  The Internet of Medical Things (IoMT) offers a promising solution to improve
patient health and reduce human error. Wearable smart infusion pumps that
accurately administer medication and integrate with electronic health records
are an example of technology that can improve healthcare. They can even alert
healthcare professionals or remote servers during operational failure,
preventing distressing incidents. However, as the number of connected medical
devices increases, the risk of cyber threats also increases. Wearable
medication devices based on IoT attached to patients' bodies are prone to
significant cyber threats. Being connected to the Internet exposes these
devices to potential harm, which could disrupt or degrade device performance
and harm patients. To ensure patient safety and well-being, it is crucial to
establish secure data authentication for internet-connected medical devices. It
is also important to note that the wearability option of such devices might
downgrade the computational resources, making them more susceptible to security
risks. This paper implements a security approach to a wearable infusion pump.
We discuss practical challenges in implementing security-enabled devices and
propose initial solutions to mitigate cyber threats.


  Major depressive disorder (MDD) presents challenges in diagnosis and
treatment due to its complex and heterogeneous nature. Emerging evidence
indicates that reward processing abnormalities may serve as a behavioral marker
for MDD. To measure reward processing, patients perform computer-based
behavioral tasks that involve making choices or responding to stimulants that
are associated with different outcomes. Reinforcement learning (RL) models are
fitted to extract parameters that measure various aspects of reward processing
to characterize how patients make decisions in behavioral tasks. Recent
findings suggest the inadequacy of characterizing reward learning solely based
on a single RL model; instead, there may be a switching of decision-making
processes between multiple strategies. An important scientific question is how
the dynamics of learning strategies in decision-making affect the reward
learning ability of individuals with MDD. Motivated by the probabilistic reward
task (PRT) within the EMBARC study, we propose a novel RL-HMM framework for
analyzing reward-based decision-making. Our model accommodates learning
strategy switching between two distinct approaches under a hidden Markov model
(HMM): subjects making decisions based on the RL model or opting for random
choices. We account for continuous RL state space and allow time-varying
transition probabilities in the HMM. We introduce a computationally efficient
EM algorithm for parameter estimation and employ a nonparametric bootstrap for
inference. We apply our approach to the EMBARC study to show that MDD patients
are less engaged in RL compared to the healthy controls, and engagement is
associated with brain activities in the negative affect circuitry during an
emotional conflict task.


  We obtain a complete description of divergent Palais-Smale sequences for the
prescribed Ricci curvature functional on compact homogeneous spaces. As an
application, we prove the existence of saddle points on generalized Wallach
spaces and several types of generalized flag manifolds. We also describe the
image of the Ricci map in some of our examples.


  Purpose: To introduce two novel learning-based motion artifact removal
networks (LEARN) for the estimation of quantitative motion- and
$B0$-inhomogeneity-corrected $R_2^\ast$ maps from motion-corrupted
multi-Gradient-Recalled Echo (mGRE) MRI data.
  Methods: We train two convolutional neural networks (CNNs) to correct motion
artifacts for high-quality estimation of quantitative
$B0$-inhomogeneity-corrected $R_2^\ast$ maps from mGRE sequences. The first
CNN, LEARN-IMG, performs motion correction on complex mGRE images, to enable
the subsequent computation of high-quality motion-free quantitative $R_2^\ast$
(and any other mGRE-enabled) maps using the standard voxel-wise analysis or
machine-learning-based analysis. The second CNN, LEARN-BIO, is trained to
directly generate motion- and $B0$-inhomogeneity-corrected quantitative
$R_2^\ast$ maps from motion-corrupted magnitude-only mGRE images by taking
advantage of the biophysical model describing the mGRE signal decay. We show
that both CNNs trained on synthetic MR images are capable of suppressing motion
artifacts while preserving details in the predicted quantitative $R_2^\ast$
maps. Significant reduction of motion artifacts on experimental in vivo
motion-corrupted data has also been achieved by using our trained models.
  Conclusion: Both LEARN-IMG and LEARN-BIO can enable the computation of
high-quality motion- and $B0$-inhomogeneity-corrected $R_2^\ast$ maps.
LEARN-IMG performs motion correction on mGRE images and relies on the
subsequent analysis for the estimation of $R_2^\ast$ maps, while LEARN-BIO
directly performs motion- and $B0$-inhomogeneity-corrected $R_2^\ast$
estimation. Both LEARN-IMG and LEARN-BIO jointly process all the available
gradient echoes, which enables them to exploit spatial patterns available in
the data. The high computational speed of LEARN-BIO is an advantage that can
lead to a broader clinical application.


  BiOCu_{1-y}S was recently reported to superconduct at T=5.8 K at y~0.15. Band
structure calculations indicate that stoichiometric BiOCuS is a band insulator.
In this paper, I show that hole-doped (whether in the virtual crystal
approximation or with actual Cu vacancies) BiOCuS is on the verge of a
ferromagnetic instability (cf. Pd metal), and therefore conventional
superconductivity with Tc~6 K is quite unlikely. Presumably, hole-doped BiOCuS
is another example of superconductivity mediated by spin-fluctuations


  Any dynamical system, whether it is generated by a differential equation or a
transformation rule on a manifold, induces a dynamics on functional-spaces. The
choice of functional-space may vary, but the induced dynamics is always linear,
and codified by the Koopman operator. The eigenfunctions of the Koopman
operator are of extreme importance in the study of the dynamics. They provide a
clear distinction between the mixing and non-mixing components of the dynamics,
and also reveal embedded toral rotations. The usual choice of functional-space
is $L^2$, a class of square integrable functions. A fundamental problem with
eigenfunctions in $L^2$ is that they are often extremely discontinuous,
particularly if the system is chaotic. There are some prototypical systems
called skew-product dynamics in which $L^2$ Koopman eigenfunctions are also
smooth. The article shows that under general assumptions on an ergodic system,
these prototypical examples are the only possibility. Moreover, the smooth
eigenfunctions can be used to create a change of variables which explicitly
characterizes of the weakly mixing component.


  We present a theory for the generation of mesoscale ($kr_{g}\ll 1$, where
$r_{g}$ is the cosmic ray gyroradius) magnetic fields during diffusive shock
acceleration. The decay or modulational instability of resonantly excited
Alfven waves scattering off ambient density perturbations in the shock
environment naturally generates larger scale fields. For a broad spectrum of
perturbations, the physical mechanism of energy transfer is random refraction,
represented by diffusion of Alfven wave packet in $k-$space. The scattering
field can be produced directly by the decay instability or by the Drury
instability, a hydrodynamic instability driven by the cosmic ray pressure
gradient. This process is of interest to acceleration since it generates waves
of longer wavelength, and so enables the confinement and acceleration of higher
energy particles. This process also limits the intensity of resonantly
generated turbulent magnetic field on $r_{g}$ scales.


