  Hydrodynamical simulations of galaxy formation and evolution attempt to fully
model the physics that shapes galaxies. The agreement between the morphology of
simulated and real galaxies, and the way the morphological types are
distributed across galaxy scaling relations are important probes of our
knowledge of galaxy formation physics. Here we propose an unsupervised deep
learning approach to perform a stringent test of the fine morphological
structure of galaxies coming from the Illustris and IllustrisTNG (TNG100 and
TNG50) simulations against observations from a subsample of the Sloan Digital
Sky Survey. Our framework is based on PixelCNN, an autoregressive model for
image generation with an explicit likelihood. We adopt a strategy that combines
the output of two PixelCNN networks in a metric that isolates the fine
morphological details of galaxies from the sky background. We are able to
\emph{quantitatively} identify the improvements of IllustrisTNG, particularly
in the high-resolution TNG50 run, over the original Illustris. However, we find
that the fine details of galaxy structure are still different between observed
and simulated galaxies. This difference is driven by small, more spheroidal,
and quenched galaxies which are globally less accurate regardless of resolution
and which have experienced little improvement between the three simulations
explored. We speculate that this disagreement, that is less severe for quenched
disky galaxies, may stem from a still too coarse numerical resolution, which
struggles to properly capture the inner, dense regions of quenched spheroidal
galaxies.


  It is shown that with allowance for nonadditivity the (canonical)
distribution for a black hole transforms to a (microcanonical) distribution
which depends on nonadditive integrals of the motion and represents a nonlinear
function of energy - the square of black hole mass. From a comparison with the
standard microcanonical distribution it is found that the statistical weight of
black hole should be proportional to the black hole area. A definition of the
black hole entropy consistent with the statistical weight is proposed.


  In this article, we investigate a unique prime factorization property for
infinite tensor product factors. We provide several examples of type II and III
factors which satisfy this property, including all free product factors with
diffuse free product components. In the type III setting, this is the first
classification result for infinite tensor product non-amenable factors. Our
proof is based on Popa's intertwining techniques and a characterization of
relative amenability on the continuous cores.


  We address the generalized Nash equilibrium seeking problem in a
partial-decision information scenario, where each agent can only exchange
information with some neighbors, although its cost function possibly depends on
the strategies of all agents. The few existing methods build on projected
pseudo-gradient dynamics, and require either double-layer iterations or
conservative conditions on the step sizes. To overcome both these flaws and
improve efficiency, we design the first fully-distributed single-layer
algorithms based on proximal best-response. Our schemes are fixed-step and
allow for inexact updates, which is crucial for reducing the computational
complexity. Under standard assumptions on the game primitives, we establish
convergence to a variational equilibrium (with linear rate for games without
coupling constraints) by recasting our algorithms as proximal-point methods,
opportunely preconditioned to distribute the computation among the agents.
Since our analysis hinges on a restricted monotonicity property, we also
provide new general results that significantly extend the domain of
applicability of proximal-point methods. Besides, the operator-theoretic
approach favors the implementation of provably correct acceleration schemes
that can further improve the convergence speed. Finally, the potential of our
algorithms is demonstrated numerically, revealing much faster convergence with
respect to projected pseudo-gradient methods and validating our theoretical
findings.


  We prove a general inequality for estimating the number of points of
arbitrary complete intersections over a finite field. This extends a result of
Deligne for nonsingular complete intersections. For normal complete
intersections, this inequality generalizes also the classical Lang-Weil
inequality. Moreover, we prove the Lang-Weil inequality for affine as well as
projective varieties with an explicit description and a bound for the constant
appearing therein. We also prove a conjecture of Lang and Weil concerning the
Picard varieties and \'etale cohomology spaces of projective varieties. The
general inequality for complete intersections may be viewed as a more precise
version of the estimates given by Hooley and Katz. The proof is primarily based
on a suitable generalization of the Weak Lefschetz Theorem to singular
varieties together with some Bertini-type arguments and the
Grothendieck-Lefschetz Trace Formula. We also describe some auxiliary results
concerning the \'etale cohomology spaces and Betti numbers of projective
varieties over finite fields and a conjecture along with some partial results
concerning the number of points of projective algebraic sets over finite
fields.


  We define the notion of a partially additive Kleene algebra, which is a
Kleene algebra where the + operation need only be partially defined. These
structures formalize a number of examples that cannot be handled directly by
Kleene algebras. We relate partially additive Kleene algebras to existing
algebraic structures, by exhibiting categorical connections with Kleene
algebras, partially additive categories, and closed semirings.


  The performance characterization of communication systems assisted by large
reconfigurable intelligent surfaces (RISs) significantly depends on the adopted
models for the underlying channels. Under unrealistic channel models, the
system performance may be over- or under-estimated which yields inaccurate
conclusions for the system design. In this paper, we review five channel models
that are chosen to progressively improve the modeling accuracy for large RISs.
For each channel model, we highlight the underlying assumptions, its
advantages, and its limitations. We compare the system performance under the
aforementioned channel models using RIS configuration algorithms from the
literature and a new scalable algorithm proposed in this paper specifically for
the configuration of extremely large RISs.


  The $^{15}{\rm N}(p,\gamma)^{16}{\rm O}$ reaction provides a path from the CN
cycle to the CNO bi-cycle and CNO tri-cycle. The measured astrophysical factor
for this reaction is dominated by resonant capture through two strong
$J^{\pi}=1^{-}$ resonances at $E_{R}= 312$ and 962 keV and direct capture to
the ground state. Recently, a new measurement of the astrophysical factor for
the $^{15}{\rm N}(p,\gamma)^{16}{\rm O}$ reaction has been published [P. J.
LeBlanc {\it et al.}, Phys. Rev. {\bf C 82}, 055804 (2010)]. The analysis has
been done using the $R$-matrix approach with unconstrained variation of all
parameters including the asymptotic normalization coefficient (ANC). The best
fit has been obtained for the square of the ANC $C^{2}= 539.2$ fm${}^{-1}$,
which exceeds the previously measured value by a factor of $\approx 3$. Here we
present a new $R$-matrix analysis of the Notre Dame-LUNA data with the fixed
within the experimental uncertainties square of the ANC $C^{2}=200.34$
fm${}^{-1}$. Rather than varying the ANC we add the contribution from a
background resonance that effectively takes into account contributions from
higher levels. Altogether we present 8 fits, five unconstrained and three
constrained. In all the fits the ANC is fixed at the previously determined
experimental value $C^{2}=200.34$ fm${}^{-1}$. For the unconstrained fit with
the boundary condition $B_{c}=S_{c}(E_{2})$, where $E_{2}$ is the energy of the
second level, we get $S(0)=39.0 \pm 1.1 $ keVb and normalized ${\tilde
\chi}^{2}=1.84$, i.e. the result which is similar to [P. J. LeBlanc {\it et
al.}, Phys. Rev. {\bf C 82}, 055804 (2010)]. From all our fits we get the range
$33.1 \leq S(0) \leq 40.1$ keVb which overlaps with the result of [P. J.
LeBlanc {\it et al.}, Phys. Rev. {\bf C 82}, 055804 (2010)]. We address also
physical interpretation of the fitting parameters.


  We introduce ViLPAct, a novel vision-language benchmark for human activity
planning. It is designed for a task where embodied AI agents can reason and
forecast future actions of humans based on video clips about their initial
activities and intents in text. The dataset consists of 2.9k videos from
\charades extended with intents via crowdsourcing, a multi-choice question test
set, and four strong baselines. One of the baselines implements a neurosymbolic
approach based on a multi-modal knowledge base (MKB), while the other ones are
deep generative models adapted from recent state-of-the-art (SOTA) methods.
According to our extensive experiments, the key challenges are compositional
generalization and effective use of information from both modalities.


  Electric control of magnetization dynamics in two-dimensional (2D) magnetic
materials is an essential step for the development of novel spintronic
nanodevices. Electrostatic gating has been shown to greatly affect the static
magnetic properties of some van der Waals magnets, but the control over their
magnetization dynamics is still largely unexplored. Here we show that the
optically-induced magnetization dynamics in the van der Waals ferromagnet
Cr$_2$Ge$_2$Te$_6$ can be effectively controlled by electrostatic gates, with a
one order of magnitude change in the precession amplitude and over 10% change
in the internal effective field. In contrast to the purely thermally-induced
mechanisms previously reported for 2D magnets, we find that coherent
opto-magnetic phenomena play a major role in the excitation of magnetization
dynamics in Cr$_2$Ge$_2$Te$_6$. Our work sets the first steps towards electric
control over the magnetization dynamics in 2D ferromagnetic semiconductors,
demonstrating their potential for applications in ultrafast opto-magnonic
devices.


  In March 2020, college campuses underwent a sudden transformation to online
learning due to the COVID-19 outbreak. To understand the impact of COVID-19 on
students' expectations, this study conducted a three-year survey from ten core
courses within the Project Management Center for Excellence at the University
of Maryland. The study involved two main steps: 1) a statistical analysis to
evaluate students' expectations regarding "student," "class," "instructor," and
"effort;" and 2) a lexical salience-valence analysis (LSVA) through the lens of
the Community of Inquiry (CoI) framework to show the changes of students'
expectations. The results revealed that students' overall evaluations
maintained relatively consistent amid the COVID-19 teaching period. However,
there were significant shifts of the student expectations toward Cognitive,
Social and Teaching Presence course elements based on LSVA results. Also, clear
differences emerged between under-graduates and graduates in their expectations
and preferences in course design and delivery. These insights provide practical
recommendations for course instructors in designing effective online courses.


  China's policy interventions to reduce the spread of the coronavirus disease
2019 have environmental and economic impacts. Tropospheric nitrogen dioxide
indicates economic activities, as nitrogen dioxide is primarily emitted from
fossil fuel consumption. Satellite measurements show a 48% drop in tropospheric
nitrogen dioxide vertical column densities from the 20 days averaged before the
2020 Lunar New Year to the 20 days averaged after. This is 20% larger than that
from recent years. We relate to this reduction to two of the government's
actions: the announcement of the first report in each province and the date of
a province's lockdown. Both actions are associated with nearly the same
magnitude of reductions. Our analysis offers insights into the unintended
environmental and economic consequences through reduced economic activities.


