title:
von Willebrand Factor unfolding mediates platelet deposition in a model
  of high-shear thrombosis

abstract:
  Thrombosis under high-shear conditions is mediated by the mechanosensitive
blood glycoprotein von Willebrand Factor (vWF). vWF unfolds in response to
strong flow gradients and facilitates rapid recruitment of platelets in flowing
blood. While the thrombogenic effect of vWF is well recognized, its
conformational response in complex flows has largely been omitted from
numerical models of thrombosis. We recently presented a continuum model for the
unfolding of vWF, where we represented vWF transport and its flow-induced
conformational change using convection-diffusion-reaction equations. Here, we
incorporate the vWF component into our multi-constituent model of thrombosis,
where the local concentration of stretched vWF amplifies the deposition rate of
free-flowing platelets and reduces the shear cleaning of deposited platelets.
We validate the model using three benchmarks: in vitro model of
atherothrombosis, a stagnation point flow, and the PFA-100, a clinical blood
test commonly used for screening for von Willebrand Disease (vWD). The
simulations reproduced the key aspects of vWF-mediated thrombosis observed in
these experiments, such as the thrombus location, thrombus growth dynamics, and
the effect of blocking platelet-vWF interactions. The PFA-100 simulations
closely matched the reported occlusion times for normal blood and several
hemostatic deficiencies, namely, thrombocytopenia, vWD Type 1, and vWD Type 3.
Overall, the multi-constituent model of thrombosis presented in this work
enables macro-scale 3-D simulations of thrombus formation in complex geometries
over a wide range of shear rates and accounts for qualitative and quantitative
hemostatic deficiencies in patient blood. The results also demonstrate the
utility of the continuum model of vWF unfolding that could be adapted to other
numerical models of thrombosis.


title: MMTAfrica: Multilingual Machine Translation for African Languages

abstract:
  In this paper, we focus on the task of multilingual machine translation for
African languages and describe our contribution in the 2021 WMT Shared Task:
Large-Scale Multilingual Machine Translation. We introduce MMTAfrica, the first
many-to-many multilingual translation system for six African languages: Fon
(fon), Igbo (ibo), Kinyarwanda (kin), Swahili/Kiswahili (swa), Xhosa (xho), and
Yoruba (yor) and two non-African languages: English (eng) and French (fra). For
multilingual translation concerning African languages, we introduce a novel
backtranslation and reconstruction objective, BT\&REC, inspired by the random
online back translation and T5 modeling framework respectively, to effectively
leverage monolingual data. Additionally, we report improvements from MMTAfrica
over the FLORES 101 benchmarks (spBLEU gains ranging from $+0.58$ in Swahili to
French to $+19.46$ in French to Xhosa). We release our dataset and code source
at https://github.com/edaiofficial/mmtafrica.


title: Weight modules over Bell--Rogalski algebras

abstract:
  We study a class of $\mathbb{Z}$-graded algebras introduced by Bell and
Rogalski. Their construction generalizes in large part that of rank one
generalized Weyl algebras (GWAs). We establish certain ring-theoretic
properties of these algebras and study their connection to GWAs. We classify
the simple weight modules in the infinite orbit case and provide a partial
classification in the case of orbits of finite order.


title:
Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning
  for Robotics

abstract:
  This paper focuses on robotic reinforcement learning with sparse rewards for
natural language goal representations. An open problem is the
sample-inefficiency that stems from the compositionality of natural language,
and from the grounding of language in sensory data and actions. We address
these issues with three contributions. We first present a mechanism for
hindsight instruction replay utilizing expert feedback. Second, we propose a
seq2seq model to generate linguistic hindsight instructions. Finally, we
present a novel class of language-focused learning tasks. We show that
hindsight instructions improve the learning performance, as expected. In
addition, we also provide an unexpected result: We show that the learning
performance of our agent can be improved by one third if, in a sense, the agent
learns to talk to itself in a self-supervised manner. We achieve this by
learning to generate linguistic instructions that would have been appropriate
as a natural language goal for an originally unintended behavior. Our results
indicate that the performance gain increases with the task-complexity.


title:
Long-term effect estimation when combining clinical trial and
  observational follow-up datasets

abstract:
  Combining experimental and observational follow-up datasets has received a
lot of attention lately. In a time-to-event setting, recent work has used
medicare claims to extend the follow-up period for participants in a prostate
cancer clinical trial. This allows the estimation of the long-term effect that
cannot be estimated by clinical trial data alone. In this paper, we study the
estimation of long-term effect when participants in a clinical trial are linked
to an observational follow-up dataset with incomplete data. Such data linkages
are often incomplete for various reasons. We formulate incomplete linkages as a
missing data problem with careful considerations of the relationship between
the linkage status and the missing data mechanism. We use the popular Cox
proportional hazard model as a working model to define the long-term effect. We
propose a conditional linking at random (CLAR) assumption and an inverse
probability of linkage weighting (IPLW) partial likelihood estimator. We show
that our IPLW partial likelihood estimator is consistent and asymptotically
normal. We further extend our approach to incorporate time-dependent
covariates. Simulations results confirm the validity of our method, and we
further apply our methods to the SWOG study.


title:
Risk-Bounded Temporal Logic Control of Continuous-Time Stochastic
  Systems

abstract:
  Motivated by the recent interest in risk-aware control, we study a
continuous-time control synthesis problem to bound the risk that a stochastic
linear system violates a given specification. We use risk signal temporal logic
as a specification formalism in which distributionally robust risk predicates
are considered and equipped with the usual Boolean and temporal operators. Our
control approach relies on reformulating these risk predicates as deterministic
predicates over mean and covariance states of the system. We then obtain a
timed sequence of sets of mean and covariance states from the timed automata
representation of the specification. To avoid an explosion in the number of
automata states, we propose heuristics to find candidate sequences effectively.
To execute and check dynamic feasibility of these sequences, we present a
sampled-data control technique based on time discretization and constraint
tightening that allows to perform timed transitions while satisfying the
continuous-time constraints.


title: On the generation of simple groups by Sylow subgroups

abstract:
  Let $G$ be a finite simple group of Lie type and let $P$ be a Sylow
$2$-subgroup of $G$. In this paper, we prove that for any nontrivial element $x
\in G$, there exists $g \in G$ such that $G = \langle P, x^g \rangle$. By
combining this result with recent work of Breuer and Guralnick, we deduce that
if $G$ is a finite nonabelian simple group and $r$ is any prime divisor of
$|G|$, then $G$ is generated by a Sylow $2$-subgroup and a Sylow $r$-subgroup.


title: The History of the Grid

abstract:
  With the widespread availability of high-speed networks, it becomes feasible
to outsource computing to remote providers and to federate resources from many
locations. Such observations motivated the development, from the mid-1990s
onwards, of a range of innovative Grid technologies, applications, and
infrastructures. We review the history, current status, and future prospects
for Grid computing.


title:
Machine learning model to predict solar radiation, based on the
  integration of meteorological data and data obtained from satellite images

abstract:
  Knowing the behavior of solar radiation at a geographic location is essential
for the use of energy from the sun using photovoltaic systems; however, the
number of stations for measuring meteorological parameters and for determining
the size of solar fields in remote areas is limited. In this work, images
obtained from the GOES-13 satellite were used, from which variables were
extracted that could be integrated into datasets from meteorological stations.
From this, 3 different models were built, on which the performance of 5 machine
learning algorithms in predicting solar radiation was evaluated. The neural
networks had the highest performance in the model that integrated the
meteorological variables and the variables obtained from the images, according
to an analysis carried out using four evaluation metrics; although if the rRMSE
is considered, all results obtained were higher than 20%, which classified the
performance of the algorithms as fair. In the 2012 dataset, the estimation
results according to the metrics MBE, R2, RMSE, and rRMSE corresponded to
-0.051, 0.880, 90.99 and 26.7%, respectively. In the 2017 dataset, the results
of MBE, R2, RMSE, and rRMSE were -0.146, 0.917, 40.97 and 22.3%, respectively.
Although it is possible to calculate solar radiation from satellite images, it
is also true that some statistical methods depend on radiation data and
sunshine captured by ground-based instruments, which is not always possible
given that the number of measurement stations on the surface is limited.


title:
Aging or DEAD: origin of the non-monotonic response to weak
  self-propulsion in active glasses

abstract:
  Among amorphous states, glass is defined by relaxation times longer than the
observation time. This nonergodic nature makes the understanding of glassy
systems an involved topic, with complex aging effects or responses to further
out-of-equilibrium external drivings. In this respect active glasses made of
self-propelled particles have recently emerged as stimulating systems which
broadens and challenges our current understanding of glasses by considering
novel internal out-of-equilibrium degrees of freedom. In previous experimental
studies we have shown that in the ergodicity broken phase, the dynamics of
dense passive particles first slows down as particles are made slightly active,
before speeding up at larger activity. Here, we show that this nonmonotonic
behavior also emerges in simulations of soft active Brownian particles and
explore its cause. We refute that the Deadlock by Emergence of Active
Directionality (DEAD) model we proposed earlier describes our data. However, we
demonstrate that the nonmonotonic response is due to activity enhanced aging,
and thus confirm the link with ergodicity breaking. Beyond self-propelled
systems, our results suggest that aging in active glasses is not fully
understood.


title: Weak solutions to the master equation of potential mean field games

abstract:
  The purpose of this work is to introduce a notion of weak solution to the
master equation of a potential mean field game and to prove that existence and
uniqueness hold under quite general assumptions. Remarkably, this is achieved
without any monotonicity constraint on the coefficients. The key point is to
interpret the master equation in a conservative sense and then to adapt to the
infinite dimensional setting earlier arguments for hyperbolic systems deriving
from a Hamilton-Jacobi-Bellman equation. Here, the master equation is indeed
regarded as an infinite dimensional system set on the space of probability
measures and is formally written as the derivative of the
Hamilton-Jacobi-Bellman equation associated with the mean field control problem
lying above the mean field game. To make the analysis easier, we assume that
the coefficients are periodic, which allows to represent probability measures
through their Fourier coefficients. Most of the analysis then consists in
rewriting the master equation and the corresponding Hamilton-Jacobi-Bellman
equation for the mean field control problem as partial differential equations
set on the Fourier coefficients themselves. In the end, we establish existence
and uniqueness of functions that are displacement semi-concave in the measure
argument and that solve the Hamilton-Jacobi-Bellman equation in a suitable
generalized sense and, subsequently, we get existence and uniqueness of
functions that solve the master equation in an appropriate weak sense and that
satisfy a weak one-sided Lipschitz inequality. As another new result, we also
prove that the optimal trajectories of the associated mean field control
problem are unique for almost every starting point, for a suitable probability
measure on the space of probability measures.


