title:
On the Stability of Super-Resolution and a Beurling-Selberg Type
  Extremal Problem

abstract:
  Super-resolution estimation is the problem of recovering a stream of spikes
(point sources) from the noisy observation of a few number of its first
trigonometric moments. The performance of super-resolution is recognized to be
intimately related to the separation between the spikes to recover. A novel
notion of stability of the Fisher information matrix (FIM) of the
super-resolution problem is introduced, when the minimal eigenvalue of the FIM
is not asymptotically vanishing. The regime where the minimal separation is
inversely proportional to the number of acquired moments is considered. It is
shown that there is a separation threshold above which the eigenvalues of the
FIM can be bounded by a quantity that does not depend on the number of moments.
The proof relies on characterizing the connection between the stability of the
FIM and a generalization of the Beurling-Selberg box approximation problem.


title: Quantification of 242Pu with a Microcalorimeter Gamma Spectrometer

abstract:
  We report measurements of the 103-keV and 159-keV gamma ray signatures of
242Pu using microcalorimetry. This is the first observation of these gamma rays
in a non-destructive measurement of an unprepared sample, and so represents an
important advance in nuclear material accountancy. The measurement campaign
also serves as the first demonstration of a field campaign with a portable
microcalorimeter gamma-ray spectrometer. For the 103-keV gamma ray we report an
improved centroid energy and emission probability.


title: Quantized MIMO: Channel Capacity and Spectrospatial Power Distribution

abstract:
  Millimeter wave systems suffer from high power consumption and are
constrained to use low resolution quantizers --digital to analog and analog to
digital converters (DACs and ADCs). However, low resolution quantization leads
to reduced data rate and increased out-of-band emission noise. In this paper, a
multiple-input multiple-output (MIMO) system with linear transceivers using low
resolution DACs and ADCs is considered. An information-theoretic analysis of
the system to model the effect of quantization on spectrospatial power
distribution and capacity of the system is provided. More precisely, it is
shown that the impact of quantization can be accurately described via a linear
model with additive independent Gaussian noise. This model in turn leads to
simple and intuitive expressions for spectrospatial power distribution of the
transmitter and a lower bound on the achievable rate of the system.
Furthermore, the derived model is validated through simulations and numerical
evaluations, where it is shown to accurately predict both spectral and spatial
power distributions.


title:
Quantitative bounds for large deviations of heavy tailed random
  variables

abstract:
  We quantify the error in the local central limit theorem for heavy-tailed
random variables outside their CLT range. This in turn gives us quantitative
bounds on the total variation distance between the conditional law and its
limiting distribution.


title:
Absolutely Continuous Spectrum For Schr\"odinger Operators With Random
  Decaying Matrix Potentials on The Strip

abstract:
  We consider a family of random Schr\"odinger operators on the discrete strip
with decaying random $\ell^2$ matrix potential. We prove that the spectrum is
almost surely pure absolutely continuous, apart from random, possibly embedded
eigenvalues, which may accumulate at band edges.


title: Persistent Homology for Effective Non-Prehensile Manipulation

abstract:
  This work explores the use of topological tools for achieving effective
non-prehensile manipulation in cluttered, constrained workspaces. In
particular, it proposes the use of persistent homology as a guiding principle
in identifying the appropriate non-prehensile actions, such as pushing, to
clean a cluttered space with a robotic arm so as to allow the retrieval of a
target object. Persistent homology enables the automatic identification of
connected components of blocking objects in the space without the need for
manual input or tuning of parameters. The proposed algorithm uses this
information to push groups of cylindrical objects together and aims to minimize
the number of pushing actions needed to reach to the target. Simulated
experiments in a physics engine using a model of the Baxter robot show that the
proposed topology-driven solution is achieving significantly higher success
rate in solving such constrained problems relatively to state-of-the-art
alternatives from the literature. It manages to keep the number of pushing
actions low, is computationally efficient and the resulting decisions and
motion appear natural for effectively solving such tasks.


title: Entropy, Symmetry, and the Difficulty of Self-Replication

abstract:
  The defining property of an artificial physical self-replicating system, such
as a self-replicating robot, is that it has the ability to make copies of
itself from basic parts. Three questions that immediately arises in the study
of such systems are: 1) How complex is the whole robot in comparison to each
basic part ? 2) How disordered can the parts be while having the robot
successfully replicate ? 3) What design principles can enable complex
self-replicating systems to function in disordered environments generation
after generation ? Consequently, much of this article focuses on exploring
different concepts of entropy as a measure of disorder, and how symmetries can
help in reliable self replication, both at the level of assembly (by reducing
the number of wrong ways that parts could be assembled), and also as a parity
check when replicas manufacture parts generation after generation. The
mathematics underpinning these principles that quantify artificial physical
self-replicating systems are articulated here by integrating ideas from
information theory, statistical mechanics, ergodic theory, group theory, and
integral geometry.


title: Distance-regular Cayley graphs over dicyclic groups

abstract:
  The characterization of distance-regular Cayley graphs originated from the
problem of identifying strongly regular Cayley graphs, or equivalently, regular
partial difference sets. In this paper, a classification of distance-regular
Cayley graphs on dicyclic groups is obtained. More specifically, it is shown
that every distance-regular Cayley graph on a dicyclic group is a complete
graph, a complete multipartite graph, or a non-antipodal bipartite
distance-regular graph with diameter $3$ satisfying some additional conditions.


title: Condensation Droplet Sieve

abstract:
  Large droplets emerging during dropwise condensation impair surface
properties such as anti-fogging/frosting ability and heat transfer efficiency.
How to spontaneously detach massive randomly distributed droplets with
controlled sizes has remained a great challenge. Herein, we present a general
solution called condensation droplet sieve, through fabricating microscale
thin-walled lattice (TWL) structures coated with a superhydrophobic layer.
Growing droplets were observed to jumped off this TWL surface with 100%
probability once becoming slightly larger than the lattices. The maximum radius
and residual volume of droplets were strictly confined to 16 {\mu}m and 3.2
nl/mm2 respectively, greatly surpassing the current state of the art. We reveal
that this extremely efficient jumping is attributed to the large tolerance of
coalescence mismatch and effective isolation of droplets between neighbouring
lattices. Our work provides a new perspective for the design and fabrication of
high-performance anti-dew materials.


title: Revisiting the ringdown of GW150914

abstract:
  We examine recent claims that evidence for an overtone in the ringdown of the
GW150914 binary black hole merger was a result of noise anomalies. We cannot
reproduce these claims, finding that our previous analysis of this event is
robust to data analysis choices and consistent with the expectation that strain
after the peak is well described as a superposition of quasinormal modes of the
remnant black hole. We discuss the meaning and implications of establishing
that any specific ringdown mode was detected, and argue that it is misguided to
expect actual LIGO-Virgo data to inform the discussion of whether or why the
merger looks linear.


title: Tractable Boolean and Arithmetic Circuits

abstract:
  Tractable Boolean and arithmetic circuits have been studied extensively in AI
for over two decades now. These circuits were initially proposed as "compiled
objects," meant to facilitate logical and probabilistic reasoning, as they
permit various types of inference to be performed in linear-time and a
feed-forward fashion like neural networks. In more recent years, the role of
tractable circuits has significantly expanded as they became a computational
and semantical backbone for some approaches that aim to integrate knowledge,
reasoning and learning. In this article, we review the foundations of tractable
circuits and some associated milestones, while focusing on their core
properties and techniques that make them particularly useful for the broad aims
of neuro-symbolic AI.


title:
Learning fair representation with a parametric integral probability
  metric

abstract:
  As they have a vital effect on social decision-making, AI algorithms should
be not only accurate but also fair. Among various algorithms for fairness AI,
learning fair representation (LFR), whose goal is to find a fair representation
with respect to sensitive variables such as gender and race, has received much
attention. For LFR, the adversarial training scheme is popularly employed as is
done in the generative adversarial network type algorithms. The choice of a
discriminator, however, is done heuristically without justification. In this
paper, we propose a new adversarial training scheme for LFR, where the integral
probability metric (IPM) with a specific parametric family of discriminators is
used. The most notable result of the proposed LFR algorithm is its theoretical
guarantee about the fairness of the final prediction model, which has not been
considered yet. That is, we derive theoretical relations between the fairness
of representation and the fairness of the prediction model built on the top of
the representation (i.e., using the representation as the input). Moreover, by
numerical experiments, we show that our proposed LFR algorithm is
computationally lighter and more stable, and the final prediction model is
competitive or superior to other LFR algorithms using more complex
discriminators.


title: Prompt-Guided Injection of Conformation to Pre-trained Protein Model

abstract:
  Pre-trained protein models (PTPMs) represent a protein with one fixed
embedding and thus are not capable for diverse tasks. For example, protein
structures can shift, namely protein folding, between several conformations in
various biological processes. To enable PTPMs to produce task-aware
representations, we propose to learn interpretable, pluggable and extensible
protein prompts as a way of injecting task-related knowledge into PTPMs. In
this regard, prior PTPM optimization with the masked language modeling task can
be interpreted as learning a sequence prompt (Seq prompt) that enables PTPMs to
capture the sequential dependency between amino acids. To incorporate
conformational knowledge to PTPMs, we propose an interaction-conformation
prompt (IC prompt) that is learned through back-propagation with the
protein-protein interaction task. As an instantiation, we present a
conformation-aware pre-trained protein model that learns both sequence and
interaction-conformation prompts in a multi-task setting. We conduct
comprehensive experiments on nine protein datasets. Results confirm our
expectation that using the sequence prompt does not hurt PTPMs' performance on
sequence-related tasks while incorporating the interaction-conformation prompt
significantly improves PTPMs' performance on tasks where conformational
knowledge counts. We also show the learned prompts can be combined and extended
to deal with new complex tasks.


