title:
Quantized and half-quantized Anomalous Hall effect induced by in-plane
  magnetic field

abstract:
  In this paper we propose that, quantized and nearly half-quantized intrinsic
anomalous Hall effect can be induced by in-plane external magnetic field
through the Zeeman coupling in non-magnetic 2D systems with sizeable
spin-orbital coupling but without two-fold rotational symmetry. An analytical
result is derived for 2D electron gas model with $C_{3v}$ symmetry. Based on
the $\boldsymbol{k\cdot p}$ Hamiltonian derived from first principle
calculations, we find that quantized and nearly half-quantized conductance can
be observed in $\mathrm{Sb_2Te_3}$ thin film in the clean limit with strong
in-plane magnetic field $B>20\ \mathrm{T}$ and low temperature $T<100\
\mathrm{mK}$.


title: Multiqubit Toffoli gates and optimal geometry with Rydberg atoms

abstract:
  Due to its potential for implementing a scalable quantum computer, multiqubit
Toffoli gate lies in the heart of quantum information processing. In this
article, we demonstrate a multiqubit blockade gate with atoms arranged in a
three-dimension spheroidal array. The gate performance is greatly improved by
the method of optimization of control-qubit distributions via evolutionary
algorithm on the spherical surface, which leads to an enhanced asymmetric
Rydberg blockade. This spheroidal configuration, not only arises a well
preservation for the dipole blockade energy between arbitrary control-target
pairs, which keeps the asymmetric blockade error at a very low level; but also
manifests an unprecedented robustness to the spatial position variations,
leading to a negligible position error. Taking account of intrinsic errors and
with typical experimental parameters, we numerically show that a C$_6$NOT
Rydberg gate can be created with a fidelity of 0.9920 which is only limited by
Rydberg decays.


title:
TREND: TempoRal Event and Node Dynamics for Graph Representation
  Learning

abstract:
  Temporal graph representation learning has drawn significant attention for
the prevalence of temporal graphs in the real world. However, most existing
works resort to taking discrete snapshots of the temporal graph, or are not
inductive to deal with new nodes, or do not model the exciting effects which is
the ability of events to influence the occurrence of another event. In this
work, We propose TREND, a novel framework for temporal graph representation
learning, driven by TempoRal Event and Node Dynamics and built upon a Hawkes
process-based graph neural network (GNN). TREND presents a few major
advantages: (1) it is inductive due to its GNN architecture; (2) it captures
the exciting effects between events by the adoption of the Hawkes process; (3)
as our main novelty, it captures the individual and collective characteristics
of events by integrating both event and node dynamics, driving a more precise
modeling of the temporal process. Extensive experiments on four real-world
datasets demonstrate the effectiveness of our proposed model.


title:
An Extended Simplified Laplace strategy for Approximate Bayesian
  inference of Latent Gaussian Models using R-INLA

abstract:
  Various computational challenges arise when applying Bayesian inference
approaches to complex hierarchical models. Sampling-based inference methods,
such as Markov Chain Monte Carlo strategies, are renowned for providing
accurate results but with high computational costs and slow or questionable
convergence. On the contrary, approximate methods like the Integrated Nested
Laplace Approximation (INLA) construct a deterministic approximation to the
univariate posteriors through nested Laplace Approximations. This method
enables fast inference performance in Latent Gaussian Models, which encode a
large class of hierarchical models. R-INLA software mainly consists of three
strategies to compute all the required posterior approximations depending on
the accuracy requirements. The Simplified Laplace approximation (SLA) is the
most attractive because of its speed performance since it is based on a Taylor
expansion up to order three of a full Laplace Approximation. Here we enhance
the methodology by simplifying the computations necessary for the skewness and
modal configuration. Then we propose an expansion up to order four and use the
Extended Skew Normal distribution as a new parametric fit. The resulting
approximations to the marginal posterior densities are more accurate than those
calculated with the SLA, with essentially no additional cost.


title: Budget-Constrained Reinforcement of Ranked Objects

abstract:
  Commercial entries, such as hotels, are ranked according to score by a search
engine or recommendation system, and the score of each can be improved upon by
making a targeted investment, e.g., advertising. We study the problem of how a
principal, who owns or supports a set of entries, can optimally allocate a
budget to maximize their ranking. Representing the set of ranked scores as a
probability distribution over scores, we treat this question as a game between
distributions.
  We show that, in the general case, the best ranking is achieved by equalizing
the scores of several disjoint score ranges. We show that there is a unique
optimal reinforcement strategy, and provide an efficient algorithm implementing
it.


title:
Analysis of Connection Times in Bipartite Network Data: Development of
  the Latent Space Accumulator Model with Applications to Assessment Data

abstract:
  Conventional social network analysis typically focuses on analyzing the
structure of the connections between pairs of nodes in a sample dataset.
However, the process and the consequences of how long it takes pairs of nodes
to be connected, i.e., node connection times, on the network structure have
been understudied in the literature. In this article, we propose a novel
statistical approach, so-called the latent space accumulator model, for
modeling connection times and their influence on the structure of connections.
We focus on a special type of bipartite network composed of respondents and
test items, where connection outcomes are binary and mutually exclusive. To
model connection times for each connection outcome, we leverage ideas from the
competing risk modeling approach and embed latent spaces into the competing
risk models to capture heterogeneous dependence structures of connection times
across connection outcome types. The proposed approach is applied and
illustrated with two real data examples.


title:
CGUA: Context-Guided and Unpaired-Assisted Weakly Supervised Person
  Search

abstract:
  Recently, weakly supervised person search is proposed to discard
human-annotated identities and train the model with only bounding box
annotations. A natural way to solve this problem is to separate it into
detection and unsupervised re-identification (Re-ID) steps. However, in this
way, two important clues in unconstrained scene images are ignored. On the one
hand, existing unsupervised Re-ID models only leverage cropped images from
scene images but ignore its rich context information. On the other hand, there
are numerous unpaired persons in real-world scene images. Directly dealing with
them as independent identities leads to the long-tail effect, while completely
discarding them can result in serious information loss. In light of these
challenges, we introduce a Context-Guided and Unpaired-Assisted (CGUA) weakly
supervised person search framework. Specifically, we propose a novel
Context-Guided Cluster (CGC) algorithm to leverage context information in the
clustering process and an Unpaired-Assisted Memory (UAM) unit to distinguish
unpaired and paired persons by pushing them away. Extensive experiments
demonstrate that the proposed approach can surpass the state-of-the-art weakly
supervised methods by a large margin (more than 5% mAP on CUHK-SYSU). Moreover,
our method achieves comparable or better performance to the state-of-the-art
supervised methods by leveraging more diverse unlabeled data. Codes and models
will be released soon.


title: Temporal Transductive Inference for Few-Shot Video Object Segmentation

abstract:
  Few-shot video object segmentation (FS-VOS) aims at segmenting video frames
using a few labelled examples of classes not seen during initial training. In
this paper, we present a simple but effective temporal transductive inference
(TTI) approach that leverages temporal consistency in the unlabelled video
frames during few-shot inference. Key to our approach is the use of both global
and local temporal constraints. The objective of the global constraint is to
learn consistent linear classifiers for novel classes across the image
sequence, whereas the local constraint enforces the proportion of
foreground/background regions in each frame to be coherent across a local
temporal window. These constraints act as spatiotemporal regularizers during
the transductive inference to increase temporal coherence and reduce
overfitting on the few-shot support set. Empirically, our model outperforms
state-of-the-art meta-learning approaches in terms of mean intersection over
union on YouTube-VIS by 2.8%. In addition, we introduce improved benchmarks
that are exhaustively labelled (i.e. all object occurrences are labelled,
unlike the currently available), and present a more realistic evaluation
paradigm that targets data distribution shift between training and testing
sets. Our empirical results and in-depth analysis confirm the added benefits of
the proposed spatiotemporal regularizers to improve temporal coherence and
overcome certain overfitting scenarios.


title: DeepDPM: Deep Clustering With an Unknown Number of Clusters

abstract:
  Deep Learning (DL) has shown great promise in the unsupervised task of
clustering. That said, while in classical (i.e., non-deep) clustering the
benefits of the nonparametric approach are well known, most deep-clustering
methods are parametric: namely, they require a predefined and fixed number of
clusters, denoted by K. When K is unknown, however, using model-selection
criteria to choose its optimal value might become computationally expensive,
especially in DL as the training process would have to be repeated numerous
times. In this work, we bridge this gap by introducing an effective
deep-clustering method that does not require knowing the value of K as it
infers it during the learning. Using a split/merge framework, a dynamic
architecture that adapts to the changing K, and a novel loss, our proposed
method outperforms existing nonparametric methods (both classical and deep
ones). While the very few existing deep nonparametric methods lack scalability,
we demonstrate ours by being the first to report the performance of such a
method on ImageNet. We also demonstrate the importance of inferring K by
showing how methods that fix it deteriorate in performance when their assumed K
value gets further from the ground-truth one, especially on imbalanced
datasets. Our code is available at https://github.com/BGU-CS-VIL/DeepDPM.


title: Sublinear Dynamic Interval Scheduling (on one or multiple machines)

abstract:
  We revisit the complexity of the classical Interval Scheduling in the dynamic
setting. In this problem, the goal is to maintain a set of intervals under
insertions and deletions and report the size of the maximum size subset of
pairwise disjoint intervals after each update. Nontrivial approximation
algorithms are known for this problem, for both the unweighted and weighted
versions [Henzinger, Neumann, Wiese, SoCG 2020]. Surprisingly, it was not known
if the general exact version admits an exact solution working in sublinear
time, that is, without recomputing the answer after each update.
  Our first contribution is a structure for Dynamic Interval Scheduling with
amortized $\tilde{\mathcal{O}}(n^{1/3})$ update time. Then, building on the
ideas used for the case of one machine, we design a sublinear solution for any
constant number of machines: we describe a structure for Dynamic Interval
Scheduling on $m\geq 2$ machines with amortized $\tilde{\mathcal{O}}(n^{1 -
1/m})$ update time.
  We complement the above results by considering Dynamic Weighted Interval
Scheduling on one machine, that is maintaining (the weight of) the maximum
weight subset of pairwise disjoint intervals. We show an almost linear lower
bound (conditioned on the hardness of Minimum Weight $k$-Clique) for the
update/query time of any structure for this problem. Hence, in the weighted
case one should indeed seek approximate solutions.


