human subject.


title:
A hierarchy of efficient bounds on quantum capacities exploiting
  symmetry

abstract:
  Optimal rates for achieving an information processing task are often
characterized in terms of regularized information measures. In many cases of
quantum tasks, we do not know how to compute such quantities. Here, we exploit
the symmetries in the recently introduced $D^{\#}$ in order to obtain a
hierarchy of semidefinite programming bounds on various regularized quantities.
As applications, we give a general procedure to give efficient bounds on the
regularized Umegaki channel divergence as well as the classical capacity and
two-way assisted quantum capacity of quantum channels. In particular, we obtain
slight improvements for the capacity of the amplitude damping channel. We also
prove that for fixed input and output dimensions, the regularized sandwiched
R\'enyi divergence between any two quantum channels can be approximated up to
an $\epsilon$ accuracy in time that is polynomial in $1/\epsilon$.


title: Distributionally Robust Bayesian Optimization with $\phi$-divergences

abstract:
  The study of robustness has received much attention due to its inevitability
in data-driven settings where many systems face uncertainty. One such example
of concern is Bayesian Optimization (BO), where uncertainty is multi-faceted,
yet there only exists a limited number of works dedicated to this direction. In
particular, there is the work of Kirschner et al. (2020), which bridges the
existing literature of Distributionally Robust Optimization (DRO) by casting
the BO problem from the lens of DRO. While this work is pioneering, it
admittedly suffers from various practical shortcomings such as finite contexts
assumptions, leaving behind the main question Can one devise a computationally
tractable algorithm for solving this DRO-BO problem? In this work, we tackle
this question to a large degree of generality by considering robustness against
data-shift in $\phi$-divergences, which subsumes many popular choices, such as
the $\chi^2$-divergence, Total Variation, and the extant Kullback-Leibler (KL)
divergence. We show that the DRO-BO problem in this setting is equivalent to a
finite-dimensional optimization problem which, even in the continuous context
setting, can be easily implemented with provable sublinear regret bounds. We
then show experimentally that our method surpasses existing methods, attesting
to the theoretical results


title:
Coexistence of extended and localized states in one-dimensional
  non-Hermitian Anderson model

abstract:
  In one-dimensional Hermitian tight-binding models, mobility edges separating
extended and localized states can appear in the presence of properly engineered
quasi-periodical potentials and coupling constants. On the other hand, mobility
edges don't exist in a one-dimensional Anderson lattice since localization
occurs whenever a diagonal disorder through random numbers is introduced. Here,
we consider a nonreciprocal non-Hermitian lattice and show that the coexistence
of extended and localized states appears with or without diagonal disorder in
the topologically nontrivial region. We discuss that the mobility edges appear
basically due to the boundary condition sensitivity of the nonreciprocal
non-Hermitian lattice.


title: Mapping evolving population geography in China

abstract:
  China's demographic changes have important global economic and geopolitical
implications. Yet, our understanding of such transitions at the micro-spatial
scale remains limited due to spatial inconsistency of the census data caused by
administrative boundary adjustments. To fill this gap, we manually collected
and built a population census panel from 2010 to 2020 at both the county and
prefectural-city levels. We show that the massive internal migration drives
China's increasing population concentration and regional disparity, resulting
in severe population aging in shrinking cities and increasing gender imbalance
in growing cities.


title: 3D endoscopic depth estimation using 3D surface-aware constraints

abstract:
  Robotic-assisted surgery allows surgeons to conduct precise surgical
operations with stereo vision and flexible motor control. However, the lack of
3D spatial perception limits situational awareness during procedures and
hinders mastering surgical skills in the narrow abdominal space. Depth
estimation, as a representative perception task, is typically defined as an
image reconstruction problem. In this work, we show that depth estimation can
be reformed from a 3D surface perspective. We propose a loss function for depth
estimation that integrates the surface-aware constraints, leading to a faster
and better convergence with the valid information from spatial information. In
addition, camera parameters are incorporated into the training pipeline to
increase the control and transparency of the depth estimation. We also
integrate a specularity removal module to recover more buried image
information. Quantitative experimental results on endoscopic datasets and user
studies with medical professionals demonstrate the effectiveness of our method.