title:
Chern Number Tunable Quantum Anomalous Hall Effect in Monolayer
  Transitional Metal Oxides via Manipulating Magnetization Orientation

abstract:
  Although much effort has been made to explore quantum anomalous Hall effect
(QAHE) in both theory and experiment, the QAHE systems with tunable Chern
numbers are yet limited. Here, we theoretically propose that NiAsO$_3$ and
PdSbO$_3$, monolayer transitional metal oxides, can realize QAHE with tunable
Chern numbers via manipulating their magnetization orientations. When the
magnetization lies in the \textit{x-y} plane and all mirror symmetries are
broken, the low-Chern-number (i.e., $\mathcal{C}=\pm1$) phase emerges. When the
magnetization exhibits non-zero \textit{z}-direction component, the system
enters the high-Chern-number (i.e., $\mathcal{C}=\pm3$) phase, even in the
presence of canted magnetization. The global band gap can approach the
room-temperature energy scale in monolayer PdSbO$_3$ (23.4 meV), when the
magnetization is aligned to \textit{z}-direction. By using Wannier-based
tight-binding model, we establish the phase diagram of magnetization induced
topological phase transition. Our work provides a high-temperature QAHE system
with tunable Chern number for the practical electronic application.


title: Scalar perturbation of gravitating double-kink solutions

abstract:
  In this letter, a two-dimensional (2D) gravity-scalar model is studied. This
model supports interesting double-kink solutions, and the corresponding metric
solutions can be derived analytically. Depending on a tunable parameter $c$,
the metric can be symmetric or asymmetric. The Schr\"odinger-like equation for
normal modes of the physical linear perturbation is derived. As $c$ varies, the
effective potential can have one or two singular barriers. If $c$ is larger
than a critical value, the zero mode will be normalizable, despite of the
appearance of a strong repulsive singularity. The double-kink solution is
always stable against linear perturbations.


title:
Parallel Successive Learning for Dynamic Distributed Model Training over
  Heterogeneous Wireless Networks

abstract:
  Federated learning (FedL) has emerged as a popular technique for distributing
model training over a set of wireless devices, via iterative local updates (at
devices) and global aggregations (at the server). In this paper, we develop
parallel successive learning (PSL), which expands the FedL architecture along
three dimensions: (i) Network, allowing decentralized cooperation among the
devices via device-to-device (D2D) communications. (ii) Heterogeneity,
interpreted at three levels: (ii-a) Learning: PSL considers heterogeneous
number of stochastic gradient descent iterations with different mini-batch
sizes at the devices; (ii-b) Data: PSL presumes a dynamic environment with data
arrival and departure, where the distributions of local datasets evolve over
time, captured via a new metric for model/concept drift. (ii-c) Device: PSL
considers devices with different computation and communication capabilities.
(iii) Proximity, where devices have different distances to each other and the
access point. PSL considers the realistic scenario where global aggregations
are conducted with idle times in-between them for resource efficiency
improvements, and incorporates data dispersion and model dispersion with local
model condensation into FedL. Our analysis sheds light on the notion of cold
vs. warmed up models, and model inertia in distributed machine learning. We
then propose network-aware dynamic model tracking to optimize the model
learning vs. resource efficiency tradeoff, which we show is an NP-hard
signomial programming problem. We finally solve this problem through proposing
a general optimization solver. Our numerical results reveal new findings on the
interdependencies between the idle times in-between the global aggregations,
model/concept drift, and D2D cooperation configuration.


title: Improved Bounds for Fractional Online Matching Problems

abstract:
  Online bipartite matching with one-sided arrival and its variants have been
extensively studied since the seminal work of Karp, Vazirani, and Vazirani
(STOC 1990). Motivated by real-life applications with dynamic market
structures, e.g. ride-sharing, two generalizations of the classical one-sided
arrival model are proposed to allow non-bipartite graphs and to allow all
vertices to arrive online. Namely, online matching with general vertex arrival
is introduced by Wang and Wong (ICALP 2015), and fully online matching is
introduced by Huang et al. (JACM 2020).
  In this paper, we study the fractional versions of the two models. We improve
three out of the four state-of-the-art upper and lower bounds of the two
models. For fully online matching, we design a $0.6$-competitive algorithm and
prove no algorithm can be $0.613$-competitive. For online matching with general
vertex arrival, we prove no algorithm can be $0.584$-competitive. Moreover, we
give an arguably more intuitive algorithm for the general vertex arrival model,
compared to the algorithm of Wang and Wong, while attaining the same
competitive ratio of $0.526$.


title:
A Deformed Supersymmetric $w_{1+\infty}$ Symmetry in the Celestial
  Conformal Field Theory

abstract:
  By using the $K$-free complex bosons and the $K$-free complex fermions, we
construct the ${\cal N}=2$ supersymmetric $W_{\infty}^{K,K}$ algebra which is
the matrix generalization of previous ${\cal N}=2$ supersymmetric $W_{\infty}$
algebra. By twisting this ${\cal N}=2$ supersymmetric $W_{\infty}^{K,K}$
algebra, we obtain the ${\cal N}=1$ supersymmetric $W_{\infty}^{K}$ algebra
which is the matrix generalization of known ${\cal N}=1$ supersymmetric
topological $W_{\infty}$ algebra. From this two-dimensional symmetry algebra,
we propose the operator product expansion (OPE) between the soft graviton and
gravitino (as a first example), at nonzero deformation parameter, in the
supersymmetric Einstein-Yang-Mills theory explicitly. Other six OPEs between
the graviton, gravitino, gluon and gluino can be determined completely. At
vanishing deformation parameter, we reproduce the known result of Fotopoulos,
Stieberger, Taylor and Zhu on the above OPEs via celestial holography.


title:
Jury Learning: Integrating Dissenting Voices into Machine Learning
  Models

abstract:
  Whose labels should a machine learning (ML) algorithm learn to emulate? For
ML tasks ranging from online comment toxicity to misinformation detection to
medical diagnosis, different groups in society may have irreconcilable
disagreements about ground truth labels. Supervised ML today resolves these
label disagreements implicitly using majority vote, which overrides minority
groups' labels. We introduce jury learning, a supervised ML approach that
resolves these disagreements explicitly through the metaphor of a jury:
defining which people or groups, in what proportion, determine the classifier's
prediction. For example, a jury learning model for online toxicity might
centrally feature women and Black jurors, who are commonly targets of online
harassment. To enable jury learning, we contribute a deep learning architecture
that models every annotator in a dataset, samples from annotators' models to
populate the jury, then runs inference to classify. Our architecture enables
juries that dynamically adapt their composition, explore counterfactuals, and
visualize dissent.


title:
Deep Deterministic Independent Component Analysis for Hyperspectral
  Unmixing

abstract:
  We develop a new neural network based independent component analysis (ICA)
method by directly minimizing the dependence amongst all extracted components.
Using the matrix-based R{\'e}nyi's $\alpha$-order entropy functional, our
network can be directly optimized by stochastic gradient descent (SGD), without
any variational approximation or adversarial training. As a solid application,
we evaluate our ICA in the problem of hyperspectral unmixing (HU) and refute a
statement that "\emph{ICA does not play a role in unmixing hyperspectral
data}", which was initially suggested by \cite{nascimento2005does}. Code and
additional remarks of our DDICA is available at
https://github.com/hongmingli1995/DDICA.


title: SUD: Supervision by Denoising for Medical Image Segmentation

abstract:
  Training a fully convolutional network for semantic segmentation typically
requires a large, labeled dataset with little label noise if good
generalization is to be guaranteed. For many segmentation problems, however,
data with pixel- or voxel-level labeling accuracy are scarce due to the cost of
manual labeling. This problem is exacerbated in domains where manual annotation
is difficult, resulting in large amounts of variability in the labeling even
across domain experts. Therefore, training segmentation networks to generalize
better by learning from both labeled and unlabeled images (called
semi-supervised learning) is problem of both practical and theoretical
interest. However, traditional semi-supervised learning methods for
segmentation often necessitate hand-crafting a differentiable regularizer
specific to a given segmentation problem, which can be extremely
time-consuming. In this work, we propose "supervision by denoising" (SUD), a
framework that enables us to supervise segmentation models using their denoised
output as targets. SUD unifies temporal ensembling and spatial denoising
techniques under a spatio-temporal denoising framework and alternates denoising
and network weight update in an optimization framework for semi-supervision. We
validate SUD on three tasks-kidney and tumor (3D), and brain (3D) segmentation,
and cortical parcellation (2D)-demonstrating a significant improvement in the
Dice overlap and the Hausdorff distance of segmentations over supervised-only
and temporal ensemble baselines.


title:
Stellar models of Betelgeuse constrained using observed surface
  conditions

abstract:
  We study stellar models for Betelgeuse using the HR diagram and surface
abundances as observational constraints. Previous studies on Betelgeuse have
not systematically investigated the surface abundances, but we believe they can
be impacted by, and thus be used as an observational constraint for various
parameters such as initial mass, rotation, and overshoot scheme. We investigate
stellar models with varying initial mass as they evolve past the main sequence,
and we examine the red supergiant (RSG) properties in detail. For each mass, we
vary the initial rotation up to $\sim$ 300 $km\:s^{-1}$, and test two different
overshoot parameters. Overall, the acceptable initial mass range is 12 to 25
$M_\odot$, but for non-rotating models only, the range is decreased to 15 to 24
$M_\odot$. Also for rotating models, we find that $v/v_{\rm K} = 0.3$ is the
upper limit for initial rotation, as more rapidly rotating models are unable to
fit to Betelgeuse's surface abundances as an RSG. In addition, we report two
possibilities for the current stage of evolution, core helium burning or core
carbon burning and beyond. We find that certain 17 $M_\odot$ models could fit
to both stages. Finally, we discuss the implications of our results in the
context of merger scenarios which have been suggested as a mechanism to attain
the observed surface velocity of Betelgeuse.


title: A Simple Model of Dark Matter and CP Violation

abstract:
  We propose a simple model of dark matter and CP violation and consider the
associated triple and quadruple productions of 125 GeV Higgs bosons at the
Large Hadron Collider (LHC). In the model, the dark matter is a vector-like
dark fermion $(\bar{\chi}, \chi)$ interacting with the Standard Model only
through a complex messenger scalar $S$ which is an electroweak singlet. New
sources of CP violation reside in the most general scalar potential involving
the doublet $H$ and the singlet $S$, as well as in the dark Yukawa coupling
between $S$ and $(\bar{\chi}, \chi)$. We study current experimental constraints
from Higgs measurements, searches for new scalars at the LHC, precision
electroweak measurements, EDM measurements, dark matter relic density, as well
as direct and indirect detections of dark matter. A smoking-gun signature of CP
violation could come from the Higgs-to-Higgs decays, $h_3\to h_2h_1$, where
$h_3/h_2/h_1$ are the heaviest scalar, second heaviest scalar and the SM-like
125-GeV Higgs, respectively. Taking into account other Higgs-to-Higgs decays,
such as $h_3\to 2h_2$ and $h_3/h_2\to 2h_1$, then gives rise to novel $3h_1$
and $4h_1$ final states, which have yet to be searched for experimentally. We
present four benchmarks and show the event rates for $3h_1$ and $4h_1$ final
states could be as large as ${\cal O}(10)\ {\rm fb}$ and ${\cal O}(1)\ {\rm
fb}$, respectively, at the 14-TeV LHC. This work opens up a new frontier of
searching for triple and quadruple Higgs bosons at a high energy collider.