title:
Global martingale solutions to a stochastic superquadratic
  cross-diffusion population system

abstract:
  The existence of a global martingale solution to a stochastic cross-diffusion
population system with superquadratic transition rate is shown. We have also
shown this martingale solution is nonnegative, $\mathbb{P}$-a.s. If the power
of the transition rate $s>2$, the existence result holds under a strengthened
coefficients of the diffusion matrix condition, comparing to the deterministic
population system when we apply the entropy method. Nonnegative property is
proved at first when the random factor be fixed, and an entropy with a
"variable-nonnegative" factor be applied to a sequence of deterministic
equations provides us with the nonnegativeness of our approximated solutions
obtained through the Galerkin method, $\mathbb{P}$-a.s. An application of the
It$\rm\hat{o}$ formula is suffice to provide us with strong enough uniform
estimates, under our coefficients scheme. After the tightness property be
proved based on the estimation, a space changing result be used to confirm the
limit is a weak solution to the cross-diffusion system.


title: The Early Universe was Dust-Rich and Extremely Hot

abstract:
  We investigate the dust properties and star-formation signature of galaxies
in the early universe by stacking 111,227 objects in the recently released
COSMOS catalogue on maps at wavelengths bracketing the peak of warmed dust
emission. We find an elevated far-infrared luminosity density to redshift 10,
indicating abundant dust in the early universe. We further find an increase of
dust temperature with redshift, reaching ~ 119 +- 7 K at z ~ 9, suggesting
either the presence of silicate rich dust originating from Population II stars,
or sources of heating beyond simply young hot stars. Lastly, we try to
understand how these objects have been missed in previous surveys, and how to
design observations to target them. All code, links to the data, and
instructions to reproduce this research in full is located at
https://github.com/marcoviero/simstack3/.


title:
Beyond Masking: Demystifying Token-Based Pre-Training for Vision
  Transformers

abstract:
  The past year has witnessed a rapid development of masked image modeling
(MIM). MIM is mostly built upon the vision transformers, which suggests that
self-supervised visual representations can be done by masking input image parts
while requiring the target model to recover the missing contents. MIM has
demonstrated promising results on downstream tasks, yet we are interested in
whether there exist other effective ways to `learn by recovering missing
contents'. In this paper, we investigate this topic by designing five other
learning objectives that follow the same procedure as MIM but degrade the input
image in different ways. With extensive experiments, we manage to summarize a
few design principles for token-based pre-training of vision transformers. In
particular, the best practice is obtained by keeping the original image style
and enriching spatial masking with spatial misalignment -- this design achieves
superior performance over MIM in a series of downstream recognition tasks
without extra computational cost. The code is available at
https://github.com/sunsmarterjie/beyond_masking.


title: Quiver Yangians and Crystal Melting: A Concise Summary

abstract:
  The goal of this short article is to summarize some of the recent
developments in the quiver Yangians and crystal meltings. This article is based
on a lecture delivered by the author at International Congress on Mathematical
Physics (ICMP), Geneva, 2021.


title: Adaptive Frequency Learning in Two-branch Face Forgery Detection

abstract:
  Face forgery has attracted increasing attention in recent applications of
computer vision. Existing detection techniques using the two-branch framework
benefit a lot from a frequency perspective, yet are restricted by their fixed
frequency decomposition and transform. In this paper, we propose to Adaptively
learn Frequency information in the two-branch Detection framework, dubbed AFD.
To be specific, we automatically learn decomposition in the frequency domain by
introducing heterogeneity constraints, and propose an attention-based module to
adaptively incorporate frequency features into spatial clues. Then we liberate
our network from the fixed frequency transforms, and achieve better performance
with our data- and task-dependent transform layers. Extensive experiments show
that AFD generally outperforms.


title:
MutexMatch: Semi-supervised Learning with Mutex-based Consistency
  Regularization

abstract:
  The core issue in semi-supervised learning (SSL) lies in how to effectively
leverage unlabeled data, whereas most existing methods tend to put a great
emphasis on the utilization of high-confidence samples yet seldom fully explore
the usage of low-confidence samples. In this paper, we aim to utilize
low-confidence samples in a novel way with our proposed mutex-based consistency
regularization, namely MutexMatch. Specifically, the high-confidence samples
are required to exactly predict "what it is" by conventional True-Positive
Classifier, while the low-confidence samples are employed to achieve a simpler
goal -- to predict with ease "what it is not" by True-Negative Classifier. In
this sense, we not only mitigate the pseudo-labeling errors but also make full
use of the low-confidence unlabeled data by consistency of dissimilarity
degree. MutexMatch achieves superior performance on multiple benchmark
datasets, i.e., CIFAR-10, CIFAR-100, SVHN, STL-10, and mini-ImageNet. More
importantly, our method further shows superiority when the amount of labeled
data is scarce, e.g., 92.23% accuracy with only 20 labeled data on CIFAR-10.
Code has been released at https://github.com/NJUyued/MutexMatch4SSL.


title:
Bridging separate communities with common interest in distributed social
  networks through the use of social objects

abstract:
  In light of the growing number of user privacy violations in centralized
social networks, the need to define effective platforms for decentralized
online social networks (DOSNs) is deeply felt. Interesting solutions have been
proposed in the past, which own the necessary mechanisms to allow users keeping
control over their personal information and setting the rules to regulate the
access of other users. Unfortunately, the effectiveness of this type of
solutions is severely reduced by the fact that different user communities with
a shared interest could be disconnected/separated from each other. This
translates into a reduced ability in effectively spreading data of common
interest towards all interested users, as it currently happens in centralized
social networks. In order to overcome the cited limitation, this paper proposes
a disruptive approach, which exploits the availability of a new class of
Internet of Things (IoT) devices with autonomous social behaviors and cognitive
abilities. Such devices can be leveraged as friendship intermediaries between
devices' owners who are connected to a DOSN platform and share the same
interest. We will demonstrate that clear advantages can be achieved in terms of
increased percentage of Interested Reachable Nodes (a specific measure of
Delivery Ratio) in distributed social networks among humans, when enhanced with
so called Mediator Objects adhering to the well-known social IoT (SIoT)
paradigm.


title:
Optimal Design for Estimating the Mean Ability over Time in Repeated
  Item Response Testing

abstract:
  We present general results on D-optimal designs for estimating the mean
response in repeated measures growth curve models with metric outcomes. For
this situation, we derive a novel equivalence theorem for checking design
optimality. The motivation of this work originates from designing a study in
psychological item response testing with multiple retests to measure the
improvement in ability. Besides introductory linear growth curves for which
analytical results can be obtained, we consider two non-linear growth curve
models incorporating an increasing mean ability and a saturation effect. For
these models, D-optimal designs are determined by computational methods and are
validated by means of the equivalence theorem.


title: Quantum statistical mechanics near a black hole horizon

abstract:
  We undertake a first-principles analysis of the thermodynamics of a small
body near a black hole horizon. In particular, we study the paradigmatic system
of a quantum ideal gas in a small box hovering over the Schwarzschild horizon.
We describe the gas in terms of free scalar quantum fields, both massive and
massless, and we identify thermodynamic properties through the microcanonical
distribution. The physics depends strongly on the distance of the box from the
horizon, which we treat as a {\em macroscopic} thermodynamic variable. We find
that the effective dimension of the system transitions from three-dimensional
to two-dimensional as we approach the horizon, that Bekenstein's bound fails
when the box is adiabatically lowered towards the black hole, and that the
pressure is highly anisotropic. The pressure difference between the upper and
lower wall leads to an effective force that must be added to the gravitational
acceleration. We also show that the approximation of quantum fields propagating
on a fixed background for matter breaks down when the system is brought to
microscopic distances from the horizon, in which case backreaction effects must
be included.


title: Matrix method for perturbed black hole metric with discontinuity

abstract:
  Recent studies based on the notion of black hole pseudospectrum indicated
substantial instability of the fundamental and high-overtone quasinormal modes.
Besides its theoretical novelty, the details about the migration of the
quasinormal mode spectrum due to specific perturbations may furnish valuable
information on the properties of associated gravitational waves in a more
realistic context. This work generalizes the matrix method for black hole
quasinormal modes to cope with a specific class of perturbations to the metric
featured by discontinuity, which is known to be intimately connected with the
quasinormal mode structural instability. In practice, the presence of
discontinuity poses a difficulty so that many well-known approaches for
quasinormal modes cannot be straightforwardly applied. By comparing with other
methods, we show that the modified matrix method is efficient, which can be
used to solve for the low-lying modes with reasonable precision. Therefore, it
might serve as an alternative gadget for relevant studies.


title:
Local one-dimensional reggeon model of the interaction of several
  pomerons

abstract:
  We consider the one-dimensional local reggeon theory describing the leading
pomeron with the conformal spin $l=0$ and two subdominant pomerons with $l=\pm
2$. The dependence of the propagators of pomerons and the $hA$ amplitude on
rapidity are found numerically by integrating the evolution equation.


title:
Generation and evaluation of multipartite entanglement with multi-rail
  encoding in linear optics networks

abstract:
  A linear optics network (LON) is a multimode interferometer system, where
indistinguishable photon inputs can create nonclassical interference that can
not be simulated with classical computers. Such nonclassical interference
implies the existence of entanglement among its subsystems, if we divide its
modes into different parties. Entanglement in such systems is naturally encoded
in multi-rail (multi-mode) quantum registers. For bipartite entanglement, a
generation and detection scheme with multi-rail encoding has been theoretically
proposed [NJP 19(10):103032, 2017] and experimentally realized [Optica,
7(11):1517, 2020]. In this paper, we will take a step further to establish a
theory for the generation and detection of genuine multipartite entanglement
(GME) of LONs in multi-rail encoding sources. We show that with
indistinguishable single-photon (DV) or squeezed-state (CV) inputs one can
always generate GME in LONs. The effect of photon losses is also numerically
analyzed for the generation scheme based on CV inputs.


title: Quantum free-electron laser: Exact solution

abstract:
  A quantum model of a free-electron laser is considered for the many electron
system. An exact expression for the evolution of the laser amplitude is
obtained. Reliable conditions for the superradiance of the high-gained laser is
discussed in the framework of the obtained exact solution.


