  We offer an explanation for the anti-correlation between the minimum
variability timescale ($MTS$) in the prompt emission light curve of gamma-ray
bursts (GRBs) and the estimated bulk Lorentz factor of these GRBs, in the
context of a magnetically arrested disk (MAD) model. In particular, we show
that previously derived limits on the maximum available energy per baryon in a
Blandford-Znajek jet leads to a relationship between the characteristic MAD
timescale, $t_{MAD}$, in GRBs and the maximum bulk Lorentz factor: $t_{MAD}
\propto \Gamma^{-6}$, somewhat steeper than (although within the error bars of)
the fitted relationship found in the GRB data. Similarly, the MAD model also
naturally accounts for the observed anti-correlation between $MTS$ and
gamma-ray luminosity $L$ in the GRB data, and we estimate the accretion rates
of the GRB disks (given these luminosities) in the context of this model. Both
of these correlations ($MTS-\Gamma$ and $MTS-L$) are also observed in the AGN
data, and we discuss the implications of our results in the context of both GRB
and blazar systems.


  As is well known, during the Last Glacial Maximum, about 20'000 years ago,
the ice was asymmetrically distributed around the present North Pole. It
reached the region of New York, while east Siberia remained ice free. Mammoths
lived in arctic regions of east Siberia, where now their food cannot grow.
Therefore the globe must have been turned in such a way that the North Pole was
in Greenland. The required rapid geographic pole shift at the end of the ice
ages has been shown to be physically possible, on condition that an
astronomical object of planetary size in an extremely eccentric orbit existed.
In this postulated situation it was red hot and a disk shaped gas cloud reduced
the solar radiation on Earth in a time dependent way. A frequent objection to
this hypothesis is that the phenomena should be reported in old traditions.
This paper quotes such traditions from passages of Platon, Herodotus, Ovid,
papyrus Ipuwer, Gilgamesh, the Bible, American Indians and other civilizations.
Far from being exhaustive the examples show that apparently strange traditions
can report observed facts. This connection is of mutual benefit for science and
humanities.


  We consider the decay of the Higgs boson to W$^{+}$ W$^{-}$ at a proposed
Large Hadron Electron Collider and determine the likelihood of detecting a
signal for the Higgs mass from its decay product W jets by imposing cuts to
select candidate jet pairs and optimizing the value of the angular separation
$\Delta R$. It was found that at the LHeC experiment (CM energy $\sqrt{s}=1.3$
TeV and luminosity of 100 fb$^{-1}$ per year), the highest efficiency is
obtained with $\Delta R = 0.4$, along with a selection scheme of $|\Delta \eta|
<1, 10<m<85$ GeV, $p_T$ of jets 1 and 2 between $10 - 20$ GeV and $p_T$ of jets
3 and 4 $>10$ GeV: this led to an efficiency between $7.1 - 7.5 \%$ for finding
the invariant 4-jet mass in a mass region $<140$ GeV. Under
signal-to-background comparison, the signal showed a $3.8 \sigma$ excess
compared to the charged current W$^{-}$ background.


  We reconsider the issue of localization in open-closed B-twisted
Landau-Ginzburg models with arbitrary Calabi-Yau target. Through careful
analsysis of zero-mode reduction, we show that the closed model allows for a
one-parameter family of localization pictures, which generalize the standard
residue representation. The parameter $\lambda$ which indexes these pictures
measures the area of worldsheets with $S^2$ topology, with the residue
representation obtained in the limit of small area. In the boundary sector, we
find a double family of such pictures, depending on parameters $\lambda$ and
$\mu$ which measure the area and boundary length of worldsheets with disk
topology. We show that setting $\mu=0$ and varying $\lambda$ interpolates
between the localization picture of the B-model with a noncompact target space
and a certain residue representation proposed recently. This gives a complete
derivation of the boundary residue formula, starting from the explicit
construction of the boundary coupling. We also show that the various
localization pictures are related by a semigroup of homotopy equivalences.


  We present several new easy ways of generating smooth one-dimensional maps
displaying robust chaos, i.e., chaos for whole intervals of the parameter.
Unlike what happens with previous methods, the Lyapunov exponent of the maps
constructed here varies widely with the parameter. We show that the condition
of negative Schwarzian derivative, which was used in previous works, is not a
necessary condition for robust chaos. Finally we show that the maps constructed
in previous works have always the Lyapunov exponent $\ln 2$ because they are
conjugated to each other and to the tent map by means of smooth homeomorphisms.
In the methods presented here, the maps have variable Lyapunov coefficients
because they are conjugated through non-smooth homeomorphisms similar to
Minkowski's question mark function.


  Using ideas of Sansuc and results of Colliot-Th\'el\`ene and of Tate, we
compute the defect of weak approximation for a reductive group G over a global
field K in terms of the algebraic fundamental group of G.


  In this paper, distributed Bayesian detection problems with unknown prior
probabilities of hypotheses are considered. The sensors obtain observations
which are conditionally dependent across sensors and their probability density
functions (pdf) are not exactly known. The observations are quantized and are
sent to the fusion center. The fusion center fuses the current quantized
observations and makes a final decision. It also designs (updated) quantizers
to be used at the sensors and the fusion rule based on all previous quantized
observations. Information regarding updated quantizers is sent back to the
sensors for use at the next time. In this paper, the conditional joint pdf is
represented in a parametric form by using the copula framework. The unknown
parameters include dependence parameters and marginal parameters. Maximum
likelihood estimation (MLE) with feedback based on quantized data is proposed
to estimate the unknown parameters. These estimates are iteratively used to
refine the quantizers and the fusion rule to improve distributed detection
performance by using feedback. Numerical examples show that the new detection
method based on MLE with feedback is much better than the usual detection
method based on the assumption of conditionally independent observations.


  In this paper we introduce an algorithm of construction of cyclic
space-filling curves. One particular construction provides a family of
space-filling curves in all dimensions (H-curves). They are compared here with
the Hilbert curve in the sense of clustering properties, and it turns out that
the constructed curve is very close and sometimes a bit better than the Hilbert
curve. At the same time, its construction is more simple and evaluation is
significantly faster.


  Finding a basis/coordinate system that can efficiently represent an input
data stream by viewing them as realizations of a stochastic process is of
tremendous importance in many fields including data compression and
computational neuroscience. Two popular measures of such efficiency of a basis
are sparsity (measured by the expected $\ell^p$ norm, $0 < p \leq 1$) and
statistical independence (measured by the mutual information). Gaining deeper
understanding of their intricate relationship, however, remains elusive.
Therefore, we chose to study a simple synthetic stochastic process called the
spike process, which puts a unit impulse at a random location in an
$n$-dimensional vector for each realization. For this process, we obtained the
following results: 1) The standard basis is the best both in terms of sparsity
and statistical independence if $n \geq 5$ and the search of basis is
restricted within all possible orthonormal bases in $R^n$; 2) If we extend our
basis search in all possible invertible linear transformations in $R^n$, then
the best basis in statistical independence differs from the one in sparsity; 3)
In either of the above, the best basis in statistical independence is not
unique, and there even exist those which make the inputs completely dense; 4)
There is no linear invertible transformation that achieves the true statistical
independence for $n > 2$.


  We propose a novel feed-forward network for video inpainting. We use a set of
sampled video frames as the reference to take visible contents to fill the hole
of a target frame. Our video inpainting network consists of two stages. The
first stage is an alignment module that uses computed homographies between the
reference frames and the target frame. The visible patches are then aggregated
based on the frame similarity to fill in the target holes roughly. The second
stage is a non-local attention module that matches the generated patches with
known reference patches (in space and time) to refine the previous global
alignment stage. Both stages consist of large spatial-temporal window size for
the reference and thus enable modeling long-range correlations between distant
information and the hole regions. Therefore, even challenging scenes with large
or slowly moving holes can be handled, which have been hardly modeled by
existing flow-based approach. Our network is also designed with a recurrent
propagation stream to encourage temporal consistency in video results.
Experiments on video object removal demonstrate that our method inpaints the
holes with globally and locally coherent contents.


  Existing contrastive language-image pre-training aims to learn a joint
representation by matching abundant image-text pairs. However, the number of
image-text pairs in medical datasets is usually orders of magnitude smaller
than that in natural datasets. Besides, medical image-text pairs often involve
numerous complex fine-grained correspondences. This paper aims to enhance the
data efficiency by introducing multiple-to-multiple local relationship modeling
to capture denser supervisions. More specifically, we propose a Medical
Language-Image Pre-training (MLIP) framework, which exploits the limited
image-text medical data more efficiently through patch-sentence matching.
Furthermore, we introduce a masked contrastive learning strategy with semantic
integrity estimation to reduce redundancy in images while preserving the
underlying semantics. Our evaluation results show that MLIP outperforms
previous work in zero/few-shot classification and few-shot segmentation tasks
by a large margin.


  We consider a generalization of the diffusion equation on graphs. This
generalized diffusion equation gives rise to both normal and superdiffusive
processes on infinite one-dimensional graphs. The generalization is based on
the $k$-path Laplacian operators $L_{k}$, which account for the hop of a
diffusive particle to non-nearest neighbours in a graph. We first prove that
the $k$-path Laplacian operators are self-adjoint. Then, we study the
transformed $k$-path Laplacian operators using Laplace, factorial and Mellin
transforms. We prove that the generalized diffusion equation using the Laplace-
and factorial-transformed operators always produce normal diffusive processes
independently of the parameters of the transforms. More importantly, the
generalized diffusion equation using the Mellin-transformed $k$-path Laplacians
$\sum_{k=1}^{\infty}k^{-s}L_{k}$ produces superdiffusive processes when
$1<s<3$.


  It is a very old conjecture that the crossing number of knots is additive
under connected sum. In other words, if K#K' is the connected sum of knots K
and K', then does the equality c(K#K') = c(K) + c(K') hold? We prove that
c(K#K') is at most c(K) + c(K') and at least (c(K) + c(K'))/152.


  We study the Tutte polynomial of two infinite families of finite graphs.
These are the Schreier graphs associated with the action of two well-known
self-similar groups acting on the binary rooted tree by automorphisms: the
first Grigorchuk group of intermediate growth, and the iterated monodromy group
of the complex polynomial $z^2-1$ known as the Basilica group. For both of
them, we describe the Tutte polynomial and we compute several special
evaluations of it, giving further information about the combinatorial structure
of these graphs.