  Here we report our recent results of variability studies in optical and X-ray
bands of three blazars namely 3C 273, PKS 2155 - 304 and BL Lacertae with
XMM-Newton. We found large amplitude optical to X-rays variability in 3C 273,
and PKS 2155 - 304 on year time scale. In 3C 273, we noticed that synchrotron
cooling and particle acceleration are at work at different epoch of
observations. In PKS 2155 - 304, spectral energy distribution from optical to
X-ray is fitted with LPPL (log parabolic + power law) model. In BL Lacertae,
optical flux and degree of polarization were anti-correlated.


  In this paper, motivated by the setting of white-space detection [1], we
present theoretical and empirical results for detection of the zero-support E
of x \in Cp (xi = 0 for i \in E) with reduced-dimension linear measurements. We
propose two low- complexity algorithms based on one-step thresholding [2] for
this purpose. The second algorithm is a variant of the first that further
assumes the presence of group-structure in the target signal [3] x. Performance
guarantees for both algorithms based on the worst- case and average coherence
(group coherence) of the measurement matrix is presented along with the
empirical performance of the algorithms.


  The local-constant-field approximation (LCFA) is an essential theoretical
tool for investigating strong-field QED phenomena in background electromagnetic
fields with complex spacetime structure. In our previous work
[Phys.~Rev.~A~\textbf{98}, 012134 (2018)] we have analyzed the shortcomings of
the LCFA in nonlinear Compton scattering at low emitted photon energies for the
case of a background plane-wave field. Here, we generalize that analysis to
background fields, which can feature a virtually arbitrary spacetime structure.
In addition, we provide an explicit and simple implementation of an improved
expression of the nonlinear Compton scattering differential probability that
solves the main shortcomings of the standard LCFA in the infrared region, and
is suitable for background electromagnetic fields with arbitrary spacetime
structure such as those occurring in particle-in-cell simulations. Finally, we
carry out a systematic procedure to calculate the probability of nonlinear
Compton scattering per unit of emitted photon light-cone energy and of
nonlinear Breit-Wheeler pair production per unit of produced positron
light-cone energy beyond the LCFA in a plane-wave background field, which
allows us to identify the limits of validity of this approximation
quantitatively.


  This article uses a dataset of answers to questions to generate student
similarity networks. Two similarity functions to determine the weights between
each pair of students are used, one that assumes a power-law distribution of
answers response times, and one that does not. The resulting networks are then
clustered using different community finding algorithms and their resulting
modularity is compared.


  We compute probability distributions of trajectory observables for reversible
and irreversible growth processes. These results reveal a correspondence
between reversible and irreversible processes, at particular points in
parameter space, in terms of their typical and atypical trajectories. Thus key
features of growth processes can be insensitive to the precise form of the rate
constants used to generate them, recalling the insensitivity to microscopic
details of certain equilibrium behavior. We obtained these results using a
sampling method, inspired by the "s-ensemble" large-deviation formalism, that
amounts to umbrella sampling in trajectory space. The method is a simple
variant of existing approaches, and applies to ensembles of trajectories
controlled by the total number of events. It can be used to determine
large-deviation rate functions for trajectory observables in or out of
equilibrium.


  We present the spectroscopy from 5254 galaxies that hosted supernovae (SNe)
or other transient events in the Sloan Digital Sky Survey II (SDSS-II).
Obtained during SDSS-I, SDSS-II, and the Baryon Oscillation Spectroscopic
Survey (BOSS), this sample represents the largest systematic, unbiased,
magnitude limited spectroscopic survey of supernova (SN) host galaxies. Using
the host galaxy redshifts, we test the impact of photometric SN classification
based on SDSS imaging data with and without using spectroscopic redshifts of
the host galaxies. Following our suggested scheme, there are a total of 1166
photometrically classified SNe Ia when using a flat redshift prior and 1126 SNe
Ia when the host spectroscopic redshift is assumed. For 1024 (87.8%) candidates
classified as likely SNe Ia without redshift information, we find that the
classification is unchanged when adding the host galaxy redshift. Using
photometry from SDSS imaging data and the host galaxy spectra, we also report
host galaxy properties for use in future nalysis of SN astrophysics. Finally,
we investigate the differences in the interpretation of the light curve
properties with and without knowledge of the redshift. When using the SALT2
light curve fitter, we find a 21% increase in the number of fits that converge
when using the spectroscopic redshift. Without host galaxy redshifts, we find
that SALT2 light curve fits are systematically biased towards lower photometric
redshift estimates and redder colors in the limit of low signal-to-noise data.
The general improvements in performance of the light curve fitter and the
increased diversity of the host galaxy sample highlights the importance of host
galaxy spectroscopy for current photometric SN surveys such as the Dark Energy
Survey and future surveys such as the Large Synoptic Survey Telescope.


  We study $6$-transposition groups, i.e. groups generated by a normal set of
involutions $D$, such that the order of the product of any two elements from
$D$ does not exceed $6$. We classify most of the groups generated by $3$
elements from $D$, two of which commute, and prove they are finite.


  We investigate the role that nonlinearity in the interatomic potential has on
the thermal conductance of a suspended nanoribbon when it is subjected to a
longitudinal strain. To focus on the first cubic and quartic nonlinear terms of
a general potential, we propose an atomic system based on an $\alpha$-$\beta$
Fermi-Pasta-Ulam nearest neighbor interaction.
  We perform classical molecular dynamics simulations to investigate the
contribution of longitudinal, transversal and flexural modes to the thermal
conductance as a function of the $\alpha$-$\beta$ parameters and the applied
strain. We compare the cases where atoms are allowed to vibrate only {\it in}
plane (2D) with the case of vibrations {\it in} and {\it out} of plane (3D). We
find that the dependence of conductance on $\alpha$ and $\beta$ relies on a
crossover phenomenon between linear/nonlinear delocalized/localized flexural
and transversal modes, driven by an on/off switch of the strain.