title:
Manipulating the transverse spin angular momentum and Belinfante
  momentum of spin-polarized light by a tilted stratified medium in optical
  tweezers

abstract:
  In the recent past, optical tweezers incorporating a stratified medium have
been exploited to generate complex translational and rotational dynamics in
mesoscopic particles due to the coupling between the spin and orbital angular
momentum of the light, generated as a consequence of the tight focusing of
light by a high numerical aperture objective lens into the stratified medium.
Here, we consider an optical tweezers system with a tilted stratified medium
(direction of stratification at an angle with the axis of the incident beam),
and show that for input circularly polarized Gaussian beams, the resulting
spin-orbit interaction deeply influences the generation of transverse spin
angular momentum (TSAM) and Belinfante momentum of light, and allows additional
control on their magnitude. Importantly, the TSAM generated in our system
consists of both the orthogonal components, which is in sharp contrast to the
case of evanescent waves and surface plasmons, where only one of the TSAM
components are generated. The broken symmetry due to the tilt ensures that,
depending upon the helicity of the input beam, the magnitude of the mutually
orthogonal components of the TSAM depend entirely on the tilt angle. This may
prove to be an effective handle in exotic spin-controlled manipulation of
particles in experiments.


title:
On the regularity of harmonic maps from ${\sf RCD}(K,N)$ to ${\sf
  CAT}(0)$ spaces and related results

abstract:
  For an harmonic map $u$ from a domain $U\subset{\rm X}$ in an ${\sf
RCD}(K,N)$ space ${\rm X}$ to a ${\sf CAT}(0)$ space ${\rm Y}$ we prove the
Lipschitz estimate \[ {\rm Lip}(u|_B)\leq \frac {C(K^-R^2,N)}r\inf_{{\sf o}\in
{\rm Y}}\,\sqrt{\frac1{{\mathfrak m}(2B)}\int_{2B}{\sf d}_{\rm
Y}^2(u(\cdot),{\sf o})\, {\rm d}{\mathfrak m}}, \qquad \forall 2B\subset U \]
where $r\in(0,R)$ is the radius of $B$. This is obtained by combining classical
Moser's iteration, a Bochner-type inequality that we derive (guided by recent
works of Zhang-Zhu) together with a reverse Poincar\'e inequality that is also
established here. A direct consequence of our estimate is a Lioville-Yau type
theorem in the case $K=0$.
  Among the ingredients we develop for the proof, a variational principle valid
in general ${\sf RCD}$ spaces is particularly relevant. It can be roughly
stated as: if $({\rm X},{\sf d},{\mathfrak m})$ is ${\sf RCD}(K,\infty)$ and
$f\in C_b({\rm X})$ is so that $\Delta f\leq C$ for some constant $C>0$, then
for every $t>0$ and ${\mathfrak m}$-a.e.\ $x\in{\rm X}$ there is a unique
minimizer $F_t(x)$ for $ y\ \mapsto\ f(y)+\frac{{\sf d}^2(x,y)}{2t} $ and the
map $F_t$ satisfies \[ (F_t)_*{\mathfrak m}\leq e^{t(C+2K^-{\sf
Osc}(f))}{\mathfrak m},\qquad\text{where}\qquad {\sf Osc}(f):=\sup f-\inf f. \]
Here existence is in place without any sort of compactness assumption and
uniqueness should be intended in a sense analogue to that in place for Regular
Lagrangian Flows and Optimal Maps (and is related to both these concepts).
  Finally, we also obtain a Rademacher-type result for Lipschitz maps between
spaces as above.


title:
Towards Understanding Barriers and Mitigation Strategies of Software
  Engineers with Non-traditional Educational and Occupational Backgrounds

abstract:
  The traditional path to a software engineering career involves a
post-secondary diploma in Software Engineering, Computer Science, or a related
field. However, many software engineers take a non-traditional path to their
career, starting from other industries or fields of study. This paper proposes
a study on barriers faced by software engineers with non-traditional
educational and occupational backgrounds, and possible mitigation strategies
for those barriers. We propose a two-stage methodology, consisting of an
exploratory study, followed by a validation study. The exploratory study will
involve a grounded-theory-based qualitative analysis of relevant Reddit data to
yield a framework around the barriers and possible mitigation strategies. These
findings will then be validated using a survey in the validation study. Making
software engineering more accessible to those with non-traditional backgrounds
will not only bring about the benefits of functional diversity, but also serves
as a method of filling in the labour shortages of the software engineering
industry.


title: A Mathematical Framework for Transformations of Physical Processes

abstract:
  We observe that the existence of sequential and parallel composition
supermaps in higher order physics can be formalized using enriched category
theory. Encouraged by physically relevant examples such as unitary supermaps
and layers within higher order causal categories (HOCCs), we treat the modeling
of higher order physics with enriched monoidal categories in analogy with the
process theoretic framework in which physical theories are modeled with
monoidal categories. We use the enriched monoidal setting to construct a
suitable definition of structure-preserving map between higher order physical
theories via the Grothendieck construction. We then show that the convenient
feature of currying in higher order physical theories can be seen as a
consequence of combining the primitive assumption of the existence of parallel
and sequential composition supermaps with an additional feature of "linking".
In a second application, we show more generally that categories containing
infinite towers of enriched monoidal categories with full and faithful
structure-preserving maps between them inevitably lead to closed monoidal
structure. The aim of the proposed definitions is to give a broad framework for
the study and comparison of novel causal structures in quantum theory, and,
more broadly, provide a paradigm of physical theory where static and dynamical
features are treated in a unified way.


title: Numerical Simulations of Nearly Incompressible Viscoelastic Membranes

abstract:
  This work presents a novel numerical investigation of the dynamics of
free-boundary flows of viscoelastic liquid membranes. The governing equation
describes the balance of linear momentum, in which the stresses include the
viscoelastic response to deformations of Maxwell type. A penalty method is
utilized to enforce near incompressibility of the viscoelastic media, in which
the penalty constant is proportional to the viscosity of the fluid. A finite
element method is used, in which the slender geometry representing the liquid
membrane, is discretized by linear three-node triangular elements under plane
stress conditions. Two applications of interest are considered for the
numerical framework provided: shear flow, and extensional flow in drawing
processes.


title: Performance portable ice-sheet modeling with MALI

abstract:
  High resolution simulations of polar ice-sheets play a crucial role in the
ongoing effort to develop more accurate and reliable Earth-system models for
probabilistic sea-level projections. These simulations often require a massive
amount of memory and computation from large supercomputing clusters to provide
sufficient accuracy and resolution. The latest exascale machines poised to come
online contain a diverse set of computing architectures. In an effort to avoid
architecture specific programming and maintain productivity across platforms,
the ice-sheet modeling code known as MALI uses high level abstractions to
integrate Trilinos libraries and the Kokkos programming model for performance
portable code across a variety of different architectures. In this paper, we
analyze the performance portable features of MALI via a performance analysis on
current CPU-based and GPU-based supercomputers. The analysis highlights
performance portable improvements made in finite element assembly and multigrid
preconditioning within MALI with speedups between 1.26-1.82x across CPU and GPU
architectures but also identifies the need to further improve performance in
software coupling and preconditioning on GPUs. We also perform a weak
scalability study and show that simulations on GPU-based machines perform
1.24-1.92x faster when utilizing the GPUs. The best performance is found in
finite element assembly which achieved a speedup of up to 8.65x and a weak
scaling efficiency of 82.9% with GPUs. We additionally describe an automated
performance testing framework developed for this code base using a changepoint
detection method. The framework is used to make actionable decisions about
performance within MALI. We provide several concrete examples of scenarios in
which the framework has identified performance regressions, improvements, and
algorithm differences over the course of two years of development.


title:
Iterative Depth-First Search for Fully Observable Non-Deterministic
  Planning

abstract:
  Fully Observable Non-Deterministic (FOND) planning models uncertainty through
actions with non-deterministic effects. Existing FOND planning algorithms are
effective and employ a wide range of techniques. However, most of the existing
algorithms are not robust for dealing with both non-determinism and task size.
In this paper, we develop a novel iterative depth-first search algorithm that
solves FOND planning tasks and produces strong cyclic policies. Our algorithm
is explicitly designed for FOND planning, addressing more directly the
non-deterministic aspect of FOND planning, and it also exploits the benefits of
heuristic functions to make the algorithm more effective during the iterative
searching process. We compare our proposed algorithm to well-known FOND
planners, and show that it has robust performance over several distinct types
of FOND domains considering different metrics.


title:
Thin viscoelastic dewetting films of Jeffreys type subjected to gravity
  and substrate interactions

abstract:
  This work presents a study of the interfacial dynamics of thin viscoelastic
films subjected to the gravitational force and substrate interactions induced
by the disjoining pressure, in two spatial dimensions. The governing equation
is derived as a long-wave approximation of the Navier-Stokes equations for
incompressible viscoelastic liquids under the effect of gravity, with the
Jeffreys model for viscoelastic stresses. For the particular cases of
horizontal or inverted planes, the linear stability analysis is performed to
investigate the influence of the physical parameters involved on the growth
rate and length scales of instabilities. Numerical simulations of the nonlinear
regime of the dewetting process are presented for the particular case of an
inverted plane. Both gravity and the disjoining pressure are found to affect
not only the length scale of instabilities, but also the final configuration of
dewetting, by favoring the formation of satellite droplets, that are suppressed
by the slippage with the solid substrate.


title:
Approximate discounting-free policy evaluation from transient and
  recurrent states

abstract:
  In order to distinguish policies that prescribe good from bad actions in
transient states, we need to evaluate the so-called bias of a policy from
transient states. However, we observe that most (if not all) works in
approximate discounting-free policy evaluation thus far are developed for
estimating the bias solely from recurrent states. We therefore propose a system
of approximators for the bias (specifically, its relative value) from transient
and recurrent states. Its key ingredient is a seminorm LSTD (least-squares
temporal difference), for which we derive its minimizer expression that enables
approximation by sampling required in model-free reinforcement learning. This
seminorm LSTD also facilitates the formulation of a general unifying procedure
for LSTD-based policy value approximators. Experimental results validate the
effectiveness of our proposed method.


