Abstract:
  In this study, we propose a decision-making strategy for pinning-based
distributed multi-agent (PDMA) automatic generation control (AGC) in islanded
microgrids against stochastic communication disruptions. The target microgrid
is construed as a cyber-physical system, wherein the physical microgrid is
modeled as an inverter-interfaced autonomous grid with detailed system dynamic
formulation, and the communication network topology is regarded as a
cyber-system independent of its physical connection. The primal goal of the
proposed method is to decide the minimum number of generators to be pinned and
their identities amongst all distributed generators (DGs). The pinningdecisions
are made based on complex network theories using the genetic algorithm (GA),
for the purpose of synchronizing and regulating the frequencies and voltages of
all generator busbars in a PDMA control structure, i.e., without resorting to a
central AGC agent. Thereafter, the mapping of cyber-system topology and the
pinning decision is constructed using deeplearning (DL) technique, so that the
pinning-decision can be made nearly instantly upon detecting a new cyber-system
topology after stochastic communication disruptions. The proposed
decision-making approach is verified using a 10-generator, 38-bus microgrid
through time-domain simulation for transient stability analysis.


Abstract:
  Consider the problem of finding the maximal nonpositive solvent $\Phi$ of the
quadratic matrix equation (QME) $X^2 + BX + C =0$ with $B$ being a nonsingular
$M$-matrix and $C$ an $M$-matrix such that $B^{-1}C\ge 0$, and $B - C - I$ a
nonsingular $M$-matrix. Such QME arises from an overdamped vibrating system.
Recently, Yu et al. ({\em Appl. Math. Comput.}, 218: 3303--3310, 2011) proved
that $\rho(\Phi)\le 1$ for this QME. In this paper, we slightly improve their
result and prove $\rho(\Phi)< 1$, which is important for the quadratic
convergence of the structure-preserving doubling algorithm. Then, a new
globally monotonically and quadratically convergent structure-preserving
doubling algorithm to solve the QME is developed. Numerical examples are
presented to demonstrate the feasibility and effectiveness of our method.


Abstract:
  Low lattice thermal conductivity is essential for high thermoelectric
performance of a material. Lattice thermal conductivity is often computed using
density functional theory (DFT), typically at a high computational cost.
Training machine learning models to predict lattice thermal conductivity could
offer an effective procedure to identify low lattice thermal conductivity
compounds. However, in doing so, we must face the fact that such compounds can
be quite rare and distinct from those in a typical training set. This
distinctness can be problematic as standard machine learning methods are
inaccurate when predicting properties of compounds with features differing
significantly from those in the training set. By computing the lattice thermal
conductivity of 122 half-Heusler compounds, using the temperature-dependent
effective potential method, we generate a data set to explore this issue. We
first show how random forest regression can fail to identify low lattice
thermal conductivity compounds with random selection of training data. Next, we
show how active selection of training data using feature and principal
component analysis can be used to improve model performance and the ability to
identify low lattice thermal conductivity compounds. Lastly, we find that
active learning without the use of DFT-based features can be viable as a
quicker way of selecting samples.


Abstract:
  Ridesharing has great potential to improve transportation efficiency while
reducing congestion and pollution. To realize this potential, mechanisms are
needed that allocate vehicles optimally and provide the right incentives to
riders. However, many existing approaches consider restricted settings (e.g.,
only one rider per vehicle or a common origin for all riders). Moreover, naive
applications of standard approaches, such as the Vickrey-Clarke-Groves or
greedy mechanisms, cannot achieve a polynomial-time, truthful, individually
rational and budget balanced mechanism. To address this, we formulate a general
ridesharing problem and apply mechanism design to develop a novel mechanism
which satisfies all four properties and whose social cost is within 8.6% of the
optimal on average.


Abstract:
  Agents trained by reinforcement learning (RL) often fail to generalize beyond
the environment they were trained in, even when presented with new scenarios
that seem similar to the training environment. We study the query complexity
required to train RL agents that generalize to multiple environments.
Intuitively, tractable generalization is only possible when the environments
are similar or close in some sense. To capture this, we introduce Weak
Proximity, a natural structural condition that requires the environments to
have highly similar transition and reward functions and share a policy
providing optimal value. Despite such shared structure, we prove that tractable
generalization is impossible in the worst case. This holds even when each
individual environment can be efficiently solved to obtain an optimal linear
policy, and when the agent possesses a generative model. Our lower bound
applies to the more complex task of representation learning for the purpose of
efficient generalization to multiple environments. On the positive side, we
introduce Strong Proximity, a strengthened condition which we prove is
sufficient for efficient generalization.


Abstract:
  Finite volume schemes often have difficulties to resolve the low Mach number
(incompressible) limit of the Euler equations. Incompressibility is only
non-trivial in multiple spatial dimensions. Low Mach fixes, however generally
are applied to the one-dimensional method and the method is then used in a
dimensionally split way. This often reduces its stability. Here, it is
suggested to keep the one-dimensional method as it is, and only to extend it to
multiple dimensions in a particular, all-speed way. This strategy is found to
lead to much more stable numerical methods. Apart from the conceptually
pleasing property of modifying the scheme only when it becomes necessary, the
multi-dimensional all-speed extension also does not include any free parameters
or arbitrary functions, which generally are difficult to choose, or might be
problem dependent. The strategy is exemplified on a Lagrange Projection method
and on a relaxation solver.


Abstract:
  A trace ratio optimization problem over the Stiefel manifold is investigated
from the perspectives of both theory and numerical computations. At least three
special cases of the problem have arisen from Fisher linear discriminant
analysis, canonical correlation analysis, and unbalanced Procrustes problem,
respectively. Necessary conditions in the form of nonlinear eigenvalue problem
with eigenvector dependency are established and a numerical method based on the
self-consistent field (SCF) iteration is designed and proved to be always
convergent. As an application to multi-view subspace learning, a new framework
and its instantiated concrete models are proposed and demonstrated on real
world data sets. Numerical results show that the efficiency of the proposed
numerical methods and effectiveness of the new multi-view subspace learning
models.


Abstract:
  We present a systematic investigation of muon-stopping states in
superconductors that reportedly exhibit spontaneous magnetic fields below their
transition temperatures due to time-reversal symmetry breaking. These materials
include elemental rhenium, several intermetallic systems and Sr$_2$RuO$_4$. We
demonstrate that the presence of the muon leads to only a limited and
relatively localized perturbation to the local crystal structure, while any
small changes to the electronic structure occur several electron volts below
the Fermi energy leading to only minimal changes in the charge density on ions
close to the muon. Our results imply that the muon-induced perturbation alone
is unlikely to lead to the observed spontaneous fields in these materials,
whose origin is more likely intrinsic to the time-reversal symmetry broken
superconducting state.


Abstract:
  We present a pulsar candidate identification and confirmation procedure based
on a position-switch mode during the pulsar search observations. This method
enables the simultaneous search and confirmation of a pulsar in a single
observation, by utilizing the different spatial features of a pulsar signal and
a radio frequency interference (RFI). Based on this method, we performed test
pulsar search observations in globular clusters M3, M15, and M92. We discovered
and confirmed a new pulsar, M3F, and detected the known pulsars M3B, M15 A to G
(except C), and M92A.


Abstract:
  Detecting groups of people who are jointly deceptive in video conversations
is crucial in settings such as meetings, sales pitches, and negotiations. Past
work on deception in videos focuses on detecting a single deceiver and uses
facial or visual features only. In this paper, we propose the concept of
Face-to-Face Dynamic Interaction Networks (FFDINs) to model the interpersonal
interactions within a group of people. The use of FFDINs enables us to leverage
network relations in detecting group deception in video conversations for the
first time. We use a dataset of 185 videos from a deception-based game called
Resistance. We first characterize the behavior of individual, pairs, and groups
of deceptive participants and compare them to non-deceptive participants. Our
analysis reveals that pairs of deceivers tend to avoid mutual interaction and
focus their attention on non-deceivers. In contrast, non-deceivers interact
with everyone equally. We propose Negative Dynamic Interaction Networks to
capture the notion of missing interactions. We create the DeceptionRank
algorithm to detect deceivers from NDINs extracted from videos that are just
one minute long. We show that our method outperforms recent state-of-the-art
computer vision, graph embedding, and ensemble methods by at least 20.9% AUROC
in identifying deception from videos.


Abstract:
  Browser fingerprinting is a stateless identification technique based on
browser properties. Together, they form an identifier that can be collected
without users' notice and has been studied to be unique and stable. As this
technique relies on browser properties that serve legitimate purposes, the
detection of this technique is challenging. While several studies propose
classification techniques, none of these are publicly available, making them
difficult to reproduce. This paper proposes a new browser fingerprinting
detection technique. Based on an incremental process, it relies on both
automatic and manual decisions to be both reliable and fast. The automatic step
matches API calls similarities between scripts while the manual step is
required to classify a script with different calls. We publicly share our
algorithm and implementation to improve the general knowledge on the subject.


Abstract:
  Linear stability analysis is performed using a combination of two-dimensional
Direct Simulation Monte Carlo (DSMC) method for the computation of the basic
state and solution of the pertinent eigenvalue problem, as applied to the
canonical boundary layer on a semi-infinite flat plate. Three different gases
are monitored, namely nitrogen, argon and air, the latter as a mixture of 79\%
Nitrogen and 21\% Oxygen at a range of free-stream Mach numbers corresponding
to flight at an altitude of 55km. A neural network has been utilised to predict
and smooth the raw DSMC data; the steady laminar profiles obtained are in very
good agreement with those computed by (self-similar) boundary layer theory,
under isothermal or adiabatic wall conditions, subject to the appropriate slip
corrections computed in the DSMC method.
  The leading eigenmode results pertaining to the unsmoothed DSMC profiles are
compared against those of the classic boundary layer theory. Small
quantitative, but no significant qualitative differences between the results of
the two classes of steady base flows have been found at all parameters
examined. The frequencies of the leading eigenmodes at all conditions examined
are practically identical, while perturbations corresponding to the DSMC
profiles are found to be systematically more damped than their counterparts
arising in the boundary layer at the conditions examined, when the correct
velocity slip and temperature jump boundary conditions are imposed in the base
flow profiles; by contrast, when the classic no-slip boundary conditions are
used, less damped/more unstable profiles are obtained, which would lead the
flow to earlier transition. On the other hand, the DSMC profiles smoothed by
the neural network are marginally more stable than their unsmoothed
counterparts.


