  For $C^{*}$-algebras $A$ and $B$, the operator space projective tensor
product $A\hat{\otimes}B$ and the Banach space projective tensor product
$A\otimes_{\gamma}B$ are shown to be symmetric. We also show that
$A\hat{\otimes}B$ is weakly Wiener algebra. Finally, quasi-centrality, and the
unitary group of $A\hat{\otimes}B$ are discussed.


  Limited flight range is a common problem for multicopters. To alleviate this
problem, we propose a method for finding the optimal speed and heading of a
multicopter when flying a given path to achieve the longest flight range. Based
on a novel multivariable extremum seeking controller with adaptive step size,
the method (a) does not require any power consumption model of the vehicle, (b)
can adapt to unknown disturbances, (c) can be executed online, and (d)
converges faster than the standard extremum seeking controller with constant
step size. We conducted indoor experiments to validate the effectiveness of
this method under different payloads and initial conditions, and showed that it
is able to converge more than 30% faster than the standard extremum seeking
controller. This method is especially useful for applications such as package
delivery, where the size and weight of the payload differ for different
deliveries and the power consumption of the vehicle is hard to model.


  A scheme of laser wakefield acceleration, when a relatively rare and long
bunch of non-relativistic or weakly-relativistic electrons is initially in
front of the laser pulse, is suggested and considered. The motion of test
electrons is studied both in the one-dimensional case (1D wakefield) and in the
three-dimensional laser wakefield excited in a plasma channel. It is shown that
the bunch is trapped, effectively compressed both in longitudinal and
transverse directions and accelerated to ultra-relativistic energies in the
region of first accelerating maximum of the wakefield. The accelerated bunch
has sizes much less than the plasma wavelength and relatively small energy
spread.


  The gap between the known randomized and deterministic local distributed
algorithms underlies arguably the most fundamental and central open question in
distributed graph algorithms. In this paper, we develop a generic and clean
recipe for derandomizing LOCAL algorithms. We also exhibit how this simple
recipe leads to significant improvements on a number of problem. Two main
results are:
  - An improved distributed hypergraph maximal matching algorithm, improving on
Fischer, Ghaffari, and Kuhn [FOCS'17], and giving improved algorithms for
edge-coloring, maximum matching approximation, and low out-degree edge
orientation. The first gives an improved algorithm for Open Problem 11.4 of the
book of Barenboim and Elkin, and the last gives the first positive resolution
of their Open Problem 11.10.
  - An improved distributed algorithm for the Lov\'{a}sz Local Lemma, which
gets closer to a conjecture of Chang and Pettie [FOCS'17], and moreover leads
to improved distributed algorithms for problems such as defective coloring and
$k$-SAT.


  This report presents an empirical evaluation of four algorithms for
automatically extracting keywords and keyphrases from documents. The four
algorithms are compared using five different collections of documents. For each
document, we have a target set of keyphrases, which were generated by hand. The
target keyphrases were generated for human readers; they were not tailored for
any of the four keyphrase extraction algorithms. Each of the algorithms was
evaluated by the degree to which the algorithm's keyphrases matched the
manually generated keyphrases. The four algorithms were (1) the AutoSummarize
feature in Microsoft's Word 97, (2) an algorithm based on Eric Brill's
part-of-speech tagger, (3) the Summarize feature in Verity's Search 97, and (4)
NRC's Extractor algorithm. For all five document collections, NRC's Extractor
yields the best match with the manually generated keyphrases.


  We point out that the works described by Foot et al. in arXiv:0706.1829
[hep-ph] and arXiv:0709.2750 [hep-ph] are derivatives of our work described in
arXiv:hep-th/0403039, the extended version of which was published in "Standard
Model and SU(5) GUT with Local Scale Invariance and the Weylon", AIP Conf.
Proc. 881 (2007) pp. 82, Melville, New York, 2006. We are wondering how many
motions (and publications!) they will go through before finally admitting that
they have re-discovered our model, and of course, as is the usual practice
these days, claiming afterwards to the world of their independent arrival at
our model. Reference to our original work is long overdue.


  We have performed accurate calculations of the hyperfine structure of the
$2\,^3\!P$ state in the $^9$Be atom with the help of highly optimized,
explicitly correlated functions, accounting for the leading finite nuclear
mass, radiative, nuclear structure and relativistic effects. By comparison with
measurements, we have determined the $^9$Be nuclear quadrupole moment to be
$Q_{\rm N} = 0.05350(14)$ barns, which is not only the most accurate result,
but also disagrees with previous determinations.


  Discovering relevant, but possibly hidden, variables is a key step in
constructing useful and predictive theories about the natural world. This brief
note explains the connections between three approaches to this problem: the
recently introduced information-bottleneck method, the computational mechanics
approach to inferring optimal models, and Salmon's statistical relevance basis.


  The "broad iron spectral features" are often seen in X-ray spectra of Active
Galactic Nuclei (AGN) and black-hole binaries (BHB). These features may be
explained either by the "relativistic disc reflection" scenario or the "partial
covering" scenario: It is hardly possible to determine which model is valid
from time-averaged spectral analysis. Thus, X-ray spectral variability has been
investigated to constrain spectral models. To that end, it is crucial to study
iron structure of BHBs in detail at short time-scales, which is, for the first
time, made possible with the Parallel-sum clocking (P-sum) mode of XIS
detectors on board Suzaku. This observational mode has a time-resolution of
7.8~ms as well as a CCD energy-resolution. We have carried out systematic
calibration of the P-sum mode, and investigated spectral variability of the BHB
GRS 1915+105. Consequently, we found that the spectral variability of GRS
1915+105 does not show iron features at sub-seconds. This is totally different
from variability of AGN such as 1H0707--495, where the variation amplitude
significantly drops at the iron K-energy band. This difference can be naturally
explained in the framework of the "partial covering" scenario.


  The problem of jam formation during the discharge by gravity of granular
material through a two-dimensional silo has a number of practical applications.
In many problems the estimation of the minimum outlet size which guarantees
that the time to the next jamming event is long enough is crucial. Assuming
that the time is modeled by an exponential distribution with two unknown
parameters, this goal translates to the optimal estimation of a non-linear
transformation of the parameters. We obtain $c$-optimum experimental designs
with that purpose, applying the graphic Elfving method. Since the optimal
designs depend on the nominal values of the parameters, a sensitivity study is
additionally provided. Finally, a simulation study checks the performance of
the approximations made, first with the Fisher Information matrix, then with
the linearization of the function to be estimated. The results are useful for
experimenting in a laboratory and translating then the results to a larger
scenario. Apart from the application a general methodology is developed in the
paper for the problem of precise estimation of a one-dimensional parametric
transformation in a non-linear model.


  Legacy AD/ADAS development from OEMs centers around developing functions on
ECUs using services provided by AUTOSAR Classic Platform (CP) to meet
automotive-grade and mass-production requirements. The AUTOSAR CP couples
hardware and software components statically and encounters challenges to
provide sufficient capacities for the processing of high-level intelligent
driving functions, whereas the new platform, AUTOSAR Adaptive Platform (AP) is
designed to support dynamically communication and provide richer services and
function abstractions for those resource-intensive (memory, CPU) applications.
Yet for both platforms, application development and the supporting system
software are still closely coupled together, and this makes application
development and the enhancement less scalable and flexible, resulting in longer
development cycles and slower time-to-market. This paper presents a
multi-layered, service-oriented intelligent driving operating system foundation
(we named it as Digital Foundation Platform) that provides abstractions for
easier adoption of heterogeneous computing hardware. It features a multi-layer
SOA software architecture with each layer providing adaptive service API at
north-bound for application developers. The proposed Digital Foundation
Platform (DFP) has significant advantages of decoupling hardware, operating
system core, middle-ware, functional software and application software
development. It provides SOA at multiple layers and enables application
developers from OEMs, to customize and develop new applications or enhance
existing applications with new features, either in autonomous domain or
intelligent cockpit domain, with great agility, and less code through
re-usability, and thus reduce the time-to-market.


  Large-amplitude collective dynamics of shape phase transition in the
low-lying states of 30-36Mg is investigated by solving the five-dimensional
(5D) quadrupole collective Schroedinger equation. The collective masses and
potentials of the 5D collective Hamiltonian are microscopically derived with
use of the constrained Hartree-Fock-Bogoliubov plus local quasiparticle RPA
method. Good agreement with the recent experimental data is obtained for the
excited 0+ states as well as the ground bands. For 30Mg, the shape coexistence
picture that the deformed excited 0+ state coexists with the spherical ground
state approximately holds. On the other hand, large-amplitude quadrupole-shape
fluctuations dominate in both the ground and the excited 0+ states in 32Mg, so
that the interpretation of 'coexisting spherical excited 0+ state' based on the
naive inversion picture of the spherical and deformed configurations does not
hold.


  The neutron resonance spin echo (NRSE) technique has the potential to
increase the Fourier time and energy resolution in neutron scattering by using
radio-frequency (rf) neutron spin-flippers. However, aberrations arising from
variations in the neutron path length between the rf flippers reduce the
polarization. Here, we develop and test a transverse static-field magnet, a
series of which are placed between the rf flippers, to correct for these
aberrations. The prototype correction magnet was both simulated in an NRSE
beamline using McStas, a Monte Carlo neutron ray-tracing software package, and
measured using neutrons. The results from the prototype demonstrate that this
static-field design corrects for transverse-field NRSE aberrations.


  Zero-shot voice conversion is becoming an increasingly popular research
topic, as it promises the ability to transform speech to sound like any
speaker. However, relatively little work has been done on end-to-end methods
for this task, which are appealing because they remove the need for a separate
vocoder to generate audio from intermediate features. In this work, we propose
LVC-VC, an end-to-end zero-shot voice conversion model that uses
location-variable convolutions (LVCs) to jointly model the conversion and
speech synthesis processes. LVC-VC utilizes carefully designed input features
that have disentangled content and speaker information, and it uses a neural
vocoder-like architecture that utilizes LVCs to efficiently combine them and
perform voice conversion while directly synthesizing time domain audio.
Experiments show that our model achieves especially well balanced performance
between voice style transfer and speech intelligibility compared to several
baselines.


  A possible method to solve the sign problem is developed by modifying the
original theory. Considering several modifications of the partition function,
the observable in the original theory is reconstructed from the identity
connecting the observables in the original and modified theories. We
demonstrate that our method gives the correct results even if the original
theory has the severe sign problem by using a simple 1-dimensional integral.