title: The global inverse fractional conductivity problem

abstract:
  We prove \emph{global} uniqueness for an inverse problem for the fractional
conductivity equation on domains that are bounded in one direction. The
conductivities are assumed to be isotropic and nontrivial in the exterior of
the domain, while the data is given in the form of partial Dirichlet-to-Neumann
(DN) maps measured in nondisjoint open subsets of the exterior. This can be
seen as the fractional counterpart of the classical inverse conductivity
problem. The proof is based on a unique continuation property (UCP) for the DN
maps and an exterior determination method from the partial exterior DN maps.
This is analogous to the classical boundary determination method by Kohn and
Vogelius. The most important technical novelty is the construction of sequences
of special solutions to the fractional conductivity equation whose Dirichlet
energies in the limit can be concentrated at any given point in the exterior.
This is achieved independently of the UCP and despite the nonlocality of the
equation. Due to the recent counterexamples by the last two authors, our
results almost completely characterize uniqueness for the inverse fractional
conductivity problem with partial data for isotropic global conductivities.


title:
Perturbative renormalization of $\phi_4^4$ theory on the half space
  $\mathbb{R}^+ \times\mathbb{R}^3$ with flow equations

abstract:
  In this paper, we give a rigorous proof of the renormalizability of the
massive $\phi_4^4$ theory on a half-space, using the renormalization group flow
equations. We find that five counter-terms are needed to make the theory
finite, namely $\phi^2$, $\phi\partial_z\phi$, $\phi\partial_z^2\phi$,
$\phi\Delta_x\phi$ and $\phi^4$ for $(z,x)\in\mathbb{R}^+\times\mathbb{R}^3$.
The amputated correlation functions are distributions in position space. We
consider a suitable class of test functions and prove inductive bounds for the
correlation functions folded with these test functions. The bounds are uniform
in the cutoff and thus directly lead to renormalizability.


title:
Show, Don't Tell: Demonstrations Outperform Descriptions for
  Schema-Guided Task-Oriented Dialogue

abstract:
  Building universal dialogue systems that can seamlessly operate across
multiple domains/APIs and generalize to new ones with minimal supervision and
maintenance is a critical challenge. Recent works have leveraged natural
language descriptions for schema elements to enable such systems; however,
descriptions can only indirectly convey schema semantics. In this work, we
propose Show, Don't Tell, a prompt format for seq2seq modeling which uses a
short labeled example dialogue to show the semantics of schema elements rather
than tell the model via descriptions. While requiring similar effort from
service developers, we show that using short examples as schema representations
with large language models results in stronger performance and better
generalization on two popular dialogue state tracking benchmarks: the
Schema-Guided Dialogue dataset and the MultiWoZ leave-one-out benchmark.


title: Decoupling-limit healthiness of the generalized SU(2) Proca theory

abstract:
  We study the healthiness of the decoupling limit of the generalized SU(2)
Proca theory (GSU2P). Namely, we study the healthiness of those terms whose
analysis in the scalar limit was not originally established in the
reconstruction of the full theory (see Phys. Rev. D 102 (2020) 104066). Those
terms are the parity-violating $\tilde{\mathcal{L}}_{4,2}^1$ and the
parity-conserving beyond SU(2) Proca terms $\mathcal{L}_{4,2}^3$ and
$\mathcal{L}_{4,2}^4$. Using the 3+1 Arnowitt-Deser-Misner formalism, we write
down the kinetic Lagrangian of these terms in the decoupling limit and show
that their corresponding kinetic matrices are degenerate. This degeneracy is a
necessary condition for the propagation of the right number of degrees of
freedom, as required by the primary constraint-enforcing relation.
Interestingly, the $\tilde{\mathcal{L}}_{4,2}^1$ term, which is purely
non-Abelian, does not contribute to the kinetic Lagrangian of the theory, so
its contribution is trivially degenerate. Similarly, but not trivially in these
cases, the contributions of the $\mathcal{L}_{4,2}^3$ and $\mathcal{L}_{4,2}^4$
terms to the kinetic Lagrangian turn out to be degenerate as well. The results
presented in this paper represent a progress in the construction of the fully
healthy GSU2P.


title: An Adaptive Black-box Backdoor Detection Method for Deep Neural Networks

abstract:
  With the surge of Machine Learning (ML), An emerging amount of intelligent
applications have been developed. Deep Neural Networks (DNNs) have demonstrated
unprecedented performance across various fields such as medical diagnosis and
autonomous driving. While DNNs are widely employed in security-sensitive
fields, they are identified to be vulnerable to Neural Trojan (NT) attacks that
are controlled and activated by stealthy triggers. In this paper, we target to
design a robust and adaptive Trojan detection scheme that inspects whether a
pre-trained model has been Trojaned before its deployment. Prior works are
oblivious of the intrinsic property of trigger distribution and try to
reconstruct the trigger pattern using simple heuristics, i.e., stimulating the
given model to incorrect outputs. As a result, their detection time and
effectiveness are limited. We leverage the observation that the pixel trigger
typically features spatial dependency and propose the first trigger
approximation based black-box Trojan detection framework that enables a fast
and scalable search of the trigger in the input space. Furthermore, our
approach can also detect Trojans embedded in the feature space where certain
filter transformations are used to activate the Trojan. We perform extensive
experiments to investigate the performance of our approach across various
datasets and ML models. Empirical results show that our approach achieves a
ROC-AUC score of 0.93 on the public TrojAI dataset. Our code can be found at
https://github.com/xinqiaozhang/adatrojan


title: Improved Object Pose Estimation via Deep Pre-touch Sensing

abstract:
  For certain manipulation tasks, object pose estimation from head-mounted
cameras may not be sufficiently accurate. This is at least in part due to our
inability to perfectly calibrate the coordinate frames of today's high degree
of freedom robot arms that link the head to the end-effectors. We present a
novel framework combining pre-touch sensing and deep learning to more
accurately estimate pose in an efficient manner. The use of pre-touch sensing
allows our method to localize the object directly with respect to the robot's
end effector, thereby avoiding error caused by miscalibration of the arms.
Instead of requiring the robot to scan the entire object with its pre-touch
sensor, we use a deep neural network to detect object regions that contain
distinctive geometric features. By focusing pre-touch sensing on these regions,
the robot can more efficiently gather the information necessary to adjust its
original pose estimate. Our region detection network was trained using a new
dataset containing objects of widely varying geometries and has been labeled in
a scalable fashion that is free from human bias. This dataset is applicable to
any task that involves a pre-touch sensor gathering geometric information, and
has been made publicly available. We evaluate our framework by having the robot
re-estimate the pose of a number of objects of varying geometries. Compared to
two simpler region proposal methods, we find that our deep neural network
performs significantly better. In addition, we find that after a sequence of
scans, objects can typically be localized to within 0.5 cm of their true
position. We also observe that the original pose estimate can often be
significantly improved after collecting a single quick scan.


title:
The boundedness of Fractional Hardy-Littlewood maximal operator on
  variable lp spaces using Calderon-Zygmund decomposition

abstract:
  In this paper, we prove strong type, weak type inequalities of
Hardy-Littlewood maximal operator and fractional Hardy-Littlewood maximal
operator on variable sequence spaces lp(Z). This is achieved using
Calderon-Zygmund decomposition for sequences, properties of modular functional
and Log Holder continuity


title:
Fundamental Limits on Detection With a Dual-function Radar Communication
  System

abstract:
  This paper investigates the fundamental limits on the target detection
performance with a dual-function multiple-input-multiple-output (MIMO) radar
communication (RadCom) systems. By assuming the presence of a point-like target
and a communication receiver, closed-form expressions for the maximum detection
probability and the transmit waveforms achieving the optimal performance are
derived. Results show that for the considered case, the dual-function system
should transmit coherent waveforms to achieve the optimal detection
performance. Moreover, the angle separation between the target and
communication receiver has a great impact on the achievable detection
performance.


title: A Study of Using Cepstrogram for Countermeasure Against Replay Attacks

abstract:
  In this paper, we investigate the properties of the cepstrogram and
demonstrate its effectiveness as a powerful feature for countermeasure against
replay attacks. Cepstrum analysis of replay attacks suggests that crucial
information for anti-spoofing against replay attacks may retain in the
cepstrogram. Experimental results on the ASVspoof 2019 physical access (PA)
database demonstrate that, compared with other features, the cepstrogram
dominates in both single and fusion systems when building countermeasures
against replay attacks. Our LCNN-based single and fusion systems with the
cepstrogram feature outperform the corresponding LCNN-based systems without
using the cepstrogram feature and several state-of-the-art (SOTA) single and
fusion systems in the literature.


title:
Spectral signatures of excess-proton waiting and transfer-path dynamics
  in aqueous hydrochloric acid solutions

abstract:
  Signatures of solvated excess protons in infrared difference absorption
spectra, such as the continuum band between the water bend and stretch bands,
have been experimentally known for a long time, but the theoretical basis for
linking spectral signatures with the microscopic proton-transfer mechanism so
far relied on normal-mode analysis. We analyze the excess-proton dynamics in ab
initio molecular-dynamics simulations of aqueous hydrochloric acid solutions by
trajectory-decomposition techniques. The continuum band in the 2000 - 3000
cm$^{-1 }$ range is shown to be due to normal-mode oscillations of temporary
H$_3$O$^+$ complexes. An additional prominent peak at 400 cm$^{-1}$ reports on
the coupling of excess-proton motion to the relative vibrations of the two
flanking water molecules. The actual proton transfer between two water
molecules, which for large water separations involves crossing of a barrier and
thus is not a normal mode, is characterized by two characteristic time scales:
Firstly, the waiting time for transfer to occur in the range of 200 - 300 fs,
which leads to a broad weak shoulder around ~100 cm$^{-1}$, consistent with our
experimental THz spectra. Secondly, the mean duration of a transfer event of
about 14 fs, which produces a rather well-defined spectral contribution around
1200 cm$^{-1}$ and agrees in location and width with previous experimental
mid-infrared spectra.


title: Measuring and improving community resilience: a Fuzzy Logic approach

abstract:
  Due to the increasing frequency of natural and man-made disasters worldwide,
the scientific community has paid considerable attention to the concept of
resilience engineering in recent years. Authorities and decision-makers, on the
other hand, have been focusing their efforts to develop strategies that can
help increase community resilience to different types of extreme events. Since
it is often impossible to prevent every risk, the focus is on adapting and
managing risks in ways that minimize impacts to communities (e.g., humans and
other systems). Several resilience strategies have been proposed in the
literature to reduce disaster risk and improve community resilience. Generally,
resilience assessment is challenging due to uncertainty and unavailability of
data necessary for the estimation process. This paper proposes a Fuzzy Logic
method for quantifying community resilience. The methodology is based on the
PEOPLES framework, an indicator-based hierarchical framework that defines all
aspects of the community. A fuzzy-based approach is implemented to quantify the
PEOPLES indicators using descriptive knowledge instead of hard data, accounting
also for the uncertainties involved in the analysis. To demonstrate the
applicability of the methodology, data regarding the functionality of the city
San Francisco before and after the Loma Prieta earthquake are used to obtain a
resilience index of the Physical Infrastructure dimension of the PEOPLES
framework. The results show that the methodology can provide good estimates of
community resilience despite the uncertainty of the indicators. Hence, it
serves as a decision-support tool to help decision-makers and stakeholders
assess and improve the resilience of their communities.