  In this work we investigate the effectiveness of continuous-variable (CV)
entangled states, transferred through high-loss atmospheric channels, as a
means of viable quantum key distribution (QKD) between terrestrial stations and
low-Earth orbit (LEO) satellites. In particular, we investigate the role played
by the Gaussian CV states as compared to non-Gaussian states. We find that
beam-wandering induced atmospheric losses lead to QKD performance levels that
are in general quite different from those found in fixed-attenuation channels.
For example, circumstances can be found where no QKD is viable at some fixed
loss in fiber but is viable at the same mean loss in fading channels. We also
find that, in some circumstances, the QKD relative performance of Gaussian and
non-Gaussian states can in atmospheric channels be the reverse of that found in
fixed-attenuation channels. These findings show that the nature of the
atmospheric channel can have a large impact on the QKD performance. Our results
should prove useful for emerging global quantum communications that use LEO
satellites as communication relays.


  Using the most general, model independent form of effective Hamiltonian, the
transverse polarization P_T of Lambda in Lambda_b -> Lambda l^+ l^- decay is
studied. It is observed that the averaged <P_T> is very sensitive to the
existence of new physics and can attain large values, which can be measured at
future colliders.


  The cryptocurrency Ethereum is the most widely used execution platform for
smart contracts. Smart contracts are distributed applications, which govern
financial assets and, hence, can implement advanced financial instruments, such
as decentralized exchanges or autonomous organizations (DAOs). Their financial
nature makes smart contracts an attractive attack target, as demonstrated by
numerous exploits on popular contracts resulting in financial damage of
millions of dollars. This omnipresent attack hazard motivates the need for
sound static analysis tools, which assist smart contract developers in
eliminating contract vulnerabilities a priori to deployment. Vulnerability
assessment that is sound and insightful for EVM contracts is a formidable
challenge because contracts execute low-level bytecode in a largely unknown and
potentially hostile execution environment. So far, there exists no provably
sound automated analyzer that allows for the verification of security
properties based on program dependencies, even though prevalent attack classes
fall into this category. In this work, we present HoRStify, the first automated
analyzer for dependency properties of Ethereum smart contracts based on sound
static analysis. HoRStify grounds its soundness proof on a formal proof
framework for static program slicing that we instantiate to the semantics of
EVM bytecode. We demonstrate that HoRStify is flexible enough to soundly verify
the absence of famous attack classes such as timestamp dependency and, at the
same time, performant enough to analyze real-world smart contracts.


  A subcategory $\mathscr{W}$ of an abelian category is called wide if it is
closed under kernels, cokernels, and extensions. Wide subcategories are of
interest in representation theory because of their links to other homological
and combinatorial objects, established among others by Ingalls-Thomas and
Marks-\v{S}\v{t}ov\'i\v{c}ek.
  If $d \geqslant 1$ is an integer, then Jasso introduced the notion of
$d$-abelian categories, where kernels, cokernels, and extensions have been
replaced by longer complexes. Wide subcategories can be generalised to this
situation.
  Important examples of $d$-abelian categories arise as the $d$-cluster tilting
subcategories $\mathscr{M}_{n,d}$ of $\operatorname{mod} A_n^{d-1}$, where
$A_n^{d-1}$ is a higher Auslander algebra of type $A$ in the sense of Iyama.
This paper gives a combinatorial description of the wide subcategories of
$\mathscr{M}_{n,d}$ in terms of what we call non-interlacing collections.


  Satellite conjunctions involving "near misses" of space objects are becoming
increasingly likely. One approach to risk analysis for them involves the
computation of the collision probability, but this has been regarded as having
some counter-intuitive properties and its interpretation has been debated. This
paper formulates an approach to satellite conjunction based on a simple
statistical model and discusses inference on the miss distance between the two
objects, both when the relative velocity can be taken as known and when its
uncertainty must be taken into account. It is pointed out that the usual
collision probability estimate can be badly biased, but that highly accurate
inference on the miss distance is possible. The ideas are illustrated with case
studies and Monte Carlo results that show its excellent performance.


  Let $n\ge 1, e\ge 1, k\ge 2$ and $c$ be integers. An integer $u$ is called a
unit in the ring $\mathbb{Z}_n$ of residue classes modulo $n$ if $\gcd(u,
n)=1$. A unit $u$ is called an exceptional unit in the ring $\mathbb{Z}_n$ if
$\gcd(1-u,n)=1$. We denote by $\mathcal{N}_{k,c,e}(n)$ the number of solutions
$(x_1,...,x_k)$ of the congruence $x_1^e+...+x_k^e\equiv c \pmod n$ with all
$x_i$ being exceptional units in the ring $\mathbb{Z}_n$. In 2017,
Mollahajiaghaei presented a formula for the number of solutions $(x_1,...,x_k)$
of the congruence $x_1^2+...+x_k^2\equiv c\pmod n$ with all $x_i$ being the
units in the ring $\mathbb{Z}_n$. Meanwhile, Yang and Zhao gave an exact
formula for $\mathcal{N}_{k,c,1}(n)$. In this paper, by using Hensel's lemma,
exponential sums and quadratic Gauss sums, we derive an explicit formula for
the number $\mathcal{N}_{k,c,2}(n)$. Our result extends Mollahajiaghaei's
theorem and that of Yang and Zhao.


  In this article, we present a leap-forward expansion to the study of
explainability in neural networks by considering explanations as answers to
abstract reasoning-based questions. With $P$ as the prediction from a neural
network, these questions are `Why P?', `What if not P?', and `Why P, rather
than Q?' for a given contrast prediction $Q$. The answers to these questions
are observed correlations, observed counterfactuals, and observed contrastive
explanations respectively. Together, these explanations constitute the
abductive reasoning scheme. We term the three explanatory schemes as observed
explanatory paradigms. The term observed refers to the specific case of
post-hoc explainability, when an explanatory technique explains the decision
$P$ after a trained neural network has made the decision $P$. The primary
advantage of viewing explanations through the lens of abductive reasoning-based
questions is that explanations can be used as reasons while making decisions.
The post-hoc field of explainability, that previously only justified decisions,
becomes active by being involved in the decision making process and providing
limited, but relevant and contextual interventions. The contributions of this
article are: ($i$) realizing explanations as reasoning paradigms, ($ii$)
providing a probabilistic definition of observed explanations and their
completeness, ($iii$) creating a taxonomy for evaluation of explanations, and
($iv$) positioning gradient-based complete explanainability's replicability and
reproducibility across multiple applications and data modalities, ($v$) code
repositories, publicly available at
https://github.com/olivesgatech/Explanatory-Paradigms.


  In this paper we revisited phenomenological potentials. We studied S-wave
heavy quarkonium spectra by two potential models. The first one is power
potential and the second one is logarithmic potential. We calculated spin
averaged masses, hyperfine splittings, decay constants, leptonic decay widhts,
two-photon and two-gluon decay widths and some allowed M1 transitions. We
studied ground and 4 radially excited S-wave charmonium and bottomonium states
via solving nonrelativistic Schr\"odinger equation. Although the potentials
were studied in this paper is not directly QCD motivated potential, obtained
results are agree well with experimental data and other theoretical studies.


  When a proposition has no proof in an inference system, it is sometimes
useful to build a counter-proof explaining, step by step, the reason of this
non-provability. In general, this counter-proof is a (possibly) infinite
co-inductive proof in a different inference system. In this paper, we show
that, for some decidable inference systems, this (possibly) infinite proof has
a representation as a finite proof in yet another system, equivalent to the
previous one.


  Most binaries are undetected. Astrometric reductions of a system using the
assumption that the object moves like a single point mass can be biased by
unresolved binary stars. The discrepancy between the centre of mass of the
system (which moves like a point mass) and the centre of light (which is what
we observe) introduces additional motion. We explore the extent to which binary
systems affect single object models fit to astrometric data. This tells us how
observations are diluted by binaries and which systems cause the largest
discrepancies - but also allows us to make inferences about the binarity of
populations based on observed astrometric error. By examining a sample of mock
observations, we show that binaries with periods close to one year can mimic
parallax and thus bias distance measurements, whilst long period binaries can
introduce significant apparent proper motion. Whilst these changes can soak up
some of the error introduced by the binary, the total deviation from the best
fitting model can be translated into a lower limit on the on-sky separation of
the pair. Throughout we link these predictions to data from the Gaia satellite,
whilst leaving the conclusions generalizable to other surveys.


  Demagnetization in ferromagnetic transition metals driven by a femtosecond
laser pulse is a fundamental problem in solid state physics, and its
understanding is essential to the development of spintronics devices. Ab initio
calculation of time-dependent magnetic moment in the velocity gauge so far has
not been successful in reproducing the large amount of demagnetization observed
in experiments. In this work, we propose a method to incorporate intraband
transitions within the velocity gauge through a convective derivative in the
crystal momentum space. Our results for transition-element bulk crystals (bcc
Fe, hcp Co and fcc Ni) based on the time-dependent quantum Liouville equation
show a dramatic enhancement in the amount of demagnetization after the
inclusion of an intraband term, in agreement with experiments. We also find
that the effect of intraband transitions to each ferromagnetic material is
distinctly different because of their band structure and spin property
differences. Our finding has a far-reaching impact on understanding of
ultrafast demagnetization.


  Fourier transformations of pseudo-Boolean functions are popular tools for
analyzing functions of binary sequences. Real-world functions often have
structures that manifest in a sparse Fourier transform, and previous works have
shown that under the assumption of sparsity the transform can be computed
efficiently. But what if we want to compute the Fourier transform of functions
defined over a $q$-ary alphabet? These types of functions arise naturally in
many areas including biology. A typical workaround is to encode the $q$-ary
sequence in binary, however, this approach is computationally inefficient and
fundamentally incompatible with the existing sparse Fourier transform
techniques. Herein, we develop a sparse Fourier transform algorithm
specifically for $q$-ary functions of length $n$ sequences, dubbed $q$-SFT,
which provably computes an $S$-sparse transform with vanishing error as $q^n
\rightarrow \infty$ in $O(Sn)$ function evaluations and $O(S n^2 \log q)$
computations, where $S = q^{n\delta}$ for some $\delta < 1$. Under certain
assumptions, we show that for fixed $q$, a robust version of $q$-SFT has a
sample complexity of $O(Sn^2)$ and a computational complexity of $O(Sn^3)$ with
the same asymptotic guarantees. We present numerical simulations on synthetic
and real-world RNA data, demonstrating the scalability of $q$-SFT to massively
high dimensional $q$-ary functions.