  This paper presents a teleoperation system that includes robot perception and
intent prediction from hand gestures. The perception module identifies the
objects present in the robot workspace and the intent prediction module which
object the user likely wants to grasp. This architecture allows the approach to
rely on traded control instead of direct control: we use hand gestures to
specify the goal objects for a sequential manipulation task, the robot then
autonomously generates a grasping or a retrieving motion using trajectory
optimization. The perception module relies on the model-based tracker to
precisely track the 6D pose of the objects and makes use of a state of the art
learning-based object detection and segmentation method, to initialize the
tracker by automatically detecting objects in the scene. Goal objects are
identified from user hand gestures using a trained a multi-layer perceptron
classifier. After presenting all the components of the system and their
empirical evaluation, we present experimental results comparing our pipeline to
a direct traded control approach (i.e., one that does not use prediction) which
shows that using intent prediction allows to bring down the overall task
execution time.


  Stutter removal is an essential scenario in the field of speech editing.
However, when the speech recording contains stutters, the existing text-based
speech editing approaches still suffer from: 1) the over-smoothing problem in
the edited speech; 2) lack of robustness due to the noise introduced by
stutter; 3) to remove the stutters, users are required to determine the edited
region manually. To tackle the challenges in stutter removal, we propose
FluentSpeech, a stutter-oriented automatic speech editing model. Specifically,
1) we propose a context-aware diffusion model that iteratively refines the
modified mel-spectrogram with the guidance of context features; 2) we introduce
a stutter predictor module to inject the stutter information into the hidden
sequence; 3) we also propose a stutter-oriented automatic speech editing (SASE)
dataset that contains spontaneous speech recordings with time-aligned stutter
labels to train the automatic stutter localization model. Experimental results
on VCTK and LibriTTS datasets demonstrate that our model achieves
state-of-the-art performance on speech editing. Further experiments on our SASE
dataset show that FluentSpeech can effectively improve the fluency of
stuttering speech in terms of objective and subjective metrics. Code and audio
samples can be found at https://github.com/Zain-Jiang/Speech-Editing-Toolkit.


  Starting from the definition of A-Fredholm and semi-A-Fredholm operator on
the standard module over a unital C*- algebra A, introduced in [8] and [4], we
construct various generalizations of these operators and obtain several results
as an analogue or a generalization of the results in [1], [2], [3],[7].
Moreover, we study also non-adjointable semi-A-Fredholm operators as a natural
continuation of the work in [6] on non-adjointable A-Fredholm operators and
obtain an analogue or a generalization in this setting of the results in [4],
[5].


  In this paper we consider evolution equations in the abstract Hilbert space
under the special conditions imposed on the operator at the right-hand side of
the equation. We establish the method that allows us to formulate the existence
and uniqueness theorem and find a solution in the form of a series on the root
vectors of the right-hand side. As an application we consider fractional
differential equations of various kinds. Such operators as the
Riemann-Liouville fractional differential operator, the Riesz potential, the
difference operator have been involved.


  Microscopes are vital pieces of equipment in much of biological research and
medical diagnostics. However, access to a microscope can represent a bottleneck
in research, especially in lower-income countries. `Smart' computer controlled
motorized microscopes, which can perform automated routines or acquire images
in a range of modalities are even more expensive and inaccessible. Developing
low-cost, open-source, smart microscopes enables more researchers to conceive
and execute optimized or more complex experiments. Here we present the
OpenFlexure Delta Stage, a 3D-printed microscope designed for researchers.
Powered by the OpenFlexure software stack, it is capable of performing
automated experiments. The design files and assembly instructions are freely
available under an open licence. Its intuitive and modular design -- along with
detailed documentation -- allows researchers to implement a variety of imaging
modes with ease. The versatility of this microscope is demonstrated by imaging
biological and non-biological samples (red blood cells with Plasmodium
parasites and colloidal particles in brightfield, epi-fluorescence, darkfield,
Rheinberg and differential phase contrast. We present the design strategy and
choice of tools to develop devices accessible to researchers from lower-income
countries, as well as the advantages of an open-source project in this context.
This microscope, having been open-source since its conception, has already been
built and tested by researchers around the world, promoting a community of
expertise and an environment of reproducibility in science.


  We present a theory of electronic transport in graphene in the presence of
randomly placed adsorbates. Our analysis predicts a marked asymmetry of the
conductivity about the Dirac point, as well as a negative weak-localization
magnetoresistivity. In the region of strong scattering, renormalization group
corrections drive the system further towards insulating behavior. These results
explain key features of recent experiments, and are validated by numerical
transport computations.


  The LOw Frequency ARray (LOFAR) was used to track the propagation of a TID
containing embedded plasma structures which generated type 1 asymmetric quasi
periodic scintillations (QPS: Maruyama, 1991) over a distance of >1200 km
across Northern Europe. Broadband trans ionospheric radio scintillation
observations of these phenomena are, to our knowledge, unreported in the
literature as is the ability to track asymmetric QPS generating plasma
structures over such a distance. Type 1 asymmetric QPS are characterised by an
initial broadband signal fade and enhancement which is then followed by
'ringing pattern' interference fringes. These are caused by diffractive
fringing as the radio signal transitions through regions of relatively steep
plasma density gradient at the trailing edge of the plasma structures. That the
QPS retained their characteristics consistently over the full observing window
implies that the plasma structures generating them likewise held their form for
several hours, and over the full 1200 km distance. The most likely TID
propagation altitude of 110 km was consistent with a persistent and non
blanketing sporadic E region detected by the Juliusruh ionosondes, and direct
measurements from co-located medium frequency radar. Co-temporal GNSS data was
used to establish that these plasma density variations were very small, with a
maximum likely amplitude of no more than +/- 0.02 TECu deviation from the
background average. The observations were made between 0430-0800 UT on 17
December 2018 under very quiet geophysical conditions which possibly indicated
a terrestrial source. Given the TID propagation direction, the source was
likely located at high-latitude.


  Medical images come in high resolutions. A high resolution is vital for
finding malignant tissues at an early stage. Yet, this resolution presents a
challenge in terms of modeling long range dependencies. Shallow transformers
eliminate this problem, but they suffer from quadratic complexity. In this
paper, we tackle this complexity by leveraging a linear self-attention
approximation. Through this approximation, we propose an efficient vision model
called HCT that stands for High resolution Convolutional Transformer. HCT
brings transformers' merits to high resolution images at a significantly lower
cost. We evaluate HCT using a high resolution mammography dataset. HCT is
significantly superior to its CNN counterpart. Furthermore, we demonstrate
HCT's fitness for medical images by evaluating its effective receptive
field.Code available at https://bit.ly/3ykBhhf


  Background: Insomnia makes people more physically unable of doing daily
duties, which results in a lack of strength, leads to lacking in strength.
However, the effects of insomnia on muscle function have not yet been
thoroughly investigated. So, the objectives of this study were to clarify how
insomnia contributes to the decrease of muscular function and to investigate
the mechanisms behind this phenomenon. Methods: To understand how insomnia
influence muscle function, we analyzed the expression level of factors
associated with muscle protein degradation, muscle protein synthesis , protein
synthesis and degradation pathways and muscle clock. Results: The results
showed that lower BMI and grip strength were observed in insomnia patients. The
mice in the sleep deprivation(SD) group saw a 7.01 g loss in body mass. The SD
group's tibialis anterior and gastrocnemius muscle mass decreased after 96 h of
SD). The grip strength reduced in SD group. Using the RT-PCR approaches, we
found a significant increase in muscle degradation factors expression in SD
group versus normal control group. Conclusions: Insomnia can impair muscle
function. The mechanism may be associated with the increased expression of
muscle degradation related factors , as well as the abnormal expression of
Clock gene.


  This paper reviews and summarizes human evaluation practices described in 97
style transfer papers with respect to three main evaluation aspects: style
transfer, meaning preservation, and fluency. In principle, evaluations by human
raters should be the most reliable. However, in style transfer papers, we find
that protocols for human evaluations are often underspecified and not
standardized, which hampers the reproducibility of research in this field and
progress toward better human and automatic evaluation methods.


  Phishing (password + fishing) is a form of cyber crime based on social
engineering and site spoofing techniques. The name of 'phishing' is a conscious
misspelling of the word 'fishing' and involves stealing confidential data from
a user's computer and subsequently using the data to steal the user's money. In
this paper, we study, discuss and propose the phishing attack stages and types,
technologies for detection of phishing web pages, and conclude our paper with
some important recommendations for preventing phishing for both consumer and
company.


  Sulfuric acid aerosol is suspected to be a major contributor to mortality and
morbidity associated with air pollution. The objective of the study is to
determine if exposure of human participants to anticipated levels of sulfuric
acid aerosol ($\sim 100\mu g/m^3 $) in the near future would have an adverse
effect on respiratory function. We used data from 28 adults exposed to sulfuric
acid for 4 hours in a controlled exposure chamber over a 3 day period with
repeated measures of pulmonary function (FEV1) recorded at 2-hour intervals.
Measurements were also recorded after 2 and 24 hours post exposure. We
formulated a linear mixed effect model for FEV1 with fixed effects (day of
treatment, hour, day-hour interaction, and smoking status), a random intercept
and an AR1 covariance structure to estimate the effect of aerosol exposure on
FEV1. We further assessed whether smoking status modified the exposure effects
and compared the analysis to the method used by Kerr et al.,1981. The findings
of the study show that the effect of day 3 exposure is negatively associated
with lung function (coefficient ($\beta$), -0.08; 95% CI, -0.16 to -0.01). A
weak negative association is also observed with increasing hours of exposure
($\beta$, -0.01; 95% CI, -0.03 to 0.00). Among the smokers, we found a
significant negative association with hours of exposure ($\beta$, -0.02; 95%
CI, -0.03 to -0.00), day 3 exposure ($\beta$, -0.11; 95% CI, -0.14 to -0.02)
and a borderline adverse effect for day 2 treatment ($\beta$, -0.06; 95% CI,
-0.14 to 0.03) whilst no significant association was observed for nonsmokers.
In conclusion, anticipated deposits of sulfuric acid aerosol in the near would
adversely affect respiratory function. The effect observed in smokers is
significantly more adverse than in nonsmokers.


