Abstract:
  In recent years, knowledge distillation has been proved to be an effective
solution for model compression. This approach can make lightweight student
models acquire the knowledge extracted from cumbersome teacher models. However,
previous distillation methods of detection have weak generalization for
different detection frameworks and rely heavily on ground truth (GT), ignoring
the valuable relation information between instances. Thus, we propose a novel
distillation method for detection tasks based on discriminative instances
without considering the positive or negative distinguished by GT, which is
called general instance distillation (GID). Our approach contains a general
instance selection module (GISM) to make full use of feature-based,
relation-based and response-based knowledge for distillation. Extensive results
demonstrate that the student model achieves significant AP improvement and even
outperforms the teacher in various detection frameworks. Specifically,
RetinaNet with ResNet-50 achieves 39.1% in mAP with GID on COCO dataset, which
surpasses the baseline 36.2% by 2.9%, and even better than the ResNet-101 based
teacher model with 38.1% AP.


Abstract:
  Quadratic Unconstrained Binary Optimization models are useful for solving a
diverse range of optimization problems. Constraints can be added by
incorporating quadratic penalty terms into the objective, often with the
introduction of slack variables needed for conversion of inequalities. This
transformation can lead to a significant increase in the size and density of
the problem. Herein, we propose an efficient approach for recasting inequality
constraints that reduces the number of linear and quadratic variables.
Experimental results illustrate the efficacy.


Abstract:
  The spin-$1/2$ XXZ chain is an integrable lattice model and parts of its spin
current can be protected by local conservation laws for anisotropies
$-1<\Delta<1$. In this case, the Drude weight $D(T)$ is non-zero at finite
temperatures $T$. Here we obtain analytical results for $D(T)$ at low
temperatures for zero external magnetic field and anisotropies
$\Delta=\cos(n\pi/m)$ with $n,m$ coprime integers, using the thermodynamic
Bethe ansatz. We show that to leading orders
$D(T)=D(0)-a(\Delta)T^{2K-2}-b_1(\Delta)T^2$ where $K$ is the Luttinger
parameter and the prefactor $a(\Delta)$, obtained in closed form, has a fractal
structure as function of anisotropy $\Delta$. The prefactor $b_1(\Delta)$, on
the other hand, does not have a fractal structure and can be obtained in a
standard field-theoretical approach. Including both temperature corrections, we
obtain an analytic result for the low-temperature asymptotics of the Drude
weight in the entire regime $-1<\Delta=\cos(n\pi/m)<1$.


Abstract:
  Silicon-germanium heterojunction bipolar transistors (HBTs) are of interest
as low-noise microwave amplifiers due to their competitive noise performance
and low cost relative to III-V devices. The fundamental noise performance
limits of HBTs are thus of interest, and several studies report that
quasiballistic electron transport across the base is a mechanism leading to
cryogenic non-ideal IV characteristics that affects these limits. However, this
conclusion has not been rigorously tested against theoretical predictions
because prior studies modeled electron transport with empirical approaches or
approximate solutions of the Boltzmann equation. Here, we study non-diffusive
transport in narrow-base SiGe HBTs using an exact, semi-analytic solution of
the Boltzmann equation based on an asymptotic expansion approach. We find that
the computed transport characteristics are inconsistent with experiment,
implying that quasiballistic electron transport is unlikely to be the origin of
cryogenic non-ideal IV characteristics. Our work helps to identify the
mechanisms governing the lower limits of the microwave noise figure of
cryogenic HBT amplifiers.


Abstract:
  The NLC2CMD Competition hosted at NeurIPS 2020 aimed to bring the power of
natural language processing to the command line. Participants were tasked with
building models that can transform descriptions of command line tasks in
English to their Bash syntax. This is a report on the competition with details
of the task, metrics, data, attempted solutions, and lessons learned.


Abstract:
  We perform a detailed numerical study of diffusion in the $\varepsilon$
stadium of Bunimovich, and propose an empirical model of the local and global
diffusion for various values of $\varepsilon$ with the following conclusions:
(i) the diffusion is normal for all values of $\varepsilon \leq(0.3)$ and all
initial conditions, (ii) the diffusion constant is a parabolic function of the
momentum (i.e., we have inhomogeneous diffusion), (iii) the model describes the
diffusion very well including the boundary effects, (iv) the approach to the
asymptotic equilibrium steady state is exponential, (v) the so-called random
model (Robnik et al., 1997) is confirmed to apply very well, (vi) the diffusion
constant extracted from the distribution function in momentum space and the one
derived from the second moment agree very well. The classical transport time,
an important parameter in quantum chaos, is thus determined.


Abstract:
  The group testing problem consists of determining a small set of defective
items from a larger set of items based on a number of possibly-noisy tests, and
has numerous practical applications. One of the defining features of group
testing is whether the tests are adaptive (i.e., a given test can be chosen
based on all previous outcomes) or non-adaptive (i.e., all tests must be chosen
in advance). In this paper, building on the success of binary splitting
techniques in noiseless group testing (Hwang, 1972), we introduce noisy group
testing algorithms that apply noisy binary search as a subroutine. We provide
three variations of this approach with increasing complexity, culminating in an
algorithm that succeeds using a number of tests that matches the best known
previously (Scarlett, 2019), while overcoming fundamental practical limitations
of the existing approach, and more precisely capturing the dependence of the
number of tests on the error probability. We provide numerical experiments
demonstrating that adaptive group testing strategies based on noisy binary
search can be highly effective in practice, using significantly fewer tests
compared to state-of-the-art non-adaptive strategies.


Abstract:
  To realize autonomous collaborative robots, it is important to increase the
trust that users have in them. Toward this goal, this paper proposes an
algorithm which endows an autonomous agent with the ability to explain the
transition from the current state to the target state in a Markov decision
process (MDP). According to cognitive science, to generate an explanation that
is acceptable to humans, it is important to present the minimum information
necessary to sufficiently understand an event. To meet this requirement, this
study proposes a framework for identifying important elements in the
decision-making process using a prediction model for the world and generating
explanations based on these elements. To verify the ability of the proposed
method to generate explanations, we conducted an experiment using a grid
environment. It was inferred from the result of a simulation experiment that
the explanation generated using the proposed method was composed of the minimum
elements important for understanding the transition from the current state to
the target state. Furthermore, subject experiments showed that the generated
explanation was a good summary of the process of state transition, and that a
high evaluation was obtained for the explanation of the reason for an action.


Abstract:
  An interpretable system for open-domain reasoning needs to express its
reasoning process in a transparent form. Natural language is an attractive
representation for this purpose -- it is both highly expressive and easy for
humans to understand. However, manipulating natural language statements in
logically consistent ways is hard: models must cope with variation in how
meaning is expressed while remaining precise. In this paper, we describe
ParaPattern, a method for building models to generate deductive inferences from
diverse natural language inputs without direct human supervision. We train
BART-based models (Lewis et al., 2020) to generate the result of applying a
particular logical operation to one or more premise statements. Crucially, we
develop a largely automated pipeline for constructing suitable training
examples from Wikipedia. We evaluate our models using out-of-domain sentence
compositions from the QASC (Khot et al., 2020) and EntailmentBank (Dalvi et
al., 2021) datasets as well as targeted perturbation sets. Our results show
that our models are substantially more accurate and flexible than baseline
systems. ParaPattern achieves 85% validity on examples of the 'substitution'
operation from EntailmentBank without the use of any in-domain training data,
matching the performance of a model fine-tuned for EntailmentBank. The full
source code for our method is publicly available.


Abstract:
  We present a uniform (and unambiguous) procedure for scaling the matter
fields in implementing the conformal method to parameterize and construct
solutions of Einstein constraint equations with coupled matter sources. The
approach is based on a phase space representation of the space-time matter
fields after a careful $n+1$ decomposition into spatial fields $B$ and
conjugate momenta $\Pi_B$, which are specified directly and are conformally
invariant quantities. We show that if the Einstein-matter field theory is
specified by a Lagrangian which is diffeomorphism invariant and involves no
dependence on derivatives of the space-time metric in the matter portion of the
Lagrangian, then fixing $B$ and $\Pi_B$ results in conformal constraint
equations that, for constant-mean curvature initial data, semi-decouple just as
they do for the vacuum Einstein conformal constraint equations. We prove this
result by establishing a structural property of the Einstein momentum
constraint that is independent of the conformal method: For an Einstein-matter
field theory which satisfies the conditions just stated, if $B$ and $\Pi_B$
satisfy the matter Euler-Lagrange equations, then (in suitable form) the
right-hand side of the momentum constraint on each spatial slice depends only
on $B$ and $\Pi_B$ and is independent of the space-time metric. We discuss the
details of our construction in the special cases of the following models:
Einstein-Maxwell-charged scalar field, Einstein-Proca, Einstein-perfect fluid,
and Einstein-Maxwell-charged dust. In these examples we find that our technique
gives a theoretical basis for scaling rules, such as those for
electromagnetism, that have worked pragmatically in the past, but also
generates new equations with advantageous features for perfect fluids that
allow direct specification of total rest mass and total charge in any spatial
region.


Abstract:
  We obtain an array of consistency results concerning trees and stationary
reflection at double successors of regular cardinals $\kappa$, updating some
classical constructions in the process. This includes models of
$\mathsf{CSR}(\kappa^{++})\wedge \mathsf{TP}(\kappa^{++})$ (both with and
without $\mathsf{AP}(\kappa^{++})$) and models of the conjunctions
$\mathsf{SR}(\kappa^{++}) \wedge \mathsf{wTP}(\kappa^{++}) \wedge
\mathsf{AP}(\kappa^{++})$ and $\neg \mathsf{AP}(\kappa^{++}) \wedge
\mathsf{SR}(\kappa^{++})$ (the latter was originally obtained in joint work by
Krueger and the first author \cite{GilKru:8fold}, and is here given using
different methods). Analogs of these results with the failure of
$\mathsf{SH}(\kappa^{++})$ are given as well. Finally, we obtain all of our
results with an arbitrarily large $2^\kappa$, applying recent joint work by
Honzik and the third author.


Abstract:
  Nonlinear distortion of an OFDM signal is a serious problem when it comes to
energy-efficient Power Amplifier(PA) utilization. Typically, Peak-to-Average
Power Ratio(PAPR) reduction algorithms and digital predistortion algorithms are
used independently to fight the same phenomenon. This paper proposes an
Amplifier-Coupled Tone Reservation (ACTR)algorithm for the reduction of
nonlinear distortion power, utilizing knowledge on thep redistorted PA
characteristic. The optimization problem is defined. Its convexity is proved. A
computationally-efficient solution is presented. Finally, its performance is
compared against two state-of-the-art TR algorithms by means of simulations and
measurements. The results show the proposed solution is advantageous, both in
terms of nonlinear distortion power and the required number of computations.


