  Double neutrons are especially important because they give most accurate
informations on the masses of neutron stars. Observations on double neutron
stars show that all masses of the neutron stars are below 1.5$\msun$.
Furthermore, two neutron stars in a given double pulsar are nearly equal in
mass. With hypercritical accretion, we found that the probability of having
companion mass $>1.5\msun$ is larger than 90%, while there is no observations
on such systems. We believe that those companions with masses higher than
$1.5\msun$ went into black holes, which is consistent with our preferred
maximum neutron star mass $M_{NS}^{max} \approx 1.5\msun$ due to the kaon
condensation. In this work, we point out that the black-hole neutron star
binaries are 10 times more dominant than double neutron star binaries. As a
result, black-hole, neutron star binaries can increase the LIGO detection rate
by a factor 20.


  In recent times a new kind of representations has been used to describe
superselection sectors of the observable net over a curved spacetime, taking
into account of the effects of the fundamental group of the spacetime. Using
this notion of representation, we prove that any net of C*-algebras over S^1
admits faithful representations, and when the net is covariant under Diff(S^1),
it admits representations covariant under any amenable subgroup of Diff(S^1).


  We consider scalar two-dimensional quantum field theories with the
factorizing S-matrix which has poles in the physical strip. In our previous
work, we introduced the bound state operators and constructed candidate
operators for observables in wedges.
  Under some additional assumptions on the S-matrix, we show that, in order to
obtain their strong commutativity, it is enough to prove the essential
self-adjointness of the sum of the left and right bound state operators. This
essential self-adjointness is shown up to the two-particle component.


  In this paper, we report on the $\tau$-tilting finiteness of some classes of
finite-dimensional algebras over an algebraically closed field, including
symmetric algebras of polynomial growth, $0$-Hecke algebras, and $0$-Schur
algebras. Consequently, we find that derived equivalence preserves the
$\tau$-tilting finiteness over symmetric algebras of polynomial growth, and
self-injective cellular algebras of polynomial growth are $\tau$-tilting
finite. Furthermore, the representation-finiteness and $\tau$-tilting
finiteness over 0-Hecke algebras and 0-Schur algebras (with few exceptions)
coincide.


  Given two multivariate copulas with corresponding tail dependence functions,
we investigate the relation between a natural tail dependence ordering
$\leq_{tdo}$ and the order $\leq_{loc}$ of local stochastic dominance. We show
that, although the two orderings are not equivalent in general, they coincide
for various important classes of copulas, among them all multivariate
Archimedean and bivariate lower extreme value copulas. We illustrate the
relevance of our results by an implication to risk management.


  Context: Observation of Balmer lines from the region around the forward shock
of supernova remnants may provide precious information on the shock dynamics
and on the efficiency of particle acceleration at the shock.
  Aims: We calculate the Balmer line emission and the shape of the broad Balmer
line for parameter values suitable for SNR 0509-67.5, as a function of the
cosmic ray acceleration efficiency and of the level of thermal equilibration
between electrons and protons behind the shock. This calculation aims at using
the width of the broad Balmer line emission to infer the cosmic ray
acceleration efficiency in this remnant.
  Methods: We use the recently developed non-linear theory of diffusive shock
acceleration in the presence of neutrals. The semi-analytical approach that we
developed includes a description of magnetic field amplification as due to
resonant streaming instability, the dynamical reaction of both accelerated
particles and turbulent magnetic field on the shock, and all channels of
interaction between neutral atoms and background plasma that change the shock
dynamics.
  Results: We achieve a quantitative assessment of the CR acceleration
efficiency in SNR 0509-67.5 as a function of the shock velocity and different
levels of electron-proton thermalization in the shock region. If the shock
moves faster than ~4500 km/s, one can conclude that particle acceleration must
be taking place with efficiency of several tens of percent. For lower shock
velocity the evidence for particle acceleration becomes less clear because of
the uncertainty in the electron-ion equilibration downstream. We also discuss
the role of future measurements of the narrow Balmer line.


  It is shown that the determinant line bundle associated to a family of Dirac
operators over a closed partitioned manifold has a canonical Hermitian metric
with compatible connection whose curvature satisfies an additivity formula with
contributions from the families of Dirac operators over the two halves. This
curvature form is the natural differential representative which satisifies the
same splitting principle as the Chern class of the determinant line bundle.


  Greybox fuzzing is a proven and effective testing method for the detection of
security vulnerabilities and other bugs in modern software systems. Greybox
fuzzing can also be used in combination with a sanitizer, such as
AddressSanitizer (ASAN), to further enhance the detection of certain classes of
bugs such as buffer overflow and use-after-free errors. However, sanitizers
also introduce additional performance overheads, and this can degrade the
performance of greybox mode fuzzing -- measured in the order of 2.36X for
fuzzing with ASAN -- partially negating the benefit of using a sanitizer in the
first place. Recent research attributes the extra overhead to program
startup/teardown costs that can dominate fork-mode fuzzing.
  In this paper, we present a new memory error sanitizer design that is
specifically optimized for fork-mode fuzzing. The basic idea is to mark object
boundaries using randomized tokens rather than disjoint metadata (as used by
traditional sanitizer designs). All read/write operations are then instrumented
to check for the token, and if present, a memory error will be detected. Since
our design does not use a disjoint metadata, it is also very lightweight,
meaning that program startup and teardown costs are minimized for the benefit
of fork-mode fuzzing. We implement our design in the form of the ReZZan tool,
and show an improved fuzzing performance overhead of 1.14-1.27X, depending on
the configuration.


  There is growing interest in nuclear physics applications of effective field
theory. I give a brief account of some of the latest developments in this area.
I also describe interplay between this new approach and the traditional nuclear
physics approach.


  We describe our system for finding good answers in a community forum, as
defined in SemEval-2016, Task 3 on Community Question Answering. Our approach
relies on several semantic similarity features based on fine-tuned word
embeddings and topics similarities. In the main Subtask C, our primary
submission was ranked third, with a MAP of 51.68 and accuracy of 69.94. In
Subtask A, our primary submission was also third, with MAP of 77.58 and
accuracy of 73.39.


  Bipolar disorder, an illness characterized by manic and depressive episodes,
affects more than 60 million people worldwide. We present a preliminary study
on bipolar disorder prediction from user-generated text on Reddit, which relies
on users' self-reported labels. Our benchmark classifiers for bipolar disorder
prediction outperform the baselines and reach accuracy and F1-scores of above
86%. Feature analysis shows interesting differences in language use between
users with bipolar disorders and the control group, including differences in
the use of emotion-expressive words.


  We study the possibilities to analyse the data on leading neutrons production
at first LHC runs. These data could be used to extract from it $\pi^+ p$ and
$\pi^+\pi^+$ cross-sections. In this note we estimate relative contributions of
$\pi$, $\rho$ and $a_2$ reggeons to charge exchanges and discuss related
problems of measurements.


  I review recent developments in the CGC approach to high-energy collisions.
The focus is on topics related to the Quark Matter conference, specifically on
predictions for the p+Pb run at the LHC; as an added bonus some of these
predictions are confronted with first data taken during the p+Pb machine
performance test at the LHC performed in september 2012. I also highlight
recent work related to particle production fluctuations, suppression of nuclear
modification factors in p+A from rcBK evolution, (partial) NLO corrections in
forward production, and photon-hadron as well as di-hadron correlations.


  In 2008 a sequence of geomagnetic storms occurred triggered by high-speed
solar wind streams from coronal holes. Improved estimates of precipitating
fluxes of energetic electrons are derived from measurements on board the
NOAA/POES 18 satellite using a new analysis technique. These fluxes are used to
quantify the direct impact of energetic electron precipitation (EEP) during
solar minimum on middle atmospheric hydroxyl (OH) measured from the Aura
satellite. During winter, localized longitudinal density enhancements in the OH
are observed over northern Russia and North America at corrected geomagnetic
latitudes poleward of 55$^{\circ}$. Although the northern Russia OH enhancement
is closely associated with increased EEP at these longitudes, the strength and
location of the North America enhancement appear to be unrelated to EEP. This
OH density enhancement is likely due to vertical motion induced by atmospheric
wave dynamics that transports air rich in atomic oxygen and atomic hydrogen
downward into the middle atmosphere, where it plays a role in the formation of
OH. In the Southern Hemisphere, localized enhancements of the OH density over
West Antarctica can be explained by a combination of enhanced EEP due to the
local minimum in Earth's magnetic field strength and atmospheric dynamics. Our
findings suggest that even during solar minimum, there is substantial
EEP-driven OH production. However, to quantify this effect, a detailed
knowledge of where and when the precipitation occurs is required in the context
of the background atmospheric dynamics.


  Spin projection noise sets a limit for the sensitivity of spin-based
magnetometers and experiments searching for parity- and
time-reversal-invariance-violating dipole moments. The limit is described by a
simple equation that appears to have universal applicability.


  Line lists for magnesium oxide are computed and extensive comparisons are
made with existing experimental spectra. The LiTY line lists cover all
ro-vibration transitions within the five lowest-lying electronic states
($X\,{}^1\Sigma^+$, $a\,{}^3\Pi$, $A\,{}^1\Pi$, $B\,{}^1\Sigma^+$ and
$b\,{}^3\Sigma^{+}$) and five isotopologues: $^{24}$Mg$^{16}$O,
$^{25}$Mg$^{16}$O, $^{26}$Mg$^{16}$O, $^{24}$Mg$^{17}$O, $^{24}$Mg$^{18}$O,
$^{24}$Mg$^{17}$O and $^{24}$Mg$^{18}$O. The calculation use potential energy
cures, spin-orbit and electronic angular momentum couplings curves determined
by fitting to empirical energy levels; these levels are reproduced to within
0.01 \cm\ in most cases. Computed nuclear-motion wavefunctions are combined
with {\it ab initio} dipole moment curves to give transition intensities and
excited state radiative lifetimes which are compared with laboraroty
measurements. The $^{24}$Mg$^{16}$O line list comprises 186 842 ($J\le 320$)
ro-vibronic states and 72 833 173 transitions with angular momenta, $J$, up to
300 and covering wavenumbers up to 33 000 cm$^{-1}$ ($\lambda > 0.3$ $\mu$m).
The line lists are suitable for temperatures up to about 5000 K. They are
relevant to astrophysical studies of exoplanet atmospheres, cool stars and
brown dwarfs, and are made available in electronic form at the CDS and ExoMol
databases.