  The growing popularity of cloud-based machine learning raises a natural
question about the privacy guarantees that can be provided in such a setting.
Our work tackles this problem in the context where a client wishes to classify
private images using a convolutional neural network (CNN) trained by a server.
Our goal is to build efficient protocols whereby the client can acquire the
classification result without revealing their input to the server, while
guaranteeing the privacy of the server's neural network.
  To this end, we design Gazelle, a scalable and low-latency system for secure
neural network inference, using an intricate combination of homomorphic
encryption and traditional two-party computation techniques (such as garbled
circuits). Gazelle makes three contributions. First, we design the Gazelle
homomorphic encryption library which provides fast algorithms for basic
homomorphic operations such as SIMD (single instruction multiple data)
addition, SIMD multiplication and ciphertext permutation. Second, we implement
the Gazelle homomorphic linear algebra kernels which map neural network layers
to optimized homomorphic matrix-vector multiplication and convolution routines.
Third, we design optimized encryption switching protocols which seamlessly
convert between homomorphic and garbled circuit encodings to enable
implementation of complete neural network inference.
  We evaluate our protocols on benchmark neural networks trained on the MNIST
and CIFAR-10 datasets and show that Gazelle outperforms the best existing
systems such as MiniONN (ACM CCS 2017) by 20 times and Chameleon (Crypto Eprint
2017/1164) by 30 times in online runtime. Similarly when compared with fully
homomorphic approaches like CryptoNets (ICML 2016) we demonstrate three orders
of magnitude faster online run-time.


  We extend the definition of the conditional min-entropy from bipartite
quantum states to bipartite quantum channels. We show that many of the
properties of the conditional min-entropy carry over to the extended version,
including an operational interpretation as a guessing probability when one of
the subsystems is classical. We then show that the extended conditional
min-entropy can be used to fully characterize when two bipartite quantum
channels are related to each other via a superchannel (also known as supermap
or a comb) that is acting on one of the subsystems. This relation is a
pre-order that extends the definition of "quantum majorization" from bipartite
states to bipartite channels, and can also be characterized with semidefinite
programming. As a special case, our characterization provides necessary and
sufficient conditions for when a set of quantum channels is related to another
set of channels via a single superchannel. We discuss the applications of our
results to channel discrimination, and to resource theories of quantum
processes. Along the way we study channel divergences, entropy functions of
quantum channels, and noise models of superchannels, including random unitary
superchannels, and doubly-stochastic superchannels. For the latter we give a
physical meaning as being completely-uniformity preserving.


  We uncover novel solutions of the 't Hooft anomaly matching conditions for
scalarless gauge theories with matter transforming according to higher
dimensional representations of the underlying gauge group. We argue that, if
the duals exist, they are gauge theories with fermions transforming according
to the defining representation of the dual gauge group. The resulting conformal
windows match the one stemming from the all-orders beta function results when
taking the anomalous dimension of the fermion mass to be unity which are also
very close to the ones obtained using the Schwinger-Dyson approximation. We use
the solutions to gain useful insight on the conformal window of the associated
electric theory. A consistent picture emerges corroborating previous results
obtained via different analytic methods and in agreement with first principle
lattice explorations.


  The popular Alternating Least Squares (ALS) algorithm for tensor
decomposition is efficient and easy to implement, but often converges to poor
local optima---particularly when the weights of the factors are non-uniform. We
propose a modification of the ALS approach that is as efficient as standard
ALS, but provably recovers the true factors with random initialization under
standard incoherence assumptions on the factors of the tensor. We demonstrate
the significant practical superiority of our approach over traditional ALS for
a variety of tasks on synthetic data---including tensor factorization on exact,
noisy and over-complete tensors, as well as tensor completion---and for
computing word embeddings from a third-order word tri-occurrence tensor.


  This paper presents a comprehensive investigation into the modeling of rotor
wake velocities, in a simplistic manner, using the Viscous Vortex Particle
Method (VVPM). The study aims to accurately simulate wind velocities in the
wake of helicopter rotors while comparing the VVPM with other established
methods such as Momentum theory, Blade element theory, and the Free wake
method. The VVPM is particularly promising due to its ability to simulate the
interaction of rotor blade vorticities with a natural and efficient approach.
The method employs a grid-free Lagrangian formulation, eliminating artificial
numerical dissipation in the solution. To demonstrate the efficacy of the
method, a Python code has been developed, enabling the visualisation of wake
velocities for basic scenarios.


  In recent years several compressed indexes based on variants of the
Burrows-Wheeler transformation have been introduced. Some of these index
structures far more complex than a single string, as was originally done with
the FM-index [Ferragina and Manzini, J. ACM 2005]. As such, there has been an
effort to better understand under which conditions such an indexing scheme is
possible. This led to the introduction of Wheeler graphs [Gagie it et al.,
Theor. Comput. Sci., 2017]. A Wheeler graph is a directed graph with edge
labels which satisfies two simple axioms. Wheeler graphs can be indexed in a
way which is space efficient and allows for fast traversal. Gagie et al. showed
that de Bruijn graphs, generalized compressed suffix arrays, and several other
BWT related structures can be represented as Wheeler graphs. Here we answer the
open question of whether or not there exists an efficient algorithm for
recognizing if a graph is a Wheeler graph. We demonstrate:(i) Recognizing if a
graph is a Wheeler graph is NP-complete for any edge label alphabet of size
$\sigma \geq 2$, even for DAGs. It can be solved in linear time for $\sigma
=1$; (ii) An optimization variant called Wheeler Graph Violation (WGV) which
aims to remove the minimum number of edges needed to obtain a Wheeler graph is
APX-hard, even for DAGs. Hence, unless P = NP, there exists constant $C > 1$
such that there is no $C$-approximation algorithm. We show conditioned on the
Unique Games Conjecture, for every constant $C \geq 1$, it is NP-hard to find a
$C$-approximation to WGV; (iii) The Wheeler Subgraph problem (WS) which aims to
find the largest Wheeler subgraph is in APX for $\sigma=O(1)$; (iv) For the
above problems there exist efficient exponential time exact algorithms, relying
on graph isomorphism being computed in strictly sub-exponential time; (v) A
class of graphs where the recognition problem is polynomial time solvable.


  Let $\{\tau_n\}_{n=1}^\infty$ and $\{\omega_m\}_{m=1}^\infty$ be two modular
Parseval frames for a Hilbert C*-module $\mathcal{E}$. Then for every $x \in
\mathcal{E}\setminus\{0\}$, we show that \begin{align} (1) \quad \quad \quad
\quad \|\theta_\tau x \|_0 \|\theta_\omega x \|_0 \geq \frac{1}{\sup_{n, m \in
\mathbb{N}} \|\langle \tau_n, \omega_m\rangle \|^2}. \end{align} We call
Inequality (1) as \textbf{Noncommutative
Donoho-Stark-Elad-Bruckstein-Ricaud-Torr\'{e}sani Uncertainty Principle}.
Inequality (1) is the noncommutative analogue of breakthrough
Ricaud-Torr\'{e}sani uncertainty principle \textit{[IEEE Trans. Inform. Theory,
2013]}. In particular, Inequality (1) extends Elad-Bruckstein uncertainty
principle \textit{[IEEE Trans. Inform. Theory, 2002]} and Donoho-Stark
uncertainty principle \textit{[SIAM J. Appl. Math., 1989]}.


  A counterintuitive effect of a repulsion between uncharged and charged
conductors is considered. Our numerical calculations prove this effect for
various axially symmetric systems of a neutral conductor in an electric field
of a point charge. The presence of a cavity in the conductor is very important
for the repulsion effect. Our simple analytical model explains this effect in
terms of specific spatial positions of induced charges.


  The goal of the relativistic heavy ion collisions (RHIC) program is to create
a state of matter where color degrees of freedom are deconfined. The dynamics
of matter in this state, in spite of the complexities of quantum
chromodynamics, is largely determined by the conservation laws of energy
momentum and color currents. Therefore it is possible to describe its main
features in hydrodynamic terms, the very short color neutralization time
notwithstanding. In this lecture we shall give a simple derivation of the
hydrodynamics of a color charged fluid, by generalizing the usual derivation of
hydrodynamics from kinetic theory to the non abelian case.


  The complex conductivity of a superconducting thin film is related to the
quasiparticle density, which depends on the physical temperature and can also
be modified by external pair breaking with photons and phonons. This
relationship forms the underlying operating principle of Kinetic Inductance
Detectors (KIDs), where the detection threshold is governed by the
superconducting energy gap. We investigate the electromagnetic properties of
thin-film aluminum that is proximitized with either a normal metal layer of
copper or a superconducting layer with a lower $T_C$, such as iridium, in order
to extend the operating range of KIDs. Using the Usadel equations along with
the Nam expressions for complex conductivity, we calculate the density of
states and the complex conductivity of the resulting bilayers to understand the
dependence of the pair breaking threshold, surface impedance, and intrinsic
quality factor of superconducting bilayers on the relative film thicknesses.
The calculations and analyses provide theoretical insights in designing
aluminum-based bilayer kinetic inductance detectors for detection of microwave
photons and athermal phonons at the frequencies well below the pair breaking
threshold of a pure aluminum film.


  We calculate the entropy of a two-dimensional Fermi Liquid(FL) using a model
with a contact interaction between fermions. We find that there are $T^2$
contributions to the entropy from interactions separate from those due to the
collective modes. These $T^2$ contributions arise from non-analytic corrections
to the real part of the self-energy which may be calculated from the leading
log dependence of the imaginary part of the self-energy through the
Kramers-Kronig relation. We find no evidence of a breakdown in Fermi Liquid
theory in 2D and conclude that FL in 2D are similar to 3D FL's.


  In the present paper we study the structure of the WKB series for the
polynomial potential $V(x)=x^N$ ($N$ even). In particular, we obtain relatively
simple recurrence formula of the coefficients $\s'_k$ of the semiclassical
approximation and of the WKB terms for the energy eigenvalues.


  Discoveries of RR Lyrae and Cepheid variable stars with multiple modes of
pulsation have increased tremendously in recent years. The Fourier spectra of
these stars can be quite complicated due to the large number of combination
frequencies that can exist between their modes. As a result, light-curve fits
to these stars often suffer from undesirable ringing effects that arise from
noisy observations and poor phase coverage. These non-physical overfitting
artifacts also occur when fitting the harmonics of single-mode stars as well.
Here we present a new method for fitting light curves that is much more robust
against these effects. We prove that the amplitude measurement problem is very
difficult (NP-hard) and provide a heuristic algorithm for solving it quickly
and accurately.