title:
Blind Source Separation for Mixture of Sinusoids with Near-Linear
  Computational Complexity

abstract:
  We propose a multi-tone decomposition algorithm that can find the
frequencies, amplitudes and phases of the fundamental sinusoids in a noisy
observation sequence. Under independent identically distributed Gaussian noise,
our method utilizes a maximum likelihood approach to estimate the relevant tone
parameters from the contaminated observations. When estimating $M$ number of
sinusoidal sources, our algorithm successively estimates their frequencies and
jointly optimizes their amplitudes and phases. Our method can also be
implemented as a blind source separator in the absence of the information about
$M$. The computational complexity of our algorithm is near-linear, i.e.,
$\tilde{O}(N)$.


title:
PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch
  Recognition

abstract:
  Face anti-spoofing (FAS) plays a critical role in securing face recognition
systems from different presentation attacks. Previous works leverage auxiliary
pixel-level supervision and domain generalization approaches to address unseen
spoof types. However, the local characteristics of image captures, i.e.,
capturing devices and presenting materials, are ignored in existing works and
we argue that such information is required for networks to discriminate between
live and spoof images. In this work, we propose PatchNet which reformulates
face anti-spoofing as a fine-grained patch-type recognition problem. To be
specific, our framework recognizes the combination of capturing devices and
presenting materials based on the patches cropped from non-distorted face
images. This reformulation can largely improve the data variation and enforce
the network to learn discriminative feature from local capture patterns. In
addition, to further improve the generalization ability of the spoof feature,
we propose the novel Asymmetric Margin-based Classification Loss and
Self-supervised Similarity Loss to regularize the patch embedding space. Our
experimental results verify our assumption and show that the model is capable
of recognizing unseen spoof types robustly by only looking at local regions.
Moreover, the fine-grained and patch-level reformulation of FAS outperforms the
existing approaches on intra-dataset, cross-dataset, and domain generalization
benchmarks. Furthermore, our PatchNet framework can enable practical
applications like Few-Shot Reference-based FAS and facilitate future
exploration of spoof-related intrinsic cues.


title:
DeepXRD, a Deep Learning Model for Predicting of XRD spectrum from
  Materials Composition

abstract:
  One of the long-standing problems in materials science is how to predict a
material's structure and then its properties given only its composition.
Experimental characterization of crystal structures has been widely used for
structure determination, which is however too expensive for high-throughput
screening. At the same time, directly predicting crystal structures from
compositions remains a challenging unsolved problem. Herein we propose a deep
learning algorithm for predicting the XRD spectrum given only the composition
of a material, which can then be used to infer key structural features for
downstream structural analysis such as crystal system or space group
classification or crystal lattice parameter determination or materials property
predictions. Benchmark studies on two datasets show that our DeepXRD algorithm
can achieve good performance for XRD prediction as evaluated over our test
sets. It can thus be used in high-throughput screening in the huge materials
composition space for new materials discovery.


title:
Local-Adaptive Face Recognition via Graph-based Meta-Clustering and
  Regularized Adaptation

abstract:
  Due to the rising concern of data privacy, it's reasonable to assume the
local client data can't be transferred to a centralized server, nor their
associated identity label is provided. To support continuous learning and fill
the last-mile quality gap, we introduce a new problem setup called
Local-Adaptive Face Recognition (LaFR). Leveraging the environment-specific
local data after the deployment of the initial global model, LaFR aims at
getting optimal performance by training local-adapted models automatically and
un-supervisely, as opposed to fixing their initial global model. We achieve
this by a newly proposed embedding cluster model based on Graph Convolution
Network (GCN), which is trained via meta-optimization procedure. Compared with
previous works, our meta-clustering model can generalize well in unseen local
environments. With the pseudo identity labels from the clustering results, we
further introduce novel regularization techniques to improve the model
adaptation performance. Extensive experiments on racial and internal sensor
adaptation demonstrate that our proposed solution is more effective for
adapting face recognition models in each specific environment. Meanwhile, we
show that LaFR can further improve the global model by a simple federated
aggregation over the updated local models.


title:
On the Neural Tangent Kernel Analysis of Randomly Pruned Wide Neural
  Networks

abstract:
  We study the behavior of ultra-wide neural networks when their weights are
randomly pruned at the initialization, through the lens of neural tangent
kernels (NTKs). We show that for fully-connected neural networks when the
network is pruned randomly at the initialization, as the width of each layer
grows to infinity, the empirical NTK of the pruned neural network converges to
that of the original (unpruned) network with some extra scaling factor.
Further, if we apply some appropriate scaling after pruning at the
initialization, the empirical NTK of the pruned network converges to the exact
NTK of the original network, and we provide a non-asymptotic bound on the
approximation error in terms of pruning probability. Moreover, when we apply
our result to an unpruned network (i.e., we set the probability of pruning a
given weight to be zero), our analysis is optimal up to a logarithmic factor in
width compared with the result in \cite{arora2019exact}. We conduct experiments
to validate our theoretical results. We further test our theory by evaluating
random pruning across different architectures via image classification on MNIST
and CIFAR-10 and compare its performance with other pruning strategies.


title: Most probable escape paths in periodically driven nonlinear oscillators

abstract:
  The dynamics of mechanical systems such as turbomachinery with multiple
blades are often modeled by arrays of periodically driven coupled nonlinear
oscillators. It is known that such systems may have multiple stable vibrational
modes, and transitions between them may occur under the influence of random
factors. A methodology for finding most probable escape paths and estimating
the transition rates in the small noise limit is developed and applied to a
collection of arrays of coupled monostable oscillators with cubic nonlinearity,
weak damping, and harmonic external forcing. The methodology is built upon the
action plot method (Beri {\it et al}., 2005) and relies on the large deviation
theory, optimal control theory, and Floquet theory. The action plot method is
promoted to non-autonomous high-dimensional systems, and a method is proposed
for solving the arising optimization problem with discontinuous objective
function restricted to a certain manifold. The most probable escape paths
between stable vibrational modes in arrays of up to five oscillators and the
corresponding quasipotential barriers are computed and visualized. The
dependence of the quasipotential barrier of the parameters on the system is
discussed.


title: ${\cal C}$osmological ${\cal K}$rylov ${\cal C}$omplexity

abstract:
  In this paper, we study the Krylov complexity ($K$) from the
planar/inflationary patch of the de Sitter space using the two mode squeezed
state formalism in the presence of an effective field having sound speed $c_s$.
From our analysis, we obtain the explicit behavior of Krylov complexity ($K$)
and lancoz coefficients ($b_n$) with respect to the conformal time scale and
scale factor in the presence of effective sound speed $c_s$. Since lancoz
coefficients ($b_n$) grow linearly with integer $n$, this suggests that
universe acts like a chaotic system during this period. We also obtain the
corresponding Lyapunov exponent $\lambda$ in presence of effective sound speed
$c_s$. We show that the Krylov complexity ($K$) for this system is equal to
average particle numbers suggesting it's relation to the volume. Finally, we
give a comparison of Krylov complexity ($K$) with entanglement entropy
(Von-Neumann) where we found that there is a large difference between Krylov
complexity ($K$) and entanglement entropy for large values of squeezing
amplitude. This suggests that Krylov complexity ($K$) can be a significant
probe for studying the dynamics of the cosmological system even after the
saturation of entanglement entropy.

categories: hep-th cond-mat.stat-mech gr-qc hep-ph quant-ph


title: SuperMVS: Non-Uniform Cost Volume For High-Resolution Multi-View Stereo

abstract:
  Different from most state-of-the-art~(SOTA) algorithms that use static and
uniform sampling methods with a lot of hypothesis planes to get fine depth
sampling. In this paper, we propose a free-moving hypothesis plane method for
dynamic and non-uniform sampling in a wide depth range to build the cost
volume, which not only greatly reduces the number of planes but also finers
sampling, for both of reducing computational cost and improving accuracy, named
Non-Uniform Cost Volume. We present the SuperMVS network to implement
Multi-View Stereo with Non-Uniform Cost Volume. SuperMVS is a coarse-to-fine
framework with four cascade stages. It can output higher resolution and
accurate depth map. Our SuperMVS achieves the SOTA results with low memory, low
runtime, and fewer planes on the DTU datasets and Tanks \& Temples dataset.


title:
Stability threshold for 2D shear flows near Couette of the Navier-Stokes
  equation

abstract:
  In this paper, we consider the stability threshold of the 2D shear flow
$(U(y),0)^{\top}$ of the Navier-Stokes equation at high Reynolds number $Re$.
When the shear flow is near in Sobolev norm to the Couette flow $(y,0)^{\top}$
in some sense, we prove that if the initial data $u_0$ satisfies
$\|u_0-(U(y),0)^{\top}\|\leq \epsilon Re^{-1/3}$, then the solution of the 2D
Navier-Stokes equation approaches to some shear flow which is also close to the
Couette flow for $t\gg Re^{1/3}$, as $t\to\infty$.


title:
Locality-Aware Inter-and Intra-Video Reconstruction for Self-Supervised
  Correspondence Learning

abstract:
  Our target is to learn visual correspondence from unlabeled videos. We
develop LIIR, a locality-aware inter-and intra-video reconstruction framework
that fills in three missing pieces, i.e., instance discrimination, location
awareness, and spatial compactness, of self-supervised correspondence learning
puzzle. First, instead of most existing efforts focusing on intra-video
self-supervision only, we exploit cross video affinities as extra negative
samples within a unified, inter-and intra-video reconstruction scheme. This
enables instance discriminative representation learning by contrasting desired
intra-video pixel association against negative inter-video correspondence.
Second, we merge position information into correspondence matching, and design
a position shifting strategy to remove the side-effect of position encoding
during inter-video affinity computation, making our LIIR location-sensitive.
Third, to make full use of the spatial continuity nature of video data, we
impose a compactness-based constraint on correspondence matching, yielding more
sparse and reliable solutions. The learned representation surpasses
self-supervised state-of-the-arts on label propagation tasks including objects,
semantic parts, and keypoints.