Abstract:
  We proposed a convolutional neural network for vertex classification on
3-dimensional dental meshes, and used it to detect teeth margins. An expanding
layer was constructed to collect statistic values of neighbor vertex features
and compute new features for each vertex with convolutional neural networks. An
end-to-end neural network was proposed to take vertex features, including
coordinates, curvatures and distance, as input and output each vertex
classification label. Several network structures with different parameters of
expanding layers and a base line network without expanding layers were designed
and trained by 1156 dental meshes. The accuracy, recall and precision were
validated on 145 dental meshes to rate the best network structures, which were
finally tested on another 144 dental meshes. All networks with our expanding
layers performed better than baseline, and the best one achieved an accuracy of
0.877 both on validation dataset and test dataset.


Abstract:
  Dynamic graph representation learning is a task to learn node embeddings over
dynamic networks, and has many important applications, including knowledge
graphs, citation networks to social networks. Graphs of this type are usually
large-scale but only a small subset of vertices are related in downstream
tasks. Current methods are too expensive to this setting as the complexity is
at best linear-dependent on both the number of nodes and edges. In this paper,
we propose a new method, namely Dynamic Personalized PageRank Embedding
(\textsc{DynamicPPE}) for learning a target subset of node representations over
large-scale dynamic networks. Based on recent advances in local node embedding
and a novel computation of dynamic personalized PageRank vector (PPV),
\textsc{DynamicPPE} has two key ingredients: 1) the per-PPV complexity is
$\mathcal{O}(m \bar{d} / \epsilon)$ where $m,\bar{d}$, and $\epsilon$ are the
number of edges received, average degree, global precision error respectively.
Thus, the per-edge event update of a single node is only dependent on $\bar{d}$
in average; and 2) by using these high quality PPVs and hash kernels, the
learned embeddings have properties of both locality and global consistency.
These two make it possible to capture the evolution of graph structure
effectively. Experimental results demonstrate both the effectiveness and
efficiency of the proposed method over large-scale dynamic networks. We apply
\textsc{DynamicPPE} to capture the embedding change of Chinese cities in the
Wikipedia graph during this ongoing COVID-19 pandemic
(https://en.wikipedia.org/wiki/COVID-19_pandemic). Our results show that these
representations successfully encode the dynamics of the Wikipedia graph.


Abstract:
  The ability to map and estimate the activity of radiological source
distributions in unknown three-dimensional environments has applications in the
prevention and response to radiological accidents or threats as well as the
enforcement and verification of international nuclear non-proliferation
agreements. Such a capability requires well-characterized detector response
functions, accurate time-dependent detector position and orientation data, a
digitized representation of the surrounding 3D environment, and appropriate
image reconstruction and uncertainty quantification methods. We have previously
demonstrated 3D mapping of gamma-ray emitters with free-moving detector systems
on a relative intensity scale using a technique called Scene Data Fusion (SDF).
Here we characterize the detector response of a multi-element gamma-ray imaging
system using experimentally benchmarked Monte Carlo simulations and perform 3D
mapping on an absolute intensity scale. We present experimental reconstruction
results from hand-carried and airborne measurements with point-like and
distributed sources in known configurations, demonstrating quantitative SDF in
complex 3D environments.


Abstract:
  We study the prime-to-$p$ Hecke action on the projective limit of the sets of
connected components of Shimura varieties with fixed parahoric or Bruhat--Tits
level at $p$. In particular, we construct infinitely many Shimura varieties for
CM unitary groups in odd variables for which the considering actions are not
transitive. We prove this result by giving negative examples on the question of
Bruhat--Colliot-Th\'el\`ene--Sansuc--Tits or its variant, which is related to
the weak approximation on tori over $\mathbb{Q}$.


Abstract:
  We formulate a nonlinear optimal control problem for intra-day operation of a
natural gas pipeline network that includes storage reservoirs. The dynamics of
compressible gas flow through pipes, compressors, reservoirs, and wells are
considered. In particular, a reservoir is modeled as a rigid, hollow container
that stores gas under isothermal conditions and uniform density, and a well is
modeled as a vertical pipe. For each pipe, flow dynamics are described by a
coupled partial differential equation (PDE) system in density and mass flux
variables, with momentum dissipation modeled using the Darcy-Wiesbach friction
approximation. Compressors are modeled as scaling up the pressure of gas
between inlet and outlet. The governing equations for all network components
are spatially discretized and assembled into a nonlinear differential-algebraic
equation (DAE) system, which synthesizes above-ground pipeline and subsurface
reservoir dynamics into a single reduced-order model. We seek to maximize an
objective function that quantifies economic profit and network efficiency
subject to the flow equations and inequalities that represent operating
limitations. The problem is solved using a primal-dual interior point solver
and the solutions are validated in computational experiments and simulations on
several pipeline test networks to demonstrate the effectiveness of the proposed
methodology.


Abstract:
  There is still a limited understanding of the necessary skill, talent, and
expertise to manage digital technologies as a crucial enabler of the hospitals
ability to adequately sense and respond to patient needs and wishes, i.e.,
patient agility. Therefore, this investigates how hospital departments can
leverage a digital dy-namic capability to enable the departments patient
agility. This study embraces the dynamic capabilities theory, develops a
research model, and tests it accordingly using data from 90 clinical hospital
departments from the Netherlands through an online survey. The model's
hypothesized relationships are tested using structural equation modeling (SEM).
The outcomes demonstrate the significance of digital dynamic capability in
developing patient sensing and responding capabili-ties that, in turn,
positively influence patient service performance. Outcomes are very relevant
for the hospital practice now, as hospitals worldwide need to trans-form
healthcare delivery processes using digital technologies and increase clinical
productivity.


Abstract:
  The knowledge of a deep learning model may be transferred to a student model,
leading to intellectual property infringement or vulnerability propagation.
Detecting such knowledge reuse is nontrivial because the suspect models may not
be white-box accessible and/or may serve different tasks. In this paper, we
propose ModelDiff, a testing-based approach to deep learning model similarity
comparison. Instead of directly comparing the weights, activations, or outputs
of two models, we compare their behavioral patterns on the same set of test
inputs. Specifically, the behavioral pattern of a model is represented as a
decision distance vector (DDV), in which each element is the distance between
the model's reactions to a pair of inputs. The knowledge similarity between two
models is measured with the cosine similarity between their DDVs. To evaluate
ModelDiff, we created a benchmark that contains 144 pairs of models that cover
most popular model reuse methods, including transfer learning, model
compression, and model stealing. Our method achieved 91.7% correctness on the
benchmark, which demonstrates the effectiveness of using ModelDiff for model
reuse detection. A study on mobile deep learning apps has shown the feasibility
of ModelDiff on real-world models.


Abstract:
  Affine rank minimization problem is the generalized version of low rank
matrix completion problem where linear combinations of the entries of a low
rank matrix are observed and the matrix is estimated from these measurements.
We propose a trainable deep neural network by unrolling a popular iterative
algorithm called the singular value thresholding (SVT) algorithm to perform
this generalized matrix completion which we call Learned SVT (LSVT). We show
that our proposed LSVT with fixed layers (say T) reconstructs the matrix with
lesser mean squared error (MSE) compared with that incurred by SVT with fixed
(same T) number of iterations and our method is much more robust to the
parameters which need to be carefully chosen in SVT algorithm.


Abstract:
  For a connected reductive group $G$ defined over $\mathbb{F}_q$ and equipped
with the induced Frobenius endomorphism $F$, we study the relation among the
following three $\mathbb{Z}$-algebras: (i) the $\mathbb{Z}$-model
$\mathsf{E}_G$ of endomorphism algebras of Gelfand-Graev representations of
$G^F$; (ii) the Grothendieck group $\mathsf{K}_{G^\ast}$ of the category of
representations of $G^{\ast F^\ast}$ over $\overline{\mathbb{F}_q}$
(Deligne-Lusztig dual side); (iii) the ring $\mathsf{B}_{G^\vee}$ of the scheme
$(T^\vee/\!\!/ W)^{F^\vee}$ over $\mathbb{Z}$ (Langlands dual side). The
comparison between (i) and (iii) is motivated by recent advances in the local
Langlands program.


Abstract:
  Whilst contrastive learning has recently brought notable benefits to deep
clustering of unlabelled images by learning sample-specific discriminative
visual features, its potential for explicitly inferring class decision
boundaries is less well understood. This is because its instance discrimination
strategy is not class sensitive, therefore, the clusters derived on the
resulting sample-specific feature space are not optimised for corresponding to
meaningful class decision boundaries. In this work, we solve this problem by
introducing Semantic Contrastive Learning (SCL). SCL imposes explicitly
distance-based cluster structures on unlabelled training data by formulating a
semantic (cluster-aware) contrastive learning objective. Moreover, we introduce
a clustering consistency condition to be satisfied jointly by both instance
visual similarities and cluster decision boundaries, and concurrently
optimising both to reason about the hypotheses of semantic ground-truth classes
(unknown/unlabelled) on-the-fly by their consensus. This semantic contrastive
learning approach to discovering unknown class decision boundaries has
considerable advantages to unsupervised learning of object recognition tasks.
Extensive experiments show that SCL outperforms state-of-the-art contrastive
learning and deep clustering methods on six object recognition benchmarks,
especially on the more challenging finer-grained and larger datasets.


Abstract:
  Let $H$ be a digraph possibly with loops and $D$ a loopless digraph whose
arcs are colored with the vertices of $H$ ($D$ is said to be an $H-$colored
digraph). If $W=(x_{0},\ldots,x_{n})$ is an open walk in $D$ and $i\in
\{1,\ldots,n-1\}$, we say that there is an obstruction on $x_{i}$ whenever
$(color(x_{i-1},x_{i}),color(x_{i},x_{i+1}))\notin A(H)$. A $(k,l,H)$-kernel by
walks in an $H$-colored digraph $D$ ($k\geq 2$, $l\geq 1$), is a subset $S$ of
vertices of $D$, such that, for every pair of different vertices in $S$, every
walk between them has at least $k-1$ obstructions, and for every $x\in
V(D)\setminus S$ there exists an $xS$-walk with at most $l-1$ obstructions.
This concept generalize the concepts of kernel, $(k,l)$-kernel, kernel by
monochromatic paths, and kernel by $H$-walks. If $D$ is an $H$-colored digraph,
an $H$-class partition is a partition $\mathscr{F}$ of $A(D)$ such that, for
every $\{(u,v),(v,w)\}\subseteq A(D)$, $(color(u,v),color(v,w))\in A(H)$ iff
there exists $F\in \mathscr{F}$ such that $\{(u,v),(v,w)\}\subseteq F$. The
$H$-class digraph relative to $\mathscr{F}$, denoted by $C_{\mathscr{F}}(D)$,
is the digraph such that $V(C_{\mathscr{F}}(D))=\mathscr{F}$, and $(F,G)\in
A(C_{\mathscr{F}}(D))$ iff there exist $(u,v)\in F$ and $(v,w)\in G$ with
$\{u,v,w\}\subseteq V(D)$.
  We will show sufficient conditions on $\mathscr{F}$ and $C_{\mathscr{F}}(D)$
to guarantee the existence of $(k,l,H)$-kernels by walks in $H$-colored
digraphs, and we will show that some conditions are tight. For instance, we
will show that if an $H$-colored digraph $D$ has an $H$-class partition in
which every class induces a strongly connected digraph, and has a
obstruction-free vertex, then for every $k\geq 2$, $D$ has a $(k,k-1,H)$-kernel
by walks. Despite finding $(k,l)$-kernels is a $NP$-complete problem, some
hypothesis presented in this paper can be verified in polynomial time.