title: Thermal Relaxation of Dark Matter Admixed Neutron Star

abstract:
  Motivated by the various theoretical studies regarding the efficient
capturing of dark matter by neutron stars, we explore the possible indirect
effects of captured dark matter on the cooling mechanism of a neutron star. The
equation of states for different configurations of dark matter admixed star at
finite temperature is obtained using the relativistic mean-field formalism with
the IOPB-I parameter set. We show that the variation in the dark matter
momentum vastly modifies the neutrino emissivity through specific neutrino
generating processes of the star. The specific heat and the thermal
conductivity of a dark matter admixed star have also been investigated to
explore the propagation of cooling waves in the interior of the star. The
dependence of theoretical surface temperature cooling curves on the equation of
state and chemical composition of the stellar matter has also been discussed
along with the observational data of thermal radiation from various sources. We
observed that the dark matter admixed canonical stars with $k_{f}^{\rm DM} >
0.04$ comply with the fast cooling scenario. Further, the metric for internal
thermal relaxation epoch has also been calculated with different dark matter
momentum and we deduced that increment of dark matter segment amplify the
cooling and internal relaxation rates of the star.


title:
A Versatile Multi-View Framework for LiDAR-based 3D Object Detection
  with Guidance from Panoptic Segmentation

abstract:
  3D object detection using LiDAR data is an indispensable component for
autonomous driving systems. Yet, only a few LiDAR-based 3D object detection
methods leverage segmentation information to further guide the detection
process. In this paper, we propose a novel multi-task framework that jointly
performs 3D object detection and panoptic segmentation. In our method, the 3D
object detection backbone in Bird's-Eye-View (BEV) plane is augmented by the
injection of Range-View (RV) feature maps from the 3D panoptic segmentation
backbone. This enables the detection backbone to leverage multi-view
information to address the shortcomings of each projection view. Furthermore,
foreground semantic information is incorporated to ease the detection task by
highlighting the locations of each object class in the feature maps. Finally, a
new center density heatmap generated based on the instance-level information
further guides the detection backbone by suggesting possible box center
locations for objects. Our method works with any BEV-based 3D object detection
method, and as shown by extensive experiments on the nuScenes dataset, it
provides significant performance gains. Notably, the proposed method based on a
single-stage CenterPoint 3D object detection network achieved state-of-the-art
performance on nuScenes 3D Detection Benchmark with 67.3 NDS.


title: Stochastic hydrodynamics meets hydro-kinetics

abstract:
  The hydro-kinetic formalism has been used as a complementary approach to
solving the Stochastic Differential Equations (SDE) corresponding to noisy
hydrodynamics. The hydro-kinetic formalism consists of a deterministic set of
relaxation type equations that tracks the evolution of 2-point correlation
functions of stochastic hydrodynamic quantities. Hence they are comparatively
easier to solve than the SDEs, which are computationally intensive and need to
deal with arbitrarily large gradients. This work compares the two approaches
for the propagation and diffusion of conserved charge fluctuations in the
Bjorken hydrodynamic model. For white noise, the two approaches agree. For
colored Catteneo noise, which is causal, the two approaches diverge. This is
because white noise only induces two-point correlations, while Catteneo noise
also induces higher-order correlations. This difference is quantified from the
effects of causal evolution and influence from higher-order correlations
induced by the Catteneo noise.


title:
Continual Few-shot Relation Learning via Embedding Space Regularization
  and Data Augmentation

abstract:
  Existing continual relation learning (CRL) methods rely on plenty of labeled
training data for learning a new task, which can be hard to acquire in real
scenario as getting large and representative labeled data is often expensive
and time-consuming. It is therefore necessary for the model to learn novel
relational patterns with very few labeled data while avoiding catastrophic
forgetting of previous task knowledge. In this paper, we formulate this
challenging yet practical problem as continual few-shot relation learning
(CFRL). Based on the finding that learning for new emerging few-shot tasks
often results in feature distributions that are incompatible with previous
tasks' learned distributions, we propose a novel method based on embedding
space regularization and data augmentation. Our method generalizes to new
few-shot tasks and avoids catastrophic forgetting of previous tasks by
enforcing extra constraints on the relational embeddings and by adding extra
{relevant} data in a self-supervised manner. With extensive experiments we
demonstrate that our method can significantly outperform previous
state-of-the-art methods in CFRL task settings.


title:
Dynamical Control of Interlayer Excitons and Trions in
  WSe$_2$/Mo$_{0.5}$W$_{0.5}$Se$_2$ Heterobilayer via Tunable Near-Field Cavity

abstract:
  Emerging photo-induced excitonic processes in transition metal dichalcogenide
(TMD) heterobilayers, e.g., coupling, dephasing, and energy transfer of intra-
and inter-layer excitons, allow new opportunities for ultrathin photonic
devices. Yet, with the associated large degree of spatial heterogeneity,
understanding and controlling their complex competing interactions at the
nanoscale remains a challenge. Here, we present an all-round dynamic control of
intra- and inter-layer excitonic processes in a
WSe$_2$/Mo$_{0.5}$W$_{0.5}$Se$_2$ heterobilayer using multifunctional
tip-enhanced photoluminescence (TEPL) spectroscopy. Specifically, we control
the radiative recombination path and emission rate, electronic bandgap energy,
and neutral to charged exciton conversion with <20 nm spatial resolution in a
reversible manner. It is achieved through the tip-induced engineering of Au
tip-heterobilayer distance and interlayer distance, GPa scale local pressure,
and plasmonic hot-electron injection respectively, with simultaneous
spectroscopic TEPL measurements. This unique nano-opto-electro-mechanical
control approach provides new strategies for developing versatile
nano-excitonic devices based on TMD heterobilayers.