  It is well known that due to its divergence at large impact parameters, the
Boltzmann collision integral in the kinetic equation for 3D systems of
particles interacting through a $1/r$ potential must be replaced by a
Balescu-Lenard-like collision term. However, the latter diverges at small
impact parameters. This comes from the fact that only weak interactions are
considered while strong collisions between close particles are neglected in its
derivation. We show that a solution to this dilemma exists in the framework of
the BBGKY formulation of statistical mechanics. It is based on a separate
treatment of the contribution of the strong interactions from that of the weak
interactions. The strong interaction part leads to a new term that involves a
fractional Laplacian operator in velocity space while the weak interaction
component yields the Balescu-Lenard collision term with an explained lower
cut-off at the Landau length. For spatially uniform initial conditions, the
fractional Laplacian contribution leads to a long-tailed velocity distribution
as long as the spatial inhomogeneity remains small. We present results from
molecular dynamics simulations confirming the existence of such long tails.


  Future nuclear arms reduction efforts will require technologies to verify
that warheads slated for dismantlement are authentic without revealing any
sensitive weapons design information to international inspectors. Despite
several decades of research, no technology has met these requirements
simultaneously. Recent work by Kemp et al. [Kemp RS, Danagoulian A, Macdonald
RR, Vavrek JR (2016) Proc Natl Acad Sci USA 113:8618--8623] has produced a
novel physical cryptographic verification protocol that approaches this treaty
verification problem by exploiting the isotope-specific nature of nuclear
resonance fluorescence (NRF) measurements to verify the authenticity of a
warhead. To protect sensitive information, the NRF signal from the warhead is
convolved with that of an encryption foil that contains key warhead isotopes in
amounts unknown to the inspector. The convolved spectrum from a candidate
warhead is statistically compared against that from an authenticated template
warhead to determine whether the candidate itself is authentic. Here we report
on recent proof-of-concept warhead verification experiments conducted at the
Massachusetts Institute of Technology. Using high-purity germanium (HPGe)
detectors, we measured NRF spectra from the interrogation of proxy 'genuine'
and 'hoax' objects by a 2.52 MeV endpoint bremsstrahlung beam. The observed
differences in NRF intensities near 2.2 MeV indicate that the physical
cryptographic protocol can distinguish between proxy genuine and hoax objects
with high confidence in realistic measurement times.


  Tracking the ball is critical for video-based analysis of team sports.
However, it is difficult, especially in low-resolution images, due to the small
size of the ball, its speed that creates motion blur, and its often being
occluded by players. In this paper, we propose a generic and principled
approach to modeling the interaction between the ball and the players while
also imposing appropriate physical constraints on the ball's trajectory. We
show that our approach, formulated in terms of a Mixed Integer Program, is more
robust and more accurate than several state-of-the-art approaches on real-life
volleyball, basketball, and soccer sequences.


  In this paper, we study the Ext-algebras of graded skew extensions. For a
connected graded algebra $A$ and a graded automorphism $\sigma$, we analyze the
Yoneda product of the Ext-algebra of graded skew extension $A[z;\sigma]$, and
prove this Ext-algebra is an $R$-smash product of the Ext-algebra of $A$ and
the one of polynomial algebra $k[z]$ as an associative algebra.


  Summarizing our discussions, in the Extended Comment on the Article, that we
received, we again did not find any general solution of CPP including any
method of solution of inequalities either. This is rather a confirmation of the
specific nature of the contact criterion and the corresponding solution plus
algorithmic scheme. Surprisingly, the authors started to criticize our
alternative Curve-To-Solid Beam formulation, which is based on the CPP for
surfaces and not based neither on CPP for PTC, nor on CPP for CTC as a closer
look should have revealed. While doing so, the aforementioned authors are
apparently not fully following the logic in our paper. Thus, as an example,
while criticizing our parallel curves, they claim in several places that it is
not possible to construct any parallel curves with an arbitrary angle between
tangent lines. However, one can easily wind a wire on a cylinder at any desired
angle -- in this case the distance between the axis of a cylinder and the
mid-curve of a wire will be constant -- solution is, of course, multiple
excluding application of CTC algorithm. We are emphasizing in detail again the
fact, that a very general criterion for the existence and uniqueness of the CPP
procedure for Curve-To-Curve algorithm by no means can be derived by performing
ad-hoc and not consistent mathematical operations.


  The basic question in perturbation analysis of Markov chains is: how do small
changes in the transition kernels of Markov chains translate to chains in their
stationary distributions? Many papers on the subject have shown, roughly, that
the change in stationary distribution is small as long as the change in the
kernel is much less than some measure of the convergence rate. This result is
essentially sharp for generic Markov chains. In this paper we show that much
larger errors, up to size roughly the square root of the convergence rate, are
permissible for many target distributions associated with graphical models. The
main motivation for this work comes from computational statistics, where there
is often a tradeoff between the per-step error and per-step cost of approximate
MCMC algorithms. Our results show that larger perturbations (and thus
less-expensive chains) still give results with small error.


  Tunnelling is one of the most paradigmatic and evocative phenomena of quantum
physics, underlying processes such as photosynthesis and nuclear fusion, as
well as devices ranging from SQUID magnetometers to superconducting qubits for
quantum computers. The question of how long a particle takes to tunnel,
however, has remained controversial since the first attempts to calculate it,
which relied on the group delay. It is now well understood that this delay (the
arrival time of the transmitted wave packet peak at the far side of the
barrier) can be smaller than the barrier thickness divided by the speed of
light, without violating causality. There have been a number of experiments
confirming this, and even a recent one claiming that tunnelling may take no
time at all. There have also been efforts to identify another timescale, which
would better describe how long a given particle spends in the barrier region.
Here we present a direct measurement of such a time, studying Bose-condensed
$^{87}$Rb atoms tunnelling through a 1.3-${\mu}$m thick optical barrier. By
localizing a pseudo-magnetic field inside the barrier, we use the spin
precession of the atoms as a clock to measure the time it takes them to cross
the classically forbidden region. We find a traversal time of 0.62(7) ms and
study its dependence on incident energy. In addition to finally shedding light
on the fundamental question of the tunnelling time, this experiment lays the
groundwork for addressing deep foundational questions about history in quantum
mechanics: for instance, what can we learn about where a particle was at
earlier times by observing where it is now?


  We present the results of the near-infrared (IR) spectroscopy of nine IRAS
galaxies (NGC 1266, NGC 1320, NGC 2633, NGC 2903, NGC 3034, Mrk 33, NGC 7331,
NGC 7625, NGC 7714) with the ISLE imager and spectrograph mounted on the
Okayama Astrophysical Observatory 1.88 m telescope. [Fe II] 1.257 um and Pa
beta emission lines were observed for the whole sample while H2 2.121 um and Br
gamma lines were additionally obtained for two sources, whose flux ratios are
used as a diagnostic tool of dominant energy sources of the galaxies. We find
that the nucleus of NGC 1266 is most likely a low ionization nuclear
emission-line region (LINER), while NGC 2633 and NGC 2903 possibly harbor
active galactic nuclei (AGNs). No AGN or LINER signal is found for other
objects. In addition, we find the spectral features which is indicative of some
unusual phenomena occurring in the galaxies, such as the large [Fe II] line
widths compared to the local escape velocity in NGC 1266. The present work
shows the potential ability of the ISLE to shed new light on the nature of
infrared galaxies, either through a statistical survey of galaxies or an
exploration of spectral features found in individual objects.


  Recent studies show the importance of the star formation feedback in changing
the energetic and structure of galaxies. Dissecting the physics of the feedback
is hence crucial to understand the evolution of galaxies. Full polarization
radio continuum surveys can be ideally performed to trace not only star
formation but also the energetic components of the interstellar medium (ISM),
the magnetic fields and cosmic ray electrons. Using the SKA precursors, we
investigate the effect of the massive star formation on the ISM energy balance
in nearby galaxies. Our multi-scale and multi-frequency surveys show that
cosmic rays are injected in star forming regions and lose energy propagating
away from their birth place. Due to the star formation feedback, cosmic ray
electron population becomes younger and more energetic. Star formation also
amplifies the turbulent magnetic field inserting a high pressure which is
important in energy balance in the ISM and structure formation in the host
galaxy.


  Laser drilling becomes of increasing importance when hole diameter is in the
range of 10 to 50 mum, for which conventional alternative approaches are
becoming difficult and cost inefficient. Furthermore, it is viewed as the
technique of choice for a number of composite materials and hard materials
which are not readily processed at the microscopic level by contact mechanical
tools. We have demonstrated that suitable experimental conditions are capable
of producing microholes with record aspect-ratio (up to 600) in pure polymers
like PET, PI, PC, PS, PMMA, PEEK,... For example holes of diameter typically 30
mum can be as long as 18 mm, depth at which the drilling rate is getting nearly
zero and the profile stationary. Other materials (metals, ceramics) which can
be similarly laser microdrilled do not exhibit such very high aspect-ratio. The
mechanisms of the drilling process have been studied in details and an original
analytical model has been constructed recently. The various experimental
results, obtained with the KrF laser, will be reviewed with emphasis on the
parameters leading to formation of good holes with high aspect-ratio. For the
application it is also important to note that such high values of aspect-ratio
are obtained with regular configuration of the KrF laser giving a standard
divergence of 3 mrad. However as shown by the model there is still room for
improvement by using a beam with a lower divergence (theoretical limit is 0.2
mrad). Further experimental work is now in progress.