  We study the relation between discrete gauge symmetries in F-theory
compactifications and torsion homology on the associated Calabi-Yau manifold.
Focusing on the simplest example of a $\mathbb Z_2$ symmetry, we show that
there are two physically distinct ways that such a discrete gauge symmetry can
arise. First, compactifications of M-Theory on Calabi-Yau threefolds which
support a genus-one fibration with a bi-section are known to be dual to
six-dimensional F-theory vacua with a $\mathbb Z_2$ gauge symmetry. We show
that the resulting five-dimensional theories do not have a $\mathbb Z_2$
symmetry but that the latter emerges only in the F-theory decompactification
limit. Accordingly the genus-one fibred Calabi-Yau manifolds do not exhibit
discrete torsion. Associated to the bi-section fibration is a Jacobian
fibration which does support a section. Compactifying on these related but
distinct varieties does lead to a $\mathbb Z_2$ symmetry in five dimensions
and, accordingly, we find explicitly an associated discrete torsion. We
identify the expected particle and membrane system of the discrete symmetry in
terms of wrapped M2 and M5 branes and present a field-theory description of the
physics for both cases in terms of circle reductions of six-dimensional
theories. Our results and methods generalise straightforwardly to larger
discrete symmetries and to four-dimensional compactifications.


  We develop algorithms to compute the differential Galois group $G$ associated
to a parameterized second-order homogeneous linear differential equation of the
form \[ \tfrac{\partial^2}{\partial x^2} Y + r_1 \tfrac{\partial}{\partial x} Y
+ r_0 Y = 0, \] where the coefficients $r_1, r_0 \in F(x)$ are rational
functions in $x$ with coefficients in a partial differential field $F$ of
characteristic zero. Our work relies on the procedure developed by Dreyfus to
compute $G$ under the assumption that $r_1 = 0$. We show how to complete this
procedure to cover the cases where $r_1 \neq 0$, by reinterpreting a classical
change of variables procedure in Galois-theoretic terms.


  We consider the nonlinear massless wave equation belonging to some family of
the Friedmann-Lema\^itre-Robertson-Walker (FLRW) spacetime. We prove the global
in time small data solutions for supercritical powers in the case of
decelerating expansion universe.


  We describe the Hamiltonian reduction of the coajoint Kac-Moody orbits to the
Virasoro coajoint orbits explicitly in terms of the Lagrangian approach for the
Wess-Zumino-Novikov-Witten theory. While a relation of the coajoint Virasoro
orbit $Diff \; S^1 /SL(2,R)$ to the Liouville theory has been already studied
we analyse the role of special coajoint Virasoro orbits $Diff \;
S^1/\tilde{T}_{\pm ,n}$ corresponding to stabilizers generated by the vector
fields with double zeros. The orbits with stabilizers with single zeros do not
appear in the model. We find an interpretation of zeros $x_i$ of the vector
field of stabilizer $\tilde{T}_{\pm ,n}$ and additional parameters $q_i$, $i =
1,...,n$, in terms of quantum mechanics for $n$ point particles on the circle.
We argue that the special orbits are generated by insertions of "wrong sign"
Liouville exponential into the path integral. The additional parmeters $q_i$
are naturally interpreted as accessory parameters for the uniformization map.
Summing up the contributions of the special Virasoro orbits we get the
integrable sinh-Gordon type theory.


  In the majority of the existing Visual Question Answering (VQA) research, the
answers consist of short, often single words, as per instructions given to the
annotators during dataset construction. This study envisions a VQA task for
natural situations, where the answers are more likely to be sentences rather
than single words. To bridge the gap between this natural VQA and existing VQA
approaches, a novel unsupervised keyword extraction method is proposed. The
method is based on the principle that the full-sentence answers can be
decomposed into two parts: one that contains new information answering the
question (i.e., keywords), and one that contains information already included
in the question. Discriminative decoders were designed to achieve such
decomposition, and the method was experimentally implemented on VQA datasets
containing full-sentence answers. The results show that the proposed model can
accurately extract the keywords without being given explicit annotations
describing them.


  We discuss two theoretical proposals for controlling the nonequilibrium
steady state of nanomechanical resonators using quantum electronic transport.
Specifically?, we analyse two approaches to achieve the ground-state cooling of
the mechanical vibration coupled to a quantum dot embedded between (i)
spin-polarised contacts or (ii) a normal metal and a superconducting contact.
Assuming a suitable coupling between the vibrational modes and the charge or
spin of the electrons in the quantum dot, we show that ground-state cooling of
the mechanical oscillator is within the state of the art for suspended carbon
nanotube quantum dots operating as electromechanical devices.


  We present a model for the description of the evolution of contacts among
individuals in a network. At each time step each individual is associated with
a domain or neighborhood of fully connected agents.The dynamics of this
changing neighborhood will later be translated into a situation where the links
between individuals are also dynamic. A characterization in terms of the
parameters that govern the evolution of the network and a comparison to
previous work on Small World networks is presented as well.


  We calculate the ${\cal O}(\alpha_s)$ radiative corrections to the spin
dependent differential decay rates of the process $t\rightarrow b+W^+$. These
are needed to study the angular distribution of the energy of hadrons produced
in polarized top quark decays at next-to-leading order (NLO). In our previous
work, we studied the angular distribution of the scaled-energy of
bottom-flavored hadrons (B) from polarized top quark decays, using a specific
helicity coordinate system where the top quark spin was measured relative to
the bottom momentum (system~1). Here, we study the angular distribution of the
energy spectrum of B-hadron in a different helicity system, where the top spin
is specified relative to the W-momentum (system~2). These energy distributions
are governed by the polarized and unpolarized rate functions which are related
to the density matrix elements of the decay $t\rightarrow W^++b$. Through this
paper, we present our predictions of the B-hadron spectrum in the polarized and
unpolarized top decay and shall compare the polarized results in two different
helicity systems. These predictions can be used to determine the polarization
states of top quarks and also provide direct access to the B-hadron
fragmentation functions (FFs) and allow us to deepen our knowledge of the
hadroniazation process.


  In this paper, we demonstrate the combination of machine learning and three
dimensional numerical simulations for multi-objective optimization of low
pressure die casting. The cooling of molten metal inside the mold is achieved
typically by passing water through the cooling lines in the die. Depending on
the cooling line location, coolant flow rate and die geometry, nonuniform
temperatures are imposed on the molten metal at the mold wall. This boundary
condition along with the initial molten metal temperature affect the product
quality quantified in terms of micro-structure parameters and yield strength. A
finite volume based numerical solver is used to determine the temperature-time
history and correlate the inputs to outputs. The objective of this research is
to develop and demonstrate a procedure to obtain the initial and wall
temperatures so as to optimize the product quality. The non-dominated sorting
genetic algorithm (NSGA-II) is used for multi-objective optimization in this
work. The number of function evaluations required for NSGA-II can be of the
order of millions and hence, the finite volume solver cannot be used directly
for optimization. Therefore, a multilayer perceptron feed-forward neural
network is first trained using the results from the numerical solution of the
fluid flow and energy equations and is subsequently used as a surrogate model.
As an assessment, simplified versions of the actual problem are designed to
first verify results of the genetic algorithm. An innovative local sensitivity
based approach is then used to rank the final Pareto optimal solutions and
select a single best design.