  The bosonic two-loop electroweak radiative corrections to the muon's
anomalous magnetic moment, $a_\mu\equiv (g_\mu-2)/2$, are presented. We find
$\Delta a_\mu^{\rm EW}({\rm 2\,loop\, bosonic})/ a_\mu^{\rm EW}({\rm
1\,loop})\approx {\alpha\over \pi}\left(-3.6 \ln\left({M_W^2\over
m_\mu^2}\right) +0.10 \right)\approx -0.11$ for $M_{\rm Higgs}\approx 250$ GeV.
Combining that result with our previous two-loop fermionic calculation, we
obtain an overall 22.6\% reduction in $a_\mu^{\rm EW}$ from $195\times
10^{-11}$ to $151(4)\times 10^{-11}$. Implications for the full standard model
prediction and an upcoming high precision measurement of $a_\mu$ are briefly
discussed. We also give the two-loop electroweak corrections to the anomalous
magnetic moments of electron and tau lepton; they result in a reduction of the
one-loop estimates by 35\% and 15\%, respectively.


  In this paper we compare different types of recurrent units in recurrent
neural networks (RNNs). Especially, we focus on more sophisticated units that
implement a gating mechanism, such as a long short-term memory (LSTM) unit and
a recently proposed gated recurrent unit (GRU). We evaluate these recurrent
units on the tasks of polyphonic music modeling and speech signal modeling. Our
experiments revealed that these advanced recurrent units are indeed better than
more traditional recurrent units such as tanh units. Also, we found GRU to be
comparable to LSTM.


  For a non-isolated point $x$ of a topological space $X$ the network character
$nw_\chi(x)$ is the smallest cardinality of a family of infinite subsets of $X$
such that each neighborhood $O(x)$ of $x$ contains a set from the family. We
prove that (1) each infinite compact Hausdorff space $X$ contains a
non-isolated point $x$ with $nw_\chi(x)=\aleph_0$; (2) for each point $x\in X$
with countable character there is an injective sequence in $X$ that
$\F$-converges to $x$ for some meager filter $\F$ on $\omega$; (3) if a
functionally Hausdorff space $X$ contains an $\F$-convergent injective sequence
for some meager filter $\F$, then for every $T_1$-space $Y$ that contains two
non-empty open sets with disjoint closures, the function space $C_p(X,Y)$ is
meager. Also we investigate properties of filters $\F$ admitting an injective
$\F$-convergent sequence in $\beta\omega$.


  In the paper we continue to study Special Bohr-Sommerfeld geometry of compact
symplectic manifolds. Using natural deformation parameters we avoid the
difficulties appeared in the definition of the moduli space of Special
Bohr-Sommerfeld cycles for compact simply connected algebraic varieties. As a
byproduct we present certain remarks on the Weinstein structures and Eliashberg
conjectures.


  This paper describes several improvements to a new method for signal
decomposition that we recently formulated under the name of Differentiable
Dictionary Search (DDS). The fundamental idea of DDS is to exploit a class of
powerful deep invertible density estimators called normalizing flows, to model
the dictionary in a linear decomposition method such as NMF, effectively
creating a bijection between the space of dictionary elements and the
associated probability space, allowing a differentiable search through the
dictionary space, guided by the estimated densities. As the initial formulation
was a proof of concept with some practical limitations, we will present several
steps towards making it scalable, hoping to improve both the computational
complexity of the method and its signal decomposition capabilities. As a
testbed for experimental evaluation, we choose the task of frame-level piano
transcription, where the signal is to be decomposed into sources whose activity
is attributed to individual piano notes. To highlight the impact of improved
non-linear modelling of sources, we compare variants of our method to a linear
overcomplete NMF baseline. Experimental results will show that even in the
absence of additional constraints, our models produce increasingly sparse and
precise decompositions, according to two pertinent evaluation measures.


  Some of the secret sharing schemes having unique quantum features like
parallelism and entanglement are supposed to be relatively secure. Different
schemes proposed by various researchers over the years have features which
could be specific to the nature and need of a situation. Following Hsu's
proposed scheme we propose a secret sharing scheme using Grover's search
algorithm for a four qubit system with several marked states. Further, the
scheme has been generalised to an n-qubit syatem.


  The aim of this paper, is to define a bivariate exponentiated generalized
linear exponential distribution based on Marshall-Olkin shock model.
Statistical and reliability properties of this distribution are discussed. This
includes quantiles, moments, stress-strength reliability, joint reliability
function, joint reversed (hazard) rates functions and joint mean waiting time
function. Moreover, the hazard rate, the availability and the mean residual
lifetime functions for a parallel system, are established. One data set is
analyzed, and it is observed that, the proposed distribution provides a better
fit than Marshall-Olkin bivariate exponential, bivariate generalized
exponential and bivariate generalized linear failure rate distributions.
Simulation studies are presented to estimate both the relative absolute bias,
and the relative mean square error for the distribution parameters based on
complete data.


  The activation gaps for fractional quantum Hall states at filling fractions
$\nu=n/(2n+1)$ are computed for heterojunction, square quantum well, as well as
parabolic quantum well geometries, using an interaction potential calculated
from a self-consistent electronic structure calculation in the local density
approximation. The finite thickness is estimated to make $\sim$30% correction
to the gap in the heterojunction geometry for typical parameters, which
accounts for roughly half of the discrepancy between the experiment and
theoretical gaps computed for a pure two dimensional system. Certain model
interactions are also considered. It is found that the activation energies
behave qualitatively differently depending on whether the interaction is of
longer or shorter range than the Coulomb interaction; there are indications
that fractional Hall states close to the Fermi sea are destabilized for the
latter.


  In Online Continual Learning (OCL) a learning system receives a stream of
data and sequentially performs prediction and training steps. Important
challenges in OCL are concerned with automatic adaptation to the particular
non-stationary structure of the data, and with quantification of predictive
uncertainty. Motivated by these challenges we introduce a probabilistic
Bayesian online learning model by using a (possibly pretrained) neural
representation and a state space model over the linear predictor weights.
Non-stationarity over the linear predictor weights is modelled using a
parameter drift transition density, parametrized by a coefficient that
quantifies forgetting. Inference in the model is implemented with efficient
Kalman filter recursions which track the posterior distribution over the linear
weights, while online SGD updates over the transition dynamics coefficient
allows to adapt to the non-stationarity seen in data. While the framework is
developed assuming a linear Gaussian model, we also extend it to deal with
classification problems and for fine-tuning the deep learning representation.
In a set of experiments in multi-class classification using data sets such as
CIFAR-100 and CLOC we demonstrate the predictive ability of the model and its
flexibility to capture non-stationarity.


  We study the local dark matter distribution in two models for the Milky Way
(MW)-Large Magellanic Cloud (LMC) interaction. The effect of the LMC on the
local dark matter distribution is dependent on the evolution of the MW-LMC
system, such that a static model is insufficient to accurately model the dark
matter velocity distribution in the solar neighbourhood. An evolved model
boosts local LMC dark matter particle velocities by nearly 50%, to a median
value of $\approx750$km/s. MW dark matter particles also experience a velocity
boost, which we identify as being caused by reflex motion owing to the infall
of the LMC. We study the implications of LMC particles in the solar
neighbourhood for dark matter detection experiments. Specifically, the
directionality of LMC particles is distinguishable from the MW particles, with
a difference in the apparent origin centroid location between the MW and LMC
particles of $26\pm6 ^\circ$. This unique identifier, along with their high
velocities, can be utilised by directional detectors to search for dark matter
particles originating in the LMC.


  Classical knots in $\mathbb{R}^3$ can be represented by diagrams in the
plane. These diagrams are formed by curves with a finite number of transverse
crossings, where each crossing is decorated to indicate which strand of the
knot passes over at that point. A pseudodiagram is a knot diagram that may be
missing crossing information at some of its crossings. At these crossings, it
is undetermined which strand passes over. Pseudodiagrams were first introduced
by Ryo Hanaki in 2010. Here, we introduce the notion of a pseudoknot, i.e. an
equivalence class of pseudodiagrams under an appropriate choice of Reidemeister
moves. In order to begin a classification of pseudoknots, we introduce the
concept of a weighted resolution set, an invariant of pseudoknots. We compute
the weighted resolution set for several pseudoknot families and discuss notions
of crossing number, homotopy, and chirality for pseudoknots.


  This Thesis presents my personal contributions to two distinct fields, namely
the recent experimental anomalies in B-meson decays, and the longstanding quest
for a theoretical explanation of lepton masses and mixings, under a unifying
umbrella: Flavour Physics. The modern view of the Standard Model as an
effective theory is motivated, and the dual role of flavour, as a probe of New
Physics effects on one side, and as a piece of the puzzle itself on the other,
is emphasized. By jointly discussing two physics cases of different nature, I
attempt to offer a broader perspective on the current status of Beyond Standard
Model searches. The discussion begins with a preliminary review of Standard
Model Effective Field Theories, in which some utility results in theory
matching are presented. The focus then moves to B-meson decay anomalies: after
introducing Lepton Flavour Universality, we discuss the phenomenology of a
specific Standard Model extension by scalar leptoquarks, and also present the
Effective Field Theory framework of Rank-One Flavor Violation, providing a less
detailed but broader picture for the case of neutral-current anomalies.
Finally, we tackle the Standard Model flavour puzzle, describing an attempt to
address charged lepton mass hierarchies in the framework of supersymmetric
modular invariant models of lepton flavour.


  We use path-integrals to derive a general expression for the semiclassical
approximation to the partition function of a one-dimensional quantum-mechanical
system. Our expression depends solely on ordinary integrals which involve the
potential. For high temperatures, the semiclassical expression is dominated by
single closed paths. As we lower the temperature, new closed paths appear,
including tunneling paths. The transition from single to multiple-path regime
corresponds to well-defined catastrophes. Tunneling sets in whenever they
occur. (Our formula fully accounts for this feature.)