Abstract:
  Abbe's resolution limit, one of the best-known physical limitations, poses a
great challenge for any wave systems in imaging, wave transport, and dynamics.
Originally formulated in linear optics, this Abbe's limit can be broken using
nonlinear optical interactions. Here we extend the Abbe theory into a nonlinear
regime and experimentally demonstrate a far-field, label-free, and scan-free
super-resolution imaging technique based on nonlinear four-wave mixing to
retrieve near-field scattered evanescent waves, achieving sub-wavelength
resolution of $\lambda/15.6$. This method paves the way for application in
biomedical imaging, semiconductor metrology, and photolithography.


Abstract:
  Despite the rapid development and great success of machine learning models,
extensive studies have exposed their disadvantage of inheriting latent
discrimination and societal bias from the training data. This phenomenon
hinders their adoption on high-stake applications. Thus, many efforts have been
taken for developing fair machine learning models. Most of them require that
sensitive attributes are available during training to learn fair models.
However, in many real-world applications, it is usually infeasible to obtain
the sensitive attributes due to privacy or legal issues, which challenges
existing fair-ensuring strategies. Though the sensitive attribute of each data
sample is unknown, we observe that there are usually some non-sensitive
features in the training data that are highly correlated with sensitive
attributes, which can be used to alleviate the bias. Therefore, in this paper,
we study a novel problem of exploring features that are highly correlated with
sensitive attributes for learning fair and accurate classifiers. We
theoretically show that by minimizing the correlation between these related
features and model prediction, we can learn a fair classifier. Based on this
motivation, we propose a novel framework which simultaneously uses these
related features for accurate prediction and enforces fairness. In addition,
the model can dynamically adjust the regularization weight of each related
feature to balance its contribution on model classification and fairness.
Experimental results on real-world datasets demonstrate the effectiveness of
the proposed model for learning fair models with high classification accuracy.


Abstract:
  Neural networks are used for many real world applications, but often they
have problems estimating their own confidence. This is particularly problematic
for computer vision applications aimed at making high stakes decisions with
humans and their lives. In this paper we make a meta-analysis of the
literature, showing that most if not all computer vision applications do not
use proper epistemic uncertainty quantification, which means that these models
ignore their own limitations. We describe the consequences of using models
without proper uncertainty quantification, and motivate the community to adopt
versions of the models they use that have proper calibrated epistemic
uncertainty, in order to enable out of distribution detection. We close the
paper with a summary of challenges on estimating uncertainty for computer
vision applications and recommendations.


Abstract:
  Purcell effect predicts that spontaneous radiation is not an intrinsic
property of matter, but is affected by the environment in which it is located,
and is the result of the interaction of matter and field. Purcell effect can be
inferred from Fermi Gold rule through strict quantum electrodynamics (QED), and
through it can achieve the enhancement or suppression of radiation. We suggest
that, in principle, the Purcell effect can be detected at the percentage level
of neutron decay in experiments with trapped ultra-cold neutrons. As a test of
our claim, we propose a currently achievable experimental protocol that can
detect whether Purcell effect has occurred in an trapped ultra-cold neutron
lifetime measurement experiment. Finally, we discuss the discrepancy in current
methods of measuring neutron lifetime, which may be caused by different
experimental setups.


Abstract:
  In Federated Learning (FL), a strong global model is collaboratively learned
by aggregating the clients' locally trained models. Although this allows no
need to access clients' data directly, the global model's convergence often
suffers from data heterogeneity. This paper suggests that forgetting could be
the bottleneck of global convergence. We observe that fitting on biased local
distribution shifts the feature on global distribution and results in
forgetting of global knowledge. We consider this phenomenon as an analogy to
Continual Learning, which also faces catastrophic forgetting when fitted on the
new task distribution. Based on our findings, we hypothesize that tackling down
the forgetting in local training relives the data heterogeneity problem. To
this end, we propose a simple yet effective framework Federated Local
Self-Distillation (FedLSD), which utilizes the global knowledge on locally
available data. By following the global perspective on local data, FedLSD
encourages the learned features to preserve global knowledge and have
consistent views across local models, thus improving convergence without
compromising data privacy. Under our framework, we further extend FedLSD to
FedLS-NTD, which only considers the not-true class signals to compensate noisy
prediction of the global model. We validate that both FedLSD and FedLS-NTD
significantly improve the performance in standard FL benchmarks in various
setups, especially in the extreme data heterogeneity cases.


Abstract:
  Given a permutation $f$, we study the positroid Catalan number $C_f$ defined
to be the torus-equivariant Euler characteristic of the associated open
positroid variety. We introduce a class of repetition-free permutations and
show that the corresponding positroid Catalan numbers count Dyck paths avoiding
a convex subset of the rectangle. We show that any convex subset appears in
this way. Conjecturally, the associated $q,t$-polynomials coincide with the
generalized $q,t$-Catalan numbers that recently appeared in relation to the
shuffle conjecture, flag Hilbert schemes, and Khovanov-Rozansky homology of
Coxeter links.


Abstract:
  We study the holomorphic symplectic geometry of (the smooth locus of) the
space of holomorphic sections of a twistor space with rotating circle action.
The twistor space has a meromorphic connection constructed by Hitchin. We give
an interpretation of Hitchin's meromorphic connection in the context of the
Atiyah--Ward transform of the corresponding hyperholomorphic line bundle.
  It is shown that the residue of the meromorphic connection serves as a moment
map for the induced circle action, and its critical points are studied.
Particular emphasis is given to the example of Deligne--Hitchin moduli spaces.


Abstract:
  The interaction between shear driven turbulence and stratification is a key
process in a wide array of geophysical flows with spatio-temporal scales that
span many orders of magnitude. A quick numerical model prediction based on
external parameters of stratified boundary layers could greatly benefit the
understanding of the interaction between velocity and scalar flux at varying
scales. For these reasons, here, we use the resolvent framework to investigate
the effects of an active scalar on incompressible wall-bounded turbulence. We
obtain the state of the flow system by applying the linear resolvent operator
to the nonlinear terms in the governing Navier-Stokes equations with the
Boussinesq approximation. This extends the formulation to include the scalar
advection equation with the scalar component acting in the wall-normal
direction in the momentum equations. We use the mean velocity profiles from a
DNS of a stably-stratified turbulent channel flow at varying friction
Richardson number. The results obtained from the resolvent analysis are
compared to the premultiplied energy spectra, auto-correlation coefficient, and
the energy budget terms obtained from the DNS. It is shown that despite using
only a very limited range of representative scales, the resolvent model is able
to reproduce the balance of energy budget terms as well as provide meaningful
insight of coherent structures occurring in the flow. Computation of the
leading resolvent models, despite considering a limited range of scales,
reproduces the balance of energy budget terms, provides meaningful predictions
of coherent structures in the flow, and is more cost-effective than performing
full-scale simulations. This quick model can provide further understanding of
stratified flows with only information about the mean profile and prior
knowledge of energetic scales of motion in the neutrally-buoyant boundary
layers.


Abstract:
  Healthcare blockchains provide an innovative way to store healthcare
information, execute healthcare transactions, and build trust for healthcare
data sharing and data integration in a decentralized open healthcare network
environment. Although the healthcare blockchain technology has attracted broad
interests and attention in industry, government and academia, the security and
privacy concerns remain the focus of debate when deploying blockchains for
information sharing in the healthcare sector from business operation to
research collaboration. This paper focuses on the security and privacy
requirements for medical data sharing using blockchain, and provides a
comprehensive analysis of the security and privacy risks and requirements,
accompanied by technical solution techniques and strategies. First, we discuss
the security and privacy requirements and attributes required for electronic
medical data sharing by deploying the healthcare blockchain. Second, we
categorize existing efforts into three reference blockchain usage scenarios for
electronic medical data sharing, and discuss the technologies for implementing
these security and privacy properties in the three categories of usage
scenarios for healthcare blockchain, such as anonymous signatures,
attribute-based encryption, zero-knowledge proofs, verification techniques for
smart contract security. Finally, we discuss other potential blockchain
application scenarios in healthcare sector. We conjecture that this survey will
help healthcare professionals, decision makers, and healthcare service
developers to gain technical and intuitive insights into the security and
privacy of healthcare blockchains in terms of concepts, risks, requirements,
development and deployment technologies and systems.


Abstract:
  The recent popularity growth of Deep Learning (DL) re-ignited the interest
towards traffic classification, with several studies demonstrating the accuracy
of DL-based classifiers to identify Internet applications' traffic. Even with
the aid of hardware accelerators (GPUs, TPUs), DL model training remains
expensive, and limits the ability to operate frequent model updates necessary
to fit to the ever evolving nature of Internet traffic, and mobile traffic in
particular. To address this pain point, in this work we explore Incremental
Learning (IL) techniques to add new classes to models without a full
retraining, hence speeding up model's updates cycle. We consider iCarl, a state
of the art IL method, and MIRAGE-2019, a public dataset with traffic from 40
Android apps, aiming to understand "if there is a case for incremental learning
in traffic classification". By dissecting iCarl internals, we discuss ways to
improve its design, contributing a revised version, namely iCarl+. Despite our
analysis reveals their infancy, IL techniques are a promising research area on
the roadmap towards automated DL-based traffic analysis systems.


Abstract:
  The optimal gain matrix of the Kalman filter is often derived by minimizing
the trace of the posterior covariance matrix. Here, I show that the Kalman gain
also minimizes the determinant of the covariance matrix, a quantity known as
the generalized variance. When the error distributions are Gaussian, the
differential entropy is also minimized.


Abstract:
  We present the latest results of our ongoing multiplicity study of
(Community) TESS Objects of Interest, using astro- and photometric data from
the ESA-Gaia mission, to detect stellar companions of these stars and to
characterize their properties. In total, 113 binary, 5 hierarchical triple star
systems, as well as one quadruple system were detected among 585 targets
surveyed, which are all located at distances closer than about 500pc around the
Sun. As proven with their accurate Gaia EDR3 astrometry the companions and the
targets are located at the same distance and share a common proper motion, as
it is expected for components of gravitationally bound stellar systems. The
companions exhibit masses in the range between about 0.09$M_\odot$ and
4.5$M_\odot$ and are most frequently found in the mass range between 0.15 and
0.6$M_{\odot}$. The companions are separated from the targets by about 120 up
to 9500au and their frequency is the highest and constant within about 500au
while it continually decreases for larger separations. Beside mainly early to
mid M dwarfs, also 5 white dwarf companions were identified in this survey,
whose true nature was revealed by their photometric properties.