  We carry out high resolution two-dimensional radiation-hydrodynamic numerical
simulations to study the formation and evolution of hot bubbles inside
planetary nebulae (PNe). We take into account the evolution of the stellar
parameters, wind velocity and mass-loss rate from the final thermal pulses
during the asymptotic giant branch (AGB) through to the post-AGB stage for a
range of initial stellar masses. The instabilities that form at the interface
between the hot bubble and the swept-up AGB wind shell lead to hydrodynamical
interactions, photoevaporation flows and opacity variations. We explore the
effects of hydrodynamical mixing combined with thermal conduction at this
interface on the dynamics, photoionization, and emissivity of our models. We
find that even models without thermal conduction mix significant amounts of
mass into the hot bubble. When thermal conduction is not included, hot gas can
leak through the gaps between clumps and filaments in the broken swept-up AGB
shell and this depressurises the bubble. The inclusion of thermal conduction
evaporates and heats material from the clumpy shell, which expands to seal the
gaps, preventing a loss in bubble pressure. The dynamics of bubbles without
conduction is dominated by the thermal pressure of the thick photoionized
shell, while for bubbles with thermal conduction it is dominated by the hot,
shocked wind.


  Our goal is to present a new shorter proof for the maximal monotonicity of
the Minkowski sum of two maximal monotone multi-valued operators defined in a
reflexive Banach space under the classical interiority condition involving
their domains.


  Reliable and precise measurements of the relative energy of band edges in
semiconductors are needed to determine band gaps and band offsets, as well as
to establish the band diagram of devices and heterostructures. These
measurements are particularly important in the field of two-dimensional
materials, in which many new semiconducting systems are becoming available
through exfoliation of bulk crystals. For two-dimensional semiconductors,
however, commonly employed techniques suffer from difficulties rooted either in
the physics of these systems, or of technical nature. The very large exciton
binding energy, for instance, prevents the band gap to be determined from a
simple spectral analysis of photoluminescence, and the limited lateral size of
atomically thin crystals makes the use of conventional scanning tunneling
spectroscopy cumbersome. Ionic gate spectroscopy is a newly developed technique
that exploits ionic gate field-effect transistors to determine quantitatively
the relative alignment of band edges of two-dimensional semiconductors in a
straightforward way, directly from transport measurements (i.e., from the
transistor electrical characteristics). The technique relies on the extremely
large geometrical capacitance of ionic gated devices that -- under suitable
conditions -- enables a change in gate voltage to be directly related to a
shift in chemical potential. Here we present an overview of ionic gate
spectroscopy, and illustrate its relevance with applications to different
two-dimensional semiconducting transition metal dichalcogenides and van der
Waals heterostructures.


  Nuclear magnetism in n-doped semiconductors with positive hyperfine constant
is revisited. Two kinds of nuclear magnetic ordering can be induced by resident
electrons in a deeply cooled nuclear spin system. At positive nuclear spin
temperature below a critical value, randomly oriented nuclear spin polarons
similar to that predicted by I. Merkulov [I. Merkulov, Physics of the Solid
State 40, 930 (1998)] should emerge. These polarons are oriented randomly and
within each polaron nuclear and electron spins are aligned
antiferromagnetically. At negative nuclear spin temperature below a critical
value we predict another type of magnetic ordering - dynamically induced
nuclear ferromagnet. This is a long-range ferromagnetically ordered state
involving both electrons and nuclei. It can form if electron spin relaxation is
dominated by the hyperfine coupling, rather than by the spin-orbit interaction.
Application of the theory to the n-doped GaAs suggests that the ferromagnetic
order may be reached at experimentally achievable nuclear spin temperature
$\Theta_N \approx 0.5$ $\mu$K and lattice temperature $T_L \approx 5$ K.


  The future of autonomous vehicles lies in the convergence of human-centric
design and advanced AI capabilities. Autonomous vehicles of the future will not
only transport passengers but also interact and adapt to their desires, making
the journey comfortable, efficient, and pleasant. In this paper, we present a
novel framework that leverages Large Language Models (LLMs) to enhance
autonomous vehicles' decision-making processes. By integrating LLMs' natural
language capabilities and contextual understanding, specialized tools usage,
synergizing reasoning, and acting with various modules on autonomous vehicles,
this framework aims to seamlessly integrate the advanced language and reasoning
capabilities of LLMs into autonomous vehicles. The proposed framework holds the
potential to revolutionize the way autonomous vehicles operate, offering
personalized assistance, continuous learning, and transparent decision-making,
ultimately contributing to safer and more efficient autonomous driving
technologies.


  We use N-body simulations to study the tidal evolution of globular clusters
(GCs) in dwarf spheroidal (dSph) galaxies. Our models adopt a cosmologically
motivated scenario in which the dSph is approximated by a static NFW halo with
a triaxial shape. We apply our models to five GCs spanning three orders of
magnitude in stellar density and two in mass, chosen to represent the
properties exhibited by the five GCs of the Fornax dSph. We show that only the
object representing Fornax's least dense GC (F1) can be fully disrupted by
Fornax's internal tidal field--the four denser clusters survive even if their
orbits decay to the centre of Fornax. For a large set of orbits and projection
angles we examine the spatial and velocity distribution of stellar debris
deposited during the complete disruption of an F1-like GC. Our simulations show
that such debris appears as shells, isolated clumps and elongated
over-densities at low surface brightness (>26 mag/arcsec^2), reminiscent of
substructure observed in several MW dSphs. Such features arise from the
triaxiality of the galaxy potential and do not dissolve in time. The kinematics
of the debris depends strongly on the progenitor's orbit. Debris associated
with box and resonant orbits does not display stream motions and may appear
"colder"/"hotter" than the dSph's field population if the viewing angle is
perpendicular/parallel to progenitor's orbital plane. In contrast, debris
associated with loop orbits shows a rotational velocity that may be detectable
out to few kpc from the galaxy centre. Chemical tagging that can distinguish GC
debris from field stars may reveal whether the merger of GCs contributed to the
formation of multiple stellar components observed in dSphs.