title:
Infrared Finite Scattering Theory in Quantum Field Theory and Quantum
  Gravity

abstract:
  Infrared (IR) divergences arise in scattering theory with massless fields and
are manifestations of the memory effect. There is nothing singular about states
with memory, but they do not lie in the standard Fock space. IR divergences are
artifacts of trying to represent states with memory in the standard Fock space.
For collider physics, one can impose an IR cutoff and calculate inclusive
quantities. But, this approach cannot treat memory as a quantum observable and
is highly unsatisfactory if one views the S-matrix as fundamental in QFT and
quantum gravity, since the S-matrix is undefined. For a well-defined S-matrix,
it is necessary to define in/out Hilbert spaces with memory. Such a
construction was given by Faddeev and Kulish (FK) for QED. Their construction
"dresses" momentum states of the charged particles by pairing them with memory
states of the electromagnetic field to produce states of vanishing large gauge
charges at spatial infinity. However, in massless QED, due to collinear
divergences, the "dressing" has an infinite energy flux so these states are
unphysical. In Yang-Mills theory the "soft particles" used for dressing also
contribute to the current flux, invalidating the FK procedure. In quantum
gravity, the analogous FK construction would attempt to produce a Hilbert space
of eigenstates of supertranslation charges at spatial infinity. However, we
prove that there are no eigenstates of supertranslation charges except the
vacuum. Thus, the FK construction fails in quantum gravity. We investigate some
alternatives to FK constructions but find that these also do not work. We
believe that to treat scattering at a fundamental level in quantum gravity - as
well as in massless QED and YM theory - it is necessary to take an algebraic
viewpoint rather than shoehorn the in/out states into some fixed Hilbert space.
We outline the framework of such an IR finite scattering theory.


title: Deep Hierarchical Semantic Segmentation

abstract:
  Humans are able to recognize structured relations in observation, allowing us
to decompose complex scenes into simpler parts and abstract the visual world in
multiple levels. However, such hierarchical reasoning ability of human
perception remains largely unexplored in current literature of semantic
segmentation. Existing work is often aware of flatten labels and predicts
target classes exclusively for each pixel. In this paper, we instead address
hierarchical semantic segmentation (HSS), which aims at structured, pixel-wise
description of visual observation in terms of a class hierarchy. We devise
HSSN, a general HSS framework that tackles two critical issues in this task: i)
how to efficiently adapt existing hierarchy-agnostic segmentation networks to
the HSS setting, and ii) how to leverage the hierarchy information to
regularize HSS network learning. To address i), HSSN directly casts HSS as a
pixel-wise multi-label classification task, only bringing minimal architecture
change to current segmentation models. To solve ii), HSSN first explores
inherent properties of the hierarchy as a training objective, which enforces
segmentation predictions to obey the hierarchy structure. Further, with
hierarchy-induced margin constraints, HSSN reshapes the pixel embedding space,
so as to generate well-structured pixel representations and improve
segmentation eventually. We conduct experiments on four semantic segmentation
datasets (i.e., Mapillary Vistas 2.0, Cityscapes, LIP, and PASCAL-Person-Part),
with different class hierarchies, segmentation network architectures and
backbones, showing the generalization and superiority of HSSN.


title:
Modified Equations of State for Dark Energy and Observational
  Limitations

abstract:
  Cosmological models with variable and modified equations of state for dark
energy are confronted with observational data, including Type Ia supernovae,
Hubble parameter data $H(z)$ from different sources, and observational
manifestations of cosmic microwave background radiation (CMB). We consider
scenarios generalizing the $\Lambda$CDM, $w$CDM, and
Chevallier--Polarski--Linder (CPL) models with nonzero curvature and compare
their predictions. The most successful model with the dark energy equation of
state $w= w_0+ w_1(1-a^2)/2$ was studied in detail. These models are
interesting in possibly alleviating the Hubble constant $H_0$ tension, but they
achieved a modest success in this direction with the considered observational
data.


title:
Itinerant G-type antiferromagnet SrCr2As2 studied by magnetization, heat
  capacity, electrical resistivity, and NMR measurements

abstract:
  The physical properties of itinerant antiferromagnetic (AFM) SrCr$_2$As$_2$
with body-centered tetragonal ThCr$_2$Si$_2$ structure were investigated in
single crystalline and polycrystalline forms by electrical resistivity $\rho$,
heat capacity $C_{\rm p}$, magnetic susceptibility $\chi$ versus
temperature~$T$ and magnetization $M$ versus applied magnetic field $H$
isotherm measurements as well as $^{75}$As and $^{53}$Cr nuclear magnetic
resonance (NMR) measurements in the wide temperature range $T$ = 1.6--900 K.
From the $\chi(T)$ and $^{75}$As NMR measurements, the G-type AFM state below
$T_{\rm N}$ = 615(15) K has been determined, consistent with the previous
neutron-diffraction measurements. Direct evidence of magnetic ordering of the
Cr spins was shown by the observation of the $^{53}$Cr NMR spectrum under $H$ =
0. From the $\chi(T)$ measurements on single-crystal SrCr$_2$As$_2$ under the
two different magnetic field directions $H||ab$ and $H||c$ in the AFM state,
the Cr ordered moments are shown to align along the $c$ axis in the G-type AFM
state. The metallic state is directly evidenced by the $\rho$, $C_{\rm p}$, and
NMR measurements, and the density of states at the Fermi energy ${\cal
D}(E_{\rm F})$ in the AFM state is estimated to be 7.53 states/eV f.u. for both
spin directions which is almost twice the bare ${\cal D}(E_{\rm F})$ estimated
from first-principles calculations, suggesting an enhancement of the
conduction-carrier mass by a factor of two in the AFM state. The ${\cal
D}(E_{\rm F})$ is found to be nearly constant below at least 100 K and is
independent of $H$. The $\rho(T)$ is found to show $T$-linear behavior above
$T_{\rm N}$ and exhibits positive curvature below $T_{\rm N}$ where significant
loss of spin-disorder scattering upon magnetic ordering is observed.


title: LibMTL: A Python Library for Multi-Task Learning

abstract:
  This paper presents LibMTL, an open-source Python library built on PyTorch,
which provides a unified, comprehensive, reproducible, and extensible
implementation framework for Multi-Task Learning (MTL). LibMTL considers
different settings and approaches in MTL, and it supports a large number of
state-of-the-art MTL methods, including 12 loss weighting strategies, 7
architectures, and 84 combinations of different architectures and loss
weighting methods. Moreover, the modular design in LibMTL makes it easy-to-use
and well extensible, thus users can easily and fast develop new MTL methods,
compare with existing MTL methods fairly, or apply MTL algorithms to real-world
applications with the support of LibMTL. The source code and detailed
documentations of LibMTL are available at
https://github.com/median-research-group/LibMTL and
https://libmtl.readthedocs.io, respectively.


title:
Distributed Link Sparsification for Scalable Scheduling Using Graph
  Neural Networks

abstract:
  Distributed scheduling algorithms for throughput or utility maximization in
dense wireless multi-hop networks can have overwhelmingly high overhead,
causing increased congestion, energy consumption, radio footprint, and security
vulnerability. For wireless networks with dense connectivity, we propose a
distributed scheme for link sparsification with graph convolutional networks
(GCNs), which can reduce the scheduling overhead while keeping most of the
network capacity. In a nutshell, a trainable GCN module generates node
embeddings as topology-aware and reusable parameters for a local decision
mechanism, based on which a link can withdraw itself from the scheduling
contention if it is not likely to win. In medium-sized wireless networks, our
proposed sparse scheduler beats classical threshold-based sparsification
policies by retaining almost $70\%$ of the total capacity achieved by a
distributed greedy max-weight scheduler with $0.4\%$ of the point-to-point
message complexity and $2.6\%$ of the average number of interfering neighbors
per link.


title:
Analytical approach to the polaron problem via the $q$-deformed Lie
  algebra

abstract:
  We present a novel approach to the polaron problem developed on the notion of
quantum groups, also known as $q$-deformed Lie algebras. In this approach, the
presence of an impurity in a bath can be depicted as a deformation of the Lie
algebra of the bosonic creation and annihilation operators of the bath. In
particular, we introduce a simple $q$-deformed bosonic \textit{parent}
Hamiltonian whose truncation on the quartic level in the usual bosonic creation
and annihilation operators corresponds to the Fr\"{o}hlich-Bogoliubov polaron
Hamiltonian. By using the $q$-deformed Lie algebra, the introduced parent
Hamiltonian allows us to derive the ground state energy of the
Fr\"{o}hlich-Bogoliubov Hamiltonian analytically for the regime where the
Bogoliubov dispersion law takes the phonon-like form.

categories: cond-mat.quant-gas cond-mat.other math-ph math.MP physics.atm-clus quant-ph


title: MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation

abstract:
  Segmentation is essential for medical image analysis to identify and localize
diseases, monitor morphological changes, and extract discriminative features
for further diagnosis. Skin cancer is one of the most common types of cancer
globally, and its early diagnosis is pivotal for the complete elimination of
malignant tumors from the body. This research develops an Artificial
Intelligence (AI) framework for supervised skin lesion segmentation employing
the deep learning approach. The proposed framework, called MFSNet (Multi-Focus
Segmentation Network), uses differently scaled feature maps for computing the
final segmentation mask using raw input RGB images of skin lesions. In doing
so, initially, the images are preprocessed to remove unwanted artifacts and
noises. The MFSNet employs the Res2Net backbone, a recently proposed
convolutional neural network (CNN), for obtaining deep features used in a
Parallel Partial Decoder (PPD) module to get a global map of the segmentation
mask. In different stages of the network, convolution features and multi-scale
maps are used in two boundary attention (BA) modules and two reverse attention
(RA) modules to generate the final segmentation output. MFSNet, when evaluated
on three publicly available datasets: $PH^2$, ISIC 2017, and HAM10000,
outperforms state-of-the-art methods, justifying the reliability of the
framework. The relevant codes for the proposed approach are accessible at
https://github.com/Rohit-Kundu/MFSNet


title:
Multispeckle diffusing wave spectroscopy as a tool to study
  heterogeneous mechanical behavior in soft solids

abstract:
  Multiple speckle diffusing wave spectroscopy (MSDWS) can be applied to
measure spatially heterogeneous mechanical behavior in soft solids, with high
sensitivity to deformation and both spatial and temporal resolution. In this
paper, we discuss the mathematical approach behind the quantification of the
deformation rate from MSDWS data and provide guidelines for optimizing the
selection of experimental parameters in measurements. After validating the
method in extensional tests on an elastomer, we provide an example of the
potentiality of MSDWS by measuring the spatial distribution of the deformation
rate during shear debonding of adhesive tapes. We quantitatively characterize
the deformation rate distribution related to shearing and peeling under
loading. A highly heterogeneous deformation rate distribution is observed, and
time-dependent measurements reveal an increase in deformation localization
hundreds of seconds before full debonding. This behavior, previously predicted
by theory and simulation, is here demonstrated experimentally for the first
time.


