title:
SINR: Deconvolving Circular SAS Images Using Implicit Neural
  Representations

abstract:
  Circular Synthetic aperture sonars (CSAS) capture multiple observations of a
scene to reconstruct high-resolution images. We can characterize resolution by
modeling CSAS imaging as the convolution between a scene's underlying point
scattering distribution and a system-dependent point spread function (PSF). The
PSF is a function of the transmitted waveform's bandwidth and determines a
fixed degree of blurring on reconstructed imagery. In theory, deconvolution
overcomes bandwidth limitations by reversing the PSF-induced blur and
recovering the scene's scattering distribution. However, deconvolution is an
ill-posed inverse problem and sensitive to noise. We propose a self-supervised
pipeline (does not require training data) that leverages an implicit neural
representation (INR) for deconvolving CSAS images. We highlight the performance
of our SAS INR pipeline, which we call SINR, by implementing and comparing to
existing deconvolution methods. Additionally, prior SAS deconvolution methods
assume a spatially-invariant PSF, which we demonstrate yields subpar
performance in practice. We provide theory and methods to account for a
spatially-varying CSAS PSF, and demonstrate that doing so enables SINR to
achieve superior deconvolution performance on simulated and real acoustic SAS
data. We provide code to encourage reproducibility of research.


title:
Curriculum Learning for Goal-Oriented Semantic Communications with a
  Common Language

abstract:
  Goal-oriented semantic communication will be a pillar of next-generation
wireless networks. Despite significant recent efforts in this area, most prior
works are focused on specific data types (e.g., image or audio), and they
ignore the goal and effectiveness aspects of semantic transmissions. In
contrast, in this paper, a holistic goal-oriented semantic communication
framework is proposed to enable a speaker and a listener to cooperatively
execute a set of sequential tasks in a dynamic environment. A common language
based on a hierarchical belief set is proposed to enable semantic
communications between speaker and listener. The speaker, acting as an observer
of the environment, utilizes the beliefs to transmit an initial description of
its observation (called event) to the listener. The listener is then able to
infer on the transmitted description and complete it by adding related beliefs
to the transmitted beliefs of the speaker. As such, the listener reconstructs
the observed event based on the completed description, and it then takes
appropriate action in the environment based on the reconstructed event. An
optimization problem is defined to determine the perfect and abstract
description of the events while minimizing the transmission and inference costs
with constraints on the task execution time and belief efficiency. Then, a
novel bottom-up curriculum learning (CL) framework based on reinforcement
learning is proposed to solve the optimization problem and enable the speaker
and listener to gradually identify the structure of the belief set and the
perfect and abstract description of the events. Simulation results show that
the proposed CL method outperforms traditional RL in terms of convergence time,
task execution cost and time, reliability, and belief efficiency.


title:
A simple denoising approach to exploit multi-fidelity data for machine
  learning materials properties

abstract:
  Machine-learning models have recently encountered enormous success for
predicting the properties of materials. These are often trained based on data
that present various levels of accuracy, with typically much less high- than
low-fidelity data. In order to extract as much information as possible from all
available data, we here introduce an approach which aims to improve the quality
of the data through denoising. We investigate the possibilities that it offers
in the case of the prediction of the band gap relying on both limited
experimental data and density-functional theory relying different
exchange-correlation functionals (with an increasing amount of data as the
accuracy of the functional decreases). We explore different ways to combine the
data into training sequences and analyze the effect of the chosen denoiser.
Finally, we analyze the effect of applying the denoising procedure several
times until convergence. Our approach provides an improvement over existing
methods to exploit multi-fidelity data.


title: Fibrewise stratification of group representations

abstract:
  Given a finite cocommutative Hopf algebra $A$ over a commutative regular ring
$R$, the lattice of localising tensor ideals of the stable category of
Gorenstein projective $A$-modules is described in terms of the corresponding
lattices for the fibres of $A$ over the spectrum of $R$. Under certain natural
conditions on the cohomology of $A$ over $R$, this yields a stratification of
the stable category. These results apply when $A$ is the group algebra over $R$
of a finite group, and also when $A$ is the exterior algebra on a finite free
$R$-module.


title:
Improving the Generalizability of Depression Detection by Leveraging
  Clinical Questionnaires

abstract:
  Automated methods have been widely used to identify and analyze mental health
conditions (e.g., depression) from various sources of information, including
social media. Yet, deployment of such models in real-world healthcare
applications faces challenges including poor out-of-domain generalization and
lack of trust in black box models. In this work, we propose approaches for
depression detection that are constrained to different degrees by the presence
of symptoms described in PHQ9, a questionnaire used by clinicians in the
depression screening process. In dataset-transfer experiments on three social
media datasets, we find that grounding the model in PHQ9's symptoms
substantially improves its ability to generalize to out-of-distribution data
compared to a standard BERT-based approach. Furthermore, this approach can
still perform competitively on in-domain data. These results and our
qualitative analyses suggest that grounding model predictions in
clinically-relevant symptoms can improve generalizability while producing a
model that is easier to inspect.


title:
Solitons in media with mixed, high-order dispersion and cubic
  nonlinearity

abstract:
  Although most soliton research has traditionally considered dominant
quadratic dispersion, the recent discovery of pure-quartic solitons has
inspired analysis of soliton solutions with large higher orders of dispersion.
Here we present analytic expressions for families of bright soliton solutions
at arbitrary dispersion orders and practical methods to obtain the associated
dispersion relations. These results provide a framework for considering higher
order dispersion solitons and show the potential for further investigation of
solitons in higher order dispersion systems.


title:
On the relationship between nonlinearities and thermalization in
  classical open systems

abstract:
  We discuss results on the dynamics of thermalization for a model with
Gaussian interactions between two classical many-body systems trapped in
external harmonic potentials. Previous work showed an approximate, power-law
scaling of the interaction energy with the number of particles, with particular
focus on the dependence of the anomalous exponent on the interaction strength.
Here we explore the role of the interaction range in determining anomalous
exponents, showing that it is a more relevant parameter to differentiate
distinct regimes of responses of the system. More specifically, on varying the
interaction range from its largest values while keeping the interaction
strength constant, we observe a crossover from an integrable system,
approximating the Caldeira-Leggett interaction term in the long range limit, to
an intermediate interaction range in which the system manifests anomalous
scaling, and finally to a regime of local interactions in which anomalous
scaling disappears. A Fourier analysis of the interaction energy shows that
nonlinearities give rise to an effective bath with a broad band of frequencies,
even when starting with only two distinct trapping frequencies, yielding
efficient thermalization in the intermediate regime of interaction range. We
provide qualitative arguments, based on an analogous Fourier analysis of the
standard map, supporting the view that anomalous scaling and features of the
Fourier spectrum may be used as proxies to identify the role of chaotic
dynamics. Our work, that encompasses models developed in different contexts and
unifies them in a common framework, may be relevant to the general
understanding of the role of nonlinearities in a variety of many-body classical
systems, ranging from plasmas to trapped atoms and ions.

categories: cond-mat.stat-mech cond-mat.quant-gas nlin.CD physics.flu-dyn physics.plasm-ph


title: PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map

abstract:
  Deep learning has recently achieved significant progress in trajectory
forecasting. However, the scarcity of trajectory data inhibits the data-hungry
deep-learning models from learning good representations. While mature
representation learning methods exist in computer vision and natural language
processing, these pre-training methods require large-scale data. It is hard to
replicate these approaches in trajectory forecasting due to the lack of
adequate trajectory data (e.g., 34K samples in the nuScenes dataset). To work
around the scarcity of trajectory data, we resort to another data modality
closely related to trajectories-HD-maps, which is abundantly provided in
existing datasets. In this paper, we propose PreTraM, a self-supervised
pre-training scheme via connecting trajectories and maps for trajectory
forecasting. Specifically, PreTraM consists of two parts: 1) Trajectory-Map
Contrastive Learning, where we project trajectories and maps to a shared
embedding space with cross-modal contrastive learning, and 2) Map Contrastive
Learning, where we enhance map representation with contrastive learning on
large quantities of HD-maps. On top of popular baselines such as AgentFormer
and Trajectron++, PreTraM boosts their performance by 5.5% and 6.9% relatively
in FDE-10 on the challenging nuScenes dataset. We show that PreTraM improves
data efficiency and scales well with model size.


title:
Scale-Equivariant Unrolled Neural Networks for Data-Efficient
  Accelerated MRI Reconstruction

abstract:
  Unrolled neural networks have enabled state-of-the-art reconstruction
performance and fast inference times for the accelerated magnetic resonance
imaging (MRI) reconstruction task. However, these approaches depend on
fully-sampled scans as ground truth data which is either costly or not possible
to acquire in many clinical medical imaging applications; hence, reducing
dependence on data is desirable. In this work, we propose modeling the proximal
operators of unrolled neural networks with scale-equivariant convolutional
neural networks in order to improve the data-efficiency and robustness to
drifts in scale of the images that might stem from the variability of patient
anatomies or change in field-of-view across different MRI scanners. Our
approach demonstrates strong improvements over the state-of-the-art unrolled
neural networks under the same memory constraints both with and without data
augmentations on both in-distribution and out-of-distribution scaled images
without significantly increasing the train or inference time.


title:
DiRA: Discriminative, Restorative, and Adversarial Learning for
  Self-supervised Medical Image Analysis

abstract:
  Discriminative learning, restorative learning, and adversarial learning have
proven beneficial for self-supervised learning schemes in computer vision and
medical imaging. Existing efforts, however, omit their synergistic effects on
each other in a ternary setup, which, we envision, can significantly benefit
deep semantic representation learning. To realize this vision, we have
developed DiRA, the first framework that unites discriminative, restorative,
and adversarial learning in a unified manner to collaboratively glean
complementary visual information from unlabeled medical images for fine-grained
semantic representation learning. Our extensive experiments demonstrate that
DiRA (1) encourages collaborative learning among three learning ingredients,
resulting in more generalizable representation across organs, diseases, and
modalities; (2) outperforms fully supervised ImageNet models and increases
robustness in small data regimes, reducing annotation cost across multiple
medical imaging applications; (3) learns fine-grained semantic representation,
facilitating accurate lesion localization with only image-level annotation; and
(4) enhances state-of-the-art restorative approaches, revealing that DiRA is a
general mechanism for united representation learning. All code and pre-trained
models are available at https: //github.com/JLiangLab/DiRA.