  The theoretical study of ultracold few-body systems is often done using an
idealized 1D model with zero range interactions. Here we study these systems
using a more realistic 3D model with finite range interactions. We place
three-particles, two identical and one impurity, in an axial symmetric harmonic
trap and solve the corresponding stationary Schr\"odinger equation using the
correlated Gaussian method for different particle types, aspect ratios and
interactions strength. We show that the idealized model is accurate for small
and intermediate strength interactions at aspect ratios larger than four,
independently of the particle types. In the strongly interacting limit, the
idealized model is acceptable for bosonic systems, but not for fermionic
systems even at large aspect ratios.


  Graphene is an ideal material for fabricating atomically thin nanometre
spaced electrodes. Recently, carbon-based nanoelectrodes have been employed to
create single-molecule transistors and phase change memory devices. In spite of
the significant recent interest in their use in a range of nanoscale devices
from phase change memories to molecular electronics, the operating and scaling
limits of these electrodes are completely unknown. In this paper, we report on
our observations of consistent voltage driven resistance switching in sub-5 nm
graphene nanogaps. We find that we are able to reversibly cycle between a low
and a high resistance state using feedback-controlled voltage ramps.We
attribute this unexplained switching in the gap to the formation and breakdown
of carbon filaments.By increasing the gap, we find that such intrinsic
resistance switching of graphene nanogaps imposes a scaling limit of 10 nm
(approx.) on the gap-size for devices with operating voltages of 1 to 2 volts.


  The linear quadratic regulator (LQR) problem has reemerged as an important
theoretical benchmark for reinforcement learning-based control of complex
dynamical systems with continuous state and action spaces. In contrast with
nearly all recent work in this area, we consider multiplicative noise models,
which are increasingly relevant because they explicitly incorporate inherent
uncertainty and variation in the system dynamics and thereby improve robustness
properties of the controller. Robustness is a critical and poorly understood
issue in reinforcement learning; existing methods which do not account for
uncertainty can converge to fragile policies or fail to converge at all.
Additionally, intentional injection of multiplicative noise into learning
algorithms can enhance robustness of policies, as observed in ad hoc work on
domain randomization. Although policy gradient algorithms require optimization
of a non-convex cost function, we show that the multiplicative noise LQR cost
has a special property called gradient domination, which is exploited to prove
global convergence of policy gradient algorithms to the globally optimum
control policy with polynomial dependence on problem parameters. Results are
provided both in the model-known and model-unknown settings where samples of
system trajectories are used to estimate policy gradients.


  The analysis of the thermal spectrum of geometrically thin and optically
thick accretion disks of black holes, the so-called continuum-fitting method,
is one of the leading techniques for measuring black hole spins. Current models
normally approximate the disk as infinitesimally thin, while in reality the
disk thickness is finite and increases as the black hole mass accretion rate
increases. Here we present an XSPEC model to calculate the multi-temperature
blackbody spectrum of a thin accretion disk of finite thickness around a Kerr
black hole. We test our new model with an RXTE observation of the black hole
binary GRS 1915+105. We find that the spin value inferred with the new model is
slightly higher than the spin value obtained with a model with an
infinitesimally thin disk, but the difference is small and the effect is
currently subdominant with respect to other sources of uncertainties in the
final spin measurement.


  One of the main atmospheric features in exoplanet atmospheres, detectable
both from ground- and space-based facilities, is Rayleigh scattering. In
hydrogen-dominated planetary atmospheres, Rayleigh scattering causes the
measured planetary radius to increase toward blue wavelengths in the optical
range. We obtained a spectrophotometric time series of one transit of the
Saturn-mass planet WASP-69b using the OSIRIS instrument at the Gran Telescopio
Canarias. From the data we constructed 19 spectroscopic transit light curves
representing 20 nm wide wavelength bins spanning from 515 nm to 905 nm. We
derived the transit depth for each curve individually by fitting an analytical
model together with a Gaussian process to account for systematic noise in the
light curves. We find that the transit depth increases toward bluer
wavelengths, indicative of a larger effective planet radius. Our results are
consistent with space-based measurements obtained in the near infrared using
the Hubble Space Telescope, which show a compatible slope of the transmission
spectrum. We discuss the origin of the detected slope and argue between two
possible scenarios: a Rayleigh scattering detection originating in the planet's
atmosphere or a stellar activity induced signal from the host star.


  There are two standard ways of classifying transport behavior of systems. The
first is via time scaling of spread of correlations in the isolated system in
thermodynamic limit. The second is via system size scaling of conductance in
the steady state of the open system. We show here that these correspond to
taking the thermodynamic limit and the long time limit of the integrated
equilibrium current-current correlations of the open system in different order.
In general, the limits may not commute leading to a conflict between the two
standard ways of transport classification. Nevertheless, the full information
is contained in the equilibrium current-current correlations of the open
system. We show this analytically by rigorously deriving the open-system
current fluctuation dissipation relations (OCFDR) starting from an extremely
general open quantum set-up and then carefully taking the proper limits. We
test our theory numerically on the non-trivial example of the critical
Aubry-Andr\'e-Harper (AAH) model, where, it has been recently shown that, the
two standard classifications indeed give different results. We find that both
the total current autocorrelation and the long-range local current correlations
of the open system in equilibrium show signatures of diffusive transport up to
a time scale. This time scale grows as square of system size. Beyond this time
scale a steady state value is reached. The steady state value is conductance,
which shows sub-diffusive scaling with system size.


  Spontaneous subtle emotions are expressed through micro-expressions, which
are tiny, sudden and short-lived dynamics of facial muscles; thus poses a great
challenge for visual recognition. The abrupt but significant dynamics for the
recognition task are temporally sparse while the rest, irrelevant dynamics, are
temporally redundant. In this work, we analyze and enforce sparsity constrains
to learn significant temporal and spectral structures while eliminate
irrelevant facial dynamics of micro-expressions, which would ease the challenge
in the visual recognition of spontaneous subtle emotions. The hypothesis is
confirmed through experimental results of automatic spontaneous subtle emotion
recognition with several sparsity levels on CASME II and SMIC, the only two
publicly available spontaneous subtle emotion databases. The overall
performances of the automatic subtle emotion recognition are boosted when only
significant dynamics are preserved from the original sequences.


  For the fractional order systems \[D^\alpha x(t)=f(x),\quad 0<\alpha\leq 1,\]
one can have a critical value of $\alpha$ viz $\alpha_*$ such that the system
is stable for $0<\alpha<\alpha_*$ and unstable for $\alpha_*<\alpha\leq 1$. In
general, if such system is stable for some $\alpha_0\in(0,1)$ then it remains
stable for all $\alpha<\alpha_0.$ In this paper, we show that there are some
delay differential equations \[D^\alpha x(t)=f(x(t),x(t-\tau))\] of the
fractional order which behave in an exactly opposite way. These systems are
unstable for higher values of fractional order and stable for the lower values.
The striking observation is the example which is chaotic for $\alpha=0.27$ but
stable for $\alpha=1$. This cannot be observed in the fractional differential
equations (FDEs) without delay. We provide the complete bifurcation scenarios
in the scalar FDEs.