title: Diagonal State Spaces are as Effective as Structured State Spaces

abstract:
  Modeling long range dependencies in sequential data is a fundamental step
towards attaining human-level performance in many modalities such as text,
vision and audio. While attention-based models are a popular and effective
choice in modeling short-range interactions, their performance on tasks
requiring long range reasoning has been largely inadequate. In a breakthrough
result, Gu et al. (2022) proposed the $\textit{Structured State Space}$ (S4)
architecture delivering large gains over state-of-the-art models on several
long-range tasks across various modalities. The core proposition of S4 is the
parameterization of state matrices via a diagonal plus low rank structure,
allowing efficient computation. In this work, we show that one can match the
performance of S4 even without the low rank correction and thus assuming the
state matrices to be diagonal. Our $\textit{Diagonal State Space}$ (DSS) model
matches the performance of S4 on Long Range Arena tasks, speech classification
on Speech Commands dataset, while being conceptually simpler and
straightforward to implement.


title:
On generalizations of discrete and integral Cauchy-Bunyakovskii
  inequalities by the method of mean values. Some applications

abstract:
  In this preprint we consider generalizations of discrete and integral
Cauchy--Bunyakovskii inequalities by the method of mean values with some
applications. Mostly the material is compiled as a short survey but some
results are proved. Main results are listed, including an interesting
inequality with maximum and minimum values. Some applications are considered
from different fields of mathematics. Among them are estimates for some special
functions, including Euler gamma and incomplete gamma function, the Legendre
complete elliptic integrals of the first kind. Also some further possible
generalizations are considered and outlined, including generalizations of the
Acz\'el and Minkovskii inequalities, a case of spaces with sign-indefinite
form, the Jackson's $q$-integrals.


title: Evolution of HEP Processing Frameworks

abstract:
  HEP data-processing software must support the disparate physics needs of many
experiments. For both collider and neutrino environments, HEP experiments
typically use data-processing frameworks to manage the computational
complexities of their large-scale data processing needs. Data-processing
frameworks are being faced with new challenges this decade. The computing
landscape has changed from the past three decades of homogeneous single-core
x86 batch jobs running on grid sites. Frameworks must now work on a
heterogeneous mixture of different platforms: multi-core machines, different
CPU architectures, and computational accelerators; and different computing
sites: grid, cloud, and high-performance computing. We describe these
challenges in more detail and how frameworks may confront them. Given their
historic success, frameworks will continue to be critical software systems that
enable HEP experiments to meet their computing needs. Frameworks have weathered
computing revolutions in the past; they will do so again with support from the
HEP community


title: A new approach for amplitudes with multiple fermion lines

abstract:
  A new approach for tree-level amplitudes with multiple fermion lines is
presented. It mainly focuses on the simplication of fermion lines. By
calculating two vectors recursively without any matrix multiplications, the
result of a fermion line is reduced to a very compact form depending only on
the two vectors. The comparison with other packages is presented, and the
results show that this new approach greatly improves the computing efficiency
and one can expect even better performance as the number of fermion lines
increases.


title:
Light-weight and highly thermally conductive support structures for
  future tracking detectors

abstract:
  Detector mechanics can play a significant role in a detector's performance,
improvements typically require in-depth study of total mass, novel ways to
reduce the total mass, as well as more integrated design concepts to save on
material budgets and optimize performance. Particle detectors at future
colliders rely on ever more precise charged particle tracking devices, which
are supported by structures manufactured from composite materials. This article
lays out engineering techniques able to solve challenges related to the design
and manufacturing of future support structures. Examples of current efforts at
Purdue University related to the high-luminosity upgrade of the CMS detector
are provided to demonstrate the prospects of suggested approaches for detectors
at new colliders: a future circular collider or a muon collider. Detectors at
electron-positron machines have significantly smaller material budgets and
require targeted concepts.


title: Unentangled quantum reinforcement learning agents in the OpenAI Gym

abstract:
  Classical reinforcement learning (RL) has generated excellent results in
different regions; however, its sample inefficiency remains a critical issue.
In this paper, we provide concrete numerical evidence that the sample
efficiency (the speed of convergence) of quantum RL could be better than that
of classical RL, and for achieving comparable learning performance, quantum RL
could use much (at least one order of magnitude) fewer trainable parameters
than classical RL. Specifically, we employ the popular benchmarking
environments of RL in the OpenAI Gym, and show that our quantum RL agent
converges faster than classical fully-connected neural networks (FCNs) in the
tasks of CartPole and Acrobot under the same optimization process. We also
successfully train the first quantum RL agent that can complete the task of
LunarLander in the OpenAI Gym. Our quantum RL agent only requires a
single-qubit-based variational quantum circuit without entangling gates,
followed by a classical neural network (NN) to post-process the measurement
output. Finally, we could accomplish the aforementioned tasks on the real IBM
quantum machines. To the best of our knowledge, none of the earlier quantum RL
agents could do that.


title:
Reinforcement Guided Multi-Task Learning Framework for Low-Resource
  Stereotype Detection

abstract:
  As large Pre-trained Language Models (PLMs) trained on large amounts of data
in an unsupervised manner become more ubiquitous, identifying various types of
bias in the text has come into sharp focus. Existing "Stereotype Detection"
datasets mainly adopt a diagnostic approach toward large PLMs. Blodgett et. al
(2021a) show that there are significant reliability issues with the existing
benchmark datasets. Annotating a reliable dataset requires a precise
understanding of the subtle nuances of how stereotypes manifest in text. In
this paper, we annotate a focused evaluation set for "Stereotype Detection"
that addresses those pitfalls by de-constructing various ways in which
stereotypes manifest in text. Further, we present a multi-task model that
leverages the abundance of data-rich neighboring tasks such as hate speech
detection, offensive language detection, misogyny detection, etc., to improve
the empirical performance on "Stereotype Detection". We then propose a
reinforcement-learning agent that guides the multi-task learning model by
learning to identify the training examples from the neighboring tasks that help
the target task the most. We show that the proposed models achieve significant
empirical gains over existing baselines on all the tasks.


title:
Aspects of higher dimensional quantum Hall effect: Bosonization,
  entanglement entropy

abstract:
  I give a brief review of higher dimensional quantum Hall effect (QHE) and how
one can use a general framework to describe the lowest Landau level dynamics as
a noncommutative field theory whose semiclassical limit leads to anomaly free
bulk-edge effective actions in any dimension. I then present the case of QHE on
complex projective spaces and focus on the entanglement entropy for integer QHE
in even spatial dimensions. In the case of $\nu=1$, a semiclassical analysis
shows that the entanglement entropy is proportional to the phase-space area of
the entangling surface with a universal overall constant, same for any
dimension as well as abelian or nonabelian background magnetic fields. This is
modified for higher Landau levels.


title: Entanglement Entropy and Matter-Gravity Couplings for Fuzzy Geometry

abstract:
  In this talk I discuss some features of the entanglement entropy for fuzzy
geometry, focusing on its dependence on the background fields and the spin
connection of the emergent continuous manifold in a large $N$ limit. Using the
Landau-Hall paradigm for fuzzy geometry, this is argued to be given by a
generalized Chern-Simons form, making a point of connection with the
thermodynamic view of gravity. Matter-gravity couplings are also considered in
the same framework; they naturally lead to certain specific nonminimal
couplings involving powers of the curvature.


title:
Physics Guided Generative Adversarial Networks for Generations of
  Crystal Materials with Symmetry Constraints

abstract:
  Discovering new materials is a long-standing challenging task that is
critical to the progress of human society. Conventional approaches such as
trial-and-error experiments and computational simulations are labor-intensive
or costly with their success heavily depending on experts' heuristics. Recently
deep generative models have been successfully proposed for materials generation
by learning implicit knowledge from known materials datasets, with performance
however limited by their confinement to a special material family or failing to
incorporate physical rules into the model training process. Here we propose a
Physics Guided Crystal Generative Model (PGCGM) for new materials generation,
which captures and exploits the pairwise atomic distance constraints among
neighbor atoms and symmetric geometric constraints. By augmenting the base atom
sites of materials, our model can generates new materials of 20 space groups.
With atom clustering and merging on generated crystal structures, our method
increases the generator's validity by 8 times compared to one of the baselines
and by 143\% compared to the previous CubicGAN along with its superiority in
properties distribution and diversity. We further validated our generated
candidates by Density Functional Theory (DFT) calculation, which successfully
optimized/relaxed 1869 materials out of 2000, of which 39.6\% are with negative
formation energy, indicating their stability.


title:
Enhancement of the superconducting critical temperature realized in the
  La-Ce-H system at moderate pressures

abstract:
  Covalent and ionic polyhydrides have become the two main camps in searching
for the high-temperature superconductors under pressure. They have been
considered as important platforms for exploring ternary or multiple hydrides in
order to further increase the Tc or decrease the stabilization pressure. In
this work, we successfully synthesized ternary hexagonal La-Ce polyhydrides
stable in the pressure range of 95-130 GPa by laser-heating the La-Ce alloy
(initial ratio La:Ce=2.5-3.5) in ammonia borane. Superconductivity at 176 K was
strikingly preserved to about 100 GPa. The extrapolated upper critical field
Hc2(0) reached 216 T at 100 GPa, the highest value among the synthesized
polyhydrides. We also performed the contrast experiments and stabilized binary
high-temperature superconducting LaHx with Tc-103 K at 78 GPa. In the pressure
range of 95-130 GPa, the ternary hexagonal La-Ce-H system exhibits higher Tc
than the binary La-H system, with the maximum difference of 100 K, and the
compounds of both systems were synthesized at the same pressure and temperature
conditions. These results clearly indicate that the discovered La-Ce-H system
not only enriches the high-temperature superconducting hydrides but also
realizes high-Tc at moderate pressures.


title: GPU-based optical simulation of the DARWIN detector

abstract:
  Understanding propagation of scintillation light is critical for maximizing
the discovery potential of next-generation liquid xenon detectors that use
dual-phase time projection chamber technology. This work describes a detailed
optical simulation of the DARWIN detector implemented using Chroma, a GPU-based
photon tracking framework. To evaluate the framework and to explore ways of
maximizing efficiency and minimizing the time of light collection, we simulate
several variations of the conventional detector design. Results of these
selected studies are presented. More generally, we conclude that the approach
used in this work allows one to investigate alternative designs faster and in
more detail than using conventional Geant4 optical simulations, making it an
attractive tool to guide the development of the ultimate liquid xenon
observatory.