title: A new family of smooth copulas with arbitrarily irregular densities

abstract:
  Copulas are known to satisfy a number of regularity properties, and one might
therefore believe that their densities, when they exist, admit a certain degree
of regularity themselves. We show that this is not true in general by
constructing a broad family of copulas which admit densities that can hardly be
considered regular. The copula densities are constructed from arbitrary
univariate densities supported on the unit interval, and we show by example
that the copula densities can inherit pathological behaviour from the
underlying univariate densities. In particular, we construct a nontrivial
univariate density which is unbounded in every open set of the unit interval,
and show that it induces a copula density which is finite everywhere but
unbounded in every neighborhood of the unit hypercube. Nevertheless, all of our
copulas are shown to enjoy attractive smoothness properties.


title: Helton-Howe Trace, the Connes-Chern character and Quantization

abstract:
  We study the Helton-Howe trace and the Connes-Chern character for Toeplitz
operators on weighted Bergman spaces via the idea of quantization. We prove a
local formula for the large $t$-limit of the Connes-Chern character as the
weight goes to infinity. And we show that the Helton-Howe trace of Toeplitz
operators is independent of the weight $t$ and obtain a local formula for the
Helton-Howe trace for all weighted Bergman spaces using harmonic analysis and
quantization.


title:
Fuzzy temporal convolutional neural networks in P300-based
  Brain-computer interface for smart home interaction

abstract:
  The processing and classification of electroencephalographic signals (EEG)
are increasingly performed using deep learning frameworks, such as
convolutional neural networks (CNNs), to generate abstract features from brain
data, automatically paving the way for remarkable classification prowess.
However, EEG patterns exhibit high variability across time and uncertainty due
to noise. It is a significant problem to be addressed in P300-based Brain
Computer Interface (BCI) for smart home interaction. It operates in a
non-optimal natural environment where added noise is often present. In this
work, we propose a sequential unification of temporal convolutional networks
(TCNs) modified to EEG signals, LSTM cells, with a fuzzy neural block (FNB),
which we called EEG-TCFNet. Fuzzy components may enable a higher tolerance to
noisy conditions. We applied three different architectures comparing the effect
of using block FNB to classify a P300 wave to build a BCI for smart home
interaction with healthy and post-stroke individuals. Our results reported a
maximum classification accuracy of 98.6% and 74.3% using the proposed method of
EEG-TCFNet in subject-dependent strategy and subject-independent strategy,
respectively. Overall, FNB usage in all three CNN topologies outperformed those
without FNB. In addition, we compared the addition of FNB to other
state-of-the-art methods and obtained higher classification accuracies on
account of the integration with FNB. The remarkable performance of the proposed
model, EEG-TCFNet, and the general integration of fuzzy units to other
classifiers would pave the way for enhanced P300-based BCIs for smart home
interaction within natural settings.


title:
A Little Too Personal: Effects of Standardization versus Personalization
  on Job Acquisition, Work Completion, and Revenue for Online Freelancers

abstract:
  As more individuals consider permanently working from home, the online labor
market continues to grow as an alternative working environment. While the
flexibility and autonomy of these online gigs attracts many workers, success
depends critically upon self-management and workers' efficient allocation of
scarce resources. To achieve this, freelancers may develop alternative work
strategies, employing highly standardized schedules and communication patterns
while taking on large work volumes, or engaging in smaller numbers of jobs
whilst tailoring their activities to build relationships with individual
employers. In this study, we consider this contrast in relation to worker
communication patterns. We demonstrate the heterogeneous effects of
standardization versus personalization across different stages of a project and
examine the relative impact on job acquisition, project completion, and
earnings. Our findings can inform the design of platforms and various worker
support tools for the gig economy.


title: Sim-to-Real Learning for Bipedal Locomotion Under Unsensed Dynamic Loads

abstract:
  Recent work on sim-to-real learning for bipedal locomotion has demonstrated
new levels of robustness and agility over a variety of terrains. However, that
work, and most prior bipedal locomotion work, have not considered locomotion
under a variety of external loads that can significantly influence the overall
system dynamics. In many applications, robots will need to maintain robust
locomotion under a wide range of potential dynamic loads, such as pulling a
cart or carrying a large container of sloshing liquid, ideally without
requiring additional load-sensing capabilities. In this work, we explore the
capabilities of reinforcement learning (RL) and sim-to-real transfer for
bipedal locomotion under dynamic loads using only proprioceptive feedback. We
show that prior RL policies trained for unloaded locomotion fail for some loads
and that simply training in the context of loads is enough to result in
successful and improved policies. We also compare training specialized policies
for each load versus a single policy for all considered loads and analyze how
the resulting gaits change to accommodate different loads. Finally, we
demonstrate sim-to-real transfer, which is successful but shows a wider
sim-to-real gap than prior unloaded work, which points to interesting future
research.


title:
Phase Diagram of a Non-Hermitian Chern Insulator: Destabilization of
  Chiral Edge States and Bulk-Boundary Correspondence

abstract:
  A non-Hermitian Chern insulator with gain/loss-type non-Hermiticity shows a
peculiar gap closing; when a nontrivial Chern insulator phase changes to a
gapless phase, conduction and valence bands are combined into one band owing to
destabilization of chiral edge states. A previous recipe of non-Hermitian
bulk-boundary correspondence is insufficient for this system since such a gap
closing is beyond its scope. Here, we revise the recipe by taking the peculiar
gap closing into account and apply it to the non-Hermitian Chern insulator. We
demonstrate that the bulk-boundary correspondence holds in the system including
a destabilization of chiral edge states. A phase diagram derived from the
bulk-boundary correspondence is shown to be consistent with spectra of the
system.


title: An Improved Integer Modular Multiplicative Inverse (modulo $2^w$)

abstract:
  This paper presents an algorithm for the integer multiplicative inverse (mod
$2^w$) which completes in the fewest cycles known for modern microprocessors,
when using the native bit width $w$ for the modulus $2^w$. The algorithm is a
modification of a method by Dumas, and for computers it slightly increases
generality and efficiency. A proof is given, and the algorithm is shown to be
closely related to the better known Newton's method algorithm for the inverse.
Simple direct formulas, which are needed by this algorithm and by Newton's
method, are reviewed and proven for the integer inverse modulo $2^k$ with $k$ =
1, 2, 3, 4, or 5, providing the first proof of the preferred formula with $k$=4
or 5.


title: Simple synthetic molecular dynamics for efficient trajectory generation

abstract:
  Synthetic molecular dynamics (synMD) trajectories from learned generative
models have been proposed as a useful addition to the biomolecular simulation
toolbox. The computational expense of explicitly integrating the equations of
motion in molecular dynamics currently is a severe limit on the number and
length of trajectories which can be generated for complex systems. Approximate,
but more computationally efficient, generative models can be used in place of
explicit integration of the equations of motion, and can produce meaningful
trajectories at greatly reduced computational cost. Here, we demonstrate a very
simple synMD approach using a fine-grained Markov state model (MSM) with states
mapped to specific atomistic configurations, which provides an exactly solvable
reference. We anticipate this simple approach will enable rapid, effective
testing of enhanced sampling algorithms in highly non-trivial models for both
equilibrium and non-equilibrium problems. We demonstrate the use of a MSM to
generate atomistic synMD trajectories for the fast-folding miniprotein
Trp-cage, at a rate of over 200 milliseconds per day on a standard workstation.
We employ a non-standard clustering for MSM generation that appears to better
preserve kinetic properties at shorter lag times than a conventional MSM. We
also show a parallelizable workflow that backmaps discrete synMD trajectories
to full-coordinate representations at dynamic resolution for efficient
analysis.


title:
Towards Better Chinese-centric Neural Machine Translation for
  Low-resource Languages

abstract:
  The last decade has witnessed enormous improvements in science and
technology, stimulating the growing demand for economic and cultural exchanges
in various countries. Building a neural machine translation (NMT) system has
become an urgent trend, especially in the low-resource setting. However, recent
work tends to study NMT systems for low-resource languages centered on English,
while few works focus on low-resource NMT systems centered on other languages
such as Chinese. To achieve this, the low-resource multilingual translation
challenge of the 2021 iFLYTEK AI Developer Competition provides the
Chinese-centric multilingual low-resource NMT tasks, where participants are
required to build NMT systems based on the provided low-resource samples. In
this paper, we present the winner competition system that leverages monolingual
word embeddings data enhancement, bilingual curriculum learning, and
contrastive re-ranking. In addition, a new Incomplete-Trust (In-trust) loss
function is proposed to replace the traditional cross-entropy loss when
training. The experimental results demonstrate that the implementation of these
ideas leads better performance than other state-of-the-art methods. All the
experimental codes are released at:
https://github.com/WENGSYX/Low-resource-text-translation.


title: Neutron capture measurement and resonance parameter analysis of natSm

abstract:
  Multiple isotopes of samarium element are the isotopes produced by the s
process, and 154Sm is produced by the r process. In addition, 144Sm is p nuclei
in nuclear astrophysics. The measurement of these can help us to better
understand the results of relevant photonuclear reaction experiments. On the
other hand, 149Sm is a 235U fission product with a 1% yield, its cross sections
are important to reactor neutronics. In this work, the neutron capture yield of
the natural samarium target was measured at the back-streaming white neutron
beamline (Back-n) of the China Spallation Neutron Source (CSNS), and the
resonance parameters were analyzed by SAMMY code. The resonance peaks and the
neutron separation energies contributed by the different isotopes are
considered individually. The results of the capture yield found signs of the
possibility of two resonance peaks at 8 eV, which awaits further experimental
examination. Cross-section was calculated according to resonance parameters and
was compared with other experimental results and evaluation databases of
ENDF/B-VIII.0 and CENDL-3.2. A clear difference between ENDF/B VIII.0 and
CENDL-3.2 database appears at 23.2 eV, the experimental result at this energy
is smaller than data of ENDF/B VIII.0 database but CENDL-3.2 database. Most of
the controversial experimental results invariably come from the samarium 149
isotope.