title: Evolution of Transparent Explainable Rule-sets

abstract:
  Most AI systems are black boxes generating reasonable outputs for given
inputs. Some domains, however, have explainability and trustworthiness
requirements that cannot be directly met by these approaches. Various methods
have therefore been developed to interpret black-box models after training.
This paper advocates an alternative approach where the models are transparent
and explainable to begin with. This approach, EVOTER, evolves rule-sets based
on simple logical expressions. The approach is evaluated in several
prediction/classification and prescription/policy search domains with and
without a surrogate. It is shown to discover meaningful rule sets that perform
similarly to black-box models. The rules can provide insight to the domain, and
make biases hidden in the data explicit. It may also be possible to edit them
directly to remove biases and add constraints. EVOTER thus forms a promising
foundation for building trustworthy AI systems for real-world applications in
the future.


title: On the primality of totally ordered $q$-factorization graphs

abstract:
  We introduce the combinatorial notion of a $q$-fatorization graph intended as
a tool to study and express results related to the classification of prime
simple modules for quantum affine algebras. These are directed graphs equipped
with three decorations: a coloring and a weight map on vertices, and an
exponent map on arrows (the exponent map can be seen as a weight map on
arrows). Such graphs do not contain oriented cycles and, hence, the set of
arrows induces a partial order on the set of vertices. In this first paper on
the topic, beside setting the theoretical base of the concept, we establish
several criteria for deciding whether or not a tensor product of two simple
modules is a highest-$\ell$-weight module and use such criteria to prove, for
type $A$, that a simple module whose $q$-factorization graph has a totally
ordered vertex set is prime.


title:
Thermodynamics and hydrodynamics of spontaneous and forced imbibition in
  conical capillaries: A theoretical study of conical liquid diode

abstract:
  Thermodynamics and hydrodynamics of spontaneous and forced imbibition of
liquid into conical capillaries are studied to assess the feasibility of a
conical liquid diode. The analytical formulas for the Laplace pressure and the
critical Young's contact angle of the capillary for the onset of spontaneous
imbibition are derived using the classical capillary model of thermodynamics.
The critical contact angle below which the spontaneous imbibition can occur
belongs to the hydrophilic region for the capillary with a diverging radius
while it belongs to the hydrophobic region for the capillary with a converging
radius. Thus, by choosing Young's contact angle between these two critical
contact angles, only the spontaneous imbibition toward the converging radius
occurs. Therefore, the capillary with a converging radius acts as the forward
direction and that with a diverging radius as the reverse direction of diode.
Even under the external applied pressure, the free-energy landscape implies
that the forced imbibition occurs only to the forward direction by tuning the
applied pressure. Furthermore, the scaling rule of the time scale of imbibition
is derived by assuming Hagen-Poiseuille steady flow. Again, the time scale of
the forward direction is advantageous compared to the reverse direction when
the imbibition to both direction is possible. Therefore, our theoretical
analysis shows that a conical capillary acts as a liquid diode.


title: Frostman lemma revisited

abstract:
  We study sharpness of various generalizations of Frostman's lemma. These
generalizations provide better estimates for the lower Hausdorff dimension of
measures. As a corollary, we prove that if a generalized anisotropic gradient
$(\partial_1^{m_1} f, \partial_2^{m_2} f,\ldots, \partial_d^{m_d} f)$ of a
function $f$ in $d$ variables is a measure of bounded variation, then this
measure is absolutely continuous with respect to the Hausdorff $d-1$
dimensional measure.


title: Three-vertex prime graphs and reality of trees

abstract:
  We continue the study of prime simple modules for quantum affine algebras
from the perspective of $q$-fatorization graphs. In this paper we establish
several properties related to simple modules whose $q$-factorization graphs are
afforded by trees. The two most important of them are proved for type $A$. The
first completes the classification of the prime simple modules with three
$q$-factors by giving a precise criterion for the primality of a $3$-vertex
line which is not totally ordered. Using a very special case of this criterion,
we then show that a simple module whose $q$-factorization graph is afforded by
an arbitrary tree is real. Indeed, the proof of the latter works for all types,
provided the aforementioned special case is settled in general.


title: $L^2$-complexes and twistor complexes of tame harmonic bundles

abstract:
  Let $f:X\to Y$ be a morphism of complex manifolds. Suppose that $X$ is a
K\"ahler manifold. Let $(\mathcal{T},\mathcal{S})$ be a regular polarized pure
twistor $\mathcal{D}$-module of weight $w$ on $X$ whose support is proper over
$Y$. We prove the Hard Lefschetz Theorem for the push-forward of
$(\mathcal{T},\mathcal{S})$ by $f$. As one of the key steps, we obtain the
twistor version of a theorem of Kashiwara and Kawai about the Hodge structure
on the intersection complex of polarized variation of Hodge structure.


title:
Advances in actinide thin films: synthesis, properties, and future
  directions

abstract:
  Actinide-based compounds exhibit unique physics due to the presence of 5f
electrons, and serve in many cases as important technological materials.
Targeted thin film synthesis of actinide materials has been successful in
generating high-purity specimens in which to study individual physical
phenomena. These films have enabled the study of the unique electron
configuration, strong mass renormalization, and nuclear decay in actinide
metals and compounds. The growth of these films, as well as their
thermophysical, magnetic, and topological properties, have been studied in a
range of chemistries, albeit far fewer than most classes of thin film systems.
This relative scarcity is the result of limited source material availability
and safety constraints associated with the handling of radioactive materials.
Here, we review recent work on the synthesis and characterization of
actinide-based thin films in detail, describing both synthesis methods and
modelling techniques for these materials. We review reports on
pyrometallurgical, solution-based, and vapor deposition methods. We highlight
the current state-of-the-art in order to construct a path forward to higher
quality actinide thin films and heterostructure devices.

categories: cond-mat.mtrl-sci nucl-ex physics.atom-ph quant-ph


title: MTE with Misspecification

abstract:
  This paper studies the implication of a fraction of the population not
responding to the instrument when selecting into treatment. We show that, in
general, the presence of non-responders biases the Marginal Treatment Effect
(MTE) curve and many of its functionals. Yet, we show that, when the propensity
score is fully supported on the unit interval, it is still possible to restore
identification of the MTE curve and its functionals with an appropriate
re-weighting.


title: Overtaking Maneuvers on a Nonplanar Racetrack

abstract:
  We leverage game theory and a new vehicle modeling approach to compute
overtaking maneuvers for racecars on a nonplanar surface. We solve for
equilibria between noncooperative racing agents and demonstrate that by
leveraging the novel nonplanar vehicle dynamics, overtaking can be achieved in
situations where simpler models can do not provide a winning strategy.


title: Design of Adaptive Compliance Controllers for Safe Robotic Assembly

abstract:
  Insertion operations are a critical element of most robotic assembly
operation, and peg-in-hole (PiH) insertion is one of the most widely studied
tasks in the industrial and academic manipulation communities. PiH insertion is
in fact an entire class of problems, where the complexity of the problem can
depend on the type of misalignment and contact formation during an insertion
attempt. In this paper, we present the design and analysis of adaptive
compliance controllers which can be used in insertion-type assembly tasks,
including learning-based compliance controllers which can be used for insertion
problems in the presence of uncertainty in the goal location during robotic
assembly. We first present the design of compliance controllers which can
ensure safe operation of the robot by limiting experienced contact forces
during contact formation. Consequently, we present analysis of the force
signature obtained during the contact formation to learn the corrective action
needed to perform insertion. Finally, we use the proposed compliance
controllers and learned models to design a policy that can successfully perform
insertion in novel test conditions with almost perfect success rate. We
validate the proposed approach on a physical robotic test-bed using a 6-DoF
manipulator arm.


title:
Hypergraph Transformer: Weakly-supervised Multi-hop Reasoning for
  Knowledge-based Visual Question Answering

abstract:
  Knowledge-based visual question answering (QA) aims to answer a question
which requires visually-grounded external knowledge beyond image content
itself. Answering complex questions that require multi-hop reasoning under weak
supervision is considered as a challenging problem since i) no supervision is
given to the reasoning process and ii) high-order semantics of multi-hop
knowledge facts need to be captured. In this paper, we introduce a concept of
hypergraph to encode high-level semantics of a question and a knowledge base,
and to learn high-order associations between them. The proposed model,
Hypergraph Transformer, constructs a question hypergraph and a query-aware
knowledge hypergraph, and infers an answer by encoding inter-associations
between two hypergraphs and intra-associations in both hypergraph itself.
Extensive experiments on two knowledge-based visual QA and two knowledge-based
textual QA demonstrate the effectiveness of our method, especially for
multi-hop reasoning problem. Our source code is available at
https://github.com/yujungheo/kbvqa-public.


title: Strongly Lipschitz structure of the singular set of distance functions

abstract:
  For the distance function from any closed subset of any smooth complete
Finsler manifold, we prove that the singular set is equal to a countable union
of Lipschitz hypersurfaces up to an exceptional set of codimension two. The
result is new even in the standard Euclidean space.


title: Quadratic pseudospectrum for identifying localized states

abstract:
  We examine the utility of the quadratic pseudospectrum in photonics and
condensed matter. Specifically, the quadratic pseudospectrum represents a
method for approaching systems with incompatible observables, as it both
minimizes the "eigen-error" in the joint approximate spectrum of the
incompatible observables and does not increase the system's computational
complexity. Moreover, we derive an important estimate relating the Clifford and
quadratic pseudospectra. Finally, we prove that the quadratic pseudospectrum is
local, and derive the bounds on the errors that are incurred by truncating the
system in the vicinity of where the pseudospectrum is being calculated.