title:
Universal estimates and Liouville theorems for superlinear problems
  without scale invariance

abstract:
  We revisit rescaling methods for nonlinear elliptic and parabolic problems
and show that, by suitable modifications, they may be used for nonlinearities
that are not scale invariant even asymptotically and whose behavior can be
quite far from power like.
  In this enlarged framework, by adapting the doubling-rescaling method from
[37, 38], we show that the equivalence found there between universal estimates
and Liouville theorems remains valid. In the parabolic case we also prove a
Liouville type theorem for a rather large class of non scale invariant
nonlinearities. This leads to a number of new results for non scale invariant
elliptic and parabolic problems, concerning space or space-time singularity
estimates, initial and final blow-up rates, universal and a priori bounds for
global solutions, and decay rates in space and/or time.
  We illustrate our approach by a number of examples, which in turn give
indication about the optimality of the estimates and of the assumptions.


title: Emerging ordinary superhumps as the standard candle for WZ Sge stars

abstract:
  In Kato (2015, arXiv:1507.07659), I suggested that the magnitude when
ordinary superhumps appear can be a standard candle for WZ Sge stars. Using
Gaia EDR3 parallaxes, I studied 53 WZ Sge stars to examine this suggestion. The
analysis indicated that the absolute magnitudes when ordinary superhumps appear
are strongly dependent on orbital inclinations, which is consistent with what
is expected for the projected area for optical thick accretion disks. I showed
that there is a linear relation between these absolute magnitudes and
logarithmic amplitudes of early superhumps, which are also dependent on the
inclinations. I confirmed that the magnitude when ordinary superhumps appear
can be used as the standard candle for WZ Sge stars particularly when the
amplitude of early superhumps is observationally known. The resultant median
absolute magnitude when ordinary superhumps appear was $+$5.4 (for an average
inclination of 1 radian). A few objects with multiple rebrightenings, which are
good candidates for period bouncers, showed slightly fainter absolute
magnitudes, although the majority of the same class of objects follows the
relation. Using the relation, I derived an empirical relation between the
inclination and the amplitude of early superhumps. I applied the relation to
MASTER OT J030227.28$+$191754.5, which was initially considered as a possible
optical counterpart of the high-energy neutrino event IceCube-211125A, and
concluded that this WZ Sge star was one of the faintest before the outburst and
one of the brightest around the optical peak in terms of absolute magnitudes.


title: Is Abell 2261 a fossil galaxy cluster in a transitional dynamical state?

abstract:
  Abell 2261 (A2661) is a well-studied fossil cluster, but previous studies
give contradictory results on its dynamical states, such as its X-ray central
entropy and magnitude gap. To improve our understanding of its dynamical state,
we conduct multi-object spectroscopic observations with Hectospec on the MMT,
covering an area out to 5 virial radii from the cluster center, and get
improved completeness and membership. Using this new data, we calculate
multiple dynamical indicators including gaussianity, distance offset, and
velocity offset. These indicators suggest that A2261 is moderately relaxed.
However, Dressler-Shectman test reveals a group candidate to the south, at a
projected distance that is near the virial radius, and that overlaps with an
X-ray tail-like feature. One of the galaxies associated with that group would
be sufficiently bright to reduce the fossil magnitude gap. This raises the
possibility that A2261 could have recently transit in fossil status, if the
group had previously crossed the cluster and is only now found outside. In the
cluster outskirts, we see an extended feature of galaxies located on the
opposite side of the cluster to the group candidate. On even larger scales, we
find this feature connects, both on the sky and in velocity space, with a long
(~4.4Mpc) filamentary structure in the Sloan Digital Sky Survey data. This
could support the idea that a group was fed into the cluster through the
filament, temporarily breaking the fossil status, and resulting in a minor
merger that weakly disturbed the intracluster medium of the cluster.


title:
A comprehensive survey on computational learning methods for analysis of
  gene expression data in genomics

abstract:
  Computational analysis methods including machine learning have a significant
impact in the fields of genomics and medicine. High-throughput gene expression
analysis methods such as microarray technology and RNA sequencing produce
enormous amounts of data. Traditionally, statistical methods are used for
comparative analysis of the gene expression data. However, more complex
analysis for classification and discovery of feature genes or sample
observations requires sophisticated computational approaches. In this review,
we compile various statistical and computational tools used in analysis of
expression microarray data. Even though, the methods are discussed in the
context of expression microarray data, they can also be applied for the
analysis of RNA sequencing or quantitative proteomics datasets. We specifically
discuss methods for missing value (gene expression) imputation, feature gene
scaling, selection and extraction of features for dimensionality reduction, and
learning and analysis of expression data. We discuss the types of missing
values and the methods and approaches usually employed in their imputation. We
also discuss methods of data transformation and feature scaling viz.
normalization and standardization. Various approaches used in feature selection
and extraction are also reviewed. Lastly, learning and analysis methods
including class comparison, class prediction, and class discovery along with
their evaluation parameters are described in detail. We have described the
process of generation of a microarray gene expression data along with
advantages and limitations of the above-mentioned techniques. We believe that
this detailed review will help the users to select appropriate methods based on
the type of data and the expected outcome.


title:
A Machine Learning Approach for Material Type Logging and Chemical
  Assaying from Autonomous Measure-While-Drilling (MWD) Data

abstract:
  Understanding the structure and mineralogical composition of a region is an
essential step in mining, both during exploration (before mining) and in the
mining process. During exploration, sparse but high-quality data are gathered
to assess the overall orebody. During the mining process, boundary positions
and material properties are refined as the mine progresses. This refinement is
facilitated through drilling, material logging, and chemical assaying. Material
type logging suffers from a high degree of variability due to factors such as
the diversity in mineralization and geology, the subjective nature of human
measurement even by experts, and human error in manually recording results.
While laboratory-based chemical assaying is much more precise, it is
time-consuming and costly and does not always capture or correlate boundary
positions between all material types. This leads to significant challenges and
financial implications for the industry, as the accuracy of production
blasthole logging and assaying processes is essential for resource evaluation,
planning, and execution of mine plans. To overcome these challenges, this work
reports on a pilot study to automate the process of material logging and
chemical assaying. A machine learning approach has been trained on features
extracted from measurement-while-drilling (MWD) data, logged from autonomous
drilling systems (ADS). MWD data facilitate the construction of profiles of
physical drilling parameters as a function of hole depth. A hypothesis is
formed to link these drilling parameters to the underlying mineral composition.
The results of the pilot study discussed in this paper demonstrate the
feasibility of this process, with correlation coefficients of up to 0.92 for
chemical assays and 93% accuracy for material detection, depending on the
material or assay type and their generalization across the different spatial
regions.


title:
Comprehensive Performance Analysis of Homomorphic Cryptosystems for
  Practical Data Processing

abstract:
  Oblivious data processing has been an on and off topic for the last decade or
so. It provides great opportunities for secure data management and processing,
especially in the cloud. At the same time, modern computing resources seem to
be affordable enough to allow for practical use of homomorphic cryptography.
Yet, the availability of products that offer practical homomorphic data
processing is extremely scarce. As part of a project aimed at developing a
practical homomorphic data management platform, we have conducted an extensive
study of homomorphic cryptosystems' performance, the results of which are
presented in this work. For this work we chose the following five
cryptosystems: fully homomorphic HElib and SEAL, somewhat fully homomorphic
PyAono, and partially homomorphic Paillier and ElGamal. In the discussion of
the aggregated results, we suggest that partially homomorphic cryptosystems
could be used today in certain practical applications, whereas time has not yet
come for the fully homomorphic ones.


title:
Efficient ADMM Decoder for Non-binary LDPC Codes with
  Codeword-Independent Performance

abstract:
  In this paper, we devote to devise a non-binary low-density parity-check
(LDPC) decoder in Galois fields of characteristic two ($\mathbb{F}_{2^q}$) via
the alternating direction method of multipliers (ADMM) technique. Through the
proposed bit embedding technique and the decomposition technique of the
three-variables parity-check equation, an efficient ADMM decoding algorithm for
non-binary LDPC codes is proposed. The computation complexity in each ADMM
iteration is roughly $\mathcal{O}(nq)$, which is significantly lower than the
existing LDPC decoders. Moreover, we prove that the proposed decoder satisfies
the favorable property of the codeword-independent. Simulation results
demonstrate the outstanding performance of the proposed decoder in contrast
with state-of-the-art LDPC decoders.


title:
Coordinately Assisted Distillation of Quantum Coherence in Multipartite
  System

abstract:
  We investigate the issue of assisted coherence distillation in the asymptotic
limit (considering infinite copies of the resource states), by coordinately
performing the identical local operations on the auxiliary systems of each
copy. When we further restrict the coordinate operations to projective
measurements, the distillation process is branched into many sub-processes.
Finally, a simple formula is given that the assisted distillable coherence
should be the maximal average coherence of the residual states. The formula
makes the experimental research of assisted coherence distillation possible and
convenient, especially for the case that the system and its auxiliary are in
mixed states. By using the formula,\ we for the first time study the assisted
coherence distillation in multipartite systems. Monogamy-like inequalities are
given to constrain the distribution of the assisted distillable coherence in
the subsystems. Taking three-qubit system for example, we experimentally
prepare two types of tripartite correlated states, i.e., the $W$-type and
GHZ-type states in a linear optical setup, and experimentally explore the
assisted coherence distillation. Theoretical and experimental results agree
well to verify the distribution inequalities given by us. Three measures of
multipartite quantum correlation are also considered. The close relationship
between the assisted coherence distillation and the genuine multipartite
correlation is revealed.