  We present results of a lattice computation of the vector and axial-vector
current matrix elements relevant for the semileptonic decay B^0-bar -> rho^+
l^- nu_l-bar. The computations are performed in the quenched approximation of
lattice QCD on a 24^3 x 48 lattice at beta = 6.2, using an O(a) improved
fermionic action. Our principal result is for the differential decay rate,
dGamma/dq^2, for the decay B^0-bar -> rho^+ l^- nu_l-bar in a region beyond the
charm threshold, allowing a model-independent extraction of |V_{ub}| from
experimental measurements. Heavy quark symmetry relations between radiative and
semileptonic decays of B-bar mesons into light vector mesons are also
discussed.


  Unbound states of $^{10}$C nuclei produced as quasi-projectiles in
$^{12}$C+$^{24}$Mg collisions at E/A = 53 and 95 MeV are studied with the Indra
detector array. Multi-particle correlation function analyses provide
experimental evidence of sequential de-excitation mechanisms through the
production of intermediate $^{9}$B, $^{6}$Be and $^{8}$Be unbound nuclei. The
relative contributions of different decay sequences to the total decay width of
the explored states is estimated semi-quantitatively. The obtained results show
that heavy-ion collisions can be used as a tool to access spectroscopic
information about exotic nuclei.


  Despite the popularity and success of deep learning, there is limited
understanding of when, how, and why neural networks generalize to unseen
examples. Since learning can be seen as extracting information from data, we
formally study information captured by neural networks during training.
Specifically, we start with viewing learning in presence of noisy labels from
an information-theoretic perspective and derive a learning algorithm that
limits label noise information in weights. We then define a notion of unique
information that an individual sample provides to the training of a deep
network, shedding some light on the behavior of neural networks on examples
that are atypical, ambiguous, or belong to underrepresented subpopulations. We
relate example informativeness to generalization by deriving nonvacuous
generalization gap bounds. Finally, by studying knowledge distillation, we
highlight the important role of data and label complexity in generalization.
Overall, our findings contribute to a deeper understanding of the mechanisms
underlying neural network generalization.


  This paper considers how the motions of a telepresence robot moving
autonomously affect a person immersed in the robot through a head-mounted
display. In particular, we explore the preference, comfort, and naturalness of
elements of piecewise linear paths compared to the same elements on a smooth
path. In a user study, thirty-six subjects watched panoramic videos of three
different paths through a simulated museum in virtual reality and responded to
questionnaires regarding each path. Preference for a particular path was
influenced the most by comfort, forward speed, and characteristics of the
turns. Preference was also strongly associated with the users' perceived
naturalness, which was primarily determined by the ability to see salient
objects, the distance to the walls and objects, as well as the turns.
Participants favored the paths that had a one meter per second forward speed
and rated the path with the least amount of turns as the most comfortable


  The classical relativistic least action principle is revisited from the
vacuum field theory approach. New physically motivated versions of relativistic
Lorentz type forces are derived, a new relativistic hadronic string model is
proposed and analyzed in detail.


  In this article we investigate the Casimir effect in the presence of a medium
by quantizing the Electromagnetic (EM) field in the presence of a
magnetodielectric medium by using the path integral formalism. For a given
medium with definite electric and magnetic susceptibilities, explicit
expressions for the Casimir force are obtained which are in agree with the
original Casimir force between two conducting parallel plates immersed in the
quantum electromagnetic vacuum.


  We consider N = 1 supersymmetric U(N) field theories in four dimensions with
adjoint chiral matter and a multi-trace tree-level superpotential. We show that
the computation of the effective action as a function of the glueball
superfield localizes to computing matrix integrals. Unlike the single-trace
case, holomorphy and symmetries do not forbid non-planar contributions.
Nevertheless, only a special subset of the planar diagrams contributes to the
exact result. Some of the data of this subset can be computed from the large-N
limit of an associated multi-trace Matrix model. However, the prescription
differs in important respects from that of Dijkgraaf and Vafa for single-trace
superpotentials in that the field theory effective action is not the derivative
of a multi-trace matrix model free energy. The basic subtlety involves the
correct identification of the field theory glueball as a variable in the Matrix
model, as we show via an auxiliary construction involving a single-trace matrix
model with additional singlet fields which are integrated out to compute the
multi-trace results. Along the way we also describe a general technique for
computing the large-N limits of multi-trace Matrix models and raise the
challenge of finding the field theories whose effective actions they may
compute. Since our models can be treated as N = 1 deformations of pure N =2
gauge theory, we show that the effective superpotential that we compute also
follows from the N = 2 Seiberg-Witten solution. Finally, we observe an
interesting connection between multi-trace local theories and non-local field
theory.


  We study randomly initialized residual networks using mean field theory and
the theory of difference equations. Classical feedforward neural networks, such
as those with tanh activations, exhibit exponential behavior on the average
when propagating inputs forward or gradients backward. The exponential forward
dynamics causes rapid collapsing of the input space geometry, while the
exponential backward dynamics causes drastic vanishing or exploding gradients.
We show, in contrast, that by adding skip connections, the network will,
depending on the nonlinearity, adopt subexponential forward and backward
dynamics, and in many cases in fact polynomial. The exponents of these
polynomials are obtained through analytic methods and proved and verified
empirically to be correct. In terms of the "edge of chaos" hypothesis, these
subexponential and polynomial laws allow residual networks to "hover over the
boundary between stability and chaos," thus preserving the geometry of the
input space and the gradient information flow. In our experiments, for each
activation function we study here, we initialize residual networks with
different hyperparameters and train them on MNIST. Remarkably, our
initialization time theory can accurately predict test time performance of
these networks, by tracking either the expected amount of gradient explosion or
the expected squared distance between the images of two input vectors.
Importantly, we show, theoretically as well as empirically, that common
initializations such as the Xavier or the He schemes are not optimal for
residual networks, because the optimal initialization variances depend on the
depth. Finally, we have made mathematical contributions by deriving several new
identities for the kernels of powers of ReLU functions by relating them to the
zeroth Bessel function of the second kind.


  Using our extensive radio, optical, near-IR and X-ray imaging and
spectroscopy, we consider the reason for the unusually large number of radio
detected galaxies, mostly found outside the cluster core, in Abell 2125
(z=0.2465, richness class 4). With 20-cm VLA data, we detect continuum emission
from 90 cluster members. The multiwavelength properties of these galaxies
suggest that most of the radio emission is due to an enhanced star-formation
rate. The dynamical study of Miller et al (2004) suggests that Abell 2125 is
undergoing a major cluster-cluster merger, with our view within 30 degrees of
the merger axis and within 0.2 Gyr of core passage. The combination of
projection effects and the physical processes at work during this special time
in the cluster's evolution seem likely to be responsible for the unusual level
of activity we see in the cluster. We argue that tidal effects on individual
cluster members, often far from the cluster core, are responsible for the
increased star formation. Our results are consistent with the idea that disk
galaxies during this phase of a cluster's evolution undergo rapid evolution,
through a burst of star formation, on their way to becoming S0's.


  In this paper, we examine team ball sports to investigate how the likelihood
of weaker teams winning against stronger ones, referred to as underdog
achievement, is influenced by inherent randomness factors that affect match
outcomes in such sports. To address our research question, we collected data on
match scores and computed corresponding team rankings from major international
competitions for 12 popular team ball sports: basketball, cricket, field
hockey, futsal, handball, ice hockey, lacrosse, roller hockey, rugby, soccer,
volleyball, and water polo. Then, we developed an underdog achievement score to
identify the sports with the highest occurrences of weaker teams prevailing
over stronger ones, and we designed a randomness model consisting of factors
that contribute to unexpected match outcomes within each sport. Our findings
indicate that soccer is among the sports in which a weaker team is most likely
to win. Through principal component analysis (PCA) and correlation analysis, we
demonstrate that our randomness model can explain such a phenomenon, showing
that the underdog achievement can be attributed to numerous factors that can
randomly influence match outcomes.


  This text contains lecture notes of the course taught to Ph.D. students of
Jagiellonian University in Krakow on 25-28 November, 2013.


  We give a classification of non-removable isolated singularities for real
analytic solutions of the prescribed mean curvature equation in Minkowski
$3$-space.


  The van der Waals layered material CuInP2S6 features interesting functional
behavior, including the existence of four uniaxial polarization states,
polarization reversal against the electric field through Cu ion migration, a
negative-capacitance regime, and reversible extraction of Cu ions. At the heart
of these characteristics lies the high mobility of Cu ions, which also
determines the spontaneous polarization. Therefore, Cu migration across the
lattice results in unusual ferroelectric behavior. Here, we demonstrate how the
interplay of polar and ionic properties provides a path to ionically controlled
ferroelectric behavior, achieved by applying selected DC voltage pulses and
subsequently probing ferroelectric switching during fast triangular voltage
sweeps. Using current measurements and theoretical calculations, we observe
that increasing DC pulse duration results in higher ionic currents, the
build-up of an internal electric field that shifts polarization loops, and an
increase in total switchable polarization by ~50% due to the existence of a
high polarization phase which is stabilized by the internal electric field.
Apart from tuning ferroelectric behavior by selected square pulses, hysteretic
polarization switching can even be entirely deactivated and reactivated,
resulting in three-state systems where polarization switching is either
inhibited or can be performed in two different directions.


  The dispersion (van der Waals and Casimir) force between two semi-spaces are
calculated using the Lifshitz theory for different materials relevant for micro
and nanodevices fabrication, namely, gold, silicon, gallium arsenide, diamond
and two types of diamond-like carbon (DLC), silicon carbide, silicon nitride
and silicon dioxide. The calculations were performed using recent experimental
optical data available in the literature, usually ranging from the far infrared
up to the extreme ultraviolet bands of the electromagnetic spectrum. The
results are presented in the form of a correction factor to the Casimir force
predicted between perfect conductors, for the separation between the
semi-spaces varying from 1 nanometre up to 1 micrometre. The relative
importance of the contributions to the dispersion force of the optical
properties in different spectral ranges is analyzed. The role of the
temperature for semiconductors and insulators is also addressed. The results
are meant to be useful for the estimation of the impact of the Casimir and van
der Waals forces on the operational parameters of micro and nanodevices.