  Inspired by the similarity between the fractal Weierstrass function and
quantum systems with discrete scaling symmetry, we establish general conditions
under which the dynamics of a quantum system will exhibit fractal structure in
the time domain. As an example, we discuss the dynamics of the Loschmidt
amplitude and the zero-momentum occupation of a single particle moving in a
scale invariant $1/r^2$ potential. In order to show these conditions can be
realized in ultracold atomic gases, we perform numerical simulations with
practical experimental parameters, which shows that the dynamical fractal can
be observed in realistic time scales. The predication can be directly verified
in current cold atom experiments.


  We propose a protocol for sympathetically cooling neutral atoms without
destroying the quantum information stored in their internal states. This is
achieved by designing state-insensitive Rydberg interactions between the
data-carrying atoms and cold auxiliary atoms. The resulting interactions give
rise to an effective phonon coupling, which leads to the transfer of heat from
the data atoms to the auxiliary atoms, where the latter can be cooled by
conventional methods. This can be used to extend the lifetime of quantum
storage based on neutral atoms and can have applications for long quantum
computations. The protocol can also be modified to realize state-insensitive
interactions between the data and the auxiliary atoms but tunable and
non-trivial interactions among the data atoms, allowing one to simultaneously
cool and simulate a quantum spin-model.


  Let $G=(V,E)$ be a bipartite graph embedded in a plane (or $n$-holed torus).
Two subgraphs of $G$ differ by a {\it $Z$-transformation} if their symmetric
difference consists of the boundary edges of a single face---and if each
subgraph contains an alternating set of the edges of that face. For a given
$\phi: V \mapsto \mathbb Z^+$, $S_{\phi}$ is the set of subgraphs of $G$ in
which each $v\in V$ has degree $\phi(v)$. Two elements of $S_{\phi}$ are said
to be adjacent if they differ by a $Z$-transformation. We determine the
connected components of $S_{\phi}$ and assign a {\it height function} to each
of its elements.
  If $\phi$ is identically two, and $G$ is a grid graph, $S_{\phi}$ contains
the partitions of the vertices of $G$ into cycles. We prove that we can always
apply a series of $Z$-transformations to decrease the total number of cycles
provided there is enough ``slack'' in the corresponding height function. This
allows us to determine in polynomial time the minimal number of cycles into
which $G$ can be partitioned provided $G$ has a limited number of non-square
faces. In particular, we determine the Hamiltonicity of polyomino graphs in
$O(|V|^2)$ steps. The algorithm extends to $n$-holed-torus-embedded graphs that
have grid-like properties. We also provide Markov chains for sampling and
approximately counting the Hamiltonian cycles of $G$.


  In reverberant conditions with multiple concurrent speakers, each microphone
acquires a mixture signal of multiple speakers at a different location. In
over-determined conditions where the microphones out-number speakers, we can
narrow down the solutions to speaker images and realize unsupervised speech
separation by leveraging each mixture signal as a constraint (i.e., the
estimated speaker images at a microphone should add up to the mixture).
Equipped with this insight, we propose UNSSOR, an algorithm for
$\textbf{u}$nsupervised $\textbf{n}$eural $\textbf{s}$peech
$\textbf{s}$eparation by leveraging $\textbf{o}$ver-determined training
mixtu$\textbf{r}$es. At each training step, we feed an input mixture to a deep
neural network (DNN) to produce an intermediate estimate for each speaker,
linearly filter the estimates, and optimize a loss so that, at each microphone,
the filtered estimates of all the speakers can add up to the mixture to satisfy
the above constraint. We show that this loss can promote unsupervised
separation of speakers. The linear filters are computed in each sub-band based
on the mixture and DNN estimates through the forward convolutive prediction
(FCP) algorithm. To address the frequency permutation problem incurred by using
sub-band FCP, a loss term based on minimizing intra-source magnitude scattering
is proposed. Although UNSSOR requires over-determined training mixtures, we can
train DNNs to achieve under-determined separation (e.g., unsupervised monaural
speech separation). Evaluation results on two-speaker separation in reverberant
conditions show the effectiveness and potential of UNSSOR.


  In derivative-free and blackbox optimization, the objective function is often
evaluated through the execution of a computer program seen as a blackbox. It
can be noisy, in the sense that its outputs are contaminated by random errors.
Sometimes, the source of these errors is identified and controllable, in the
sense that it is possible to reduce the standard deviation of the stochastic
noise it generates. A common strategy to deal with such a situation is to
monotonically diminish this standard deviation, to asymptotically make it
converge to zero and ensure convergence of algorithms because the noise is
dismantled. This work presents MpMads, an algorithm which follows this
approach. However, in practice a reduction of the standard deviation increases
the computation time, and makes the optimization process long. Therefore, a
second algorithm called DpMads is introduced to explore another strategy, which
does not force the standard deviation to monotonically diminish. Although these
strategies are proved to be theoretically equivalents, tests on analytical
problems and an industrial blackbox are presented to illustrate practical
differences.


  Separating benign domains from domains generated by DGAs with the help of a
binary classifier is a well-studied problem for which promising performance
results have been published. The corresponding multiclass task of determining
the exact DGA that generated a domain enabling targeted remediation measures is
less well studied. Selecting the most promising classifier for these tasks in
practice raises a number of questions that have not been addressed in prior
work so far. These include the questions on which traffic to train in which
network and when, just as well as how to assess robustness against adversarial
attacks. Moreover, it is unclear which features lead a classifier to a decision
and whether the classifiers are real-time capable. In this paper, we address
these issues and thus contribute to bringing DGA detection classifiers closer
to practical use. In this context, we propose one novel classifier based on
residual neural networks for each of the two tasks and extensively evaluate
them as well as previously proposed classifiers in a unified setting. We not
only evaluate their classification performance but also compare them with
respect to explainability, robustness, and training and classification speed.
Finally, we show that our newly proposed binary classifier generalizes well to
other networks, is time-robust, and able to identify previously unknown DGAs.


  We extend topological string methods in order to perform WKB approximations
for quantum mechanical problems with higher order potentials efficiently. This
requires techniques for the evaluation of the relevant quantum periods for
Riemann surfaces beyond genus one. The basis of these quantum periods is fixed
using the leading behaviour of the classical periods. The full expansion of the
quantum periods is obtained using a system of Picard-Fuchs like operators for a
sequence of integrals of meromorphic forms of the second kind. Discrete
automorphisms of simple higher order potentials allow to view the corresponding
higher genus curves as covering of a genus one curve. In this case the quantum
periods can be alternatively obtained using the holomorphic anomaly solved in
the holomorphic limit within the ring of quasi modular forms of a congruent
subgroup of SL$(2,\mathbb{Z})$ as we check for a symmetric sextic potential.


  We consider the effect of weak uncorrelated quenched disorder (point defects)
on a strongly fluctuating flux-line liquid. We use a hydrodynamic model which
is based on mapping the flux-line system onto a quantum liquid of relativistic
charged bosons in 2+1 dimensions [P. Benetatos and M. C. Marchetti, Phys. Rev.
B 64, 054518, (2001)]. In this model, flux lines are allowed to be arbitrarily
curved and can even form closed loops. Point defects can be scalar or polar. In
the latter case, the direction of their dipole moments can be random or
correlated. Within the Gaussian approximation of our hydrodynamic model, we
calculate disorder-induced corrections to the correlation functions of the
flux-line fields and the elastic moduli of the flux-line liquid. We find that
scalar disorder enhances loop nucleation, and polar (magnetic) defects decrease
the tilt modulus.


  Binary sources of gravitational waves in the early inspiral phase are
accurately described by a post-Newtonian expansion in small velocity and weak
interaction. We compute the conservative dynamics to fifth and partial sixth
order using a non-relativistic effective field theory. We give predictions for
central observables and determine the required coefficients for the
construction of an Effective One-Body Hamiltonian, extending the applicability
of our results to the late inspiral and merger phases.


  The aim of this paper is to analyze a class of random motions which models
the motion of a particle on the real line with random velocity and subject to
the action of the friction. The speed randomly changes when a Poissonian event
occurs. We study the characteristic and the moment generating function of the
position reached by the particle at time $t>0$. We are able to derive the
explicit probability distributions in few cases for which discuss the
connections with the random flights. The moments are also widely analyzed.
  For the random motions having an explicit density law, further interesting
probabilistic interpretations emerge if we deal with them varying up a random
time. Essentially, we consider two different type of random times, namely
Bessel and Gamma times, which contain, as particular cases, some important
probability distributions (e.g. Gaussian, Exponential). In particular, for the
random processes built by means of these compositions, we derive the
probability distributions fixed the number of Poisson events.
  Some remarks on the possible extensions to the random motions in higher
spaces are proposed. We focus our attention on the persistent planar random
motion.


  We have investigated the magnetocaloric effect in single and polycrystalline
samples of quantum paraelectric EuTiO3 by magnetization and heat capacity
measurements. Single crystalline EuTiO3 shows antiferromagnetic ordering due to
Eu2+ magnetic moments below TN = 5.6 K. This compound shows a giant
magnetocaloric effect around its Neel temperature. The isothermal magnetic
entropy change is 49 Jkg-1K-1, the adiabatic temperature change is 21 K and the
refrigeration capacity is 500 JKg-1 for a field change of 7 T at TN. The single
crystal and polycrystalline samples show similar values of the magnetic entropy
change and adiabatic temperature changes. The large magnetocaloric effect is
due to suppression of the spin entropy associated with localized 4f moment of
Eu2+ ions. The giant magnetocaloric effect together with negligible hysteresis,
suggest that EuTiO3 could be a potential material for magnetic refrigeration
below 20 K.


  We raise the question of the realizability of permutation modules in the
context of Kahn's realizability problem for abstract groups and the $G$-Moore
space problem. Specifically, given a finite group $G$, we consider a collection
$\{M_i\}_{i=1}^n$ of finitely generated $\Z G$-modules that admit a submodule
decomposition on which $G$ acts by permuting the summands. Then we prove the
existence of connected finite spaces $X$ that realize each $M_i$ as its $i$-th
homology, $G$ as its group of self-homotopy equivalences $\E(X)$, and the
action of $G$ on each $M_i$ as the action of $\E(X)$ on $H_i(X; \Z)$.