title:
Global wellposedness of the 3D compressible Navier-Stokes equations with
  free surface in the maximal regularity class

abstract:
  This paper concerns the global well posedness issue of the Navier-Stokes
equations (CNS) describing barotropic compressible fluid flow with free surface
occupied in the three dimensional exterior domain. Combining the maximal
$L_p$-$L_q$ estimate and the $L_p$-$L_q$ decay estimate of solutions to the
linearized equations, we prove the unique existence of global in time solutions
in the time weighted maximal $L_p$-$L_q$ regularity class for some $p>2$ and
$q>3.$ Namely, the solution is bounded as $L_p$ in time and $L_q$ in space.
Compared with the previous results of the free boundary value problem of (CNS)
in unbounded domains, we relax the regularity assumption on the initial states,
which is the advantage by using the maximal $L_p$-$L_q$ regularity framework.
On the other hand, the equilibrium state of the moving boundary of the exterior
domain is not necessary the sphere. To our knowledge, this paper is the first
result on the long time solvability of the free boundary value problem of (CNS)
in the exterior domain.


title:
HDCoin: A Proof-of-Useful-Work Based Blockchain for Hyperdimensional
  Computing

abstract:
  Various blockchain systems and schemes have been proposed since Bitcoin was
first introduced by Nakamoto Satoshi as a distributed ledger. However,
blockchains usually face criticisms, particularly on environmental concerns as
their ``proof-of-work'' based mining process usually consumes a considerable
amount of energy which hardly makes any useful contributions to the real world.
Therefore, the concept of ``proof-of-useful-work'' (PoUW) is proposed to
connect blockchain with practical application domain problems so the
computation power consumed in the mining process can be spent on useful
activities, such as solving optimization problems or training machine learning
models. This paper introduces HDCoin, a blockchain-based framework for an
emerging machine learning scheme: the brain-inspired hyperdimensional computing
(HDC). We formulate the model development of HDC as a problem that can be used
in blockchain mining. Specifically, we define the PoUW under the HDC scenario
and develop the entire mining process of HDCoin. During mining, miners are
competing to obtain the highest test accuracy on a given dataset. The winner
also has its model recorded in the blockchain and are available for the public
as a trustworthy HDC model. In addition, we also quantitatively examine the
performance of mining under different HDC configurations to illustrate the
adaptive mining difficulty.


title:
Energy-efficient Dynamic-subarray with Fixed True-time-delay Design for
  Terahertz Wideband Hybrid Beamforming

abstract:
  Hybrid beamforming for Terahertz (THz) ultra-massive multiple-input
multiple-output (UM-MIMO) systems is a promising technology for 6G
space-air-ground integrated networks, which can overcome huge propagation loss
and offer unprecedented data rates. With ultra-wide bandwidth and
ultra-large-scale antennas array in THz band, the beam squint becomes one of
the critical problems which could reduce the array gain and degrade the data
rate substantially. However, the traditional phase-shifters-based hybrid
beamforming architectures cannot tackle this issue due to the frequency-flat
property of the phase shifters. In this paper, to combat the beam squint while
keeping high energy efficiency, a novel dynamic-subarray with fixed
true-time-delay (DS-FTTD) architecture is proposed. Compared to the existing
studies which use the complicated adjustable TTDs, the DS-FTTD architecture has
lower power consumption and hardware complexity, thanks to the low-cost FTTDs.
Furthermore, a low-complexity row-decomposition (RD) algorithm is proposed to
design hybrid beamforming matrices for the DS-FTTD architecture. Extensive
simulation results show that, by using the RD algorithm, the DS-FTTD
architecture achieves near-optimal array gain and significantly higher energy
efficiency than the existing architectures. Moreover, the spectral efficiency
of DS-FTTD architecture with the RD algorithm is robust to the imperfect
channel state information.


title: Large induced distance matchings in certain sparse random graphs

abstract:
  For a fixed integer $k\geqslant 2$, let $G\in \mathcal{G}(n,p)$ be a simple
connected graph on $n\rightarrow\infty$ vertices with the expected degree
$d=np$ satisfying $d\geqslant c$ and $d^{k-1}= o(n)$ for some large enough
constant $c$. We show that the asymptotical size of any maximal collection of
edges $M$ in $G$ such that no two edges in $M$ are within distance $k$, which
is called a distance $k$-matching, is between $ \frac{(k-1)n\log d}{4d^{k-1}}$
and $ \frac{k n \log d}{2d^{k-1}}$. We also design a randomized greedy
algorithm to generate one large distance $k$-matching in $G$ with asymptotical
size $ \frac{kn\log d}{4d^{k-1}}$. Our results partially generalize the results
on the size of the largest distance $k$-matchings from the case $k=2$ or $d=c$
for some large constant $c$.


title:
Learning from Imperfect Demonstrations via Adversarial Confidence
  Transfer

abstract:
  Existing learning from demonstration algorithms usually assume access to
expert demonstrations. However, this assumption is limiting in many real-world
applications since the collected demonstrations may be suboptimal or even
consist of failure cases. We therefore study the problem of learning from
imperfect demonstrations by learning a confidence predictor. Specifically, we
rely on demonstrations along with their confidence values from a different
correspondent environment (source environment) to learn a confidence predictor
for the environment we aim to learn a policy in (target environment -- where we
only have unlabeled demonstrations.) We learn a common latent space through
adversarial distribution matching of multi-length partial trajectories to
enable the transfer of confidence across source and target environments. The
learned confidence reweights the demonstrations to enable learning more from
informative demonstrations and discarding the irrelevant ones. Our experiments
in three simulated environments and a real robot reaching task demonstrate that
our approach learns a policy with the highest expected return.


title: Shotgun Assembly of Random Geometric Graphs

abstract:
  In a recent work, Huang and Tikhomirov considered the shotgun assembly for
Erd\H os-R\'enyi graphs $\mathcal G(n,p_n)$ with $p_n=n^{-\alpha}$, and showed
that the graph is reconstructable if $0<\alpha < \frac{1}{2}$ and not
reconstructable if $\frac{1}{2}<\alpha<1$ from its $1$-neighbourhoods. In this
article, we consider random geometric graphs $G(n,r)$, where $r^2=n^{-\alpha}$
and $ 0<\alpha<1$, on flat torus. Interestingly, unlike the results for the
Erd\H os-R\'enyi random graphs, we show that the random geometric graph is
always reconstructable from its 1-neighbourhoods.


title:
Doubled Quantum Spin Hall Effect with High-Spin Chern Number in
  $\alpha$-Antimonene and $\alpha$-Bismuthene

abstract:
  The discovery of quantum spin Hall effect has ignited the field of
topological physics with vast variety of exotic properties. Here, we present
the emergence of doubled quantum spin Hall effect in two dimensions
characterized with a high spin Chern number of ${\mathcal C_S}=2$ and two pairs
of helical edge states. Although is overlooked and invisible in topological
quantum chemistry and symmetry indicator theory, the already experimentally
synthesized $\alpha$-antimonene and $\alpha$-bismuthene are revealed as
realistic material candidates of predicted topological states with band
inversions emerging at generic $k$-points, rather than the high-symmetry
momenta. Remarkably, the nontrivial energy gap can be as large as 464 meV for
$\alpha$-bismuthene, indicating the high possibility of room-temperature
observation of the doubled quantum spin Hall effect. Moreover, a four-band
effective model is constructed to demonstrate further the feasibility of
attaining this type of nontrivial topology. Our results not only uncover a
novel topological character of antimony and bismuth, but will also facilitate
the experimental characterization of the previously overlooked hidden topology.


title:
Think-Aloud Verbalizations for Identifying User Experience Problems:
  Effects of Language Proficiency with Chinese Non-Native English Speakers

abstract:
  Subtle patterns in users' think-aloud (TA) verbalizations (i.e., utterances)
are shown to be telltale signs of user experience (UX) problems and used to
build artificial intelligence (AI) models or AI-assisted tools to help UX
evaluators identify UX problems automatically or semi-automatically. Despite
the potential of such verbalization patterns, they were uncovered with native
English speakers. As most people who speak English are non-native speakers, it
is important to investigate whether similar patterns exist in non-native
English speakers' TA verbalizations. As a first step to answer this question,
we conducted think-aloud usability testing with Chinese non-native English
speakers and native English speakers using three common TA protocols. We
compared their verbalizations and UX problems that they encountered to
understand the effects of language and TA protocols. Our findings show that
both language groups had similar amounts and proportions of verbalization
categories, encountered similar problems, and had similar verbalization
patterns that indicate UX problems. Furthermore, TA protocols did not
significantly affect the correlations between verbalizations and problems.
Based on the findings, we present three design implications for UX
practitioners and the design of AI-assisted analysis tools.


title:
Locally Differentially Private Distributed Deep Learning via Knowledge
  Distillation

abstract:
  Deep learning often requires a large amount of data. In real-world
applications, e.g., healthcare applications, the data collected by a single
organization (e.g., hospital) is often limited, and the majority of massive and
diverse data is often segregated across multiple organizations. As such, it
motivates the researchers to conduct distributed deep learning, where the data
user would like to build DL models using the data segregated across multiple
different data owners. However, this could lead to severe privacy concerns due
to the sensitive nature of the data, thus the data owners would be hesitant and
reluctant to participate. We propose LDP-DL, a privacy-preserving distributed
deep learning framework via local differential privacy and knowledge
distillation, where each data owner learns a teacher model using its own
(local) private dataset, and the data user learns a student model to mimic the
output of the ensemble of the teacher models. In the experimental evaluation, a
comprehensive comparison has been made among our proposed approach (i.e.,
LDP-DL), DP-SGD, PATE and DP-FL, using three popular deep learning benchmark
datasets (i.e., CIFAR10, MNIST and FashionMNIST). The experimental results show
that LDP-DL consistently outperforms the other competitors in terms of privacy
budget and model accuracy.


title: Hardy-Littlewood-Sobolev and related inequalities: stability

abstract:
  The purpose of this text is twofold. We present a review of the existing
stability results for Sobolev, Hardy-Littlewood-Sobolev (HLS) and related
inequalities. We also contribute to the topic with some observations on
constructive stability estimates for (HLS).