Abstract:
  Place recognition is critical for both offline mapping and online
localization. However, current single-sensor based place recognition still
remains challenging in adverse conditions. In this paper, a heterogeneous
measurements based framework is proposed for long-term place recognition, which
retrieves the query radar scans from the existing lidar maps. To achieve this,
a deep neural network is built with joint training in the learning stage, and
then in the testing stage, shared embeddings of radar and lidar are extracted
for heterogeneous place recognition. To validate the effectiveness of the
proposed method, we conduct tests and generalization experiments on the
multi-session public datasets compared to other competitive methods. The
experimental results indicate that our model is able to perform multiple place
recognitions: lidar-to-lidar, radar-to-radar and radar-to-lidar, while the
learned model is trained only once. We also release the source code publicly:
https://github.com/ZJUYH/radar-to-lidar-place-recognition.


Abstract:
  This paper analyzes a discretization of a stochastic parabolic optimal
control problem, where the diffusion term contains the control variable. With
rough data, the convergence of the discretization is derived. In addition, a
Monte-Carlo method is presented.


Abstract:
  We present OffRoadTranSeg, the first end-to-end framework for semi-supervised
segmentation in unstructured outdoor environment using transformers and
automatic data selection for labelling. The offroad segmentation is a scene
understanding approach that is widely used in autonomous driving. The popular
offroad segmentation method is to use fully connected convolution layers and
large labelled data, however, due to class imbalance, there will be several
mismatches and also some classes may not be detected. Our approach is to do the
task of offroad segmentation in a semi-supervised manner. The aim is to provide
a model where self supervised vision transformer is used to fine-tune offroad
datasets with self-supervised data collection for labelling using depth
estimation. The proposed method is validated on RELLIS-3D and RUGD offroad
datasets. The experiments show that OffRoadTranSeg outperformed other state of
the art models, and also solves the RELLIS-3D class imbalance problem.


Abstract:
  Modern programming languages (e.g., Java and C#) provide features to separate
error-handling code from regular code, seeking to enhance software
comprehensibility and maintainability. Nevertheless, the way exception handling
(EH) code is structured in such languages may lead to multiple, different, and
complex control flows, which may affect the software testability. Previous
studies have reported that EH code is typically neglected, not well tested, and
its misuse can lead to reliability degradation and catastrophic failures.
However, little is known about the relationship between testing practices and
EH testing effectiveness. In this exploratory study, we (i) measured the
adequacy degree of EH testing concerning code coverage (instruction, branch,
and method) criteria; and (ii) evaluated the effectiveness of the EH testing by
measuring its capability to detect artificially injected faults (i.e., mutants)
using 7 EH mutation operators. Our study was performed using test suites of 27
long-lived Java libraries from open-source ecosystems. Our results show that
instructions and branches within $\mathtt{catch}$ blocks and $\mathtt{throw}$
instructions are less covered, with statistical significance than the overall
instructions and branches. Nevertheless, most of the studied libraries
presented test suites capable of detecting more than 70% of the injected
faults. From a total of 12,331 mutants created in this study, the test suites
were able to detect 68% of them.


Abstract:
  Deep learning technology promotes the rapid development of person
re-identifica-tion (re-ID). However, some challenges are still existing in the
open-world. First, the existing re-ID research usually assumes only one factor
variable (view, clothing, pedestrian pose, pedestrian occlusion, image
resolution, RGB/IR modality) changing, ignoring the complexity of multi-factor
variables in the open-world. Second, the existing re-ID methods are over depend
on clothing color and other apparent features of pedestrian, which are easily
disguised or changed. In addition, the lack of benchmark datasets containing
multi-factor variables is also hindering the practically application of re-ID
in the open-world. In this paper, we propose a low-cost and high-efficiency
method to solve shortcomings of the existing re-ID research, such as unreliable
feature selection, low efficiency of feature extraction, single research
variable, etc. Our approach based on pose estimation model improved by group
convolution to obtain the continuous key points of pedestrian, and utilize
dynamic time warping (DTW) to measure the similarity of features between
different pedestrians. At the same time, to verify the effectiveness of our
method, we provide a miniature dataset which is closer to the real world and
includes pedestrian changing clothes and cross-modality factor variables
fusion. Extensive experiments are conducted and the results show that our
method achieves Rank-1: 60.9%, Rank-5: 78.1%, and mAP: 49.2% on this dataset,
which exceeds most existing state-of-art re-ID models.


Abstract:
  Though nearest neighbor Machine Translation ($k$NN-MT)
\cite{khandelwal2020nearest} has proved to introduce significant performance
boosts over standard neural MT systems, it is prohibitively slow since it uses
the entire reference corpus as the datastore for the nearest neighbor search.
This means each step for each beam in the beam search has to search over the
entire reference corpus. $k$NN-MT is thus two-order slower than vanilla MT
models, making it hard to be applied to real-world applications, especially
online services. In this work, we propose Fast $k$NN-MT to address this issue.
Fast $k$NN-MT constructs a significantly smaller datastore for the nearest
neighbor search: for each word in a source sentence, Fast $k$NN-MT first
selects its nearest token-level neighbors, which is limited to tokens that are
the same as the query token. Then at each decoding step, in contrast to using
the entire corpus as the datastore, the search space is limited to target
tokens corresponding to the previously selected reference source tokens. This
strategy avoids search through the whole datastore for nearest neighbors and
drastically improves decoding efficiency. Without loss of performance, Fast
$k$NN-MT is two-order faster than $k$NN-MT, and is only two times slower than
the standard NMT model. Fast $k$NN-MT enables the practical use of $k$NN-MT
systems in real-world MT applications.\footnote{Code is available at
\url{https://github.com/ShannonAI/fast-knn-nmt.}}


Abstract:
  We study the cosmological evolution for a scalar field dark matter model, by
considering a parameterization of the evolution equations that allow us to
unify in a single parameter a family of potentials: quadratic (free case),
trigonometric (Axion-like case), and hyperbolic. After exploring the
cosmological dynamics of this model, we perform a statistical analysis to study
the viability of such model in comparison with the standard Cold Dark Matter
model. We found that the free case is preferred over the other scalar field
potentials, but in any case all of them are disfavored by the cosmological
observations with respect to the standard model.


Abstract:
  Generative adversarial networks built from deep convolutional neural networks
(GANs) lack the ability to exactly replicate the high-frequency components of
natural images. To alleviate this issue, we introduce two novel training
techniques called frequency dropping (F-Drop) and frequency matching (F-Match).
The key idea of F-Drop is to filter out unnecessary high-frequency components
from the input images of the discriminators. This simple modification prevents
the discriminators from being confused by perturbations of the high-frequency
components. In addition, F-Drop makes the GANs focus on fitting in the
low-frequency domain, in which there are the dominant components of natural
images. F-Match minimizes the difference between real and fake images in the
frequency domain for generating more realistic images. F-Match is implemented
as a regularization term in the objective functions of the generators; it
penalizes the batch mean error in the frequency domain. F-Match helps the
generators to fit in the high-frequency domain filtered out by F-Drop to the
real image. We experimentally demonstrate that the combination of F-Drop and
F-Match improves the generative performance of GANs in both the frequency and
spatial domain on multiple image benchmarks.


Abstract:
  This paper describes our submission for the End-to-end Multi-domain Task
Completion Dialog shared task at the 9th Dialog System Technology Challenge
(DSTC-9). Participants in the shared task build an end-to-end task completion
dialog system which is evaluated by human evaluation and a user simulator based
automatic evaluation. Different from traditional pipelined approaches where
modules are optimized individually and suffer from cascading failure, we
propose an end-to-end dialog system that 1) uses Generative Pretraining 2
(GPT-2) as the backbone to jointly solve Natural Language Understanding, Dialog
State Tracking, and Natural Language Generation tasks, 2) adopts Domain and
Task Adaptive Pretraining to tailor GPT-2 to the dialog domain before
finetuning, 3) utilizes heuristic pre/post-processing rules that greatly
simplify the prediction tasks and improve generalizability, and 4) equips a
fault tolerance module to correct errors and inappropriate responses. Our
proposed method significantly outperforms baselines and ties for first place in
the official evaluation. We make our source code publicly available.


Abstract:
  Non-perturbatively computing the hadronic vacuum polarization at large photon
virtualities and making contact with perturbation theory enables a precision
determination of the electromagnetic coupling at the $Z$ pole, which enters
global electroweak fits. In order to achieve this goal ab initio using lattice
QCD, one faces the challenge that, at the short distances which dominate the
observable, discretization errors are hard to control. Here we address
challenges of this type with the help of static screening correlators in the
high-temperature phase of QCD, yet without incurring any bias. The idea is
motivated by the observations that (a) the cost of high-temperature simulations
is typically much lower than their vacuum counterpart, and (b) at distances
$x_3$ far below the inverse temperature $1/T$, the operator-product expansion
guarantees the thermal correlator of two local currents to deviate from the
vacuum correlator by a relative amount that is power-suppressed in $(x_3\:T)$.
The method is first investigated in lattice perturbation theory, where we point
out the appearance of an O$(a^2 \log(1/a))$ lattice artifact in the vacuum
polarization with a prefactor that we calculate. It is then applied to
non-perturbative lattice QCD data with two dynamical flavors of quarks. Our
lattice spacings range down to 0.049 fm for the vacuum simulations and down to
0.033 fm for the simulations performed at a temperature of 250 MeV.


Abstract:
  We use the Keck Cosmic Web Imager integral-field unit spectrograph to: 1)
measure the global stellar population parameters for the ultra-diffuse galaxy
(UDG) Dragonfly 44 (DF44) to much higher precision than previously possible for
any UDG, and 2) for the first time measure spatially-resolved stellar
population parameters of a UDG. We find that DF44 falls below the
mass--metallicity relation established by canonical dwarf galaxies both in and
beyond the Local Group. We measure a flat radial age gradient ($m_{\rm age}
\sim +0.01_{-0.08}^{+0.07}$ log Gyr kpc$^{-1}$) and a flat-to-positive
metallicity gradient ($m_{\rm [Fe/H]} \sim +0.08_{-0.11}^{+0.11}$ dex
kpc$^{-1}$), which are inconsistent with the gradients measured in similarly
pressure-supported dwarf galaxies. We also measure a flat-to-negative [Mg/Fe]
gradient ($m_{\rm [Mg/Fe]} \sim -0.18_{-0.17}^{+0.17}$ dex kpc$^{-1}$) such
that the central $1.5$ kpc of DF44 has stellar population parameters comparable
to metal-poor globular clusters. Overall, DF44 does not have internal
properties similar to other dwarf galaxies and is inconsistent with it having
been puffed up through a prolonged, bursty star-formation history, as suggested
by some simulations. Rather, the evidence indicates that DF44 experienced an
intense epoch of "inside-out" star formation and then quenched early and
catastrophically, such that star-formation was cut off more quickly than in
canonical dwarf galaxies.