title:
Product structure and regularity theorem for totally nonnegative flag
  varieties

abstract:
  The totally nonnegative flag variety was introduced by Lusztig. It has
enriched combinatorial, geometric, and Lie-theoretic structures. In this paper,
we introduce a (new) $J$-total positivity on the full flag variety of an
arbitrary Kac-Moody group, generalizing the (ordinary) total positivity.
  We show that the $J$-totally nonnegative flag variety has a cellular
decomposition into totally positive $J$-Richardson varieties. Moreover, each
totally positive $J$-Richardson variety admits a favorable decomposition,
called a product structure. Combined with the generalized Poincare conjecture,
we prove that the closure of each totally positive $J$-Richardson variety is a
regular CW complex homeomorphic to a closed ball. Moreover, the $J$-total
positivity on the full flag provides a model for the (ordinary) totally
nonnegative partial flag variety. As a consequence, we prove that the closure
of each (ordinary) totally positive Richardson variety is a regular CW complex
homeomorphic to a closed ball, confirming conjectures of Galashin, Karp and
Lam.


title:
Spontaneous Polarization Induced Photovoltaic Effect In Rhombohedrally
  Stacked MoS$_2$

abstract:
  Stacking order in van der Waals materials determines the coupling between
atomic layers and is therefore key to the materials' properties. By exploring
different stacking orders, many novel physical phenomena have been realized in
artificial vdW stacks. Recently, 2D ferroelectricity has been observed in
zero-degree aligned hBN and graphene-hBN heterostructures, holding promise in a
range of electronic applications. In those artificial stacks, however, the
single domain size is limited by the stacking-angle misalignment to about 0.1
to 1 $\mu$m, which is incompatible with most optical or optoelectronic
applications. Here we show MoS$_2$ in the rhombohedral phase can host a
homogeneous spontaneous polarization throughout few-$\mu$m-sized exfoliated
flakes, as it is a natural crystal requiring no stacking and is, therefore free
of misalignment. Utilizing this homogeneous polarization and its induced
depolarization field (DEP), we build a graphene-MoS$_2$ based photovoltaic
device with high efficiency. The few-layer MoS$_2$ is thinner than most
oxide-based ferroelectric films, which allows us to maximize the DEP and study
its impact at the atomically thin limit, while the highly uniform polarization
achievable in the commensurate crystal enables a tangible path for up-scaling.
The external quantum efficiency of our device is up to 16% at room temperature,
over one order larger than the highest efficiency observed in bulk photovoltaic
devices, owing to the reduced screening in graphene, the exciton-enhanced
light-matter interaction, and the ultrafast interlayer relaxation in MoS$_2$.
In view of the wide range of bandgap energy in other TMDs, our findings make
rhombohedral TMDs a promising and versatile candidate for applications such as
energy-efficient photo-detection with high speed and programmable polarity.


title:
First Search for the Majorana Nature of Neutrinos in the Inverted Mass
  Ordering Region with KamLAND-Zen

abstract:
  The KamLAND-Zen experiment has provided stringent constraints on the
neutrinoless double-beta ($0\nu\beta\beta$) decay half-life in $^{136}$Xe using
a xenon-loaded liquid scintillator. We report an improved search using an
upgraded detector with almost double the amount of xenon and an ultra-low
radioactivity container, corresponding to an exposure of 970 kg yr of
$^{136}$Xe. This new data provides valuable insight into backgrounds,
especially from cosmic muon spallation of xenon, and has required the use of
novel background rejection techniques. We obtain a lower limit for the
$0\nu\beta\beta$ decay half-life of $T_{1/2}^{0\nu} > 2.3 \times 10^{26}$ yr at
90% C.L., corresponding to upper limits on the effective Majorana neutrino mass
of 36-156 meV using commonly adopted nuclear matrix element calculations.