title:
Empirical Analysis and Offering of Various Historical Spot Instance
  Datasets

abstract:
  Public cloud service vendors provide a surplus of computing resources at a
cheaper price as a spot instance. The first spot instance provider, Amazon Web
Services(AWS), releases the history of spot price changes so that users can
estimate the availability of spot instances, and it triggered lots of research
work in literature. However, a change in spot pricing policy in 2017 rendered a
large portion of spot instance availability analysis work obsolete. Instead,
AWS publishes new spot instance datasets, the spot placement score, and the
interruption frequency for the previous month, but the new datasets received
far less attention than the spot price history dataset. Furthermore, the
datasets only provide the current dataset without historical information, and
there are few restrictions when querying the dataset. In addition, the various
spot datasets can provide contradicting information about spot instance
availability of the same entity at the same time quite often. In this work, we
develop a web-based spot instance data-archive service that provides historical
information of various spot instance datasets. We describe heuristics how we
overcame limitations imposed when querying multiple spot instance datasets. We
conducted a real-world evaluation measuring spot instance fulfillment and
interruption events to identify a credible spot instance dataset, especially
when different datasets represent contradictory information. Based on the
findings of the empirical analysis, we propose SpotScore, a new spot instance
metric that provides a spot recommendation score based on the composition of
spot price savings, spot placement score, and interruption frequency. The
data-archive service with the SpotScore is now publicly available as a web
service to speed up system research in the spot instance community and to
improve spot instance usage with a higher level of availability and cost
savings.


title: What Makes a Good Commit Message?

abstract:
  A key issue in collaborative software development is communication among
developers. One modality of communication is a commit message, in which
developers describe the changes they make in a repository. As such, commit
messages serve as an "audit trail" by which developers can understand how the
source code of a project has changed-and why. Hence, the quality of commit
messages affects the effectiveness of communication among developers. Commit
messages are often of poor quality as developers lack time and motivation to
craft a good message. Several automatic approaches have been proposed to
generate commit messages. However, these are based on uncurated datasets
including considerable proportions of poorly phrased commit messages. In this
multi-method study, we first define what constitutes a "good" commit message,
and then establish what proportion of commit messages lack information using a
sample of almost 1,600 messages from five highly active open source projects.
We find that an average of circa 44% of messages could be improved, suggesting
the use of uncurated datasets may be a major threat when commit message
generators are trained with such data. We also observe that prior work has not
considered semantics of commit messages, and there is surprisingly little
guidance available for writing good commit messages. To that end, we develop a
taxonomy based on recurring patterns in commit messages' expressions. Finally,
we investigate whether "good" commit messages can be automatically identified;
such automation could prompt developers to write better commit messages.


title:
Competitive Online Optimization with Multiple Inventories: A
  Divide-and-Conquer Approach

abstract:
  We study a competitive online optimization problem with multiple inventories.
In the problem, an online decision maker seeks to optimize the allocation of
multiple capacity-limited inventories over a slotted horizon, while the
allocation constraints and revenue function come online at each slot. The
problem is challenging as we need to allocate limited inventories under
adversarial revenue functions and allocation constraints, while our decisions
are coupled among multiple inventories and different slots. We propose a
divide-and-conquer approach that allows us to decompose the problem into
several single inventory problems and solve it in a two-step manner with almost
no optimality loss in terms of competitive ratio (CR). Our approach provides
new angles, insights and results to the problem, which differs from the
widely-adopted primal-and-dual framework. Specifically, when the gradients of
the revenue functions are bounded in a positive range, we show that our
approach can achieve a tight CR that is optimal when the number of inventories
is small, which is better than all existing ones. For an arbitrary number of
inventories, the CR we achieve is within an additive constant of one to a lower
bound of the best possible CR among all online algorithms for the problem. We
further characterize a general condition for generalizing our approach to
different applications. For example, for a generalized one-way trading problem
with price elasticity, where no previous results are available, our approach
obtains an online algorithm that achieves the optimal CR up to a constant
factor.


title:
Measuring and Reducing Model Update Regression in Structured Prediction
  for NLP

abstract:
  Recent advance in deep learning has led to rapid adoption of machine learning
based NLP models in a wide range of applications. Despite the continuous gain
in accuracy, backward compatibility is also an important aspect for industrial
applications, yet it received little research attention. Backward compatibility
requires that the new model does not regress on cases that were correctly
handled by its predecessor. This work studies model update regression in
structured prediction tasks. We choose syntactic dependency parsing and
conversational semantic parsing as representative examples of structured
prediction tasks in NLP. First, we measure and analyze model update regression
in different model update settings. Next, we explore and benchmark existing
techniques for reducing model update regression including model ensemble and
knowledge distillation. We further propose a simple and effective method,
Backward-Congruent Re-ranking (BCR), by taking into account the characteristics
of structured output. Experiments show that BCR can better mitigate model
update regression than model ensemble and knowledge distillation approaches.


title:
Performance of Hamamatsu VUV4 SiPMs for detecting liquid argon
  scintillation

abstract:
  Detection of light signals is crucial to a wide range of particle detectors.
In particular, efficient detection of vacuum ultraviolet (VUV) light will
provide new opportunities for some novel detectors currently being developed,
but is technically challenging. In this article, we characterized the
performance of Hamamatsu VUV4 silicon photomultipliers (SiPMs) for detecting
VUV argon scintillation light without wavelength shifting. Using a customized
cryogenic amplifier design, we operated two models of VUV4 SiPMs inside liquid
argon and thoroughly examined their direct sensitivities to liquid argon
scintillation. In addition to describing their cryogenic performance, we
measured a photon detection efficiency of $14.7^{+1.1}_{-2.4}$% and
$17.2^{+1.6}_{-3.0}$% at 128 nm for these two VUV4 models for operation at 4 V
of overvoltage, with the main uncertainty arising from the SiPM reflectivity
for VUV light.


title: Demonstration of non-Abelian frame charge flow using photonic crystals

abstract:
  In PT symmetric systems, non-Abelian frame charges enable multiband
topological characterization of the degeneracy nodes through the eigenvector
frame rotations. Interestingly, these frame charges can be viewed as an analogy
of the electric charge confined in conducting wire, flowing in momentum space
along nodal lines. Different from the traditional charge flow behaviour,
non-Abelian signatures emerge when braiding between adjacent band nodal lines
occurs, which flips the direction of the frame charge flow. In photonic
systems, we discover that the photonic {\Gamma} point serves as the source or
sink of such frame charge flow due to hidden braiding induced by the
often-ignored electrostatic mode at zero-frequency. By designing a biaxial
photonic crystal, the non-Abelian frame charge flow in momentum space is
experimentally demonstrated.


title:
Deep-Learning-Aided Extraction of Optical Constants in Scanning
  Near-Field Optical Microscopy

abstract:
  Scanning near-field optical microscopy is one of the most effective
techniques for spectroscopy of nanoscale systems. However, inferring optical
constants from the measured near-field signal can be challenging because of a
complicated and highly nonlinear interaction between the scanned probe and the
sample. Conventional fitting methods applied to this problem often suffer from
the lack of convergence or require human intervention. Here we develop an
alternative approach where the optical parameter extraction is automated by a
deep learning network. Compared to its traditional counterparts, our method
demonstrates superior accuracy, stability against noise, and computational
speed when applied to simulated near-field spectra.


title: 3D Object Detection from Images for Autonomous Driving: A Survey

abstract:
  3D object detection from images, one of the fundamental and challenging
problems in autonomous driving, has received increasing attention from both
industry and academia in recent years. Benefiting from the rapid development of
deep learning technologies, image-based 3D detection has achieved remarkable
progress. Particularly, more than 200 works have studied this problem from 2015
to 2021, encompassing a broad spectrum of theories, algorithms, and
applications. However, to date no recent survey exists to collect and organize
this knowledge. In this paper, we fill this gap in the literature and provide
the first comprehensive survey of this novel and continuously growing research
field, summarizing the most commonly used pipelines for image-based 3D
detection and deeply analyzing each of their components. Additionally, we also
propose two new taxonomies to organize the state-of-the-art methods into
different categories, with the intent of providing a more systematic review of
existing methods and facilitating fair comparisons with future works. In
retrospect of what has been achieved so far, we also analyze the current
challenges in the field and discuss future directions for image-based 3D
detection research.


title: Neural Tangent Kernel Analysis of Deep Narrow Neural Networks

abstract:
  The tremendous recent progress in analyzing the training dynamics of
overparameterized neural networks has primarily focused on wide networks and
therefore does not sufficiently address the role of depth in deep learning. In
this work, we present the first trainability guarantee of infinitely deep but
narrow neural networks. We study the infinite-depth limit of a multilayer
perceptron (MLP) with a specific initialization and establish a trainability
guarantee using the NTK theory. We then extend the analysis to an infinitely
deep convolutional neural network (CNN) and perform brief experiments


title:
Sr$_2$IrO$_4$/Sr$_3$Ir$_2$O$_7$ superlattice for a model 2D quantum
  Heisenberg antiferromagnet

abstract:
  Spin-orbit entangled pseudospins hold promise for a wide array of exotic
magnetism ranging from a Heisenberg antiferromagnet to a Kitaev spin liquid
depending on the lattice and bonding geometry, but many of the host materials
suffer from lattice distortions and deviate from idealized models in part due
to inherent strong pseudospin-lattice coupling. Here, we report on the
synthesis of a magnetic superlattice comprising the single ($n$=1) and the
double ($n$=2) layer members of the Ruddlesden-Popper series iridates
Sr$_{n+1}$Ir$_{n}$O$_{3n+1}$ alternating along the $c$-axis, and provide a
comprehensive study of its lattice and magnetic structures using scanning
transmission electron microscopy, resonant elastic and inelastic x-ray
scattering, third harmonic generation measurements and Raman spectroscopy. The
superlattice is free of the structural distortions reported for the parent
phases and has a higher point group symmetry, while preserving the magnetic
orders and pseudospin dynamics inherited from the parent phases, featuring two
magnetic transitions with two symmetry-distinct orders. We infer weaker
pseudospin-lattice coupling from the analysis of Raman spectra and attribute it
to frustrated magnetic-elastic couplings. Thus, the superlattice expresses a
near ideal network of effective spin-one-half moments on a square lattice.