  The carbon-oxygen white dwarf (CO WD) + He star channel is one of the
promising ways for producing type Ia supernovae (SNe Ia) with short delay
times. Recent studies found that carbon under the He-shell can be ignited if
the mass-accretion rate of CO WD is higher than a critical rate (about 2 *
10^-6 Msun/yr), triggering an inwardly propagating carbon flame. Previous
studies usually supposed that the off-centre carbon flame would reach the
centre, resulting in the formation of an oxygen-neon (ONe) WD that will
collapse into a neutron star. However, the process of off-centre carbon burning
is not well studied. This may result in some uncertainties on the final fates
of CO WDs. By employing MESA, we simulated the long-term evolution of
off-centre carbon burning in He-accreting CO WDs. We found that the inwardly
propagating carbon flame transforms the CO WDs into OSi cores directly but not
ONe cores owing to the high temperature of the burning front. We suggest that
the final fates of the CO WDs may be OSi WDs under the conditions of off-centre
carbon burning, or explode as iron-core-collapse SNe if the mass-accretion
continues. We also found that the mass-fractions of silicon in the OSi cores
are sensitive to the mass-accretion rates.


  Eigenvalues and eigenfunctions of the QCD Dirac operator are studied for an
instanton liquid partition function. We find that for energy differences
$\delta E$ below an energy scale $E_c$, identified as the Thouless energy, the
eigenvalue correlations are given by Random Matrix Theory. The value of $E_c$
shows a weak volume dependence for eigenvalues near zero and is consistent with
a scaling of $E_c \sim 1/L^2$ in the bulk of the spectrum in agreement with
estimates from chiral perturbation theory, that $E_c/\Delta \approx F_\pi^2
L^2/\pi$ (with average level spacing $\Delta$). For $\delta E> E_c$ the number
variance shows a linear dependence. For the wave functions we find a small
nonzero multifractality index.


  Let $X$ and $Y$ be independent variance-gamma random variables with zero
location parameter; then the exact probability density function of the product
$XY$ is derived. Some basic distributional properties are also derived,
including formulas for the cumulative distribution function and the
characteristic function, as well as asymptotic approximations for the density,
tail probabilities and the quantile function. As special cases, we deduce some
key distributional properties for the product of two independent asymmetric
Laplace random variables as well as the product of four jointly correlated zero
mean normal random variables with a particular block diagonal covariance
matrix. As a by-product of our analysis, we deduce some new reduction formulas
for the Meijer $G$-function.


  We present an implementation of the Bethe-Salpeter equation (BSE) for
core-conduction band pairs within the framework of the projector augmented-wave
method. For validation, the method is applied to the $K$-edges of diamond,
graphite, hexagonal boron-nitride, as well as four lithium-halides (LiF, LiCl,
LiI, LiBr). We compare our results with experiment, previous theoretical BSE
results, and the density functional theory-based supercell core-hole method. In
all considered cases, the agreement with experiment is excellent, in particular
for the position of the peaks as well as the fine structure. Comparing BSE to
supercell core-hole spectra we find that the latter often qualitatively
reproduces the experimental spectrum, however, it sometimes lacks important
details. This is shown for the $K$-edges of diamond and nitrogen in hexagonal
boron-nitride, where we are capable to resolve within the BSE experimental
features that are lacking in the core-hole method. Additionally, we show that
in certain systems the supercell core-hole method performs better if the
excited electron is added to the background charge. We attribute this improved
performance to a reduced self-interaction.


  We provide a complete elaboration of the $L^2$-Hilbert space hypocoercivity
theorem for the degenerate Langevin dynamics with multiplicative noise,
studying the longtime behaviour of the strongly continuous contraction
semigroup solving the abstract Cauchy problem for the associated backward
Kolmogorov operator. Hypocoercivity for the Langevin dynamics with constant
diffusion matrix was proven previously by Dolbeault, Mouhot and Schmeiser in
the corresponding Fokker-Planck framework, and made rigorous in the Kolmogorov
backwards setting by Grothaus and Stilgenbauer. We extend these results to
weakly differentiable diffusion coefficient matrices, introducing
multiplicative noise for the corresponding stochastic differential equation.
The rate of convergence is explicitly computed depending on the choice of these
coefficients and the potential giving the outer force. In order to obtain a
solution to the abstract Cauchy problem, we first prove essential
self-adjointness of non-degenerate elliptic Dirichlet operators on Hilbert
spaces, using prior elliptic regularity results and techniques from Bogachev,
Krylov and R\"ockner. We apply operator perturbation theory to obtain essential
m-dissipativity of the Kolmogorov operator, extending the m-dissipativity
results from Conrad and Grothaus. We emphasize that the chosen Kolmogorov
approach is natural, as the theory of generalized Dirichlet forms implies a
stochastic representation of the Langevin semigroup as the transition kernel of
a diffusion process which provides a martingale solution to the Langevin
equation with multiplicative noise. Moreover, we show that even a weak solution
is obtained this way.


  Graded composition CdxZn1-xTe films were prepared by growing several
alternate layers of CdTe and ZnTe by the Isothermal Close Space Sublimation
technique. The thickness of both kinds of layers was modified along the
structure to produce an increase of the average concentration of CdTe towards
the surface of the films. Due to Zn/Cd inter-diffusion during the growth
process the sequence of layers was converted into a relatively smooth graded
composition film. According to X- ray diffraction characterization the layers
grew coherently with the (100) oriented GaAs substrates although they showed a
relatively high mosaicity. Theta-2theta plots show very wide diffraction peaks
as expected from variable composition samples. The band gap grading effect in
light absorption was also verified through transmission measurements, using
transparent substrates. Graded composition profiles of the thin films were
confirmed by x-ray photoelectron and secondary ion mass spectroscopies.
Moreover, quantitative Cd, Zn and Te profiles were obtained by the analysis of
Rutherford backscattering spectra of the samples. This analysis showed a CdTe
molar fraction profile ranging from approximately x = 0.8 at the surface of the
sample and x = 0.35 at the interface with the substrate. The possibility of
growing graded composition layers using a simple and cost-effective procedure
was demonstrated. This could be interesting in technological applications like
CdxZn1-xTe layers of variable composition in CdS/CdTe solar cells


  We study the RKKY interaction mediated by the helical edge states of a
quantum spin Hall insulator in the presence of the Rashba spin-orbital coupling
induced by an external electric field and the electron-electron interaction. We
show that in the presence of the Rashba coupling, the RKKY interaction induced
by the helical edge states contains not only the Heisenberg-like and the
Dzyaloshinskii-Moria terms but also the nematic-type term that is not present
originally, with the range functions depending on the strength of the Rashba
coupling. We also show that the electron-electron interaction changes the
strength of the RKKY interaction by modifying the power of the $1/|x|$
dependence of the range functions. In particular, by varying the strength of
the interaction or the Rashba coupling, there is an (impurity) quantum phase
transition involving the sign change of the RKKY interaction at the value of
the Luttinger liquid parameter $K=1/2$. Since both the strength of the Rashba
coupling and the chemical potential of the helical edge states are electrically
controllable by external gate voltages, our results not only shed light on the
nature of magnetic impurity correlations in the edge of a two-dimensional
topological insulator, but also pave a way to manipulate the qubits in quantum
computing.


  Person re-identification aims to identify the same pedestrian across
non-overlapping camera views. Deep learning techniques have been applied for
person re-identification recently, towards learning representation of
pedestrian appearance. This paper presents a novel Contextual-Attentional
Attribute-Appearance Network (CA3Net) for person re-identification. The CA3Net
simultaneously exploits the complementarity between semantic attributes and
visual appearance, the semantic context among attributes, visual attention on
attributes as well as spatial dependencies among body parts, leading to
discriminative and robust pedestrian representation. Specifically, an attribute
network within CA3Net is designed with an Attention-LSTM module. It
concentrates the network on latent image regions related to each attribute as
well as exploits the semantic context among attributes by a LSTM module. An
appearance network is developed to learn appearance features from the full
body, horizontal and vertical body parts of pedestrians with spatial
dependencies among body parts. The CA3Net jointly learns the attribute and
appearance features in a multi-task learning manner, generating comprehensive
representation of pedestrians. Extensive experiments on two challenging
benchmarks, i.e., Market-1501 and DukeMTMC-reID datasets, have demonstrated the
effectiveness of the proposed approach.


  We review here the prospects of a long-term upgrade programme for the Large
Hadron Collider (LHC), CERN laboratory's new proton-proton collider. The
super-LHC, which is currently under evaluation and design, is expected to
deliver of the order of ten times the statistics of the LHC. In addition to a
non-technical summary of the principal physics arguments for the upgrade, I
present a pedagogical introduction to the technological challenges on the
accelerator and experimental fronts, and a review of the current status of the
planning.


  Quantum electromagnetic fluctuations induce forces between neutral particles,
known as the van der Waals (vdW) and Casimir interactions. These fundamental
forces, mediated by virtual photons from the vacuum, play an important role in
basic physics and chemistry, and in emerging technologies involving, e.g.
micro-electromechanical systems or quantum information processing. Here we show
that these interactions can be enhanced by many orders of magnitude upon
changing the character of the mediating vacuum-modes. By considering two
polarizable particles in the vicinity of any standard electric transmission
line, along which photons can propagate in one dimension (1d), we find a much
stronger and longer-range interaction than in free-space. This enhancement may
have profound implications on many-particle and bulk systems, and impact the
quantum technologies mentioned above. The predicted giant vacuum force is
estimated to be measurable in a coplanar waveguide line.


  We present a GPU-accelerated numerical approach for fast kernel and
differential background solutions. The model image proposed in the Bramich
(2008) difference image analysis algorithm is analogous to a very simple
Convolutional Neural Network (CNN), with a single convolutional filter (i.e.
the kernel) and an added scalar bias (i.e. the differential background). Here,
we do not solve for the discrete pixel array in the classical, analytical
linear least-squares sense. Instead, by making use of PyTorch tensors (GPU
compatible multi-dimensional matrices) and associated deep learning tools, we
solve for the kernel via an inherently massively parallel optimisation. By
casting the Difference Image Analysis (DIA) problem as a GPU-accelerated
optimisation which utilises automatic differentiation tools, our algorithm is
both flexible to the choice of scalar objective function, and can perform DIA
on astronomical data sets at least an order of magnitude faster than its
classical analogue. More generally, we demonstrate that tools developed for
machine learning can be used to address generic data analysis and modelling
problems.


  Reinforcement learning is important part of artificial intelligence. In this
paper, we review model-free reinforcement learning that utilizes the average
reward optimality criterion in the infinite horizon setting. Motivated by the
solo survey by Mahadevan (1996a), we provide an updated review of work in this
area and extend it to cover policy-iteration and function approximation methods
(in addition to the value-iteration and tabular counterparts). We present a
comprehensive literature mapping. We also identify and discuss opportunities
for future work.