title: Robust and Efficient Bayesian Inference for Non-Probability Samples

abstract:
  The declining response rates in probability surveys along with the widespread
availability of unstructured data has led to growing research into
non-probability samples. Existing robust approaches are not well-developed for
non-Gaussian outcomes and may perform poorly in presence of influential
pseudo-weights. Furthermore, their variance estimator lacks a unified framework
and rely often on asymptotic theory. To address these gaps, we propose an
alternative Bayesian approach using a partially linear Gaussian process
regression that utilizes a prediction model with a flexible function of the
pseudo-inclusion probabilities to impute the outcome variable for the reference
survey. By efficiency, we mean not only computational scalability but also
superiority with respect to variance. We also show that Gaussian process
regression behaves as a kernel matching technique based on the estimated
propensity scores, which yields double robustness and lowers sensitivity to
influential pseudo-weights. Using the simulated posterior predictive
distribution, one can directly quantify the uncertainty of the proposed
estimator and derive associated $95\%$ credible intervals. We assess the
repeated sampling properties of our method in two simulation studies. The
application of this study deals with modeling count data with varying exposures
under a non-probability sample setting.


title:
Onset of charge incompressibility and Mott gaps in the Honeycomb-Lattice
  SU(4) Hubbard Model: Lessons for Twisted Bilayer Graphene systems

abstract:
  We use finite temperature strong coupling expansions to calculate
thermodynamic properties of the Honeycomb-lattice SU(4) Hubbard model. We
present numerical results for various properties including chemical potential,
compressibility, entropy and specific heat as a function of temperature and
density at several $U/t$ values. We study the onset of charge incompressibility
and Mott gaps as the temperature is lowered at integer densities. In the
incompressible Mott regime, the expansions are recast into a high temperature
expansion for a generalized spin model with SU(4) symmetry, which is then used
to study the convergence of strong coupling expansions in t/U. We discuss
lessons that can be drawn from high temperature properties of a simple Hubbard
model regarding Twisted Bilayer Graphene (TBG) and other magic-angle flat-band
systems.


title:
Sums of squares in function fields over henselian discretely valued
  fields

abstract:
  Let $n\in\mathbb{N}$ and let $K$ be a field with a henselian discrete
valuation of rank $n$ with hereditarily euclidean residue field. Let $F/K$ be
an algebraic function field in one variable. We show that the Pythagoras number
of $F$ is $2$ or $3$ and we determine the order of the group of nonzero sums of
squares modulo sums of two squares in $F$ in terms of the number of equivalence
classes of discrete valuations on $F$ of rank at most $n.$ In the case of
function fields of hyperelliptic curves of genus $g$, K.J. Becher and J. Van
Geel showed that the order of this quotient group is bounded by $2^{n(g+1)}$.
We show in Example 4.6 that this bound is optimal.


title:
A Memristive Based Design of a Core Digital Circuit for Elliptic Curve
  Cryptography

abstract:
  The new emerging non-volatile memory (NVM) devices known as memristors could
be the promising candidate for future digital architecture, owing to their
nanoscale size and its ability to integrate with the exciting CMOS technology.
In this paper, a combination of memristor devices and CMOS transistors are
working together to form a hybrid CMOS-memristor circuit for XAX- Module, a
core element for the finite field multiplier. The proposed design was
implemented using Pt /TaOx/Ta memristor device and simulated in Cadence
Virtuoso. The simulation results demonstrate the design functionality. The
proposed module appears to be efficient in terms of layout area, delay and
power consumption since the design utilizes the hybrid CMOS/memristor gates.


title: Online Meta-Learning For Hybrid Model-Based Deep Receivers

abstract:
  Recent years have witnessed growing interest in the application of deep
neural networks (DNNs) for receiver design, which can potentially be applied in
complex environments without relying on knowledge of the channel model.
However, the dynamic nature of communication channels often leads to rapid
distribution shifts, which may require periodically retraining. This paper
formulates a data-efficient two-stage training method that facilitates rapid
online adaptation. Our training mechanism uses a predictive meta-learning
scheme to train rapidly from data corresponding to both current and past
channel realizations. Our method is applicable to any deep neural network
(DNN)-based receiver, and does not require transmission of new pilot data for
training. To illustrate the proposed approach, we study DNN-aided receivers
that utilize an interpretable model-based architecture, and introduce a modular
training strategy based on predictive meta-learning. We demonstrate our
techniques in simulations on a synthetic linear channel, a synthetic non-linear
channel, and a COST 2100 channel. Our results demonstrate that the proposed
online training scheme allows receivers to outperform previous techniques based
on self-supervision and joint-learning by a margin of up to 2.5 dB in coded bit
error rate in rapidly-varying scenarios.


title:
Observation-Centric SORT: Rethinking SORT for Robust Multi-Object
  Tracking

abstract:
  Multi-Object Tracking (MOT) has rapidly progressed with the development of
object detection and re-identification. However, motion modeling, which
facilitates object association by forecasting short-term trajectories with past
observations, has been relatively under-explored in recent years. Current
motion models in MOT typically assume that the object motion is linear in a
small time window and needs continuous observations, so these methods are
sensitive to occlusions and non-linear motion and require high frame-rate
videos. In this work, we show that a simple motion model can obtain
state-of-the-art tracking performance without other cues like appearance. We
emphasize the role of "observation" when recovering tracks from being lost and
reducing the error accumulated by linear motion models during the lost period.
We thus name the proposed method as Observation-Centric SORT, OC-SORT for
short. It remains simple, online, and real-time but improves robustness over
occlusion and non-linear motion. It achieves 63.2 and 62.1 HOTA on MOT17 and
MOT20, respectively, surpassing all published methods. It also sets new states
of the art on KITTI Pedestrian Tracking and DanceTrack where the object motion
is highly non-linear. The code and model are available at
https://github.com/noahcao/OC_SORT.


title: Computing Equivariant Homology with a Splitting Method

abstract:
  We improve the splitting method in the author's previous paper and apply this
method to the computation of more general equivariant homology. In particular,
we will consider the case $G=A_5$ and compute the homotopy of
$H\underline{\mathbb{Z}}$ as an $RO(A_5)$-graded ring.


title:
GPU-Powered Spatial Database Engine for Commodity Hardware: Extended
  Version

abstract:
  Given the massive growth in the volume of spatial data, there is a great need
for systems that can efficiently evaluate spatial queries over large data sets.
These queries are notoriously expensive using traditional database solutions.
While faster response times can be attained through powerful clusters or
servers with large main-memory, these options, due to cost and complexity, are
out of reach to many data scientists and analysts making up the long tail.
  Graphics Processing Units (GPUs), which are now widely available even in
commodity desktops and laptops, provide a cost-effective alternative to support
high-performance computing, opening up new opportunities to the efficient
evaluation of spatial queries. While GPU-based approaches proposed in the
literature have shown great improvements in performance, they are tied to
specific GPU hardware and only handle specific queries over fixed geometry
types.
  In this paper we present SPADE, a GPU-powered spatial database engine that
supports a rich set of spatial queries. We discuss the challenges involved in
attaining efficient query evaluation over large datasets as well as portability
across different GPU hardware, and how these are addressed in SPADE. We
performed a detailed experimental evaluation to assess the effectiveness of the
system for wide range of queries and datasets, and report results which show
that SPADE is scalable and able to handle data larger than main-memory, and its
performance on a laptop is on par with that other systems that require clusters
or large-memory servers.


title:
piRank: A Probabilistic Intent Based Ranking Framework for Facebook
  Search

abstract:
  While numerous studies have been conducted in the literature exploring
different types of machine learning approaches for search ranking, most of them
are focused on specific pre-defined problems but only a few of them have
studied the ranking framework which can be applied in a commercial search
engine in a scalable way. In the meantime, existing ranking models are often
optimized for normalized discounted cumulative gains (NDCG) or online
click-through rate (CTR), and both types of machine learning models are built
based on the assumption that high-quality training data can be easily obtained
and well applied to unseen cases. In practice at Facebook search, we observed
that our training data for ML models have certain issues. First, tail query
intents are hardly covered in our human rating dataset. Second, search click
logs are often noisy and hard to clean up due to various reasons. To address
the above issues, in this paper, we propose a probabilistic intent based
ranking framework (short for piRank), which can: 1) provide a scalable
framework to address various ranking issues for different query intents in a
divide-and-conquer way; 2) improve system development agility including
iteration speed and system debuggability; 3) combine both machine learning and
empirical-based algorithmic methods in a systematic way. We conducted extensive
experiments and studies on top of Facebook search engine system and validated
the effectiveness of this new ranking architecture.


title:
Hollenbeck-Verbitsky conjecture on best constant inequalities for
  analytic and co-analytic projections

abstract:
  \begin{abstract}
  In this paper we address the problem of finding the best constants in
inequalities of the form:
  $$ \|\big(|P_+f|^s+|P_-f|^s\big)^{\frac{1}{s}}\|_{L^p({\mathbb{T}})}\leq
A_{p,s} \|f\|_{L^p({\mathbb{T}})},$$ where $P_+f$ and $P_-f$ denote analytic
and co-analytic projection of a complex-valued function $f \in
L^p({\mathbb{T}}),$ for $p \geq 2$ and all $s>0$, thus proving
Hollenbeck-Verbitsky conjecture from \cite{HV.OTAA}. We also prove the same
inequalities for\\ $1<p\leq\frac{4}{3}$ and $s\leq \sec^2\frac{\pi}{2p}$ and
confirm that $s=\sec^2\frac{\pi}{2p}$ is the sharp cutoff for $s.$ The proof
uses a method of plurisubharmonic minorants and an approach of proving the
appropriate "elementary" inequalities that seems to be new in this topic.
  We show that this result implies best constants inequalities for the
projections on the real-line and half-space multipliers on $\mathbb{R}^n$ and
an analog for analytic martingales. A remark on an isoperimetric inequality for
harmonic functions in the unit disk is also given.
  \end{abstract}


title:
On the Linear Components Space of S-boxes Generated by Orthogonal
  Cellular Automata

abstract:
  We investigate S-boxes defined by pairs of Orthogonal Cellular Automata
(OCA), motivated by the fact that such CA always define bijective vectorial
Boolean functions, and could thus be interesting for the design of block
ciphers. In particular, we perform an exhaustive search of all nonlinear OCA
pairs of diameter $d=4$ and $d=5$, which generate S-boxes of size $6\times 6$
and $8\times 8$, respectively. Surprisingly, all these S-boxes turn out to be
linear, and thus they are not useful for the design of confusion layers in
block ciphers. However, a closer inspection of these S-boxes reveals a very
interesting structure. Indeed, we remark that the linear components space of
the OCA-based S-boxes found by our exhaustive search are themselves the kernels
of linear CA, or, equivalently, \emph{polynomial codes}. We finally classify
the polynomial codes of the S-boxes obtained in our exhaustive search and
observe that, in most cases, they actually correspond to the cyclic code with
generator polynomial $X^{b}+1$, where $b=d-1$. Although these findings rule out
the possibility of using OCA to design good S-boxes in block ciphers, they give
nonetheless some interesting insights for a theoretical characterization of
nonlinear OCA pairs, which is still an open question in general.