Abstract:
  In spite of considerable practical importance, current algorithmic fairness
literature lacks in technical methods to account for underlying geographic
dependency while evaluating or mitigating fairness issues for spatial data. We
initiate the study of spatial fairness in this paper, taking the first step
towards formalizing this line of quantitative methods. Fairness considerations
for spatial data often get confounded by the underlying spatial
autocorrelation. We propose hypothesis testing methodology to detect the
presence and strength of this effect, then mitigate it using a spatial
filtering-based approach -- in order to enable application of existing bias
detection metrics. We evaluate our proposed methodology through numerical
experiments on real and synthetic datasets, demonstrating that in presence of
several types of confounding effects due to the underlying spatial structure
our testing methods perform well in maintaining low type-II errors and nominal
type-I errors.


Abstract:
  We present the first numerical solutions of the causal, stable relativistic
Navier-Stokes equations as formulated by Bemfica, Disconzi, Noronha, and Kovtun
(BDNK). For this initial investigation we restrict to plane-symmetric
configurations of a conformal fluid in Minkowski spacetime. We consider
evolution of three classes of initial data: a smooth (initially) stationary
concentration of energy, a standard shock tube setup, and a smooth shockwave
setup. We compare these solutions to those obtained with the
Muller-Israel-Stewart (MIS) formalism, variants of which are the common tools
used to model relativistic, viscous fluids. We find that for the two smooth
initial data cases, simple finite difference methods are adequate to obtain
stable, convergent solutions to the BDNK equations. For low viscosity, the MIS
and BDNK evolutions show good agreement. At high viscosity the solutions begin
to differ in regions with large gradients, and there the BDNK solutions can (as
expected) exhibit violation of the weak energy condition. This behavior is
transient, and the solutions evolve toward a hydrodynamic regime in a way
reminiscent of an approach to a universal attractor. For the shockwave problem,
we give evidence that if a hydrodynamic frame is chosen so that the maximum
characteristic speed of the BDNK system is the speed of light (or larger),
arbitrarily strong shockwaves are smoothly resolved. Regarding the shock tube
problem, it is unclear whether discontinuous initial data is mathematically
well-posed for the BDNK system, even in a weak sense. Nevertheless we attempt
numerical solution, and then need to treat the perfect fluid terms using
high-resolution shock-capturing methods. When such methods can successfully
evolve the solution beyond the initial time, subsequent evolution agrees with
corresponding MIS solutions, as well as the perfect fluid solution in the limit
of zero viscosity.


Abstract:
  We develop an analytical ringdown waveform model, including both the
fundamental and the overtone quasinormal modes, for charged black holes and
show that it is precise enough to analyze current gravitational wave data.
Applying this waveform model to GW150914, the charge to mass ratio of the
remnant black hole ($\lambda_f$) has been constrained with the sole ringdown
gravitational wave data for the first time and the $90\%$ upper limit is
$\lambda_f\leq 0.38$. Correspondingly, the deviation parameter of the
scalar-tensor vector gravity ($\alpha_{\rm s}$) is limited to be $\alpha_{\rm
s}\leq 0.17$. Our approach can be directly applied to other binary black hole
mergers with loud ringdown radiation.


Abstract:
  Although cyberattacks on machine learning (ML) production systems can be
harmful, today, security practitioners are ill equipped, lacking methodologies
and tactical tools that would allow them to analyze the security risks of their
ML-based systems. In this paper, we performed a comprehensive threat analysis
of ML production systems. In this analysis, we follow the ontology presented by
NIST for evaluating enterprise network security risk and apply it to ML-based
production systems. Specifically, we (1) enumerate the assets of a typical ML
production system, (2) describe the threat model (i.e., potential adversaries,
their capabilities, and their main goal), (3) identify the various threats to
ML systems, and (4) review a large number of attacks, demonstrated in previous
studies, which can realize these threats. In addition, to quantify the risk of
adversarial machine learning (AML) threat, we introduce a novel scoring system,
which assign a severity score to different AML attacks. The proposed scoring
system utilizes the analytic hierarchy process (AHP) for ranking, with the
assistance of security experts, various attributes of the attacks. Finally, we
developed an extension to the MulVAL attack graph generation and analysis
framework to incorporate cyberattacks on ML production systems. Using the
extension, security practitioners can apply attack graph analysis methods in
environments that include ML components; thus, providing security practitioners
with a methodological and practical tool for evaluating the impact and
quantifying the risk of a cyberattack targeting an ML production system.


Abstract:
  A framework is presented for fitting inverse problem models via variational
Bayes approximations. This methodology guarantees flexibility to statistical
model specification for a broad range of applications, good accuracy
performances and reduced model fitting times, when compared with standard
Markov chain Monte Carlo methods. The message passing and factor graph fragment
approach to variational Bayes we describe facilitates streamlined
implementation of approximate inference algorithms and forms the basis to
software development. Such approach allows for supple inclusion of numerous
response distributions and penalizations into the inverse problem model. Even
though our work is circumscribed to one- and two-dimensional response
variables, we lay down an infrastructure where efficient algorithm updates
based on nullifying weak interactions between variables can also be derived for
inverse problems in higher dimensions. Image processing applications motivated
by biomedical and archaeological problems are included as illustrations.


Abstract:
  Recently deep neural networks have been successfully applied in channel
coding to improve the decoding performance. However, the state-of-the-art
neural channel decoders cannot achieve high decoding performance and low
complexity simultaneously. To overcome this challenge, in this paper we propose
doubly residual neural (DRN) decoder. By integrating both the residual input
and residual learning to the design of neural channel decoder, DRN enables
significant decoding performance improvement while maintaining low complexity.
Extensive experiment results show that on different types of channel codes, our
DRN decoder consistently outperform the state-of-the-art decoders in terms of
decoding performance, model sizes and computational cost.


Abstract:
  We propose a universal and physically realizable adversarial attack on a
cascaded multi-modal deep learning network (DNN), in the context of
self-driving cars. DNNs have achieved high performance in 3D object detection,
but they are known to be vulnerable to adversarial attacks. These attacks have
been heavily investigated in the RGB image domain and more recently in the
point cloud domain, but rarely in both domains simultaneously - a gap to be
filled in this paper. We use a single 3D mesh and differentiable rendering to
explore how perturbing the mesh's geometry and texture can reduce the
robustness of DNNs to adversarial attacks. We attack a prominent cascaded
multi-modal DNN, the Frustum-Pointnet model. Using the popular KITTI benchmark,
we showed that the proposed universal multi-modal attack was successful in
reducing the model's ability to detect a car by nearly 73%. This work can aid
in the understanding of what the cascaded RGB-point cloud DNN learns and its
vulnerability to adversarial attacks.


Abstract:
  Autonomous vehicle following systems are playing a decisive role to increase
vehicle density on roads by shortening inter-vehicle time gaps. However,
disturbance attenuation along a platoon of vehicles, i.e., string stability, is
being a challenging task while time gap is getting shorter. In order to
guarantee the string stability of a vehicle platoon, a multi-objective
$H_{\infty}$ control formulation for adaptive cruise control and cooperative
adaptive cruise control structures has been investigated in this paper. The
proposed control method solves an optimization problem and achieves a
controller that is able to provide not only the system stability, but also the
string stability as distinct from the traditional $H_{\infty}$ control.


Abstract:
  StyleGAN is one of the state-of-the-art image generators which is well-known
for synthesizing high-resolution and hyper-realistic face images. Though images
generated by vanilla StyleGAN model are visually appealing, they sometimes
contain prominent circular artifacts which severely degrade the quality of
generated images. In this work, we provide a systematic investigation on how
those circular artifacts are formed by studying the functionalities of
different stages of vanilla StyleGAN architecture, with both mechanism analysis
and extensive experiments. The key modules of vanilla StyleGAN that promote
such undesired artifacts are highlighted. Our investigation also explains why
the artifacts are usually circular, relatively small and rarely split into 2 or
more parts. Besides, we propose a simple yet effective solution to remove the
prominent circular artifacts for vanilla StyleGAN, by applying a novel
pixel-instance normalization (PIN) layer.


Abstract:
  We study the time-dependent neutralization of a slow highly charged ion that
penetrates a hexagonal hollow-centred graphene nanoflake. To compute the
ultrafast charge transfer dynamics, we apply an effective Hubbard nanocluster
model and use the method of nonequilibrium Green functions (NEGF) in
conjunction with an embedding self-energy scheme which allows one to follow the
temporal changes of the number of electrons in the nanoflake. We perform
extensive simulations of the charge transfer dynamics for a broad range of ion
charge states and impact velocities. The results are used to put forward a
simple semi-analytical model of the neutralization dynamics that is in very
good agreement with transmission experiments, in which highly charged xenon
ions pass through sheets of single-layer graphene.


Abstract:
  We use the general notion of 2-dimensional adjunction with given coherence
equations as introduced by MacDonald-Stone, building on earlier work by Gray,
to derive coherence equations for a general 2-monad, which we refer to as a
lax-Gray monad. The free lax-Gray 2-monad on one object may be regarded as the
suspension of a lax 2-dimensional analogue of the simplicial category Delta. We
call this analogue Delta LG for lax-Gray Delta. This is analogous to the way
that the free 1-monad Mnd (as presented in Schanuel-Street) is a concrete
example of the suspension of the simplicial category Delta, described by Mac
Lane.


Abstract:
  We study the local analytic classification of affine structures with
logarithmic pole on complex surfaces. With this result in hand, we can get the
local classification of the logarithmic parallelizable d-webs, d $\ge$ 3.