  We extend some aspects of vacuum string field theory to superstring field
theory in Berkovits' formulation, and we study the star algebra in the
fermionic matter sector. After clarifying the structure of the interaction
vertex in the operator formalism of Gross and Jevicki, we provide an algebraic
construction of the supersliver state in terms of infinite-dimensional
matrices. This state is an idempotent string field and solves the matter part
of the equation of motion of superstring field theory with a pure ghost BRST
operator. We determine the spectrum of eigenvalues and eigenvectors of the
infinite-dimensional matrices of Neumann coefficients in the fermionic matter
sector. We then analyze coherent states based on the supersliver and use them
in order to construct higher-rank projector solutions, as well as to construct
closed subalgebras of the star algebra in the fermionic matter sector. Finally,
we show that the geometric supersliver is a solution to the superstring field
theory equations of motion, including the (super)ghost sector, with the
canonical choice of vacuum BRST operator recently proposed by Gaiotto,
Rastelli, Sen and Zwiebach.


  The miscibility of two interacting quantum systems is an important testing
ground for the understanding of complex quantum systems. Two-component
Bose-Einstein condensates enable the investigation of this scenario in a
particularly well controlled setting. In a homogeneous system, the transition
between mixed and separated phases is fully characterised by a `miscibility
parameter', based on the ratio of intra- to inter-species interaction
strengths. Here we show, however, that this parameter is no longer the optimal
one for trapped gases, for which the location of the phase boundary depends
critically on atom numbers. We demonstrate how monitoring of damping rates and
frequencies of dipole oscillations enables the experimental mapping of the
phase diagram by numerical implementation of a fully self-consistent
finite-temperature kinetic theory for binary condensates. The change in damping
rate is explained in terms of surface oscillation in the immiscible regime, and
counterflow instability in the miscible regime, with collisions becoming only
important in the long time evolution.


  In this paper, we develop necessary and sufficient conditions for the
validity of a martingale approximation for the partial sums of a stationary
process in terms of the maximum of consecutive errors. Such an approximation is
useful for transferring the conditional functional central limit theorem from
the martingale to the original process. The condition found is simple and well
adapted to a variety of examples, leading to a better understanding of the
structure of several stochastic processes and their asymptotic behaviors. The
approximation brings together many disparate examples in probability theory. It
is valid for classes of variables defined by familiar projection conditions
such as the Maxwell--Woodroofe condition, various classes of mixing processes,
including the large class of strongly mixing processes, and for additive
functionals of Markov chains with normal or symmetric Markov operators.


  Metagenomic studies have increasingly utilized sequencing technologies in
order to analyze DNA fragments found in environmental samples.One important
step in this analysis is the taxonomic classification of the DNA fragments.
Conventional read classification methods require large databases and vast
amounts of memory to run, with recent deep learning methods suffering from very
large model sizes. We therefore aim to develop a more memory-efficient
technique for taxonomic classification. A task of particular interest is
abundance estimation in metagenomic samples. Current attempts rely on
classifying single DNA reads independently from each other and are therefore
agnostic to co-occurence patterns between taxa. In this work, we also attempt
to take these patterns into account. We develop a novel memory-efficient read
classification technique, combining deep learning and locality-sensitive
hashing. We show that this approach outperforms conventional mapping-based and
other deep learning methods for single-read taxonomic classification when
restricting all methods to a fixed memory footprint. Moreover, we formulate the
task of abundance estimation as a Multiple Instance Learning (MIL) problem and
we extend current deep learning architectures with two different types of
permutation-invariant MIL pooling layers: a) deepsets and b) attention-based
pooling. We illustrate that our architectures can exploit the co-occurrence of
species in metagenomic read sets and outperform the single-read architectures
in predicting the distribution over taxa at higher taxonomic ranks.


  Recent advances in deep learning algorithms have led to significant benefits
for solving many medical image analysis problems. Training deep learning models
commonly requires large datasets with expert-labeled annotations. However,
acquiring expert-labeled annotation is not only expensive but also is
subjective, error-prone, and inter-/intra- observer variability introduces
noise to labels. This is particularly a problem when using deep learning models
for segmenting medical images due to the ambiguous anatomical boundaries.
Image-based medical diagnosis tools using deep learning models trained with
incorrect segmentation labels can lead to false diagnoses and treatment
suggestions. Multi-rater annotations might be better suited to train deep
learning models with small training sets compared to single-rater annotations.
The aim of this paper was to develop and evaluate a method to generate
probabilistic labels based on multi-rater annotations and anatomical knowledge
of the lesion features in MRI and a method to train segmentation models using
probabilistic labels using normalized active-passive loss as a "noise-tolerant
loss" function. The model was evaluated by comparing it to binary ground truth
for 17 knees MRI scans for clinical segmentation and detection of bone marrow
lesions (BML). The proposed method successfully improved precision 14, recall
22, and Dice score 8 percent compared to a binary cross-entropy loss function.
Overall, the results of this work suggest that the proposed normalized
active-passive loss using soft labels successfully mitigated the effects of
noisy labels.


  The programmable artificial lattice, based on the controllability of coupling
strengths and the scalability of multiple sites, is desperately desired in
engineering metamaterials and exploring fundamental physics. In this work, we
experimentally present a programmable lattice consisting of multiple paralleled
nanomechanical resonators, whose internal interactions can be linearly
manipulated by external voltages. Flexural modes of nearest-neighboring (NN)
and next-nearest-neighboring (NNN) resonators are parametrically coupled
through modulated electrostatic interactions. Particularly, in a wide range up
to deep strong coupling regime, both the NN and NNN coupling strengths are
precisely proportional to manipulation voltage. The realization of long-range
coupling provides a promising prospect in constructing complex lattice
structure, which is essential in investigating mechanical logic devices,
topological physics and coherent phononic dynamics.


  The advent of modern genome sequencing techniques allows for a more stringent
test of the neutrality hypothesis of Darwinian evolution, where all individuals
have the same fitness. Using the individual based model of Wright and Fisher,
we compute the amplitude of neutral aggregation in the genome space, i.e., the
probability of finding two individuals at genetic (hamming) distance k as a
function of genome size L, population size N and mutation probability per base
\nu. In well mixed populations, we show that for N\nu\textless{}1/L, neutral
aggregation is the dominant force and most individuals are found at short
genetic distances from each other. For N\nu\textgreater{}1 on the contrary,
individuals are randomly dispersed in genome space. The results are extended to
geographically dispersed population, where the controlling parameter is shown
to be a combination of mutation and migration probability. The theory we
develop can be used to test the neutrality hypothesis in various ecological and
evolutionary systems.


  We present a comprehensive investigation, combining numerical simulations and
experimental measurements, into the manipulation of water waves and resonance
characteristics within closed cavities utilizing anisotropic metamaterials. We
engineer the anisotropic media with subwavelength-scale layered bathymetry
through the application of coordinate transformation theory and the
homogenization technique to a fully three-dimensional linear water wave
problem. Experimental and numerical analyses of deformed cavities employing
anisotropic metamaterial bathymetry demonstrate regular sloshing mode patterns
and eigenfrequencies akin to those observed in rectangular reference cavities
with flat bathymetry. Our study underscores the potential of water wave
metamaterials in establishing robust anisotropic metabathymetry for the precise
control of sloshing modes.


  To achieve high-levels of autonomy, modern robots require the ability to
detect and recover from anomalies and failures with minimal human supervision.
Multi-modal sensor signals could provide more information for such anomaly
detection tasks; however, the fusion of high-dimensional and heterogeneous
sensor modalities remains a challenging problem. We propose a deep learning
neural network: supervised variational autoencoder (SVAE), for failure
identification in unstructured and uncertain environments. Our model leverages
the representational power of VAE to extract robust features from
high-dimensional inputs for supervised learning tasks. The training objective
unifies the generative model and the discriminative model, thus making the
learning a one-stage procedure. Our experiments on real field robot data
demonstrate superior failure identification performance than baseline methods,
and that our model learns interpretable representations. Videos of our results
are available on our website:
https://sites.google.com/illinois.edu/supervised-vae .


  Decentralized learning with private data is a central problem in machine
learning. We propose a novel distillation-based decentralized learning
technique that allows multiple agents with private non-iid data to learn from
each other, without having to share their data, weights or weight updates. Our
approach is communication efficient, utilizes an unlabeled public dataset and
uses multiple auxiliary heads for each client, greatly improving training
efficiency in the case of heterogeneous data. This approach allows individual
models to preserve and enhance performance on their private tasks while also
dramatically improving their performance on the global aggregated data
distribution. We study the effects of data and model architecture heterogeneity
and the impact of the underlying communication graph topology on learning
efficiency and show that our agents can significantly improve their performance
compared to learning in isolation.


  We give the classification of constant mean curvature rotational surfaces of
elliptic, hyperbolic, and parabolic type in the four-dimensional
pseudo-Euclidean space with neutral metric.


  We consider the existence of strong solution to liquid crystals system in
critical Besov space,then give a criterion which is similar to Serrin's
criterion on regularity of weak solution to Navier-Stokes equations.


  Brown dwarfs straddle the mass range transition from planetary to stellar
objects. There is a relative paucity of brown dwarfs companions around FGKM
stars compared to exoplanets for orbital periods less than a few years, but
most of the short-period brown dwarf companions fully characterised by transits
and radial velocities are found around F-type stars. We examine the hypothesis
that brown dwarf companions could not survive on close orbit around stars with
important convective envelopes because the tides and angular momentum loss
through magnetic breaking should lead to a rapid orbital decay and quick
engulfment of the companion. We use a classical Skumanich-type braking law, and
constant time-lag tidal theory to assess the characteristic timescale for
orbital decay for the brown dwarf mass range as a function of the host
properties. We find that F-type stars may host massive companions for a
significantly longer time than G-type stars for a given orbital period, which
may explain the paucity of G-type hosts for brown dwarfs with orbital period
less than 5 days. On the other hand, we show that the small radius of early
M-type stars contributes to orbital decay timescales that are only half those
of F-type stars, despite their more efficient tidal dissipation and magnetic
braking. For fully convective later type M-dwarfs, orbital decay timescales
could be orders of magnitude greater than for F-type stars. For orbital periods
greater than 10 days, brown dwarf occurrence should largely be unaffected by
tidal decay, whatever the mass of the host. On closer orbital periods, the
rapid engulfment of massive companions could explain the lack of G and K-type
hosts in the sample of known systems with transiting brown dwarfs. However, the
paucity of M-type hosts can not be an effect of tidal decay alone, but may be
the result of a selection effect in the sample and/or the formation mechanism.