Abstract:
  We use an iteration procedure propped up by a a classical form of the maximum
principle to show the existence of solutions to a nonlinear Poisson equation
with Dirichlet boundary conditions. These methods can be applied to the case of
special unbounded domains, and can be adapted to show the existence of
nontrivial solutions to systems, which we show via some examples.


Abstract:
  The rapid growth of the e-commerce market in Indonesia, making various
e-commerce companies appear and there has been high competition among them.
Marketing intelligence is an important activity to measure competitive
position. One element of marketing intelligence is to assess customer
satisfaction. Many Indonesian customers express their sense of satisfaction or
dissatisfaction towards the company through social media. Hence, using social
media data provides a new practical way to measure marketing intelligence
effort. This research performs sentiment analysis using the naive bayes
classifier classification method with TF-IDF weighting. We compare the
sentiments towards of top-3 e-commerce sites visited companies, are Bukalapak,
Tokopedia, and Elevenia. We use Twitter data for sentiment analysis because
it's faster, cheaper, and easier from both the customer and the researcher
side. The purpose of this research is to find out how to process the huge
customer sentiment Twitter to become useful information for the e-commerce
company, and which of those top-3 e-commerce companies has the highest level of
customer satisfaction. The experiment results show the method can be used to
classify customer sentiments in social media Twitter automatically and Elevenia
is the highest e-commerce with customer satisfaction.


Abstract:
  Marchenko methods are based on integral representations which express Green's
functions for virtual sources and/or receivers in the subsurface in terms of
the reflection response at the surface. An underlying assumption is that inside
the medium the wave field can be decomposed into downgoing and upgoing waves
and that evanescent waves can be neglected. We present a new derivation of
Green's function representations which circumvents these assumptions, both for
the acoustic and the elastodynamic situation. These representations form the
basis for research into new Marchenko methods which have the potential to
handle refracted and evanescent waves and to more accurately image steeply
dipping reflectors.


Abstract:
  We investigate fast and communication-efficient algorithms for the classic
problem of minimizing a sum of strongly convex and smooth functions that are
distributed among $n$ different nodes, which can communicate using a limited
number of bits. Most previous communication-efficient approaches for this
problem are limited to first-order optimization, and therefore have
\emph{linear} dependence on the condition number in their communication
complexity. We show that this dependence is not inherent:
communication-efficient methods can in fact have sublinear dependence on the
condition number. For this, we design and analyze the first
communication-efficient distributed variants of preconditioned gradient descent
for Generalized Linear Models, and for Newton's method. Our results rely on a
new technique for quantizing both the preconditioner and the descent direction
at each step of the algorithms, while controlling their convergence rate. We
also validate our findings experimentally, showing fast convergence and reduced
communication.


Abstract:
  We propose to accelerate existing linear bandit algorithms to achieve
per-step time complexity sublinear in the number of arms $K$. The key to
sublinear complexity is the realization that the arm selection in many linear
bandit algorithms reduces to the maximum inner product search (MIPS) problem.
Correspondingly, we propose an algorithm that approximately solves the MIPS
problem for a sequence of adaptive queries yielding near-linear preprocessing
time complexity and sublinear query time complexity. Using the proposed MIPS
solver as a sub-routine, we present two bandit algorithms (one based on UCB,
and the other based on TS) that achieve sublinear time complexity. We
explicitly characterize the tradeoff between the per-step time complexity and
regret, and show that our proposed algorithms can achieve $O(K^{1-\alpha(T)})$
per-step complexity for some $\alpha(T) > 0$ and $\widetilde O(\sqrt{T})$
regret, where $T$ is the time horizon. Further, we present the theoretical
limit of the tradeoff, which provides a lower bound for the per-step time
complexity. We also discuss other choices of approximate MIPS algorithms and
other applications to linear bandit problems.


Abstract:
  Lokshtanov et al.~[STOC 2017] introduced \emph{lossy kernelization} as a
mathematical framework for quantifying the effectiveness of preprocessing
algorithms in preserving approximation ratios. \emph{$\alpha$-approximate
reduction rules} are a central notion of this framework. We propose that
carefully crafted $\alpha$-approximate reduction rules can yield improved
approximation ratios in practice, while being easy to implement as well. This
is distinctly different from the (theoretical) purpose for which Lokshtanov et
al. designed $\alpha$-approximate Reduction Rules. As evidence in support of
this proposal we present a new 2-approximate reduction rule for the
\textsc{Dominating Set} problem. This rule, when combined with an approximation
algorithm for \textsc{Dominating Set}, yields significantly better
approximation ratios on a variety of benchmark instances as compared to the
latter algorithm alone.
  The central thesis of this work is that $\alpha$-approximate reduction rules
can be used as a tool for designing approximation algorithms which perform
better in practice. To the best of our knowledge, ours is the first exploration
of the use of $\alpha$-approximate reduction rules as a design technique for
practical approximation algorithms. We believe that this technique could be
useful in coming up with improved approximation algorithms for other
optimization problems as well.


Abstract:
  This paper studies the convergence of three temporal semi-discretizations for
a backward semilinear stochastic evolution equation. For general terminal value
and general coefficient with Lipschitz continuity, the convergence of three
Euler type temporal semi-discretizations is established without regularity
assumption on the solution. Moreover, the third temporal semi-discretization is
applied to a stochastic linear quadratic control problem, and an explicit
convergence rate is derived.


Abstract:
  Discrete mechanics is presented as an alternative to the equations of fluid
mechanics, in particular to the Navier-Stokes equation. The derivation of the
discrete equation of motion is built from the intuitions of Galileo, the
principles of Galilean equivalence and relativity. Other more recent concepts
such as the equivalence between mass and energy and the Helmholtz-Hodge
decomposition complete the formal framework used to write a fundamental law of
motion such as the conservation of accelerations, the intrinsic acceleration of
the material medium, and the sum of the accelerations applied to it. The two
scalar and vector potentials of the acceleration resulting from the
decomposition into two contributions, to curl-free and to divergence-free,
represent the energies per unit of mass of compression and shear.
  The solutions obtained by the incompressible Navier-Stokes equation and the
discrete equation of motion are the same, with constant physical properties.
This new formulation of the equation of motion makes it possible to
significantly modify the treatment of surface discontinuities, thanks to the
intrinsic properties established from the outset for a discrete geometrical
description directly linked to the decomposition of acceleration. The treatment
of the jump conditions of density, viscosity and capillary pressure is
explained in order to understand the two-phase flows. The choice of the
examples retained, mainly of the exact solutions of the continuous equations,
serves to show that the treatment of the conditions of jumps does not affect
the precision of the method of resolution.


Abstract:
  Federated Learning (FL) enables multiple distributed clients (e.g., mobile
devices) to collaboratively train a centralized model while keeping the
training data locally on the client. Compared to traditional centralized
machine learning, FL offers many favorable features such as offloading
operations which would usually be performed by a central server and reducing
risks of serious privacy leakage. However, Byzantine clients that send
incorrect or disruptive updates due to system failures or adversarial attacks
may disturb the joint learning process, consequently degrading the performance
of the resulting model. In this paper, we propose to mitigate these failures
and attacks from a spatial-temporal perspective. Specifically, we use a
clustering-based method to detect and exclude incorrect updates by leveraging
their geometric properties in the parameter space. Moreover, to further handle
malicious clients with time-varying behaviors, we propose to adaptively adjust
the learning rate according to momentum-based update speculation. Extensive
experiments on 4 public datasets demonstrate that our algorithm achieves
enhanced robustness comparing to existing methods under both cross-silo and
cross-device FL settings with faulty/malicious clients.


Abstract:
  Ensemble Kalman inversion is a parallelizable derivative-free method to solve
inverse problems. The method uses an ensemble that follows the Kalman update
formula iteratively to solve an optimization problem. The ensemble size is
crucial to capture the correct statistical information in estimating the
unknown variable of interest. Still, the ensemble is limited to a size smaller
than the unknown variable's dimension for computational efficiency. This study
proposes a strategy to correct the sampling error due to a small ensemble size,
which improves the performance of the ensemble Kalman inversion. This study
validates the efficiency and robustness of the proposed strategy through a
suite of numerical tests, including compressive sensing, image deblurring,
parameter estimation of a nonlinear dynamical system, and a PDE-constrained
inverse problem.


Abstract:
  On edge devices, data scarcity occurs as a common problem where transfer
learning serves as a widely-suggested remedy. Nevertheless, transfer learning
imposes a heavy computation burden to resource-constrained edge devices.
Existing task allocation works usually assume all submitted tasks are equally
important, leading to inefficient resource allocation at a task level when
directly applied in Multi-task Transfer Learning (MTL). To address these
issues, we first reveal that it is crucial to measure the impact of tasks on
overall decision performance improvement and quantify \emph{task importance}.
We then show that task allocation with task importance for MTL (TATIM) is a
variant of the NP-complete Knapsack problem, where the complicated computation
to solve this problem needs to be conducted repeatedly under varying contexts.
To solve TATIM with high computational efficiency, we propose a Data-driven
Cooperative Task Allocation (DCTA) approach. Finally, we evaluate the
performance of DCTA by not only a trace-driven simulation, but also a new
comprehensive real-world AIOps case study that bridges model and practice via a
new architecture and main components design within the AIOps system. Extensive
experiments show that our DCTA reduces 3.24 times of processing time, and saves
48.4\% energy consumption compared with the state-of-the-art when solving
TATIM.