  Reentry around non-conducting ventricular scar tissue, which often causes
lethal arrhythmias, is typically treated by rapid stimulation from an
implantable defibrillator. However, the mechanisms of termination (success and
failure) are poorly understood. To elucidate such mechanisms, we studied pacing
of anatomical reentry in 1-D and 2-D excitable cardiac media models. Our
results suggest that the existence of inhomogeneity in the reentry circuit is
essential for pacing termination of tachycardia to be successful. Considering
the role of such inhomogeneities may lead to more effective pacing algorithms.


  In this paper we discuss the boson/vortex duality by mapping the (3+1)D
Gross-Pitaevskii theory into an effective string theory in the presence of
boundaries. Via the effective string theory, we find the Seiberg-Witten map
between the commutative and the noncommutative tachyon field theories, and
consequently identify their soliton solutions with $D$-branes in the effective
string theory. We perform various checks of the duality map and the
identification of soliton solutions. This new insight between the
Gross-Pitaevskii theory and the effective string theory explains the similarity
of these two systems at quantitative level.


  Many interactive image segmentation techniques are based on semi-supervised
learning. The user may label some pixels from each object and the SSL algorithm
will propagate the labels from the labeled to the unlabeled pixels, finding
object boundaries. This paper proposes a new SSL graph-based interactive image
segmentation approach, using undirected and unweighted kNN graphs, from which
the unlabeled nodes receive contributions from other nodes (either labeled or
unlabeled). It is simpler than many other techniques, but it still achieves
significant classification accuracy in the image segmentation task. Computer
simulations are performed using some real-world images, extracted from the
Microsoft GrabCut dataset. The segmentation results show the effectiveness of
the proposed approach.


  Graph neural networks (GNNs) have shown impressive performance in recommender
systems, particularly in collaborative filtering (CF). The key lies in
aggregating neighborhood information on a user-item interaction graph to
enhance user/item representations. However, we have discovered that this
aggregation mechanism comes with a drawback, which amplifies biases present in
the interaction graph. For instance, a user's interactions with items can be
driven by both unbiased true interest and various biased factors like item
popularity or exposure. However, the current aggregation approach combines all
information, both biased and unbiased, leading to biased representation
learning. Consequently, graph-based recommenders can learn distorted views of
users/items, hindering the modeling of their true preferences and
generalizations. To address this issue, we introduce a novel framework called
Adversarial Graph Dropout (AdvDrop). It differentiates between unbiased and
biased interactions, enabling unbiased representation learning. For each
user/item, AdvDrop employs adversarial learning to split the neighborhood into
two views: one with bias-mitigated interactions and the other with bias-aware
interactions. After view-specific aggregation, AdvDrop ensures that the
bias-mitigated and bias-aware representations remain invariant, shielding them
from the influence of bias. We validate AdvDrop's effectiveness on five public
datasets that cover both general and specific biases, demonstrating significant
improvements. Furthermore, our method exhibits meaningful separation of
subgraphs and achieves unbiased representations for graph-based CF models, as
revealed by in-depth analysis. Our code is publicly available at
https://github.com/Arthurma71/AdvDrop.


  A previously successful model for purely hadronic total cross-sections, based
on QCD minijets and soft-gluon resummation, is here applied to the total
photoproduction cross section. We find that our model in the gamma p case
predicts a rise with energy stronger than in the pp -pbarp case.


  Foreign Object Debris (FOD) detection has attracted increased attention in
the area of machine learning and computer vision. However, a robust and
publicly available image dataset for FOD has not been initialized. To this end,
this paper introduces an image dataset of FOD, named FOD in Airports (FOD-A).
FOD-A object categories have been selected based on guidance from prior
documentation and related research by the Federal Aviation Administration
(FAA). In addition to the primary annotations of bounding boxes for object
detection, FOD-A provides labeled environmental conditions. As such, each
annotation instance is further categorized into three light level categories
(bright, dim, and dark) and two weather categories (dry and wet). Currently,
FOD-A has released 31 object categories and over 30,000 annotation instances.
This paper presents the creation methodology, discusses the publicly available
dataset extension process, and demonstrates the practicality of FOD-A with
widely used machine learning models for object detection.


  Although there is no force on the electron in Aharonov-Bohm solenoid effect,
the electron exerts a force on the solenoid related to the inequality of action
and reaction forces of two subsystems in three-system configuration. The AB
phase which is related to the force exerted by the electron on the solenoid .
The momentum changes of the mechanical oscillator are equal in magnitude and
opposite in sign to the changes in the momentum of the em fields. It is
proposed to investigate momentum changes of "micro" bodies producing magnetic
fields in AB systems which will clarify the nature of these effects. The
problem of magnetic fields shielded from the electron wave packet is also
discussed.


  We compute the rates for $q \bar{q}$ annihilation into charginos and
neutralinos by taking into account the effects of supersymmetric soft phases.
In particular, the phase of the $\mu$ parameter gains direct accessibility via
the production of dissimilar charginos and neutralinos. The phases of the
trilinear soft masses do not have a significant effect on the cross sections.
Our results can be important for sparticle searches at the LHC.


  With the emergence of the Quantum Internet, the need for advanced quantum
networking techniques has significantly risen. Various models of quantum
repeaters have been presented, each delineating a unique strategy to ensure
quantum communication over long distances. We focus on repeaters that employ
entanglement generation and swapping. This revolves around establishing remote
end-to-end entanglement through repeaters, a concept we denote as the
"quantum-native" repeaters (also called "first-generation" repeaters in some
literature). The challenges in routing with quantum-native repeaters arise from
probabilistic entanglement generation and restricted coherence time. Current
approaches use synchronized time slots to search for entanglement-swapping
paths, resulting in inefficiencies. Here, we propose a new set of asynchronous
routing protocols for quantum networks by incorporating the idea of maintaining
a dynamic topology in a distributed manner, which has been extensively studied
in classical routing for lossy networks, such as using a destination-oriented
directed acyclic graph (DODAG) or a spanning tree. The protocols update the
entanglement-link topology asynchronously, identify optimal
entanglement-swapping paths, and preserve unused direct-link entanglements. Our
results indicate that asynchronous protocols achieve a larger upper bound with
an appropriate setting and significantly higher entanglement rate than existing
synchronous approaches, and the rate increases with coherence time, suggesting
that it will have a much more profound impact on quantum networks as technology
advances.


  In this work we theoretically propose scattering cancellation-based cloaks
for heat waves that obey the Maxwell-Cattaneo equation. The proposed cloaks
possess carefully tailored diffusivity to cancel the dipole scattering from the
object that they surround, and thus can render a small object invisible in the
near and far fields, as demonstrated by full-wave finite-element simulations.
Mantle heat cloaking is further analyzed and proposed to simplify the design
and bring this cloaking technology one step closer to its practical
implementation, with promising applications in nanoelectronics and defense
related applications.


  Let $\mathcal{F}$ be a Morse-Bott foliation on the solid torus $T=S^1\times
D^2$ into $2$-tori parallel to the boundary and one singular central circle.
Gluing two copies of $T$ by some diffeomorphism between their boundaries, one
gets a lens space $L_{p,q}$ with a Morse-Bott foliation $\mathcal{F}_{p,q}$
obtained from $\mathcal{F}$ on each copy of $T$ and thus consisting of two
singluar circles and parallel $2$-tori. In the previous paper [O. Khokliuk, S.
Maksymenko, Journ. Homot. Rel. Struct., 2024, 18, 313-356] there were computed
weak homotopy types of the groups $\mathcal{D}^{lp}(\mathcal{F}_{p,q})$ of leaf
preserving (i.e. leaving invariant each leaf) diffeomorphisms of such
foliations. In the present paper it is shown that the inclusion of these groups
into the corresponding group $\mathcal{D}_{+}^{fol}(\mathcal{F}_{p,q})$ of
foliated (i.e. sending leaves to leaves) diffeomorphisms which do not
interchange singular circles are homotopy equivalences.


  We conjecture that the pseudogap is an inhomogeneous condensate above the
homogeneous state whose existence is granted by topological stability. We
consider the simplest possible order parameter theory that provides this
interpretation of the pseudogap and study its angular momentum states. The
normal state gap density, the breaking of the time reversal symmetry and the
checkerboard pattern are naturally explained under this view. The pseudogap is
a lattice of skyrmions and the inner weak local magnetic field falls below the
experimental threshold of observation given by NMR/NQR and $\mu$SR experiments.


  We give an example of a unital commutative complex Banach algebra having a
normalized state which is not a spectral state and admitting an extreme
normalized state which is not multiplicative. This disproves two results by
Golfarshchi and Khalilzadeh.


  Online scheduling has been a well studied and challenging research problem
over the last five decades since the pioneering work of Graham with immense
practical significance in various applications such as interactive parallel
processing, routing in communication networks, distributed data management,
client-server communications, traffic management in transportation, industrial
manufacturing and production. In this problem, a sequence of jobs is received
one by one in order by the scheduler for scheduling over a number of machines.
On arrival of a job, the scheduler assigns the job irrevocably to a machine
before the availability of the next job with an objective to minimize the
completion time of the scheduled jobs. This paper highlights the state of the
art contributions for online scheduling of a sequence of independent jobs on
identical and uniform related machines with a special focus on preemptive and
non-preemptive processing formats by considering makespan minimization as the
optimality criterion. We present the fundamental aspects of online scheduling
from a beginner's perspective along with a background of general scheduling
framework. Important competitive analysis results obtained by well-known
deterministic and randomized online scheduling algorithms in the literature are
presented along with research challenges and open problems. Two of the emerging
recent trends such as resource augmentation and semi-online scheduling are
discussed as a motivation for future research work.


  We demonstrate control over optical coupling between air-suspended carbon
nanotubes and air-mode nanobeam cavities by spectral tuning. Taking advantage
of the large dielectric screening effects caused by adsorbed molecules, laser
heating is used to blueshift the nanotube photoluminescence. Significant
increase of the cavity peak is observed when the nanotube emission is brought
into resonance, and the spontaneous emission enhancement is estimated from the
photoluminescence spectra. We find that the enhancement shows good correlation
to the spectral overlap of the nanotube emission and the cavity peak. Our
technique offers a convenient method for controlling the optical coupling of
air-suspended nanotubes to photonic structures.