  The solar wind is a magnetized and turbulent plasma. Its turbulence is often
dominated by Alfv\'enic fluctuations and often deemed as nearly incompressible
far away from the Sun, as shown by in-situ measurements near 1AU. However, for
solar wind closer to the Sun, the plasma $\beta$ decreases (often lower than
unity) while the turbulent Mach number $M_t$ increases (can approach unity,
e.g., transonic fluctuations). These conditions could produce significantly
more compressible effects, characterized by enhanced density fluctuations, as
seen by several space missions. In this paper, a series of 3D MHD simulations
of turbulence are carried out to understand the properties of compressible
turbulence, particularly the generation of density fluctuations. We find that,
over a broad range of parameter space in plasma $\beta$, cross helicity and
polytropic index, the turbulent density fluctuations scale linearly as a
function of $M_t$, with the scaling coefficients showing weak dependence on
parameters. Furthermore, through detailed spatio-temporal analysis, we show
that the density fluctuations are dominated by low-frequency nonlinear
structures, rather than compressible MHD eigen-waves. These results could be
important for understanding how compressible turbulence contributes to solar
wind heating near the Sun.


  The genome sequence contains the blueprint for governing cellular processes.
While the availability of genomes has vastly increased over the last decades,
experimental annotation of the various functional, non-coding and regulatory
elements encoded in the DNA sequence remains both expensive and challenging.
This has sparked interest in unsupervised language modeling of genomic DNA, a
paradigm that has seen great success for protein sequence data. Although
various DNA language models have been proposed, evaluation tasks often differ
between individual works, and might not fully recapitulate the fundamental
challenges of genome annotation, including the length, scale and sparsity of
the data. In this study, we introduce BEND, a Benchmark for DNA language
models, featuring a collection of realistic and biologically meaningful
downstream tasks defined on the human genome. We find that embeddings from
current DNA LMs can approach performance of expert methods on some tasks, but
only capture limited information about long-range features. BEND is available
at https://github.com/frederikkemarin/BEND.


  Throughout the past few years, various transmission system operators (TSOs)
and research institutes have defined several functional specifications for
grid-forming (GFM) converters via grid codes, white papers, and technical
documents. These institutes and organisations also proposed testing
requirements for general inverter-based resources (IBRs) and specific GFM
converters. This paper initially reviews functional specifications and testing
requirements from several sources to create an understanding of GFM
capabilities in general. Furthermore, it proposes an outlook of the defined GFM
capabilities, functional specifications, and testing requirements for offshore
wind power plant (OF WPP) applications from an original equipment manufacturer
(OEM) perspective. Finally, this paper briefly establishes the relevance of new
testing methodologies for equipment-level certification and model validation,
focusing on GFM functional specifications.


  Very faint X-ray binaries (VFXBs), defined as having peak luminosities Lx of
10^34-10^36 erg/s, have been uncovered in significant numbers, but remain
poorly understood. We analyse three published outburst light curves of two
transient VFXBs using the exponential and linear decay formalism of King and
Ritter (1998). The decay timescales and brink luminosities suggest orbital
periods of order 1 hour. We review various estimates of VFXB properties, and
compare these with suggested explanations of the nature of VFXBs. We suggest
that: 1) VFXB outbursts showing linear decays might be explained as partial
drainings of the disc of "normal" X-ray transients, and many VFXB outbursts may
belong to this category; 2) VFXB outbursts showing exponential decays are best
explained by old, short-period systems involving mass transfer from a low-mass
white dwarf or brown dwarf; 3) persistent (or quasi-persistent) VFXBs, which
maintain an Lx of 10^34-10^35 erg/s for years, may be explained by
magnetospheric choking of the accretion flow in a propeller effect, permitting
a small portion of the flow to accrete onto the neutron star's surface. We thus
predict that (quasi-)persistent VFXBs may also be transitional millisecond
pulsars, turning on as millisecond radio pulsars when their Lx drops below
10^32 erg/s.


  This paper investigates an error covariance matrix splitting technique for
multiuser multiple input and multiple output (MIMO) interference downlink
channel. Most of the related work has thus far considered the traditional error
covariance matrix which has not been well-shaped for maximizing the system
capacity. Thus, we split and propose a new iterative error covariance matrix to
mitigate the system error and maximize the system capacity in this paper.
Numerical results illustrate that our proposed method is strictly better than
the traditional method.


  Our previous research has confirmed that the USD/JPY rate tends to rise
toward 9:55 every morning in the Gotobi days, which are divisible by five. This
is called the Gotobi anomaly. In the present study, we verify the possible
trading strategy and its validity under the condition that investors recognize
the existence of the anomaly. Moreover, we illustrate the possibility that the
wealth of Japanese companies might leak to FX traders due to the arbitrage
opportunity if Japanese companies blindly keep making payments in the Gotobi
days as a business custom.


  Bayesian inference with empirical likelihood faces a challenge as the
posterior domain is a proper subset of the original parameter space due to the
convex hull constraint. We propose a regularized exponentially tilted empirical
likelihood to address this issue. Our method removes the convex hull constraint
using a novel regularization technique, incorporating a continuous exponential
family distribution to satisfy a Kullback-Leibler divergence criterion. The
regularization arises as a limiting procedure where pseudo-data are added to
the formulation of exponentially tilted empirical likelihood in a structured
fashion. We show that this regularized exponentially tilted empirical
likelihood retains certain desirable asymptotic properties of (exponentially
tilted) empirical likelihood and has improved finite sample performance.
Simulation and data analysis demonstrate that the proposed method provides a
suitable pseudo-likelihood for Bayesian inference. The implementation of our
method is available as the R package retel. Supplementary materials for this
article are available online.


  The collective operations are considered critical for improving the
performance of exascale-ready and high-performance computing applications. On
this paper we focus on the Message-Passing Interface (MPI) Allgather many to
many collective, which is amongst the most called and time-consuming
operations. Each MPI algorithm for this call suffers from different operational
and performance limitations, that might include only working for restricted
cases, requiring linear amounts of communication steps with the growth in
number of processes, memory copies and shifts to assure correct data
organization, and non-local data exchange patterns, most of which negatively
contribute to the total operation time. All these characteristics create an
environment where there is no algorithm which is the best for all cases and
this consequently implies that careful choices of alternatives must be made to
execute the call. Considering such aspects, we propose the Stripe Parallel
Binomial Trees (Sparbit) algorithm, which has optimal latency and bandwidth
time costs with no usage restrictions. It also maintains a much more local
communication pattern that minimizes the delays due to long range exchanges,
allowing the extraction of more performance from current systems when compared
with asymptotically equivalent alternatives. On its best scenario, Sparbit
surpassed the traditional MPI algorithms on 46.43% of test cases with mean
(median) improvements of 34.7% (26.16%) and highest reaching 84.16%.


  The methods of light-front quantization and Pauli-Villars regularization are
applied to a nonperturbative calculation of the dressed-electron state in
quantum electrodynamics. This is intended as a test of the methods in a gauge
theory, as a precursor to possible methods for the nonperturbative solution of
quantum chromodynamics. The electron state is truncated to include at most two
photons and no positrons in the Fock basis, and the wave functions of the
dressed state are used to compute the electrons's anomalous magnetic moment. A
choice of regularization that preserves the chiral symmetry of the massless
limit is critical for the success of the calculation.


  We prove bounds on the variance of a function $f$ under the empirical measure
of the samples obtained by the Sequential Monte Carlo (SMC) algorithm, with
time complexity depending on local rather than global Markov chain mixing
dynamics. SMC is a Markov Chain Monte Carlo (MCMC) method, which starts by
drawing $N$ particles from a known distribution, and then, through a sequence
of distributions, re-weights and re-samples the particles, at each instance
applying a Markov chain for smoothing. In principle, SMC tries to alleviate
problems from multi-modality. However, most theoretical guarantees for SMC are
obtained by assuming global mixing time bounds, which are only efficient in the
uni-modal setting. We show that bounds can be obtained in the truly multi-modal
setting, with mixing times that depend only on local MCMC dynamics.


  Most commercially available fixed-wing aerial vehicles (FWV) can carry only
small, lightweight computing hardware such as Jetson TX2 onboard. Solving
non-linear trajectory optimization on these computing resources is
computationally challenging even while considering only the kinematic motion
model. Most importantly, the computation time increases sharply as the
environment becomes more cluttered. In this paper, we take a step towards
overcoming this bottleneck and propose a trajectory optimizer that achieves
online performance on both conventional laptops/desktops and Jetson TX2 in a
typical urban environment setting. Our optimizer builds on the novel insight
that the seemingly non-linear trajectory optimization problem for FWV has an
implicit multi-convex structure. Our optimizer exploits these computational
structures by bringing together diverse concepts from Alternating Minimization,
Bregman iteration, and Alternating Direction Method of Multipliers. We show
that our optimizer outperforms the state-of-the-art implementation of
sequential quadratic programming approach in optimal control solver ACADO in
computation time and solution quality measured in terms of control and goal
reaching cost.


  Mixture of experts method is a neural network based ensemble learning that
has great ability to improve the overall classification accuracy. This method
is based on the divide and conquer principle, in which the problem space is
divided between several experts by supervisition of gating network. In this
paper, we propose an ensemble learning method based on mixture of experts which
is named mixture of ELM based experts with trainable gating network (MEETG) to
improve the computing cost and to speed up the learning process of ME. The
structure of ME consists of multi layer perceptrons (MLPs) as base experts and
gating network, in which gradient-based learning algorithm is applied for
training the MLPs which is an iterative and time consuming process. In order to
overcome on these problems, we use the advantages of extreme learning machine
(ELM) for designing the structure of ME. ELM as a learning algorithm for single
hidden-layer feed forward neural networks provides much faster learning process
and better generalization ability in comparision with some other traditional
learning algorithms. Also, in the proposed method a trainable gating network is
applied to aggregate the outputs of the experts dynamically according to the
input sample. Our experimental results and statistical analysis on 11 benchmark
datasets confirm that MEETG has an acceptable performance in classification
problems. Furthermore, our experimental results show that the proposed approach
outperforms the original ELM on prediction stability and classification
accuracy.