categories: quant-ph cond-mat.mes-hall math-ph math.FA math.MP physics.optics


title: SCOPE: Safe Exploration for Dynamic Computer Systems Optimization

abstract:
  Modern computer systems need to execute under strict safety constraints
(e.g., a power limit), but doing so often conflicts with their ability to
deliver high performance (i.e. minimal latency). Prior work uses machine
learning to automatically tune hardware resources such that the system
execution meets safety constraints optimally. Such solutions monitor past
system executions to learn the system's behavior under different hardware
resource allocations before dynamically tuning resources to optimize the
application execution. However, system behavior can change significantly
between different applications and even different inputs of the same
applications. Hence, the models learned using data collected a priori are often
suboptimal and violate safety constraints when used with new applications and
inputs. To address this limitation, we introduce the concept of an execution
space, which is the cross product of hardware resources, input features, and
applications. To dynamically and safely allocate hardware resources from the
execution space, we present SCOPE, a resource manager that leverages a novel
safe exploration framework. We evaluate SCOPE's ability to deliver improved
latency while minimizing power constraint violations by dynamically configuring
hardware while running a variety of Apache Spark applications. Compared to
prior approaches that minimize power constraint violations, SCOPE consumes
comparable power while improving latency by up to 9.5X. Compared to prior
approaches that minimize latency, SCOPE achieves similar latency but reduces
power constraint violation rates by up to 45.88X, achieving almost zero safety
constraint violations across all applications.


title:
On Borel $\sigma$-algebras of topologies generated by two-point
  selections

abstract:
  A two-point selection on a set $X$ is a function $f:[X]^2 \to X$ such that
$f(F) \in F$ for every $F \in [X]^2$. It is known that every two-point
selection $f:[X]^2 \to X$ induced a topology $\tau_f$ on $X$ by using the
relation: $x \leq y$ if either $f(\{x,y\}) = x$ or $x = y$, for every $x, y \in
X$. We are mainly concern with the two-point selections on the real line
$\mathbb{R}$. In this paper, we study the $\sigma$-algebras of Borel, each one
denoted by $\mathcal{B}_f(\mathbb{R})$, of the topologies $\tau_f$'s defined by
a two-point selection $f$ on $\mathbb{R}$. We prove that the assumption
$\mathfrak{c} = 2^{< \mathfrak{c}}$ implies the existence of a family $\{ f_\nu
: \nu < 2^\mathfrak{c} \}$ of two-point selections on $\mathbb{R}$ such that
$\mathcal{B}_{f_\mu}(\mathbb{R}) \neq \mathcal{B}_{f_\nu}(\mathbb{R})$ for
distinct $\mu, \nu < 2^\mathfrak{c}$. By assuming that $\mathfrak{c} = 2^{<
\mathfrak{c}}$ and $\mathfrak{c}$ is regular, we also show that there are
$2^{2^\mathfrak{c}}$ many $\sigma$-algebras on $\mathbb{R}$ that contain
$[\mathbb{R}]^{\leq \omega}$ and none of them is the $\sigma$-algebra of Borel
of $\tau_f$ for any two-point selection $f: [\mathbb{R}]^2 \to \mathbb{R}$.
Several examples are given to illustrate some properties of these Borel
$\sigma$-algebras.


title: Fixed-Time Parameter Adaptation for Safe Control Synthesis

abstract:
  We propose a fixed-time stable parameter adaptation law that guarantees that
an additive, parameter-affine disturbance appearing in the dynamics of a class
of nonlinear, control-affine systems is learned within a fixed time. We then
provide an upper bound on the parameter estimation error as an explicit
function of time, and use such knowledge to formulate a control barrier
function condition to guarantee safety of the system trajectories at all times.
We further show that our proposed parameter adaptation law is robust against
bounded perturbations to the system dynamics in that the resulting estimated
parametric disturbance converges to a known neighborhood of the true parametric
disturbance within a fixed time. To illustrate the advantages of our proposed
approach, we conduct a comparative numerical study against various controllers
from the literature. Finally, we validate our approach on a trajectory-tracking
problem subject to safety requirements using a 6 degree-of-freedom quadrotor
model in an unknown wind field.


title:
A Data-Efficient Model-Based Learning Framework for the Closed-Loop
  Control of Continuum Robots

abstract:
  Traditional dynamic models of continuum robots are in general computationally
expensive and not suitable for real-time control. Recent approaches using
learning-based methods to approximate the dynamic model of continuum robots for
control have been promising, although real data hungry -- which may cause
potential damage to robots and be time consuming -- and getting poorer
performance when trained with simulation data only. This paper presents a
model-based learning framework for continuum robot closed-loop control that, by
combining simulation and real data, shows to require only 100 real data to
outperform a real-data-only controller trained using up to 10000 points. The
introduced data-efficient framework with three control policies has utilized a
Gaussian process regression (GPR) and a recurrent neural network (RNN). Control
policy A uses a GPR model and a RNN trained in simulation to optimize control
outputs for simulated targets; control policy B retrains the RNN in policy A
with data generated from the GPR model to adapt to real robot physics; control
policy C utilizes policy A and B to form a hybrid policy. Using a continuum
robot with soft spines, we show that our approach provides an efficient
framework to bridge the sim-to-real gap in model-based learning for continuum
robots.


title: Optimal Heap Limits for Reducing Browser Memory Use

abstract:
  Garbage collected language runtimes must carefully tune heap limits to reduce
garbage collection time and memory usage. However, there's a trade-off: a lower
heap limit reduces memory use but increases garbage collection time. Classic
methods for setting heap limits include manually-tuned heap limits and
multiple-of-working-memory rules of thumb. But because it's a trade-off, it's
not clear what heap limit rule is best or how even to compare them.
  We address this problem with a new framework where heap limits are set for
multiple heaps at once. In this framework, standard heap limit rules are
non-compositional: multiple heaps using the same heap limit rule allocate
memory in non-Pareto-optimal ways. We use our framework to derive a
compositional "square-root" heap limit rule, which achieves minimizes total
memory usage for any amount of total garbage collection time. Paradoxically,
the square-root heap limit rule achieves coordination without communication: it
allocates memory optimally across multiple heaps without requiring any
communication between heaps.
  To demonstrate that this heap limit rule is effective, we prototype it for
V8, the JavaScript runtime used in Google Chrome, Microsoft Edge, and other
browsers, as well as in server-side frameworks like node.js and Deno. On
real-world web pages, our prototype achieves reductions of approximately 16.99%
of memory usage. On memory-intensive benchmarks, reductions of up to 6.55% of
garbage collection time are possible with no change in total memory usage.


title: Variations of generalised pairs

abstract:
  In this paper we investigate various properties of generalised pairs in
families, especially boundedness of several kinds. We show that many statements
for usual pairs do not hold for generalised pairs. In particular, we construct
an unexpected counter-example to boundedness of generalised lc models with
fixed appropriate invariants. We also show that the DCC of Iitaka volumes and
existence of nef reduction maps fail in families of generalised pairs. In a
positive direction we show boundedness of bases of log Calabi-Yau fibrations
with their induced generalised pair structure under natural assumptions.
Roughly speaking we prove this boundedness for fibrations whose general fibres
belong to a bounded family and whose Iitaka volume is fixed.


title:
Stackelberg Routing of Autonomous Cars in Mixed-Autonomy Traffic
  Networks

abstract:
  As autonomous cars are becoming tangible technologies, road networks will
soon be shared by human-driven and autonomous cars. However, humans normally
act selfishly which may result in network inefficiencies. In this work, we
study increasing the efficiency of mixed-autonomy traffic networks by routing
autonomous cars altruistically. We consider a Stackelberg routing setting where
a central planner can route autonomous cars in the favor of society such that
when human-driven cars react and select their routes selfishly, the overall
system efficiency is increased. We develop a Stackelberg routing strategy for
autonomous cars in a mixed-autonomy traffic network with arbitrary geometry. We
bound the price of anarchy that our Stackelberg strategy induces and prove that
our proposed Stackelberg routing will reduce the price of anarchy, i.e. it
increases the network efficiency. Specifically, we consider a non-atomic
routing game in a mixed-autonomy setting with affine latency functions and
develop an extension of the SCALE Stackelberg strategy for mixed-autonomy
networks. We derive an upper bound on the price of anarchy that this
Stackelberg routing induces and demonstrate that in the limit, our bound
recovers the price of anarchy bounds for networks of only human-driven cars.


title: Path Integral Quantum Monte Carlo Calculations of Light Nuclei

abstract:
  We describe a path-integral ground-state quantum Monte Carlo method for light
nuclei in continuous space. We show how to efficiently update and sample the
paths with spin-isospin dependent and spin-orbit interactions. We apply the
method to the triton and alpha particle using both local chiral interactions
with next-to-next-to-leading-order %(N$^2$LO) and the Argonne interactions. For
operators, like the total energy, that commute with the Hamiltonian, our
results agree with Green's function Monte Carlo and auxiliary field diffusion
Monte Carlo calculations. For operators that do not commute with the
Hamiltonian and for Euclidean response functions, the path-integral formulation
allows straightforward calculation without forward walking or the increased
variance typical of diffusion methods. We demonstrate this by calculating
density distributions, root mean square radii, and Euclidean response functions
for single-nucleon couplings.


title:
Robust estimation and model diagnostic of insurance loss data: a
  weighted likelihood approach

abstract:
  This paper presents a score-based weighted likelihood estimator (SWLE) for
robust estimations of generalized linear model (GLM) for insurance loss data.
The SWLE exhibits a limited sensitivity to the outliers, theoretically
justifying its robustness against model contaminations. Also, with the
specially designed weight function to effectively diminish the contributions of
extreme losses to the GLM parameter estimations, most statistical quantities
can still be derived analytically, minimizing the computational burden for
parameter calibrations. Apart from robust estimations, the SWLE can also act as
a quantitative diagnostic tool to detect outliers and systematic model
misspecifications. Motivated by the coverage modifications which make insurance
losses often random censored and truncated, the SWLE is extended to accommodate
censored and truncated data. We exemplify the SWLE on three simulation studies
and two real insurance datasets. Empirical results suggest that the SWLE
produces more reliable parameter estimates than the MLE if outliers contaminate
the dataset. The SWLE diagnostic tool also successfully detects any systematic
model misspecifications with high power, accompanying some potential model
improvements.


title:
Symmetric and antisymmetric constitutive tensors for bi-isotropic and
  bi-anisotropic media

abstract:
  The classical propagation of electromagnetic waves in continuous matter is
described by the constitutive relations and the Maxwell equations. This work
investigates the effects stemming from extended constitutive relations on the
propagation of waves in bi-isotropic and bi-anisotropic media using a classical
general approach, based on the evaluation of dispersion relations and
refractive indices. For the bi-anisotropic media, the magnetoelectric
parameters were represented by specific symmetric and antisymmetric
configurations. The three cases examined have provided real and distinct
refractive indices for two propagating modes, which implies birefringence. The
propagating modes were also carried out in all cases. The anisotropy or
birefringence effect, given by the rotatory power or phase difference, was
evaluated in terms of the magnetoelectric parameters of the theory in each
case. The propagation orthogonal to the vectors used to parametrize the
symmetric and antisymmetric magnetoelectric tensors is described by distinct
modes, which may represent a route to identify the kind of bi-anisotropic
medium examined.