  Semiconductor microcavities, in which strong coupling of excitons to confined
photon modes leads to the formation of exciton-polariton modes, have
increasingly become a focus for the study of spontaneous coherence, lasing, and
condensation in solid state systems. This review discusses the significant
experimental progress to date, the phenomena associated with coherence which
have been observed, and also discusses in some detail the different theoretical
models that have been used to study such systems. We consider both the case of
non-resonant pumping, in which coherence may spontaneously arise, and the
related topics of resonant pumping, and the optical parametric oscillator.


  We employ several unsupervised machine learning techniques, including
autoencoders, random trees embedding, and t-distributed stochastic neighboring
ensemble (t-SNE), to reduce the dimensionality of, and therefore classify, raw
(auxiliary) spin configurations generated, through Monte Carlo simulations of
small clusters, for the Ising and Fermi-Hubbard models at finite temperatures.
Results from a convolutional autoencoder for the three-dimensional Ising model
can be shown to produce the magnetization and the susceptibility as a function
of temperature with a high degree of accuracy. Quantum fluctuations distort
this picture and prevent us from making such connections between the output of
the autoencoder and physical observables for the Hubbard model. However, we are
able to define an indicator based on the output of the t-SNE algorithm that
shows a near perfect agreement with the antiferromagnetic structure factor of
the model in two and three spatial dimensions in the weak-coupling regime.
t-SNE also predicts a transition to the canted antiferromagnetic phase for the
three-dimensional model when a strong magnetic field is present. We show that
these techniques cannot be expected to work away from half filling when the
"sign problem" in quantum Monte Carlo simulations is present.


  We prove that a Sobolev map from a Riemannian manifold into a complete metric
space pushes forward almost every compactly supported integral current to an
Ambrosio--Kirchheim integral current in the metric target, where "almost every"
is understood in a modulus sense.
  As an application, we prove that when the target supports an isoperimetric
inequality of Euclidean type for integral currents, an isoperimetric inequality
for Sobolev mappings relative to bounded, closed and additive cochains follows.
  Using the results above, we answer positively to an open question by Onninen
and Pankka on sharp H\"older continuity for quasiregular curves. A key tool in
the continuity proof is Almgren's isoperimetric inequality for integral
currents.


  Typically clustering algorithms provide clustering solutions with
prespecified number of clusters. The lack of a priori knowledge on the true
number of underlying clusters in the dataset makes it important to have a
metric to compare the clustering solutions with different number of clusters.
This article quantifies a notion of persistence of clustering solutions that
enables comparing solutions with different number of clusters. The persistence
relates to the range of data-resolution scales over which a clustering solution
persists; it is quantified in terms of the maximum over two-norms of all the
associated cluster-covariance matrices. Thus we associate a persistence value
for each element in a set of clustering solutions with different number of
clusters. We show that the datasets where natural clusters are a priori known,
the clustering solutions that identify the natural clusters are most persistent
- in this way, this notion can be used to identify solutions with true number
of clusters. Detailed experiments on a variety of standard and synthetic
datasets demonstrate that the proposed persistence-based indicator outperforms
the existing approaches, such as, gap-statistic method, $X$-means, $G$-means,
$PG$-means, dip-means algorithms and information-theoretic method, in
accurately identifying the clustering solutions with true number of clusters.
Interestingly, our method can be explained in terms of the phase-transition
phenomenon in the deterministic annealing algorithm, where the number of
distinct cluster centers changes (bifurcates) with respect to an annealing
parameter.


  We argue that the first order folding transitions of proteins observed at
physiological chemical conditions end in a critical point for a given
temperature and chemical potential of the surrounding water. We investigate
this critical point using a hierarchical Hamiltonian and determine its
universality class. This class differs qualitatively from those of other known
models.


  In this paper, we investigate the usage of autoencoders in modeling textual
data. Traditional autoencoders suffer from at least two aspects: scalability
with the high dimensionality of vocabulary size and dealing with
task-irrelevant words. We address this problem by introducing supervision via
the loss function of autoencoders. In particular, we first train a linear
classifier on the labeled data, then define a loss for the autoencoder with the
weights learned from the linear classifier. To reduce the bias brought by one
single classifier, we define a posterior probability distribution on the
weights of the classifier, and derive the marginalized loss of the autoencoder
with Laplace approximation. We show that our choice of loss function can be
rationalized from the perspective of Bregman Divergence, which justifies the
soundness of our model. We evaluate the effectiveness of our model on six
sentiment analysis datasets, and show that our model significantly outperforms
all the competing methods with respect to classification accuracy. We also show
that our model is able to take advantage of unlabeled dataset and get improved
performance. We further show that our model successfully learns highly
discriminative feature maps, which explains its superior performance.


  Atom interferometry is a rapidly advancing field and this Letter proposes an
experiment based on existing technology that can search for new short distance
forces. With current technology it is possible to improve the sensitivity by up
to a factor of 10^2 and near-future advances will be able to rewrite the limits
for forces with ranges from 100 um to 1 km.


  Remotely operated vehicles (ROVs) have drawn much attention to underwater
tasks, such as the inspection and maintenance of infrastructure. The workload
of ROV operators tends to be high, even for the skilled ones. Therefore,
assistance methods for the operators are desired. This study focuses on a task
in which a human operator controls an underwater robot to follow a certain path
while visually inspecting objects in the vicinity of the path. In such a task,
it is desirable to realize the speed of trajectory control manually because the
visual inspection is performed by a human operator. However, to allocate
resources to visual inspection, it is desirable to minimize the workload on the
path-following by assisting with the automatic control. Therefore, the
objective of this study was to develop a cooperative path-following control
method that achieves the above-mentioned task by expanding a robust
path-following control law of nonholonomic wheeled vehicles. To simplify this
problem, we considered a path-following and visual objects recognition task in
a two-dimensional plane. We conducted an experiment with participants (n=16)
who completed the task using the proposed method and manual control. The
results showed that both the path-following errors and the workload of the
participants were significantly smaller with the proposed method than with
manual control. In addition, subjective responses demonstrated that operator
attention tended to be allocated to objects recognition rather than robot
operation tasks with the proposed method. These results indicate the
effectiveness of the proposed cooperative path-following control method.