  The existence of conserved spin Hall currents is shown in a strongly
correlated system without involving spin-orbit coupling. The spin Hall
conductivity is determined by intrinsic bulk properties, which remains finite
even when the charge resistivity diverges in strong magnetic fields at zero
temperature. The state in the latter limit corresponds to a spin Hall
insulator. Such a system is a doped Mott insulator described by the phase
string theory, and spin Hall effect is predicted to coexist with the Nernst
effect to characterize the intrinsic properties of the low-temperature
pseudogap phase. Possible applications in spintronics are also mentioned.


  The paper investigates the existence and upper semicontinuity of uniform
attractors of the perturbed non-autonomous Kirchhoff wave equations with strong
damping and supercritical nonlinearity: $u_{tt}-\Delta
u_{t}-(1+\epsilon\|\nabla u\|^{2})\Delta u+f(u)=g(x,t)$, where $\epsilon\in
[0,1]$ is a perturbed parameter. It shows that when the nonlinearity $f(u)$ is
of supercritical growth $p: \frac{N+2}{N-2}=p^*<p<p^{**}=\frac{N+4}{(N-4)^+}$:
(i) the related evolution process has a compact uniform attractor
$\mathcal{A}_\ls^\e $ for each $\epsilon\in [0,1]$; (ii) the family of uniform
attractor $\mathcal{A}_\ls^\e $ is upper semicontinuous on the perturbed
parameter $\epsilon$ in the sense of partially strong topology.


  Monte Carlo switching moves ("perturbations") are defined between two or more
classical Hamiltonians sharing a common ground-state energy. The ratio of the
density of states (DOS) of one system to that of another is related to the
ensemble averages of the microcanonical acceptance probabilities of switching
between these Hamiltonians, analogously to the case of Bennett's acceptance
ratio method for the canonical ensemble [C. H. Bennett, J. Comput. Phys., 22,
245 (1976)]. Thus, if the DOS of one of the systems is known, one obtains those
of the others and, hence, the partition functions. As a simple test case, the
vapor pressure of an anharmonic Einstein crystal is computed, using the
harmonic Einstein crystal as the reference system in one dimension; an
auxiliary calculation is also performed in three dimensions. As a further
example of the algorithm, the energy dependence of the ratio of the DOS of the
square-well and hard-sphere tetradecamers is determined, from which the
temperature dependence of the constant-volume heat capacity of the square-well
system is calculated and compared with canonical Metropolis Monte Carlo
estimates. For these cases and reference systems, the perturbation calculations
exhibit a higher degree of convergence per Monte Carlo cycle than Wang-Landau
(WL) sampling, although for the one-dimensional oscillator the WL sampling is
ultimately more efficient for long runs. Last, we calculate the vapor pressure
of liquid gold using an empirical Sutton-Chen many-body potential and the ideal
gas as the reference state. Although this proves the general applicability of
the method, by its inherent perturbation approach, the algorithm is suitable
for those particular cases where the properties of a related system are well
known.


  Studies of the growth of cosmic perturbations are typically focused on
galactic scales and above. In this paper we investigate the evolution of
perturbations in baryons, photons, and dark matter for masses below 10^6
M_\odot (or wavenumbers above 100 Mpc^-1). Fluctuations on these scales are of
interest and importance because they grow to become the earliest collapsed
objects and provide the first light sources in the so called dark ages. We
investigate both the linear evolution and the second-order nonlinear effects
arising from the coupling of large-scale velocity fields to small-scale
perturbations in the baryon density and the electron ionization fraction. We
find that this second order nonlinear coupling dominates the growth of
perturbations with masses \la 10^3 M_\odot immediately after recombination,
enhancing the baryon fluctuation amplitudes by a factor of ~ 5, but the
nonlinear effect does not persist at late times.


  We show that in AB stacked bilayer graphene low energy excitations around the
semimetallic points are described by massless, four dimensional Dirac fermions.
There is an effective reconstruction of the 4 dimensional spacetime, including
in particular the dimension perpendicular to the sheet, that arises dynamically
from the physical graphene sheet and the interactions experienced by the
carriers. The effective spacetime is the Eisenhart-Duval lift of the dynamics
experienced by Galilei invariant L\'evy-Leblond spin $\frac{1}{2}$ particles
near the Dirac points. We find that changing the intrinsic curvature of the
bilayer sheet induces a change in the energy level of the electronic bands,
switching from a conducting regime for negative curvature to an insulating one
when curvature is positive. In particular, curving graphene bilayers allows
opening or closing the energy gap between conduction and valence bands, a key
effect for electronic devices. Thus using curvature as a tunable parameter
opens the way for the beginning of curvatronics in bilayer graphene.


  Recently, motivated by the pioneer works that reveal the small-world effect
and scale-free property of various real-life networks, many scientists devote
themselves into studying complex networks. One of the ultimate goals is to
understand how the topological structures of networks affect the dynamics upon
them. In this paper, we give a brief review on the studies of epidemic dynamics
on complex networks, including the description of classical epidemic models,
the epidemic spread on small-world and scale-free networks, and network
immunization. Finally, a prospect is addressed and some interesting problems
are listed.


  Conversational explainable artificial intelligence (ConvXAI) systems based on
large language models (LLMs) have garnered significant interest from the
research community in natural language processing (NLP) and human-computer
interaction (HCI). Such systems can provide answers to user questions about
explanations in dialogues, have the potential to enhance users' comprehension
and offer more information about the decision-making and generation processes
of LLMs. Currently available ConvXAI systems are based on intent recognition
rather than free chat, as this has been found to be more precise and reliable
in identifying users' intentions. However, the recognition of intents still
presents a challenge in the case of ConvXAI, since little training data exist
and the domain is highly specific, as there is a broad range of XAI methods to
map requests onto. In order to bridge this gap, we present CoXQL, the first
dataset for user intent recognition in ConvXAI, covering 31 intents, seven of
which require filling multiple slots. Subsequently, we enhance an existing
parsing approach by incorporating template validations, and conduct an
evaluation of several LLMs on CoXQL using different parsing strategies. We
conclude that the improved parsing approach (MP+) surpasses the performance of
previous approaches. We also discover that intents with multiple slots remain
highly challenging for LLMs.


  Classical novae are potential sources of gamma-rays, like the 1.275 MeV gamma
emission following 22Na beta decay, that could be detected by appropriate
instruments on board of future satellites like INTEGRAL. It has been shown that
the production of 22Na by novae is affected by the uncertainty on the
21Na(p,gamma)22Mg rate and in particular by the unknown partial widths of the
Ex = 5.714 MeV, J^pi = 2^+, 22Mg level. To reduce these uncertainties, we
performed shell model calculations with the OXBASH code, compared the results
with available spectroscopic data and calculated the missing partial widths.
Finally, we discuss the influence of these results on the 21Na(p,gamma)22Mg
reaction rate and 22Na synthesis.


  Uncertainty quantification in prediction presents a compelling challenge with
vast applications across various domains, including biomedical science,
economics, and weather forecasting. There exists a wide array of methods for
constructing prediction intervals, such as quantile regression and conformal
prediction. However, practitioners often face the challenge of selecting the
most suitable method for a specific real-world data problem. In response to
this dilemma, we introduce a novel and universally applicable strategy called
Universally Trainable Optimal Predictive Intervals Aggregation (UTOPIA). This
technique excels in efficiently aggregating multiple prediction intervals while
maintaining a small average width of the prediction band and ensuring coverage.
UTOPIA is grounded in linear or convex programming, making it straightforward
to train and implement. In the specific case where the prediction methods are
elementary basis functions, as in kernel and spline bases, our method becomes
the construction of a prediction band. Our proposed methodologies are supported
by theoretical guarantees on the coverage probability and the average width of
the aggregated prediction interval, which are detailed in this paper. The
practicality and effectiveness of UTOPIA are further validated through its
application to synthetic data and two real-world datasets in finance and
macroeconomics.


  We propose an attitude and gyro-bias estimation scheme for accelerated rigid
body systems using an inertial measurement unit (IMU) and a global positioning
system (GPS). The proposed scheme allows to obtain attitude estimates directly
on the Special Orthogonal group $SO(3)$ while estimating the gyro bias and the
unknown apparent acceleration of the vehicle. We prove semi-global exponential
stability of the estimation errors. Furthermore, a new switching technique for
the attitude state is introduced which results in a velocity-aided hybrid
attitude observer with proven global exponential stability.


  The development of supervised deep learning-based methods for multi-label
scene classification (MLC) is one of the prominent research directions in
remote sensing (RS). Yet, collecting annotations for large RS image archives is
time-consuming and costly. To address this issue, several data augmentation
methods have been introduced in RS. Among others, the data augmentation
technique CutMix, which combines parts of two existing training images to
generate an augmented image, stands out as a particularly effective approach.
However, the direct application of CutMix in RS MLC can lead to the erasure or
addition of class labels (i.e., label noise) in the augmented (i.e., combined)
training image. To address this problem, we introduce a label propagation (LP)
strategy that allows the effective application of CutMix in the context of MLC
problems in RS without being affected by label noise. To this end, our proposed
LP strategy exploits pixel-level class positional information to update the
multi-label of the augmented training image. We propose to access such class
positional information from reference maps associated to each training image
(e.g., thematic products) or from class explanation masks provided by an
explanation method if no reference maps are available. Similarly to pairing two
training images, our LP strategy carries out a pairing operation on the
associated pixel-level class positional information to derive the updated
multi-label for the augmented image. Experimental results show the
effectiveness of our LP strategy in general and its robustness in the case of
various simulated and real scenarios with noisy class positional information in
particular.


  Gottlob Frege ingeniously presented a purely logical definition of the
concept of number. However, one can claim that his definition is, in some way,
circular, as it relies on the concept of one-to-one relation. The concept of
number only makes sense when it presents the property of projection/reflection
or binding. When we consider a number as an abstraction of objects, whatever
they may be, saying that a number that belongs to the concept F is the same as
that which belongs to the concept G means there is a projection/reflection, or
binding, between the objects in F and the objects in G. We present a definition
based on both equivalent approaches. First, we introduce the definition based
on the relations of projection and reflection; then, we present the definition
based on the relation of binding.