title:
WaBERT: A Low-resource End-to-end Model for Spoken Language
  Understanding and Speech-to-BERT Alignment

abstract:
  Historically lower-level tasks such as automatic speech recognition (ASR) and
speaker identification are the main focus in the speech field. Interest has
been growing in higher-level spoken language understanding (SLU) tasks
recently, like sentiment analysis (SA). However, improving performances on SLU
tasks remains a big challenge. Basically, there are two main methods for SLU
tasks: (1) Two-stage method, which uses a speech model to transfer speech to
text, then uses a language model to get the results of downstream tasks; (2)
One-stage method, which just fine-tunes a pre-trained speech model to fit in
the downstream tasks. The first method loses emotional cues such as intonation,
and causes recognition errors during ASR process, and the second one lacks
necessary language knowledge. In this paper, we propose the Wave BERT (WaBERT),
a novel end-to-end model combining the speech model and the language model for
SLU tasks. WaBERT is based on the pre-trained speech and language model, hence
training from scratch is not needed. We also set most parameters of WaBERT
frozen during training. By introducing WaBERT, audio-specific information and
language knowledge are integrated in the short-time and low-resource training
process to improve results on the dev dataset of SLUE SA tasks by 1.15% of
recall score and 0.82% of F1 score. Additionally, we modify the serial
Continuous Integrate-and-Fire (CIF) mechanism to achieve the monotonic
alignment between the speech and text modalities.


title:
Parameter-robust Braess-Sarazin-type smoothers for linear elasticity
  problems

abstract:
  In this work, we propose three Braess-Sarazin-type multigrid relaxation
schemes for solving linear elasticity problems, where the marker and cell
scheme, a finite difference method, is used for the discretization. The three
relaxation schemes are Jacobi-Braess-Sarazin, Mass-Braess-Sarazin, and
Vanka-Braess-Sarazin. A local Fourier analysis (LFA) for the block-structured
relaxation schemes is presented to study multigrid convergence behavior. From
LFA, we derive optimal LFA smoothing factor for each case. We obtain highly
efficient smoothing factors, which are independent of Lam\'{e} constants.
Vanka-Braess-Sarazin relaxation scheme leads to the most efficient one. In each
relaxation, a Schur complement system needs to be solved. Due to the fact that
direct solve is often expensive, an inexact version is developed, where we
simply use at most three weighted Jacobi iterations on the Schur complement
system. Finally, two-grid and V-cycle multigrid performances are presented to
validate our theoretical results. Our numerical results show that inexact
versions can achieve the same performance as that of exact versions and our
methods are robust to the Lam\'{e} constants.


title:
Mostra: A Flexible Balancing Framework to Trade-off User, Artist and
  Platform Objectives for Music Sequencing

abstract:
  We consider the task of sequencing tracks on music streaming platforms where
the goal is to maximise not only user satisfaction, but also artist- and
platform-centric objectives, needed to ensure long-term health and
sustainability of the platform. Grounding the work across four objectives: Sat,
Discovery, Exposure and Boost, we highlight the need and the potential to
trade-off performance across these objectives, and propose Mostra, a Set
Transformer-based encoder-decoder architecture equipped with submodular
multi-objective beam search decoding. The proposed model affords system
designers the power to balance multiple goals, and dynamically control the
impact on one objective to satisfy other objectives. Through extensive
experiments on data from a large-scale music streaming platform, we present
insights on the trade-offs that exist across different objectives, and
demonstrate that the proposed framework leads to a superior, just-in-time
balancing across the various metrics of interest.


title: Towards Involving End-users in Interactive Human-in-the-loop AI Fairness

abstract:
  Ensuring fairness in artificial intelligence (AI) is important to counteract
bias and discrimination in far-reaching applications. Recent work has started
to investigate how humans judge fairness and how to support machine learning
(ML) experts in making their AI models fairer. Drawing inspiration from an
Explainable AI (XAI) approach called \emph{explanatory debugging} used in
interactive machine learning, our work explores designing interpretable and
interactive human-in-the-loop interfaces that allow ordinary end-users without
any technical or domain background to identify potential fairness issues and
possibly fix them in the context of loan decisions. Through workshops with
end-users, we co-designed and implemented a prototype system that allowed
end-users to see why predictions were made, and then to change weights on
features to "debug" fairness issues. We evaluated the use of this prototype
system through an online study. To investigate the implications of diverse
human values about fairness around the globe, we also explored how cultural
dimensions might play a role in using this prototype. Our results contribute to
the design of interfaces to allow end-users to be involved in judging and
addressing AI fairness through a human-in-the-loop approach.


title:
Hardness of Approximation in P via Short Cycle Removal: Cycle Detection,
  Distance Oracles, and Beyond

abstract:
  We present a new technique for efficiently removing almost all short cycles
in a graph without unintentionally removing its triangles. Consequently,
triangle finding problems do not become easy even in almost $k$-cycle free
graphs, for any constant $k\geq 4$.
  Triangle finding is at the base of many conditional lower bounds in P, mainly
for distance computation problems, and the existence of many $4$- or $5$-cycles
in a worst-case instance had been the obstacle towards resolving major open
questions.
  Hardness of approximation: Are there distance oracles with $m^{1+o(1)}$
preprocessing time and $m^{o(1)}$ query time that achieve a constant
approximation? Existing algorithms with such desirable time bounds only achieve
super-constant approximation factors, while only $3-\epsilon$ factors were
conditionally ruled out (P\u{a}tra\c{s}cu, Roditty, and Thorup; FOCS 2012). We
prove that no $O(1)$ approximations are possible, assuming the $3$-SUM or APSP
conjectures. In particular, we prove that $k$-approximations require
$\Omega(m^{1+1/ck})$ time, which is tight up to the constant $c$. The lower
bound holds even for the offline version where we are given the queries in
advance, and extends to other problems such as dynamic shortest paths.
  The $4$-Cycle problem: An infamous open question in fine-grained complexity
is to establish any surprising consequences from a subquadratic or even
linear-time algorithm for detecting a $4$-cycle in a graph. We prove that
$\Omega(m^{1.1194})$ time is needed for $k$-cycle detection for all $k\geq 4$,
unless we can detect a triangle in $\sqrt{n}$-degree graphs in
$O(n^{2-\delta})$ time; a breakthrough that is not known to follow even from
optimal matrix multiplication algorithms.


title:
AgilePkgC: An Agile System Idle State Architecture for Energy
  Proportional Datacenter Servers

abstract:
  This paper presents the design of AgilePkgC (APC): a new C-state architecture
that improves the energy proportionality of servers that operate at low
utilization while running microservices of user-facing applications. APC
targets the reduction of power when all cores are idle in a shallow C-state,
ready to transition back to service. In particular, APC targets the power of
the resources shared by the cores (e.g., LLC, network-on-chip, IOs, DRAM) which
remain active while no core is active to use them. APC realizes its objective
by using low-overhead hardware to facilitate sub-microsecond entry/exit latency
to a new package C-state and judiciously selecting intermediate power modes for
the different shared resources that offer fast transition and, yet, substantial
power savings. Our experimental evaluation supports that APC holds the
potential to reduce server power by up to 41% with a worst-case performance
degradation of less than 0.1% for several representative workloads. Our results
clearly support the research and development and eventual adoption of new deep
and fast package C-states, like APC, for future server CPUs targeting
datacenters running microservices.


title:
Neural Contrastive Clustering: Fully Unsupervised Bias Reduction for
  Sentiment Classification

abstract:
  Background: Neural networks produce biased classification results due to
correlation bias (they learn correlations between their inputs and outputs to
classify samples, even when those correlations do not represent
cause-and-effect relationships).
  Objective: This study introduces a fully unsupervised method of mitigating
correlation bias, demonstrated with sentiment classification on COVID-19 social
media data.
  Methods: Correlation bias in sentiment classification often arises in
conversations about controversial topics. Therefore, this study uses
adversarial learning to contrast clusters based on sentiment classification
labels, with clusters produced by unsupervised topic modeling. This discourages
the neural network from learning topic-related features that produce biased
classification results.
  Results: Compared to a baseline classifier, neural contrastive clustering
approximately doubles accuracy on bias-prone sentences for human-labeled
COVID-19 social media data, without adversely affecting the classifier's
overall F1 score. Despite being a fully unsupervised approach, neural
contrastive clustering achieves a larger improvement in accuracy on bias-prone
sentences than a supervised masking approach.
  Conclusions: Neural contrastive clustering reduces correlation bias in
sentiment text classification. Further research is needed to explore
generalizing this technique to other neural network architectures and
application domains.


title: Pythagoras number of quartic orders containing $\sqrt{2}$

abstract:
  Let $K$ be a quartic number field containing $\sqrt{2}$ and let
$\mathcal{O}\subseteq K$ be an order such that $\sqrt{2}\in \mathcal{O}$. We
prove that the Pythagoras number of $\mathcal{O}$ is at most 5. This confirms a
conjecture of Kr\'{a}sensk\'{y}, Ra\v{s}ka and Sgallov\'a. The proof makes use
of Beli's theory of bases of norm generators for quadratic lattices over dyadic
local fields.


title:
Origami-controlled strain engineering of tunable flat bands and
  correlated states in folded graphene

abstract:
  Flat electronic bands with tunable structures offer opportunities for the
exploitation and manipulation of exotic interacting quantum states. Here, we
present a controllable route to construct easily tunable flat bands in folded
graphene, by nano origami-controlled strain engineering, and discover
correlated states in this system. Via tearing and folding graphene monolayer at
arbitrary step edges with scanning tunneling microscope manipulation, we create
strain-induced pseudo-magnetic fields as well as resulting flat electronic
bands in the curved edges of folded graphene. We show that the intensity of the
pseudo-magnetic field can be readily tuned by changing the width of the folding
edge due to the edge-width-dependent lattice deformation, leading to the well
adjustability of the geometry of flat bands in folded graphene. Furthermore, by
creating expected dispersionless flat bands using this technique, the
correlation-induced splits of flat bands are successfully observed in the
density of states when these bands are partially filled. Our experiment
provides a feasible and effective pathway to engineer the system with tunable
flat band structures, and establishes a new platform that can be used to
realize devisable strain and interaction induced quantum phases.