  Quantum key distribution (QKD) is a crucial technology for information
security in the future. Developing simple and efficient ways to establish QKD
among multiple users are important to extend the applications of QKD in
communication networks. Herein, we proposed a scheme of symmetric dispersive
optics QKD (DO-QKD) and demonstrated an entanglement-based quantum network
based on it. In the experiment, a broadband entanglement photon pair source was
shared by end users via wavelength and space division multiplexing. The wide
spectrum of generated entangled photon pairs was divided into 16 combinations
of frequency-conjugate channels. Photon pairs in each channel combination
supported a fully-connected subnet with 8 users by a passive beam splitter.
Eventually, it showed that an entanglement-based QKD network over 100 users
could be supported by one entangled photon pair source in this architecture. It
has great potential on applications of local quantum networks with large user
number.


  We report on the discovery of hard spectral tails for energies above 10 keV
in the total and pulsed spectra of anomalous X-ray pulsars 1RXS J1708-4009, 4U
0142+61 and 1E 2259+586 using RXTE PCA (2-60 keV) and HEXTE (15-250 keV) data
and INTEGRAL IBIS ISGRI (20-300 keV) data. Improved spectral information on 1E
1841-045 is presented. The pulsed and total spectra measured above 10 keV have
power-law shapes and there is so far no significant evidence for spectral
breaks or bends up to ~150 keV. The pulsed spectra are exceptionally hard with
indices measured for 4 AXPs approximately in the range -1.0 -- 1.0. We also
reanalyzed archival CGRO COMPTEL (0.75-30 MeV) data to search for signatures
from our set of AXPs. No detections can be claimed, but the obtained
upper-limits in the MeV band indicate that for 1RXS J1708-4009, 4U 0142+61 and
1E 1841-045 strong breaks must occur somewhere between 150 and 750 keV.


  Charging of a clean two-dimensional island is studied in the regime of small
concentration of electrons when they form the Wigner crystal. Two forms of
electron-electron interaction potential are studied: the pure Coulomb
interaction and the exponential interaction corresponding to the screening by a
pair of close metallic gates. The electrons are assumed to reside in a
parabolic external confining potential. Due to the crystalline symmetry the
center of the confinement can be situated at distinct positions with respect to
the crystal. With the increasing number of electrons $N$ the center
periodically hops from one such a location to another providing the lowest
total energy. These events occur with the period $\sim N^{1/2}$. At these
moments in the case of the pure Coulomb interaction the charging energy of the
island has a negative correction $\approx 15%$. For the case of the exponential
interaction at the moments of switching the capacitance becomes negative and
$\sim N^{1/4}$ new electrons enter the island simultaneously. The
configurations of disclinations and dislocations in the island are also
studied.


  Context: Technical debt (TD) refers to the additional costs incurred due to
compromises in software quality, providing short-term advantages during
development but potentially compromising long-term quality. Accurate TD
forecasting and prediction are vital for informed software maintenance and
proactive management. However, this research area lacks comprehensive
documentation on the available forecasting techniques. Objective: This study
aims to explore existing knowledge in software engineering to gain insights
into approaches proposed in research and industry for forecasting TD evolution.
Methods: To achieve this objective, we conducted a Systematic Literature Review
encompassing 646 distinct papers published until 2023. Following established
methodology in software engineering, we identified and included 14 primary
studies for analysis. Result: Our analysis unveiled various approaches for TD
evolution forecasting. Notably, random forest and temporal convolutional
networks demonstrated superior performance compared to other methods based on
the result from the primary studies. However, these approaches only address two
of the fifteen identified TD types, specifically Code debt and Architecture
debt, while disregarding the remaining types. Conclusion: Our findings indicate
that research on TD evolution forecasting is still in its early stages, leaving
numerous challenges unaddressed. Therefore, we propose several research
directions that require further investigation to bridge the existing gaps.
Keywords: Systematic literature review, Technical debt, Technical debt
prediction, Technical debt forecasting, Technical debt metrics


  We study the topological triviality and the Whitney equisingularity of a
family of isolated determinantal singularities. On one hand, we give a
L\^e-Ramanujam type theorem for this kind of singularities by using the
vanishing Euler characteristic. On the other hand, we extend the results of
Teissier and Gaffney about the Whitney equisingularity of hypersurfaces and
complete intersections, respectively, in terms of the constancy of the polar
multiplicities.


  As large language models achieve impressive scores on traditional benchmarks,
an increasing number of researchers are becoming concerned about benchmark data
leakage during pre-training, commonly known as the data contamination problem.
To ensure fair evaluation, recent benchmarks release only the training and
validation sets, keeping the test set labels closed-source. They require anyone
wishing to evaluate his language model to submit the model's predictions for
centralized processing and then publish the model's result on their
leaderboard. However, this submission process is inefficient and prevents
effective error analysis. To address this issue, we propose to variabilize
benchmarks and evaluate language models dynamically. Specifically, we extract
variables from each test case and define a value range for each variable. For
each evaluation, we sample new values from these value ranges to create unique
test cases, thus ensuring a fresh evaluation each time. We applied this
variable perturbation method to four datasets: GSM8K, ARC, CommonsenseQA, and
TruthfulQA, which cover mathematical generation and multiple-choice tasks. Our
experimental results demonstrate that this approach provides a more accurate
assessment of the true capabilities of language models, effectively mitigating
the contamination problem.


  We have obtained H$\alpha$ CCD images of 21 compact planetary nebulae in the
galactic bulge. All objects are resolved and, after deconvolving the seeing
disk, we find diameters of 1--2$''$ with typical uncertainties of 20\%. The
values are generally in good agreement with radio measurements but are smaller
than older optical measurements. Based on the new values, we find that the
Shklovsky method for distance determination greatly overestimates the actual
distance for all of our objects.


  The microscopic origins of terahertz (THz) vibrational modes in biological
systems are an active and open area of current research. Recent experiments
[Physical Review X 8, 031061 (2018)] have revealed the presence of a pronounced
mode at $\sim$0.3 THz in fluorophore-decorated bovine serum albumin (BSA)
protein in aqueous solution under nonequilibrium conditions induced by optical
pumping. This result was heuristically interpreted as a collective elastic
fluctuation originating from the activation of a low-frequency phonon mode. In
this work, we show that the sub-THz spectroscopic response emerges in a
statistically significant manner (> 2$\sigma$) from such collective behavior,
illustrating how specific THz vibrational modes can be triggered through
optical excitations and other charge reorganization processes. We revisit the
theoretical analysis with proof-of-concept molecular dynamics that introduce
optical excitations into the simulations. Using information theory techniques,
we show that these excitations can induce a multiscale response involving the
two optically excited chromophores (tryptophans), other amino acids in the
protein, ions, and water. Our results motivate new experiments and fully
nonequilibrium simulations to probe these phenomena, as well as the refinement
of atomistic models of Fr\"ohlich condensates that are fundamentally determined
by nonlinear interactions in biology.


  Turbulence simulation codes can exploit the flute-like nature of plasma
turbulence to reduce the effective number of degrees of freedom necessary to
represent fluctuations. This can be achieved by employing magnetic coordinates
of which one is aligned along the magnetic field. This work presents an
approach in which the position along the field lines is identified by the
toroidal angle, rather than the most commonly used poloidal angle. It will be
shown that this approach has several advantages. Among these, periodicity in
both angles is retained. This property allows moving to an equivalent
representation in Fourier space with a reduced number of toroidal components.
It will be shown how this duality can be exploited to transform conventional
codes that use a spectral representation on the magnetic surface into codes
with a field-aligned coordinate. It is also shown that the new approach can be
generalised to get rid of magnetic coordinates in the poloidal plane
altogether, for a large class of models. Tests are carried out by comparing the
new approach with the conventional approach employing a uniform grid, for a
basic ion temperature gradient (ITG) turbulence model implemented by the two
corresponding versions of the ETAI3D code. These tests uncover an unexpected
property of the model, that localized large parallel gradients can
intermittently appear in the turbulent regime. This leaves open the question
whether this is a general property of plasma turbulence, which may lead one to
reconsider some of the usual assumptions on micro-turbulence dynamics.


  We calculate the Emptiness Formation Probability (EFP) in the spin-Calogero
Model (sCM) and Haldane-Shastry Model (HSM) using their hydrodynamic
description. The EFP is the probability that a region of space is completely
void of particles in the ground state of a quantum many body system. We
calculate this probability in an instanton approach, by considering the more
general problem of an arbitrary depletion of particles (DFP). In the limit of
large size of depletion region the probability is dominated by a classical
configuration in imaginary time that satisfies a set of boundary conditions and
the action calculated on such solution gives the EFP/DFP with exponential
accuracy. We show that the calculation for sCM can be elegantly performed by
representing the gradientless hydrodynamics of spin particles as a sum of two
spin-less Calogero collective field theories in auxiliary variables.
Interestingly, the result we find for the EFP can be casted in a form
reminiscing of spin-charge separation, which should be violated for a
non-linear effect such as this. We also highlight the connections between sCM,
HSM and $\lambda=2$ spin-less Calogero model from a EFP/DFP perspective.


  We present the ESO Distant Cluster Survey (EDisCS) a survey of 20 fields
containing distant galaxy clusters with redshifts ranging from 0.4 to almost
1.0. Candidate clusters were chosen from among the brightest objects identified
in the Las Campanas Distant Cluster Survey, half with estimated redshift
z_est~0.5 and half with z_est~0.8. They were confirmed by identifying red
sequences in moderately deep two colour data from VLT/FORS2. For confirmed
candidates we have assembled deep three-band optical photometry using
VLT/FORS2, deep near-infrared photometry in one or two bands using NTT/SOFI,
deep optical spectroscopy using VLT/FORS2, wide field imaging in two or three
bands using the ESO Wide Field Imager, and HST/ACS mosaic images for 10 of the
most distant clusters. This first paper presents our sample and the VLT
photometry we have obtained. We present images, colour-magnitude diagrams and
richness estimates for our clusters, as well as giving redshifts and positions
for the brightest cluster members. Subsequent papers will present our infrared
photometry, spectroscopy, HST and wide-field imaging, as well as a wealth of
further analysis and science results. Our reduced data become publicly
available as these papers are accepted.