title: Weighted Tutte-Grothendieck polynomials of graphs

abstract:
  In this paper, we introduce the concept of the weighted (harmonic) chromatic
polynomials of graphs and discuss some of its properties. We also present the
notion of the weighted (harmonic) Tutte--Grothendieck polynomials of graphs and
give a generalization of the recipe theorem between the harmonic
Tutte--Grothendieck polynomials graphs and the harmonic Tutte polynomials of
matroids. Moreover, we give some constructions of the weighted (harmonic)
Tutte--Grothendieck invariants for graphs and the weighted (harmonic) Tutte
invariants for matroids. Finally, we give a remark on the categorification of
the harmonic chromatic polynomials of graphs and harmonic Tutte polynomials of
matroids.


title: Thin-Plate Spline Motion Model for Image Animation

abstract:
  Image animation brings life to the static object in the source image
according to the driving video. Recent works attempt to perform motion transfer
on arbitrary objects through unsupervised methods without using a priori
knowledge. However, it remains a significant challenge for current unsupervised
methods when there is a large pose gap between the objects in the source and
driving images. In this paper, a new end-to-end unsupervised motion transfer
framework is proposed to overcome such issue. Firstly, we propose thin-plate
spline motion estimation to produce a more flexible optical flow, which warps
the feature maps of the source image to the feature domain of the driving
image. Secondly, in order to restore the missing regions more realistically, we
leverage multi-resolution occlusion masks to achieve more effective feature
fusion. Finally, additional auxiliary loss functions are designed to ensure
that there is a clear division of labor in the network modules, encouraging the
network to generate high-quality images. Our method can animate a variety of
objects, including talking faces, human bodies, and pixel animations.
Experiments demonstrate that our method performs better on most benchmarks than
the state of the art with visible improvements in pose-related metrics.


title:
Random coordinate descent methods for nonseparable composite
  optimization

abstract:
  In this paper we consider large-scale composite optimization problems having
the objective function formed as a sum of two terms (possibly nonconvex), one
has block coordinate-wise Lipschitz continuous gradient and the other is
differentiable but nonseparable. Under these general settings we derive and
analyze two new random coordinate descent methods. The first algorithm,
referred to as random coordinate proximal gradient method, considers the
composite form of the objective function, while the other algorithm disregards
the composite form of the objective and uses the partial gradient of the full
objective, yielding a random coordinate gradient descent scheme with novel
adaptive stepsize rules. We prove that these new stepsize rules make the random
coordinate gradient scheme a descent method, provided that additional
assumptions hold for the second term in the objective function. We also present
a complete worst-case complexity analysis for these two new methods in both,
convex and nonconvex settings. Preliminary numerical results also confirm the
efficiency of our two algorithms on practical problems.


title:
The VMC survey -- XLVI. Stellar proper motions in the centre of the
  Large Magellanic Cloud

abstract:
  We present proper motion (PM) measurements within the central region of the
Large Magellanic Cloud (LMC) using near-infrared data from the VISTA survey of
the Magellanic Cloud system (VMC). This work encompasses 18 VMC tiles covering
a total sky area of $\sim$28~deg$^2$. We computed absolute stellar PMs from
multi-epoch observations in the $K_s$ filter over time baselines between
$\sim$12 and 47 months. Our final catalogue contains $\sim$6,322,000 likely LMC
member stars with derived PMs. We employed a simple flat-rotating disc model to
analyse and interpret the PM data. We found a stellar centre of rotation
($\alpha_0$ = 79.95 deg +0.22 -0.23, $\delta_0$ = -69.31 deg +0.12 -0.11) that
is in agreement with that resulting from Hubble Space Telescope data. The
inferred viewing angles of the LMC disc (i = 33.5 deg +1.2 -1.3, $\Theta$ =
129.8 deg +1.9 -1.9) are in good agreement with values from the literature but
suggest a higher inclination of the central parts of the LMC. Our data confirm
a higher rotation amplitude for the young ($\lesssim$0.5~Gyr) stars compared to
the intermediate-age/old ($\gtrsim$1~Gyr) population, which can be explained by
asymmetric drift. We constructed spatially resolved velocity maps of the
intermediate-age/old and young populations. Intermediate-age/old stars follow
elongated orbits parallel to the bar's major axis, providing first
observational evidence for $x_1$ orbits within the LMC bar. In the innermost
regions, the motions show more chaotic structures. Young stars show motions
along a central filamentary bar structure.


title:
CaCo: Both Positive and Negative Samples are Directly Learnable via
  Cooperative-adversarial Contrastive Learning

abstract:
  As a representative self-supervised method, contrastive learning has achieved
great successes in unsupervised training of representations. It trains an
encoder by distinguishing positive samples from negative ones given query
anchors. These positive and negative samples play critical roles in defining
the objective to learn the discriminative encoder, avoiding it from learning
trivial features. While existing methods heuristically choose these samples, we
present a principled method where both positive and negative samples are
directly learnable end-to-end with the encoder. We show that the positive and
negative samples can be cooperatively and adversarially learned by minimizing
and maximizing the contrastive loss, respectively. This yields cooperative
positives and adversarial negatives with respect to the encoder, which are
updated to continuously track the learned representation of the query anchors
over mini-batches. The proposed method achieves 71.3% and 75.3% in top-1
accuracy respectively over 200 and 800 epochs of pre-training ResNet-50
backbone on ImageNet1K without tricks such as multi-crop or stronger
augmentations. With Multi-Crop, it can be further boosted into 75.7%. The
source code and pre-trained model are released in
https://github.com/maple-research-lab/caco.


title:
MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical
  domain Question Answering

abstract:
  This paper introduces MedMCQA, a new large-scale, Multiple-Choice Question
Answering (MCQA) dataset designed to address real-world medical entrance exam
questions. More than 194k high-quality AIIMS \& NEET PG entrance exam MCQs
covering 2.4k healthcare topics and 21 medical subjects are collected with an
average token length of 12.77 and high topical diversity. Each sample contains
a question, correct answer(s), and other options which requires a deeper
language understanding as it tests the 10+ reasoning abilities of a model
across a wide range of medical subjects \& topics. A detailed explanation of
the solution, along with the above information, is provided in this study.


title:
Algorithmic support of a personal virtual assistant for automating the
  processing of client requests

abstract:
  This article describes creating algorithmic support for the functioning of a
personal virtual assistant, which allows automating the processing of customer
requests. The study aims to reduce errors and processing time for a client
request in business systems - text chats or voice channels using a text
transcription system. The results of the development of algorithmic support and
an assessment of the quality of work on synthetic data presented.


title:
Sustainable and effective antimicrobial surface based on cellulose thin
  films

abstract:
  In the present work, we developed a sustainable and effective antimicrobial
surface film based on Micro-Fibrillated Cellulose. The resulting porous
cellulose thin film is barely noticeable to human eyes due to its sub-micron
thickness, of which the coverage, porosity and microstructure can be modulated
by the formulations developed. Using goniometers and a quartz crystal
microbalance (QCM), we observed a threefold reduction in water contact angles
and accelerated (more than 50% faster) water evaporation kinetics on the
cellulose film. The thin film exhibits not only a rapid inactivation effect
against SARS-CoV-2 in 5 minutes, following deposition of the virus loaded
droplets, but also an exceptional ability to reduce contact transfer of liquid,
e.g. respiratory droplets, onto surfaces such as artificial skin by more than
90%. It also exhibits excellent antimicrobial performance in inhibiting the
growth of both gram-negative and gram-positive bacteria (E.coli and
S.epidermidis) due to the excellent porosity and hydrophilicity. Additionally,
the cellulose film shows nearly 100% resistance to skin scraping in dry
condition thanks to its strong attachment to the substrate, whilst good
removability once wetted, suggesting its practical suitability for daily use.
Importantly, the coating can be formed on solid substrates readily by spraying
and requires solely a simple formulation of a plant-based cellulose material
with no additives, rendering it a scalable, affordable and green solution for
antimicrobial surfaces. Implementing such cellulose films could thus play a
significant role in controlling future pan- and epidemics, in particularly
during the first phase when appropriate medication needs to be developed.


title: On the Gleason-Kahane-\.{Z}elazko theorem for associative algebras

abstract:
  The classical Gleason-Kahane-\.{Z}elazko Theorem states that a linear
functional on a complex Banach algebra not vanishing on units, and such that
$\Lambda(\mathbf 1)=1$, is multiplicative, that is,
$\Lambda(ab)=\Lambda(a)\Lambda(b)$ for all $a,b\in A$. We study the GK\.Z
property for associative unital algebras, especially for function algebras. In
a GK\.Z algebra $A$ over a field of at least $3$ elements, and having an ideal
of codimension $1$, every element is a finite sum of units. A real or complex
algebra with just countably many maximal left (right) ideals, is a GK\.Z
algebra. If $A$ is a commutative algebra, then the localisation $A_{P}$ is a
GK\.Z-algebra for every prime ideal $P$ of $A$. Hence the GK\.Z property is not
a local-global property. The class of GK\.Z algebras is closed under
homomorphic images. If a function algebra $A\subseteq \mathbb F^{X}$ over a
subfield $\mathbb F$ of $\mathbb C$, contains all the bounded functions in
$\mathbb F^{X}$, then each element of $A$ is a sum of two units. If $A$
contains also a discrete function, then $A$ is a GK\.Z algebra. We prove that
the algebra of periodic distributions, and the unitisation of the algebra of
distributions with support in $(0,\infty)$ satisfy the GK\.Z property, while
the algebra of compactly supported distributions does not.