title:
Design and Commissioning of A Beam Distribution System for Multiple
  Undulator Line Operation of the SXFEL-UF

abstract:
  As an important measure of improving the efficiency and usability of X-ray
free electron laser facilities, simultaneous operation of multiple undulator
lines realized by a beam distribution system has become a standard
configuration in the recent built XFEL facilities. In Shanghai, SXFEL-UF, the
first soft X-ray free electron laser user facility in China, has finished
construction and started commissioning recently. Electron beam from linac is
alternately distributed between the two parallel undulator beam lines by a beam
distribution system with a 6{\deg} deflection line. The beam distribution
system is designed to keep the beam properties like low emittance, high peak
charge and small bunch length from being spoiled. Beam collective effects such
as the dispersion, coherent synchrotron radiation and micro-bunching
instability should be well suppressed to guarantee the beam quality. In this
work, the detailed physics design of the beam distribution system is described
and the recent commissioning result is reported.


title:
A general framework for the composition of quantum homomorphic
  encryption \& quantum error correction

abstract:
  Two essential primitives for universal, cloud-based quantum computation with
security based on the laws of quantum mechanics, are quantum homomorphic
encryption with information-theoretic security and quantum error correction.
The former enables information-theoretic security of outsourced quantum
computation, while the latter allows reliable and scalable quantum computations
in the presence of errors. Previously these ingredients have been considered in
isolation from one another. By establishing group-theoretic requirements that
these two ingredients must satisfy, we provide a general framework for
composing them. Namely, a quantum homomorphic encryption scheme enhanced with
quantum error correction can directly inherit its properties from its
constituent quantum homomorphic encryption and quantum error correction
schemes. We apply our framework to both discrete- and continuous-variable
models for quantum computation, such as Pauli-key and permutation-key
encryptions in the qubit model, and displacement-key encryptions in a
continuous-variable model based on Gottesman-Kitaev-Preskill codes.


title:
Sparse dynamical system identification with simultaneous structural
  parameters and initial condition estimation

abstract:
  Sparse Identification of Nonlinear Dynamics (SINDy) has been shown to
successfully recover governing equations from data; however, this approach
assumes the initial condition to be exactly known in advance and is sensitive
to noise. In this work we propose an integral SINDy (ISINDy) method to
simultaneously identify model structure and parameters of nonlinear ordinary
differential equations (ODEs) from noisy time-series observations. First, the
states are estimated via penalized spline smoothing and then substituted into
the integral-form numerical discretization solver, leading to a pseudo-linear
regression. The sequential threshold least squares is performed to extract the
fewest active terms from the overdetermined set of candidate features, thereby
estimating structural parameters and initial condition simultaneously and
meanwhile, making the identified dynamics parsimonious and interpretable.
Simulations detail the method's recovery accuracy and robustness to noise.
Examples include a logistic equation, Lokta-Volterra system, and Lorenz system.


title:
Gene Function Prediction with Gene Interaction Networks: A Context Graph
  Kernel Approach

abstract:
  Predicting gene functions is a challenge for biologists in the post genomic
era. Interactions among genes and their products compose networks that can be
used to infer gene functions. Most previous studies adopt a linkage assumption,
i.e., they assume that gene interactions indicate functional similarities
between connected genes. In this study, we propose to use a gene's context
graph, i.e., the gene interaction network associated with the focal gene, to
infer its functions. In a kernel-based machine-learning framework, we design a
context graph kernel to capture the information in context graphs. Our
experimental study on a testbed of p53-related genes demonstrates the advantage
of using indirect gene interactions and shows the empirical superiority of the
proposed approach over linkage-assumption-based methods, such as the algorithm
to minimize inconsistent connected genes and diffusion kernels.


title: A note on periods of Calabi--Yau fractional complete intersections

abstract:
  We prove that the GKZ $\mathscr{D}$-module $\mathcal{M}_{A}^{\beta}$ arising
from Calabi--Yau fractional complete intersections in toric varieties is
complete, i.e., all the solutions to $\mathcal{M}_{A}^{\beta}$ are period
integrals. This particularly implies that $\mathcal{M}_{A}^{\beta}$ is
equivalent to the Picard--Fuchs system. As an application, we give explicit
formulae of the period integrals of Calabi--Yau threefolds coming from double
covers of $\mathbf{P}^{3}$ branch over eight hyperplanes in general position.


title: Revisiting D-term inflation with gravitational waves and Hubble tension

abstract:
  We revisit D-term inflation with the bounds of the cosmic string tension from
gravitational wave observations and consider the possible deviation of the
spectral index compared with $\Lambda$CDM model in light of pre-recombination
resolutions of the Hubble tension. D-term inflation requires very small
coupling constants under these constraints. We show that large coupling
constants can be achieved in the case of D-term inflation on the brane.


title:
Global Mapping of Gene/Protein Interactions in PubMed Abstracts: A
  Framework and an Experiment with P53 Interactions

abstract:
  Gene/protein interactions provide critical information for a thorough
understanding of cellular processes. Recently, considerable interest and effort
has been focused on the construction and analysis of genome-wide gene networks.
The large body of biomedical literature is an important source of gene/protein
interaction information. Recent advances in text mining tools have made it
possible to automatically extract such documented interactions from free-text
literature. In this paper, we propose a comprehensive framework for
constructing and analyzing large-scale gene functional networks based on the
gene/protein interactions extracted from biomedical literature repositories
using text mining tools. Our proposed framework consists of analyses of the
network topology, network topology-gene function relationship, and temporal
network evolution to distill valuable information embedded in the gene
functional interactions in literature. We demonstrate the application of the
proposed framework using a testbed of P53-related PubMed abstracts, which shows
that literature-based P53 networks exhibit small-world and scale-free
properties. We also found that high degree genes in the literature-based
networks have a high probability of appearing in the manually curated database
and genes in the same pathway tend to form local clusters in our
literature-based networks. Temporal analysis showed that genes interacting with
many other genes tend to be involved in a large number of newly discovered
interactions.


title: Learning Dynamic View Synthesis With Few RGBD Cameras

abstract:
  There have been significant advancements in dynamic novel view synthesis in
recent years. However, current deep learning models often require (1) prior
models (e.g., SMPL human models), (2) heavy pre-processing, or (3) per-scene
optimization. We propose to utilize RGBD cameras to remove these limitations
and synthesize free-viewpoint videos of dynamic indoor scenes. We generate
feature point clouds from RGBD frames and then render them into free-viewpoint
videos via a neural renderer. However, the inaccurate, unstable, and incomplete
depth measurements induce severe distortions, flickering, and ghosting
artifacts. We enforce spatial-temporal consistency via the proposed Cycle
Reconstruction Consistency and Temporal Stabilization module to reduce these
artifacts. We introduce a simple Regional Depth-Inpainting module that
adaptively inpaints missing depth values to render complete novel views.
Additionally, we present a Human-Things Interactions dataset to validate our
approach and facilitate future research. The dataset consists of 43 multi-view
RGBD video sequences of everyday activities, capturing complex interactions
between human subjects and their surroundings. Experiments on the HTI dataset
show that our method outperforms the baseline per-frame image fidelity and
spatial-temporal consistency. We will release our code, and the dataset on the
website soon.


title:
On the Robustness of Second-Price Auctions in Prior-Independent
  Mechanism Design

abstract:
  Classical Bayesian mechanism design relies on the common prior assumption,
but the common prior is often not available in practice. We study the design of
prior-independent mechanisms that relax this assumption: the seller is selling
an indivisible item to $n$ buyers such that the buyers' valuations are drawn
from a joint distribution that is unknown to both the buyers and the seller;
buyers do not need to form beliefs about competitors, and the seller assumes
the distribution is adversarially chosen from a specified class. We measure
performance through the worst-case regret, or the difference between the
expected revenue achievable with perfect knowledge of buyers' valuations and
the actual mechanism revenue.
  We study a broad set of classes of valuation distributions that capture a
wide spectrum of possible dependencies: independent and identically distributed
(i.i.d.) distributions, mixtures of i.i.d. distributions, affiliated and
exchangeable distributions, exchangeable distributions, and all joint
distributions. We derive in quasi closed form the minimax values and the
associated optimal mechanism. In particular, we show that the first three
classes admit the same minimax regret value, which is decreasing with the
number of competitors, while the last two have the same minimax regret equal to
that of the case $n = 1$. Furthermore, we show that the minimax optimal
mechanisms have a simple form across all settings: a second-price auction with
random reserve prices, which shows its robustness in prior-independent
mechanism design. En route to our results, we also develop a principled
methodology to determine the form of the optimal mechanism and worst-case
distribution via first-order conditions that should be of independent interest
in other minimax problems.


title: Analysis of Temporal Difference Learning: Linear System Approach

abstract:
  The goal of this technical note is to introduce a new finite-time convergence
analysis of temporal difference (TD) learning based on stochastic linear system
models. TD-learning is a fundamental reinforcement learning (RL) to evaluate a
given policy by estimating the corresponding value function for a Markov
decision process. While there has been a series of successful works in
theoretical analysis of TDlearning, it was not until recently that researchers
found some guarantees on its statistical efficiency by developing finite-time
error bounds. In this paper, we propose a simple control theoretic finite-time
analysis of TD-learning, which exploits linear system models and standard
notions in linear system communities. The proposed work provides new simple
templets for RL analysis, and additional insights on TD-learning and RL based
on ideas in control theory.


title: Restricting Representations from a Complex Group to a Real Form

abstract:
  Let $G$ be a complex connected reductive algebraic group and let
$G_{\mathbb{R}}$ be a real form of $G$. We construct a sequence of functors
$L_i\mathcal{R}$ from admissible (resp. finite-length) representations of $G$
to admissible (resp. finite-length) representations of $G_{\mathbb{R}}$. We
establish many basic properties of these functors, including their behavior
with respect to infinitesimal character, associated variety, and restriction to
a maximal compact subgroup. We deduce that each $L_i\mathcal{R}$ takes
unipotent representations of $G$ to unipotent representations of
$G_{\mathbb{R}}$. Taking the alternating sum of $L_i\mathcal{R}$, we get a
well-defined homomorphism on the level of characters. We compute this
homomorphism in the case when $G_{\mathbb{R}}$ is split.