title: SpinQ Triangulum: a commercial three-qubit desktop quantum computer

abstract:
  SpinQ Triangulum is the second generation of the desktop quantum computers
designed and manufactured by SpinQ Technology. SpinQ's desktop quantum computer
series, based on room temperature NMR spectrometer, provide light-weighted,
cost-effective and maintenance-free quantum computing platforms that aim to
provide real-device experience for quantum computing education for K-12 and
college level. These platforms also feature quantum control design capabilities
for studying quantum control and quantum noise. Compared with the first
generation product, the two-qubit SpinQ Gemini, Triangulum features a
three-qubit QPU, smaller dimensions (61 * 33 * 56 cm^3) and lighter (40 kg).
Furthermore, the magnetic field is more stable and the performance of quantum
control is more accurate. This paper introduces the system design of Triangulum
and its new features. As an example of performing quantum computing tasks, we
present the implementation of the Harrow-Hassidim-Lloyd (HHL) algorithm on
Triangulum, demonstrating Triangulum's capability of undertaking complex
quantum computing tasks. SpinQ will continue to develop desktop quantum
computing platform with more qubits. Meanwhile, a simplified version of SpinQ
Gemini, namely Gemini Mini (https://www.spinq.cn/products#geminiMini-anchor) ,
has been recently realised. Gemini Mini is much more portable (20* 35 * 26
cm^3, 14 kg) and affordable for most K-12 schools around the world.


title: Deep Residual Shrinkage Networks for EMG-based Gesture Identification

abstract:
  This work introduces a method for high-accuracy EMG based gesture
identification. A newly developed deep learning method, namely, deep residual
shrinkage network is applied to perform gesture identification. Based on the
feature of EMG signal resulting from gestures, optimizations are made to
improve the identification accuracy. Finally, three different algorithms are
applied to compare the accuracy of EMG signal recognition with that of DRSN.
The result shows that DRSN excel traditional neural networks in terms of EMG
recognition accuracy. This paper provides a reliable way to classify EMG
signals, as well as exploring possible applications of DRSN.


title: Space as relation

abstract:
  Here we will discuss the philosophical differences between an approach to the
deep nature of physical space based on the concept of coordinates and one based
on the concept of relation. The philosophical superiority of the second
approach will be analysed and maintained, attempting to bring it to its extreme
consequences. We will propose considering the concept of spatial dimension
completely superfluous in favour of an idea of space as a pure relational
structure, capable of including within itself the information necessary to
define the properties of the things in the world.


title: Geometric Algebra and Algebraic Geometry of Loop and Potts Models

abstract:
  We uncover a connection between two seemingly separate subjects in integrable
models: the representation theory of the affine Temperley-Lieb algebra, and the
algebraic structure of solutions to the Bethe equations of the XXZ spin chain.
We study the solution of Bethe equations analytically by computational
algebraic geometry, and find that the solution space encodes rich information
about the representation theory of Temperley-Lieb algebra. Using these
connections, we compute the partition function of the completely-packed loop
model and of the closely related random-cluster Potts model, on medium-size
lattices with toroidal boundary conditions, by two quite different methods. We
consider the partial thermodynamic limit of infinitely long tori and analyze
the corresponding condensation curves of the zeros of the partition functions.
Two components of these curves are obtained analytically in the full
thermodynamic limit.


title: One to Rule them All? A First Look at DNS over QUIC

abstract:
  The DNS is one of the most crucial parts of the Internet. Since the original
DNS specifications defined UDP and TCP as the underlying transport protocols,
DNS queries are inherently unencrypted, making them vulnerable to eavesdropping
and on-path manipulations. Consequently, concerns about DNS privacy have gained
attention in recent years, which resulted in the introduction of the encrypted
protocols DNS over TLS (DoT) and DNS over HTTPS (DoH). Although these protocols
address the key issues of adding privacy to the DNS, they are inherently
restrained by their underlying transport protocols, which are at strife with,
e.g., IP fragmentation or multi-RTT handshakes - challenges which are addressed
by QUIC. As such, the recent addition of DNS over QUIC (DoQ) promises to
improve upon the established DNS protocols. However, no studies focusing on
DoQ, its adoption, or its response times exist to this date - a gap we close
with our study. Our active measurements show a slowly but steadily increasing
adoption of DoQ and reveal a high week-over-week fluctuation, which reflects
the ongoing development process: As DoQ is still in standardization,
implementations and services undergo rapid changes. Analyzing the response
times of DoQ, we find that roughly 40% of measurements show considerably higher
handshake times than expected, which traces back to the enforcement of the
traffic amplification limit despite successful validation of the client's
address. However, DoQ already outperforms DoT as well as DoH, which makes it
the best choice for encrypted DNS to date.


title:
Detecting Structural Breaks in Foreign Exchange Markets by using the
  group LASSO technique

abstract:
  This article proposes an estimation method to detect breakpoints for linear
time series models with their parameters that jump scarcely. Its basic idea
owes the group LASSO (group least absolute shrinkage and selection operator).
The method practically provides estimates of such time-varying parameters of
the models. An example shows that our method can detect each structural
breakpoint's date and magnitude.


title: Graph Self-supervised Learning with Accurate Discrepancy Learning

abstract:
  Self-supervised learning of graph neural networks (GNNs) aims to learn an
accurate representation of the graphs in an unsupervised manner, to obtain
transferable representations of them for diverse downstream tasks. Predictive
learning and contrastive learning are the two most prevalent approaches for
graph self-supervised learning. However, they have their own drawbacks. While
the predictive learning methods can learn the contextual relationships between
neighboring nodes and edges, they cannot learn global graph-level similarities.
Contrastive learning, while it can learn global graph-level similarities, its
objective to maximize the similarity between two differently perturbed graphs
may result in representations that cannot discriminate two similar graphs with
different properties. To tackle such limitations, we propose a framework that
aims to learn the exact discrepancy between the original and the perturbed
graphs, coined as Discrepancy-based Self-supervised LeArning (D-SLA).
Specifically, we create multiple perturbations of the given graph with varying
degrees of similarity and train the model to predict whether each graph is the
original graph or a perturbed one. Moreover, we further aim to accurately
capture the amount of discrepancy for each perturbed graph using the graph edit
distance. We validate our method on various graph-related downstream tasks,
including molecular property prediction, protein function prediction, and link
prediction tasks, on which our model largely outperforms relevant baselines.


title:
Comparison and Combination of Sentence Embeddings Derived from Different
  Supervision Signals

abstract:
  We have recently seen many successful applications of sentence embedding
methods. It has not been well understood, however, what kind of properties are
captured in the resulting sentence embeddings, depending on the supervision
signals. In this paper, we focus on two types of sentence embeddings obtained
by using natural language inference (NLI) datasets and definition sentences
from a word dictionary and investigate their properties by comparing their
performance with the semantic textual similarity (STS) task using the STS data
partitioned by two perspectives: 1) the sources of sentences, and 2) the
superficial similarity of the sentence pairs, and their performance on the
downstream and probing tasks. We also demonstrate that combining the two types
of embeddings yields substantially better performances than respective models
on unsupervised STS tasks and downstream tasks.


title:
The statistical theory of self-gravitating collisionless dark matter
  flow and high order kinematic and dynamic relations for velocity correlations
  on small and large scales

abstract:
  Statistical theory for self-gravitating collisionless dark matter flow is not
fully developed because of 1) intrinsic complexity involving constant
divergence flow on small scale and irrotational flow on large scale; 2) lack of
self-closed description for peculiar velocity; and 3) mathematically
challenging. To better understand dark matter flow, kinematic and dynamic
relations among different statistical measures of velocity must be developed
for different types of flow. In this paper, a compact derivation is presented
to formulate general kinematic relations on any order for incompressible,
constant divergence, and irrotational flow. Results are validated by N-body
simulation. Dynamic relations can only be determined from self-closed
description of velocity evolution. On large scale, we found i) third order
correlation can be related to density correlation or pairwise velocity; ii)
effective viscosity in adhesion model originates from velocity fluctuations;
iii) negative viscosity is due to inverse energy cascade; iv) velocity
correlations follow $\propto a^{(q+2)/2}$ for odd q and $\propto a^{q/2}$ for
even q; v) overdensity is proportional to density correlation on the same
scale, $\langle\delta\rangle\propto\langle\delta\delta'\rangle$; vi) (reduced)
velocity dispersion is proportional to density correlation on the same scale;
vii) combined kinematic and dynamic relations determines exponential velocity
correlations on large scale. On small scale, self-closed description for
velocity evolution is developed by decomposing velocity into motion in halo and
motion of halo. Vorticity, enstrophy, and energy evolution can all be derived
from self-closed equation for velocity. Dynamic relation is derived to relate
third and second order correlations. Third moment of pairwise velocity is
determined by energy production rate $\epsilon_u$ or $\langle(\Delta
u_L)^3\rangle\propto\epsilon_uar$.


title: Dark Matter in Galaxy Clusters: a Parametric Strong Lensing Approach

abstract:
  We present a parametric strong lensing analysis of three massive clusters.
Our aim is to probe the inner shape of dark matter haloes, in particular the
existence of a core. We adopt the following working hypothesis: any
group/cluster scale dark matter clump introduced in the modelling should be
associated with a luminous counterpart. We also adopt some additional well
motivated priors in the analysis, even if this degrades the quality of the fit,
quantified using the RMS between the observed and model generated images. In
particular, in order to alleviate the degeneracy between the smooth underlying
component and the galaxy scale perturbers, we use the results from spectrocopic
campaigns by Bergamini et al. (2019) allowing to fix the mass of the galaxy
scale component. In the unimodal galaxy cluster AS1063, a cored mass model is
favored with respect to a non cored mass model. This is also the case in the
multimodal cluster MACSJ0416, but we consider that the RMS difference between
both models is not large enough to disentangle between a cored and a non cored
mass model. In the unimodal cluster MACSJ1206, we fail to reproduce the strong
lensing constraints using a parametric approach within the adopted working
hypothesis. We then sucessfully add a mild perturbation in the form of a
superposition of B-spline potentials which allows to get a decent fit
(RMS=0.5"), finally finding that a cored mass model is favored. Overall, our
analysis suggest evidence for cored cluster scale dark matter haloes. These
findings may be usefull to interpret within alternative dark matter scenario,
as self interacting dark matter. We propose a working hypothesis for parametric
strong lensing modelling where the quest for the best fit model will be
balanced by the quest for presenting a physically motivated mass model, in
particular by imposing priors.