Abstract:
  Extra-large datasets are becoming increasingly accessible, and computing
tools designed to handle huge amount of data efficiently are democratizing
rapidly. However, conventional statistical and econometric tools are still
lacking fluency when dealing with such large datasets. This paper dives into
econometrics on big datasets, specifically focusing on the logistic regression
on Spark. We review the robustness of the functions available in Spark to fit
logistic regression and introduce a package that we developed in PySpark which
returns the statistical summary of the logistic regression, necessary for
statistical inference.


Abstract:
  A search for neutrinos produced in coincidence with Gamma-Ray Bursts(GRB) was
conducted with the Super-Kamiokande (SK) detector. Between December 2008 and
March 2017, the Gamma-ray Coordinates Network recorded 2208 GRBs that occurred
during normal SK operation. Several time windows around each GRB were used to
search for coincident neutrino events. No statistically significant signal in
excess of the estimated backgrounds was detected. The $\bar\nu_e$ fluence in
the range from 8 MeV to 100 MeV in positron total energy for
$\bar\nu_e+p\rightarrow e^{+}+n$ was found to be less than $\rm 5.07\times10^5$
cm$^{-2}$ per GRB in 90\% C.L. Upper bounds on the fluence as a function of
neutrino energy were also obtained.


Abstract:
  In the race to bring Artificial Intelligence (AI) to the edge, collaborative
intelligence has emerged as a promising way to lighten the computation load on
edge devices that run applications based on Deep Neural Networks (DNNs).
Typically, a deep model is split at a certain layer into edge and cloud
sub-models. The deep feature tensor produced by the edge sub-model is
transmitted to the cloud, where the remaining computationally intensive
workload is performed by the cloud sub-model. The communication channel between
the edge and cloud is imperfect, which will result in missing data in the deep
feature tensor received at the cloud side. In this study, we examine the
effectiveness of four low-rank tensor completion methods in recovering missing
data in the deep feature tensor. We consider both sparse tensors, such as those
produced by the VGG16 model, as well as non-sparse tensors, such as those
produced by ResNet34 model. We study tensor completion effectiveness in both
conplexity-constrained and unconstrained scenario.


Abstract:
  We study the finite groups $G$ with the curious property that there exists an
element $k\in G$ and a function $f\colon G\to G$ such that $f(xk)=xf(x)$ holds
for all $x\in G$. This property arose from the study of near-rings and
input-output automata on groups. We call a group with this property a
$J$-group. Finite $J$-groups must have odd order, and hence are solvable. We
prove that every finite nilpotent group of odd order is a $J$-group if its
nilpotency class $c$ satisfies $c\le6$. If $G$ is a finite $p$-group, with
$p>2$ and $p^2>2c-1$, then we prove that $G$ is $J$-group. Finally, if $p>2$
and $G$ is a regular $p$-group or, more generally, a power-closed one (i.e., in
each section and for each $m\geq1$ the subset of $p^m$-th powers is a
subgroup), then we prove that $G$ is a $J$-group.


Abstract:
  We consider the stochastic combinatorial semi-bandit problem with adversarial
corruptions. We provide a simple combinatorial algorithm that can achieve a
regret of $\tilde{O}\left(C+d^2K/\Delta_{min}\right)$ where $C$ is the total
amount of corruptions, $d$ is the maximal number of arms one can play in each
round, $K$ is the number of arms. If one selects only one arm in each round, we
achieves a regret of $\tilde{O}\left(C+\sum_{\Delta_i>0}(1/\Delta_i)\right)$.
Our algorithm is combinatorial and improves on the previous combinatorial
algorithm by [Gupta et al., COLT2019] (their bound is
$\tilde{O}\left(KC+\sum_{\Delta_i>0}(1/\Delta_i)\right)$), and almost matches
the best known bounds obtained by [Zimmert et al., ICML2019] and [Zimmert and
Seldin, AISTATS2019] (up to logarithmic factor). Note that the algorithms in
[Zimmert et al., ICML2019] and [Zimmert and Seldin, AISTATS2019] require one to
solve complex convex programs while our algorithm is combinatorial, very easy
to implement, requires weaker assumptions and has very low oracle complexity
and running time. We also study the setting where we only get access to an
approximation oracle for the stochastic combinatorial semi-bandit problem. Our
algorithm achieves an (approximation) regret bound of
$\tilde{O}\left(d\sqrt{KT}\right)$. Our algorithm is very simple, only worse
than the best known regret bound by $\sqrt{d}$, and has much lower oracle
complexity than previous work.


Abstract:
  A new kinematic condition for soliton motions of an $n$-dimensional continuum
in $R^{n+m}$, independent of the underlying physics, is proven. The condition
and its consequences for different cases are demonstrated. A soliton in a 1D
string that rocks back and forth, a rotating soliton in a 2D membrane, and
various other cases are presented as examples. It is shown that traveling knots
based on classical wave equation are plausible. Cases in which all the motions
are solitons are also presented. Compatibility of equations of motion with the
kinematic constraint is explored and demonstrated.


Abstract:
  We prove that both local Galois representations and
$(\varphi,\Gamma)$-modules can be recovered from prismatic F-crystals, from
which we obtain a new proof of the equivalence of Galois representations and
$(\varphi,\Gamma)$-modules.


Abstract:
  A signification fraction of Galactic massive stars (> 8Mo) are ejected from
their parent cluster and supersonically sail away through the interstellar
medium (ISM). The winds of these fast-moving stars blow asymmetric bubbles thus
creating a circumstellar environment in which stars eventually die with a
supernova explosion. The morphology of the resulting remnant is largely
governed by the circumstellar medium of the defunct progenitor star. In this
paper, we present 2D magneto-hydrodynamical simulations investigating the
effect of the ISM magnetic field on the shape of the supernova remnants of a
35Mo star evolving through a Wolf-Rayet phase and running with velocity 20 and
40 km/s, respectively. A 7 microG ambient magnetic field is sufficient to
modify the properties of the expanding supernova shock front and in particular
to prevent the formation of filamentary structures. Prior to the supernova
explosion, the compressed magnetic field in the circumstellar medium stabilises
the wind/ISM contact discontinuity in the tail of the wind bubble. A
consequence is a reduced mixing efficiency of ejecta and wind materials in the
inner region of the remnant, where the supernova shock wave propagates.
Radiative transfer calculations for synchrotron emission reveal that the
non-thermal radio emission has characteristic features reflecting the asymmetry
of exiled core-collapse supernova remnants from Wolf-Rayet progenitors. Our
models are qualitatively consistent with the radio appearance of several
remnants of high-mass progenitors, namely the bilateral G296.5+10.0 and the
shell-type remnants CTB109 and Kes 17, respectively.


Abstract:
  The density fluctuations of quantum vortex lines are measured in a turbulent
flow of superfluid He, at temperatures corresponding to superfluid fraction of
16%, 47% and 81%. The probe is a micro-fabricated second sound resonator that
allows for local and small-scale measurements in the core of the flow, at a
10-mesh-size behind a grid. Remarkably, all the vortex power spectra collapse
on a single master curve, independently from the superfluid fraction and the
mean velocity. By contrast with previous measurements, we report an peculiar
shape of the power spectra. The vortex density probability distributions are
found to be strongly skewed, similarly to the vorticity distributions observed
in classical turbulence. Implications of those results are discussed.


Abstract:
  Distributed arithmetic coding (DAC) has been shown to be effective for
Slepian-Wolf coding, especially for short data blocks. In this letter, we
propose to use the DAC to compress momery-correlated sources. More
specifically, the correlation between sources is modeled as a hidden Markov
process. Experimental results show that the performance is close to the
theoretical Slepian-Wolf limit.


Abstract:
  High levels of missing data and strong class imbalance are ubiquitous
challenges that are often presented simultaneously in real-world time series
data. Existing methods approach these problems separately, frequently making
significant assumptions about the underlying data generation process in order
to lessen the impact of missing information. In this work, we instead
demonstrate how a general self-supervised training method, namely
Autoregressive Predictive Coding (APC), can be leveraged to overcome both
missing data and class imbalance simultaneously without strong assumptions.
Specifically, on a synthetic dataset, we show that standard baselines are
substantially improved upon through the use of APC, yielding the greatest gains
in the combined setting of high missingness and severe class imbalance. We
further apply APC on two real-world medical time-series datasets, and show that
APC improves the classification performance in all settings, ultimately
achieving state-of-the-art AUPRC results on the Physionet benchmark.


Abstract:
  We study the BCS energy gap $\Xi$ in the high-density limit and derive an
asymptotic formula, which strongly depends on the strength of the interaction
potential $V$ on the Fermi surface. In combination with the recent result by
one of us (arXiv:2106.02015) on the critical temperature $T_c$ at high
densities, we prove the universality of the ratio of the energy gap and the
critical temperature.


Abstract:
  Semiconductors with very low lattice thermal conductivities are highly
desired for applications relevant to thermal energy conversion and management,
such as thermoelectrics and thermal barrier coatings. Although the crystal
structure and chemical bonding are known to play vital roles in shaping heat
transfer behavior, material design approaches of lowering lattice thermal
conductivity using chemical bonding principles are uncommon. In this work, we
present an effective strategy of weakening interatomic interactions and
therefore suppressing lattice thermal conductivity based on chemical bonding
principles and develop a high-efficiency approach of discovering low
$\kappa_{\rm L}$ materials by screening the local coordination environments of
crystalline compounds. The followed first-principles calculations uncover 30
hitherto unexplored compounds with (ultra)low lattice thermal conductivities
from thirteen prototype crystal structures contained in the inorganic crystal
structure database. Furthermore, we demonstrate an approach of rationally
designing high-performance thermoelectrics by additionally incorporating
cations with stereochemically active lone-pair electrons. Our results not only
provide fundamental insights into the physical origin of the low lattice
thermal conductivity in a large family of copper-based compounds but also offer
an efficient approach to discovery and design materials with targeted thermal
transport properties.


Abstract:
  We introduce a new renormalization procedure on double rotations, which is
reminiscent of the classical Rauzy induction. Using this renormalization we
prove that the set of parameters which induce infinite type double rotations
has Hausdorff dimension strictly smaller than 3. Moreover, we construct a
natural invariant measure supported on these parameters and show that, with
respect to this measure, almost all double rotations are uniquely ergodic.