  Studies of the genetic loci that contribute to variation in gene expression
frequently identify loci with broad effect on gene expression: expression
quantitative trait locus (eQTL) hotspots. We describe a set of exploratory
graphical methods as well as a formal likelihood-based test for assessing
whether a given hotspot is due to one or multiple polymorphisms. We first look
at the pattern of effects of the locus on the expression traits that map to the
locus: the direction of the effects, as well as the degree of dominance. A
second technique is to focus on the individuals that exhibit no recombination
event in the region, apply dimensionality reduction (such as with linear
discriminant analysis) and compare the phenotype distribution in the
non-recombinants to that in the recombinant individuals: If the recombinant
individuals display a different expression pattern than the non-recombinants,
this indicates the presence of multiple causal polymorphisms. In the formal
likelihood-based test, we compare a two-locus model, with each expression trait
affected by one or the other locus, to a single-locus model. We apply our
methods to a large mouse intercross with gene expression microarray data on six
tissues.


  Future manufacturing requires complex systems that connect simulation
platforms and virtualization with physical data from industrial processes.
Digital twins incorporate a physical twin, a digital twin, and the connection
between the two. Benefits of using digital twins, especially in manufacturing,
are abundant as they can increase efficiency across an entire manufacturing
life-cycle. The digital twin concept has become increasingly sophisticated and
capable over time, enabled by rises in many technologies. In this paper, we
detail the cognitive digital twin as the next stage of advancement of a digital
twin that will help realize the vision of Industry 4.0. Cognitive digital twins
will allow enterprises to creatively, effectively, and efficiently exploit
implicit knowledge drawn from the experience of existing manufacturing systems.
They also enable more autonomous decisions and control, while improving the
performance across the enterprise (at scale). This paper presents graph
learning as one potential pathway towards enabling cognitive functionalities in
manufacturing digital twins. A novel approach to realize cognitive digital
twins in the product design stage of manufacturing that utilizes graph learning
is presented.


  In a remarkable development Bender and coworkers have shown that it is
possible to formulate quantum mechanics consistently even if the Hamiltonian
and other observables are not Hermitian. Their formulation, dubbed PT quantum
mechanics, replaces hermiticity by another set of requirements, notably that
the Hamiltonian should be invariant under the discrete symmetry PT, where P
denotes parity and T denotes time reversal. All prior work has focused on the
case that time reversal is even (T^2 = 1). We generalize the formalism to the
case of odd time reversal (T^2 = -1). We discover an analogue of Kramer's
theorem for PT quantum mechanics, present a prototypical example of a PT
quantum system with odd time reversal, and discuss potential applications of
the formalism. Odd time reversal symmetry applies to fermionic systems
including quarks and leptons and a plethora of models in nuclear, atomic and
condensed matter physics. PT quantum mechanics makes it possible to enlarge the
set of possible Hamiltonians that physicists could deploy to describe
fundamental physics beyond the standard model or for the effective description
of condensed matter phenomena.


  In this paper we present a technique for procedurally generating 3D maps
using a set of premade meshes which snap together based on designer-specified
visual constraints. The proposed approach avoids size and layout limitations,
offering the designer control over the look and feel of the generated maps, as
well as immediate feedback on a given map's navigability. A prototype
implementation of the method, developed in the Unity game engine, is discussed,
and a number of case studies are analyzed. These include a multiplayer game
where the method was used, together with a number of illustrative examples
which highlight various parameterizations and piece selection methods. The
technique can be used as a designer-centric map composition method and/or as a
prototyping system in 3D level design, opening the door for quality map and
level creation in a fraction of the time of a fully human-based approach.


  We solve robust optimization problem and show the example of the market model
for which the worst case measure is not a martingale measure. In our model the
instantaneous interest rate is determined by the Hull-White model and the
investor employs the HARA utility to measure his satisfaction.To protect
against the model uncertainty he uses the worst case measure approach. The
problem is formulated as a stochastic game between the investor and the market
from the other side. PDE methods are used to find the saddle point and the
precise verification argument is provided.


  Saturating sets are combinatorial objects in projective spaces over finite
fields that have been intensively investigated in the last three decades. They
are related to the so-called covering problem of codes in the Hamming metric.
In this paper, we consider the recently introduced linear version of such sets,
which is, in turn, related to the covering problem in the rank metric. The main
questions in this context are how small the rank of a saturating linear set can
be and how to construct saturating linear sets of small rank. Recently, Bonini,
Borello, and Byrne provided a lower bound on the rank of saturating linear sets
in a given projective space, which is shown to be tight in some cases. In this
paper, we provide construction of saturating linear sets meeting the lower
bound and we develop a link between the saturating property and the
scatteredness of linear sets. The last part of the paper is devoted to show
some parameters for which the bound is not tight.


  We report spectroscopic observations of 23 candidates for Kepler
asteroseismic targets and 10 other stars in the Kepler field, carried out at
two observatories (see the footnote). For all these stars, we derive the radial
velocities, effective temperature, surface gravity, metallicity, the projected
rotational velocity, and estimate the MK type. HIP 97513 and HIP 92132 are
classified as suspected new single-lined spectroscopic binaries. For 28 stars,
the radial velocity is measured for the first time.


  In this paper we discuss quantization of the Fayet-Iliopoulos parameter in
supergravity theories with altered nonperturbative sectors, which were recently
used to argue a fractional quantization condition. Nonlinear sigma models with
altered nonperturbative sectors are the same as nonlinear sigma models on
special stacks known as gerbes. After reviewing the existing results on such
theories in two dimensions, we discuss examples of gerby moduli `spaces'
appearing in four-dimensional field theory and string compactifications, and
the effect of various dualities. We discuss global topological defects arising
when a field or string theory moduli space has a gerbe structure. We also
outline how to generalize results of Bagger-Witten and more recent authors on
quantization issues in supergravities from smooth manifolds to smooth moduli
stacks, focusing particular attention on stacks that have gerbe structures.


  State statistics of linear systems satisfy certain structural constraints
that arise from the underlying dynamics and the directionality of input
disturbances. In the present paper we study the problem of completing partially
known state statistics. Our aim is to develop tools that can be used in the
context of control-oriented modeling of large-scale dynamical systems. For the
type of applications we have in mind, the dynamical interaction between state
variables is known while the directionality and dynamics of input excitation is
often uncertain. Thus, the goal of the mathematical problem that we formulate
is to identify the dynamics and directionality of input excitation in order to
explain and complete observed sample statistics. More specifically, we seek to
explain correlation data with the least number of possible input disturbance
channels. We formulate this inverse problem as rank minimization, and for its
solution, we employ a convex relaxation based on the nuclear norm. The
resulting optimization problem is cast as a semidefinite program and can be
solved using general-purpose solvers. For problem sizes that these solvers
cannot handle, we develop a customized alternating minimization algorithm
(AMA). We interpret AMA as a proximal gradient for the dual problem and prove
sub-linear convergence for the algorithm with fixed step-size. We conclude with
an example that illustrates the utility of our modeling and optimization
framework and draw contrast between AMA and the commonly used alternating
direction method of multipliers (ADMM) algorithm.


  Humans spend vast hours in bed -- about one-third of the lifetime on average.
Besides, a human at rest is vital in many healthcare applications. Typically,
humans are covered by a blanket when resting, for which we propose a multimodal
approach to uncover the subjects so their bodies at rest can be viewed without
the occlusion of the blankets above. We propose a pyramid scheme to effectively
fuse the different modalities in a way that best leverages the knowledge
captured by the multimodal sensors. Specifically, the two most informative
modalities (i.e., depth and infrared images) are first fused to generate good
initial pose and shape estimation. Then pressure map and RGB images are further
fused one by one to refine the result by providing occlusion-invariant
information for the covered part, and accurate shape information for the
uncovered part, respectively. However, even with multimodal data, the task of
detecting human bodies at rest is still very challenging due to the extreme
occlusion of bodies. To further reduce the negative effects of the occlusion
from blankets, we employ an attention-based reconstruction module to generate
uncovered modalities, which are further fused to update current estimation via
a cyclic fashion. Extensive experiments validate the superiority of the
proposed model over others.


  The Praesepe open cluster represents a puzzle since it has about the same age
as the Hyades, and only slightly different metallicity, yet previous ROSAT
observations resulted in a detection rate of cluster sources significantly
lower than for the Hyades. We present a new 50 ksec observation of Praesepe
performed with the EPIC instrument on board XMM-Newton, which resulted in the
detection of ~ 200 sources, including 48 cluster members. We detected all
solar-type (F-G) stars in the field of view, 90% of the K stars and 70% of the
M stars. We find that the distribution of X-ray luminosities of solar-type
Praesepe members is comparable to that of the Hyades, in contrast with the
previous ROSAT results; however, the disagreement between the ROSAT and
XMM-Newton results appears to be mostly due to X-ray faint Praesepe members
falling outside the XMM-Newton field of view, while it is considerably reduced
when considering only the subsample of stars in the ROSAT survey in common with
the present observation. The finding supports an earlier suggestion that
Praesepe may be formed by two merged clusters of different age.


  Despite the great potential of Federated Learning (FL) in large-scale
distributed learning, the current system is still subject to several privacy
issues due to the fact that local models trained by clients are exposed to the
central server. Consequently, secure aggregation protocols for FL have been
developed to conceal the local models from the server. However, we show that,
by manipulating the client selection process, the server can circumvent the
secure aggregation to learn the local models of a victim client, indicating
that secure aggregation alone is inadequate for privacy protection. To tackle
this issue, we leverage blockchain technology to propose a verifiable client
selection protocol. Owing to the immutability and transparency of blockchain,
our proposed protocol enforces a random selection of clients, making the server
unable to control the selection process at its discretion. We present security
proofs showing that our protocol is secure against this attack. Additionally,
we conduct several experiments on an Ethereum-like blockchain to demonstrate
the feasibility and practicality of our solution.


  In this paper, we discuss an initial boundary value problem for the
stochastic wave equation involving the nonlinear damping term $|u_t|^{q-2}u_t$
and a source term of the type $|u|^{p-2}u$. We firstly establish the local
existence and uniqueness of solution by the Galerkin approximation method and
show that the solution is global for $q\geq p$. Secondly, by an appropriate
energy inequality, the local solution of the stochastic equations will blow up
with positive probability or explosive in energy sense for $p>q$.