  The highest fidelity of quantum error-correcting codes of length n and rate R
is proven to be lower bounded by 1 - exp [-n E(R)+ o(n)] for some function E(R)
on noisy quantum channels that are subject to not necessarily independent
errors. The E(R) is positive below some threshold R', which implies R' is a
lower bound on the quantum capacity. This work is an extension of the author's
previous works [M. Hamada, Phys. Rev. A, 65, 052305, 2002 (e-Print
quant-ph/0109114, LANL, 2001), and M. Hamada, submitted to IEEE Trans. Inf.
Theory, 2002 (e-Print quant-ph/0112103, LANL, 2001)], which presented the bound
for channels subject to independent errors, or channels modeled as tensor
products of copies of a completely positive linear map. The relation of the
channel class treated in this paper to those in the previous works are similar
to that of Markov chains to sequences of independent identically distributed
random variables.


  Sparse estimation for Gaussian graphical models is a crucial technique for
making the relationships among numerous observed variables more interpretable
and quantifiable. Various methods have been proposed, including graphical
lasso, which utilizes the $\ell_1$ norm as a regularization term, as well as
methods employing non-convex regularization terms. However, most of these
methods approximate the $\ell_0$ norm with convex functions. To estimate more
accurate solutions, it is desirable to treat the $\ell_0$ norm directly as a
regularization term. In this study, we formulate the sparse estimation problem
for Gaussian graphical models using the $\ell_0$ norm and propose a method to
solve this problem using the Difference of Convex functions Algorithm (DCA).
Specifically, we convert the $\ell_0$ norm constraint into an equivalent
largest-$K$ norm constraint, reformulate the constrained problem into a
penalized form, and solve it using the DC algorithm (DCA). Furthermore, we
designed an algorithm that efficiently computes using graphical lasso.
Experimental results with synthetic data show that our method yields results
that are equivalent to or better than existing methods. Comparisons of model
learning through cross-validation confirm that our method is particularly
advantageous in selecting true edges.


  In this paper, we consider the solutions of the relaxed Q-tensor flow in
$\R^3$ with small parameter $\epsilon$. Firstly, we show that the limiting map
is the so called harmonic map flow; Secondly, we also present a new proof for
the global existence of weak solution for the harmonic map flow in three
dimensions as in \cite{struwe88} and \cite{keller}, where Ginzburg-Landau
approximation approach was used.


  The most fundamental approach to an understanding of electronic, optical, and
transport phenomena which the condensed matter physics (of conventional as well
as nonconventional systems) offers is generally founded on two experiments: the
inelastic electron scattering and the inelastic light scattering. This work
embarks on providing a systematic framework for the theory of inelastic
electron scattering and of inelastic light scattering from the electronic
excitations in GaAs/Ga$_{1-x}$Al$_{x}$As quantum wells. To this end, we start
with the Kubo's correlation function to derive the generalized nonlocal,
dynamic dielectric function, and the inverse dielectric function within the
framework of Bohm-Pines' random-phase approximation. This is followed by a
thorough development of the theory of inelastic electron scattering and of
inelastic light scattering. The methodological part is then subjected to the
analytical diagnoses which allow us to sense the subtlety of the analytical
results and the importance of their applications. The general analytical
results, which know no bounds regarding, e.g., the subband occupancy, are then
specified so as to make them applicable to practicality. After trying and
testing the eigenfunctions, we compute the density of states, the Fermi energy,
the full excitation spectrum made up of intrasubband and intersubband --
single-particle and collective (plasmon) -- excitations, the loss functions for
all the principal geometries envisioned for the inelastic electron scattering,
and the Raman intensity, which provides a measure of the real transitions
induced by the (laser) probe, for the inelastic light scattering...


  Astrophysical objects frequently exhibit some irregularities or complex
behaviour in their light curves. We focus primarily on hot stars, where both
radial and non-radial pulsations are observed. One of the primary research
goals is to determine physical parameters of stellar pulsations by analyzing
their light curves or spectra, focusing on periodic or quasiperiodic behaviour.
  We analyse the feasibility of classical methods for period searches in a
nonlinear chaotic system, such as the R\"ossler system, where a period does not
exist at all. As an astrophysical application of the chaotic system, we utilize
a simple model of stellar pulsation with two different sets of parameters
corresponding to periodic and chaotic behaviour. For both models we create a
synthetic signal, and then apply widely used methods for period finding, such
as the phase dispersion method and periodograms. For comparison, a
quasi-periodic signal is employed as well.
  The period analysis indicates periods even for the chaotic signal. Such
periods are apparently spurious. This implies that it is very problematic to
distinguish chaotic and quasiperiodic process by such an analysis only.


  Automated speaker recognition uses data processing to identify speakers by
their voice. Today, automated speaker recognition is deployed on billions of
smart devices and in services such as call centres. Despite their wide-scale
deployment and known sources of bias in related domains like face recognition
and natural language processing, bias in automated speaker recognition has not
been studied systematically. We present an in-depth empirical and analytical
study of bias in the machine learning development workflow of speaker
verification, a voice biometric and core task in automated speaker recognition.
Drawing on an established framework for understanding sources of harm in
machine learning, we show that bias exists at every development stage in the
well-known VoxCeleb Speaker Recognition Challenge, including data generation,
model building, and implementation. Most affected are female speakers and
non-US nationalities, who experience significant performance degradation.
Leveraging the insights from our findings, we make practical recommendations
for mitigating bias in automated speaker recognition, and outline future
research directions.


  The study of dense matter at ultra-high density has a very long history,
which is meaningful for us to understand not only cosmic events in extreme
circumstances but also fundamental laws of physics. It is well known that the
state of cold matter at supra-nuclear density depends on the non-perturbative
nature of quantum chromo-dynamics (QCD) and is essential for modeling pulsars.
A so-called H-cluster matter is proposed in this paper as the nature of dense
matter in reality.
  In compact stars at only a few nuclear densities but low temperature, quarks
could be interacting strongly with each other there. That might render quarks
grouped in clusters, although the hypothetical quark-clusters in cold dense
matter has not been confirmed due to the lack of both theoretical and
experimental evidence. Motivated by recent lattice QCD simulations of the
H-dibaryons (with structure uuddss), we are therefore considering here a
possible kind of quark-clusters, H-clusters, that could emerge inside compact
stars during their initial cooling, as the dominant components inside (the
degree of freedom could then be H-clusters there).Taking into account the
in-medium stiffening effect, we find that at baryon densities of compact stars
$H$-cluster matter could be more stable than nuclear matter. We also find that
for the H-cluster matter with lattice structure, the equation of state could be
so stiff that it would seem to be "superluminal" in most dense region. However,
the real sound speed for H-cluster matter is in fact hard to calculate, so at
this stage we do not put constraints on our model from the usual requirment of
causality.


  Limited feedback is a paradigm for the feedback of channel state information
in wireless systems. In multiple antenna wireless systems, limited feedback
usually entails quantizing a source that lives on the Grassmann manifold. Most
work on limited feedback beamforming considered single-shot quantization. In
wireless systems, however, the channel is temporally correlated, which can be
used to reduce feedback requirements. Unfortunately, conventional predictive
quantization does not incorporate the non-Euclidean structure of the Grassmann
manifold. In this paper, we propose a Grassmannian predictive coding algorithm
where the differential geometric structure of the Grassmann manifold is used to
formulate a predictive vector quantization encoder and decoder. We analyze the
quantization error and derive bounds on the distortion attained by the proposed
algorithm. We apply the algorithm to a multiuser multiple-input multiple-output
wireless system and show that it improves the achievable sum rate as the
temporal correlation of the channel increases.


  Quantum Chromodynamics predicts that the strong coupling strength $\alpha_s$
decreases with increasing energy or momentum transfer, and vanishes at
asymptotically high energies. The history and the status of experimental tests
of asymptotic freedom are summarised in this review.


  From the formation of animal flocks to the emergence of coordinate motion in
bacterial swarms, at all scales populations of motile organisms display
coherent collective motion. This consistent behavior strongly contrasts with
the difference in communication abilities between the individuals. Guided by
this universal feature, physicists have proposed that solely alignment rules at
the individual level could account for the emergence of unidirectional motion
at the group level. This hypothesis has been supported by agent-based
simulations. However, more complex collective behaviors have been
systematically found in experiments including the formation of vortices,
fluctuating swarms, clustering and swirling. All these model systems
predominantly rely on actual collisions to display collective motion. As a
result, the potential local alignment rules are entangled with more complex,
often unknown, interactions. The large-scale behavior of the populations
therefore depends on these uncontrolled microscopic couplings. Here, we
demonstrate a new phase of active matter. We reveal that dilute populations of
millions of colloidal rollers self-organize to achieve coherent motion along a
unique direction, with very few density and velocity fluctuations. Identifying
the microscopic interactions between the rollers allows a theoretical
description of this polar-liquid state. Comparison of the theory with
experiment suggests that hydrodynamic interactions promote the emergence of
collective motion either in the form of a single macroscopic flock at low
densities, or in that of a homogenous polar phase at higher densities.
Furthermore, hydrodynamics protects the polar-liquid state from the giant
density fluctuations. Our experiments demonstrate that genuine physical
interactions at the individual level are sufficient to set homogeneous active
populations into stable directed motion.


  A wide variety of engineered and natural systems are modelled as networks of
coupled nonlinear oscillators. In nature, the intrinsic frequencies of these
oscillators are not constant in time. Here, we probe the effect of such a
temporal heterogeneity on coupled oscillator networks, through the lens of the
Kuramoto model. To do this, we shuffle repeatedly the intrinsic frequencies
among the oscillators at either random or regular time intervals. What emerges
is the remarkable effect that frequent shuffling induces earlier onset (i.e.,
at a lower coupling) of synchrony among the oscillator phases. Our study
provides a novel strategy to induce and control synchrony under resource
constraints. We demonstrate our results analytically and in experiments with a
network of Wien Bridge oscillators with internal frequencies being shuffled in
time.