Abstract:
  The one-dimensional Bose-Hubbard model in large-$U$ limit has been studied
via reducing and mapping the Hamiltonian to a simpler one. The eigenstates and
eigenvalues have been obtained exactly in the subspaces with fixed numbers of
single- and double-occupancies but without multiple-occupancies, and the
thermodynamic properties of the system have been calculated further. These
eigenstates and eigenvalues also enable us to develop a new perturbation
treatment of the model, with which the ground-state energy has been calculated
exactly to first order in $1/U$.


Abstract:
  In magnetic Cataclysmic Variables (mCVs), X-ray radiation originates from the
shock heated multi-temperature plasma in the post-shock region near the white
dwarf surface. These X-rays are modified by a complex distribution of absorbers
in the pre-shock region. The presence of photo-ionized lines and warm absorber
features in the soft X-ray spectra of these mCVs suggests that these absorbers
are ionized. We developed the ionized complex absorber model zxipab, which is
represented by a power-law distribution of ionized absorbers in the pre-shock
flow. Using the ionized absorber model zxipab along with a cooling flow model
and a reflection component, we model the broadband Chandra/HETG and NuSTAR
spectra of two IPs: NY Lup and V1223 Sgr. We find that this model describes
well many of the H and He like emission lines from medium Z elements, which
arises from the collisionally excited plasma. However the model fails to
account for some of the He like triplets from medium Z elements, which points
towards its photo-ionization origin. We do not find a compelling evidence for a
blackbody component to model the soft excess seen in the residuals of the
Chandra/HETG spectra, which could be due to the uncertainties in estimation of
the interstellar absorption of these sources using Chandra/HETG data and/or
excess fluxes seen in some photo-ionized emission lines which are not accounted
by the cooling flow model. We describe the implications of this model with
respect to the geometry of the pre-shock region in these two IPs.


Abstract:
  This text is written based on the author's publications during the period
from 1991 to 2001. The work is devoted to the theory of Markov intertwining
operators and joinings of measure-preserving group actions, as well as to their
applications to study asymptotic properties of dynamical systems. Special
attention is paid to Rokhlin's problems on multiple mixing and multiple
spectrum. The development of these topics over the past twenty years has not
been discussed. In fact many results on joinings have frozen in time, many
questions have remained open without losing their relevance, but probably have
ceased to excite interest due to difficulties. For example, it is not known
whether the minimal self-joinings of order 2 imply all orders? Is there a
non-trivial pairwise independent joining for a weakly mixing system of zero
entropy? What can be said about such joinings for transformations with small
local rank? These questions are ripe for a long time, and the author reminds
the reader about them, combining his story with numerous partial and related
results.


Abstract:
  Let $ \sigma$ be a partition of the set of all primes and $\mathfrak{F}$ be a
hereditary formation. We described all formations $\mathfrak{F}$ for which the
$\mathfrak{F}$-hypercenter and the intersection of weak
$K$-$\mathfrak{F}$-subnormalizers of all Sylow subgroups coincide in every
group. In particular the formation of all $\sigma$-nilpotent groups has this
property. With the help of our results we solve a particular case of
L.A.~Shemetkov's problem about the intersection of $\mathfrak{F}$-maximal
subgroups and the $\mathfrak{F}$-hypercenter. As corollaries we obtained P.
Hall's and R. Baer's classical results about the hypercenter. We proved that
the non-$\sigma$-nilpotent graph of a group is connected and its diameter is at
most 3.


Abstract:
  Andrews' $(k, i)$-singular overpartition function $\overline{C}_{k, i}(n)$
counts the number of overpartitions of $n$ in which no part is divisible by $k$
and only parts $\equiv \pm i\pmod{k}$ may be overlined. In recent times,
divisibility of $\overline{C}_{3\ell, \ell}(n)$, $\overline{C}_{4\ell,
\ell}(n)$ and $\overline{C}_{6\ell, \ell}(n)$ by $2$ and $3$ are studied for
certain values of $\ell$. In this article, we study divisibility of
$\overline{C}_{3\ell, \ell}(n)$, $\overline{C}_{4\ell, \ell}(n)$ and
$\overline{C}_{6\ell, \ell}(n)$ by primes $p\geq 5$. For all positive integer
$\ell$ and prime divisors $p\geq 5$ of $\ell$, we prove that
$\overline{C}_{3\ell, \ell}(n)$, $\overline{C}_{4\ell, \ell}(n)$ and
$\overline{C}_{6\ell, \ell}(n)$ are almost always divisible by arbitrary powers
of $p$. For $s\in \{3, 4, 6\}$, we next show that the set of those $n$ for
which $\overline{C}_{s\cdot\ell, \ell}(n) \not\equiv 0\pmod{p_i^k}$ is
infinite, where $k$ is a positive integer satisfying $p_i^{k-1}\geq \ell$. We
further improve a result of Gordon and Ono on divisibility of $\ell$-regular
partitions by powers of certain primes. We also improve a result of Ray and
Chakraborty on divisibility of $\ell$-regular overpartitions by powers of
certain primes.


Abstract:
  Affordance detection refers to identifying the potential action possibilities
of objects in an image, which is an important ability for robot perception and
manipulation. To empower robots with this ability in unseen scenarios, we
consider the challenging one-shot affordance detection problem in this paper,
i.e., given a support image that depicts the action purpose, all objects in a
scene with the common affordance should be detected. To this end, we devise a
One-Shot Affordance Detection (OS-AD) network that firstly estimates the
purpose and then transfers it to help detect the common affordance from all
candidate images. Through collaboration learning, OS-AD can capture the common
characteristics between objects having the same underlying affordance and learn
a good adaptation capability for perceiving unseen affordances. Besides, we
build a Purpose-driven Affordance Dataset (PAD) by collecting and labeling 4k
images from 31 affordance and 72 object categories. Experimental results
demonstrate the superiority of our model over previous representative ones in
terms of both objective metrics and visual quality. The benchmark suite is at
ProjectPage.


Abstract:
  Federated learning has emerged as a popular technique for distributing
machine learning (ML) model training across the wireless edge. In this paper,
we propose two timescale hybrid federated learning (TT-HF), a
semi-decentralized learning architecture that combines the conventional
device-to-server communication paradigm for federated learning with
device-to-device (D2D) communications for model training. In TT-HF, during each
global aggregation interval, devices (i) perform multiple stochastic gradient
descent iterations on their individual datasets, and (ii) aperiodically engage
in consensus procedure of their model parameters through cooperative,
distributed D2D communications within local clusters. With a new general
definition of gradient diversity, we formally study the convergence behavior of
TT-HF, resulting in new convergence bounds for distributed ML. We leverage our
convergence bounds to develop an adaptive control algorithm that tunes the step
size, D2D communication rounds, and global aggregation period of TT-HF over
time to target a sublinear convergence rate of O(1/t) while minimizing network
resource utilization. Our subsequent experiments demonstrate that TT-HF
significantly outperforms the current art in federated learning in terms of
model accuracy and/or network energy consumption in different scenarios where
local device datasets exhibit statistical heterogeneity. Finally, our numerical
evaluations demonstrate robustness against outages caused by fading channels,
as well favorable performance with non-convex loss functions.


Abstract:
  The ALMA Spectroscopic Survey in the Hubble Ultra Deep Field (ASPECS) Band 6
scan (212-272 GHz) covers potential [CII] emission in galaxies at $6\leq z
\leq8$ throughout a 2.9 arcmin$^2$ area. By selecting on known Lyman-$\alpha$
emitters (LAEs) and photometric dropout galaxies in the field, we perform
targeted searches down to a 5$\sigma$ [CII] luminosity depth
$L_{\mathrm{[CII]}}\sim2.0\times10^8$ L$_{\odot}$, corresponding roughly to
star formation rates (SFRs) of $10$-$20$ M$_{\odot}$ yr$^{-1}$ when applying a
locally calibrated conversion for star-forming galaxies, yielding zero
detections. While the majority of galaxies in this sample are characterized by
lower SFRs, the resulting upper limits on [CII] luminosity in these sources are
consistent with the current literature sample of targeted ALMA observations of
$z=6$-$7$ LAEs and Lyman-break galaxies (LBGs), as well as the locally
calibrated relations between $L_{\mathrm{[CII]}}$ and SFR -- with the exception
of a single [CII]-deficient, UV luminous LBG. We also perform a blind search
for [CII]-bright galaxies that may have been missed by optical selections,
resulting in an upper limit on the cumulative number density of [CII] sources
with $L_{\mathrm{[CII]}}>2.0\times10^8$ L$_{\odot}$ ($5\sigma $) to be less
than $1.8\times10^{-4}$ Mpc$^{-3}$ (90% confidence level). At this luminosity
depth and volume coverage, we present an observed evolution of the [CII]
luminosity function from $z=6$-$8$ to $z\sim0$ by comparing the ASPECS
measurement to literature results at lower redshift.


Abstract:
  We construct two-dimensional non-commutative topological quantum field
theories (TQFTs), one for each Hecke algebra corresponding to a finite Coxeter
system. These TQFTs associate an invariant to each ciliated surface, which is a
Laurent polynomial for punctured surfaces. There is a graphical way to compute
the invariant using minimal colored graphs. We give explicit formulas in terms
of the Schur elements of the Hecke algebra and prove positivity properties for
the invariants when the Coxeter group is of classical type, or one of the
exceptional types $H_3$, $E_6$ and $E_7$.


Abstract:
  Let $\mathbf{P} \subset [H_0,H]$ be a set of primes, where $\log H_0 \geq
(\log H)^{2/3 + \epsilon}$. Let $\mathscr{L} = \sum_{p \in \mathbf{P}} 1/p$.
Let $N$ be such that $\log H \leq (\log N)^{1/2-\epsilon}$. We show there
exists a subset $\mathscr{X} \subset (N, 2N]$ of density close to $1$ such that
all the eigenvalues of the linear operator $$(A_{|\mathscr{X}} f)(n) =
\sum_{\substack{p \in \mathbf{P} : p | n \\ n, n \pm p \in \mathscr{X}}} f(n
\pm p) \; - \sum_{\substack{p \in\mathbf{P} \\ n, n \pm p \in \mathscr{X}}}
\frac{f(n \pm p)}{p}$$ are $O(\sqrt{\mathscr{L}})$. This bound is optimal up to
a constant factor. In other words, we prove that a graph describing
divisibility by primes is a strong local expander almost everywhere, and indeed
within a constant factor of being "locally Ramanujan" (a.e.).
  Specializing to $f(n) = \lambda(n)$ with $\lambda(n)$ the Liouville function,
and using an estimate by Matom\"aki, Radziwi{\l}{\l} and Tao on the average of
$\lambda(n)$ in short intervals, we derive that \[\frac{1}{\log x} \sum_{n\leq
x} \frac{\lambda(n) \lambda(n+1)}{n} = O\Big(\frac{1}{\sqrt{\log \log
x}}\Big),\]
  improving on a result of Tao's. We also prove that $\sum_{N<n\leq 2 N}
\lambda(n) \lambda(n+1)=o(N)$ at almost all scales with a similar error term,
improving on a result by Tao and Ter\"av\"ainen. (Tao and Tao-Ter\"av\"ainen
followed a different approach, based on entropy, not expansion; significantly,
we can take a much larger value of $H$, and thus consider many more primes.)
  We can also prove sharper results with ease. For instance: let $S_{N,k}$ the
set of all $N<n\leq 2N$ such that $\Omega(n) = k$. Then, for any fixed value of
$k$ with $k = \log \log N + O(\sqrt{\log \log N})$ (that is, any "popular"
value of $k$) the average of $\lambda(n+1)$ over $S_{N,k}$ is $o(1)$ at almost
all scales.