  In this paper, we present the systematic measurements of the temperature and
magnetic field dependences of the thermodynamic and transport properties of the
Yb-based heavy fermion YbPtBi for temperatures down to 0.02 K with magnetic
fields up to 140 kOe to address the possible existence of a field-tuned quantum
critical point. Measurements of magnetic field and temperature dependent
resistivity, specific heat, thermal expansion, Hall effect, and thermoelectric
power indicate that the AFM order can be suppressed by applied magnetic field
of $H_{c}$ $\sim$ 4 kOe. In the $H-T$ phase diagram of YbPtBi, three regimes of
its low temperature states emerges: (I) AFM state, characterized by spin
density wave (SDW) like feature, which can be suppressed to $T$ = 0 by the
relatively small magnetic field of $H_{c}$ $\sim$ 4\,kOe, (II) field induced
anomalous state in which the electrical resistivity follows $\Delta\rho(T)
\propto T^{1.5}$ between $H_{c}$ and $\sim$ 8 kOe, and (III) Fermi liquid (FL)
state in which $\Delta\rho(T) \propto T^{2}$ for $H \geq$ 8 kOe. Regions I and
II are separated at $T$ = 0 by what appears to be a quantum critical point.
Whereas region III appears to be a FL associated with the hybridized 4$f$
states of Yb, region II may be a manifestation of a spin liquid state.


  Deep Neural Network (DNN) Inference in Edge Computing, often called Edge
Intelligence, requires solutions to insure that sensitive data confidentiality
and intellectual property are not revealed in the process. Privacy-preserving
Edge Intelligence is only emerging, despite the growing prevalence of Edge
Computing as a context of Machine-Learning-as-a-Service. Solutions are yet to
be applied, and possibly adapted, to state-of-the-art DNNs. This position paper
provides an original assessment of the compatibility of existing techniques for
privacy-preserving DNN Inference with the characteristics of an Edge Computing
setup, highlighting the appropriateness of secret sharing in this context. We
then address the future role of model compression methods in the research
towards secret sharing on DNNs with state-of-the-art performance.


  Feature selection with specific multivariate performance measures is the key
to the success of many applications, such as image retrieval and text
classification. The existing feature selection methods are usually designed for
classification error. In this paper, we propose a generalized sparse
regularizer. Based on the proposed regularizer, we present a unified feature
selection framework for general loss functions. In particular, we study the
novel feature selection paradigm by optimizing multivariate performance
measures. The resultant formulation is a challenging problem for
high-dimensional data. Hence, a two-layer cutting plane algorithm is proposed
to solve this problem, and the convergence is presented. In addition, we adapt
the proposed method to optimize multivariate measures for multiple instance
learning problems. The analyses by comparing with the state-of-the-art feature
selection methods show that the proposed method is superior to others.
Extensive experiments on large-scale and high-dimensional real world datasets
show that the proposed method outperforms $l_1$-SVM and SVM-RFE when choosing a
small subset of features, and achieves significantly improved performances over
SVM$^{perf}$ in terms of $F_1$-score.


  We study the problem of stabilization for the acoustic system with a
spatially distributed damping. Without imposing any hypotheses on the
structural properties of the damping term, we identify logarithmic decay of
solutions with growing time. Logarithmic decay rate is shown by using a
frequency domain method and combines a contradiction argument with the
multiplier technique and a new Carleman estimate to carry out a special
analysis for the resolvent.


  There has been great interest in applying the results of statistical
mechanics to single molecule experiements. Recent work has highlighted
so-called non-equilibrium work-energy relations and Fluctuation Theorems which
take on an equilibrium-like (time independent) form. Here I give a very simple
heuristic example where an equilibrium result (the barometric law for colloidal
particles) arises from theory describing the {\em thermodynamically}
non-equilibrium phenomenon of a single colloidal particle falling through
solution due to gravity. This simple result arises from the fact that the
particle, even while falling, is in {\em mechanical} equilibrium (gravitational
force equal the viscous drag force) at every instant. The results are
generalized by appeal to the central limit theorem. The resulting time
independent equations that hold for thermodynamically non-equilibrium (and even
non-stationary) processes offer great possibilities for rapid determination of
thermodynamic parameters from single molecule experiments.


  This paper presents the measurement problem from the point of view of the
thermal interpretation of quantum physics introduced in Part II. The
measurement of a Hermitian quantity $A$ is regarded as giving an uncertain
value approximating the q-expectation $\langle A\rangle$ rather than (as
tradition wanted to have it) as an exact revelation of an eigenvalue of $A$.
Single observations of microscopic systems are (except under special
circumstances) very uncertain measurements only. The thermal interpretation
  * treats detection events as a statistical measurement of particle beam
intensity.
  * claims that the particle concept is only asymptotically valid, under
conditions where particles are essentially free.
  * claims that the unmodeled environment influences the results enough to
cause all randomness in quantum physics.
  * allows one to derive Born's rule for scattering and in the limit of ideal
measurements; but in general, only part of Born's rule holds exactly: Whenever
a quantity A with zero uncertainty is measured exactly, its value is an
eigenvalue of A.
  * has no explicit collapse -- the latter emerges approximately in
non-isolated subsystems.
  * gives a valid interpretation of systems modeled by a quantum-classical
dynamics.
  * explains the peculiar features of the Copenhagen interpretation (lacking
realism between measurements) and the minimal statistical interpretation
(lacking realism for the single case) where these interpretations apply -- in
the microscopic domain.
  The thermal interpretation is an interpretation of quantum physics that is in
principle refutable by theoretical arguments leading to a negative answer to a
number of open issues collected at the end of the paper, since there is plenty
of experimental evidence for each of the points mentioned there.


  Using tools and concepts from contact topology we show that non-vanishing
twist implies conservation of the layer structure in cholesteric liquid
crystals. This leads to a number of additional topological invariants for
cholesteric textures, such as layer numbers, that are not captured by
traditional descriptions, characterises the nature and size of the chiral
energy barriers between metastable configurations, and gives a geometric
characterisation of cholesteric dynamics in any context, including active
systems, those in confined geometries or under the influence of an external
field.


  Theories that spontaneously break Lorentz invariance also violate
diffeomorphism symmetries, implying the existence of extra degrees of freedom
and modifications of gravity. In the minimal model (``ghost condensation'')
with only a single extra degree of freedom at low energies, the scale of
Lorentz violation cannot be larger than about M ~ 100GeV due to an infrared
instability in the gravity sector. We show that Lorentz symmetry can be broken
at much higher scales in a non-minimal theory with additional degrees of
freedom, in particular if Lorentz symmetry is broken by the vacuum expectation
value of a vector field. This theory can be constructed by gauging ghost
condensation, giving a systematic effective field theory description that
allows us to estimate the size of all physical effects. We show that nonlinear
effects become important for gravitational fields with strength \sqrt{\Phi} >
g, where g is the gauge coupling, and we argue that the nonlinear dynamics is
free from singularities. We then analyze the phenomenology of the model,
including nonlinear dynamics and velocity-dependent effects. The strongest
bounds on the gravitational sector come from either black hole accretion or
direction-dependent gravitational forces, and imply that the scale of
spontaneous Lorentz breaking is M < Min(10^{12}GeV, g^2 10^{15}GeV). If the
Lorentz breaking sector couples directly to matter, there is a spin-dependent
inverse-square law force, which has a different angular dependence from the
force mediated by the ghost condensate, providing a distinctive signature for
this class of models.


  Here is one of the results of this paper (with the convention ${{1}\over
{0}}=+\infty$): Let $X$ be a real Hilbert space and let $J:X\to {\bf R}$ be a
$C^1$ functional, with compact derivative, such that $$\alpha^*:=\max\left
\{0,\limsup_{\|x\|\to +\infty}{{J(x)}\over {\|x\|^2}}\right
\}<\beta^*:=\sup_{x\in X\setminus \{0\}}{{J(x)}\over {\|x\|^2}}<+\infty\ .$$
Then, for every $\lambda\in \left ]{{1}\over {2\beta^*}}, {{1}\over
{2\alpha^*}}\right [$ and for every convex set $C\subseteq X$ dense in $X$,
there exists $\tilde y\in C$ such that the equation $$x=\lambda J'(x)+\tilde
y$$ has at least three solutions, two of which are global minima of the
functional $x\to {{1}\over {2}}\|x\|^2-\lambda J(x)-\langle x,\tilde y\rangle$ .


  The emergence of generative models has revolutionized the field of remote
sensing (RS) image generation. Despite generating high-quality images, existing
methods are limited in relying mainly on text control conditions and thus don't
always generate images accurately and stablely. In this paper, we propose
CRS-Diff, a new RS generative foundation framework specifically tailored for RS
image generation, leveraging the inherent advantages of diffusion models while
integrating more advanced control mechanisms. Specifically, CRS-Diff can
simultaneously support text-condition, metadata-condition, and image-condition
control inputs, thus enabling more precise control to refine the generation
process. To effectively integrate multiple condition control information, we
introduce a new conditional control mechanism to achieve multi-scale feature
fusion, thus enhancing the guiding effect of control conditions. To our
knowledge, CRS-Diff is the first multiple-condition controllable generative RS
foundation model. Experimental results in single-condition and
multiple-condition cases have demonstrated the superior ability of our CRS-Diff
to generate RS images both quantitatively and qualitatively compared with
previous methods. Additionally, our CRS-Diff can serve as a data engine that
generates high-quality training data for downstream tasks, e.g., road
extraction. The code is available at https://github.com/Sonettoo/CRS-Diff.


  We consider the elliptic equation $-\Delta u = f(u)$ in the whole $\R^{2m}$,
where $f$ is of bistable type. It is known that there exists a saddle-shaped
solution in $\R^{2m}$. This is a solution which changes sign in $\R^{2m}$ and
vanishes only on the Simons cone ${\mathcal C}=\{(x^1,x^2)\in\R^m\times\R^m:
|x^1|=|x^2|\}$. It is also known that these solutions are unstable in
dimensions 2 and 4.
  In this article we establish that when $2m=6$ every saddle-shaped solution is
unstable outside of every compact set and, as a consequence has infinite Morse
index. For this we establish the asymptotic behavior of saddle-shaped solutions
at infinity. Moreover we prove the existence of a minimal and a maximal
saddle-shaped solutions and derive monotonicity properties for the maximal
solution.
  These results are relevant in connection with a conjecture of De Giorgi on 1D
symmetry of certain solutions. Saddle-shaped solutions are the simplest
candidates, besides 1D solutions, to be global minimizers in high dimensions, a
property not yet established.