  Few-shot segmentation enables the model to recognize unseen classes with few
annotated examples. Most existing methods adopt prototype learning
architecture, where support prototype vectors are expanded and concatenated
with query features to perform conditional segmentation. However, such
framework potentially focuses more on query features while may neglect the
similarity between support and query features. This paper proposes a
contrastive enhancement approach using latent prototypes to leverage latent
classes and raise the utilization of similarity information between prototype
and query features. Specifically, a latent prototype sampling module is
proposed to generate pseudo-mask and novel prototypes based on features
similarity. The module conveniently conducts end-to-end learning and has no
strong dependence on clustering numbers like cluster-based method. Besides, a
contrastive enhancement module is developed to drive models to provide
different predictions with the same query features. Our method can be used as
an auxiliary module to flexibly integrate into other baselines for a better
segmentation performance. Extensive experiments show our approach remarkably
improves the performance of state-of-the-art methods for 1-shot and 5-shot
segmentation, especially outperforming baseline by 5.9% and 7.3% for 5-shot
task on Pascal-5^i and COCO-20^i. Source code is available at
https://github.com/zhaoxiaoyu1995/CELP-Pytorch


  This study explores and analyzes the learning tendencies of second-year
students enrolled in different lines of study related to the Databases course.
There were 79 answers collected from 191 enrolled students that were analyzed
and interpreted using thematic analysis. The participants in the study provided
two sets of answers, anonymously collected (at the beginning and at the end of
the course), thus allowing us to have clear data regarding their interests and
to find out their tendencies. We looked into their expectations and if they
were met; we concluded that the students want to learn only database basics.
Their main challenges were related to the course homework. We combined the
information and the answers related to 1) other database-related topics that
they would like to learn, 2) how they plan to use the acquired information, and
3) overall interest in learning other database-related topics. The conclusion
was that students prefer learning only the basic information that could help
them achieve their goals: creating an application or using it at work. For
these students, Getting the work done is preferred to Learning in-depth.


  The e^+ jet invariant mass spectrum produced in the reaction e^+ p -> e^+ X
has been studied at a center-of-mass energy of 300 GeV. The data were collected
using the ZEUS detector operating at the HERA collider, and correspond to an
integrated luminosity of 47.7 pb^{-1}. The observed mass spectrum is in good
agreement with Standard Model expectations up to an e^+ jet mass of 210 GeV.
Above this mass, some excess is seen. The angular distribution of these events
is typical of high-Q^2 neutral current events and does not give convincing
evidence for the presence of a narrow scalar or vector state. Limits are
presented on the product of cross section and branching ratio for such a state
and are interpreted as limits on leptoquark or R-parity-violating squark
production. Specific leptoquark types are ruled out at 95% confidence level for
coupling strength lambda=0.3 for masses between 150 and 280 GeV.


  Machine vision is an important sensing technology used in mobile robotic
systems. Advancing the autonomy of such systems requires accurate
characterisation of sensor uncertainty. Vision includes intrinsic uncertainty
due to the camera sensor and extrinsic uncertainty due to environmental
lighting and texture, which propagate through the image processing algorithms
used to produce visual measurements. To faithfully characterise visual
measurements, we must take into account these uncertainties.
  In this paper, we propose a new class of likelihood functions that
characterises the uncertainty of the error distribution of two-frame optical
flow that enables a heteroscedastic dependence on texture. We employ the
proposed class to characterise the Farneback and Lucas Kanade optical flow
algorithms and achieve close agreement with their respective empirical error
distributions over a wide range of texture in a simulated environment. The
utility of the proposed likelihood model is demonstrated in a visual odometry
ego-motion study, which results in performance competitive with contemporary
methods. The development of an empirically congruent likelihood model advances
the requisite tool-set for vision-based Bayesian inference and enables sensor
data fusion with GPS, LiDAR and IMU to advance robust autonomous navigation.


  The future long baseline experiments such as DUNE and T2HKK have promising
prospects to determine the neutrino mass hierarchy and measuring standard $CP$
phase $\delta$. However, presence of possible non-standard interactions of
neutrinos with matter may intricate this picture and is the subject matter of
the present work. We have studied the standard parameter degeneracies in
presence of non-standard interactions(NSI) with DUNE and T2HKK experiments. We
examine the mass hierarchy degeneracy assuming (i) all NSI parameters to be
non-zero and (ii) one NSI parameter($\epsilon_{e\mu}$) and its corresponding
$CP$ phase($\delta_{e\mu}$) to be non-zero. We find that the later case is more
appropriate to resolve mass hierarchy degeneracy with DUNE and T2HKK
experiments due to relatively small uncertainties emanating from the NSI
sector. We have, also, investigated the octant degeneracy with
neutrino($\nu_{\mu}\rightarrow\nu_{e}$) and
antineutrino($\bar{\nu}_{\mu}\rightarrow\bar{\nu}_{e}$) mode separately. We
find that to resolve this degeneracy the long baseline experiment with
combination of neutrino and antineutrino mode is essential. Furthermore, we
have considered DUNE in conjunction with T2HKK experiment to study $CP$ phase
degeneracy due to standard($\delta$) and non-standard($\delta_{e\mu}$) $CP$
phases. We find that DUNE and T2HKK, in conjunction, has more sensitivity for
$CP$ violation effects(10$\sigma$ for true NH and 8.2$\sigma$ for true IH).


  Phylogenetics is now fundamental in life sciences, providing insights into
the earliest branches of life and the origins and spread of epidemics. However,
finding suitable phylogenies from the vast space of possible trees remains
challenging. To address this problem, for the first time, we perform both tree
exploration and inference in a continuous space where the computation of
gradients is possible. This continuous relaxation allows for major leaps across
tree space in both rooted and unrooted trees, and is less susceptible to
convergence to local minima. Our approach outperforms the current best methods
for inference on unrooted trees and, in simulation, accurately infers the tree
and root in ultrametric cases. The approach is effective in cases of empirical
data with negligible amounts of data, which we demonstrate on the phylogeny of
jawed vertebrates. Indeed, only a few genes with an ultrametric signal were
generally sufficient for resolving the major lineages of vertebrates.
Optimisation is possible via automatic differentiation and our method presents
an effective way forwards for exploring the most difficult, data-deficient
phylogenetic questions.


  We investigate the interplay between the lattice, charge, and spin dynamics
in charge ordered high $T_c$ cuprate La$_{1.875}$Ba$_{0.125}$CuO$_{4}$ ($T_{c}
=4$~K) based on the inverse Laplace transform (ILT) analysis of the $^{139}$La
nuclear spin-lattice relaxation rate $1/T_1$ (dubbed ILTT$_{1}$ analysis here
after). A major thrust of the ILTT$_{1}$ analysis is that one can deduce the
probability density function $P(1/T_1)$ of distributed $1/T_1$. We demonstrate
that $1/T_{1}^{lm}$, defined as the log-mean (i.e. the center of gravity on a
logarithmic scale) of $P(1/T_1)$, can be well approximated by $1/T_{1}^{str}$
deduced from the phenomenological stretched fit, however, $P(1/T_1)$ can
provide much richer insight into how the lattice, charge, and spin fluctuations
and their distribution develop near and below the long range charge order at
$T_{charge} \sim 54$~K. Upon entering the charge ordered state, a divergent
increase of $1/T_{1}^{lm}$ toward the spin ordering at $T_{spin}^{\mu SR}
\simeq 35$~K is accompanied by an asymmetric broadening of $P(1/T_1)$. Even
deep inside the charge ordered state, $1/T_{1}$ at a gradually diminishing
fraction of $^{139}$La sites continues to slow down as temperature is lowered,
as expected for canonical superconducting CuO$_2$ planes without enhanced spin
fluctuations. The fraction of such canonical $^{139}$La sites almost disappears
by $\simeq 40$~K. In contrast, nearly a half of the $^{139}$La sites in
La$_{1.885}$Sr$_{0.115}$CuO$_{4}$ ($T_{charge} \simeq 80$~K) still exhibits the
canonical behavior without enhanced spin fluctuations even near its $T_{c} =
31$~K. These contrasting behaviors explain why superconductivity in
La$_{1.875}$Ba$_{0.125}$CuO$_{4}$ is more strongly suppressed than in
La$_{1.885}$Sr$_{0.115}$CuO$_{4}$ despite the lower onset temperature of the
charge order.


  The unexpected discoveries at the beginning of the century, particularly
thanks to Heisenberg, Bohr, and Godel, has driven the science to drastic
changes, opening new, extraordinary, and infinite research fields. After this,
many scientists saw, and still today see, a crisis, with dreadful meaning, in
the science. However, this crisis is only present in that type of science,
driven by determinism, which is strictly linked to the common sense.


  In this paper, we give some new identities of Carlitz q-Bernoulli polynomials
under symmetry group S 3 . The derivatives of identities are based on the
q-Volkenborn integral expression of the generating function for the Carlitz
q-Bernoulli polynomials and the q-Volkenborn integral equations that can be
expressed as the exponential generating functions for the q-power sums.


  Many algorithms require discriminative boundaries, such as separating
hyperplanes or hyperballs, or are specifically designed to work on spherical
data. By applying inversive geometry, we show that the two discriminative
boundaries can be used interchangeably, and that general Euclidean data can be
transformed into spherical data, whenever a change in point distances is
acceptable. We provide explicit formulae to embed general Euclidean data into
spherical data and to unembed it back. We further show a duality between
hyperspherical caps, i.e., the volume created by a separating hyperplane on
spherical data, and hyperballs and provide explicit formulae to map between the
two. We further provide equations to translate inner products and Euclidean
distances between the two spaces, to avoid explicit embedding and unembedding.
We also provide a method to enforce projections of the general Euclidean space
onto hemi-hyperspheres and propose an intrinsic dimensionality based method to
obtain "all-purpose" parameters. To show the usefulness of the
cap-ball-duality, we discuss example applications in machine learning and
vector similarity search.


  We present a novel offline-online method to mitigate the computational burden
of the characterization of posterior random variables in statistical learning.
In the offline phase, the proposed method learns the joint law of the parameter
random variables and the observable random variables in the tensor-train (TT)
format. In the online phase, the resulting order-preserving conditional
transport can characterize the posterior random variables given newly observed
data in real time. Compared with the state-of-the-art normalizing flow
techniques, the proposed method relies on function approximation and is
equipped with a thorough performance analysis. The function approximation
perspective also allows us to further extend the capability of transport maps
in challenging problems with high-dimensional observations and high-dimensional
parameters. On the one hand, we present novel heuristics to reorder and/or
reparametrize the variables to enhance the approximation power of TT. On the
other hand, we integrate the TT-based transport maps and the parameter
reordering/reparametrization into layered compositions to further improve the
performance of the resulting transport maps. We demonstrate the efficiency of
the proposed method on various statistical learning tasks in ordinary
differential equations (ODEs) and partial differential equations (PDEs).