title: Global stability of multi-group SAIRS epidemic models

abstract:
  We study a multi-group SAIRS-type epidemic model with vaccination. The role
of asymptomatic and symptomatic infectious individuals is explicitly considered
in the transmission pattern of the disease among the groups in which the
population is divided. This is a natural extension of the homogeneous mixing
SAIRS model with vaccination studied in Ottaviano et. al (2021). We provide a
global stability analysis for the model. We determine the value of the basic
reproduction number $\mathcal{R}_0$ and prove that the disease-free equilibrium
is globally asymptotically stable if $\mathcal{R}_0 < 1$. In the case of the
SAIRS model without vaccination, we prove the global asymptotic stability of
the disease-free equilibrium also when $\mathcal{R}_0=1$. Moreover, if
$\mathcal{R}_0 > 1$, the disease-free equilibrium is unstable and a unique
endemic equilibrium exists. First, we investigate the local asymptotic
stability of the endemic equilibrium and subsequently its global stability, for
two variations of the original model. Last, we provide numerical simulations to
compare the epidemic spreading on different networks topologies.


title: Withdrawal Success Estimation

abstract:
  Given a geometric Levy alpha-stable wealth process, a log-Levy alpha-stable
lower bound is constructed for the terminal wealth of a regular investing
schedule. Using a transformation, the lower bound is applied to a schedule of
withdrawals occurring after an initial investment. As a result, an upper bound
is described on the probability to complete a given schedule of withdrawals.
For withdrawals of a constant amount at equidistant times, necessary conditions
are given on the initial investment and parameters of the wealth process such
that $k$ withdrawals can be made with 95% confidence. When the initial
investment is in the S&P Composite Index and $2\leq k\leq 16$, then the initial
investment must be at least $k$ times the amount of each withdrawal.


title: Active Random Walks in One and Two Dimensions

abstract:
  We investigate active lattice walks: biased continuous time Random Walks
which perform orientational diffusion between lattice directions in one and two
spatial dimensions. We study the occupation probability of an arbitrary site on
the lattice in one and two dimensions, and derive exact results in the
continuum limit. Next, we compute the large deviation free energy function in
both one and two dimensions, which we use to compute the moments and the
cumulants of the displacements exactly at late times. Our exact results
demonstrate that the cross-correlations between the motion in the $x$ and $y$
directions in two dimensions persist in the large deviation function. We also
demonstrate that the large deviation function of an active particle with
diffusion displays two regimes, with differing diffusive behaviors. We verify
our analytic results with kinetic Monte Carlo simulations of an active lattice
walker in one and two dimensions.


title:
An effective weighted K-stability condition for polytopes and semisimple
  principal toric fibratons

abstract:
  The second author has shown that existence of extremal K\"ahler metrics on
semisimple principal toric fibrations is equivalent to a notion of weighted
uniform K-stability, read off from the moment polytope. The purpose of this
article is to prove various sufficient conditions of weighted uniform
K-stability which can be checked effectively and explore the low dimensional
new examples of extremal K\"ahler metrics it provides.


title:
Inverse problem for recovery of temporal component of source term for
  multi-term time fractional parabolic equation with nonlocal boundary datum

abstract:
  Inverse problem for multi-term fractional parabolic equation in two
dimensional space, involving m + 1 Caputo fractional derivatives in time, is
investigated. Presence of nonlocal boundary conditions leads to a
non-self-adjoint spectral problem. A bi-orthogonal system of functions is used
to construct the solution that involves double infinite series. Properties of
multinomial Mittag-Leffler function and eigenfunctions are used to prove the
classical nature of the solution under certain regularity conditions on the
given datum.


title: Automatic defect segmentation by unsupervised anomaly learning

abstract:
  This paper addresses the problem of defect segmentation in semiconductor
manufacturing. The input of our segmentation is a scanning-electron-microscopy
(SEM) image of the candidate defect region. We train a U-net shape network to
segment defects using a dataset of clean background images. The samples of the
training phase are produced automatically such that no manual labeling is
required. To enrich the dataset of clean background samples, we apply defect
implant augmentation. To that end, we apply a copy-and-paste of a random image
patch in the clean specimen. To improve robustness to the unlabeled data
scenario, we train the features of the network with unsupervised learning
methods and loss functions. Our experiments show that we succeed to segment
real defects with high quality, even though our dataset contains no defect
examples. Our approach performs accurately also on the problem of supervised
and labeled defect segmentation.


title: Beyond Windability: An FPRAS for The Six-Vertex Model

abstract:
  The six-vertex model is an important model in statistical physics and has
deep connections with counting problems. There have been some fully polynomial
randomized approximation schemes (FPRAS) for the six-vertex model [30, 10],
which all require that the constraint functions are windable. In the present
paper, we give an FPRAS for the six-vertex model with an unwindable constraint
function by Markov Chain Monte Carlo method (MCMC). Different from [10], we use
the Glauber dynamics to design the Markov Chain depending on a circuit
decomposition of the underlying graph. Moreover, we prove the rapid mixing of
the Markov Chain by coupling, instead of canonical paths in [10].


title:
The Reidemeister spectrum of 2-step nilpotent groups determined by
  graphs

abstract:
  In this paper we study the Reidemeister spectrum of 2-step nilpotent groups
associated to graphs. We develop three methods, based on the structure of the
graph, that can be used to determine the Reidemeister spectrum of the
associated group in terms of the Reidemeister spectra of groups associated to
smaller graphs. We illustrate our methods for several families of graphs,
including all the groups associated to a graph with at most four vertices. We
also apply our results in the context of topological fixed point theory for
nilmanifolds.


title: Kaon meson condensate in neutron star matter including hyperons

abstract:
  The recent measurement of the mass of neutron stars (PSR J1614 - 2230, PSR
J0348 + 0432, MSP J0740 + 6620) restricts the lower limit $\sim 2M_{\odot}$ of
the maximum mass of such compact stars, making it possible for dense matter to
exist in massive stars. The relativistic mean field theory with parameter sets
FSUGold including Kaon condensation is used to describe the properties of
neutron stars in $\beta$ equilibrium. Through careful choice of the parameter
of the $\sigma$-cut $c_{\sigma}$, we are able to produce a maximum mass neutron
star with Kaon condensation heavier than $2M_{\odot}$, and we find that the
parameter $\Lambda_{\nu}$ of the $\rho-\omega$ interaction term in this model
has a significant effect on $K^{-}$ condensation. In the case of using
$\sigma$-cut scheme, $K^{-}$ condensation occurs only when the $\rho-\omega$
interaction $\Lambda_{\nu}$ is switched off.


title:
Partial Encryption after Encoding for Security and Reliability in Data
  Systems

abstract:
  We consider the problem of secure and reliable communication over a noisy
multipath network. Previous work considering a noiseless version of our problem
proposed a hybrid universal network coding cryptosystem (HUNCC). By combining
an information-theoretically secure encoder together with partial encryption,
HUNCC is able to obtain security guarantees, even in the presence of an
all-observing eavesdropper. In this paper, we propose a version of HUNCC for
noisy channels (N-HUNCC). This modification requires four main novelties.
First, we present a network coding construction which is jointly, individually
secure and error-correcting. Second, we introduce a new security definition
which is a computational analogue of individual security, which we call
individual indistinguishability under chosen ciphertext attack (individual
IND-CCA1), and show that NHUNCC satisfies it. Third, we present a noise based
decoder for N-HUNCC, which permits the decoding of the encoded-thenencrypted
data. Finally, we discuss how to select parameters for N-HUNCC and its
error-correcting capabilities.


title:
Non-diagonal disorder enhanced topological properties of graphene with
  laser irradiation

abstract:
  Laser irradiation, as a versatile tool to tune topological properties of
electronic systems, is under intensive studies. Experimentally, laser
irradiation induced anomalous Hall effect in graphene has been observed (McIver
et al., Nat. Phys. 16, 38 (2020)). Disorder is ubiquitous in real materials,
and it has been shown that diagonal disorders, i.e., onsite disorder, can
enhance topological properties of time-periodically driven quantum materials
(Titum et al., Phys. Rev. Lett. 114, 056801 (2015)). Here, we investigate
circularly polarized laser irradiated graphene with non-diagonal disorders,
i.e., disordered tunneling, and find that disorder can induce nontrivial
topological properties, characterized by Bott index and the real-space Chern
number. Moreover, we show that one can turn on the laser irradiation
non-adiabatically to drive the disordered graphene into non-trivial topological
phase. It is a scheme which is especially interesting for experimental
implementations.


title:
Network Calculus with Flow Prolongation -- A Feedforward FIFO Analysis
  enabled by ML

abstract:
  The derivation of upper bounds on data flows' worst-case traversal times is
an important task in many application areas. For accurate bounds, model
simplifications should be avoided even in large networks. Network Calculus (NC)
provides a modeling framework and different analyses for delay bounding. We
investigate the analysis of feedforward networks where all queues implement
First-In First-Out (FIFO) service. Correctly considering the effect of data
flows onto each other under FIFO is already a challenging task. Yet, the
fastest available NC FIFO analysis suffers from limitations resulting in
unnecessarily loose bounds. A feature called Flow Prolongation (FP) has been
shown to improve delay bound accuracy significantly. Unfortunately, FP needs to
be executed within the NC FIFO analysis very often and each time it creates an
exponentially growing set of alternative networks with prolongations. FP
therefore does not scale and has been out of reach for the exhaustive analysis
of large networks. We introduce DeepFP, an approach to make FP scale by
predicting prolongations using machine learning. In our evaluation, we show
that DeepFP can improve results in FIFO networks considerably. Compared to the
standard NC FIFO analysis, DeepFP reduces delay bounds by 12.1% on average at
negligible additional computational cost.


title:
B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization
  Modules for Neural Architecture Search

abstract:
  The early pioneering Neural Architecture Search (NAS) works were multi-trial
methods applicable to any general search space. The subsequent works took
advantage of the early findings and developed weight-sharing methods that
assume a structured search space typically with pre-fixed hyperparameters.
Despite the amazing computational efficiency of the weight-sharing NAS
algorithms, it is becoming apparent that multi-trial NAS algorithms are also
needed for identifying very high-performance architectures, especially when
exploring a general search space. In this work, we carefully review the latest
multi-trial NAS algorithms and identify the key strategies including
Evolutionary Algorithm (EA), Bayesian Optimization (BO), diversification, input
and output transformations, and lower fidelity estimation. To accommodate the
key strategies into a single framework, we develop B2EA that is a surrogate
assisted EA with two BO surrogate models and a mutation step in between. To
show that B2EA is robust and efficient, we evaluate three performance metrics
over 14 benchmarks with general and cell-based search spaces. Comparisons with
state-of-the-art multi-trial algorithms reveal that B2EA is robust and
efficient over the 14 benchmarks for three difficulty levels of target
performance. The B2EA code is publicly available at
\url{https://github.com/snu-adsl/BBEA}.