Abstract:
  Coronavirus disease (COVID-19) pandemic has changed various aspects of
people's lives and behaviors. At this stage, there are no other ways to control
the natural progression of the disease than adopting mitigation strategies such
as wearing masks, watching distance, and washing hands. Moreover, at this time
of social distancing, social media plays a key role in connecting people and
providing a platform for expressing their feelings. In this study, we tap into
social media to surveil the uptake of mitigation and detection strategies, and
capture issues and concerns about the pandemic. In particular, we explore the
research question, "how much can be learned regarding the public uptake of
mitigation strategies and concerns about COVID-19 pandemic by using natural
language processing on Reddit posts?" After extracting COVID-related posts from
the four largest subreddit communities of North Carolina over six months, we
performed NLP-based preprocessing to clean the noisy data. We employed a custom
Named-entity Recognition (NER) system and a Latent Dirichlet Allocation (LDA)
method for topic modeling on a Reddit corpus. We observed that 'mask', 'flu',
and 'testing' are the most prevalent named-entities for "Personal Protective
Equipment", "symptoms", and "testing" categories, respectively. We also
observed that the most discussed topics are related to testing, masks, and
employment. The mitigation measures are the most prevalent theme of discussion
across all subreddits.


Abstract:
  Motivated by the mathematical beauty and the recent experimental realizations
of fractal systems, we study the spin-$1/2$ antiferromagnetic Heisenberg model
on a Sierpi\'nski gasket. The fractal porous feature generates new kinds of
frustration to exhibit exotic quantum states. Using advanced tensor network
techniques, we identify a quantum gapless-spin-liquid ground state in
fractional spatial dimension. This fractal spin system also demonstrates
nontrivial non-local properties. While the extremely short-range correlation
causes a highly degenerate spin form factor, the entanglement in this fractal
system suggests scaling behaviors significantly different from those in integer
dimensions. We also study the dynamic structure factor and clearly identify the
gapless excitation with a stable corner excitation emerged from the
ground-state entanglement. Our results unambiguously point out multiple
essential properties of this fractal spin system, and open a new route to
explore spin liquid and frustrated magnetism.


Abstract:
  Injection locking of diode lasers is commonly used to amplify low power laser
light, but is extremely sensitive to perturbations in the laser current and
temperature. To counter such perturbations, active stabilization is often
applied to the current of the injection locked diode. We observe that the diode
laser's polarization extinction ratio (PER) greatly increases when injection
locked, and therefore the PER provides a measure of injection lock quality. We
report robust active stabilization of a diode laser injection lock based on the
PER, demonstrating the technique at 399 nm wavelength where injection locking
is typically less stable than at longer wavelengths. The PER provides a
feedback error signal that is compatible with standard PID servo controllers,
requires no additional optical components beyond the optical isolator typically
used in injection locking, and enables a large feedback bandwidth.


Abstract:
  Layered ternary transition-metal chalcogenides have been focused as a vein of
exploration for superconductors. In this study, TiGeTe$_{6}$ single crystals
were synthesized and characterized by structural and valence state analyses and
electrical transport measurements. The transport properties were measured under
various pressures up to 71 GPa. The activation energy gets smaller as the
applied pressure increases, and a signature of a pressure-induced metallization
was observed under around 8.4 GPa. Under 13 GPa, pressure-induced
superconductivity was discovered in this compound for the first time, with
successive drops at 3 K and 6 K in the resistance, indicating the presence of
multiple superconducting transitions. The superconducting transition
temperature kept increasing as we further applied the pressure to the
TiGeTe$_{6}$ single crystal in the performed pressure range, reaching as high
as 8.1 K under 71 GPa.


Abstract:
  In this paper we study the existence and uniqueness of the random periodic
solution for a stochastic differential equation with a one-sided Lipschitz
condition (also known as monotonicity condition) and the convergence of its
numerical approximation via the backward Euler-Maruyama method. The existence
of the random periodic solution is shown as the limits of the pull-back flows
of the SDE and discretized SDE respectively. We establish a convergence rate of
the strong error for the backward Euler-Maruyama method and obtain the weak
convergence result for the approximation of the periodic measure.


Abstract:
  This report is dedicated to a short motivation and description of our
contribution to the AAPM DL-Sparse-View CT Challenge (team name:
"robust-and-stable"). The task is to recover breast model phantom images from
limited view fanbeam measurements using data-driven reconstruction techniques.
The challenge is distinctive in the sense that participants are provided with a
collection of ground truth images and their noiseless, subsampled sinograms (as
well as the associated limited view filtered backprojection images), but not
with the actual forward model. Therefore, our approach first estimates the
fanbeam geometry in a data-driven geometric calibration step. In a subsequent
two-step procedure, we design an iterative end-to-end network that enables the
computation of near-exact solutions.


Abstract:
  Accurately forecasting air quality is critical to protecting general public
from lung and heart diseases. This is a challenging task due to the complicated
interactions among distinct pollution sources and various other influencing
factors. Existing air quality forecasting methods cannot effectively model the
diffusion processes of air pollutants between cities and monitoring stations,
which may suddenly deteriorate the air quality of a region. In this paper, we
propose HighAir, i.e., a hierarchical graph neural network-based air quality
forecasting method, which adopts an encoder-decoder architecture and considers
complex air quality influencing factors, e.g., weather and land usage.
Specifically, we construct a city-level graph and station-level graphs from a
hierarchical perspective, which can consider city-level and station-level
patterns, respectively. We design two strategies, i.e., upper delivery and
lower updating, to implement the inter-level interactions, and introduce
message passing mechanism to implement the intra-level interactions. We
dynamically adjust edge weights based on wind direction to model the
correlations between dynamic factors and air quality. We compare HighAir with
the state-of-the-art air quality forecasting methods on the dataset of Yangtze
River Delta city group, which covers 10 major cities within 61,500 km2. The
experimental results show that HighAir significantly outperforms other methods.


Abstract:
  Computationally efficient and accurate quantum mechanical approximations to
solve the many-electron Schr\"odinger equation are at the heart of
computational materials science. In that respect the coupled cluster hierarchy
of methods plays a central role in molecular quantum chemistry because of its
systematic improvability and computational efficiency. In this hierarchy,
coupled cluster singles and doubles (CCSD) is one of the most important steps
in moving towards chemical accuracy and, in recent years, its scope has
successfully been expanded to the study of insulating surfaces and solids.
Here, we show that CCSD theory can also be applied to real metals. In so doing,
we overcome the limitation of needing extremely large supercells to capture
long range electronic correlation effects. An effective Hamiltonian can be
found using the transition structure factor--a map of electronic excitations
from the Hartree--Fock wavefunction--which has fewer finite size effects than
conventional periodic boundary conditions. This not only paves the way of
applying coupled cluster methods to real metals but also reduces the
computational cost by two orders of magnitude compared to previous methods. Our
applications to phases of lithium and silicon show a resounding success in
reaching the thermodynamic limit, taking the first step towards a truly
universal quantum chemical treatment of solids.


Abstract:
  We perform an extensive analysis of the statistics of axion masses and
interactions in compactifications of type IIB string theory, and we show that
black hole superradiance excludes some regions of Calabi-Yau moduli space.
Regardless of the cosmological model, a theory with an axion whose mass falls
in a superradiant band can be probed by the measured properties of
astrophysical black holes, unless the axion self-interaction is large enough to
disrupt formation of a condensate. We study a large ensemble of
compactifications on Calabi-Yau hypersurfaces, with $1 \leq h^{1,1} \leq 491$
closed string axions, and determine whether the superradiance conditions on the
masses and self-interactions are fulfilled. The axion mass spectrum is largely
determined by the K\"ahler parameters, for mild assumptions about the
contributing instantons, and takes a nearly-universal form when $h^{1,1} \gg
1$. When the K\"ahler moduli are taken at the tip of the stretched K\"ahler
cone, the fraction of geometries excluded initially grows with $h^{1,1}$, to a
maximum of $\approx 0.5$ at $h^{1,1} \approx 160$, and then falls for larger
$h^{1,1}$. Further inside the K\"ahler cone, the superradiance constraints are
far weaker, but for $h^{1,1} \gg 100$ the decay constants are so small that
these geometries may be in tension with astrophysical bounds, depending on the
realization of the Standard Model.


Abstract:
  Writers such as journalists often use automatic tools to find relevant
content to include in their narratives. In this paper, we focus on supporting
writers in the news domain to develop event-centric narratives. Given an
incomplete narrative that specifies a main event and a context, we aim to
retrieve news articles that discuss relevant events that would enable the
continuation of the narrative. We formally define this task and propose a
retrieval dataset construction procedure that relies on existing news articles
to simulate incomplete narratives and relevant articles. Experiments on two
datasets derived from this procedure show that state-of-the-art lexical and
semantic rankers are not sufficient for this task. We show that combining those
with a ranker that ranks articles by reverse chronological order outperforms
those rankers alone. We also perform an in-depth quantitative and qualitative
analysis of the results that sheds light on the characteristics of this task.


Abstract:
  This paper proposes a multi-task learning network with phoneme-aware and
channel-wise attentive learning strategies for text-dependent Speaker
Verification (SV). In the proposed structure, the frame-level multi-task
learning along with the segment-level adversarial learning is adopted for
speaker embedding extraction. The phoneme-aware attentive pooling is exploited
on frame-level features in the main network for speaker classifier, with the
corresponding posterior probability for the phoneme distribution in the
auxiliary subnet. Further, the introduction of Squeeze and Excitation
(SE-block) performs dynamic channel-wise feature recalibration, which improves
the representational ability. The proposed method exploits speaker
idiosyncrasies associated with pass-phrases, and is further improved by the
phoneme-aware attentive pooling and SE-block from temporal and channel-wise
aspects, respectively. The experiments conducted on RSR2015 Part 1 database
confirm that the proposed system achieves outstanding results for textdependent
SV.