  We introduce a framework for generating samples of a distribution given a
finite number of its moments, targeted to particle-based solutions of kinetic
equations and rarefied gas flow simulations. Our model, referred to as the
Wasserstein-Entropy distribution (WE), couples a physically-motivated
Wasserstein penalty term to the traditional maximum-entropy distribution (MED)
functions, which serves to regularize the latter. The penalty term becomes
negligible near the local equilibrium, reducing the proposed model to the MED,
known to reproduce the hydrodynamic limit. However, in contrast to the standard
MED, the proposed WE closure can cover the entire physically realizable moment
space, including the so-called Junk line. We also propose an efficient Monte
Carlo algorithm for generating samples of the unknown distribution which is
expected to outperform traditional non-linear optimization approaches used to
solve the MED problem. Numerical tests demonstrate that, given moments up to
the heat flux -- that is equivalent to the information contained in the
Chapman-Enskog distribution -- the proposed methodology provides a reliable
closure in the collision-dominated and early transition regime. Applications to
larger rarefaction demand information from higher-order moments, which can be
incorporated within the proposed closure.


  Robust principal component analysis (RPCA) is a widely used tool for
dimension reduction. In this work, we propose a novel non-convex algorithm,
coined Iterated Robust CUR (IRCUR), for solving RPCA problems, which
dramatically improves the computational efficiency in comparison with the
existing algorithms. IRCUR achieves this acceleration by employing CUR
decomposition when updating the low rank component, which allows us to obtain
an accurate low rank approximation via only three small submatrices.
Consequently, IRCUR is able to process only the small submatrices and avoid
expensive computing on the full matrix through the entire algorithm. Numerical
experiments establish the computational advantage of IRCUR over the
state-of-art algorithms on both synthetic and real-world datasets.


  We perform a detailed abundance analysis on integrated-light spectra of 20
globular clusters (GCs) in the early-type galaxy NGC 5128 (Centaurus A). The
GCs were observed with X-Shooter on the VLT. The cluster sample spans a
metallicity range of $-1.92 < $ [Fe/H] $< -0.13$ dex. Using theoretical
isochrones we compute synthetic integrated-light spectra and iterate the
individual abundances until the best fit to the observations is obtained. We
measured abundances of Mg, Ca, and Ti, and find a slightly higher enhancement
in NGC 5128 GCs with metallicities [Fe/H] < $-$0.75 dex, of the order of
$\sim$0.1 dex, than in the average values observed in the MW for GCs of the
same metallicity. If this $\alpha$-enhancement in the metal-poor GCs in NGC
5128 is genuine, it could hint at a chemical enrichment history different than
that experienced by the MW. We also measure Na abundances in 9 out of 20 GCs.
We find evidence for intra-cluster abundance variations in 6 of these clusters
where we see enhanced [Na/Fe] > $+$0.25 dex. We obtain the first abundance
measurements of Cr, Mn, and Ni for a sample of the GC population in NGC 5128
and find consistency with the overall trends observed in the MW, with a slight
enhancement ($<$0.1 dex) in the Fe-peak abundances measured in the NGC 5128.


  We discuss the issue of perturbativity in single-field inflationary models
with a phase of ultra slow-roll (USR) tailor suited to generate an order-one
abundance of primordial black holes (PBHs). More in detail, we impose the
condition that loop corrections made up of short-wavelength modes enhanced by
the USR dynamics do not alter the tree-level power spectrum of curvature
perturbations. In our analysis, the USR phase is preceded and followed by two
stages of ordinary slow-roll (SR), and we model the resulting SR/USR/SR
dynamics using both instantaneous and smooth transitions. Focusing on scales
relevant for CMB observations, we find that it is not possible, with these
arguments, to rule out the scenario of PBH formation via USR, not even in the
limit of instantaneous transition. However, we also find that loop corrections
of short modes on the power spectrum of long modes, even though not large
enough to violate perturbativity requirements, remain appreciable and, most
importantly, are not tamed in realistic realisations of smooth SR/USR/SR
transitions. This makes perturbativity a powerful theoretical tool to constrain
USR dynamics. We extend the analysis at any scale beyond those relevant for CMB
observations. We find that loop corrections of short modes remain within the
few percent if compared to the tree-level power spectrum. However, we also find
one notable exception of phenomenological relevance: we show that the so-called
dip in the power spectrum of curvature perturbation is an artifact of the
tree-level computation.


  Parametric converters are parametric amplifiers that mix two spatially
separate nondegenerate modes and are commonly used for amplifying and squeezing
microwave signals in quantum computing and sensing. In Josephson parametric
converters, the strong localized nonlinearity of the Josephson Junction limits
the amplification and squeezing, as well as the dynamic range, in current
devices. In contrast, a weak distributed nonlinearity can provide higher gain
and dynamic range, when implemented as a kinetic inductance (KI) nanowire of a
dirty superconductor, and has additional benefits such as resilience to
magnetic field, higher-temperature operation, and simplified fabrication. Here,
we propose, demonstrate, and analyze the performance of a KI parametric
converter that relies on the weak distributed nonlinearity of a KI nanowire.
The device utilizes three-wave mixing induced by a DC current bias. We
demonstrate its operation as a nondegenerate parametric amplifier with high
phase-sensitive gain, reaching two-mode amplification and deamplification of
$\sim$30 dB for two resonances separated by 0.8 GHz, in excellent agreement
with our theory of the device. We observe a dynamic range of -108~dBm at 30 dB
gain. Our device can significantly broaden applications of quantum-limited
signal processing devices including phase-preserving amplification and two-mode
squeezing.


  In this paper, we show how a presence of the exotic scalar, tensor weak
interactions in addition to the standard vector-axial (V-A) one may help to
distinguish the Dirac from Majorana neutrinos in the elastic scattering of
(anti)neutrino beam off the unpolarized electrons in the limit of vanishing
(anti)neutrino mass. We assume that the incoming (anti)neutrino beam comes from
the polarized muon decay at rest and is the left-right chiral mixture with
assigned direction of the transversal spin polarization with respect to the
production plane. We display that the azimuthal asymmetry in the angular
distribution of recoil electrons is generated by the interference terms between
the standard and exotic couplings, which are proportional to the transversal
(anti)neutrino spin polarization and independent of the neutrino mass. This
asymmetry for the Majorana neutrinos is distinct from the one for the Dirac
neutrinos through the absence of interference between the standard and tensor
couplings. Additionally, the interference term between the standard and scalar
coupling of the only left chiral neutrinos, absent in the Dirac case, appears.
We also indicate the possibility of utilizing the azimuthal asymmetry
measurements to search for the new CP-violating phases. Our analysis is
model-independent and consistent with the current upper limits on the
non-standard couplings.


  The recent deep X-ray surveys at both soft (0.5--2 keV) and hard (2--10 keV)
energies have greatly extended our knowledge of the X-ray source density and
spectral shapes at relatively faint fluxes adding further evidence on the fact
that discrete sources, mainly AGNs, are responsible for the X-ray background
(XRB) emission over a broad energy range. In addition the first complete
optically identified samples of soft X-ray sources are becoming available
allowing to test the XRB AGN synthesis models in the light of recent results.
In this paper I will briefly compare the model predictions with some new
observational data.


  Orthogonality is a much desired property for MIMO coding. It enables
symbol-wise decoding, where the errors in other symbol estimates do not affect
the result, thus providing an optimality that is worth pursuing. Another
beneficial property is a low complexity soft decision decoder, which for
orthogonal complex MIMO codes is known for two transmit (Tx) antennas i.e. for
the Alamouti code. We propose novel soft decision decoders for the orthogonal
complex MIMO codes on three and four Tx antennas and extend the old result of
maximal ratio combining (MRC) to cover all orthogonal codes up to four Tx
antennas.
  As a rule, a sophisticated transmission scheme encompasses forward error
correction (FEC) coding, and its performance is measured at the FEC decoder
instead of at the MIMO decoder. We introduce the receiver structure that
delivers the MIMO decoder's soft decisions to the demodulator, which in turn
cranks out the logarithm of likelihood ratio (LLR) of each bit and delivers
them to the FEC decoder. This makes a significant improvement on the receiver,
where a maximum likelihood (ML) MIMO decoder makes hard decisions at a too
early stage. Further, the additional gain is achieved with stunningly low
complexity.


  A recent study of Mejia-Rodriguez and Trickey [Phys. Rev. A 96, 052512
(2017)] showed that the deorbitalization procedure (replacing the exact
Kohn-Sham kinetic-energy density by an approximate orbital-free expression)
applied to exchange-correlation functionals of the meta-generalized gradient
approximation (MGGA) can lead to important changes in the results for molecular
properties. For the present work, the deorbitalization of MGGA functionals is
further investigated by considering various properties of solids. It is shown
that depending on the MGGA, common orbital-free approximations to the
kinetic-energy density can be sufficiently accurate for the lattice constant,
bulk modulus, and cohesive energy. For the band gap, calculated with the
modified Becke-Johnson MGGA potential, the deorbitalization has a larger impact
on the results.


  The paper describes the effect of anion I-3 on thermotropic mesomorphism of
fully hydrated dipalmitoylphosphatidylcholine bilayers. The biphasic behaviour
of melting temperature - I-3 concentration dependence and the increase of DELTA
T values with I-3 concentration (DELTA T = melting temperature - freezing
temperature) suggest the appearance of interdigitated phase of bilayers induced
by I-3 ions.


  We investigate the validity and failure of Liouville theorems and Harnack
inequalities for parabolic and elliptic operators with low regularity
coefficients. We are particularly interested in operators of the form
$\partial_t - \Delta +b\cdot\nabla$ and $-\Delta +b\cdot\nabla$ with a
divergence-free drift $b$. We prove the Liouville theorem and Harnack
inequality when $b\in L_\infty(BMO^{-1})$ resp. $b\in BMO^{-1}$ and provide a
counterexample to such results demonstrating sharpness of our conditions on the
drift. Our results generalize to divergence-form operators with an elliptic
symmetric part and a BMO skew-symmetric part. We also prove the existence of a
modulus of continuity for solutions to the elliptic problem in two dimensions,
depending on the non-scale-invariant norm $\|b\|_{L_1}$. In three dimensions,
on the other hand, bounded solutions with $L_1$ drifts may be discontinuous.