  Strong lensing by intervening galaxies can produce multiple images of
gravitational waves from sources at cosmological distances. These images
acquire additional phase-shifts as the over-focused wavefront passes through
itself along the line of sight. Time domain waveforms of Type-II images
(associated with saddle points of the time delay) exhibit a non-trivial
distortion from the unlensed waveforms. This phenomenon is in addition to the
usual frequency-independent magnification, and happens even in the geometric
limit where the wavelength is much shorter than the deflector's gravitational
length scale. Similarly, Type-III images preserve the original waveform's shape
but exhibit a sign flip. We show that for non-precessing binaries undergoing
circular inspiral and merger, these distortions are equivalent to rotating the
line of sight about the normal to the orbital plane by $45^\circ$ (Type II) and
$90^\circ$ (Type III). This effect will enable us to distinguish between the
different topological types among a set of multiple images, and give us
valuable insight into the lens model. Furthermore, we show that for eccentric
binaries, the waveform of a Type-II image is distorted in a manner that is
inequivalent to a change of the source's orbital parameters.


  In this work, we report the growth and functional characterizations of
multiferroic TbMnO3 thin films grown on Nb-doped SrTiO3 (001) substrates using
pulsed laser deposition. By performing detailed magnetic and ferroelectric
properties measurements, we demonstrate that the multiferroicity of spin origin
known in the bulk crystals can be successfully transferred to TbMnO3 thin
films. Meanwhile, anomalous magnetic transition and unusual magnetoelectric
coupling related to Tb moments are observed, suggesting a modified magnetic
configuration of Tb in the films as compared to the bulk counterpart. In
addition, it is found that the magnetoelectric coupling enabled by Tb moments
can even be seen far above the Tb spin ordering temperature, which provides a
larger temperature range for the magnetoelectric control involving Tb moments.


  Rapid growth and proliferation of cloud computing services around the world
has increased the necessity and significance of improving the energy efficiency
of could implementations. Virtual machines (VM) comprise the backend of most,
if not all, cloud computing services. Several VMs are often consolidated on a
physical machine to better utilize its resources. We take into account the
cooling and network structure of the datacenter hosting the physical machines
when consolidating the VMs so that fewer racks and routers are employed,
without compromising the service-level agreements, so that unused routing and
cooling equipment can be turned off to reduce energy consumption. Our
experimental results on four benchmarks shows that our technique improves
energy consumption of servers, network equipment, and cooling systems by 2.5%,
18.8%, and 28.2% respectively, resulting in a total of 14.7% improvement on
average in the entire datacenter.


  We study distinguished subalgebras and automorphisms of boundary quotients
arising from algebraic dynamical systems $(G,P,\theta)$. Our work includes a
complete solution to the problem of extending Bogolubov automorphisms from the
Cuntz algebra in $2 \leq p<\infty$ generators to the $p$-adic ring
$C^*$-algebra. For the case where $P$ is abelian and $C^*(G)$ is a maximal
abelian subalgebra, we establish a picture for the automorphisms of the
boundary quotient that fix $C^*(G)$ pointwise. This allows us to show that they
form a maximal abelian subgroup of the entire automorphism group. The picture
also leads to the surprising outcome that, for integral dynamics, every
automorphism that fixes one of the natural Cuntz subalgebras pointwise is
necessarily a gauge automorphism. Many of the automorphisms we consider are
shown to be outer.


  Quantum many-body control is a central milestone en route to harnessing
quantum technologies. However, the exponential growth of the Hilbert space
dimension with the number of qubits makes it challenging to classically
simulate quantum many-body systems and consequently, to devise reliable and
robust optimal control protocols. Here, we present a novel framework for
efficiently controlling quantum many-body systems based on reinforcement
learning (RL). We tackle the quantum control problem by leveraging matrix
product states (i) for representing the many-body state and, (ii) as part of
the trainable machine learning architecture for our RL agent. The framework is
applied to prepare ground states of the quantum Ising chain, including states
in the critical region. It allows us to control systems far larger than
neural-network-only architectures permit, while retaining the advantages of
deep learning algorithms, such as generalizability and trainable robustness to
noise. In particular, we demonstrate that RL agents are capable of finding
universal controls, of learning how to optimally steer previously unseen
many-body states, and of adapting control protocols on-the-fly when the quantum
dynamics is subject to stochastic perturbations. Furthermore, we map the QMPS
framework to a hybrid quantum-classical algorithm that can be performed on
noisy intermediate-scale quantum devices and test it under the presence of
experimentally relevant sources of noise.


  An agent acquires a costly flexible signal before making a decision. We
explore to what degree knowledge of the agent's information costs helps predict
her behavior. We establish an impossibility result: learning costs alone
generate no testable restrictions on choice without also imposing constraints
on actions' state-dependent utilities. By contrast, choices from a menu often
uniquely pin down the agent's decisions in all submenus. To prove the latter
result, we define iteratively differentiable cost functions, a tractable class
amenable to first-order techniques. Finally, we construct tight tests for a
multi-menu data set to be consistent with a given cost.


  Small perturbations of the Jacobi matrix with weights \sqrt n and zero
diagonal are considered. A formula relating the asymptotics of polynomials of
the first kind to the spectral density is obtained, which is analogue of the
classical Weyl-Titchmarsh formula for the Schroedinger operator on the
half-line with summable potential. Additionally a base of generalized
eigenvectors for "free" Hermite operator is studied and asymptotics of
Plancherel-Rotach type are obtained.


  Equalized odds, as a popular notion of algorithmic fairness, aims to ensure
that sensitive variables, such as race and gender, do not unfairly influence
the algorithm prediction when conditioning on the true outcome. Despite rapid
advancements, most of the current research focuses on the violation of
equalized odds caused by one sensitive attribute, leaving the challenge of
simultaneously accounting for multiple attributes under-addressed. We address
this gap by introducing a fairness learning approach that integrates
adversarial learning with a novel inverse conditional permutation. This
approach effectively and flexibly handles multiple sensitive attributes,
potentially of mixed data types. The efficacy and flexibility of our method are
demonstrated through both simulation studies and empirical analysis of
real-world datasets.


  We study numerically the maximal and minimal height distributions (MAHD,
MIHD) of the nonlinear interface growth equations of second and fourth order
and of related lattice models in two dimensions. MAHD and MIHD are different
due to the asymmetry of the local height distribution, so that, in each class,
the sign of the relevant nonlinear term determines which one of two universal
curves is the MAHD and the MIHD. The average maximal and minimal heights scale
as the average roughness, in contrast to Edwards-Wilkinson (EW) growth. All
extreme height distributions, including the EW ones, have tails that cannot be
fit by generalized Gumbel distributions.