Abstract:
  In this article we apply proper splittings of matrices to develop an
iterative process to approximate solutions of matrix equations of the form TX =
W. Moreover, by using the partial order induced by positive semidefinite
matrices, we obtain equivalent conditions to the convergence of this process.
We also include some speed comparison results of the convergence of this
method. In addition, for all matrix T we propose a proper splitting based on
the polar decomposition of T.


Abstract:
  Adversarial attack arises due to the vulnerability of deep neural networks to
perceive input samples injected with imperceptible perturbations. Recently,
adversarial attack has been applied to visual object tracking to evaluate the
robustness of deep trackers. Assuming that the model structures of deep
trackers are known, a variety of white-box attack approaches to visual tracking
have demonstrated promising results. However, the model knowledge about deep
trackers is usually unavailable in real applications. In this paper, we propose
a decision-based black-box attack method for visual object tracking. In
contrast to existing black-box adversarial attack methods that deal with static
images for image classification, we propose IoU attack that sequentially
generates perturbations based on the predicted IoU scores from both current and
historical frames. By decreasing the IoU scores, the proposed attack method
degrades the accuracy of temporal coherent bounding boxes (i.e., object
motions) accordingly. In addition, we transfer the learned perturbations to the
next few frames to initialize temporal motion attack. We validate the proposed
IoU attack on state-of-the-art deep trackers (i.e., detection based,
correlation filter based, and long-term trackers). Extensive experiments on the
benchmark datasets indicate the effectiveness of the proposed IoU attack
method. The source code is available at
https://github.com/VISION-SJTU/IoUattack.


Abstract:
  The amplitude (Higgs) mode near the two-dimensional superfluid-Mott glass
quantum phase transition is studied. We map the Bose-Hubbard Hamiltonian of
disordered interacting bosons onto an equivalent classical XY model in (2+1)
dimensions and compute the scalar susceptibility of the order parameter
amplitude via Monte Carlo simulation. Analytic continuation of the scalar
susceptibilities from imaginary to real frequency to obtain the spectral
densities is performed by a modified maximum entropy technique. Our results
show that the introduction of disorder into the system leads to unconventional
dynamical behavior of the Higgs mode that violates naive scaling,despite the
underlying thermodynamics of the transition being of conventional power-law
type. The computed spectral densities exhibit a broad, non-critical response
for all energies, and a momentum-independent dispersion for long-wavelengths,
indicating strong evidence for the localization of the Higgs mode for all
dilutions.


Abstract:
  If $H$ is a Hilbert space, the Stiefel manifold $St(n,H)$ is formed by all
the independent $n$-tuples in $H$. In this article, we contribute to the
topological study of Stiefel manifolds by proving density and
path-connectedness-related results. Regarding the density aspect, we generalize
the fact that $St(n,H)$ is dense in $H^n$ and prove that $St(n,H) \cap S$ is
dense in $S$ whenever $S \subseteq H^n$ is connected by polynomial paths of
finite degree to some $\Theta \in St(n,H) \cap S$. We provide special examples
of such sets $S$ in the context of finite-dimensional continuous frames (we set
$H := L^2(X,\mu;\mathbb{F})$ and we identify $St(n,H)$ with
$\mathcal{F}_{(X,\mu),n}^\mathbb{F}$) which are constructed from the inverse
image of singletons by some familiar linear and pseudo-quadratic functions. In
the second part devoted to path-connectedness, we prove that the intersection
of translates of $St(n,H)$ is path-connected under a condition on the
codimension of the span of the components of the translating $n$-tuples. These
results are also a contribution to the topological theory of Hilbert space
frames which is presently an active area of research.


Abstract:
  Massive black holes often exist within dwarf galaxies, and both simulations
and observations have shown that a substantial fraction of these may be
off-center with respect to their hosts. We trace the evolution of off-center
massive black holes (MBHs) in dwarf galaxies using cosmological hydrodynamical
simulations, and show that the reason for off-center locations is mainly due to
galaxy-galaxy mergers. We calculate dynamical timescales and show that
off-center MBHs are unlikely to sink to their galaxys' centers within a Hubble
time, due to the shape of the hosts' potential wells and low stellar densities.
These wandering MBHs are unlikely to be detected electromagnetically, nor is
there a measurable dynamical effect on the galaxy's stellar population. We
conclude that off-center MBHs may be common in dwarfs, especially if the mass
of the MBH is small or the stellar mass of the host galaxy is large. However
detecting them is extremely challenging, because their accretion luminosities
are very low and they do not measurably alter the dynamics of their host
galaxies.


Abstract:
  We discuss the interplay of wave package decoherence and decoherence induced
by quantum gravity via interactions with spacetime foam for high energy
astrophysical neutrinos. In this context we point out a compelling consequence
of the expectation that quantum gravity should break global symmetries, namely
that quantum-gravity induced decoherence can provide both a powerful tool for
the search for new particles, including totally decoupled backgrounds
interacting only gravitationally, and at the same time a window into the
intricacies of black hole information processing.


Abstract:
  Semi-Supervised Learning (SSL) has seen success in many application domains,
but this success often hinges on the availability of task-specific unlabeled
data. Knowledge distillation (KD) has enabled effective optimization of compact
neural nets, achieving the best results when the knowledge of an expensive
network is distilled via fresh task-specific unlabeled data. However,
task-specific unlabeled data can be challenging to find, especially for NLP. We
investigate the use of generative models in synthesizing unlabeled data and
present a simple and general framework called "generate, annotate, and learn
(GAL)". A language model (LM) is used to synthesize in-domain unlabeled data.
Then, a classifier is used to annotate such data. Finally, synthetically
generated and annotated data is used to advance SSL, KD, and few-shot learning
on NLP and tabular tasks. To obtain a strong task-specific LM, we either
fine-tune a large LM on inputs from a specific task, or prompt a large LM with
a few input examples and conditionally generate more unlabeled examples. It
also yields a new state-of-the-art for 6-layer transformers on the GLUE
leaderboard. Finally, self-training with GAL offers large gains on four tabular
tasks from the UCI repository.


Abstract:
  This chapter is written for the welfare of the society, questioning and
enlightening the effects of the increment or decrement in the percentage of
quality of air causing pollution due to the rise in the traffic post lockdown
due to COVID 19 in metro cities, specifically in Delhi. In this chapter, we
address the question about people's preference in moving in the shared taxis to
their workplaces or their reluctance and denial of the idea of moving in the
shared vehicle because of the fear of getting infected. The sensitivity of the
situation will compel the people to move in a single occupied vehicle (SOV).
The rise in the number of vehicles on the roads will result in traffic jams and
different kinds of pollution where people battling with the pandemic will
inevitably get exposed to other health related issues. We use a BPR (Bureau of
Public Roads) model to combat this issue endangering the environment and public
health. We exploit the BPR function to relate average travel time to the
estimated number of commuters travelling by car. We collect mode share data
from the NITI Ayog, the State Resource Centre and other authentic sources,
which gives unique figures of the impact of shared mobility in India and how,
in its absence, various sectors will get affected. Using the given data and the
BPR, we evaluate increased vehicle volumes on the road if different portions of
transit and carpool users switch to single occupancy vehicles and its effect on
multiple other factors. Based on the study of densely populated city, Delhi, we
predict that cities with significant transit ridership are at risk for extreme
traffic and pollution unless transit systems can resume safe with effective
protocols.


Abstract:
  Motivated by packet routing in computer networks, online queuing systems are
composed of queues receiving packets at different rates. Repeatedly, they send
packets to servers, each of them treating only at most one packet at a time. In
the centralized case, the number of accumulated packets remains bounded (i.e.,
the system is \textit{stable}) as long as the ratio between service rates and
arrival rates is larger than $1$. In the decentralized case, individual
no-regret strategies ensures stability when this ratio is larger than $2$. Yet,
myopically minimizing regret disregards the long term effects due to the
carryover of packets to further rounds. On the other hand, minimizing long term
costs leads to stable Nash equilibria as soon as the ratio exceeds
$\frac{e}{e-1}$. Stability with decentralized learning strategies with a ratio
below $2$ was a major remaining question. We first argue that for ratios up to
$2$, cooperation is required for stability of learning strategies, as selfish
minimization of policy regret, a \textit{patient} notion of regret, might
indeed still be unstable in this case. We therefore consider cooperative queues
and propose the first learning decentralized algorithm guaranteeing stability
of the system as long as the ratio of rates is larger than $1$, thus reaching
performances comparable to centralized strategies.


Abstract:
  In this paper we study the transformation of surface envelope solitons
travelling over a bottom step in water of a finite depth. Using the
transformation coefficients earlier derived in the linear approximation, we
find the parameters of transmitted pulses and subsequent evolution of the
pulses in the course of propagation. Relying on the weakly nonlinear theory,
the analytic formulae are derived which describe the maximum attainable wave
amplitude in the neighbourhood of the step and in the far zone. Solitary waves
may be greatly amplified (within the weakly nonlinear theory formally even
without a limit) when propagating from relatively shallow water to the deeper
domain due to the constructive interference between the newly emerging envelope
solitons and the residual quasi-linear waves. The theoretical results are in a
good agreement with the data of direct numerical modelling of soliton
transformation. In particular, more than double wave amplification is
demonstrated in the performed simulations.


Abstract:
  The dramatic growth of big datasets presents a new challenge to data storage
and analysis. Data reduction, or subsampling, that extracts useful information
from datasets is a crucial step in big data analysis. We propose an orthogonal
subsampling (OSS) approach for big data with a focus on linear regression
models. The approach is inspired by the fact that an orthogonal array of two
levels provides the best experimental design for linear regression models in
the sense that it minimizes the average variance of the estimated parameters
and provides the best predictions. The merits of OSS are three-fold: (i) it is
easy to implement and fast; (ii) it is suitable for distributed parallel
computing and ensures the subsamples selected in different batches have no
common data points; and (iii) it outperforms existing methods in minimizing the
mean squared errors of the estimated parameters and maximizing the efficiencies
of the selected subsamples. Theoretical results and extensive numerical results
show that the OSS approach is superior to existing subsampling approaches. It
is also more robust to the presence of interactions among covariates and, when
they do exist, OSS provides more precise estimates of the interaction effects
than existing methods. The advantages of OSS are also illustrated through
analysis of real data.