title:
Switching fronts, plateaus and Kerr oscillations of counterpropagating
  light in ring resonators

abstract:
  We characterise stationary fronts and dark solitons for counterpropagating
waves in micro-ring and fibre resonators with two input fields, normal
dispersion and nonlocal coupling. These features are different from those in
systems with local coupling in that their existence and stability are due to a
careful balance of the areas of offset from homogeneous solutions. When
scanning one of the two cavity detunings, stable solutions formed plateaus
separated by two fronts are present in one of the counter-propagating fields
with the power of the other field being homogeneous. Two front plateau
solutions have a one-to-one correspondence to solutions of a Lugiato-Lefever
equation at the unique Maxwell point. By defining effective detunings and for
fixed values of the input powers where the fronts are found, we determine
expressions for both the Maxwell point and the distance of the stable fronts as
functions of detunings and input powers of both fields in good agreement with
numerical simulations. For certain values of the detunings we find multi-stable
states of plateaus with fronts, oscillating homogeneous states and
non-oscillating homogeneous states of the counter-propagating fields.
Robustness and parameter ranges of these unusual dynamical states coexisting
with stable non-homogeneous front solutions are provided.


title: A Whittaker category for the Symplectic Lie algebra

abstract:
  For any $n\in \mathbb{Z}_{\geq 2}$, let $\mathfrak{m}_n$ be the subalgebra of
$\mathfrak{sp}_{2n}$ spanned by all long negative root vectors
$X_{-2\epsilon_i}$, $i=1,\dots,n$. An $\mathfrak{sp}_{2n}$-module $M$ is called
a Whittaker module with respect to the Whittaker pair
$(\mathfrak{sp}_{2n},\mathfrak{m}_n)$ if the action of $\mathfrak{m}_n$ on $M$
is locally finite, according to a definition of Batra and Mazorchuk. This kind
of modules are more general than the classical Whittaker modules defined by
Kostant. In this paper, we show that each non-singular block
$\mathcal{WH}_{\mathbf{a}}^{\mu}$ with finite dimensional Whittaker vector
subspaces is equivalent to a module category $\mathcal{W}^{\mathbf{a}}$ of the
even Weyl algebra $\mathcal{D}_n^{ev}$ which is semi-simple. As a corollary,
any simple module in the block
$\mathcal{WH}_{\mathbf{i}}^{-\frac{1}{2}\omega_n}$ for the fundamental weight
$\omega_n$ is equivalent to the Nilsson's module $N_{\mathbf{i}}$ up to an
automorphism of $\mathfrak{sp}_{2n}$. We also characterize all possible algebra
homomorphisms from $U(\mathfrak{sp}_{2n})$ to the Weyl algebra $\mathcal{D}_n$
under a natural condition.


title:
Model Reduction of Consensus Network Systems via Selection of Optimal
  Edge Weights and Nodal Time-Scales

abstract:
  This paper proposes model reduction approaches for consensus network systems
based on a given clustering of the underlying graph. Namely, given a consensus
network system of time-scaled agents evolving over a weighted undirected graph
and a graph clustering, a parameterized reduced consensus network system is
constructed with its edge weights and nodal time-scales as the parameters to be
optimized. H-infinity- and H-2-based optimization approaches are proposed to
select the reduced network parameters such that the corresponding approximation
errors, i.e., the H-infinity- and H-2-norms of the error system, are minimized.
The effectiveness of the proposed model reduction methods is illustrated via a
numerical example.


title:
Precise wavefront characterization of X-ray optical elements using a
  laboratory source

abstract:
  Improvements in X-ray optics critically depend on the measurement of their
optical performance. The knowledge of wavefront aberrations, for example, can
be used to improve the fabrication of optical elements or to design phase
correctors to compensate for these errors. Nowadays, the characterization of
such optics is made using intense X-ray sources such as synchrotrons. However,
the limited access to these facilities can substantially slow down the
development process. Improvements in the brightness of lab-based X-ray
micro-sources in combination with the development of new metrology methods, and
in particular ptychographic X-ray speckle tracking, enable characterization of
X-ray optics in the lab with a precision and sensitivity not possible before.
Here, we present a laboratory set-up that utilizes a commercially available
X-ray source and can be used to characterize different types of X-ray optics.
The set-up is used in our laboratory on a routine basis to characterize
multilayer Laue lenses of high numerical aperture and other optical elements.
This typically includes measurements of the wavefront distortions, optimum
operating photon energy and focal length of the lens. To check the sensitivity
and accuracy of this laboratory set-up we compared the results to those
obtained at the synchrotron and saw no significant difference. To illustrate
the feedback of measurements on performance, we demonstrated the correction of
the phase errors of a particular multilayer Laue lens using a 3D printed
compound refractive phase plate.


title: Constructive Separations and Their Consequences

abstract:
  For a complexity class $C$ and language $L$, a constructive separation of $L
\notin C$ gives an efficient algorithm (also called a refuter) to find
counterexamples (bad inputs) for every $C$-algorithm attempting to decide $L$.
We study the questions: Which lower bounds can be made constructive? What are
the consequences of constructive separations? We build a case that
"constructiveness" serves as a dividing line between many weak lower bounds we
know how to prove, and strong lower bounds against $P$, $ZPP$, and $BPP$. Put
another way, constructiveness is the opposite of a complexity barrier: it is a
property we want lower bounds to have. Our results fall into three broad
categories.
  1. Our first set of results shows that, for many well-known lower bounds
against streaming algorithms, one-tape Turing machines, and query complexity,
as well as lower bounds for the Minimum Circuit Size Problem, making these
lower bounds constructive would imply breakthrough separations ranging from
$EXP \neq BPP$ to even $P \neq NP$.
  2. Our second set of results shows that for most major open problems in lower
bounds against $P$, $ZPP$, and $BPP$, including $P \neq NP$, $P \neq PSPACE$,
$P \neq PP$, $ZPP \neq EXP$, and $BPP \neq NEXP$, any proof of the separation
would further imply a constructive separation. Our results generalize earlier
results for $P \neq NP$ [Gutfreund, Shaltiel, and Ta-Shma, CCC 2005] and $BPP
\neq NEXP$ [Dolev, Fandina and Gutfreund, CIAC 2013].
  3. Our third set of results shows that certain complexity separations cannot
be made constructive. We observe that for all super-polynomially growing
functions $t$, there are no constructive separations for detecting high
$t$-time Kolmogorov complexity (a task which is known to be not in $P$) from
any complexity class, unconditionally.


title:
Pyramid-BERT: Reducing Complexity via Successive Core-set based Token
  Selection

abstract:
  Transformer-based language models such as BERT have achieved the
state-of-the-art performance on various NLP tasks, but are computationally
prohibitive. A recent line of works use various heuristics to successively
shorten sequence length while transforming tokens through encoders, in tasks
such as classification and ranking that require a single token embedding for
prediction. We present a novel solution to this problem, called Pyramid-BERT
where we replace previously used heuristics with a {\em core-set} based token
selection method justified by theoretical results. The core-set based token
selection technique allows us to avoid expensive pre-training, gives a
space-efficient fine tuning, and thus makes it suitable to handle longer
sequence lengths. We provide extensive experiments establishing advantages of
pyramid BERT over several baselines and existing works on the GLUE benchmarks
and Long Range Arena datasets.


title: Bayesian inference for asymptomatic COVID-19 infection rates

abstract:
  To strengthen inferences meta analyses are commonly used to summarize
information from a set of independent studies. In some cases, though, the data
may not satisfy the assumptions underlying the meta analysis. Using three
Bayesian methods that have a more general structure than the common meta
analytic ones, we can show the extent and nature of the pooling that is
justified statistically. In this paper, we re-analyze data from several reviews
whose objective is to make inference about the COVID-19 asymptomatic infection
rate. When it is unlikely that all of the true effect sizes come from a single
source researchers should be cautious about pooling the data from all of the
studies. Our findings and methodology are applicable to other COVID-19 outcome
variables, and more generally.


title: Calabi-Yau structures on (quasi-)bisymplectic algebras

abstract:
  We show that relative Calabi--Yau structures on noncommutative moment maps
give rise to (quasi-)bisymplectic structures, as introduced by
Crawley-Boevey-Etingof-Ginzburg (in the additive case) and Van den Bergh (in
the multiplicative case). We prove along the way that the fusion process (a)
corresponds to the composition of Calabi-Yau cospans with "pair-of-pants" ones,
and (b) preserves the duality between non-degenerate double quasi-Poisson
structures and quasi-bisymplectic structures. As an application we obtain that
Van den Bergh's Poisson structures on the moduli spaces of representations of
deformed multiplicative preprojective algebras coincide with the ones induced
by the 2-Calabi-Yau structures on (dg-versions of) these algebras.


title:
Continual learning: a feature extraction formalization, an efficient
  algorithm, and fundamental obstructions

abstract:
  Continual learning is an emerging paradigm in machine learning, wherein a
model is exposed in an online fashion to data from multiple different
distributions (i.e. environments), and is expected to adapt to the distribution
change. Precisely, the goal is to perform well in the new environment, while
simultaneously retaining the performance on the previous environments (i.e.
avoid "catastrophic forgetting") -- without increasing the size of the model.
  While this setup has enjoyed a lot of attention in the applied community,
there hasn't be theoretical work that even formalizes the desired guarantees.
In this paper, we propose a framework for continual learning through the
framework of feature extraction -- namely, one in which features, as well as a
classifier, are being trained with each environment. When the features are
linear, we design an efficient gradient-based algorithm $\mathsf{DPGD}$, that
is guaranteed to perform well on the current environment, as well as avoid
catastrophic forgetting. In the general case, when the features are non-linear,
we show such an algorithm cannot exist, whether efficient or not.


title: Multimarked Spatial Search by Continuous-Time Quantum Walk

abstract:
  The quantum-walk-based spatial search problem aims to find a marked vertex
using a quantum walk on a graph with marked vertices. We describe a framework
for determining the computational complexity of spatial search by
continuous-time quantum walk on arbitrary graphs by providing a recipe for
finding the optimal running time and the success probability of the algorithm.
The quantum walk is driven by a Hamiltonian that is obtained from the adjacency
matrix of the graph modified by the presence of the marked vertices. The
success of our framework depends on the knowledge of the eigenvalues and
eigenvectors of the adjacency matrix. The spectrum of the modified Hamiltonian
is then obtained from the roots of the determinant of a real symmetric matrix,
whose dimension depends on the number of marked vertices. We show all steps of
the framework by solving the spatial searching problem on the Johnson graphs
with fixed diameter and with two marked vertices. Our calculations show that
the optimal running time is $O(\sqrt{N})$ with asymptotic probability $1+o(1)$,
where $N$ is the number of vertices.