title: A three term sublevel set inequality

abstract:
  Let $B$ be a ball in ${\mathbb R}^2$. For $j=1,2,3$ let
$\varphi_j:B\to{\mathbb R}^1$ be real analytic submersions, and let $a_j$ be
real analytic coefficient functions. To any $\varepsilon>0$ and any Lebesgue
measurable functions $f_j:{\mathbb R}^1\to {\mathbb C}$ associate the sublevel
set $S = S(f_1,f_2,f_3,\varepsilon) = \{x\in B: |\sum_{j=1}^3
a_j(x)(f_j\circ\varphi_j)(x)|<\varepsilon\}$. Let $S' = \{x\in S:
\max_j|f_j\circ\varphi_j(x)|\ge 1\}$. Our main result is an upper bound, under
certain hypotheses on the data $\varphi_j,a_j$ for the Lebesgue measure of $S'$
of the form $|S'| \le c\varepsilon^\gamma$ for some constants $c,\gamma>0$ that
depend on the data $a_j,\varphi_j$ but not on the functions $f_j$ or parameter
$\varepsilon$. The main hypothesis is that in any connected open subset of $B$,
the only real analytic solution $(f_1,f_2,f_3)$ of $\sum_j
a_j(x)(f_j\circ\varphi_j)(x)\equiv 0$ is the trivial solution $f_k=0\
\forall\,k$. Certain auxiliary hypotheses, which hold for generic
$\varphi_j,a_j$, are also imposed. The case in which all coefficients $a_j$ are
constant was previously known.
  This result is a principal ingredient in an analysis, in a companion paper,
of related implicitly oscillatory integrals with four factors $f_j$.
  Certain related results are also discussed. In particular, a generalization
to arbitrarily many summands f_j is obtained for the special case in which all
mappings $\varphi_j$ are linear.


title: On the Importance of Karaka Framework in Multi-modal Grounding

abstract:
  Computational Paninian Grammar model helps in decoding a natural language
expression as a series of modifier-modified relations and therefore facilitates
in identifying dependency relations closer to language (context) semantics
compared to the usual Stanford dependency relations. However, the importance of
this CPG dependency scheme has not been studied in the context of multi-modal
vision and language applications. At IIIT Hyderabad, we plan to perform a novel
study to explore the potential advantages and disadvantages of CPG framework in
a vision-language navigation task setting, a popular and challenging
multi-modal grounding task.


title: Neural networks embrace learned diversity

abstract:
  Diversity conveys advantages in nature, yet homogeneous neurons typically
comprise the layers of artificial neural networks. Here we construct neural
networks from neurons that learn their own activation functions, quickly
diversify, and subsequently outperform their homogeneous counterparts.
Sub-networks instantiate the neurons, which meta-learn especially efficient
sets of nonlinear responses. Such learned diversity provides examples of
dynamical systems selecting diversity over uniformity and elucidates the role
of diversity in natural and artificial systems.


title: Sedimentation of a surfactant-laden drop in a liquid with particles

abstract:
  This project aims to study the sedimentation of a surfactant-laden drop in a
liquid with particles. A 2D simulation is performed with MATLAB. The interface
is captured by the front-tracking method. The local viscosity depends on the
local particle concentration, which follows a power law. The surfactants not
only decrease the surface tension but also induce a surface tension gradient.
If the surface tension decreases, the settling velocity will decrease due to a
larger deformation of the drop (i.e., the drop becomes more flat).
Additionally, increasing the Peclet number (ratio of convection to diffusion on
the interface) for surfactants will reduce the settling velocity due to a
larger surface tension gradient.


title: Hardware Trojan Insertion Using Reinforcement Learning

abstract:
  This paper utilizes Reinforcement Learning (RL) as a means to automate the
Hardware Trojan (HT) insertion process to eliminate the inherent human biases
that limit the development of robust HT detection methods. An RL agent explores
the design space and finds circuit locations that are best for keeping inserted
HTs hidden. To achieve this, a digital circuit is converted to an environment
in which an RL agent inserts HTs such that the cumulative reward is maximized.
Our toolset can insert combinational HTs into the ISCAS-85 benchmark suite with
variations in HT size and triggering conditions. Experimental results show that
the toolset achieves high input coverage rates (100\% in two benchmark
circuits) that confirms its effectiveness. Also, the inserted HTs have shown a
minimal footprint and rare activation probability.


title: Area and spectrum estimates for stable minimal surfaces

abstract:
  This note concerns the area growth and bottom spectrum of complete stable
minimal surfaces in a three-dimensional manifold with scalar curvature bounded
from below. When the ambient manifold is the Euclidean space, by an elementary
argument, it is shown directly from the stability inequality that the area of
such minimal surfaces grows exactly as the Euclidean plane. Consequently, such
minimal surfaces must be at, a well-known result due to Fisher-Colbrie and
Schoen as well as do Carmo and Peng. In the case the ambient manifold is the
hyperbolic space, explicit area growth estimate is also derived. For the bottom
spectrum, upper bound estimates are established in terms of the scalar
curvature lower bound of the ambient manifold.


title: Is thermodynamics fundamental?

abstract:
  It is a common view in philosophy of physics that thermodynamics is a
non-fundamental theory. This is motivated in particular by the fact that
thermodynamics is considered to be a paradigmatic example for a theory that can
be reduced to another one, namely statistical mechanics. For instance, the
statement "temperature is mean molecular kinetic energy" has become a textbook
example for a successful reduction, despite the fact that this statement is not
correct for a large variety of systems. In this article, we defend the view
that thermodynamics is a fundamental theory, a position that we justify based
on four case studies from recent physical research. We explain how entropic
gravity (1) and black hole thermodynamics (2) can serve as case studies for the
multiple realizability problem which blocks the reduction of thermodynamics.
Moreover, we discuss the problem of the reducibility of phase transitions and
argue that bifurcation theory (3) allows the modelling of "phase transitions"
on a thermodynamic level even in finite systems. It is also shown that the
derivation of irreversible transport equations in the Mori-Zwanzig formalism
(4) does not, despite recent claims to the contrary, constitute a reduction of
thermodynamics to quantum mechanics. Finally, we briefly discuss some arguments
against the fundamentality of thermodynamics that are not based on reduction.


title:
Should we tweet this? Generative response modeling for predicting
  reception of public health messaging on Twitter

abstract:
  The way people respond to messaging from public health organizations on
social media can provide insight into public perceptions on critical health
issues, especially during a global crisis such as COVID-19. It could be
valuable for high-impact organizations such as the US Centers for Disease
Control and Prevention (CDC) or the World Health Organization (WHO) to
understand how these perceptions impact reception of messaging on health policy
recommendations. We collect two datasets of public health messages and their
responses from Twitter relating to COVID-19 and Vaccines, and introduce a
predictive method which can be used to explore the potential reception of such
messages. Specifically, we harness a generative model (GPT-2) to directly
predict probable future responses and demonstrate how it can be used to
optimize expected reception of important health guidance. Finally, we introduce
a novel evaluation scheme with extensive statistical testing which allows us to
conclude that our models capture the semantics and sentiment found in actual
public health responses.


title:
Systematic KMTNet Planetary Anomaly Search. V. Complete Sample of 2018
  Prime-Field

abstract:
  We complete the analysis of all 2018 prime-field microlensing planets
identified by the KMTNet AnomalyFinder. Among the 10 previously unpublished
events with clear planetary solutions, 8 are either unambiguously planetary or
are very likely to be planetary in nature: OGLE-2018-BLG-1126,
KMT-2018-BLG-2004, OGLE-2018-BLG-1647, OGLE-2018-BLG-1367, OGLE-2018-BLG-1544,
OGLE-2018-BLG-0932, OGLE-2018-BLG-1212, and KMT-2018-BLG-2718. Combined with
the 4 previously published new AnomalyFinder events and 12 previously published
(or in preparation) planets that were discovered by eye, thismakes a total of
24 2018 prime-field planets discovered or recovered by AnomalyFinder. Together
with a paper in preparation on 2018 sub-prime planets, this work lays the basis
for the first statistical analysis of the planet mass-ratio function based on
planets identified in KMTNet data. By systematically applying the heuristic
analysis of Hwang et al. (2022) to each event, we identify the small
modification in their formalism that is needed to unify the so-called
close/wide and inner/outer degeneracies, as conjectured by


title: Of Shadows and Gaps in Spatial Search

abstract:
  Spatial search occurs in a connected graph if a continuous-time quantum walk
on the adjacency matrix of the graph, suitably scaled, plus a rank-one
perturbation induced by any vertex will unitarily map the principal eigenvector
of the graph to the characteristic vector of the vertex. This phenomenon is a
natural continuous-time analogue of Grover search. The spatial search is said
to be optimal if it occurs with constant fidelity and in time inversely
proportional to the shadow of the target vertex on the principal eigenvector.
Extending a result of Chakraborty et al. (Physical Review A, 102:032214, 2020),
we prove a simpler characterization of optimal spatial search. Based on this
characterization, we observe that some families of distance-regular graphs,
such as Hamming and Grassmann graphs, have optimal spatial search. We also show
a matching lower bound on time for spatial search with constant fidelity, which
extends a bound due to Farhi and Gutmann for perfect fidelity. Our elementary
proofs employ standard tools, such as Weyl inequalities and Cauchy determinant
formula.


title:
NMSSM neutralino dark matter for $W$-boson mass and muon $g-2$ and the
  promising prospect of direct detection

abstract:
  Two experiments from the Fermilab, E989 and CDF II, have reported two
anomalies for muon anomalous magnetic moment ($g$-2) and $W$-boson mass that
may indicate the new physics at the low energy scale. Here we examine the
possibility of a common origin of these two anomalies in the Next-to-Minimal
Supersymmetric Standard Model. Considering various experimental and
astrophysical constraints such as the Higgs mass, collider data, B-physics,
dark matter relic density and direct detection experiments, we find that a
neutralino in the mass range of $\sim 160-270$ GeV is a viable solution.
Moreover, the favored parameter region can be effectively probed by the ongoing
direct detection experiments like LZ, PandaX-4T and XENON-nT. The velocity
averaged annihilation cross section of the dark matter particles, however, is
suppressed.