  A conjugation-free geometric presentation of a fundamental group is a
presentation with the natural topological generators $x_1, ..., x_n$ and the
cyclic relations: $x_{i_k}x_{i_{k-1}} ... x_{i_1} = x_{i_{k-1}} ... x_{i_1}
x_{i_k} = ... = x_{i_1} x_{i_k} ... x_{i_2}$ with no conjugations on the
generators.
  We have already proved that if the graph of the arrangement is a disjoint
union of cycles, then its fundamental group has a conjugation-free geometric
presentation. In this paper, we extend this property to arrangements whose
graphs are a disjoint union of cycle-tree graphs.
  Moreover, we study some properties of this type of presentations for a
fundamental group of a line arrangement's complement. We show that these
presentations satisfy a completeness property in the sense of Dehornoy, if the
corresponding graph of the arrangement has no edges. The completeness property
is a powerful property which leads to many nice properties concerning the
presentation (such as the left-cancellativity of the associated monoid and
yields some simple criterion for the solvability of the word problem in the
group).


  Using the method of QCD Sum Rules, we derive the correlator $\Pi^Z$ for a
state consisting of two charm quarks and two light quarks, $c\bar{c}u\bar{d}$,
and carry out a Borel transform to find $\Pi^Z(M_B)$. From this we find the
solution that $M_B\simeq 3.9 \pm .2$ GeV, showing that the $Zc(3900)$ is a
tetra-quark state.


  Traction Force Microscopy (TFM) is a technique used to determine the tensions
that a biological cell conveys to the underlying surface. Typically, TFM
requires culturing cells on gels with fluorescent beads, followed by bead
displacement calculations. We present a new method allowing to predict those
forces from a regular fluorescent image of the cell. Using Deep Learning, we
trained a Bayesian Neural Network adapted for pixel regression of the forces
and show that it generalises on different cells of the same strain. The
predicted forces are computed along with an approximated uncertainty, which
shows whether the prediction is trustworthy or not. Using the proposed method
could help estimating forces when calculating non-trivial bead displacements
and can also free one of the fluorescent channels of the microscope. Code is
available at \url{https://github.com/wahlby-lab/InSilicoTFM}.


  The coherence length $\xi$ is the fundamental length scale of superconductors
which governs the sizes of Cooper pairs, vortices, Andreev bound states, and
more. In existing microscopic theories of superconductivity, it is expected
that as the strength of the attractive interaction increases, $\xi$ decreases
as the electrons are bound together more strongly. In BCS theory, the coherence
length is $\xi_\mathrm{BCS} = \hbar v_{F}/\Delta$, where $v_{F}$ is the Fermi
velocity and $\Delta$ is the pairing gap. It is clear that increasing $\Delta$
will shorten $\xi_\mathrm{BCS}$. However, the situation is puzzling for
superconductors with exactly flat bands in which $v_{F}$ goes to zero and
$\xi_\mathrm{BCS}$ is expected to be zero. In this work, we show that the
quantum metric, which is the real part of the quantum geometric tensor, gives
rise to an anomalous contribution to the coherence length. Specifically, $\xi =
\sqrt{\xi_\mathrm{BCS}^2 +\ell_{\mathrm{qm}}^{2}}$ for a superconductor where
$\ell_{\mathrm{qm}}$ is the quantum metric contribution. In the flat-band
limit, $\xi$ does not vanish but is bound below by $\ell_{\mathrm{qm}}$. We
demonstrate that under the uniform pairing condition, $\ell_{\mathrm{qm}}$ is
controlled by the quantum metric of minimal trace in the flat-band limit.
Physically, the Cooper pair size of a superconductor cannot be squeezed down to
a size smaller than $\ell_{\mathrm{qm}}$ which is a fundamental length scale
determined by the quantum geometry of the wave functions. Lastly, we compute
the quantum metric contributions for the family of superconducting moir\'{e}
graphene materials, demonstrating the significant role played by quantum metric
effects in these narrow-band superconductors.


  Ferromagnetic transition in three-dimensional double-exchange models is
studied by the Monte Carlo method. Critical temperature $T_{\rm c}$ is
precisely determined by finite-size scaling analysis. Strong spin fluctuations
in this itinerant system significantly reduce $T_{\rm c}$ from mean-field
estimates. By choosing appropriate parameters, obtained values of $T_{\rm c}$
quantitatively agree with experiments for the ferromagnetic metal regime of
(La,Sr)MnO$_{3}$, which is a typical perovskite manganite showing colossal
magnetoresistance. This indicates that the double-exchange mechanism alone is
sufficient to explain $T_{\rm c}$ in this material. Critical exponents are also
discussed.


  Fox et al. [SIAM J. Comp. 2020] introduced a new parameter, called
$c$-closure, for a parameterized study of clique enumeration problems. A graph
$G$ is $c$-closed if every pair of vertices with at least $c$ common neighbors
is adjacent. The $c$-closure of $G$ is the smallest $c$ such that $G$ is
$c$-closed. We systematically explore the impact of $c$-closure on the
computational complexity of detecting and enumerating small induced subgraphs.
More precisely, for each graph $H$ on three or four vertices, we investigate
parameterized polynomial-time algorithms for detecting $H$ and for enumerating
all occurrences of $H$ in a given $c$-closed graph.


  It is shown how certain idempotents in the Griess algebra generate the
discrete series representations for the Virasoro algebra inside the
Frenkel-Lepowsky-Meurman's moonshine module vertex operator algebra. It is also
shown that each Niemeier lattice determines (in many ways) certain maximal
associative subalgebras of the Griess algebra.


  We propose and experimentally implement a novel reconfigurable quantum key
distribution (QKD) scheme, where the users can switch in real time between
conventional QKD and the recently-introduced measurement-device-independent
(MDI) QKD. Through this setup, we demonstrate the distribution of quantum keys
between three remote parties connected by only two quantum channels, a
previously unattempted task. Moreover, as a prominent application, we extract
the first quantum digital signature (QDS) rates from a network that uses a
measurement-device-independent link. In so doing, we introduce an efficient
protocol to distil multiple signatures from the same block of data, thus
reducing the statistical fluctuations in the sample and increasing the final
QDS rate.


  Determining the equation of state of dark energy with astronomical
observations is crucially important to understand the nature of dark energy. In
performing a likelihood analysis of the data, especially of the cosmic
microwave background and large scale structure data the dark energy
perturbations have to be taken into account both for theoretical consistency
and for numerical accuracy. Usually, one assumes in the global fitting analysis
that the dark energy perturbations are adiabatic. In this paper, we study the
dark energy isocurvature perturbation analytically and discuss its implications
for the cosmic microwave background radiation and large scale structure.
Furthermore, with the current astronomical observational data and by employing
Markov Chain Monte Carlo method, we perform a global analysis of cosmological
parameters assuming general initial conditions for the dark energy
perturbations. The results show that the dark energy isocurvature perturbations
are very weakly constrained and that purely adiabatic initial conditions are
consistent with the data.


  Quantum Machine Learning (QML) has emerged as a promising intersection of
quantum computing and classical machine learning, anticipated to drive
breakthroughs in computational tasks. This paper discusses the question which
security concerns and strengths are connected to QML by means of a systematic
literature review. We categorize and review the security of QML models, their
vulnerabilities inherent to quantum architectures, and the mitigation
strategies proposed. The survey reveals that while QML possesses unique
strengths, it also introduces novel attack vectors not seen in classical
systems. We point out specific risks, such as cross-talk in superconducting
systems and forced repeated shuttle operations in ion-trap systems, which
threaten QML's reliability. However, approaches like adversarial training,
quantum noise exploitation, and quantum differential privacy have shown
potential in enhancing QML robustness. Our review discuss the need for
continued and rigorous research to ensure the secure deployment of QML in
real-world applications. This work serves as a foundational reference for
researchers and practitioners aiming to navigate the security aspects of QML.


  We demonstrate that the operation of taking disjoint unions of J-holomorphic
curves (and thus obtaining new J-holomorphic curves) has a Seiberg-Witten
counterpart. The main theorem asserts that, given two solutions (A_i, psi_i),
i=0,1 of the Seiberg-Witten equations for the Spin^c-structure W^+_{E_i}= E_i
direct sum (E_i tensor K^{-1}) (with certain restrictions), there is a solution
(A, psi) of the Seiberg-Witten equations for the Spin^c-structure W_E with E=
E_0 tensor E_1, obtained by `grafting' the two solutions (A_i, psi_i).


  While long period variables (LPVs) have been extensively investigated,
especially with MACHO and OGLE data for the Magellanic Clouds, there still
exist open questions in their pulsations regarding the excitation mechanisms,
radial order and angular degree assignment. Here, we perform asteroseismic
analyses on LPVs observed by the 4-year Kepler mission. Using a
cross-correlation method, we detect unambiguous pulsation ridges associated
with radial fundamental modes ($n=1$) and overtones ($n\geqslant2$), where the
radial order assignment is made by using theoretical frequencies and observed
frequencies. Our results confirm that the amplitude variability seen in
semiregulars is consistent with oscillations being solar-like. We identify that
the dipole modes, $l=1$, are dominant in the radial orders of $3\leq n \leq6$,
and that quadrupole modes, $l=2$, are dominant in the first overtone $n=2$. A
test of seismic scaling relations using Gaia DR2 parallaxes reveals the
possibility that the relations break down when $\nu_{\rm max}$ $\lesssim$ 3
$\mu$Hz (R $\gtrsim$ 40 R$_{\odot}$, or log $\rm L/L_{\odot}$ $\gtrsim$ 2.6).
Our homogeneous measurements of pulsation amplitude and period for 3213 LPVs
will be very valuable for probing effects of pulsation on mass loss, in
particular in those stars with periods around 60 days, which has been argued as
a threshold of substantial pulsation-triggered mass loss.


  The linear polarisation of the Crab pulsar and its close environment was
derived from observations with the high-speed photo-polarimeter OPTIMA at the
2.56-m Nordic Optical Telescope in the optical spectral range (400 - 750 nm).
Time resolution as short as 11 microseconds, which corresponds to a phase
interval of 1/3000 of the pulsar rotation, and high statistics allow the
derivation of polarisation details never achieved before. The degree of optical
polarisation and the position angle correlate in surprising details with the
light curves at optical wavelengths and at radio frequencies of 610 and 1400
MHz. Our observations show that there exists a subtle connection between
presumed non-coherent (optical) and coherent (radio) emissions. This finding
supports previously detected correlations between the optical intensity of the
Crab and the occurrence of giant radio pulses. Interpretation of our
observations require more elaborate theoretical models than those currently
available in the literature.