Abstract:
  The design of feedback control systems to block observability in a network
synchronization model, i.e. to make the dynamics unobservable from measurements
at a subset of the network's nodes, is studied. First, a general design
algorithm is presented for blocking observability at any specified group of $m$
nodes, by applying state feedback controls at $m+2$ specified actuation nodes.
The algorithm is based on a method for eigenstructure assignment, which allows
surgical modification of particular eigenvectors to block observability while
preserving the remaining open-loop eigenstructure. Next, the topological
structure of the network is exploited to reduce the number of controllers
required for blocking observability; the result is based on blocking
observability on the nodes associated with a vertex-cutset separating the
actuation and measurement locations. Also, the design is modified to encompass
regional feedback controls, which only use data from a subset of accessible
nodes. The regional feedback design does not maintain the open-loop
eigenstructure, but can be guaranteed to preserve stability via a time-scale
argument. The results are illustrated with numerical examples.


Abstract:
  Context: In the light of the swift and iterative nature of Agile Software
Development (ASD) practices, establishing deeper insights into capability
measurement within the context of team formation is crucial, as the capability
of individuals and teams can affect team performance and productivity. Although
a former Systematic Literature Review (SLR) synthesized the state of the art in
relation to capability measurement in ASD with a focus on selecting individuals
to agile teams, and capabilities related to team performance and success,
determining to what degree the SLR's results apply to practice can provide
progressive insights to both research and practice.
  Objective: Our study investigates how agile practitioners perceive the
relevance of individual and team level measures for characterizing the
capability of an agile team and its members. Furthermore, to scrutinize
variations in practitioners' perceptions, our study further analyzes
perceptions across stratified demographic groups.
  Method: We undertook a Web-based survey using a questionnaire built based on
the capability measures identified from a previously conducted SLR.
  Results: Our survey responses (60) indicate that 127 individual and 28 team
capability measures were considered as relevant by the majority of
practitioners. We also identified seven individual and one team capability
measure that have not been previously characterized by our SLR. The surveyed
practitioners suggested that an agile team member's responsibility and
questioning skills significantly represent the member's capability.
  Conclusion: Results from our survey align with our SLR's findings. Measures
associated with social aspects were observed to be dominant compared to
technical and innovative aspects. Our results can support agile practitioners
in their team composition decisions.


Abstract:
  Early speculations about the existence of heavy hadron molecules were
grounded on the idea that light-meson exchanges forces could lead to binding.
In analogy to the deuteron, the light-mesons usually considered include the
pion, sigma, rho and omega, but not the axial meson $a_1(1260)$. Though it has
been argued in the past that the coupling of the axial meson to the nucleons is
indeed strong, its mass is considerably heavier than that of the vector mesons
and thus its exchange ends up being suppressed. Yet, this is not necessarily
the case in heavy hadrons molecules: we find that even though the contribution
to binding from the axial meson is modest, it cannot be neglected in the
isovector sector where vector meson exchange cancels out. This might provide a
natural binding mechanism for molecular candidates such as the $Z_c(3900)$,
$Z_c(4020)$ or the more recently observed $Z_{cs}(3985)$. However the
$Z_{cs}(3985)$ is more dependent on a mixture of different factors, which
(besides axial meson exchange) include $\eta$ exchange and the nature of scalar
meson exchange. Together they point towards the existence of two
$Z_{cs}(3985)$-like resonances instead of one, while the observations about the
role of scalar meson exchange in the $Z_{cs}(3985)$ might be relevant for the
$P_{cs}(4459)$. Finally, the combination of axial meson exchange and flavor
symmetry breaking effects indicates that the isovector $J^{PC} = 0^{++}$
$D^*\bar{D}^*$ and the strange $J^P = 2^{+}$ $D^*\bar{D}_s^*$ molecules are the
most attractive configurations and thus the most likely molecular partners of
the $Z_c(3900)$, $Z_c(4020)$ and $Z_{cs}(3985)$.


Abstract:
  The 44.7 ms X-ray pulsar in the supernova remnant G12.82-0.02/HESS J1813-178
has the second highest spin-down luminosity of known pulsars in the Galaxy,
with E-dot=5.6e37 erg/s. Using the Green Bank Telescope, we have detected radio
pulsations from PSR J1813-1749 at 4.4-10.2 GHz. The pulse is highly scattered,
with an exponential decay timescale \tau longer than that of any other pulsar
at these frequencies. A point source detected at this position by Dzib et al.
in several observations with the Jansky Very Large Array can be attributed to
the pulsed emission. The steep dependence of \tau on observing frequency
explains why all previous pulsation searches at lower frequencies failed
(\tau~0.25 s at 2 GHz). The large dispersion measure, DM=1087 pc/cc, indicates
a distance of either 6.2 or 12 kpc according to two widely used models of the
electron density distribution in the Galaxy. These disfavor a previously
suggested association with a young stellar cluster at the closer distance of
4.8 kpc. The high X-ray measured column density of ~1e23/cm^2 also supports a
large distance. If at d~12 kpc, HESS J1813-178 would be one of the most
luminous TeV sources in the Galaxy.


Abstract:
  Two recent papers (Renou et al., arXiv:2101.10873, and Chen et al.,
arXiv:2103.08123) have indicated that complex numbers are necessary for quantum
theory. This short note is a comment on their result.


Abstract:
  This work analyzes the optical properties of a localized surface plasmon
(LSP) spaser made of a dielectric active wire coated with a graphene monolayer.
Our theoretical results, obtained by using rigorous electromagnetic methods,
illustrate the non-radiative transfer between the active medium and the
localized surface plasmons of the graphene. In particular, we focus on the
lasing conditions and the tunability of the LSP spaser in two cases: when the
wire is made of an infrared/THz transparent dielectric material and when it is
made of a metal-like material. We analyze the results by comparing them with
analytical expressions obtained by us using the quasistatic approximation. We
show that the studied systems present a high tunability of the spaser
resonances with the geometrical parameters as well as with the chemical
potential of the graphene.


Abstract:
  We prove positive mass theorems for asymptotically hyperbolic and
asymptotically locally hyperbolic Riemannian manifolds with black-hole-type
boundaries.


Abstract:
  Progress in making neural networks more robust against adversarial attacks is
mostly marginal, despite the great efforts of the research community. Moreover,
the robustness evaluation is often imprecise, making it difficult to identify
promising approaches. We analyze the classification decisions of 19 different
state-of-the-art neural networks trained to be robust against adversarial
attacks. Our findings suggest that current untargeted adversarial attacks
induce misclassification towards only a limited amount of different classes.
Additionally, we observe that both over- and under-confidence in model
predictions result in an inaccurate assessment of model robustness. Based on
these observations, we propose a novel loss function for adversarial attacks
that consistently improves attack success rate compared to prior loss functions
for 19 out of 19 analyzed models.


Abstract:
  Estimation of permutation entropy (PE) using Bayesian statistical methods is
presented for systems where the ordinal pattern sampling follows an
independent, multinomial distribution. It is demonstrated that the PE posterior
distribution is closely approximated by a standard Beta distribution, whose
hyperparameters can be estimated directly from moments computed analytically
from observed ordinal pattern counts. Equivalence with expressions derived
previously using frequentist methods is also demonstrated. Because Bayesian
estimation of PE naturally incorporates uncertainty and prior information, the
orthodox requirement that $N \gg D!$ is effectively circumvented, allowing PE
to be estimated even for very short time series. Self-similarity tests on PE
posterior distributions computed for a semiconductor laser with optical
feedback (SLWOF) system show its PE to vary periodically over time.


Abstract:
  Unconventional fermions, such as three-fold, four-fold, six-fold, and
eight-fold fermions have attracted intense attention in recent years. However,
the concrete materials hosting unconventional fermions are still in urgent
scarcity. In this work, based first-principle calculations and symmetry
analysis, we reveal rich unconventional fermions in existing compound Re2C3 (Re
= Y, La, Ce, Pr, Nd, Sm, Tb, Dy, Ho, Er, Tm, Yb, Lu). We show that these
compounds host quadratic dispersive three-fold (TP), linear dispersive
four-fold (FP) and six-fold points (SP) near the Fermi level in their electric
band structures when spin-orbital coupling (SOC) is not included. Notably, the
FP is charge-2 Dirac-like point. More importantly, among compound Re2C3, the
compound Yb2C3 has very clean band structure, and its unconventional fermions
are closed to the Fermi level. We also find that a uniaxial strain can
transform the unconventional fermions into other types fermions, depending on
the directions of strain. When SOC is considered, a SP transform to an
eightfold degenerate point and a fourfold degenerate point. Overall, our work
provides a family of realistic materials to study the unconventional fermions.


Abstract:
  We condition a Brownian motion with arbitrary starting point $y \in
\mathbb{R}$ on spending at most $1$ time unit below $0$ and provide an explicit
description of the resulting process. In particular, we provide explicit
formulas for the distributions of its last zero $g=g^y$ and of its occupation
time $\Gamma=\Gamma^y$ below $0$ as functions of $y$. This generalizes a result
of Benjamini and Berestycki from 2011, which covers the special case $y=0$.
Additionally, we study the behavior of the distributions of $g^y$ and
$\Gamma^y$, respectively, for $y \to \pm\infty$.


Abstract:
  Assessing sensitivity to unmeasured confounding is an important step in
observational studies, which typically estimate effects under the assumption
that all confounders are measured. In this paper, we develop a sensitivity
analysis framework for balancing weights estimators, an increasingly popular
approach that solves an optimization problem to obtain weights that directly
minimizes covariate imbalance. In particular, we adapt a sensitivity analysis
framework using the percentile bootstrap for a broad class of balancing weights
estimators. We prove that the percentile bootstrap procedure can, with only
minor modifications, yield valid confidence intervals for causal effects under
restrictions on the level of unmeasured confounding. We also propose an
amplification to allow for interpretable sensitivity parameters in the
balancing weights framework. We illustrate our method through extensive real
data examples.


Abstract:
  Topological band theory has achieved great success in the high-throughput
search for topological band structures both in paramagnetic and magnetic
crystal materials. However, a significant proportion of materials are
topologically trivial insulators at the Fermi level. In this paper, we show
that, remarkably, for a subset of the topologically trivial insulators, knowing
only their electron number and the Wyckoff positions of the atoms we can
separate them into two groups: the obstructed atomic insulator (OAI) and the
atomic insulator (AI). The interesting group, the OAI, have a center of charge
not localized on the atoms. Using the theory of topological quantum chemistry,
in this work we first derive the necessary and sufficient conditions for a
topologically trivial insulator to be a filling enforced obstructed atomic
insulator (feOAI) in the 1651 Shubnikov space groups. Remarkably, the filling
enforced criteria enable the identification of obstructed atomic bands without
knowing the representations of the band structures. Hence, no ab-initio
calculations are needed for the filling enforced criteria, although they are
needed to obtain the band gaps. With the help of the Topological Quantum
Chemistry website, we have performed a high-throughput search for feOAIs and
have found that 957 ICSD entries (638 unique materials) are paramagnetic
feOAIs, among which 738 (475) materials have an indirect gap. The metallic
obstructed surface states of feOAIs are also showcased by several material
examples.