  A new phase field model of microstructural evolution is presented that
includes the effects of elastic strain energy. The model's thin interface
behavior is investigated by mapping it onto a recent model developed by
Echebarria et al (Phys Rev E, 70:061604,2004). Exploiting this thin interface
analysis the growth of solid state dendrites are simulated with diffuse
interfaces and the phase field and mechanical equilibrium equations are solved
in real space on an adaptive mesh. A morphological competition between surface
energy anisotropy and elastic anisotropy is examined. Two dimensional
simulations are reported that show that solid state dendritic structures
undergo a transition from a surface dominated [10] growth direction to an
elastically driven [11] growth direction by changes in the elastic anisotropy,
the surface anisotropy and the supersaturation. Using the curvature and strain
corrections to the equilibrium interfacial composition and linear stability
theory for isotropic precipitates as calculated by Mullins and Sekerka, the
dominant growth morphology is predicted.


  Datasets (semi-)automatically collected from the web can easily scale to
millions of entries, but a dataset's usefulness is directly related to how
clean and high-quality its examples are. In this paper, we describe and
publicly release an image dataset along with pretrained models designed to
(semi-)automatically filter out undesirable images from very large image
collections, possibly obtained from the web. Our dataset focusses on
photographic and/or natural images, a very common use-case in computer vision
research. We provide annotations for coarse prediction, i.e. photographic vs.
non-photographic, and smaller fine-grained prediction tasks where we further
break down the non-photographic class into five classes: maps, drawings,
graphs, icons, and sketches. Results on held out validation data show that a
model architecture with reduced memory footprint achieves over 96% accuracy on
coarse-prediction. Our best model achieves 88% accuracy on the hardest
fine-grained classification task available. Dataset and pretrained models are
available at: https://github.com/houda96/imagi-filter.


  As blockchain technology continues to gain attention, there is a growing need
to make it more accessible to young learners in K-12 education. However, the
technical complexity and lack of accessible tools have been identified as
significant barriers to adoption. Our paper proposes a new method for
empowering NFTs by continuously updating their metadata using an API layer. The
approach aims to reduce the barriers to entry and enable K-12 students to
explore blockchain technology in the same way they learn computational thinking
through visual programming tools like Scratch. Our method utilizes Google
Blockly, a visual programming language, to make updating NFT metadata more
accessible and engaging for young learners. By leveraging a familiar and
engaging visual programming language, students can develop their computational
thinking skills and explore blockchain technology in a fun and intuitive way.
The paper discusses the benefits of using NFTs as a learning tool, including
how they can help students understand the concept of digital ownership and
value. Overall, our proposed method has the potential to promote student
engagement and understanding of blockchain technology, which could have
significant implications for the future of education.


  We present a scheme for tuning and controlling nano mechanical resonators by
subjecting them to electrostatic gradient fields, provided by nearby tip
electrodes. We show that this approach enables access to a novel regime of
optomechanics, where the intrinsic nonlinearity of the nanoresonator can be
explored. In this regime, one or several laser driven cavity modes coupled to
the nanoresonator and suitably adjusted gradient fields allow to control the
motional state of the nanoresonator at the single phonon level. Some
applications of this platform have been presented previously [New J. Phys. 14,
023042 (2012), Phys. Rev. Lett. 110, 120503 (2013)]. Here, we provide a
detailed description of the corresponding setup and its optomechanical coupling
mechanisms, together with an in-depth analysis of possible sources of damping
or decoherence and a discussion of the readout of the nanoresonator state.


  We have designed a fiber scrambler as a prototype for the Keck HIRES
spectrograph, using double scrambling to stabilize illumination of the
spectrometer and a pupil slicer to increase spectral resolution to R = 70,000
with minimal slit losses. We find that the spectral line spread function (SLSF)
for the double scrambler observations is 18 times more stable than the SLSF for
comparable slit observations and 9 times more stable than the SLSF for a single
fiber scrambler that we tested in 2010. For the double scrambler test data, we
further reduced the radial velocity scatter from an average of 2.1 m/s to 1.5
m/s after adopting a median description of the stabilized SLSF in our Doppler
model. This demonstrates that inaccuracies in modeling the SLSF contribute to
the velocity RMS. Imperfect knowledge of the SLSF, rather than stellar jitter,
sets the precision floor for chromospherically quiet stars analyzed with the
iodine technique using Keck HIRES and other slit-fed spectrometers. It is
increasingly common practice for astronomers to scale stellar noise in
quadrature with formal errors such that their Keplerian model yields a
chi-squared fit of 1.0. When this is done, errors from inaccurate modeling of
the SLSF (and perhaps from other sources) are attributed to the star and the
floor of the stellar noise is overestimated.


  In this paper we develop tools for observers to use when analysing nebular
spectra for temperatures and metallicities, with two goals: to present a new,
simple method to calculate equilibrium electron temperatures for collisionally
excited line flux ratios, using the latest atomic data; and to adapt current
methods to include the effects of possible non-equilibrium '{\kappa}' electron
energy distributions. Adopting recent collision strength data for [O iii], [S
iii], [O ii], [S ii], and [N ii], we find that existing methods based on older
atomic data seriously overestimate the electron temperatures, even when
considering purely Maxwellian statistics. If {\kappa} distributions exist in H
ii regions and planetary nebulae as they do in solar system plasmas, it is
important to investigate the observational consequences. This paper continues
our previous work on the {\kappa} distribution (Nicholls et al. 2012). We
present simple formulaic methods that allow observers to (a) measure
equilibrium electron temperatures and atomic abundances using the latest atomic
data, and (b) to apply simple corrections to existing equilibrium analysis
techniques to allow for possible non-equilibrium effects. These tools should
lead to better consistency in temperature and abundance measurements, and a
clearer understanding of the physics of H ii regions and planetary nebulae.


  For nearly 100 years, the paradox of Schr\"odinger's Cat has remained
unresolved. Why does the world we live in appear classical despite being
composed of quantum particles governed by the Schr\"odinger wave equation?
Lajos Di\'osi and Roger Penrose propose the wavefunction collapses because it
describes two incompatible space-times, demonstrating an inconsistency between
quantum mechanics and general relativity. To avoid this paradox, collapse must
occur within Heisenberg's time-energy uncertainty limit. Subatomic particles
with low mass, and correspondingly low energy, collapse in years, while
superposed cats would collapse almost instantaneously. We propose a table-top
experiment to put two small mirrors into superposition and observe them
collapse in a time consistent with the Di\'osi-Penrose model. We employ two
techniques to perform this experiment in ambient laboratory conditions. Most
experiments separate a small mass by a large distance. In contrast, we displace
a large mass by a small distance where the self-energy follows an inverse
square law with correspondingly high collapse times. We further use a
symmetrical apparatus, where a break in symmetry indicates collapse independent
of decoherence.


  We investigate the stability of the deformation modeled by the opening angle
method, often used to give a measure of residual stresses in arteries and other
biological soft tubular structures. Specifically, we study the influence of
stiffness contrast, dimensions and inner pressure on the onset of wrinkles when
an open sector of a soft tube, coated with a stiffer film, is bent into a full
cylinder. The tube and its coating are made of isotropic, incompressible,
hyperelastic materials. We provide a full analytical exposition of the
governing equations and the associated boundary value problem for the large
deformation and for the superimposed small-amplitude wrinkles. For
illustration, we solve them numerically with a robust algorithm in the case of
Mooney-Rivlin materials. We confront the results to experimental data that we
collected for soft silicone sectors. We study the influence of axial stretch
and inner pressure on the stability of closed-up coated tubes with material
parameters comparable with those of soft biological tubes such as arteries and
veins, although we do not account for anisotropy. We find that the large
deformation described in the opening angle method does not always exist, as it
can become unstable for certain combinations of dimensions and material
parameters.


  Multiple spectroscopic lines of different elements observed in winds from
X-ray binaries (XRBs), based on one zone model, indicate super-solar abundance
of elements, e.g. Mg, Si, S, Ar, Ca, Cr, Mn, Co. The one zone model considers
similar hydrodynamics of underlying winds. In order to find a possible origin
of these overabundances, we explore nucleosynthesis in advective, geometrically
thick, sub-Keplerian, accretion disc in XRBs and active galactic nuclei (AGNs),
and further in outflows launched from the disc. Based on flow hydrodynamics and
solving nuclear network code therein by semi-implicit Euler method, we obtain
abundance evolution of the elements. Although the density is very low, due to
very high temperature of advective disc than Keplerian Shakura-Sunyaev disc
(SSD), it is quite evident that significant nucleosynthesis occurs in the
former. As the temperature at the base of the outflow is constrained by the
temperature of disc, nucleosynthesis also occurs in the outflow contingent upon
its launching temperature. Till now, the outer region of XRB and AGN discs is
understood to be colder SSD and inner region to be advective disc, together
forming a disc-wind system. Hence, newly evolved abundances after processing
through outflow can change the abundances of different elements present in the
environment of the whole disc-wind system. We find 2-6 times overabundant Mg,
Si, Ar, Cr with respect to the respective solar abundances, which is consistent
observationally. Thus for most XRBs, when only iron lines are present,
inclusion of these evolved abundances is expected to change the observational
analysis drastically.


  Given an $n\times n$ array $M$ ($n\ge 7$), where each cell is colored in one
of two colors, we give a necessary and sufficient condition for the existence
of a partition of $M$ into $n$ diagonals, each containing at least one cell of
each color. As a consequence, it follows that if each color appears in at least
$2n-1$ cells, then such a partition exists. The proof uses results on
completion of partial Latin squares.