  In this paper we consider the worldsheet of superstring as a noncommutative
space. Some additional terms can be added to the superstring action, such that
for ordinary worldsheet they are zero. Expansion of this extended action up to
the first order of the noncommutativity parameter, leads to the new
supersymmetric action for string. For the closed superstring, we obtain the
boundary state that describes a brane. From the open string point of view, the
new boundary conditions on the worldsheet bosons, generalize the
noncommutativity of spacetime. Finally, we suggest some definitions for the
noncommutativity parameter of superstring worldsheet.


  Lensing of the CMB is an important effect, and is usually modelled by
remapping the unlensed CMB fields by a lensing deflection. However the lensing
deflections also change the photon path so that the emission angle is no longer
orthogonal to the background last-scattering surface. We give the first
calculation of the emission-angle corrections to the standard lensing
approximation from dipole (Doppler) sources for temperature and quadrupole
sources for temperature and polarization. We show that while the corrections
are negligible for the temperature and E-mode polarization, additional
large-scale B-modes are produced with a white spectrum that dominates those
from post-Born field rotation (curl lensing). On large scales about one percent
of the total lensing-induced B-mode amplitude is expected to be due to this
effect. However, the photon emission angle does remain orthogonal to the
perturbed last-scattering surface due to time delay, and half of the
large-scale emission-angle B modes cancel with B modes from time delay to give
a total contribution of about half a percent. While not important for planned
observations, the signal could ultimately limit the ability of delensing to
reveal low amplitudes of primordial gravitational waves.We also derive the
rotation of polarization due to multiple deflections between emission and
observation. The rotation angle is of quadratic order in the deflection angle,
and hence negligibly small: polarization typically rotates by less than an
arcsecond, orders of magnitude less than a small-scale image rotates due to
post-Born field rotation (which is quadratic in the shear). The field-rotation
B modes dominate the other effects on small scales.


  We address effects of spin-orbit coupling (SOC), phenomenologically added to
a two-component Bose-Einstein condensate composed of particles moving by Levy
flights, in one- and two-dimensional (1D and 2D) settings. The corresponding
system of coupled Gross-Pitaevskii equations includes fractional kinetic-energy
operators, characterized by the Levy index, \alpha < 2 (the normal kinetic
energy corresponds to \alpha = 2). The SOC terms, with strength \lambda,
produce strong effects in the 2D case: they create families of stable solitons
of the semi-vortex (SV) and mixed-mode (MM) types in the interval of 1 < \alpha
< 2, where the supercritical collapse does not admit the existence of stable
solitons in the absence of the SOC. At \lambda --> 0, amplitudes of these
solitons vanish as (\lambda)^{1/(\alpha - 1)}.


  Video panoptic segmentation is a challenging task that serves as the
cornerstone of numerous downstream applications, including video editing and
autonomous driving. We believe that the decoupling strategy proposed by DVIS
enables more effective utilization of temporal information for both "thing" and
"stuff" objects. In this report, we successfully validated the effectiveness of
the decoupling strategy in video panoptic segmentation. Finally, our method
achieved a VPQ score of 51.4 and 53.7 in the development and test phases,
respectively, and ultimately ranked 1st in the VPS track of the 2nd PVUW
Challenge. The code is available at https://github.com/zhang-tao-whu/DVIS


  We investigate the localization of waves in aperiodic structures that
manifest the characteristic multiscale complexity of certain arithmetic
functions with a central role in number theory. In particular, we study the
eigenspectra and wave localization properties of tight-binding Schr\"{o}dinger
equation models with on-site potentials distributed according to the Liouville
function $\lambda(n)$, the M\"{o}bius function $\mu(n)$, and the Legendre
sequence of quadratic residues modulo a prime (QRs). We employ Multifractal
Detrended Fluctuation Analysis (MDFA) and establish the multifractal scaling
properties of the energy spectra in these systems. Moreover, by systematically
analyzing the spatial eigenmodes and their level spacing distributions, we show
the absence of level repulsion with broadband localization across the entire
energy spectra. Our study introduces deterministic aperiodic systems whose
eigenmodes are all strongly localized in realistic finite one-dimensional
systems and provides opportunities for novel quantum and classical devices of
particular importance to cold-atom experiments in engineered speckle potentials
and enhanced light-matter interactions.


  Deep learning tasks are often complicated and require a variety of components
working together efficiently to perform well. Due to the often large scale of
these tasks, there is a necessity to iterate quickly in order to attempt a
variety of methods and to find and fix bugs. While participating in IARPA's
Functional Map of the World challenge, we identified challenges along the
entire deep learning pipeline and found various solutions to these challenges.
In this paper, we present the performance, engineering, and deep learning
considerations with processing and modeling data, as well as underlying
infrastructure considerations that support large-scale deep learning tasks. We
also discuss insights and observations with regard to satellite imagery and
deep learning for image classification.


  Data heterogeneity across participating devices poses one of the main
challenges in federated learning as it has been shown to greatly hamper its
convergence time and generalization capabilities. In this work, we address this
limitation by enabling personalization using multiple user-centric aggregation
rules at the parameter server. Our approach potentially produces a personalized
model for each user at the cost of some extra downlink communication overhead.
To strike a trade-off between personalization and communication efficiency, we
propose a broadcast protocol that limits the number of personalized streams
while retaining the essential advantages of our learning scheme. Through
simulation results, our approach is shown to enjoy higher personalization
capabilities, faster convergence, and better communication efficiency compared
to other competing baseline solutions.


  Quantum spin Hall insulators are a class of topological materials that has
been extensively studied during the past decade. One of their distinctive
features is the presence of a finite band gap in the bulk and gapless,
topologically protected edge states that are spin-momentum locked. These
materials are characterized by a $\mathbb{Z}_2$ topological order where, in the
2D case, a single topological invariant can be even or odd for a trivial or a
topological material, respectively. Thanks to their interesting properties,
such as the realization of dissipationless spin currents, spin pumping and spin
filtering, they are of great interest in the field of electronics, spintronics
and quantum computing. In this work we perform an high-throughput screening of
Quantum spin Hall insulators starting from a set of 783 2D exfoliable
materials, recently identified from a systematic screening of the ICSD, COD,
and MPDS databases. We find a new $\mathbb{Z}_2$ topological insulator (HgNS)
as well as 3 already known ones and 7 direect gap metals that have the
potential of becoming Quantum spin Hall insulators under a reasonably weak
external perturbation.


  We introduce, for the first time, bicoherent-state path integration as a
method for quantizing non-hermitian systems. Bicoherent-state path integrals
arise as a natural generalization of ordinary coherent-state path integrals,
familiar from hermitian quantum physics. We do all this by working out a
concrete example, namely, computation of the propagator of a certain
quasi-hermitian variant of Swanson's model, which is not invariant under
conventional $PT$-transformation. The resulting propagator coincides with that
of the propagator of the standard harmonic oscillator, which is isospectral
with the model under consideration by virtue of a similarity transformation
relating the corresponding hamiltonians. We also compute the propagator of this
model in position space by means of Feynman path integration and verify the
consistency of the two results.


  It is common to compare state changes of multiple data items and identify
which data items have changed more in various applications (e.g., annual GDP
growth of different countries and daily increase of new COVID-19 cases in
different regions). Grouped bar charts and slope graphs can visualize both
state changes and their initial and final states of multiple data items, and
are thus widely used for state change comparison. But they leverage implicit
bar differences or line slopes to indicate state changes, which has been proven
less effective for visual comparison. Both visualizations also suffer from
visual scalability issues when an increasing number of data items need to be
compared. This paper fills the research gap by proposing a novel radial
visualization called Intercept Graph to facilitate visual comparison of
multiple state changes. It consists of inner and outer axes, and leverages the
lengths of line segments intercepted by the inner axis to explicitly encode the
state changes. Users can interactively adjust the inner axis to filter large
changes of their interest and magnify the difference of relatively-similar
state changes, enhancing its visual scalability and comparison accuracy. We
extensively evaluate the Intercept Graph in comparison with baseline methods
through two usage scenarios, quantitative metric evaluations, and well-designed
crowdsourcing user studies with 50 participants. Our results demonstrate the
usefulness and effectiveness of the Intercept Graph.


  We explore the effective field theory for single and multiple interacting
pseudo-linear spin-2 fields. By applying forward limit positivity bounds, we
show that among the parameters contributing to elastic tree level scattering
amplitude, there is no region of compatibility of the leading interactions with
a standard local UV completion. Our result generalizes to any number of
interacting pseudo-linear spin-2 fields. These results have significant
implications for the organization of the effective field theory expansion for
pseudo-linear fields.


  We introduce novel results for approximate inference on planar graphical
models using the loop calculus framework. The loop calculus (Chertkov and
Chernyak, 2006b) allows to express the exact partition function Z of a
graphical model as a finite sum of terms that can be evaluated once the belief
propagation (BP) solution is known. In general, full summation over all
correction terms is intractable. We develop an algorithm for the approach
presented in Chertkov et al. (2008) which represents an efficient truncation
scheme on planar graphs and a new representation of the series in terms of
Pfaffians of matrices. We analyze in detail both the loop series and the
Pfaffian series for models with binary variables and pairwise interactions, and
show that the first term of the Pfaffian series can provide very accurate
approximations. The algorithm outperforms previous truncation schemes of the
loop series and is competitive with other state-of-the-art methods for
approximate inference.


  Authentication is a key element of security, by which a receiver confirms the
sender identity of a message. Typical approaches include either key-based
authentication at the application layer or physical layer authentication (PLA),
where a message is considered authentic if it appears to have gone through the
legitimate channel. In both cases a source of randomness is needed, whereas for
PLA the random nature of the communication channel is exploited. In this paper
we compare the various approaches using in all cases the channel as a source of
randomness. We consider a multiple-input multiple-output (MIMO) system with a
finite number of antennas. Simple auto-regressive (AR) models for its evolution
as well as the relation of the legitimate and attacker channel are considered.
In this setting the attacker can either predict the key used for key-based
authentication or forge the channel estimated at the legitimate receiver for
PLA. The analysis includes both symmetric and asymmetric key-based
authentication. We compare the schemes in terms of false alarm and missed
detection probability and we outline best attack strategies.