title:
Scrutinizing New Physics in Semi-leptonic $B_{c}\rightarrow J/\psi
  \tau\nu$ Decay

abstract:
  We perform a global analysis of the $b\to c\tau\nu$ data using the recent
lattice results on the $B_c\to J/\psi$ vector and axial-vector form factors. To
explore the effects from the tensor operator of new physics beyond the standard
model, we determine the tensor form factors by using the non-relativistic QCD
(NRQCD) relations between tensor and (axial-)vector form factors. Based on the
lattice+NRQCD form factors, we fit the Wilson coefficients and the new physics
couplings in $R_2$, $S_1$ and $U_1$ leptoquark models by including the recently
measured $R(\Lambda_c)$ and imposing the relaxed constraint $\mathcal B(B_c\to
J/\psi)<30\%$ in light of the recent studies on LEP1 data and $B_c$ lifetime.
We give predictions for the experimental observables including $R(J/\psi)$,
$P_\tau(J/\psi)$, $F_L(J/\psi)$ and $\mathcal A_{FB}(J/\psi)$ as well as their
$q^2$ distribution in new physics scenarios/models. Our results suggest that
the longitudinal $\tau$ polarization fraction $P_\tau(J/\psi)$ and the
forward-backward asymmetry $\mathcal A_{FB}(J/\psi)$ are useful for testing the
$R_2$ leptoquark model.


title:
Segmenting across places: The need for fair transfer learning with
  satellite imagery

abstract:
  The increasing availability of high-resolution satellite imagery has enabled
the use of machine learning to support land-cover measurement and inform
policy-making. However, labelling satellite images is expensive and is
available for only some locations. This prompts the use of transfer learning to
adapt models from data-rich locations to others. Given the potential for
high-impact applications of satellite imagery across geographies, a systematic
assessment of transfer learning implications is warranted. In this work, we
consider the task of land-cover segmentation and study the fairness
implications of transferring models across locations. We leverage a large
satellite image segmentation benchmark with 5987 images from 18 districts (9
urban and 9 rural). Via fairness metrics we quantify disparities in model
performance along two axes -- across urban-rural locations and across
land-cover classes. Findings show that state-of-the-art models have better
overall accuracy in rural areas compared to urban areas, through unsupervised
domain adaptation methods transfer learning better to urban versus rural areas
and enlarge fairness gaps. In analysis of reasons for these findings, we show
that raw satellite images are overall more dissimilar between source and target
districts for rural than for urban locations. This work highlights the need to
conduct fairness analysis for satellite imagery segmentation models and
motivates the development of methods for fair transfer learning in order not to
introduce disparities between places, particularly urban and rural locations.


title: Permutation-bipartition pairs

abstract:
  Permutation-partition pairs were introduced by Stahl in 1980. These pairs are
generalizations of graphs and graphs on surfaces. They were used to solve some
problems for orientable embeddings of graphs. In this paper, we introduce a
particular type of permutation-partition pair, called permutation-bipartition
pair, which can be seen as generalizations of signed graphs and signed graph
embeddings. Some applications are given.


title: Data Augmentation for Electrocardiograms

abstract:
  Neural network models have demonstrated impressive performance in predicting
pathologies and outcomes from the 12-lead electrocardiogram (ECG). However,
these models often need to be trained with large, labelled datasets, which are
not available for many predictive tasks of interest. In this work, we perform
an empirical study examining whether training time data augmentation methods
can be used to improve performance on such data-scarce ECG prediction problems.
We investigate how data augmentation strategies impact model performance when
detecting cardiac abnormalities from the ECG. Motivated by our finding that the
effectiveness of existing augmentation strategies is highly task-dependent, we
introduce a new method, TaskAug, which defines a flexible augmentation policy
that is optimized on a per-task basis. We outline an efficient learning
algorithm to do so that leverages recent work in nested optimization and
implicit differentiation. In experiments, considering three datasets and eight
predictive tasks, we find that TaskAug is competitive with or improves on prior
work, and the learned policies shed light on what transformations are most
effective for different tasks. We distill key insights from our experimental
evaluation, generating a set of best practices for applying data augmentation
to ECG prediction problems.


title:
Orthogonal-ansatz VQE: Locating excited states without modifying a
  cost-function

abstract:
  Most literature in the Variational Quantum Eigensolver (VQE) algorithm
focuses on finding the ground state of a physical system, by minimizing a
quantum-computed cost-function. When excited states are required, the
cost-function is usually modified to include additional terms ensuring
orthogonality with the ground state. This generally requires additional quantum
circuit executions and measurements, increasing algorithmic complexity. Here we
present a design strategy for the variational ansatz which enforces
orthogonality in candidate excited states while still fully exploring the
remaining subset of Hilbert space. The result is an excited-state VQE solver
which trades increasing measurement complexity for increasing circuit
complexity. The latter is anticipated to become preferable as quantum error
mitigation and correction become more refined. We demonstrate our approach with
three distinct ansatze, beginning with a simple single-body example, before
generalizing to accommodate the full Hilbert space spanned by all qubits, and a
constrained Hilbert space obeying particle number conservation.


title:
Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable
  Fine-tuning for Zero-Shot Dialogue Summarization

abstract:
  The most advanced abstractive dialogue summarizers lack generalization
ability on new domains and the existing researches for domain adaptation in
summarization generally rely on large-scale pre-trainings. To explore the
lightweight fine-tuning methods for domain adaptation of dialogue
summarization, in this paper, we propose an efficient and generalizable
Domain-Oriented Prefix-tuning model, which utilizes a domain word initialized
prefix module to alleviate domain entanglement and adopts discrete prompts to
guide the model to focus on key contents of dialogues and enhance model
generalization. We conduct zero-shot experiments and build domain adaptation
benchmarks on two multi-domain dialogue summarization datasets, TODSum and
QMSum. Adequate experiments and qualitative analysis prove the effectiveness of
our methods.


title:
Attention guided global enhancement and local refinement network for
  semantic segmentation

abstract:
  The encoder-decoder architecture is widely used as a lightweight semantic
segmentation network. However, it struggles with a limited performance compared
to a well-designed Dilated-FCN model for two major problems. First, commonly
used upsampling methods in the decoder such as interpolation and deconvolution
suffer from a local receptive field, unable to encode global contexts. Second,
low-level features may bring noises to the network decoder through skip
connections for the inadequacy of semantic concepts in early encoder layers. To
tackle these challenges, a Global Enhancement Method is proposed to aggregate
global information from high-level feature maps and adaptively distribute them
to different decoder layers, alleviating the shortage of global contexts in the
upsampling process. Besides, a Local Refinement Module is developed by
utilizing the decoder features as the semantic guidance to refine the noisy
encoder features before the fusion of these two (the decoder features and the
encoder features). Then, the two methods are integrated into a Context Fusion
Block, and based on that, a novel Attention guided Global enhancement and Local
refinement Network (AGLN) is elaborately designed. Extensive experiments on
PASCAL Context, ADE20K, and PASCAL VOC 2012 datasets have demonstrated the
effectiveness of the proposed approach. In particular, with a vanilla
ResNet-101 backbone, AGLN achieves the state-of-the-art result (56.23% mean
IoU) on the PASCAL Context dataset. The code is available at
https://github.com/zhasen1996/AGLN.


title: Application of machine learning for predicting the spread of COVID-19

abstract:
  The spread of diseases has been studied for many years, but it receives a
particular focus recently due to the outbreak and spread of COVID-19. Studies
show that the spread of COVID-19 can be characterized by the
Susceptible-Infectious-Recovered-Deceased (SIRD) model with containment
coefficients (due to quarantine and keeping social distance). This project aims
to apply the machine learning technique to predict the severity of COVID-19 and
the effect of quarantine, keeping social distance, working from home, and
wearing masks on the transmission of the disease. This work deepens our
understanding of disease transmission and reveals the importance of following
policies.


title:
Evidence of fully-gapped superconductivity in NbReSi: A combined $\mu$SR
  and NMR study

abstract:
  We report a comprehensive study of the noncentrosymmetric NbReSi
superconductor by means of muon-spin rotation and relaxation ($\mu$SR) and
nuclear magnetic resonance (NMR) techniques. NbReSi is a bulk superconductor
with $T_c = 6.5$ K, characterized by a large upper critical field, which
exceeds the Pauli limit. Both the superfluid density $\rho_\mathrm{sc}(T)$
(determined via transverse-field $\mu$SR) and the spin-lattice relaxation rate
$T_1^{-1}(T)$ (determined via NMR) suggest a nodeless superconductivity (SC) in
NbReSi. We also find signatures of multigap SC, here evidenced by the
field-dependent muon-spin relaxation rate and the electronic specific-heat
coefficient. The absence of spontaneous magnetic fields below $T_c$, as evinced
from zero-field $\mu$SR measurements, indicates a preserved time-reversal
symmetry in the superconducting state of NbReSi. Finally, we discuss possible
reasons for the unusually large upper critical field of NbReSi, most likely
arising from its anisotropic crystal structure.


title: On the slice spectral sequence for quotients of norms of Real bordism

abstract:
  In this paper, we study equivariant quotients of the multiplicative norm
$MU^{((C_{2^n}))}$ of the Real bordism spectrum by permutation summands, a
concept defined here. These quotients are interesting because of their
relationship to the so-called "higher real $K$-theories". We provide new tools
for computing the equivariant homotopy groups of such quotients of
$MU^{((C_{2^n}))}$ and quotients of the closely related spectrum
$BP^{((C_{2^n}))}$.
  As a new example, we study spectra denoted by $BP^{((C_{2^n}))}\langle
m,m\rangle$, which have non-trivial chromatic localizations only at heights
equal to $rm$ where $0\leq r\leq 2^{n-1}$. These spectra are natural
equivariant generalizations of integral Morava-$K$-theories. For $\sigma$ the
real sign representation of $C_{2^{n-1}}$, we give a complete computation of
the $a_{\sigma}$-localized slice spectral sequence of
$i^*_{C_{2^{n-1}}}BP^{((C_{2^n}))}\langle m,m\rangle$. We do this by
establishing a correspondence between this localized slice spectral sequence
and the $H\mathbb{F}_2$-based Adams spectral sequence in the category of
$H\mathbb{F}_2 \wedge H\mathbb{F}_2$-modules. We also give a complete
computation of the $a_{\lambda}$-localized slice spectral sequence of
$BP^{((C_{4}))}\langle 2,2\rangle$ for $\lambda$ a rotation of $\mathbb{R}^2$
by an angle of $\pi/2$. The non-localized slice spectral sequences can be
recovered completely from these localizations.