Abstract:
  Context. The Small Magellanic Cloud (SMC) hosts many known high-mass X-ray
binaries (HMXBs), and all but one (SMC X-1) have a Be companion star. Through
the calibration and verification phase of eROSITA on board the
Spektrum-Roentgen-Gamma (SRG) spacecraft, the Be/X-ray binary XMMU
J010429.4-723136 was in the field of view during observations of the supernova
remnant, 1E0102.2-7219, used as a calibration standard.
  Aims. We report timing and spectral analyses of XMMU J010429.4-723136 based
on three eROSITA observations of the field, two of which were performed on 2019
November 7-9, with the third on 2020 June 18-19. We also reanalyse the OGLE-IV
light curve for that source in order to determine the orbital period.
  Methods. We performed a Lomb-Scargle periodogram analysis to search for
pulsations (from the X-ray data) and for the orbital period (from the OGLE
data). X-ray spectral parameters and fluxes were retrieved from the best-fit
model.
  Results. We detect, for the first time, the pulsations of XMMU
J010429.4-723136 at a period of ~164 s, and therefore designate the source as
SXP 164. From the spectral fitting, we derive a source flux of ~1x10e-12 erg
s-1 cm-2 for all three observations, corresponding to a luminosity of ~4x10e35
erg s-1 at the distance of the SMC. Furthermore, reanalysing the OGLE light
curve, including the latest observations, we find a significant periodic signal
that we believe is likely be the orbital period; at 22.3d, this is shorter than
the previously reported values. The Swift/XRT light curve, extracted from two
long monitorings of the field and folded at the same period, suggests that a
modulation is also present in the X-ray data.


Abstract:
  Dense granular systems subjected to an imposed shear stress undergo
stick-slip dynamics with systematic patterns of dilation-compaction. During
each stick phase, as the frictional strength builds up, the granular system
dilates to accommodate shear strain, developing stronger force networks. During
each slip event, when the stored energy is released, particles experience large
rearrangements and the granular network can significantly change. Here, we use
numerical simulations of 3D, sheared frictional packings to show that the mean
betweenness centrality -- a property of network of interparticle connections --
follows consistent patterns during the stick-slip dynamics, showing sharp
spikes at each slip event. We identify the source of this behavior as arising
from the connectivity and contact arrangements of granular network during
dilation-compaction cycles, and find that a lower potential for connection
between particles leads to an increase of mean betweenness centrality in the
system. Furthermore, we show that at high confinements, few particles lose
contact during slip events, leading to a smaller change in granular
connectivity and betweenness centrality.


Abstract:
  In this paper we consider a combination of the joint replenishment problem
(JRP) and single machine scheduling with release dates. There is a single
machine and one or more item types. Each job has a release date, a positive
processing time, and it requires a subset of items. A job can be started at
time $t$ only if all the required item types were replenished between the
release date of the job and time point $t$. The ordering of item types for
distinct jobs can be combined. The objective is to minimize the total ordering
cost plus a scheduling criterion, such as total weighted completion time or
maximum flow time, where the cost of ordering a subset of items simultaneously
is the sum of a joint ordering cost, and an additional item ordering cost for
each item type in the subset. We provide several complexity results for the
offline problem, and competitive analysis for online variants with min-sum and
min-max criteria, respectively.


Abstract:
  The Clifford group is a finite subgroup of the unitary group generated by the
Hadamard, the CNOT, and the Phase gates. This group plays a prominent role in
quantum error correction, randomized benchmarking protocols, and the study of
entanglement. Here we consider the problem of finding a short quantum circuit
implementing a given Clifford group element. Our methods aim to minimize the
entangling gate count assuming all-to-all qubit connectivity. First, we
consider circuit optimization based on template matching and design
Clifford-specific templates that leverage the ability to factor out Pauli and
SWAP gates. Second, we introduce a symbolic peephole optimization method. It
works by projecting the full circuit onto a small subset of qubits and
optimally recompiling the projected subcircuit via dynamic programming. CNOT
gates coupling the chosen subset of qubits with the remaining qubits are
expressed using symbolic Pauli gates. Software implementation of these methods
finds circuits that are only 0.2% away from optimal for 6 qubits and reduces
the two-qubit gate count in circuits with up to 64 qubits by 64.7% on average,
compared with the Aaronson-Gottesman canonical form.


Abstract:
  We analyze the fine structure of Clark measures and Clark isometries
associated with two-variable rational inner functions on the bidisk. In the
degree (n,1) case, we give a complete description of supports and weights for
both generic and exceptional Clark measures, characterize when the associated
embedding operators are unitary, and give a formula for those embedding
operators. We also highlight connections between our results and both the
structure of Agler decompositions and study of extreme points for the set of
positive pluriharmonic measures on 2-torus.


Abstract:
  We study a time minimization problem on the group of motions of a plane with
admissible control in a half-disk. The considered control system describes a
model of a car that can move forward on a plane and turn in place. Optimal
trajectories of this system are used in image processing for the detection of
salient lines. In particular, such trajectories are used in the analysis of
medical images when searching for blood vessels in photos of the human retina.
The problem is of interest in geometric control theory as a model example in
which the set of admissible controls contains zero at the boundary. We prove
complete controllability and the existence of optimal trajectories. By
analyzing the Hamiltonian system of Pontryagin maximum principle we derive
explicit formulas for extremal controls and trajectories. The optimality of
extremals is partially investigated. The structure of optimal synthesis is
described.


Abstract:
  Dynamical two-particle susceptibilites are important for a wide range of
different experiments in condensed-matter physics and beyond. Nevertheless,
most textbooks avoid describing how to derive such response functions, perhaps
because they are viewed as too complex. In the literature, most derivations
work with generalized susceptibilities, which are more general, but require an
even higher layer of complexity. In this work, we show a more direct derivation
in the context of model Hamiltonians which can be mapped directly onto an
impurity model. We also present an alternative derivation for the irreducible
vertex in the context of the Falicov-Kimball model.


Abstract:
  Emerging hybrid memory systems that comprise technologies such as Intel's
Optane DC Persistent Memory, exhibit disparities in the access speeds and
capacity ratios of their heterogeneous memory components. This breaks many
assumptions and heuristics designed for traditional DRAM-only platforms. High
application performance is feasible via dynamic data movement across memory
units, which maximizes the capacity use of DRAM while ensuring efficient use of
the aggregate system resources. Newly proposed solutions use performance models
and machine intelligence to optimize which and how much data to move
dynamically; however, the decision of when to move this data is based on
empirical selection of time intervals, or left to the applications. Our
experimental evaluation shows that failure to properly configure the data
movement frequency can lead to 10%-100% slowdown for a given data movement
policy; yet, there is no established methodology on how to properly configure
this value for a given workload, platform and policy. We propose Cori, a
system-level tuning solution that identifies and extracts the necessary
application-level data reuse information, and guides the selection of data
movement frequency to deliver gains in application performance and system
resource efficiency. Experimental evaluation shows that Cori configures data
movement frequencies that provide application performance within 3% of the
optimal one, and that it can achieve this up to 5x more quickly than random or
brute-force approaches. System-level validation of Cori on a platform with DRAM
and Intel's Optane DC PMEM confirms its practicality and tuning efficiency.


Abstract:
  When the Stable Unit Treatment Value Assumption (SUTVA) is violated and there
is interference among units, there is not a uniquely defined Average Treatment
Effect (ATE), and alternative estimands may be of interest, among them average
unit-level differences in outcomes under different homogeneous treatment
policies. We term this target the Homogeneous Assignment Average Treatment
Effect (HAATE). We consider approaches to experimental design with multiple
treatment conditions under partial interference and, given the estimand of
interest, we show that difference-in-means estimators may perform better than
correctly specified regression models in finite samples on root mean squared
error (RMSE). With errors correlated at the cluster level, we demonstrate that
two-stage randomization procedures with intra-cluster correlation of treatment
strictly between zero and one may dominate one-stage randomization designs on
the same metric. Simulations demonstrate performance of this approach; an
application to online experiments at Facebook is discussed.


Abstract:
  First-order phase transitions can leave relic pockets of false vacua and
their particles, that manifest as macroscopic Dark Matter. We compute one
predictive model: a gauge theory with a dark quark relic heavier than the
confinement scale. During the first-order phase transition to confinement, dark
quarks remain in the false vacuum and get compressed, forming Fermi balls that
can undergo gravitational collapse to stable dark dwarfs (bound states
analogous to white dwarfs) near the Chandrasekhar limit, or to primordial black
holes.


Abstract:
  We describe SuperIso v4.1, a public code for calculation of flavour
observables, in the Standard Model and for New Physics scenarios in a
model-independent way as well as for specific beyond the SM models such as two
Higgs doublet models (2HDM), minimal supersymmetric Standard Model (MSSM) and
next-to-minimal supersymmetric Standard Model (NMSSM).


Abstract:
  The $A$MnO$_{2}$ delafossites ($A$=Na, Cu), are model frustrated
antiferromagnets, with triangular layers of Mn$^{3+}$~spins. At low
temperatures ($T_{N}$=65 K), a $C2/m \rightarrow P\overline{1}$ transition is
found in CuMnO$_2$, which breaks frustration and establishes magnetic order. In
contrast to this clean transition, $A$=Na only shows short-range distortions at
$T_N$. Here we report a systematic crystallographic, spectroscopic, and
theoretical investigation of CuMnO$_2$. We show that, even in stoichiometric
samples, non-zero anisotropic Cu displacements co-exist with magnetic order.
Using X-ray/neutron diffraction and Raman scattering, we show that high
pressures acts to decouple these degrees of freedom. This manifests as an
isostuctural phase transition at $\sim$10 GPa, with a reversible collapse of
the $c$-axis. This is shown to be the high pressure analog of the $c$-axis
negative thermal expansion seen at ambient pressure. DFT simulations confirm
that dynamical instabilities of the Cu$^{+}$ cations and edge-shared MnO$_{6}$
layers are intertwined at ambient pressure. However, high pressure selectively
activates the former, before an eventual predicted re-emergence of magnetism at
the highest pressures. Our results show that the lattice dynamics and local
structure of CuMnO$_2$ are quantitatively different to non-magnetic Cu
delafossites, and raise questions about the role of intrinsic inhomogeniety in
frustrated antiferromagnets.