  The first X-ray source catalog of Insight-HXMT Galactic Plane (|b|<10deg)
Scanning Survey (GPSS) is presented based on the data accumulated from June
2017 to August 2021. The 4 yr limit sensitivities at main energy bands can
reach 8.2x10^(-12) erg/s/cm^2} (2-6 keV), 4.21x10^(-11) erg/s/cm^2 (7-40 keV)
and 2.78x10^(-11) erg/s/cm^2 (25-100 keV). More than 1300 sources have been
monitored at a wide band (1$-$100\,keV), of which 223 sources have a
signal-to-noise ratio greater than 5. We combined the GPSS data of Insight-HXMT
and MAXI and found it is feasible to obtain more complete long-term light
curves from their scanning results. The flux variabilities at different energy
bands of the 223 bright sources are analyzed based on the excess variances. It
is found that the fluxes of X-ray binaries are more active than those of
supernova remnants and isolated pulsars. Different types of binaries, e.g.,
low-mass X-ray binaries (LMXBs), high-mass X-ray binaries (HMXBs), neutron star
binaries, and black hole binaries, also distinctively show different
regularities. In addition, the relations between the hardness ratio (HR) and
excess variances, and HR and source types are analyzed. It is obvious that the
HRs of HMXBs tend to be harder than those of LMXBs and HMXBs tend to be more
active than those of LMXBs.


  We propose some one-dimensional reduced models for the three-dimensional
electron magnetohydrodynamics which involves a highly nonlinear Hall term with
intricate structure. The models contain nonlocal nonlinear terms. Local
well-posedness is obtained in certain circumstances. In contrast, for a model
with nonlocal transport term, we show that finite time singularity may occur
for some initial data.


  By the Telescope Conjecture for Module Categories, we mean the following
claim: "Let R be any ring and (A, B) be a hereditary cotorsion pair in Mod-R
with A and B closed under direct limits. Then (A, B) is of finite type."
  We prove a modification of this conjecture with the word 'finite' replaced by
'countable'. We show that a hereditary cotorsion pair (A, B) of modules over an
arbitrary ring R is generated by a set of strongly countably presented modules
provided that B is closed under unions of well-ordered chains. We also
characterize the modules in B and the countably presented modules in A in terms
of morphisms between finitely presented modules, and show that (A, B) is
cogenerated by a single pure-injective module provided that A is closed under
direct limits. Then we move our attention to strong analogies between cotorsion
pairs in module categories and localizing pairs in compactly generated
triangulated categories.


  This article gives an overview over recent results on quark and gluon
helicity distributions obtained in deep inelastic lepton nucleon scattering and
proton proton interactions. Future experimental programs to study the nucleon
structure will be discussed as well.


  Let S be a smooth del Pezzo surface that is defined over a field K and splits
over a Galois extension L. Let G be either the split reductive group given by
the root system of $S_L$ in Pic $S_L$, or a form of it containing the
N\'eron-Severi torus. Let $\mathcal{G}$ be the G-torsor over $S_L$ obtained by
extension of structure group from a universal torsor $\mathcal{T}$ over $S_L$.
We prove that $\mathcal{G}$ does not descend to S unless $\mathcal{T}$ does.
This is in contrast to a result of Friedman and Morgan that such $\mathcal{G}$
always descend to singular del Pezzo surfaces over $\mathbb{C}$ from their
desingularizations.


  Brain computer interface applications can be used to overcome learning
problems, especially student anxiety, lack of focus, and lack of attention.
This paper introduces a system based on brain computer interface (BCI) to be
used in education to measure intended learning outcomes and measure the impact
of noise on the degree of system accuracy. This system works online and is
based on recorded brain signal dataset. The system can be considered as a
special case of P300 speller accepting only letters from A to D. These are the
possible answers to multiple-choice questions MCQ. The teacher makes exams,
stores them in an exam database and delivers them to students. Students enroll
into the system and record their brain signals. Brain signals go through
preprocessing phase in which signals undergo low and high pass filter. Then the
signals undergo a subsampling and segmentation. The features obtained are used
as inputs to Linear Discriminant Analysis (LDA). Gained accuracy is 91%.


  Strong interplay of spin and charge/orbital degrees of freedom is the
fundamental characteristic of the iron-based superconductors (FeSCs), which
leads to the emergence of a nematic state as a rule in the vicinity of the
antiferromagnetic state. Despite intense debate for many years, however,
whether nematicity is driven by spin or orbital fluctuations remains unsettled.
Here, by use of transport, magnetization, and $^{75}$As nuclear magnetic
resonance (NMR) measurements, we show a striking transformation of the
relationship between nematicity and spin fluctuations (SFs) in
Na$_{1-x}$Li$_x$FeAs; For $x\leq 0.02$, the nematic transition promotes SFs. In
contrast, for $x\geq 0.03$, the system undergoes a non-magnetic phase
transition at a temperature $T_0$ into a distinct nematic state that suppresses
SFs. Such a drastic change of the spin fluctuation spectrum associated with
nematicity by small doping is highly unusual, and provides insights into the
origin and nature of nematicity in FeSCs.


  Let m be a unidimensional measure with dimension d. A natural question is to
ask if the measure m is comparable with the Hausdorff measure (or the packing
measure) in dimension d. We give an answer (which is in general negative) to
this question in several situations (self-similar measures, quasi-Bernoulli
measures). More precisely we obtain fine comparisons between the mesure m and
generalized Hausdorff type (or packing type) measures. The Law of the Iterated
Logarithm or estimations of the L^q-spectrum in a neighborhood of q=1 are the
tools to obtain such results.


  The goal of this work is to study the electromagnetic scattering problem of
time-domain Maxwell's equations in an unbounded structure. An exact transparent
boundary condition is developed to reformulate the scattering problem into an
initial-boundary value problem in an infinite rectangular slab. The
well-posedness and stability are established for the reduced problem. Our proof
is based on the method of energy, the Lax--Milgram lemma, and the inversion
theorem of the Laplace transform. Moreover, a priori estimates with explicit
dependence on the time are achieved for the electric field by directly studying
the time-domain Maxwell equations.


  In this paper, we study the maximum number of edges in an $N$-vertex
$r$-uniform hypergraph with girth $g$ where $g \in \{5,6 \}$. Writing
$\textrm{ex}_r ( N, \mathcal{C}_{<g} )$ for this maximum, it is shown that
$\textrm{ex}_r ( N , \mathcal{C}_{ < 5} ) = \Omega_r ( N^{3/2 - o(1)} )$ for $r
\in \{4,5,6 \}$. We address an unproved claim from [31] asserting a technique
of Ruzsa can be used to show that this lower bound holds for all $r \geq 3$. We
carefully explain one of the main obstacles that was overlooked at the time the
claim from [31] was made, and show that this obstacle can be overcome when
$r\in \{4,5,6\}$. We use constructions from coding theory to prove nontrivial
lower bounds that hold for all $r \geq 3$. Finally, we use a recent result of
Conlon, Fox, Sudakov, and Zhao to show that the sphere packing bound from
coding theory may be improved when upper bounding the size of linear $q$-ary
codes of distance $6$.


  We define a new topology, weaker than the gap topology, on the space of
selfadjoint unbounded operators on a separable Hilbert space. We show that the
subspace of selfadjoint Fredholm operators represents the functor $K^1$ from
the category of compact spaces to the category of abelian groups and prove a
similar result for $K^0$. We define the spectral flow of a continuous path of
selfadjoint Fredholm operators generalizing the approach of
Booss-Bavnek--Lesch--Phillips.


  Wearable technologies enable continuous monitoring of various health metrics,
such as physical activity, heart rate, sleep, and stress levels. A key
challenge with wearable data is obtaining quality labels. Unlike modalities
like video where the videos themselves can be effectively used to label objects
or events, wearable data do not contain obvious cues about the physical
manifestation of the users and usually require rich metadata. As a result,
label noise can become an increasingly thorny issue when labeling such data. In
this paper, we propose a novel solution to address noisy label learning,
entitled Few-Shot Human-in-the-Loop Refinement (FHLR). Our method initially
learns a seed model using weak labels. Next, it fine-tunes the seed model using
a handful of expert corrections. Finally, it achieves better generalizability
and robustness by merging the seed and fine-tuned models via weighted parameter
averaging. We evaluate our approach on four challenging tasks and datasets, and
compare it against eight competitive baselines designed to deal with noisy
labels. We show that FHLR achieves significantly better performance when
learning from noisy labels and achieves state-of-the-art by a large margin,
with up to 19% accuracy improvement under symmetric and asymmetric noise.
Notably, we find that FHLR is particularly robust to increased label noise,
unlike prior works that suffer from severe performance degradation. Our work
not only achieves better generalization in high-stakes health sensing
benchmarks but also sheds light on how noise affects commonly-used models.


  Exploring the effects a chemical compound has on a species takes a
considerable experimental effort. Appropriate methods for estimating and
suggesting new effects can dramatically reduce the work needed to be done by a
laboratory. In this paper we explore the suitability of using a knowledge graph
embedding approach for ecotoxicological effect prediction. A knowledge graph
has been constructed from publicly available data sets, including a species
taxonomy and chemical classification and similarity. The publicly available
effect data is integrated to the knowledge graph using ontology alignment
techniques. Our experimental results show that the knowledge graph based
approach improves the selected baselines.


  P. Kabamba developed generation theory as a tool for studying
self-reproducing systems. We provide an alternative definition of a generation
system and give a complete solution to the problem of finding optimal seeds for
a finite self-replicating system. We also exhibit examples illustrating a
connection between self-replication and fixed-point theory.


  This letter presents the results of an advanced parametrization of the solar
wind electron temperature anisotropy and the instabilities resulting from the
interplay of the (bi-)Maxwellian core and (bi-)Kappa halo populations in the
slow solar wind. A large set of observational data (from the Ulysses, Helios
and Cluster missions) is used to parametrize these components and establish
their correlations. The instabilities are significantly stimulated in the
presence of suprathermals, and the instability thresholds shape the limits of
the temperature anisotropy for both the core and halo populations re-stating
the incontestable role that the selfgenerated instabilities can play in
constraining the electron anisotropy. These results confirm a particular
implication of the suprathermal electrons which are less dense but hotter than
thermal electrons.