  We propose to describe higher spins as invariant subspaces of the Casimir
operators of the Poincar\'{e} Group, P^{2}, and the squared Pauli-Lubanski
operator, W^{2}, in a properly chosen representation, \psi(p) (in momentum
space), of the Homogeneous Lorentz Group. The resulting equation of motion for
any field with s\neq0 is then just a specific combination of the respective
covariant projectors. We couple minimally electromagnetism to this equation and
show that the corresponding wave fronts of the classical solutions propagate
causally. Furthermore, for (s,0)+(0,s) representations, the formalism predicts
the correct gyromagnetic factor, g_{s}=1/s. The advocated method allows to
describe any higher spin without auxiliary conditions and by one covariant
matrix equation alone. This master equation is only quadratic in the momenta
and its dimensionality is that of \psi(p). We prove that the suggested master
equation avoids the Velo-Zwanziger problem of superluminal propagation of
higher spin waves and points toward a consistent description of higher spin
quantum fields.


  Open chemical reaction systems involve matter-exchange with the surroundings.
As a result, species can accumulate inside a system during the course of the
reaction. We study the role of network topology in governing the concentration
build-up inside a fixed reaction volume at steady state, particularly focusing
on the effect of additional paths. The problem is akin to that in traffic
networks where an extra route, surprisingly, can increase the overall travel
time. This is known as the Braess' paradox. Here, we report chemical analogues
of such a paradox in suitably chosen reaction networks, where extra reaction
step(s) can inflate the total concentration, denoted as `load', at steady
state. It is shown that, such counter-intuitive behavior emerges in a
qualitatively similar pattern in networks of varying complexities. We then
explore how such extra routes affect the load in a biochemical scheme of uric
acid degradation. From a thorough analysis of this network, we propose a
functional role of some decomposition steps that can trim the load, indicating
the importance of the latter in the evolution of reaction mechanisms in living
matter.


  We introduce an approximation to the short-range correlation energy
functional with multide-terminantal reference involved in a variant of
range-separated density-functional theory. This approximation is a local
functional of the density, the density gradient, and the on-top pair density,
which locally interpolates between the standard Perdew-Burke-Ernzerhof
correlation functional at vanishing range-separation parameter and the known
exact asymptotic expansion at large range-separation parameter. When combined
with (selected) configuration-interaction calculations for the long-range wave
function, this approximation gives accurate dissociation energy curves of the
H2, Li2, and Be2 molecules, and thus appears as a promising way to accurately
account for static correlation in range-separated density-functional theory.


  A new proof of Imprimitivity theorem for transitive systems of covariance is
given and a definition of square-integrable representation modulo a subgroup is
proposed. This clarifies the relation between coherent states, wavelet
transforms and covariant localisation observables.


  A brief overview of the phenomenology related to the seesaw mechanism and the
baryogenesis via leptogenesis is presented.


  The Detection and Classification of Acoustic Scenes and Events (DCASE) 2019
challenge focuses on audio tagging, sound event detection and spatial
localisation. DCASE 2019 consists of five tasks: 1) acoustic scene
classification, 2) audio tagging with noisy labels and minimal supervision, 3)
sound event localisation and detection, 4) sound event detection in domestic
environments, and 5) urban sound tagging. In this paper, we propose generic
cross-task baseline systems based on convolutional neural networks (CNNs). The
motivation is to investigate the performance of a variety of models across
several tasks without exploiting the specific characteristics of the tasks. We
looked at CNNs with 5, 9, and 13 layers, and found that the optimal
architecture is task-dependent. For the systems we considered, we found that
the 9-layer CNN with average pooling is a good model for a majority of the
DCASE 2019 tasks.


  We study a simplified inertial Ericksen-Leslie system for the nematic liquid
crystal flow, which can be viewed as a system coupling Navier-Stokes equations
and wave map equations. We prove the global existence of classical solution
with initial data near equilibrium.


  We use a sensitive magneto-optical microscopy technique to image the
magnetization response of micron-scale ferromagnetic disks to changes in
applied magnetic field. This differential technique relies on a modulated
applied magnetic field which allows us to measure changes in magnetization <1%
with sub-micron resolution. The disks are magnetized in single vortex domains,
with defects in the material serving to pin the vortex core at particular
positions. By applying a small AC magnetic field, we measure the deformation of
the magnetization while the core remains pinned. We can also characterize the
strength of the pinning site by increasing the AC magnetic field to unpin the
vortex core. While pinned, we find that the magnetization away from the core
reorients slightly to better align with an applied field. Additionally, an
applied field causes the pinned core itself to tilt in the direction of the
field. Once the field is large enough to unpin the core, this tilt disappears,
and the core instead translates across the disk.


  We study the model checking problem, for fixed structures A, over positive
equality-free first-order logic -- a natural generalisation of the non-uniform
quantified constraint satisfaction problem QCSP(A). We prove a complete
complexity classification for this problem when A ranges over 1.) boolean
structures and 2.) digraphs of size (less than or equal to) three. The former
class displays dichotomy between Logspace and Pspace-complete, while the latter
class displays tetrachotomy between Logspace, NP-complete, co-NP-complete and
Pspace-complete.


  We investigate the interplay of cosmic ray (CR) propagation and advection in
galaxy clusters. Propagation in form of CR diffusion and streaming tends to
drive the CR radial profiles towards being flat, with equal CR number density
everywhere. Advection of CR by the turbulent gas motions tends to produce
centrally enhanced profiles. We assume that the CR streaming velocity is of the
order of the sound velocity. This is motivated by plasma physical arguments.
The CR streaming is then usually larger than typical advection velocities and
becomes comparable or lower than this only for periods with trans- and
super-sonic cluster turbulence. As a consequence a bimodality of the CR spatial
distribution results. Strongly turbulent, merging clusters should have a more
centrally concentrated CR energy density profile with respect to relaxed ones
with very subsonic turbulence. This translates into a bimodality of the
expected diffuse radio and gamma-ray emission of clusters, since more centrally
concentrated CR will find higher target densities for hadronic CR proton
interactions, higher plasma wave energy densities for CR electron and proton
re-acceleration, and stronger magnetic fields. Thus, the observed bimodality of
cluster radio halos appears to be a natural consequence of the interplay of CR
transport processes, independent of the model of radio halo formation, be it
hadronic interactions of CR protons or re-acceleration of low-energy CR
electrons. Energy dependence of the CR propagation should lead to spectral
steepening of dying radio halos. Furthermore, we show that the interplay of CR
diffusion with advection implies first order CR re-acceleration in the
pressure-stratified atmospheres of galaxy clusters. Finally, we argue that CR
streaming could be important in turbulent cool cores of galaxy clusters since
it heats preferentially the central gas with highest cooling rate.


  The knowledge of the positions of the corotation resonance in spiral arms is
a key way to estimate their pattern speed, which is a fundamental parameter
determining the galaxy dynamics. Various methods for its estimation have been
developed, but they all demonstrate certain limitations and a lack of agreement
with each other. Here, we present a new method for estimating the corotation
radius. This method takes into account the shape of the profile across the arm
and its width and, thus, only photometric data is needed. The significance of
the method is that it can potentially be used for the farthest galaxies with
measurable spiral arms. We apply it to a sample of local galaxies from
Savchenko et al. and compare the obtained corotation radii with those
previously measured in the literature by other methods. Our results are in good
agreement with the literature. We also apply the new method to distant galaxies
from the COSMOS field. For the first time, corotation locations for galaxies
with photometric redshifts up to $z\sim0.9$ are measured.


  We have determined the terahertz spectrum of the chiral langasite
Ba$_3$NbFe$_3$Si$_2$O$_{14}$ by means of synchrotron-radiation measurements.
Two excitations are revealed that are shown to have a different nature. The
first one, purely magnetic, is observed at low temperature in the magnetically
ordered phase and is assigned to a magnon. The second one persits far into the
paramagnetic phase and exhibits both an electric and a magnetic activity at
slightly different energies. This magnetoelectric excitation is interpreted in
terms of atomic rotations and requires a helical electric polarization.


  To begin with, we identify the equations of elastostatics in a Riemannian
manifold, which generalize those of classical elasticity in the
three-dimensional Euclidean space. Our approach relies on the principle of
least energy, which asserts that the deformation of the elastic body arising in
response to given loads minimizes over a specific set of admissible
deformations the total energy of the elastic body, defined as the difference
between the strain energy and the potential of the loads. Assuming that the
strain energy is a function of the metric tensor field induced by the
deformation, we first derive the principle of virtual work and the associated
nonlinear boundary value problem of nonlinear elasticity from the expression of
the total energy of the elastic body. We then show that this boundary value
problem possesses a solution if the loads are sufficiently small (in a sense we
specify).


  In the collision of a high-energy proton beam and a strong laser field,
merging of the laser photons can occur due to the polarization of vacuum. The
probability of photon merging is calculated by accounting exactly for the laser
field and presents a highly non-perturbative dependence on the laser intensity
and frequency. It is shown that the non-perturbative vacuum-polarization
effects can be experimentally measured by combining the next-generation of
table-top petawatt lasers with presently available proton accelerators.


  Uncertainties on parton distribution functions (PDFs) compromise discovery at
the LHC for any new physics which can be described as a contact-interaction.
PDF uncertainties also limit our ability to use W and Z cross-sections as an
accurate luminosity monitor. The impact of the current level of PDF uncertainty
on LHC physics is reviewed and the possibility of reducing this uncertainty
using LHC data is investigated.


  Utilizing the known off-shell formulation of 2D, N = (2,2) supergravity,
containing a finite number of auxiliary fields, there is shown to exist a
simple form for a 'chiral projection operator' and an explicit expression for
it is given.


  The demand to process vast amounts of data generated from state-of-the-art
high resolution cameras has motivated novel energy-efficient on-device AI
solutions. Visual data in such cameras are usually captured in the form of
analog voltages by a sensor pixel array, and then converted to the digital
domain for subsequent AI processing using analog-to-digital converters (ADC).
Recent research has tried to take advantage of massively parallel low-power
analog/digital computing in the form of near- and in-sensor processing, in
which the AI computation is performed partly in the periphery of the pixel
array and partly in a separate on-board CPU/accelerator. Unfortunately,
high-resolution input images still need to be streamed between the camera and
the AI processing unit, frame by frame, causing energy, bandwidth, and security
bottlenecks. To mitigate this problem, we propose a novel
Processing-in-Pixel-in-memory (P2M) paradigm, that customizes the pixel array
by adding support for analog multi-channel, multi-bit convolution, batch
normalization, and ReLU (Rectified Linear Units). Our solution includes a
holistic algorithm-circuit co-design approach and the resulting P2M paradigm
can be used as a drop-in replacement for embedding memory-intensive first few
layers of convolutional neural network (CNN) models within
foundry-manufacturable CMOS image sensor platforms. Our experimental results
indicate that P2M reduces data transfer bandwidth from sensors and analog to
digital conversions by ~21x, and the energy-delay product (EDP) incurred in
processing a MobileNetV2 model on a TinyML use case for visual wake words
dataset (VWW) by up to ~11x compared to standard near-processing or in-sensor
implementations, without any significant drop in test accuracy.