title: Analysis of closed-loop inertial gradient dynamics

abstract:
  In this paper, we analyse the performance of the closed-loop Whiplash
gradient descent algorithm for L-smooth convex cost functions. Using numerical
experiments, we study the algorithm's performance for convex cost functions,
for different condition numbers. We analyse the convergence of the momentum
sequence using symplectic integration and introduce the concept of relaxation
sequences which analyses the non-classical character of the whiplash method.
Under the additional assumption of invexity, we establish a momentum-driven
adaptive convergence rate. Furthermore, we introduce an energy method for
predicting the convergence rate with convex cost functions for closed-loop
inertial gradient dynamics, using an integral anchored energy function and a
novel lower bound asymptotic notation, by exploiting the bounded nature of the
solutions. Using this, we establish a polynomial convergence rate for the
whiplash inertial gradient system, for a family of scalar quadratic cost
functions and an exponential rate for a quadratic scalar cost function.


title:
Weighted Simultaneous Algebra Reconstruction Technique (wSART) for
  Additive Light Field Synthesis

abstract:
  We apply an iterative weighting scheme for additive light field synthesis.
Unlike previous work optimizing additive light field evenly over viewpoints, we
constrain the optimization to deliver a reconstructed light field of high image
quality for viewpoints of large weight.


title: Benchmarking tunnel and encryption methodologies in cloud environments

abstract:
  The recent past has seen the adoption of multi-cloud deployments by
enterprises due to availability, features, and regulatory requirements. A
typical deployment involves parts of an application/workloads running inside a
private cloud with the other parts spread across multiple on-prem/public
clouds. Typical cluster-to-cluster networking in such deployments involve the
establishment of site-to-site encrypted tunnels to connect the workloads.
  In this report, we benchmark the performance of various tunneling and
encryption technologies to provide directions on their use in multi-cloud
deployments. Based on the various experiments conducted on three different
testbeds, we present quantifiable data which can be leveraged by operators and
cloud providers tasked with design and development decisions of multi-cloud
network connectivity and orchestration.


title: Generalized probability and current densities: A Field theory

abstract:
  We introduce a generalized Lagrangian density - involving a non-Hermitian
kinetic term - for a quantum particle with the generalized momentum operator.
Upon variation of the Lagrangian, we obtain the corresponding Schr\"odinger
equation. The extended probability and particle current densities are found
which satisfy the continuity equation.


title:
X-ray constraint for the unseen companion of V723 Mon: it is a mass-gap
  black hole rather than binary neutron stars

abstract:
  Recently, the red giant V723 Mon is reported to have an unseen companion with
a mass of $3.04\pm0.06M_{\odot}$, but question remains about whether it is a
single (thus the so-called mass-gap) black hole or an inner binary of two more
ordinary compact objects (neutron stars or white dwarfs). In this work, we
estimate the X-ray emission by considering the wind-fed accretion from V723 Mon
onto the compact companion. We analyze three different scenarios of the dark
companion, i.e., a single black hole, binary neutron stars and binary of a
neutron star and a white dwarf. We show that the single black hole is the most
favored scenario. We also calculate the synchrotron emission from the bow shock
caused by the interaction of the compact companion with the wind. We find this
emission peaks at $\sim$ 0.1-1 GHz, with a flux density $\sim$ 1 mJy, which is
expected to be detected by observations with higher angular resolution in the
future.


title:
Second-order magnetic responses in quantum magnets: Magnetization under
  ac magnetic fields

abstract:
  We investigate second-order magnetic responses of quantum magnets against ac
magnetic fields. We focus on the case where the $z$ component of the spin is
conserved in the unperturbed Hamiltonian and the driving field is applied in
the $xy$ plane. We find that linearly polarized driving fields induce a
second-harmonic response, while circularly polarized fields generate only a
zero frequency response, leading to a magnetization with a direction determined
by the helicity. Employing an unbiased numerical method, we demonstrate the
nonlinear magnetic effect driven by the circularly polarized field in the XXZ
model and show that the magnitude of the magnetization can be predicted by the
dynamical spin structure factor in the linear response regime.


title:
ACVNet: Attention Concatenation Volume for Accurate and Efficient Stereo
  Matching

abstract:
  Stereo matching is a fundamental building block for many vision and robotics
applications. An informative and concise cost volume representation is vital
for stereo matching of high accuracy and efficiency. In this paper, we present
a novel cost volume construction method which generates attention weights from
correlation clues to suppress redundant information and enhance
matching-related information in the concatenation volume. To generate reliable
attention weights, we propose multi-level adaptive patch matching to improve
the distinctiveness of the matching cost at different disparities even for
textureless regions. The proposed cost volume is named attention concatenation
volume (ACV) which can be seamlessly embedded into most stereo matching
networks, the resulting networks can use a more lightweight aggregation network
and meanwhile achieve higher accuracy, e.g. using only 1/25 parameters of the
aggregation network can achieve higher accuracy for GwcNet. Furthermore, we
design a highly accurate network (ACVNet) based on our ACV, which achieves
state-of-the-art performance on several benchmarks.