  Visual recognition systems mounted on autonomous moving agents face the
challenge of unconstrained data, but simultaneously have the opportunity to
improve their performance by moving to acquire new views of test data. In this
work, we first show how a recurrent neural network-based system may be trained
to perform end-to-end learning of motion policies suited for this "active
recognition" setting. Further, we hypothesize that active vision requires an
agent to have the capacity to reason about the effects of its motions on its
view of the world. To verify this hypothesis, we attempt to induce this
capacity in our active recognition pipeline, by simultaneously learning to
forecast the effects of the agent's motions on its internal representation of
the environment conditional on all past views. Results across two challenging
datasets confirm both that our end-to-end system successfully learns meaningful
policies for active category recognition, and that "learning to look ahead"
further boosts recognition performance.


  Childhood obesity is a major public health concern. Multidisciplinary
pediatric weight management programs are considered standard treatment for
children with obesity and severe obesity who are not able to be successfully
managed in the primary care setting; however, high drop-out rates (referred to
as attrition) are a major hurdle in delivering successful interventions.
Predicting attrition patterns can help providers reduce the attrition rates.
Previous work has mainly focused on finding static predictors of attrition
using statistical analysis methods. In this study, we present a machine
learning model to predict (a) the likelihood of attrition, and (b) the change
in body-mass index (BMI) percentile of children, at different time points after
joining a weight management program. We use a five-year dataset containing the
information related to around 4,550 children that we have compiled using data
from the Nemours Pediatric Weight Management program. Our models show strong
prediction performance as determined by high AUROC scores across different
tasks (average AUROC of 0.75 for predicting attrition, and 0.73 for predicting
weight outcomes). Additionally, we report the top features predicting attrition
and weight outcomes in a series of explanatory experiments.


  We focus on designing Peer-to-Peer (P2P) networks that enable efficient
communication. Over the last two decades, there has been substantial
algorithmic research on distributed protocols for building P2P networks with
various desirable properties such as high expansion, low diameter, and
robustness to a large number of deletions. A key underlying theme in all of
these works is to distributively build a \emph{random graph} topology that
guarantees the above properties. Moreover, the random connectivity topology is
widely deployed in many P2P systems today, including those that implement
blockchains and cryptocurrencies. However, a major drawback of using a random
graph topology for a P2P network is that the random topology does not respect
the \emph{underlying} (Internet) communication topology. This creates a large
\emph{propagation delay}, which is a major communication bottleneck in modern
P2P networks.
  In this paper, we work towards designing P2P networks that are
communication-efficient (having small propagation delay) with provable
guarantees. Our main contribution is an efficient, decentralized protocol,
$\textsc{Close-Weaver}$, that transforms a random graph topology embedded in an
underlying Euclidean space into a topology that also respects the underlying
metric. We then present efficient point-to-point routing and broadcast
protocols that achieve essentially optimal performance with respect to the
underlying space.


  We report on the magnetocaloric properties of two molecule-based
hexacyanochromate Prussian blue analogs, nominally CsNi[Cr(CN)_6](H_2O) and
Cr_3[Cr(CN)_6]_2x12(H_2O). The former orders ferromagnetically below Tc=90 K,
whereas the latter is a ferrimagnet below Tc=230 K. For both, we find
significantly large magnetic entropy changes DSm associated to the magnetic
phase transitions. Notably, our studies represent the first attempt to look at
molecule-based materials in terms of the magnetocaloric effect for temperatures
well above the liquid helium range.


  I review ten problems associated with the dynamical wave function collapse
program, which were described in the first of these two papers. Five of these,
the \textit{interaction, preferred basis, trigger, symmetry} and
\textit{superluminal} problems, were discussed as resolved there. In this
volume in honor of Abner Shimony, I discuss the five remaining problems,
\textit{tails, conservation law, experimental, relativity, legitimization}.
Particular emphasis is given to the tails problem, first raised by Abner. The
discussion of legitimization contains a new argument, that the energy density
of the fluctuating field which causes collapse should exert a gravitational
force. This force can be repulsive, since this energy density can be negative.
Speculative illustrations of cosmological implications are offered.


  Compared to other modalities, EEG-based emotion recognition can intuitively
respond to the emotional patterns in the human brain and, therefore, has become
one of the most concerning tasks in the brain-computer interfaces field. Since
dependencies within brain regions are closely related to emotion, a significant
trend is to develop Graph Neural Networks (GNNs) for EEG-based emotion
recognition. However, brain region dependencies in emotional EEG have
physiological bases that distinguish GNNs in this field from those in other
time series fields. Besides, there is neither a comprehensive review nor
guidance for constructing GNNs in EEG-based emotion recognition. In the survey,
our categorization reveals the commonalities and differences of existing
approaches under a unified framework of graph construction. We analyze and
categorize methods from three stages in the framework to provide clear guidance
on constructing GNNs in EEG-based emotion recognition. In addition, we discuss
several open challenges and future directions, such as Temporal full-connected
graph and Graph condensation.


  We address the experimentally observed non-Gaussian fluctuations for the
energy injected into a closed turbulent flow at fixed Reynolds number. We
propose that the power fluctuations mirror the internal kinetic energy
fluctuations. Using a stochastic cascade model, we construct the excess kinetic
energy as the sum over the energy transfers at different levels of the cascade.
We find an asymmetric distribution that strongly resembles the experimental
data. The asymmetry is an explicit consequence of intermittency and the global
measure is dominated by small scale events correlated over the entire system.
Our calculation is consistent with the statistical analogy recently made
between a confined turbulent flow and a critical system of finite size.


  Cross-coupling of ordering parameters in multiferroic materials by multiple
external stimuli other than electric field and magnetic field is highly
desirable from both practical application and fundamental study points of view.
Recently, mechanical force has attracted great attention in switching of
ferroic ordering parameters via electro-elastic coupling in ferroelectric
materials. In this work, mechanical force induced polarization and
magnetization switching were investigated in a polycrystalline multiferroic
Bi0.9La0.1FeO3 thin film using a scanning probe microscopy system. The
piezoresponse force microscopy and magnetic force microscopy responses suggest
that both the ferroelectric domains and the magnetic domains in Bi0.9La0.1FeO3
film could be switched by mechanical force as well as electric field. High
strain gradient created by mechanical force is demonstrated as able to induce
ferroelastic switching and thus induce both ferroelectric dipole and magnetic
spin flipping in our thin film, as a consequence of electro-elastic coupling
and magneto-electric coupling. The demonstration of mechanical force control of
both the ferroelectric and the magnetic domains at room temperature provides a
new freedom for manipulation of multiferroics and could result in devices with
novel functionalities.


  Fully frustrated Josephson Junction arrays (FF-JJA's) exhibit a subtle
compound phase transition in which an Ising transition associated with discrete
broken translational symmetry and a Berezinskii-Kosterlitz-Thouless (BKT)
transition associated with quasi-long-range phase coherence occur nearly
simultaneously. In this Letter we discuss a cold atom realization of the FF-JJA
system. We demonstrate that both orders can be studied by standard
momentum-distribution-function measurements and present numerical results,
based on a successful self-consistent spin-wave approximation, that illustrate
the expected behavior of observables.


  In this paper, we give some requirements under which two self-mappings have a
common fixed point in $b$-metric-like spaces.


  Full face synthesis and partial face manipulation by virtue of the generative
adversarial networks (GANs) and its variants have raised wide public concerns.
In the multi-media forensics area, detecting and ultimately locating the image
forgery has become an imperative task. In this work, we investigate the
architecture of existing GAN-based face manipulation methods and observe that
the imperfection of upsampling methods therewithin could be served as an
important asset for GAN-synthesized fake image detection and forgery
localization. Based on this basic observation, we have proposed a novel
approach, termed FakeLocator, to obtain high localization accuracy, at full
resolution, on manipulated facial images. To the best of our knowledge, this is
the very first attempt to solve the GAN-based fake localization problem with a
gray-scale fakeness map that preserves more information of fake regions. To
improve the universality of FakeLocator across multifarious facial attributes,
we introduce an attention mechanism to guide the training of the model. To
improve the universality of FakeLocator across different DeepFake methods, we
propose partial data augmentation and single sample clustering on the training
images. Experimental results on popular FaceForensics++, DFFD datasets and
seven different state-of-the-art GAN-based face generation methods have shown
the effectiveness of our method. Compared with the baselines, our method
performs better on various metrics. Moreover, the proposed method is robust
against various real-world facial image degradations such as JPEG compression,
low-resolution, noise, and blur.


  Hesitant fuzzy linguistic preference relation (HFLPR) is of interest because
it provides an efficient way for opinion expression under uncertainty. For
enhancing the theory of decision making with HFLPR, the paper introduces an
algorithm for group decision making with HFLPRs based on the acceptable
consistency and consensus measurements, which involves (1) defining a hesitant
fuzzy linguistic geometric consistency index (HFLGCI) and proposing a procedure
for consistency checking and inconsistency improving for HFLPR; (2) measuring
the group consensus based on the similarity between the original individual
HFLPRs and the overall perfect HFLPR, then establishing a procedure for
consensus ensuring including the determination of decision-makers weights. The
convergence and monotonicity of the proposed two procedures have been proved.
Some experiments are furtherly performed to investigate the critical values of
the defined HFLGCI, and comparative analyses are conducted to show the
effectiveness of the proposed algorithm. A case concerning the performance
evaluation of venture capital guiding funds is given to illustrate the
availability of the proposed algorithm. As an application of our work, an
online decision-making portal is finally provided for decision-makers to
utilize the proposed algorithms to solve decision-making problems.


  High performance ultrasonic sensor hardware is mainly used in medical
applications. Although, the development in automotive scenarios is towards
autonomous driving, the ultrasonic sensor hardware still stays low-cost and
low-performance, respectively. To overcome the strict hardware limitations, we
propose to use capsule neural networks. By the high classification capability
of this network architecture, we can achieve outstanding results for performing
a detailed height analysis of detected objects. We apply a novel resorting and
reshaping method to feed the neural network with ultrasonic data. This
increases classification performance and computation speed. We tested the
approach under different environmental conditions to verify that the proposed
method is working independent of external parameters that is needed for
autonomous driving.