  We present a completely explicit transcendence measure for $e$. This is a
continuation and an improvement to the works of Borel, Mahler and Hata on the
topic. Furthermore, we also prove a transcendence measure for an arbitrary
positive integer power of $e$. The results are based on Hermite-Pad\'e
approximations and on careful analysis of common factors in the footsteps of
Hata.


  We investigate the contributions of the Cartan generators in the static
potentials for various representations in the framework of the domain model of
center vortices for SU(3) gauge theory. Using the center domains with the cores
corresponding to only one Cartan generator H8, already given as a particular
proposal, leads to some concavities in the potentials for higher
representations. Furthermore, the string tension of the fundamental
representation is the same at Casimir scaling and N-ality regimes. We add the
contribution of the other Cartan generator H3 to the potentials and therefore
these shortcomings can be eliminated. However, we discuss that at intermediate
range of distances the potentials induced by only H8 agree with the Casimir
scaling better than those corresponding to both Cartan generators.


  Traditional supervised learning requires ground truth labels for the training
data, whose collection can be difficult in many cases. Recently, crowdsourcing
has established itself as an efficient labeling solution through resorting to
non-expert crowds. To reduce the labeling error effects, one common practice is
to distribute each instance to multiple workers, whereas each worker only
annotates a subset of data, resulting in the {\it sparse annotation}
phenomenon. In this paper, we note that when meeting with class-imbalance,
i.e., when the ground truth labels are {\it class-imbalanced}, the sparse
annotations are prone to be skewly distributed, which thus can severely bias
the learning algorithm. To combat this issue, we propose one self-training
based approach named {\it Self-Crowd} by progressively adding confident
pseudo-annotations and rebalancing the annotation distribution. Specifically,
we propose one distribution aware confidence measure to select confident
pseudo-annotations, which adopts the resampling strategy to oversample the
minority annotations and undersample the majority annotations. On one
real-world crowdsourcing image classification task, we show that the proposed
method yields more balanced annotations throughout training than the
distribution agnostic methods and substantially improves the learning
performance at different annotation sparsity levels.


  We exhibit timelike geodesic paths for a metric, introduced by Bonnor [11]
and considered also by Steadman [13], and show that coordinate time runs
backward along a portion of these geodesics.


  In this paper, we introduce poly-Bernoulli numbers with level $2$, related to
the Stirling numbers of the second kind with level $2$, and study several
properties of poly-Bernoulli numbers with level $2$ from their expressions,
relations, and congruences. Poly-Bernoulli numbers with level $2$ have strong
connections with poly-Cauchy numbers with level $2$. In a special case, we can
determine the denominators of Bernoulli numbers with level $2$ by showing a von
Staudt-Clausen like theorem.


  A $\delta N$ formalism is used to study the non-Gaussianity of the primordial
curvature perturbation on an uniform density hypersurfaces generated by the
warm inflation for the first time. After introducing the framework of the warm
inflation and the $\delta N$ formalism, we obtain an analytic expression for
the nonlinear parameter $f_{NL}$ that describes the non-Gaussianity in slow
roll approximation, and find that the $\delta N$ formalism gives a very good
result. We analyse the magnitude of $f_{NL}$ and compare our result with those
of the standard inflation. Then we discuss two concrete examples: the quartic
chaotic model and the hilltop model. The quartic potential model can again be
in very good agreement with the Planck results in the warm inflationary
scenario, and we give out the concrete results of how the nonlinear parameter
depends on the dissipation strength of the warm inflation and the amounts of
expansion. We find that the range of the nonlinear parameters in these two
cases are both well inside of the allowed region of Planck.


  The class of Poset Take-Away games includes many interesting and difficult
games. Playing on an $n$-dimensional positive quadrant (the origin being the
bottom of the poset) gives rise to nim, wythoff's nim and chomp. These are
impartial games. We introduce a partisan game motivated by chomp and the recent
chocolate-bar version. Our game is played on a chocolate bar with alternately
flavored pieces (or a checkerboard). We solve this game by showing it is
equivalent to blue-red hackenbush strings. This equivalence proves that the
values of game are numbers and it gives an algorithm for optimal play when
there is more than one chocolate bar. The checkerboard interpretation leads to
many natural questions.


  In this paper, we consider product-type operators $T^m_{u,v,\varphi}$ from
minimal M\"{o}bius invariant spaces into Zygmund type spaces. So some
characterizations for boundedness and essential norm of these operators are
obtained. As a result some conditions for the compactness will be given.


  Lateral ventricular volume (LVV) is an important biomarker for clinical
investigation. We present the first transfer learning-based LVV segmentation
method for fluid-attenuated inversion recovery (FLAIR) MRI. To mitigate
covariate shifts between source and target domains, this work proposes an
domain adaptation method that optimizes performance on three target datasets.
Silver standard (SS) masks were generated from the target domain using a novel
conventional image processing ventricular segmentation algorithm and used to
supplement the gold standard (GS) data from the source domain, Canadian
Atherosclerosis Imaging Network (CAIN). Four models were tested on held-out
test sets from four datasets: 1) SS+GS: trained on target SS masks and
fine-tuned on source GS masks, 2) GS+SS: trained on source GS masks and
fine-tuned on target SS masks, 3) trained on source GS (GS CAIN Only) and 4)
trained on target SS masks (SS Only). The SS+GS model had the best and most
consistent performance (mean DSC = 0.89, CoV = 0.05) and showed significantly
(p < 0.05) higher DSC compared to the GS-only model on three target domains.
Results suggest pre-training with noisy labels from the target domain allows
the model to adapt to the dataset-specific characteristics and provides robust
parameter initialization while fine-tuning with GS masks allows the model to
learn detailed features. This method has wide application to other medical
imaging problems where labeled data is scarce, and can be used as a per-dataset
calibration method to accelerate wide-scale adoption.


  We present time-of-arrival (TOA) measurements and timing models of 47
millisecond pulsars (MSPs) observed from 2004 to 2017 at the Arecibo
Observatory and the Green Bank Telescope by the North American Nanohertz
Observatory for Gravitational Waves (NANOGrav). The observing cadence was three
to four weeks for most pulsars over most of this time span, with weekly
observations of six sources. These data were collected for use in low-frequency
gravitational wave searches and for other astrophysical purposes. We detail our
observational methods and present a set of TOA measurements, based on
"narrowband" analysis, in which many TOAs are calculated within narrow
radio-frequency bands for data collected simultaneously across a wide
bandwidth. A separate set of "wideband" TOAs will be presented in a companion
paper. We detail a number of methodological changes compared to our previous
work which yield a cleaner and more uniformly processed data set. Our timing
models include several new astrometric and binary pulsar measurements,
including previously unpublished values for the parallaxes of PSRs J1832-0836
and J2322+2057, the secular derivatives of the projected semi-major orbital
axes of PSRs J0613-0200 and J2229+2643, and the first detection of the Shapiro
delay in PSR J2145-0750. We report detectable levels of red noise in the time
series for 14 pulsars. As a check on timing model reliability, we investigate
the stability of astrometric parameters across data sets of different lengths.
We report flux density measurements for all pulsars observed. Searches for
stochastic and continuous gravitational waves using these data will be subjects
of forthcoming publications.


  This paper introduces UQA, a novel dataset for question answering and text
comprehension in Urdu, a low-resource language with over 70 million native
speakers. UQA is generated by translating the Stanford Question Answering
Dataset (SQuAD2.0), a large-scale English QA dataset, using a technique called
EATS (Enclose to Anchor, Translate, Seek), which preserves the answer spans in
the translated context paragraphs. The paper describes the process of selecting
and evaluating the best translation model among two candidates: Google
Translator and Seamless M4T. The paper also benchmarks several state-of-the-art
multilingual QA models on UQA, including mBERT, XLM-RoBERTa, and mT5, and
reports promising results. For XLM-RoBERTa-XL, we have an F1 score of 85.99 and
74.56 EM. UQA is a valuable resource for developing and testing multilingual
NLP systems for Urdu and for enhancing the cross-lingual transferability of
existing models. Further, the paper demonstrates the effectiveness of EATS for
creating high-quality datasets for other languages and domains. The UQA dataset
and the code are publicly available at www.github.com/sameearif/UQA.


  In this note we reconsider Sen's entropy function analysis for 5D
supergravity actions containing Chern-Simons terms. The apparent lack of gauge
invariance is usually tackled via a 4D reduction. Here we motivate how a
systematic 5D procedure also works. In doing so, it becomes important to
identify the correct 5D charges. In particular, we perform explicit
calculations for the black ring and 5D black hole. In the black ring analysis,
we find Chern-Simons induced spectral flow shifts emerging out of Sen's
formalism. We find that the entropy function nevertheless remains gauge
invariant and the resulting electric charges are identified as Page charges.
For the black hole too, 5D gauge invariance is confirmed. Our 5D analysis
enables us to fix a mismatch that arose in the electric charges of Goldstein
and Jena's 4D-reduced calculation. Finally we provide an interpretation for the
e^0 - p^0 exchange in the entropy function as an interpolation between black
hole and black ring geometries in Taub-NUT.


  We show that the problem of finding a set with maximum cohesion in an
undirected network is NP-hard.


  We demonstrate a versatile, rotational-state dependent trapping scheme for
the ground and first excited rotational states of $^{23}$Na$^{40}$K molecules.
Close to the rotational manifold of a narrow electronic transition, we
determine tune-out frequencies where the polarizability of one state vanishes
while the other remains finite, and a magic frequency where both states
experience equal polarizability. The proximity of these frequencies of only 10
GHz allows for dynamic switching between different trap configurations in a
single experiment, while still maintaining sufficiently low scattering rates.