title:
Simultaneous Detection of Optical Flares of the Magnetically Active M
  Dwarf Wolf 359

abstract:
  We present detections of stellar flares of Wolf\,359, an M6.5 dwarf in the
solar neighborhood (2.41~pc) known to be prone to flares due to surface
magnetic activity. The observations were carried out from 2020 April 23 to 29
with a 1-m and a 0.5-m telescope separated by nearly 300~km in Xinjiang, China.
In 27~hr of photometric monitoring, a total of 13 optical flares were detected,
each with a total energy of $\gtrsim 5 \times 10^{29}$~erg. The measured event
rate of about once every two hours is consistent with those reported previously
in radio, X-ray and optical wavelengths for this star. One such flare, detected
by both telescopes on 26 April, was an energetic event with a released energy
of nearly $10^{33}$~erg. The two-telescope lightcurves of this major event
sampled at different cadences and exposure timings enabled us to better
estimate the intrinsic flare profile, which reached a peak of up to 1.6 times
the stellar quiescent brightness, that otherwise would have been underestimated
in the observed flare amplitudes of about $0.4$ and $0.8$, respectively, with
single telescopes alone. The compromise between fast sampling so as to resolve
a flare profile versus a longer integration time for higher photometric
signal-to-noise provides a useful guidance in the experimental design of future
flare observations.


title: Learning Sound Localization Better From Semantically Similar Samples

abstract:
  The objective of this work is to localize the sound sources in visual scenes.
Existing audio-visual works employ contrastive learning by assigning
corresponding audio-visual pairs from the same source as positives while
randomly mismatched pairs as negatives. However, these negative pairs may
contain semantically matched audio-visual information. Thus, these semantically
correlated pairs, "hard positives", are mistakenly grouped as negatives. Our
key contribution is showing that hard positives can give similar response maps
to the corresponding pairs. Our approach incorporates these hard positives by
adding their response maps into a contrastive learning objective directly. We
demonstrate the effectiveness of our approach on VGG-SS and SoundNet-Flickr
test sets, showing favorable performance to the state-of-the-art methods.


title: Algorithms that get old : the case of generative algorithms

abstract:
  Generative IA networks, like the Variational Auto-Encoders (VAE), and
Generative Adversarial Networks (GANs) produce new objects each time when asked
to do so. However, this behavior is unlike that of human artists that change
their style as times go by and seldom return to the initial point. We
investigate a situation where VAEs are requested to sample from a probability
measure described by some empirical set. Based on recent works on Radon-Sobolev
statistical distances, we propose a numerical paradigm, to be used in
conjunction with a generative algorithm, that satisfies the two following
requirements: the objects created do not repeat and evolve to fill the entire
target probability measure.


title: Encoding and Decoding of Several Optimal Rank Metric Codes

abstract:
  This paper presents encoding and decoding algorithms for several families of
optimal rank metric codes whose codes are in restricted forms of symmetric,
alternating and Hermitian matrices. First, we show the evaluation encoding is
the right choice for these codes and then we provide easily reversible encoding
methods for each family. Later unique decoding algorithms for the codes are
described. The decoding algorithms are interpolation-based and can uniquely
correct errors for each code with rank up to $\lfloor(d-1)/2\rfloor$ in
polynomial-time, where $d$ is the minimum distance of the code.


title:
On the non-vanishing of modular $L$-values and fourier coefficients of
  cusp forms

abstract:
  We prove a non-vanishing result of modular L-values with quadratic twists,
where the quadratic discriminants are in a short interval. Using this fact and
Waldspurger's theorem, we improve the results of Balog-Ono[The chebotarev
density theorem in short intervals and some questions of Serre, Journal of
number theory. 91(2):356-371(2001)] on the non-vanishing of Fourier
coefficients of half-integral weight eigenform.


title: On 1-skeleton of the polytope of pyramidal tours with step-backs

abstract:
  Pyramidal tours with step-backs are Hamiltonian tours of a special kind: the
salesperson starts in city 1, then visits some cities in ascending order,
reaches city $n$, and returns to city 1 visiting the remaining cities in
descending order. However, in the ascending and descending direction, the order
of neighboring cities can be inverted (a step-back). It is known that on
pyramidal tours with step-backs the traveling salesperson problem can be solved
by dynamic programming in polynomial time.
  We define the polytope of pyramidal tours with step-backs
$\operatorname{PSB}(n)$ as the convex hull of the characteristic vectors of all
possible pyramidal tours with step-backs in a complete directed graph. The
1-skeleton of $\operatorname{PSB}(n)$ is the graph whose vertex set is the
vertex set of the polytope, and the edge set is the set of geometric edges or
one-dimensional faces of the polytope. We present a linear-time algorithm to
verify vertex adjacencies in 1-skeleton of the polytope $\operatorname{PSB}(n)$
and estimate the diameter and the clique number of 1-skeleton: the diameter is
bounded above by 4 and the clique number grows quadratically in the parameter
$n$.


title: EDCHO: High Order Exact Dynamic Consensus

abstract:
  This article addresses the problem of average consensus in a multi-agent
system when the desired consensus quantity is a time varying signal. Although
this problem has been addressed in existing literature by linear schemes, only
bounded steady-state errors have been achieved. Other approaches have used
first order sliding modes to achieve zero steady-state error, but suffer from
the chattering effect. In this work, we propose a new exact dynamic consensus
algorithm which leverages high order sliding modes, in the form of a
distributed differentiator to achieve zero steady-state error of the average of
time varying reference signals in a group of agents. Moreover, our proposal is
also able to achieve consensus to high order derivatives of the average signal,
if desired. An in depth formal study on the stability and convergence for EDCHO
is provided for undirected connected graphs. Finally, the effectiveness and
advantages of our proposal are shown with concrete simulation scenarios.


title:
$\mu$AFL: Non-intrusive Feedback-driven Fuzzing for Microcontroller
  Firmware

abstract:
  Fuzzing is one of the most effective approaches to finding software flaws.
However, applying it to microcontroller firmware incurs many challenges. For
example, rehosting-based solutions cannot accurately model peripheral behaviors
and thus cannot be used to fuzz the corresponding driver code. In this work, we
present $\mu$AFL, a hardware-in-the-loop approach to fuzzing microcontroller
firmware. It leverages debugging tools in existing embedded system development
to construct an AFL-compatible fuzzing framework. Specifically, we use the
debug dongle to bridge the fuzzing environment on the PC and the target
firmware on the microcontroller device. To collect code coverage information
without costly code instrumentation, $\mu$AFL relies on the ARM ETM hardware
debugging feature, which transparently collects the instruction trace and
streams the results to the PC. However, the raw ETM data is obscure and needs
enormous computing resources to recover the actual instruction flow. We
therefore propose an alternative representation of code coverage, which retains
the same path sensitivity as the original AFL algorithm, but can directly work
on the raw ETM data without matching them with disassembled instructions. To
further reduce the workload, we use the DWT hardware feature to selectively
collect runtime information of interest. We evaluated $\mu$AFL on two real
evaluation boards from two major vendors: NXP and STMicroelectronics. With our
prototype, we discovered ten zero-day bugs in the driver code shipped with the
SDK of STMicroelectronics and three zero-day bugs in the SDK of NXP. Eight CVEs
have been allocated for them. Considering the wide adoption of vendor SDKs in
real products, our results are alarming.


title: US Velocimetry in Participants with Aortoiliac Occlusive Disease

abstract:
  The accurate quantification of blood flow in aortoiliac arteries is
challenging but clinically relevant because local flow patterns can influence
atherosclerotic disease. To investigate the feasibility and clinical
application of two-dimensional blood flow quantification using high-frame-rate
contrast-enhanced US (HFR-CEUS) and particle image velocimetry (PIV), or US
velocimetry, in participants with aortoiliac stenosis. In this prospective
study, participants with a recently diagnosed aortoiliac stenosis underwent
HFR-CEUS measurements of the pre- and poststenotic vessel segments.
Two-dimensional quantification of blood flow was achieved by performing PIV
analysis, which was based on pairwise cross-correlation of the HFR-CEUS images.
Visual inspection of the entire data set was performed by five observers to
evaluate the ability of the technique to enable adequate visualization of blood
flow. The contrast-to-background ratio and average vector correlation were
calculated. In two participants who showed flow disturbances, the flow
complexity and vorticity were calculated. Results: 35 participants were
included. Visual scoring showed that flow quantification was achieved in 41 of
42 locations. In 25 locations, one or multiple issues occurred that limited
optimal flow quantification, including loss of correlation during systole,
shadow regions, a short vessel segment in the image plane, and loss of contrast
during diastole. In the remaining 16 locations, optimal quantification was
achieved. The contrast-to-background ratio was higher during systole than
during diastole, whereas the vector correlation was lower. Flow complexity and
vorticity were high in regions with disturbed flow. Blood flow quantification
with US velocimetry is feasible in patients with an aortoiliac stenosis, but
several challenges must be overcome before implementation into clinical
practice.


title: Data modelling recipes for SARS-CoV-2 wastewater-based epidemiology

abstract:
  Wastewater based epidemiology is recognized as one of the monitoring pillars,
providing essential information for pandemic management. Central in the
methodology are data modelling concepts for both communicating the monitoring
results but also for analysis of the signal. It is due to the fast development
of the field that a range of modelling concepts are used but without a coherent
framework. This paper provides for such a framework, focusing on robust and
simple concepts readily applicable, rather than applying latest findings from
e.g., machine learning. It is demonstrated that data preprocessing, most
important normalization by means of biomarkers and equal temporal spacing of
the scattered data, is crucial. In terms of the latter, downsampling to a
weekly spaced series is sufficient. Also, data smoothing turned out to be
essential, not only for communication of the signal dynamics but likewise for
regressions, nowcasting and forecasting. Correlation of the signal with
epidemic indicators require multivariate regression as the signal alone cannot
explain the dynamics but simple linear regression proofed to be a suitable tool
for compensation. It was also demonstrated that short term prediction (7 days)
is accurate with simple models (exponential smoothing or autoregressive models)
but forecast accuracy deteriorates fast for longer periods.