  Cirelli, Mani\`{a} and Pizzocchero generalized quantum mechanics by
K\"{a}hler geometry. Furthermore they proved that any unital C$^{*}$-algebra is
represented as a function algebra on the set of pure states with a
noncommutative $*$-product as an application. The ordinary quantum mechanics is
regarded as a dynamical system of the projective Hilbert space ${\cal P}({\cal
H})$ of a Hilbert space ${\cal H}$. The space ${\cal P}({\cal H})$ is an
infinite dimensional K\"{a}hler manifold of positive constant holomorphic
sectional curvature. In general, such dynamical system is constructed for a
general K\"{a}hler manifold of nonzero constant holomorphic sectional curvature
$c$. The Hilbert ball $B_{{\cal H}}$ is defined by the open unit ball in ${\cal
H}$ and it is a K\"{a}hler manifold with $c<0$. We introduce the quantum
mechanics on $B_{{\cal H}}$. As an application, we show the structure of the
noncommutative function algebra on $B_{{\cal H}}$.


  We investigate the issues of achieving sufficient rigor in the arguments for
the safety of machine learning functions. By considering the known weaknesses
of DNN-based 2D bounding box detection algorithms, we sharpen the metric of
imprecise pedestrian localization by associating it with the safety goal. The
sharpening leads to introducing a conservative post-processor after the
standard non-max-suppression as a counter-measure. We then propose a
semi-formal assurance case for arguing the effectiveness of the post-processor,
which is further translated into formal proof obligations for demonstrating the
soundness of the arguments. Applying theorem proving not only discovers the
need to introduce missing claims and mathematical concepts but also reveals the
limitation of Dempster-Shafer's rules used in semi-formal argumentation.


  We provide sufficient criteria for explosion in Crump-Mode-Jagers branching
process, via the process producing an infinite path in finite time. As an
application, we deduce a curious phase-transition in the infinite tree
associated with a class of recursive tree models with fitness, showing that in
one regime every node in the tree has infinite degree, whilst in another, the
tree is locally finite, with a unique infinite path. The latter class
encompasses many models studied in the literature, including the weighted
random recursive tree, the preferential attachment tree with additive fitness,
and the Bianconi-Barab\'asi model, or preferential attachment tree with
multiplicative fitness.


  In this work, we proposed a nonresonant explanation for the Y(4260) structure
observed in the $e^+e^-\to J/\psi\pi^+\pi^-$ process, i.e., Y(4260) is not a
genuine resonance. Our result indicates that the Y(4260) structure can be
reproduced by the interference of production amplitudes of the $e^+e^-\to
J/\psi\pi^+\pi^-$ processes via direct $e^+e^-$ annihilation and through
intermediate charmonia $\psi(4160)/\psi(4415)$. Besides describing Y(4260) line
shape in $e^+e^-\to J/\psi\pi^+\pi^-$ well, such a nonresonant explanation for
the Y(4260) structure naturally answers why there is no evidence of Y(4260) in
the exclusive open-charm decay channel and $R$-value scan.


  Robust optimization is becoming increasingly important in machine learning
applications. In this paper, we study a unified framework of robust submodular
optimization. We study this problem both from a minimization and maximization
perspective (previous work has only focused on variants of robust submodular
maximization). We do this under a broad range of combinatorial constraints
including cardinality, knapsack, matroid as well as graph-based constraints
such as cuts, paths, matchings and trees. Furthermore, we also study robust
submodular minimization and maximization under multiple submodular upper and
lower bound constraints. We show that all these problems are motivated by
important machine learning applications including robust data subset selection,
robust co-operative cuts and robust co-operative matchings. In each case, we
provide scalable approximation algorithms and also study hardness bounds.
Finally, we empirically demonstrate the utility of our algorithms on synthetic
data, and real-world applications of robust cooperative matchings for image
correspondence, robust data subset selection for speech recognition, and image
collection summarization with multiple queries.


  In a domain $\Omega\subseteq \mathbb{R}^\mathbf{N}$ we consider compact,
Birman-Schwinger type, operators of the form
$\mathbf{T}_{P,\mathfrak{A}}=\mathfrak{A}^*P\mathfrak{A}$; here $P$ is a
singular Borel measure in $\Omega$ and $\mathfrak{A}$ is a noncritical order
$-l\ne -\mathbf{N}/2$ pseudodifferential operator. For a class of such
operators, we obtain estimates and a proper version of H.Weyl's asymptotic law
for eigenvalues, with order depending on dimensional characteristics of the
measure. A version of the CLR estimate for singular measures is proved. For
non-selfadjoint operators of the form $P_2 \mathfrak{A} P_1$ and
$\mathfrak{A}_2 P \mathfrak{A}_1$ with singular measures $P,P_1,P_2$ and
negative order pseudodifferential operators
$\mathfrak{A},\mathfrak{A}_1,\mathfrak{A}_2$ we obtain estimates for singular
numbers.


  We use a recently developed technique, which allows to perform few Lanczos
steps on a given wavefunction even for large system sizes, to investigate the
$t{-}J$ model in the physical parameter region and to check the stability of
the BCS d-wave variational wavefunction. Our statistical Lanczos algorithm,
which extends and improves the one Lanczos step, has been extensively tested on
the small L=18 sites cluster where many Lanczos iterations can be performed
exactly. In this case, at doping $\delta\sim 10%$ the BCS wavefunction
represents a very good initial state to achieve extremely accurate energies and
correlation functions with few Lanczos iterations. For large sizes ($L\le 98$)
the behavior is similar: the low-energy d-wave order parameter $P_d$ is weakly
affected by a couple of Lanczos iterations in the low doping $\delta\sim 10%$
region, whereas the energy is considerably lowered. As a further test of our
calculation we have computed the variance of the Hamiltonian $\Delta E_p=(<
\hat{H}^2 >-< \hat{H} >^2)/L^2$ on the BCS wavefunction with $p=0,1,2$ Lanczos
steps. For large $p$, when the Lanczos algorithm converges to the exact ground
state, the variance vanishes exponentially with increasing $p$. The remarkable
reduction of the variance, observed for $p=1,2$ Lanczos steps even for the
largest lattice size considered, suggests a smooth and rapid convergence to the
exact ground state. These results support the existence of off-diagonal
long-range d-wave superconducting order in the two-dimensional $t{-}J$ model.


  This paper examines the structure of poset matrices by formulating a set of
new construction rules for this purpose. In this direction, the technique of
partial composition operation will be introduced as the basis for the
construction of poset matrices of any given size by extending the combinatorial
setting of species of structures to poset matrices. More specifically, three
new partial composition operations that apply to poset matrices are defined as
the foundation for this study. Several new structural properties derived from
viewing any poset matrix and its dual in terms of these operations are
highlighted.


  We study two dimensional low angular momentum flow around the black hole
using the resistive magnetohydrodynamic module of PLUTO code. Simulations have
been performed for the flows with parameters of specific angular momentum,
specific energy, and magnetic field which may be expected for the flow around
Sgr A*. For flows with lower resistivity $\eta=10^{-6}$ and $0.01$, the
luminosity and the shock location on the equator vary quasi-periodically. The
power density spectra of luminosity variation show the peak frequencies which
correspond to the periods of $5 \times 10^5$, $1.4 \times 10^5$, and $5 \times
10^4$ seconds, respectively. These quasi-periodic oscillations (QPOs) occur due
to the interaction between the outer oscillating standing shock and the inner
weak shocks occurring at the innermost hot blob. While for cases with higher
resistivity $\eta=0.1$ and 1.0, the high resistivity considerably suppresses
the magnetic activity such as the MHD turbulence and the flows tend to be
steady and symmetric to the equator. The steady standing shock is formed more
outward compared with the hydrodynamical flow. The low angular momentum flow
model with the above flow parameters and with low resistivity has a possibility
for the explanation of the long-term flares with $\sim$ one per day and $\sim 5
- 10$ days of Sgr A* in the latest observations by Chandra, Swift, and
XMM-Newton monitoring of Sgr A*.


  The partial-dual genus polynomial $^\partial\varepsilon_G(z)$ of a ribbon
graph $G$ is the generating function that enumerates all partial duals of $G$.
In this paper, we give a categorification for this polynomial. The key
ingredient of the construction is an extended Frobenius algebra related to
unoriented topological quantum field theory.


  Software Quality is a major concern in software engineering development in
order to be competitive. Such a quality can be achieved by a possible technique
called Refactoring where the systems external behavior of the system is not
changed. Initially we present our work by analyzing the case studies of ongoing
researches of DMARF and GIPSY by understanding their needs and requirements
involving the major components in their respective systems. Later sections
illustrate the conceptual architecture of these case studies, for this we have
referenced the original architecture to draw the important candidate concepts
presented in the system, and analyzing their associations with other concepts
in the system and then compared this conceptual architecture with the original
architectures. Later the document throws light on identifying the code smells
exist in the architectures to find them and resolve to minimize the deeper
problems. JDeodorant, SonarQube are the tools which we go across for
identification and analyzing the source code quality, both these tools are
available as an IDE plugin or as an open source platforms. Next is to identify
the design patterns exist in the architectures along with their importance and
need for existence in respective systems. Finally, the implication is towards
introducing refactoring methods onto the smells which have been identified and
possibly refactor them accordingly by applying appropriate refactoring methods
and showcasing the respective tests to ensure that changes in the architecture
does not change the behavior much.


  A model is proposed where two $\chi^{(2)}$ nonlinear waveguides are contained
in a cavity suited for second-harmonic generation. The evanescent wave coupling
between the waveguides is considered as weak, and the interplay between this
coupling and the nonlinear interaction within the waveguides gives rise to
quantum violations of the classical limit. These violations are particularly
strong when two instabilities are competing, where twin-beam behavior is found
as almost complete noise suppression in the difference of the fundamental
intensities. Moreover, close to bistable transitions perfect twin-beam
correlations are seen in the sum of the fundamental intensities, and also the
self-pulsing instability as well as the transition from symmetric to asymmetric
states display nonclassical twin-beam correlations of both fundamental and
second-harmonic intensities. The results are based on the full quantum Langevin
equations derived from the Hamiltonian and including cavity damping effects.
The intensity correlations of the output fields are calculated
semi-analytically using a linearized version of the Langevin equations derived
through the positive-P representation. Confirmation of the analytical results
are obtained by numerical simulations of the nonlinear Langevin equations
derived using the truncated Wigner representation.