title:
Nuclear spin self compensation system for moving MEG sensing with
  optical pumped atomic spin co-magnetometer

abstract:
  Recording the moving MEGs of a person in which a person's head could move
freely as we record the brain's magnetic field is a hot topic in recent years.
Traditionally, atomic magnetometers are utilized for moving MEGs recording and
a large compensation coil system is utilized for background magnetic field
compensation. Here we described a new potential candidate: an optically pumped
atomic co-magnetometer(OPACM) for moving MEGs recording. In the OPACM,
hyper-polarized nuclear spins could produce a magnetic field which will shield
the background fluctuation low frequency magnetic field noise while the the
fast changing MEGs signal could be recorded. The nuclear spins look like an
automatic magnetic field shields and dynamically compensate the fluctuated
background magnetic field noise. In this article, the magnetic field
compensation is studied theoretically and we find that the compensation is
closely related to several parameters such as the electron spin magnetic field,
the nuclear spin magnetic field and the holding magnetic field. Based on the
model, the magnetic field compensation could be optimized. We also
experimentally studied the magnetic field compensation and the responses of the
OPACM to different frequencies of magnetic field are measured. We show that the
OPACM owns a clear suppression of low frequency magnetic field under 1Hz and
response to magnetic field's frequencies around the band of the MEGs. Magnetic
field sensitivity of $3fT/Hz^{1/2}$ has been achieved. Finally, we do a
simulation for the OPACM as it is utilized for moving MEGs recording. For
comparison, the traditional compensation system for moving MEGs recording is
based on a coil which is around 2m in dimension while our compensation system
is only 2mm in dimension. Moreover, our compensation system could work in situ
and will not affect each other.


title: Recurrent Affine Transformation for Text-to-image Synthesis

abstract:
  Text-to-image synthesis aims to generate natural images conditioned on text
descriptions. The main difficulty of this task lies in effectively fusing text
information into the image synthesis process. Existing methods usually
adaptively fuse suitable text information into the synthesis process with
multiple isolated fusion blocks (e.g., Conditional
  Batch Normalization and Instance Normalization). However, isolated fusion
blocks not only conflict with each other but also increase the difficulty of
training (see first page of the supplementary). To address these issues, we
propose a Recurrent Affine Transformation (RAT) for Generative Adversarial
Networks that connects all the fusion blocks with a recurrent neural network to
model their long-term dependency. Besides, to improve semantic consistency
between texts and synthesized images, we incorporate a spatial attention model
in the discriminator. Being aware of matching image regions, text descriptions
supervise the generator to synthesize more relevant image contents. Extensive
experiments on the CUB, Oxford-102 and COCO datasets demonstrate the
superiority of the proposed model in comparison to state-of-the-art models
\footnote{https://github.com/senmaoy/Recurrent-Affine-Transformation-for-Text-to-image-Synthesis.git}


title: NLP Based Anomaly Detection for Categorical Time Series

abstract:
  Identifying anomalies in large multi-dimensional time series is a crucial and
difficult task across multiple domains. Few methods exist in the literature
that address this task when some of the variables are categorical in nature. We
formalize an analogy between categorical time series and classical Natural
Language Processing and demonstrate the strength of this analogy for anomaly
detection and root cause investigation by implementing and testing three
different machine learning anomaly detection and root cause investigation
models based upon it.


title:
SE-GAN: Skeleton Enhanced GAN-based Model for Brush Handwriting Font
  Generation

abstract:
  Previous works on font generation mainly focus on the standard print fonts
where character's shape is stable and strokes are clearly separated. There is
rare research on brush handwriting font generation, which involves holistic
structure changes and complex strokes transfer. To address this issue, we
propose a novel GAN-based image translation model by integrating the skeleton
information. We first extract the skeleton from training images, then design an
image encoder and a skeleton encoder to extract corresponding features. A
self-attentive refined attention module is devised to guide the model to learn
distinctive features between different domains. A skeleton discriminator is
involved to first synthesize the skeleton image from the generated image with a
pre-trained generator, then to judge its realness to the target one. We also
contribute a large-scale brush handwriting font image dataset with six styles
and 15,000 high-resolution images. Both quantitative and qualitative
experimental results demonstrate the competitiveness of our proposed model.


title:
Attentions Help CNNs See Better: Attention-based Hybrid Image Quality
  Assessment Network

abstract:
  Image quality assessment (IQA) algorithm aims to quantify the human
perception of image quality. Unfortunately, there is a performance drop when
assessing the distortion images generated by generative adversarial network
(GAN) with seemingly realistic texture. In this work, we conjecture that this
maladaptation lies in the backbone of IQA models, where patch-level prediction
methods use independent image patches as input to calculate their scores
separately, but lack spatial relationship modeling among image patches.
Therefore, we propose an Attention-based Hybrid Image Quality Assessment
Network (AHIQ) to deal with the challenge and get better performance on the
GAN-based IQA task. Firstly, we adopt a two-branch architecture, including a
vision transformer (ViT) branch and a convolutional neural network (CNN) branch
for feature extraction. The hybrid architecture combines interaction
information among image patches captured by ViT and local texture details from
CNN. To make the features from shallow CNN more focused on the visually salient
region, a deformable convolution is applied with the help of semantic
information from the ViT branch. Finally, we use a patch-wise score prediction
module to obtain the final score. The experiments show that our model
outperforms the state-of-the-art methods on four standard IQA datasets and AHIQ
ranked first on the Full Reference (FR) track of the NTIRE 2022 Perceptual
Image Quality Assessment Challenge.


title:
Inverse characterization of composites using guided waves and
  convolutional neural networks with dual-branch feature fusion

abstract:
  In this work, ultrasonic guided waves and a dual-branch version of
convolutional neural networks are used to solve two different but related
inverse problems, i.e., finding layup sequence type and identifying material
properties. In the forward problem, polar group velocity representations are
obtained for two fundamental Lamb wave modes using the stiffness matrix method.
For the inverse problems, a supervised classification-based network is
implemented to classify the polar representations into different layup sequence
types (inverse problem - 1) and a regression-based network is utilized to
identify the material properties (inverse problem - 2)


title: Distributed Grover's algorithm

abstract:
  Let Boolean function $f:\{0,1\}^n\longrightarrow \{0,1\}$ where
$|\{x\in\{0,1\}^n| f(x)=1\}|=a\geq 1$. To search for an $x\in\{0,1\}^n$ with
$f(x)=1$, by Grover's algorithm we can get the objective with query times
$\lfloor \frac{\pi}{4}\sqrt{\frac{2^n}{a}} \rfloor$. In this paper, we propose
a distributed Grover's algorithm for computing $f$ with lower query times and
smaller number of input bits. More exactly, for any $k$ with $n>k\geq 1$, we
can decompose $f$ into $2^k$ subfunctions, each which has $n-k$ input bits, and
then the objective can be found out by computing these subfunctions with query
times at most $\sum_{i=1}^{r_i} \lfloor \frac{\pi}{4}\sqrt{\frac{2^{n-k}}{b_i}}
\rfloor+\lceil\sqrt{2^{n-k}}\rceil+2t_a+1$ for some $1\leq b_i\leq a$ and
$r_i\leq 2t_a+1$, where $t_a=\lceil 2\pi\sqrt{a}+11\rceil$. In particular, if
$a=1$, then our distributed Grover's algorithm only needs $\lfloor
\frac{\pi}{4}\sqrt{2^{n-k}} \rfloor$ queries, versus $\lfloor
\frac{\pi}{4}\sqrt{2^{n}} \rfloor$ of Grover's algorithm. When $n$ belongs to
middle scale but still is a bit difficult to be processed in practice, $n-k$ is
likely feasible for appropriate $k$ in physical realizability.


title: The Equivariance Criterion in a Linear Model for Fixed-X Cases

abstract:
  In this article, we explored the usage of the equivariance criterion in
linear model with fixed-X for the estimation and extended the model to allow
multiple populations, which, in turn, leads to a larger transformation group.
The minimum risk equivariant estimators of the coefficient vector and the
covariance matrix were derived via the maximal invariants, which was consistent
with earlier works. This article serves as an early exploration of the
equivariance criterion in linear model.


title: Generalized weighted composition operators on weighted Hardy spaces

abstract:
  In this paper, we investigate the complex symmetric structure of generalized
weighted composition operators $D_{m,\psi,\varphi}$ on the weighted Hardy space
$H^2(\beta)$. We obtain explicit conditions for $ D_{m,\psi,\varphi}$ to be
complex symmetric with the conjugation $J_w$. Under the assumption that $
D_{m,\psi,\varphi}$ is $J_w$-symmetric, some sufficient and necessary
conditions for $D_{m,\psi,\varphi}$ to be Hermitian and normal are given.


title:
Piercing families of convex sets in the plane that avoid a certain
  subfamily with lines

abstract:
  We define a $C(k)$ to be a family of $k$ sets $F_1,\dots,F_k$ such that
$\textrm{conv}(F_i\cup F_{i+1})\cap \textrm{conv}(F_j\cup F_{j+1})=\emptyset$
when $\{i,i+1\}\cap \{j,j+1\}=\emptyset$ (indices are taken modulo $k$). We
show that if $\mathcal{F}$ is a family of compact, convex sets that does not
contain a $C(k)$, then there are $k-2$ lines that pierce $\mathcal{F}$.
Additionally, we give an example of a family of compact, convex sets that
contains no $C(k)$ and cannot be pierced by $\left\lceil \frac{k}{2}
\right\rceil -1$ lines.


title: Some Optimization Solutions for Relief Distribution

abstract:
  Humanitarian logistics remain a challenging area of application for
operations research. In relief distribution, the main goal is to deliver all
the supplies to those that are in need in the fastest way possible. In this
paper, we present different optimization solutions for relief distribution. We
present a formalization of the three main problems in the humanitarian
logistics aspect of relief distribution. We identify the optimal location of
the distribution centers. We match the number of supplies to the number of
demands for each distribution center based on the distribution of demands. We
provide the assignment of tasks to delivery fleet according to the location and
the road network of the region. For each delivery truck, we provide an optimal
sequence of visits to pre-assigned distribution centers.