Abstract:
  Orthogonal drawings, i.e., embeddings of graphs into grids, are a classic
topic in Graph Drawing. Often the goal is to find a drawing that minimizes the
number of bends on the edges. A key ingredient for bend minimization algorithms
is the existence of an orthogonal representation that describes such drawings
combinatorially by only listing the angles between the edges around each vertex
and the directions of bends on the edges, but neglecting any kind of geometric
information such as vertex coordinates or edge lengths.
  We generalize this idea to ortho-radial representations of ortho-radial
drawings, which are embeddings into an ortho-radial grid, whose gridlines are
concentric circles around the origin and straight-line spokes emanating from
the origin but excluding the origin itself. Unlike the orthogonal case, there
exist ortho-radial representations that do not admit a corresponding drawing,
for example so-called strictly monotone cycles. An ortho-radial drawing is
called valid if it does not contain a strictly monotone cycle. Our first result
is that an ortho-radial representation admits a corresponding drawing if and
only if it is valid. Previously such a characterization was only known for
ortho-radial drawings of paths, cycles, and theta graphs, and in the special
case of rectangular drawings of cubic graphs, where the contour of each face is
required to be a rectangle. Further, we give a quadratic-time algorithm that
tests for an ortho-radial representation whether it is valid, and we show how
to draw a valid ortho-radial representation in the same running time.
  Altogether, this reduces the problem of computing a minimum-bend ortho-radial
drawing to the task of computing a valid ortho-radial representation with the
minimum number of bends, and hence establishes an ortho-radial analogue of the
topology-shape-metrics framework for planar orthogonal drawings by Tamassia.


Abstract:
  Engineering materials with high thermal conductivity are of fundamental
interest for efficiently dissipating heat in micro/nanoelectronics. Using first
principles computations we report an ultra-high thermal conductivity of 2090
Wm-1K-1 (1395 Wm-1K-1) for hexagonal pure (natural) BC6N(h-BC6N). This value is
among the highest thermal conductivities known after diamond and cubic boron
arsenide. This ultra-high lattice thermal conductivity (k) is mainly attributed
with high phonon group velocities of both acoustic and optical phonons arising
from strong C-C and B-N bonds as well as the light atomic mass of the
constituent elements such as boron (B), carbon (C) and nitrogen (N). We also
report size dependent thermal conductivity of h-BC6N nanostructures by
including boundary scattering. At room temperature (300 K) and at nanoscale
length (L) of 100 nm, a high k value of 175 Wm-1K-1 is observed (higher than
the bulk k value of silicon). Optical phonons with large group velocities are
mainly responsible for this high thermal conductivity in h-BC6N nanostructures.
High thermal conductivity of h-BC6N makes it a candidate material for heat
dissipation in micro/nano thermal management applications.


Abstract:
  The generic fluidity observed in the nature of political protest movements
across the world during the last decade weigh heavily with the presence of
social media. As such, there is a possibility to study the contemporary
movements with an interdisciplinary approach combining computational analytics
with social science perspectives. The present study has put efforts to
understand such dynamics in the context of the ongoing nationwide movement in
India opposing the NRC-CAA enactment. The transformative nature of individual
discontent into collective mobilization, especially with a reflective
intervention in social media across a sensitive region of the nation state, is
presented here with a combination of qualitative (fieldwork) and quantitative
(computing) techniques. The study is augmented further by the primary data
generation coupled with real-time application of analytical approaches.


Abstract:
  We explore the constraints on the three-nucleon force (3NF) of chiral
effective field theory ($\chi$EFT) that are provided by bound-state observables
in the $A=3$ and $A=4$ sectors. Our statistically rigorous analysis
incorporates experimental error, computational method uncertainty, and the
uncertainty due to truncation of the $\chi$EFT expansion at
next-to-next-to-leading order. A consistent solution for the ${}^3$H binding
energy, the ${}^4$He binding energy and radius, and the ${}^3$H $\beta$-decay
rate can only be obtained if $\chi$EFT truncation errors are included in the
analysis. All of these except the $\beta$-decay rate give essentially
degenerate constraints on the 3NF low-energy constants, so it is crucial for
estimating these parameters. We use eigenvector continuation for fast and
accurate emulation of No-Core Shell Model calculations of the considered
few-nucleon observables. This facilitates sampling of the posterior probability
distribution, allowing us to also determine the distributions of the
hyperparameters that quantify the truncation error. We find a $\chi$EFT
expansion parameter of $Q=0.33 \pm 0.06$ for these observables.


Abstract:
  An instance of the multiperiod binary knapsack problem (MPBKP) is given by a
horizon length $T$, a non-decreasing vector of knapsack sizes $(c_1, \ldots,
c_T)$ where $c_t$ denotes the cumulative size for periods $1,\ldots,t$, and a
list of $n$ items. Each item is a triple $(r, q, d)$ where $r$ denotes the
reward of the item, $q$ its size, and $d$ its time index (or, deadline). The
goal is to choose, for each deadline $t$, which items to include to maximize
the total reward, subject to the constraints that for all $t=1,\ldots,T$, the
total size of selected items with deadlines at most $t$ does not exceed the
cumulative capacity of the knapsack up to time $t$. We also consider the
multiperiod binary knapsack problem with soft capacity constraints (MPBKP-S)
where the capacity constraints are allowed to be violated by paying a penalty
that is linear in the violation. The goal is to maximize the total profit,
i.e., the total reward of selected items less the total penalty. Finally, we
consider the multiperiod binary knapsack problem with soft stochastic capacity
constraints (MPBKP-SS), where the non-decreasing vector of knapsack sizes
$(c_1, \ldots, c_T)$ follow some arbitrary joint distribution but we are given
access to the profit as an oracle, and we choose a subset of items to maximize
the total expected profit, i.e., the total reward less the total expected
penalty. For MPBKP, we exhibit a fully polynomial-time approximation scheme
with runtime
$\tilde{\mathcal{O}}\left(\min\left\{n+\frac{T^{3.25}}{\epsilon^{2.25}},n+\frac{T^{2}}{\epsilon^{3}},\frac{nT}{\epsilon^2},\frac{n^2}{\epsilon}\right\}\right)$
that achieves $(1+\epsilon)$ approximation; for MPBKP-S, the $(1+\epsilon)$
approximation can be achieved in $\mathcal{O}\left(\frac{n\log
n}{\epsilon}\cdot\min\left\{\frac{T}{\epsilon},n\right\}\right)$; for MPBKP-SS,
a greedy algorithm is a 2-approximation when items have the same size.


Abstract:
  This manuscript focus on in the transmission problem for one dimensional
waves with nonlinear weights on the frictional damping and time-varying delay.
We prove global existence of solutions using Kato's variable norm technique and
we show the exponential stability by the energy method with the construction of
a suitable Lyapunov functional.


Abstract:
  Predictive state representations (PSRs) are models of controlled non-Markov
observation sequences which exhibit the same generative process governing POMDP
observations without relying on an underlying latent state. In that respect, a
PSR is indistinguishable from the corresponding POMDP. However, PSRs
notoriously ignore the notion of rewards, which undermines the general utility
of PSR models for control, planning, or reinforcement learning. Therefore, we
describe a sufficient and necessary accuracy condition which determines whether
a PSR is able to accurately model POMDP rewards, we show that rewards can be
approximated even when the accuracy condition is not satisfied, and we find
that a non-trivial number of POMDPs taken from a well-known third-party
repository do not satisfy the accuracy condition. We propose reward-predictive
state representations (R-PSRs), a generalization of PSRs which accurately
models both observations and rewards, and develop value iteration for R-PSRs.
We show that there is a mismatch between optimal POMDP policies and the optimal
PSR policies derived from approximate rewards. On the other hand, optimal R-PSR
policies perfectly match optimal POMDP policies, reconfirming R-PSRs as
accurate state-less generative models of observations and rewards.


Abstract:
  This paper presents Gem, a model-agnostic approach for providing
interpretable explanations for any GNNs on various graph learning tasks.
Specifically, we formulate the problem of providing explanations for the
decisions of GNNs as a causal learning task. Then we train a causal explanation
model equipped with a loss function based on Granger causality. Different from
existing explainers for GNNs, Gem explains GNNs on graph-structured data from a
causal perspective. It has better generalization ability as it has no
requirements on the internal structure of the GNNs or prior knowledge on the
graph learning tasks. In addition, Gem, once trained, can be used to explain
the target GNN very quickly. Our theoretical analysis shows that several recent
explainers fall into a unified framework of additive feature attribution
methods. Experimental results on synthetic and real-world datasets show that
Gem achieves a relative increase of the explanation accuracy by up to $30\%$
and speeds up the explanation process by up to $110\times$ as compared to its
state-of-the-art alternatives.


Abstract:
  We report new precision measurements of the elastic electron-proton
scattering cross section for momentum transfer squared (Q$^2$) up to
15.75~\gevsq. These data allow for improved extraction of the proton magnetic
form factor at high Q$^2$ and nearly double the Q$^2$ range of direct
longitudinal/transverse separated cross sections. A comparison of our results
to polarization measurements establishes the presence of hard two-photon
exchange in the $e$-$p$ elastic scattering cross section at greater than 95\%
confidence level for Q$^2$ up to 8 (GeV/c)$^2$.


Abstract:
  Intuition dictates that a very long, very thin cavity (e.g., a fiber optic
cable) could perhaps be modeled as an approximately one dimensional system. In
this paper we rigorously explore the validity of such intuition from the
perspective of a localized probe coupling to a quantum field inside a cavity
(e.g., an atom or an Unruh-DeWitt particle detector in a fiber optic cable). To
do so, we introduce the notion of subfield decomposition in which a $D+1$
dimensional quantum field in an axially-symmetric cavity can be reduced to an
infinite collection of uncoupled, massive $1+1$ dimensional fields. We show
that the ability to approximate a higher-dimensional scenario by a $1+1$
dimensional model is equivalent to making a certain change of the probe's shape
in the higher-dimensional space. The approximation is justified whenever this
change of shape is "small enough". In this light, we identify the dynamically
relevant norm by which the magnitude of these changes in probe shape ought to
be judged. Finally, we explore this approximation in particular setups
corresponding to quantum optics and superconducting circuits.