title:
Design of wavelength division multiplexing devices based on tunable edge
  states of valley photonic crystals

abstract:
  Wavelength division multiplexing (WDM) devices are key elements of Photonic
integrated circuits (PICs). Conventional WDM devices based on silicon
waveguides and photonic crystals have limited transmittance due to high loss
introduced by the strong backward scattering from defects. In addition, it is
challenging to reduce the footprint of those devices. Here we theoretically
demonstrate a WDM device in the telecommunication range based on all-dielectric
silicon topological valley photonic crystal (VPC) structures. We tune its
effective refractive index by tuning the physical parameters of the lattice in
the silicon substrate, which can continuously tune the working wavelength range
of the topological edge states, which allows designing WDM devices with
different channels. The WDM device has two channels (1470 nm-1523 nm and 1548
nm-1609 nm), with contrast ratios of 22.4 dB and 24.9 dB, respectively. The
principle of manipulating the working bandwidth of the topological edge states
can be generally applied in designing different integratable photonic devices,
thus it will find broad applications.


title: Homotopy of manifolds stabilized by projective spaces

abstract:
  We study the homotopy of the connected sum of a manifold with a projective
space, viewed as a typical way to stabilize manifolds. In particular, we show a
loop homotopy decomposition of a manifold after stabilization by a projective
space, and provide concrete examples. To do this, we trace the effect in
homotopy theory of surgery on certain product manifolds by showing a loop
homotopy decomposition after localization away from the order of the image of
the classical $J$-homomorphism.


title:
Moment estimates in the first Borel-Cantelli Lemma with applications to
  mean deviation frequencies

abstract:
  We quantify the elementary Borel-Cantelli Lemma by higher moments of the
overlap count statistic in terms of the weighted summability of the
probabilities. Applications include mean deviation frequencies in the Strong
Law and the Law of the Iterated Logarithm.


title: QuiKo: A Quantum Beat Generation Application

abstract:
  In this chapter a quantum music generation application called QuiKo will be
discussed. It combines existing quantum algorithms with data encoding methods
from quantum machine learning to build drum and audio sample patterns from a
database of audio tracks. QuiKo leverages the physical properties and
characteristics of quantum computers to generate what can be referred to as
Soft Rules proposed by Alexis Kirke. These rules take advantage of the noise
produced by quantum devices to develop flexible rules and grammars for quantum
music generation. These properties include qubit decoherence and phase kickback
due controlled quantum gates within the quantum circuit. QuiKo builds upon the
concept of soft rules in quantum music generation and takes it a step further.
It attempts to mimic and react to an external musical inputs, similar to the
way that human musicians play and compose with one another. Audio signals are
used as inputs into the system. Feature extraction is then performed on the
signal to identify the harmonic and percussive elements. This information is
then encoded onto the quantum circuit. Measurements of the quantum circuit are
then taken providing results in the form of probability distributions for
external music applications to use to build the new drum patterns.


title:
Learning to Dispatch Multi-Server Jobs in Bipartite Graphs with Unknown
  Service Rates

abstract:
  Multi-server jobs are imperative in modern cloud computing systems. A
multi-server job has multiple components and requests multiple servers for
being served. How to allocate restricted computing devices to jobs is a topic
of great concern, which leads to the job scheduling and load balancing
algorithms thriving. However, current job dispatching algorithms require the
service rates to be changeless and knowable, which is difficult to realize in
production systems. Besides, for multi-server jobs, the dispatching decision
for each job component follows the All-or-Nothing property under service
locality constraints and resource capacity limits, which is not well supported
by mainstream algorithms. In this paper, we propose a dispatching algorithm for
multi-server jobs that learns the unknown service rates and simultaneously
maximizes the expected Accumulative Social Welfare (Asw). We formulate the Asw
as the sum of utilities of jobs and servers achieved over each time slot. The
utility of a job is proportional to the valuation for being served, which is
mainly impacted by the fluctuating but unknown service rates. We maximize the
Asw without knowing the exact valuations, but approximate them with
exploration-exploitation. From this, we bring in several evolving statistics
and maximize the statistical Asw with dynamic programming. The proposed
algorithm is proved to have a polynomial complexity and a State-of-the-Art
regret. We validate it with extensive simulations and the results show that the
proposed algorithm outperforms several benchmark policies with improvements by
up to 73%, 36%, and 28%, respectively.


title: A Siren Song of Open Source Reproducibility

abstract:
  As reproducibility becomes a greater concern, conferences have largely
converged to a strategy of asking reviewers to indicate whether code was
attached to a submission. This is part of a larger trend of taking action based
on assumed ideals, without studying if those actions will yield the desired
outcome. Our argument is that this focus on code for replication is misguided
if we want to improve the state of reproducible research. This focus can be
harmful -- we should not force code to be submitted. There is a lack of
evidence for effective actions taken by conferences to encourage and reward
reproducibility. We argue that venues must take more action to advance
reproducible machine learning research today.


title:
Tight toughness, isolated toughness and binding number bounds for the
  $\{K_2,C_n\}$-factors

abstract:
  The $\{K_2,C_n\}$-factor of a graph is a spanning subgraph whose each
component is either $K_2$ or $C_n$. In this paper, a sufficient condition with
regard to tight toughness, isolated toughness and binding number bounds to
guarantee the existence of the $\{K_2,C_{2i+1}| i\geq 2 \}$-factor for any
graph is obtained, which answers a problem due to Gao and Wang (J. Oper. Res.
Soc. China (2021), https://doi.org/10.1007/s40305-021-00357-6).


title: Collapsing domain walls beyond $Z_2$

abstract:
  Discrete symmetries are widely imposed in particle theories. It is well-known
that the spontaneous breaking of discrete symmetries leads to domain walls.
Current studies of domain walls have focused on those from the spontaneous
breaking of a $Z_2$ symmetry. Larger discrete symmetries have multiple
degenerate vacua, leading to the domain walls in principle different from the
simplest $Z_2$ domain wall. We take domain walls from $Z_N$ symmetry breaking
as an illustrative study, and study in detail the $Z_3$ case, in which
semi-analytical results for the tension and thickness of domain walls are
derived. Explicit symmetry breaking terms lead to the dynamics of domain walls
collapsing more complicated than the $Z_2$ case. Gravitational wave signals
deviate from those from $Z_2$ domain walls.


title:
Channel Pruning In Quantization-aware Training: An Adaptive
  Projection-gradient Descent-shrinkage-splitting Method

abstract:
  We propose an adaptive projection-gradient descent-shrinkage-splitting method
(APGDSSM) to integrate penalty based channel pruning into quantization-aware
training (QAT). APGDSSM concurrently searches weights in both the quantized
subspace and the sparse subspace. APGDSSM uses shrinkage operator and a
splitting technique to create sparse weights, as well as the Group Lasso
penalty to push the weight sparsity into channel sparsity. In addition, we
propose a novel complementary transformed l1 penalty to stabilize the training
for extreme compression.


title:
Small-Gain Theorem for Safety Verification under High-Relative-Degree
  Constraints

abstract:
  This paper develops a small-gain technique for the safety analysis and
verification of interconnected systems with high-relative-degree safety
constraints. In this technique, input-to-state safety (ISSf) is used to
characterize how the safety of a subsystem is influenced by the external input,
and ISSf-barrier functions (ISSf-BFs) with high relative degree are employed to
capture the safety of subsystems. With a coordination transform, the
relationship between ISSf-BFs and the existing high-relative-degree (or
high-order) barrier functions is established in order to simplify the ISSf
analysis. With the help of high-relative-degree ISSf-BFs, a small-gain theorem
is proposed for safety verification. It is shown that, under the small-gain
condition, i) the interconnection of ISSf subsystems is still ISSf; and ii) the
overall interconnected system is input-to-state stable (ISS) with respect to
the compositional safe set. The effectiveness of the proposed small-gain
theorem is illustrated on the output-constrained decentralized control of two
inverted pendulums connected by a spring mounted on two carts.


title:
Robotic Surgery Remote Mentoring via AR with 3D Scene Streaming and Hand
  Interaction

abstract:
  With the growing popularity of robotic surgery, education becomes
increasingly important and urgently needed for the sake of patient safety.
However, experienced surgeons have limited accessibility due to their busy
clinical schedule or working in a distant city, thus can hardly provide
sufficient education resources for novices. Remote mentoring, as an effective
way, can help solve this problem, but traditional methods are limited to plain
text, audio, or 2D video, which are not intuitive nor vivid. Augmented reality
(AR), a thriving technique being widely used for various education scenarios,
is promising to offer new possibilities of visual experience and interactive
teaching. In this paper, we propose a novel AR-based robotic surgery remote
mentoring system with efficient 3D scene visualization and natural 3D hand
interaction. Using a head-mounted display (i.e., HoloLens), the mentor can
remotely monitor the procedure streamed from the trainee's operation side. The
mentor can also provide feedback directly with hand gestures, which is in-turn
transmitted to the trainee and viewed in surgical console as guidance. We
comprehensively validate the system on both real surgery stereo videos and
ex-vivo scenarios of common robotic training tasks (i.e., peg-transfer and
suturing). Promising results are demonstrated regarding the fidelity of
streamed scene visualization, the accuracy of feedback with hand interaction,
and the low-latency of each component in the entire remote mentoring system.
This work showcases the feasibility of leveraging AR technology for reliable,
flexible and low-cost solutions to robotic surgical education, and holds great
potential for clinical applications.


title:
Programmable Hamiltonian engineering with quadratic quantum Fourier
  transform

abstract:
  Quantum Fourier transform (QFT) is a widely used building block for quantum
algorithms, whose scalable implementation is challenging in experiments. Here,
we propose a protocol of quadratic quantum Fourier transform (QQFT),
considering cold atoms confined in an optical lattice. This QQFT is equivalent
to QFT in the single-particle subspace, and produces a different unitary
operation in the entire Hilbert space. We show this QQFT protocol can be
implemented using programmable laser potential with the
digital-micromirror-device techniques recently developed in the experiments.
The QQFT protocol enables programmable Hamiltonian engineering, and allows
quantum simulations of Hamiltonian models, which are difficult to realize with
conventional approaches. The flexibility of our approach is demonstrated by
performing quantum simulations of one-dimensional Poincar\'{e} crystal physics
and two-dimensional topological flat bands, where the QQFT protocol effectively
generates the required long-range tunnelings despite the locality of the cold
atom system. We find the discrete Poincar\'{e} symmetry and topological
properties in the two examples respectively have robustness against a certain
degree of noise that is potentially existent in the experimental realization.
We expect this approach would open up wide opportunities for optical lattice
based programmable quantum simulations.