title: Gravity aided navigation using Viterbi map matching algorithm

abstract:
  In GNSS-denied environments, aiding a vehicle's inertial navigation system
(INS) is crucial to reducing the accumulated navigation drift caused by sensor
errors (e.g. bias and noise). One potential solution is to use measurements of
gravity as an aiding source. The measurements are matched to a geo-referenced
map of Earth's gravity in order to estimate the vehicle's position. In this
paper, we propose a novel formulation of the map matching problem using a
hidden Markov model (HMM). Specifically, we treat the spatial cells of the map
as the hidden states of the HMM and present a Viterbi style algorithm to
estimate the most likely sequence of states, i.e. most likely sequence of
vehicle positions, that results in the sequence of observed gravity
measurements. Using a realistic gravity map, we demonstrate the accuracy of our
Viterbi map matching algorithm in a navigation scenario and illustrate its
robustness compared to existing methods.


title: MITL Verification Under Timing Uncertainty

abstract:
  A Metric Interval Temporal Logic (MITL) verification algorithm is presented.
It verifies continuous-time signals without relying on high frequency sampling.
Instead, it is assumed that collections of over- and under-approximating
intervals are available for the times at which the individual atomic
propositions hold true. These are combined inductively to generate
corresponding over- and under-approximations for the specified MITL formula.
The gap between the over- and under-approximations reflects timing uncertainty
with respect to the signal being verified, thereby providing a quantitative
measure of the conservativeness of the algorithm. Numerical examples are
provided to illustrate.


title: Lengths of Cycles in Generalized Pancake Graphs

abstract:
  In this paper, we consider the lengths of cycles that can be embedded on the
edges of the \emph{generalized pancake graph} which is the Cayley graph of the
generalized symmetric group, the wreath product of the cyclic group $C_m$ and
the symmetric group, generated by prefix reversals. In the cases when the
cyclic group has one or two elements the graphs are the \emph{pancake graphs}
and \emph{burnt pancake graphs}, respectively. We prove that when the cyclic
group has three elements the underlying, undirected graph of the generalized
pancake graph is pancyclic, thus resembling a similar property of the pancake
graphs and the burnt pancake graphs. Moreover, when the cyclic group has four
elements, the resulting undirected graphs will have all the even-length cycles.
We utilize these results as base cases and show that if $m>2$ is even, the
corresponding undirected pancake graph has all cycles of even length starting
from its girth to a Hamiltonian cycle. Moreover, when $m$ is odd, the
corresponding undirected pancake graph has cycles of all lengths starting from
its girth to a Hamiltonian cycle. We furthermore show that the girth of the
undirected generalized pancake graphs is $\min\{m,8\}$ if $m\geq3$, thus
complementing the known results for $m=1,2.$


title: Adversarial Estimators

abstract:
  We develop an asymptotic theory of adversarial estimators (`A-estimators').
Like maximum-likelihood-type estimators (`M-estimators'), both the estimator
and estimand are defined as the critical points of a sample and population
average respectively. A-estimators generalize M-estimators, as their objective
is maximized by one set of parameters and minimized by another. The
continuous-updating Generalized Method of Moments estimator, popular in
econometrics and causal inference, is among the earliest members of this class
which distinctly falls outside the M-estimation framework. Since the recent
success of Generative Adversarial Networks, A-estimators received considerable
attention in both machine learning and causal inference contexts, where a
flexible adversary can remove the need for researchers to manually specify
which features of a problem are important. We present general results
characterizing the convergence rates of A-estimators under both point-wise and
partial identification, and derive the asymptotic root-n normality for plug-in
estimates of smooth functionals of their parameters. All unknown parameters may
contain functions which are approximated via sieves. While the results apply
generally, we provide easily verifiable, low-level conditions for the case
where the sieves correspond to (deep) neural networks. Our theory also yields
the asymptotic normality of general functionals of neural network M-estimators
(as a special case), overcoming technical issues previously identified by the
literature. We examine a variety of A-estimators proposed across econometrics
and machine learning and use our theory to derive novel statistical results for
each of them. Embedding distinct A-estimators into the same framework, we
notice interesting connections among them, providing intuition and formal
justification for their recent success in practical applications.


title:
Multimodal Adaptive Distillation for Leveraging Unimodal Encoders for
  Vision-Language Tasks

abstract:
  Cross-modal encoders for vision-language (VL) tasks are often pretrained with
carefully curated vision-language datasets. While these datasets reach an order
of 10 million samples, the labor cost is prohibitive to scale further.
Conversely, unimodal encoders are pretrained with simpler annotations that are
less cost-prohibitive, achieving scales of hundreds of millions to billions. As
a result, unimodal encoders have achieved state-of-art (SOTA) on many
downstream tasks. However, challenges remain when applying to VL tasks. The
pretraining data is not optimal for cross-modal architectures and requires
heavy computational resources. In addition, unimodal architectures lack
cross-modal interactions that have demonstrated significant benefits for VL
tasks. Therefore, how to best leverage pretrained unimodal encoders for VL
tasks is still an area of active research. In this work, we propose a method to
leverage unimodal vision and text encoders for VL tasks that augment existing
VL approaches while conserving computational complexity. Specifically, we
propose Multimodal Adaptive Distillation (MAD), which adaptively distills
useful knowledge from pretrained encoders to cross-modal VL encoders. Second,
to better capture nuanced impacts on VL task performance, we introduce an
evaluation protocol that includes Visual Commonsense Reasoning (VCR), Visual
Entailment (SNLI-VE), and Visual Question Answering (VQA), across a variety of
data constraints and conditions of domain shift. Experiments demonstrate that
MAD leads to consistent gains in the low-shot, domain-shifted, and
fully-supervised conditions on VCR, SNLI-VE, and VQA, achieving SOTA
performance on VCR compared to other single models pretrained with image-text
data. Finally, MAD outperforms concurrent works utilizing pretrained vision
encoder from CLIP. Code will be made available.


title:
Transferring ConvNet Features from Passive to Active Robot
  Self-Localization: The Use of Ego-Centric and World-Centric Views

abstract:
  The training of a next-best-view (NBV) planner for visual place recognition
(VPR) is a fundamentally important task in autonomous robot navigation, for
which a typical approach is the use of visual experiences that are collected in
the target domain as training data. However, the collection of a wide variety
of visual experiences in everyday navigation is costly and prohibitive for
real-time robotic applications. We address this issue by employing a novel {\it
domain-invariant} NBV planner. A standard VPR subsystem based on a
convolutional neural network (CNN) is assumed to be available, and its
domain-invariant state recognition ability is proposed to be transferred to
train the domain-invariant NBV planner. Specifically, we divide the visual cues
that are available from the CNN model into two types: the output layer cue
(OLC) and intermediate layer cue (ILC). The OLC is available at the output
layer of the CNN model and aims to estimate the state of the robot (e.g., the
robot viewpoint) with respect to the world-centric view coordinate system. The
ILC is available within the middle layers of the CNN model as a high-level
description of the visual content (e.g., a saliency image) with respect to the
ego-centric view. In our framework, the ILC and OLC are mapped to a state
vector and subsequently used to train a multiview NBV planner via deep
reinforcement learning. Experiments using the public NCLT dataset validate the
effectiveness of the proposed method.


title: Detecting quantumness in uniform precessions

abstract:
  The fact that harmonic oscillators exhibit proper quantum features is
literally textbook knowledge. It is also believed that such features are not to
be found in the dynamics, because both in classical and in quantum mechanics,
the evolution in time is a simple precession. Contrary to this belief, we
present a family of protocols that detect the non-classicality of suitable
states of a single quantum system, under the sole assumption that its time
evolution is a uniform precession. The case of the harmonic oscillator, where
the precession happens in phase space and couples position and momentum, was
anticipated in an unnoticed work by Tsirelson, which we extend. We then
introduce a similar criterion for a finite-dimensional spin that undergoes
uniform precession in real space. A gap between the classical and the quantum
expectations is present for every $j\geq \frac{3}{2}$ (excluding $j=2$).


title: Statistical $p$-convergence in lattice-normed Riesz spaces

abstract:
  A sequence $(x_n)$ in a lattice-normed space $(X,p,E)$ is statistical
$p$-convergent to $x\in X$ if there exists a statistical $p$-decreasing
sequence $q\stpd 0$ with an index set $K$ such that $\delta(K)=1$ and
$p(x_{n_k}-x)\leq q_{n_k}$ for every $n_k\in K$. This convergence has been
investigated recently for $(X,p,E)=(E,|\cdot|,E)$ under the name of statistical
order convergence and under the name of statistical multiplicative order
convergence, and also, for taking $E$ as a locally solid Riesz space under the
names statistically unbounded $\tau$-convergence and statistically
multiplicative convergence. In this paper, we study the general properties of
statistical $p$-convergence.


title:
Josephson junctions of Weyl semimetal $\text{WTe}_2$ induced by
  spontaneous nucleation of $\text{PdTe}$ superconductor

abstract:
  We report on the fabrication of Josephson junction devices with weak links
utilizing the Weyl and higher-order topological semimetal $\text{WTe}_2$. We
show that $\text{WTe}_2\text{/Pd}$ contact annealed at a low temperature of
80{\deg}C did not exhibit superconducting properties because neither
$\text{WTe}_2$ nor Pd are superconductors in the ground state. Upon 180{\deg}C
annealing, spontaneous formation of superconducting $\text{PdTe}$ due to Pd
diffusion enabled us to obtain the interface between $\text{WTe}_2$ and
superconductor suitable for the Josephson junction. This result is a facile
technique to make a Josephson junction and induce Cooper pairs into topological
telluride semimetals.


title:
Classical harmonic three-body system: An experimental electronic
  realization

abstract:
  The classical three-body harmonic system in $\mathbb{R}^d$ ($d>1$) with
finite rest lengths and zero total angular momentum $L=0$ is considered. This
model describes the dynamics of the $L=0$ near-equilibrium configurations of
three point masses $(m_1,m_2,m_3)$ with arbitrary pairwise potential
$V(r_{ij})$ that solely depends on the relative distances between bodies. It
exhibits an interesting mixed regular and chaotic dynamics as a function of the
energy and the system parameters. The corresponding harmonic quantum system
plays a fundamental role in atomic and molecular physics. In this work we
report on a novel electronic experimental realization of the model as a
complementary tool to analyze the rich dynamics of the classical system. Our
setup allows us to experimentally explore different regions of behavior due to
the fact that the system parameters and initial conditions are independently
controlled via voltage signals. Chaotic and periodic motions are characterized
employing time series, phase planes, and the largest Lyapunov exponents as a
function of the energy and system parameters. The results show an excellent
qualitative as well as quantitative agreement between theory and experiment.