  We characterize the kinematic and chemical properties of 589 Galactic
Anticenter Substructure Stars (GASS) with K-/M- giants in Integrals-of-Motion
space. These stars likely include members of previously identified
substructures such as Monoceros, A13, and the Triangulum-Andromeda cloud
(TriAnd). We show that these stars are on nearly circular orbits on both sides
of the Galactic plane. We can see velocity($V_{Z}$) gradient along Y-axis
especially for the south GASS members. Our GASS members have similar energy and
angular momentum distributions to thin disk stars. Their location in
[$\alpha$/M] vs. [M/H] space is more metal poor than typical thin disk stars,
with [$\alpha$/M] \textbf{lower} than the thick disk. We infer that our GASS
members are part of the outer metal-poor disk stars, and the outer-disk extends
to 30 kpc. Considering the distance range and $\alpha$-abundance features, GASS
could be formed after the thick disk was formed due to the molecular cloud
density decreased in the outer disk where the SFR might be less efficient than
the inner disk.


  We prove that Vop\v{e}nka's Principle implies that for every class
$\mathfrak{X}$ of modules over any ring, the class of
\textbf{$\boldsymbol{\mathfrak{X}}$-Gorenstein Projective modules}
(\textbf{$\boldsymbol{\mathfrak{X}}$-$\boldsymbol{\mathcal{GP}}$}) is a special
precovering class. In particular, it is not possible to prove (unless
Vop\v{e}nka's Principle is inconsistent) that there is a ring over which the
\textbf{Ding Projectives} ($\boldsymbol{\mathcal{DP}}$) or the
\textbf{Gorenstein Projectives} ($\boldsymbol{\mathcal{GP}}$) do not form a
precovering class (\v{S}aroch previously obtained this result for the class
$\mathcal{GP}$, using different methods). The key innovation is a new
"top-down" characterization of \emph{deconstructibility}, which is a well-known
sufficient condition for a class to be precovering. We also prove that
Vop\v{e}nka's Principle implies, in some sense, the maximum possible amount of
deconstructibility in module categories.


  One-parameter hyperbolic planar motion was first studied by S.
Y$\ddot{\texttt{u}}$ce and N. Kuruo$\tilde{\texttt{g}}$lu. Moreover, they
analyzed the relationships between the absolute, relative and sliding
velocities of one-parameter hyperbolic planar motion as well as the related
pole curves, \cite{Yuc}. One-parameter planar motions in the Euclidean plane
$\mathbb{E}^2$ and the Euler-Savary formula in one-parameter planar motions
were given by M$\ddot{\texttt{u}}$ller, \cite{Mul}. In the present article, one
hyperbolic plane moving relative to two other hyperbolic planes, one moving and
the other fixed, was taken into consideration and the relation between the
absolute, relative and sliding velocities of this movement was obtained. In
addition, a canonical relative system for one-parameter hyperbolic planar
motion was defined. Euler-Savary formula, which gives the relationship between
the curvature of trajectory curves, was obtained with the help of this relative
system.


  While most approaches to automatically recognizing entailment relations have
used classifiers employing hand engineered features derived from complex
natural language processing pipelines, in practice their performance has been
only slightly better than bag-of-word pair classifiers using only lexical
similarity. The only attempt so far to build an end-to-end differentiable
neural network for entailment failed to outperform such a simple similarity
classifier. In this paper, we propose a neural model that reads two sentences
to determine entailment using long short-term memory units. We extend this
model with a word-by-word neural attention mechanism that encourages reasoning
over entailments of pairs of words and phrases. Furthermore, we present a
qualitative analysis of attention weights produced by this model, demonstrating
such reasoning capabilities. On a large entailment dataset this model
outperforms the previous best neural model and a classifier with engineered
features by a substantial margin. It is the first generic end-to-end
differentiable system that achieves state-of-the-art accuracy on a textual
entailment dataset.


  In this article, we propose the approach to procedural optimization of a
neural network, based on the combination of information theory and braid
theory. The network studied in the article implemented with the intersections
between the braid strands, as well as simplified networks (a network with
strands without intersections and a simple convolutional deep neural network),
are used to solve various problems of multiclass image classification that
allow us to analyze the comparative effectiveness of the proposed architecture.
The simulation results showed BraidNet's comparative advantage in learning
speed and classification accuracy.


  Robust Markov Decision Processes (MDPs) are a powerful framework for modeling
sequential decision-making problems with model uncertainty. This paper proposes
the first first-order framework for solving robust MDPs. Our algorithm
interleaves primal-dual first-order updates with approximate Value Iteration
updates. By carefully controlling the tradeoff between the accuracy and cost of
Value Iteration updates, we achieve an ergodic convergence rate of $O \left(
A^{2} S^{3}\log(S)\log(\epsilon^{-1}) \epsilon^{-1} \right)$ for the best
choice of parameters on ellipsoidal and Kullback-Leibler $s$-rectangular
uncertainty sets, where $S$ and $A$ is the number of states and actions,
respectively. Our dependence on the number of states and actions is
significantly better (by a factor of $O(A^{1.5}S^{1.5})$) than that of pure
Value Iteration algorithms. In numerical experiments on ellipsoidal uncertainty
sets we show that our algorithm is significantly more scalable than
state-of-the-art approaches. Our framework is also the first one to solve
robust MDPs with $s$-rectangular KL uncertainty sets.


  Recent text-to-image diffusion-based generative models have the stunning
ability to generate highly detailed and photo-realistic images and achieve
state-of-the-art low FID scores on challenging image generation benchmarks.
However, one of the primary failure modes of these text-to-image generative
models is in composing attributes, objects, and their associated relationships
accurately into an image. In our paper, we investigate this
compositionality-based failure mode and highlight that imperfect text
conditioning with CLIP text-encoder is one of the primary reasons behind the
inability of these models to generate high-fidelity compositional scenes. In
particular, we show that (i) there exists an optimal text-embedding space that
can generate highly coherent compositional scenes which shows that the output
space of the CLIP text-encoder is sub-optimal, and (ii) we observe that the
final token embeddings in CLIP are erroneous as they often include attention
contributions from unrelated tokens in compositional prompts. Our main finding
shows that the best compositional improvements can be achieved (without harming
the model's FID scores) by fine-tuning {\it only} a simple linear projection on
CLIP's representation space in Stable-Diffusion variants using a small set of
compositional image-text pairs. This result demonstrates that the
sub-optimality of the CLIP's output space is a major error source. We also show
that re-weighting the erroneous attention contributions in CLIP can also lead
to improved compositional performances, however these improvements are often
less significant than those achieved by solely learning a linear projection
head, highlighting erroneous attentions to be only a minor error source.


  In this lecture, I summarize developments presented at the Lepton Photon 2011
conference and give my perspective on the current situation in high-energy
physics.


  Consider the wrapped Fukaya category W of a collection of exact Lagrangians
in a Liouville manifold. Under a non-degeneracy condition implying the
existence of enough Lagrangians, we show that natural geometric maps from the
Hochschild homology of W to symplectic cohomology and from symplectic
cohomology to the Hochschild cohomology of W are isomorphisms, in a manner
compatible with ring and module structures. This is a consequence of a more
general duality for the wrapped Fukaya category, which should be thought of as
a non-compact version of a Calabi-Yau structure. The new ingredients are: (1)
Fourier-Mukai theory for W via a wrapped version of holomorphic quilts, (2) new
geometric operations, coming from discs with two negative punctures and
arbitrary many positive punctures, (3) a generalization of the Cardy condition,
and (4) the use of homotopy units and A-infinity shuffle products to relate
non-degeneracy to a resolution of the diagonal.


  This paper deals with the investigation of the computational solutions of an
unified fractional reaction-diffusion equation, which is obtained from the
standard diffusion equation by replacing the time derivative of first order by
the generalized Riemann-Liouville fractional derivative defined in Hilfer et
al. , and the space derivative of second order by the Riesz-Feller fractional
derivative, and adding a function $\phi(x,t)$. The solution is derived by the
application of the Laplace and Fourier transforms in a compact and closed form
in terms of Mittag-Leffler functions. The main result obtained in this paper
provides an elegant extension of the fundamental solution for the space-time
fractional diffusion equation obtained earlier by Mainardi et al., and the
result very recently given by Tomovski et al.. At the end, extensions of the
derived results, associated with a finite number of Riesz-Feller space
fractional derivatives, are also investigated.


  Dynamically correcting for unwanted interactions between a quantum system and
its environment is vital to achieving the high-fidelity quantum control
necessary for a broad range of quantum information technologies. In recent
work, we uncovered the complete solution space of all possible driving fields
that suppress transverse quasistatic noise errors while performing single-qubit
operations. This solution space lives within a simple geometrical framework
that makes it possible to obtain globally optimal pulses subject to a set of
experimental constraints by solving certain geometrical optimization problems.
In this work, we solve such a geometrical optimization problem to find the
fastest possible pulses that implement single-qubit gates while cancelling
transverse quasistatic noise to second order. Because the time-optimized pulses
are not smooth, we provide a method based on our geometrical approach to obtain
experimentally feasible smooth pulses that approximate the time-optimal ones
with minimal loss in gate speed. We show that in the presence of realistic
constraints on pulse rise times, our smooth pulses significantly outperform
sequences based on ideal pulse shapes, highlighting the benefits of building
experimental waveform constraints directly into dynamically corrected gate
designs.


  Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the
developing brain but is not suitable for anomaly screening. For this ultrasound
(US) is employed. While expert sonographers are adept at reading US images, MR
images are much easier for non-experts to interpret. Hence in this paper we
seek to produce images with MRI-like appearance directly from clinical US
images. Our own clinical motivation is to seek a way to communicate US findings
to patients or clinical professionals unfamiliar with US, but in medical image
analysis such a capability is potentially useful, for instance, for US-MRI
registration or fusion. Our model is self-supervised and end-to-end trainable.
Specifically, based on an assumption that the US and MRI data share a similar
anatomical latent space, we first utilise an extractor to determine shared
latent features, which are then used for data synthesis. Since paired data was
unavailable for our study (and rare in practice), we propose to enforce the
distributions to be similar instead of employing pixel-wise constraints, by
adversarial learning in both the image domain and latent space. Furthermore, we
propose an adversarial structural constraint to regularise the anatomical
structures between the two modalities during the synthesis. A cross-modal
attention scheme is proposed to leverage non-local spatial correlations. The
feasibility of the approach to produce realistic looking MR images is
demonstrated quantitatively and with a qualitative evaluation compared to real
fetal MR images.