Abstract:
  One of the main challenges for arbitrary-shaped text detection is to design a
good text instance representation that allows networks to learn diverse text
geometry variances. Most of existing methods model text instances in image
spatial domain via masks or contour point sequences in the Cartesian or the
polar coordinate system. However, the mask representation might lead to
expensive post-processing, while the point sequence one may have limited
capability to model texts with highly-curved shapes. To tackle these problems,
we model text instances in the Fourier domain and propose one novel Fourier
Contour Embedding (FCE) method to represent arbitrary shaped text contours as
compact signatures. We further construct FCENet with a backbone, feature
pyramid networks (FPN) and a simple post-processing with the Inverse Fourier
Transformation (IFT) and Non-Maximum Suppression (NMS). Different from previous
methods, FCENet first predicts compact Fourier signatures of text instances,
and then reconstructs text contours via IFT and NMS during test. Extensive
experiments demonstrate that FCE is accurate and robust to fit contours of
scene texts even with highly-curved shapes, and also validate the effectiveness
and the good generalization of FCENet for arbitrary-shaped text detection.
Furthermore, experimental results show that our FCENet is superior to the
state-of-the-art (SOTA) methods on CTW1500 and Total-Text, especially on
challenging highly-curved text subset.


Abstract:
  We study celestial amplitudes in (super) Yang-Mills theory using a
parameterisation of the spinor helicity variables where their overall phase is
not fixed by the little group action. In this approach the spin constraint
$h-\bar{h}=J$ for celestial conformal primaries emerges naturally from a new
Mellin transform, and the action of conformal transformations on celestial
amplitudes is derived. Applying this approach to $\mathcal{N}\!=\!4$ super
Yang-Mills, we show how the appropriate definition of on-shell superspace
coordinates leads naturally to a formulation of chiral celestial
superamplitudes and a representation of the generators of the four-dimensional
superconformal algebra on the celestial sphere, which by construction
annihilate all tree-level celestial superamplitudes.


Abstract:
  Available data on the polarization of the secondary proton (as a function of
its momentum $K$) in the inelastic ({\it p, p$'$}) reactions with the $^{9}$Be,
$^{12}$C, and $^{40}$Ca nuclei and differential cross section data (the
momentum distributions) for the reactions at the initial proton energy 1 GeV
and scattering angles $\Theta$=21$^\circ$ and $\Theta$=24.5$^\circ$ were
analysed in a range of the high momenta $K$ close to the momentum corresponding
to the proton elastic scattering off the investigated nucleus. A structure in
the polarization and momentum distribution data at the high momenum $K$,
related probably to quasi-elastic scattering off a $^{8}$Be-like nucleon
cluster inside the nuclei, was observed.


Abstract:
  With smart devices being an essential part of our everyday lives,
unsupervised access to the mobile sensors' data can result in a multitude of
side-channel attacks. In this paper, we study potential data leaks from Apple
Pencil (2nd generation) supported by the Apple iPad Pro, the latest stylus pen
which attaches to the iPad body magnetically for charging. We observe that the
Pencil's body affects the magnetic readings sensed by the iPad's magnetometer
when a user is using the Pencil. Therefore, we ask: Can we infer what a user is
writing on the iPad screen with the Apple Pencil, given access to only the
iPad's motion sensors' data? To answer this question, we present Side-channel
attack on Stylus pencil through Sensors (S3), a system that identifies what a
user is writing from motion sensor readings. We first use the sharp
fluctuations in the motion sensors' data to determine when a user is writing on
the iPad. We then introduce a high-dimensional particle filter to track the
location and orientation of the Pencil during usage. Lastly, to guide
particles, we build the Pencil's magnetic map serving as a bridge between the
measured magnetic data and the Pencil location and orientation. We evaluate S3
with 10 subjects and demonstrate that we correctly identify 93.9%, 96%, 97.9%,
and 93.33% of the letters, numbers, shapes, and words by only having access to
the motion sensors' data.


Abstract:
  In the supervisory control framework of discrete-event systems (DES) with
infinite behavior initiated by Thistle and Wonham, a supervisor satisfying the
minimal acceptable specification and the maximal legal specification is
synthesized. However, this supervisor may incur livelocks as it cannot ensure
that the infinite behavior under supervision will always visit some marker
states. To tackle this problem, we propose the definition of markability by
requiring that all infinite cycles include at least one marker state. Then we
formulate the problem of $\omega-$nonblocking supervisory control of DES with
infinite behavior to synthesize an $\omega-$nonblocking (i.e. nonblocking,
deadlock-free and livelock-free) supervisor. An algorithm is proposed to
achieve $\omega-$nonblockingness by computing the supremal $*-$controllable,
$*-$closed, $\omega-$controllable and markable sublanguage. We utilize the
example of a robot as a running example.


Abstract:
  For many, Graphics Processing Units (GPUs) provides a source of reliable
computing power. Recently, Nvidia introduced its 9th generation HPC-grade GPUs,
the Ampere 100, claiming significant performance improvements over previous
generations, particularly for AI-workloads, as well as introducing new
architectural features such as asynchronous data movement. But how well does
the A100 perform on non-AI benchmarks, and can we expect the A100 to deliver
the application improvements we have grown used to with previous GPU
generations? In this paper, we benchmark the A100 GPU and compare it to four
previous generations of GPUs, with particular focus on empirically quantifying
our derived performance expectations, and -- should those expectations be
undelivered -- investigate whether the introduced data-movement features can
offset any eventual loss in performance? We find that the A100 delivers less
performance increase than previous generations for the well-known Rodinia
benchmark suite; we show that some of these performance anomalies can be
remedied through clever use of the new data-movement features, which we
microbenchmark and demonstrate where (and more importantly, how) they should be
used.


Abstract:
  The automation of decision procedures makes certification essential. We
suggest to use determinacy of turn-based two-player games with regular winning
conditions in order to generate certificates for the number of states that a
deterministic finite automaton (DFA) needs in order to recognize a given
language. Given a language $L$ and a bound $k$, recognizability of $L$ by a DFA
with $k$ states is reduced to a game between Prover and Refuter. The
interaction along the game then serves as a certificate. Certificates generated
by Prover are minimal DFAs. Certificates generated by Refuter are faulty
attempts to define the required DFA. We compare the length of offline
certificates, which are generated with no interaction between Prover and
Refuter, and online certificates, which are based on such an interaction, and
are thus shorter. We show that our approach is useful also for certification of
separability of regular languages by a DFA of a given size. Unlike DFA
minimization, which can be solved in polynomial time, separation is
NP-complete, and thus the certification approach is essential. In addition, we
prove NP-completeness of a strict version of separation.


Abstract:
  The recent outbreak of the COVID-19 led to the death of millions of people
worldwide. To stave off the spread of the virus, the authorities in the US
employed different strategies, including the mask mandate order issued by the
states' governors. In the current work, we defined a parameter called the
average death ratio as the monthly average of the number of daily deaths to the
monthly average number of daily cases. We utilized survey data to quantify
people's abidance by the mask mandate order. Additionally, we implicitly
addressed the extent to which people abide by the mask mandate order that may
depend on some parameters like population, income, and education level. Using
different machine learning classification algorithms, we investigated how the
decrease or increase in death ratio for the counties in the US West Coast
correlates with the input parameters. The results showed that for most counties
there, the mask mandate order decreased the death ratio reflecting the
effectiveness of this preventive measure on the West Coast. Additionally, the
changes in the death ratio demonstrated a noticeable correlation with the
socio-economic condition of each county. Moreover, the results showed a
promising classification accuracy score as high as around 90%.


Abstract:
  Question Answering (QA) tasks requiring information from multiple documents
often rely on a retrieval model to identify relevant information for reasoning.
The retrieval model is typically trained to maximize the likelihood of the
labeled supporting evidence. However, when retrieving from large text corpora
such as Wikipedia, the correct answer can often be obtained from multiple
evidence candidates. Moreover, not all such candidates are labeled as positive
during annotation, rendering the training signal weak and noisy. This problem
is exacerbated when the questions are unanswerable or when the answers are
Boolean, since the model cannot rely on lexical overlap to make a connection
between the answer and supporting evidence. We develop a new parameterization
of set-valued retrieval that handles unanswerable queries, and we show that
marginalizing over this set during training allows a model to mitigate false
negatives in supporting evidence annotations. We test our method on two
multi-document QA datasets, IIRC and HotpotQA. On IIRC, we show that joint
modeling with marginalization improves model performance by 5.5 F1 points and
achieves a new state-of-the-art performance of 50.5 F1. We also show that
retrieval marginalization results in 4.1 QA F1 improvement over a
non-marginalized baseline on HotpotQA in the fullwiki setting.


Abstract:
  Let $m\in\mathbb N_{\ge 2}$, and let $\mathcal K=\{K_\lambda: \lambda\in(0,
1/m]\}$ be a class of Cantor sets, where $K_{\lambda}=\{\sum_{i=1}^\infty
d_i\lambda^i: d_i\in\{0,1,\ldots, m-1\}, i\ge 1\}$. We investigate in this
paper the likelyhood of a fixed point in the Cantor sets of $\mathcal K$. More
precisely, for a fixed point $x\in(0,1)$ we consider the parameter set
$\Lambda(x)=\{\lambda\in(0,1/m]: x\in K_\lambda\}$, and show that $\Lambda(x)$
is a topological Cantor set having zero Lebesgue measure and full Hausdorff
dimension. Furthermore, by constructing a sequence of Cantor subsets with large
thickness in $\Lambda(x)$ we prove that the intersection
$\Lambda(x)\cap\Lambda(y)$ also has full Hausdorff dimension for any $x,
y\in(0,1)$.


Abstract:
  Although true metal-free "Population III" stars have so-far escaped
discovery, their nature, and that of their supernovae, is revealed in the
chemical products left behind in the next generations of stars. Here we report
the detection of an ultra-metal poor star in the Sculptor dwarf spheroidal
galaxy, AS0039. With [Fe/H]$_{\rm LTE}=-4.11$, it is the most metal-poor star
so far discovered in any external galaxy. Contrary to the majority of Milky Way
stars at this metallicity, AS0039 is clearly not enhanced in carbon, with
[C/Fe]$_{\rm LTE}=-0.75$ and A(C)=+3.60, making it the lowest detected carbon
abundance in any star to date. It furthermore lacks $\alpha$-element
uniformity, having extremely low [Mg/Ca]$_{\rm NLTE}=-0.60$ and [Mg/Ti]$_{\rm
NLTE}=-0.86$, in stark contrast with the near solar ratios observed in C-normal
stars within the Milky Way halo. The unique abundance pattern indicates that
AS0039 formed out of material that was predominantly enriched by a $\sim$20$
M_\odot$ progenitor star with an unusually high explosion energy
$E=10\times10^{51}$ erg. The star AS0039 is thus one of the first observational
evidence for zero-metallicity hypernovae and provides a unique opportunity to
investigate the diverse nature of Population III stars.