title: Beyond 3DMM: Learning to Capture High-fidelity 3D Face Shape

abstract:
  3D Morphable Model (3DMM) fitting has widely benefited face analysis due to
its strong 3D priori. However, previous reconstructed 3D faces suffer from
degraded visual verisimilitude due to the loss of fine-grained geometry, which
is attributed to insufficient ground-truth 3D shapes, unreliable training
strategies and limited representation power of 3DMM. To alleviate this issue,
this paper proposes a complete solution to capture the personalized shape so
that the reconstructed shape looks identical to the corresponding person.
Specifically, given a 2D image as the input, we virtually render the image in
several calibrated views to normalize pose variations while preserving the
original image geometry. A many-to-one hourglass network serves as the
encode-decoder to fuse multiview features and generate vertex displacements as
the fine-grained geometry. Besides, the neural network is trained by directly
optimizing the visual effect, where two 3D shapes are compared by measuring the
similarity between the multiview images rendered from the shapes. Finally, we
propose to generate the ground-truth 3D shapes by registering RGB-D images
followed by pose and shape augmentation, providing sufficient data for network
training. Experiments on several challenging protocols demonstrate the superior
reconstruction accuracy of our proposal on the face shape.


title:
A dataset of ant colonies motion trajectories in indoor and outdoor
  scenes for social cluster behavior study

abstract:
  Motion and interaction of social insects (such as ants) have been studied by
many researchers to understand the clustering mechanism. Most studies in the
field of ant behavior have only focused on indoor environments, while outdoor
environments are still underexplored. In this paper, we collect 10 videos of
ant colonies from different indoor and outdoor scenes. And we develop an image
sequence marking software named VisualMarkData, which enables us to provide
annotations of ants in the video. In all 5354 frames, the location information
and the identification number of each ant are recorded for a total of 712 ants
and 114112 annotations. Moreover, we provide visual analysis tools to assess
and validate the technical quality and reproducibility of our data. It is hoped
that this dataset will contribute to a deeper exploration on the behavior of
the ant colony.


title: Harmonic Centralization of Some Graph Families

abstract:
  Centrality describes the importance of nodes in a graph and is modeled by
various measures. Its global analogue, called centralization, is a general
formula for calculating a graph-level centrality score based on the node-level
centrality measure. The latter enables us to compare graphs based on the extent
to which the connections of a given network are concentrated on a single vertex
or group of vertices. One of the measures of centrality in social network
analysis is harmonic centrality. It sums the inverse of the geodesic distances
of each node to other nodes where it is 0 if there is no path from one node to
another, with the sum normalized by dividing it by $m-1$, where $m$ is the
number of nodes of the graph. In this paper, we present some results regarding
the harmonic centralization of some important families of graphs with the hope
that formulas generated herein will be of use when one determines the harmonic
centralization of more complex graphs.


title: Federated Unsupervised Domain Adaptation for Face Recognition

abstract:
  Given labeled data in a source domain, unsupervised domain adaptation has
been widely adopted to generalize models for unlabeled data in a target domain,
whose data distributions are different. However, existing works are
inapplicable to face recognition under privacy constraints because they require
sharing of sensitive face images between domains. To address this problem, we
propose federated unsupervised domain adaptation for face recognition, FedFR.
FedFR jointly optimizes clustering-based domain adaptation and federated
learning to elevate performance on the target domain. Specifically, for
unlabeled data in the target domain, we enhance a clustering algorithm with
distance constrain to improve the quality of predicted pseudo labels. Besides,
we propose a new domain constraint loss (DCL) to regularize source domain
training in federated learning. Extensive experiments on a newly constructed
benchmark demonstrate that FedFR outperforms the baseline and classic methods
on the target domain by 3% to 14% on different evaluation metrics.


title: Learning-based Bounded Synthesis for Semi-MDPs with LTL Specifications

abstract:
  This letter proposes a learning-based bounded synthesis for a semi-Markov
decision process (SMDP) with a linear temporal logic (LTL) specification. In
the product of the SMDP and the deterministic $K$-co-B\"uchi automaton
(d$K$cBA) converted from the LTL specification, we learn both the winning
region of satisfying the LTL specification and the dynamics therein based on
reinforcement learning and Bayesian inference. Then, we synthesize an optimal
policy satisfying the following two conditions. (1) It maximizes the
probability of reaching the wining region. (2) It minimizes a long-term risk
for the dwell time within the winning region. The minimization of the long-term
risk is done based on the estimated dynamics and a value iteration. We show
that, if the discount factor is sufficiently close to one, the synthesized
policy converges to the optimal policy as the number of the data obtained by
the exploration goes to the infinity.


title:
The Two Dimensions of Worst-case Training and the Integrated Effect for
  Out-of-domain Generalization

abstract:
  Training with an emphasis on "hard-to-learn" components of the data has been
proven as an effective method to improve the generalization of machine learning
models, especially in the settings where robustness (e.g., generalization
across distributions) is valued. Existing literature discussing this
"hard-to-learn" concept are mainly expanded either along the dimension of the
samples or the dimension of the features. In this paper, we aim to introduce a
simple view merging these two dimensions, leading to a new, simple yet
effective, heuristic to train machine learning models by emphasizing the
worst-cases on both the sample and the feature dimensions. We name our method
W2D following the concept of "Worst-case along Two Dimensions". We validate the
idea and demonstrate its empirical strength over standard benchmarks.


title: Divergence-aware Federated Self-Supervised Learning

abstract:
  Self-supervised learning (SSL) is capable of learning remarkable
representations from centrally available data. Recent works further implement
federated learning with SSL to learn from rapidly growing decentralized
unlabeled images (e.g., from cameras and phones), often resulted from privacy
constraints. Extensive attention has been paid to SSL approaches based on
Siamese networks. However, such an effort has not yet revealed deep insights
into various fundamental building blocks for the federated self-supervised
learning (FedSSL) architecture. We aim to fill in this gap via in-depth
empirical study and propose a new method to tackle the non-independently and
identically distributed (non-IID) data problem of decentralized data. Firstly,
we introduce a generalized FedSSL framework that embraces existing SSL methods
based on Siamese networks and presents flexibility catering to future methods.
In this framework, a server coordinates multiple clients to conduct SSL
training and periodically updates local models of clients with the aggregated
global model. Using the framework, our study uncovers unique insights of
FedSSL: 1) stop-gradient operation, previously reported to be essential, is not
always necessary in FedSSL; 2) retaining local knowledge of clients in FedSSL
is particularly beneficial for non-IID data. Inspired by the insights, we then
propose a new approach for model update, Federated Divergence-aware Exponential
Moving Average update (FedEMA). FedEMA updates local models of clients
adaptively using EMA of the global model, where the decay rate is dynamically
measured by model divergence. Extensive experiments demonstrate that FedEMA
outperforms existing methods by 3-4% on linear evaluation. We hope that this
work will provide useful insights for future research.


title:
Efficient Derivative-free Bayesian Inference for Large-Scale Inverse
  Problems

abstract:
  We consider Bayesian inference for large scale inverse problems, where
computational challenges arise from the need for repeated evaluations of an
expensive forward model. This renders most Markov chain Monte Carlo approaches
infeasible, since they typically require $O(10^4)$ model runs, or more.
Moreover, the forward model is often given as a black box or is impractical to
differentiate. Therefore derivative-free algorithms are highly desirable. We
propose a framework, which is built on Kalman methodology, to efficiently
perform Bayesian inference in such inverse problems. The basic method is based
on an approximation of the filtering distribution of a novel mean-field
dynamical system into which the inverse problem is embedded as an observation
operator. Theoretical properties of the mean-field model are established for
linear inverse problems, demonstrating that the desired Bayesian posterior is
given by the steady state of the law of the filtering distribution of the
mean-field dynamical system, and proving exponential convergence to it. This
suggests that, for nonlinear problems which are close to Gaussian, sequentially
computing this law provides the basis for efficient iterative methods to
approximate the Bayesian posterior. Ensemble methods are applied to obtain
interacting particle system approximations of the filtering distribution of the
mean-field model; and practical strategies to further reduce the computational
and memory cost of the methodology are presented, including low-rank
approximation and a bi-fidelity approach. The effectiveness of the framework is
demonstrated in several numerical experiments, including proof-of-concept
linear/nonlinear examples and two large-scale applications: learning of
permeability parameters in subsurface flow; and learning subgrid-scale
parameters in a global climate model from time-averaged statistics.


title: Dual-Stage Approach Toward Hyperspectral Image Super-Resolution

abstract:
  Hyperspectral image produces high spectral resolution at the sacrifice of
spatial resolution. Without reducing the spectral resolution, improving the
resolution in the spatial domain is a very challenging problem. Motivated by
the discovery that hyperspectral image exhibits high similarity between
adjacent bands in a large spectral range, in this paper, we explore a new
structure for hyperspectral image super-resolution (DualSR), leading to a
dual-stage design, i.e., coarse stage and fine stage. In coarse stage, five
bands with high similarity in a certain spectral range are divided into three
groups, and the current band is guided to study the potential knowledge. Under
the action of alternative spectral fusion mechanism, the coarse SR image is
super-resolved in band-by-band. In order to build model from a global
perspective, an enhanced back-projection method via spectral angle constraint
is developed in fine stage to learn the content of spatial-spectral
consistency, dramatically improving the performance gain. Extensive experiments
demonstrate the effectiveness of the proposed coarse stage and fine stage.
Besides, our network produces state-of-the-art results against existing works
in terms of spatial reconstruction and spectral fidelity.


title: Monochromatic vertex-disconnection of graphs

abstract:
  Let G be a vertex-colored graph. A vertex cut S of G is called a
monochromatic vertex cut if the vertices of S are colored with the same color.
A graph G is monochromatically vertex-disconnected if any two nonadjacent
vertices of G has a monochromatic vertex cut separating them. The monochromatic
vertex-disconnection number of G, denoted by mvd(G), is the maximum number of
colors that are used to make G monochromatically vertex-disconnected. In this
paper, the connection between the graph parameters are studied: mvd(G),
connectivity and block decomposition. We determine the value of mvd(G) for some
well known graphs, and then characterize G when n-5\leq mvd(G)\leq n and all
blocks of G are minimally 2-connected triangle-free graphs. We obtain the
maximum size of a graph G with mvd(G)=k for any k. Furthermore, we study the
Erd\H{o}s-Gallai-type results for mvd(G), and completely solve them. Finally,
we propose an algorithm to compute mvd(G) and give an mvd-coloring of G.