  A \emph{three-dimensional grid drawing} of a graph is a placement of the
vertices at distinct points with integer coordinates, such that the straight
line segments representing the edges do not cross. Our aim is to produce
three-dimensional grid drawings with small bounding box volume. We prove that
every $n$-vertex graph with bounded degeneracy has a three-dimensional grid
drawing with $O(n^{3/2})$ volume. This is the broadest class of graphs admiting
such drawings. A three-dimensional grid drawing of a directed graph is
\emph{upward} if every arc points up in the z-direction. We prove that every
directed acyclic graph has an upward three-dimensional grid drawing with
$(n^3)$ volume, which is tight for the complete dag. The previous best upper
bound was $O(n^4)$. Our main result is that every $c$-colourable directed
acyclic graph ($c$ constant) has an upward three-dimensional grid drawing with
$O(n^2)$ volume. This result matches the bound in the undirected case, and
improves the best known bound from $O(n^3)$ for many classes of directed
acyclic graphs, including planar, series parallel, and outerplanar.


  Efficient hyperparameter or architecture search methods have shown remarkable
results, but each of them is only applicable to searching for either
hyperparameters (HPs) or architectures. In this work, we propose a unified
pipeline, AutoHAS, to efficiently search for both architectures and
hyperparameters. AutoHAS learns to alternately update the shared network
weights and a reinforcement learning (RL) controller, which learns the
probability distribution for the architecture candidates and HP candidates. A
temporary weight is introduced to store the updated weight from the selected
HPs (by the controller), and a validation accuracy based on this temporary
weight serves as a reward to update the controller. In experiments, we show
AutoHAS is efficient and generalizable to different search spaces, baselines
and datasets. In particular, AutoHAS can improve the accuracy over popular
network architectures, such as ResNet and EfficientNet, on CIFAR-10/100,
ImageNet, and four more other datasets.


  We consider the problem of optimal portfolio selection under forward
investment performance criteria in an incomplete market. Given multiple traded
assets, the prices of which depend on multiple observable stochastic factors,
we construct a large class of forward performance processes with power-utility
initial data, as well as the corresponding optimal portfolios. This is done by
solving the associated non-linear parabolic partial differential equations
(PDEs) posed in the "wrong" time direction, for stock-factor correlation
matrices with eigenvalue equality (EVE) structure, which we introduce here.
Along the way we establish on domains an explicit form of the generalized
Widder's theorem of Nadtochiy and Tehranchi [NT15, Theorem 3.12] and rely
hereby on the Laplace inversion in time of the solutions to suitable linear
parabolic PDEs posed in the "right" time direction.


  Understanding the run-time behavior of concurrent programs is a challenging
task. A popular approach is to establish a happens- before relation via vector
clocks. Thus, we can identify bugs and per- formance bottlenecks, for example,
by checking if two con icting events may happen concurrently. We employ a
two-phase method to derive vector clock information for a wide range of
concurrency features that includes all of the message-passing features in Go.
The rst phase (instrumentation and tracing) yields a run-time trace that
records all events related to message-passing concurrency that took place. The
second phase (trace replay) is carried out o ine and replays the recorded
traces to infer vector clock information. Trace replay operates on thread-local
traces. Thus, we can observe behav- ior that might result from some alternative
schedule. Our approach is not tied to any speci c language. We have built a
prototype for the Go programming language and provide empirical evidence of the
usefulness of our method.


  We discuss a time-harmonic inverse scattering problem for a nonlinear
Helmholtz equation with compactly supported inhomogeneous scattering objects
that are described by a nonlinear refractive index in unbounded free space.
Assuming the knowledge of a nonlinear far field operator, which maps Herglotz
incident waves to the far field patterns of corresponding solutions of the
nonlinear scattering problem, we show that the nonlinear index of refraction is
uniquely determined. We also generalize two reconstruction methods, a
factorization method and a monotonicity method, to recover the support of such
nonlinear scattering objects. Numerical results illustrate our theoretical
findings.


  Tag recommendation relies on either a ranking function for top-$k$ tags or an
autoregressive generation method. However, the previous methods neglect one of
two seemingly conflicting yet desirable characteristics of a tag set:
orderlessness and inter-dependency. While the ranking approach fails to address
the inter-dependency among tags when they are ranked, the autoregressive
approach fails to take orderlessness into account because it is designed to
utilize sequential relations among tokens. We propose a sequence-oblivious
generation method for tag recommendation, in which the next tag to be generated
is independent of the order of the generated tags and the order of the ground
truth tags occurring in training data. Empirical results on two different
domains, Instagram and Stack Overflow, show that our method is significantly
superior to the previous approaches.


  The performance of a small prototype of a cylindrical drift chamber (CDC)
used in the COMET Phase-I experiment was studied by using an electron beam. The
prototype chamber was constructed with alternating all-stereo wire
configuration and operated with the He-iC$_{4}$H$_{10}$ (90/10) gas mixture
without a magnetic field. The drift space-time relation, drift velocity,
d$E$/d$x$ resolution, hit efficiency, and spatial resolution as a function of
distance from the wire were investigated. The average spatial resolution of 150
$\mu$m with the hit efficiency of 99% was obtained at applied voltages higher
than 1800 V. We have demonstrated that the design and gas mixture of the
prototype match the operation of the COMET CDC.


  We described an approach to achieve fine color control of fluorescent White
Organic Light-Emitting Diodes (OLED), based on an Ultra-thin Premixed emitting
Layer (UPL). The UPL consists of a mixture of two dyes (red-emitting
4-di(4'-tert-butylbiphenyl-4-yl)amino-4'-dicyanovinylbenzene or fvin and
green-emitting 4-di(4'-tert-butylbiphenyl-4-yl)aminobenzaldehyde or fcho)
premixed in a single evaporation cell: since these two molecules have
comparable structures and similar melting temperatures, a blend can be
evaporated, giving rise to thin films of identical and reproducible composition
compared to those of the pre-mixture. The principle of fine color tuning is
demonstrated by evaporating a 1-nm-thick layer of this blend within the
hole-transport layer (4,4'-bis[N-(1-naphtyl)-N-phenylamino]biphenyl
(\alpha-NPB)) of a standard fluorescent OLED structure. Upon playing on the
position of the UPL inside the hole-transport layer, as well as on the premix
composition, two independent parameters are available to finely control the
emitted color. Combined with blue emission from the heterojunction, white light
with Commission Internationale de l'Eclairage 1931 color coordinates (0.34,
0.34) was obtained, with excellent color stability with the injected current.
The spectrum reveals that the fcho material does not emit light due to
efficient energy transfer to the red-emitting fvin compound but plays the role
of a host matrix for fvin, allowing for a very precise adjustment of the red
dopant amount in the device.


  Operator regular variation reveals general power-law distribution tail decay
phenomena using operator scaling, that includes multivariate regular variation
with scalar scaling as a special case. In this paper, we show that a
multivariate Liouville distribution is operator regularly varying if its
driving function is univariate regularly varying. Our method focuses on
operator regular variation of multivariate densities, which implies, as we also
show in this paper, operator regular variation of the multivariate
distributions. This general result extends the general closure property of
multivariate regular variation established by de Haan and Resnick in 1987.


  In addition to being environment-friendly, vehicle-to-grid (V2G) systems can
help the plug-in electric vehicle (PEV) users in reducing their energy costs
and can also help stabilizing energy demand in the power grid. In V2G systems,
since the PEV users need to obtain system information (e.g., locations of
charging/discharging stations, current load and supply of the power grid) to
achieve the best charging and discharging performance, data communication plays
a crucial role. However, since the PEV users are highly mobile, information
from V2G systems is not always available for many reasons, e.g., wireless link
failures and cyber attacks. Therefore, in this paper, we introduce a novel
concept using cyber insurance to "transfer" cyber risks, e.g., unavailable
information, of a PEV user to a third party, e.g., a cyber insurance company.
Under the insurance coverage, even without information about V2G systems, a PEV
user is always guaranteed the best price for charging/discharging. In
particular, we formulate the optimal energy cost problem for the PEV user by
adopting a Markov decision process framework. We then propose a learning
algorithm to help the PEV user make optimal decisions, e.g., to charge or
discharge and to buy or not to buy insurance, in an online fashion. Through
simulations, we show that cyber insurance is an efficient solution not only in
dealing with cyber risks, but also in maximizing revenue of the PEV user.


  New method of calculation of master integrals using differential equations
and asymptotical expansion is presented. This method leads to the results exact
in space-time dimension $D$ having the form of the convergent power series. As
an application of this method, we calculate the two--loop master integral for
"crossed--triangle" topology which was previously known only up to $O(\ep)$
order. The case when a topology contains several master integrals is also
considered. We present an algorithm of the term-by-term calculation of the
asymptotical expansion in this case and analyze in detail the "crossed--box"
topology with three master integrals.


  We introduce the concept of tunable ideal magnetic dipole scattering, where a
nonmagnetic nanoparticle scatters lights as a pure magnetic dipole. High
refractive index subwavelength nanoparticles usually support both electric and
magnetic dipole responses. Thus, to achieve ideal magnetic dipole scattering
one has to suppress the electric dipole response. Such a possibility was
recently demonstrated for the so-called anapole mode, which is associated with
zero electric dipole scattering. By overlapping magnetic dipole resonance with
the anapole mode we achieve ideal magnetic dipole scattering in the far-field
with tunable high scattering resonances in near infrared spectrum. We
demonstrate that such condition can be realized for two subwavelength
geometries. One of them is core-shell nanosphere consisting of Au core and
silicon shell. It can be also achieved in other geometries, including
nanodisks, which are compatible with current nanofabrication technology.


  Classification tasks are usually evaluated in terms of accuracy. However,
accuracy is discontinuous and cannot be directly optimized using gradient
ascent. Popular methods minimize cross-entropy, hinge loss, or other surrogate
losses, which can lead to suboptimal results. In this paper, we propose a new
optimization framework by introducing stochasticity to a model's output and
optimizing expected accuracy, i.e. accuracy of the stochastic model. Extensive
experiments on linear models and deep image classification show that the
proposed optimization method is a powerful alternative to widely used
classification losses.