title: LiDetector: License Incompatibility Detection for Open Source Software

abstract:
  Open-source software (OSS) licenses dictate the conditions which should be
followed to reuse, distribute, and modify the software. Apart from widely-used
licenses such as the MIT License, developers are also allowed to customize
their own licenses (called custom licenses), whose descriptions are more
flexible. The presence of such various licenses imposes challenges to
understanding licenses and their compatibility. To avoid financial and legal
risks, it is essential to ensure license compatibility when integrating
third-party packages or reusing code accompanied with licenses. In this work,
we propose LiDetector, an effective tool that extracts and interprets OSS
licenses (including both official licenses and custom licenses), and detects
license incompatibility among these licenses. Specifically, LiDetector
introduces a learning-based method to automatically identify meaningful license
terms from an arbitrary license and employs Probabilistic Context-Free Grammar
(PCFG) to infer rights and obligations for incompatibility detection.
Experiments demonstrate that LiDetector outperforms existing methods with
93.28% precision for term identification, and 91.09% accuracy for right and
obligation inference, and can effectively detect incompatibility with a 10.06%
FP rate and 2.56% FN rate. Furthermore, with LiDetector, our large-scale
empirical study on 1,846 projects reveals that 72.91% of the projects are
suffering from license incompatibility, including popular ones such as the MIT
License and the Apache License. We highlighted lessons learned from the
perspectives of different stakeholders and made all related data and the
replication package publicly available to facilitate follow-up research.


title: Centrally Essential Semirings

abstract:
  A semiring is said to be centrally essential if for every non-zero element
$x$, there exist two non-zero central elements $y, z$ with $xy = z$. We give
some examples of non-commutative centrally essential semirings and describe
some properties of additively cancellative centrally essential semirings.


title:
Progression, Detection and Remission: Evolution of Chronic Myeloid
  Leukemia using a three-stage probabilistic model

abstract:
  We present a three-stage probabilistic model for the progression of Chronic
Myeloid Leukemia (CML), as manifested by the leukemic stem cells, progenitor
cells and mature leukemic cells. This progression is captured through the
process of cell division and cell mutation, with probabilities of occurrence
being assigned to both of them. The key contributions of this study include,
the determination of the expected number of the leukemic stem cells, progenitor
cells, mature leukemic cells, as well as total number of these cells (in terms
of probabilities, and contingent on the initial cell count), expected time to
reach a threshold level of total and injurious leukemic cells, as well as the
critical time when the disease changes its phases, the probability of
extinction of CML, and the dynamics of CML evolution consequent to primary
therapy. Finally, the results obtained are demonstrated with numerical
illustrations.


title:
Application of Federated Learning in Building a Robust COVID-19 Chest
  X-ray Classification Model

abstract:
  While developing artificial intelligence (AI)-based algorithms to solve
problems, the amount of data plays a pivotal role - large amount of data helps
the researchers and engineers to develop robust AI algorithms. In the case of
building AI-based models for problems related to medical imaging, these data
need to be transferred from the medical institutions where they were acquired
to the organizations developing the algorithms. This movement of data involves
time-consuming formalities like complying with HIPAA, GDPR, etc.There is also a
risk of patients' private data getting leaked, compromising their
confidentiality. One solution to these problems is using the Federated Learning
framework.
  Federated Learning (FL) helps AI models to generalize better and create a
robust AI model by using data from different sources having different
distributions and data characteristics without moving all the data to a central
server. In our paper, we apply the FL framework for training a deep learning
model to solve a binary classification problem of predicting the presence or
absence of COVID-19. We took three different sources of data and trained
individual models on each source. Then we trained an FL model on the complete
data and compared all the model performances. We demonstrated that the FL model
performs better than the individual models. Moreover, the FL model performed at
par with the model trained on all the data combined at a central server. Thus
Federated Learning leads to generalized AI models without the cost of data
transfer and regulatory overhead.


title: Aldaz-Kounchev-Render operators and their approximation properties

abstract:
  The approximation properties of the Aldaz-Kounchev-Render (AKR) operators are
discussed and classes of functions for which these operators approximate better
than the classical Bernstein operators are described. The new results are then
extended to the bivariate case on the square $[0,1]^2$ and compared with other
existing results known in literature. Several numerical examples, illustrating
the relevance and supporting the theoretical findings, are presented


title: Ideals and Factor Rings of Centrally Essential Rings

abstract:
  It is proved that the ring $R$ with center $Z(R)$, such that the module
$R_{Z(R)}$ is an essential extension of the module $Z(R)_{Z(R)}$, is not
necessarily right quasi-invariant, i.e., maximal right ideals of the ring $R$
are not necessarily ideals. We use central essentiality to obtain conditions
which are sufficient to the property that all maximal right ideals are ideals


title:
Imputation with verifiable identification condition for nonignorable
  missing outcomes

abstract:
  Missing data often results in undesirable bias and loss of efficiency. These
results become substantial problems when the response mechanism is
nonignorable, such that the response model depends on the unobserved variable.
It is often necessary to estimate the joint distribution of the unobserved
variables and response indicators to further manage nonignorable nonresponse.
However, model misspecification and identification issues prevent robust
estimates, despite carefully estimating the target joint distribution. In this
study we model the distribution of the observed parts and derived sufficient
conditions for model identifiability, assuming a logistic distribution of the
response mechanism and a generalized linear model as the main outcome model of
interest. More importantly, the derived sufficient conditions are testable with
the observed data and do not require any instrumental variables, which have
often been assumed to guarantee model identifiability but cannot be practically
determined beforehand. To analyse missing data, we propose a new fractional
imputation method which incorporates verifiable identifiability using only the
observed data. Furthermore, we present the performance of the proposed
estimators in numerical studies and apply the proposed method to two sets of
real data, namely, Opinion Poll for the 2022 South Korean Presidential
Election, and public data collected from the US National Supported Work
Evaluation Study.


title: Towards Multi-Turn Empathetic Dialogs with Positive Emotion Elicitation

abstract:
  Emotional support is a crucial skill for many real-world scenarios, including
caring for the elderly, mental health support, and customer service chats. This
paper presents a novel task of empathetic dialog generation with positive
emotion elicitation to promote users' positive emotions, similar to that of
emotional support between humans. In this task, the agent conducts empathetic
responses along with the target of eliciting the user's positive emotions in
the multi-turn dialog. To facilitate the study of this task, we collect a
large-scale emotional dialog dataset with positive emotion elicitation, called
PosEmoDial (about 820k dialogs, 3M utterances). In these dialogs, the agent
tries to guide the user from any possible initial emotional state, e.g.,
sadness, to a positive emotional state. Then we present a
positive-emotion-guided dialog generation model with a novel loss function
design. This loss function encourages the dialog model to not only elicit
positive emotions from users but also ensure smooth emotional transitions along
with the whole dialog. Finally, we establish benchmark results on PosEmoDial,
and we will release this dataset and related source code to facilitate future
studies.


title: Exponential Diophantine approximation and symbolic dynamics

abstract:
  We extend the key formula which intertwines multiplicative Markoff-Lagrange
spectrum and symbolic dynamics. The proof uses complex analysis and elucidates
the strategy of the problem. Moreover, the new method applies to a wide variety
of polynomials possibly having multiple roots. We derive several consequences
of this formula, which are expected on the Markoff-Lagrange spectrum.


title: Keypoint based Sign Language Translation without Glosses

abstract:
  Sign Language Translation (SLT) is a task that has not been studied
relatively much compared to the study of Sign Language Recognition (SLR).
However, the SLR is a study that recognizes the unique grammar of sign
language, which is different from the spoken language and has a problem that
non-disabled people cannot easily interpret. So, we're going to solve the
problem of translating directly spoken language in sign language video. To this
end, we propose a new keypoint normalization method for performing translation
based on the skeleton point of the signer and robustly normalizing these points
in sign language translation. It contributed to performance improvement by a
customized normalization method depending on the body parts. In addition, we
propose a stochastic frame selection method that enables frame augmentation and
sampling at the same time. Finally, it is translated into the spoken language
through an Attention-based translation model. Our method can be applied to
various datasets in a way that can be applied to datasets without glosses. In
addition, quantitative experimental evaluation proved the excellence of our
method.


title:
Gravitational Faraday rotation of gravitational waves by a Kerr black
  hole

abstract:
  Gravitational Faraday rotation (GFR) is a frame-dragging effect induced by
rotating massive objects, which is one of the important, but yet studied,
characters of the lensed gravitational waves (GWs). In this work, we calculate
the GFR angle $\chi_{g}$ of GWs in the weak deflection limit, assuming it is
lensed by a Kerr black hole (BH). We find that the dominant term of $\chi_{g}$
is a second-order correction to polarization angle, which depends on the
light-of-sight component of BH angular momentum. For GWs lensed by a
supermassive BH, the rotation angle $\chi_{g}$ is potentially detectable by the
third-generation detectors networks or the future detectors. Detection of GFR
provides an alternative method to measure BH spin and/or identify the lensed GW
signals.


title: MIPR:Automatic Annotation of Medical Images with Pixel Rearrangement

abstract:
  Most of the state-of-the-art semantic segmentation reported in recent years
is based on fully supervised deep learning in the medical domain. How?ever, the
high-quality annotated datasets require intense labor and domain knowledge,
consuming enormous time and cost. Previous works that adopt semi?supervised and
unsupervised learning are proposed to address the lack of anno?tated data
through assisted training with unlabeled data and achieve good perfor?mance.
Still, these methods can not directly get the image annotation as doctors do.
In this paper, inspired by self-training of semi-supervised learning, we
pro?pose a novel approach to solve the lack of annotated data from another
angle, called medical image pixel rearrangement (short in MIPR). The MIPR
combines image-editing and pseudo-label technology to obtain labeled data. As
the number of iterations increases, the edited image is similar to the original
image, and the labeled result is similar to the doctor annotation. Therefore,
the MIPR is to get labeled pairs of data directly from amounts of unlabled data
with pixel rearrange?ment, which is implemented with a designed conditional
Generative Adversarial Networks and a segmentation network. Experiments on the
ISIC18 show that the effect of the data annotated by our method for
segmentation task is is equal to or even better than that of doctors
annotations