Abstract:
  Timely and reliable sensing of infrastructure conditions is critical in
disaster management for planning effective infrastructure restorations. Social
media, a near real-time information source, has been widely used in disasters
for forming timely situational awareness. Yet, using social media to sense
electricity infrastructure conditions has not been explored. This study aims to
address the research gap through mining public topics from social media. To
achieve this purpose, we proposed a systematic and customized approach wherein
(1) electricity-related social media data is extracted by the classifier
developed based on Bidirectional Encoder Representations from Transformers
(BERT); and (2) public topics are modeled with unigrams, bigrams, and trigrams
to incorporate the formulaic expressions of infrastructure conditions in social
media. Electricity infrastructures in Florida impacted by Hurricane Irma are
studied for illustration and demonstration. Results show that the proposed
approach is capable of sensing the temporal evolutions and geographic
differences of electricity infrastructure conditions.


Abstract:
  We observe a system of filaments and clusters around Cl0016+1609 and
MACSJ1621.4+3810 using the SITELLE Fourier transform spectrograph at the Canada
France Hawaii Telescope. For Cl0016+1609 (z=0.546), the observations span an
11.8 Mpc x 4.3 Mpc region along an eastern filament which covers the main
cluster core, as well as two 4.3 Mpc x 4.3 Mpc regions which each cover
southern subclumps. For MACSJ1621.4+3810 (z= 0.465), 3.9 Mpc x 3.9 Mpc around
the main cluster core is covered. We present the frequency and location of the
emission line galaxies, their emission line images, and calculate the star
formation rates, specific star formation rates and merger statistics. In
Cl0016+1609, we find thirteen [OII]~3727 Angstrom emitting galaxies with star
formation rates between 0.2 and 14.0 M$_{\odot}$ yr$^{-1}$. 91$^{+3}_{-10}$%
are found in regions with moderate local galaxy density, avoiding the dense
cluster cores. These galaxies follow the main filament of the superstructure,
and are mostly blue and disky, with several showing close companions and
merging morphologies. In MACSJ1621.4+3810, we find ten emission line sources.
All are blue (100$^{+0}_{-15}$%), with 40$^{+16}_{-12}$% classified as disky
and 60$^{+12}_{-16}$% as merging systems. Eight avoid the cluster core
(80$^{+7}_{-17}$%), but two (20$^{+17}_{-7}$%) are found near high density
regions, including the brightest cluster galaxy (BCG). These observations push
the spectroscopic study of galaxies in filaments beyond z~ 0.3 to z~ 0.5. Their
efficient confirmation is paramount to their usefulness as more galaxy surveys
come online.


Abstract:
  As the convolutional neural network (CNN) gets deeper and wider in recent
years, the requirements for the amount of data and hardware resources have
gradually increased. Meanwhile, CNN also reveals salient redundancy in several
tasks. The existing magnitude-based pruning methods are efficient, but the
performance of the compressed network is unpredictable. While the accuracy loss
after pruning based on the structure sensitivity is relatively slight, the
process is time-consuming and the algorithm complexity is notable. In this
article, we propose a novel automatic channel pruning method (ACP).
Specifically, we firstly perform layer-wise channel clustering via the
similarity of the feature maps to perform preliminary pruning on the network.
Then a population initialization method is introduced to transform the pruned
structure into a candidate population. Finally, we conduct searching and
optimizing iteratively based on the particle swarm optimization (PSO) to find
the optimal compressed structure. The compact network is then retrained to
mitigate the accuracy loss from pruning. Our method is evaluated against
several state-of-the-art CNNs on three different classification datasets
CIFAR-10/100 and ILSVRC-2012. On the ILSVRC-2012, when removing 64.36%
parameters and 63.34% floating-point operations (FLOPs) of ResNet-50, the Top-1
and Top-5 accuracy drop are less than 0.9%. Moreover, we demonstrate that
without harming overall performance it is possible to compress SSD by more than
50% on the target detection dataset PASCAL VOC. It further verifies that the
proposed method can also be applied to other CNNs and application scenarios.


Abstract:
  Graph associahedra are generalized permutohedra arising as special cases of
nestohedra and hypergraphic polytopes. The graph associahedron of a graph $G$
encodes the combinatorics of search trees on $G$, defined recursively by a root
$r$ together with search trees on each of the connected components of $G-r$. In
particular, the skeleton of the graph associahedron is the rotation graph of
those search trees. We investigate the diameter of graph associahedra as a
function of some graph parameters. We give a tight bound of $\Theta(m)$ on the
diameter of trivially perfect graph associahedra on $m$ edges. We consider the
maximum diameter of associahedra of graphs on $n$ vertices and of given
tree-depth, treewidth, or pathwidth, and give lower and upper bounds as a
function of these parameters. We also prove that the maximum diameter of
associahedra of graphs of pathwidth two is $\Theta (n\log n)$. Finally, we give
the exact diameter of the associahedra of complete split and of unbalanced
complete bipartite graphs.


Abstract:
  Complex machine learning (ML) inference algorithms like recurrent neural
networks (RNNs) use standard functions from math libraries like exponentiation,
sigmoid, tanh, and reciprocal of square root. Although prior work on secure
2-party inference provides specialized protocols for convolutional neural
networks (CNNs), existing secure implementations of these math operators rely
on generic 2-party computation (2PC) protocols that suffer from high
communication. We provide new specialized 2PC protocols for math functions that
crucially rely on lookup-tables and mixed-bitwidths to address this performance
overhead; our protocols for math functions communicate up to 423x less data
than prior work. Some of the mixed bitwidth operations used by our math
implementations are (zero and signed) extensions, different forms of
truncations, multiplication of operands of mixed-bitwidths, and digit
decomposition (a generalization of bit decomposition to larger digits). For
each of these primitive operations, we construct specialized 2PC protocols that
are more communication efficient than generic 2PC, and can be of independent
interest. Furthermore, our math implementations are numerically precise, which
ensures that the secure implementations preserve model accuracy of cleartext.
We build on top of our novel protocols to build SIRNN, a library for end-to-end
secure 2-party DNN inference, that provides the first secure implementations of
an RNN operating on time series sensor data, an RNN operating on speech data,
and a state-of-the-art ML architecture that combines CNNs and RNNs for
identifying all heads present in images. Our evaluation shows that SIRNN
achieves up to three orders of magnitude of performance improvement when
compared to inference of these models using an existing state-of-the-art 2PC
framework.


Abstract:
  The cluster Westerlund~1 (Wd1) is host to a large variety of post
main-sequence (MS) massive stars. The simultaneous presence of these stars can
only be explained by stellar models if the cluster has a finely-tuned age of
4-5Myr, with several published studies independently claiming ages within this
range. At this age, stellar models predict that the cool supergiants (CSGs)
should have luminosities of $\log(L/L_\odot) \approx 5.5$, close to the
empirical luminosity limit. Here, we test that prediction using archival data
and new photometry from SOFIA to estimate bolometric luminosities for the CSGs.
We find that these stars are on average 0.4dex too faint to be 5Myr old,
regardless of which stellar evolution model is used, and instead are indicative
of a much older age of $10.4^{+1.3}_{-1.2}$Myr. We argue that neither
systematic uncertainties in the extinction law nor stellar variability can
explain this discrepancy. In reviewing various independent age estimates of Wd1
in the literature, we firstly show that those based on stellar diversity are
unreliable. Secondly, we re-analyse Wd1's pre-MS stars employing the Damineli
extinction law, finding an age of $7.2^{+1.1}_{-2.3}$Myr; older than that of
previous studies, but which is vulnerable to systematic errors that could push
the age close to 10Myr. However, there remains significant tension between the
CSG age and that inferred from the eclipsing binary W13. We conclude that
stellar evolution models cannot explain Wd1 under the single age paradigm.
Instead, we propose that the stars in the Wd1 region formed over a period of
several Myr.


Abstract:
  Explaining Graph Neural Networks predictions to end users of AI applications
in easily understandable terms remains an unsolved problem. In particular, we
do not have well developed methods for automatically evaluating explanations,
in ways that are closer to how users consume those explanations. Based on
recent application trends and our own experiences in real world problems, we
propose automatic evaluation approaches for GNN Explanations.


Abstract:
  Stellar streams from globular clusters (GCs) offer constraints on the nature
of dark matter and have been used to explore the dark matter halo structure and
substructure of our Galaxy. Detection of GC streams in other galaxies would
broaden this endeavor to a cosmological context, yet no such streams have been
detected to date. To enable such exploration, we develop the Hough Stream
Spotter (HSS), and apply it to the Pan-Andromeda Archaeological Survey (PAndAS)
photometric data of resolved stars in M31's stellar halo. We first demonstrate
that our code can re-discover known dwarf streams in M31. We then use the HSS
to blindly identify 27 linear GC stream-like structures in the PAndAS data. For
each HSS GC stream candidate, we investigate the morphologies of the streams
and the colors and magnitudes of all stars in the candidate streams. We find
that the five most significant detections show a stronger signal along the red
giant branch in color-magnitude diagrams (CMDs) than spurious non-stream
detections. Lastly, we demonstrate that the HSS will easily detect globular
cluster streams in future Nancy Grace Roman Space Telescope data of nearby
galaxies. This has the potential to open up a new discovery space for GC stream
studies, GC stream gap searches, and for GC stream-based constraints on the
nature of dark matter.


Abstract:
  We propose a method for object-aware 3D egocentric pose estimation that
tightly integrates kinematics modeling, dynamics modeling, and scene object
information. Unlike prior kinematics or dynamics-based approaches where the two
components are used disjointly, we synergize the two approaches via
dynamics-regulated training. At each timestep, a kinematic model is used to
provide a target pose using video evidence and simulation state. Then, a
prelearned dynamics model attempts to mimic the kinematic pose in a physics
simulator. By comparing the pose instructed by the kinematic model against the
pose generated by the dynamics model, we can use their misalignment to further
improve the kinematic model. By factoring in the 6DoF pose of objects (e.g.,
chairs, boxes) in the scene, we demonstrate for the first time, the ability to
estimate physically-plausible 3D human-object interactions using a single
wearable camera. We evaluate our egocentric pose estimation method in both
controlled laboratory settings and real-world scenarios.