  A general class of models is proposed that is able to estimate the whole
predictive distribution of a dependent variable $Y$ given a vector of
explanatory variables $\xb$. The models exploit that the strength of
explanatory variables to distinguish between low and high values of the
dependent variable may vary across the thresholds that are used to define low
and high. Simple linear versions of the models are generalizations of classical
linear regression models but also of widely used ordinal regression models.
They allow to visualize the effect of explanatory variables in the form of
parameter functions. More general models are based on efficient nonparametric
approaches like random forests, which are more flexible and are strong
prediction tools. A general estimation method is given that can use all the
estimation tools that have been proposed for binary regression, including
selection methods like the lasso or elastic net. For linearly structured models
maximum likelihood estimates are derived. The usefulness of the models is
illustrated by simulations and several real data set.


  Removing adverse weather conditions like rain, fog, and snow from images is
an important problem in many applications. Most methods proposed in the
literature have been designed to deal with just removing one type of
degradation. Recently, a CNN-based method using neural architecture search
(All-in-One) was proposed to remove all the weather conditions at once.
However, it has a large number of parameters as it uses multiple encoders to
cater to each weather removal task and still has scope for improvement in its
performance. In this work, we focus on developing an efficient solution for the
all adverse weather removal problem. To this end, we propose TransWeather, a
transformer-based end-to-end model with just a single encoder and a decoder
that can restore an image degraded by any weather condition. Specifically, we
utilize a novel transformer encoder using intra-patch transformer blocks to
enhance attention inside the patches to effectively remove smaller weather
degradations. We also introduce a transformer decoder with learnable weather
type embeddings to adjust to the weather degradation at hand. TransWeather
achieves improvements across multiple test datasets over both All-in-One
network as well as methods fine-tuned for specific tasks. TransWeather is also
validated on real world test images and found to be more effective than
previous methods. Implementation code can be accessed at
https://github.com/jeya-maria-jose/TransWeather .


  Automated disease detection in neuroimaging holds promise to improve the
diagnostic ability of radiologists, but routinely collected clinical data
frequently contains technical and demographic confounding factors that cause
data to both differ between sites and be systematically associated with the
disease of interest, thus negatively affecting the robustness of diagnostic
models. There is a critical need for diagnostic deep learning models that can
train on such imbalanced datasets without being influenced by these confounds.
In this work, we introduce a novel deep learning architecture, MUCRAN
(Multi-Confound Regression Adversarial Network), to train a deep learning model
on clinical brain MRI while regressing demographic and technical confounding
factors. We trained MUCRAN using 17,076 clinical T1 Axial brain MRIs collected
from Massachusetts General Hospital before 2019 and demonstrated that MUCRAN
could successfully regress major confounding factors in the vast clinical data.
We also applied a method for quantifying uncertainty across an ensemble of
these models to automatically exclude out-of-distribution data in the AD
detection. By combining MUCRAN and the uncertainty quantification method, we
showed consistent and significant increases in the AD detection accuracy for
newly collected MGH data (post-2019) and for data from other hospitals. MUCRAN
offers a generalizable approach for heterogenous clinical data for
deep-learning-based automatic disease detection.


  In this paper, we study the Schr\"odinger equation with a new quasi-exactly
solvable double-well potential. Exact expressions for the energies, the
corresponding wave functions and the allowed values of the potential parameters
are obtained using two different methods, the Bethe ansatz method and the Lie
algebraic approach. Some numerical results are reported and it is shown that
the results are in good agreement with each other and with those obtained
previously via a different method.


  In this paper, we prove Lorentzian positive mass theorem for spacetimes with
distributional curvature. To do so, we introduce distributional curvature and
generalized Arnowitt-Deser-Misner (ADM) momentum. As an application, we discuss
a junction of spacetimes.


  Starting from a Geometrothermodynamics metric for the space of thermodynamic
equilibrium states in the mass representation, we use numerical techniques to
analyse the thermodynamic geodesics of a supermassive Reissner Nordstr\"{o}m
black hole in isolation. Appropriate constraints are obtained by taking into
account the processes of Hawking radiation and Schwinger pair--production. We
model the black hole in line with the work of Hiscock and Weems. It can be
deduced that the relation which the geodesics establish between the entropy $S$
and electric charge $Q$ of the black hole extremises changes in the black
hole's mass. Indeed, the expression for the entropy of an extremal black hole
is an exact solution to the geodesic equation. We also find that in certain
cases, the geodesics describe the evolution brought about by the constant
emission of Hawking radiation and charged-particle pairs.


  This CAS talk describes the role of beam instrumentation and diagnostics in
particle therapy accelerators. It presents an extended view on instrumentation,
feedbacks, detector technology, quality assurance (QA) and their
interdependencies. Furthermore, some basics, examples and challenges in near
future concerning diagnostics and instrumentation techniques used in particle
therapy are reported.


  Real-life networks often encounter vertex dysfunctions, which are usually
followed by recoveries after appropriate maintenances. In this paper we present
our research on a model of scale-free networks whose vertices are regularly
removed and put back. Both the frequency and length of time of the
disappearance of each vertex depend on the degree of the vertex, creating a
heterogeneous control over the network. Our simulation results show very
interesting growth pattern of this kind of networks. We also find that the
scale-free property of the degree distribution is maintained in the proposed
heterogeneously controlled networks. However, the overall growth rate of the
networks in our model can be remarkably reduced if the inactive periods of the
vertices are kept long.


  Cold-start is a notoriously difficult problem which can occur in
recommendation systems, and arises when there is insufficient information to
draw inferences for users or items. To address this challenge, a contextual
bandit algorithm -- the Fast Approximate Bayesian Contextual Cold Start
Learning algorithm (FAB-COST) -- is proposed, which is designed to provide
improved accuracy compared to the traditionally used Laplace approximation in
the logistic contextual bandit, while controlling both algorithmic complexity
and computational cost. To this end, FAB-COST uses a combination of two moment
projection variational methods: Expectation Propagation (EP), which performs
well at the cold start, but becomes slow as the amount of data increases; and
Assumed Density Filtering (ADF), which has slower growth of computational cost
with data size but requires more data to obtain an acceptable level of
accuracy. By switching from EP to ADF when the dataset becomes large, it is
able to exploit their complementary strengths. The empirical justification for
FAB-COST is presented, and systematically compared to other approaches on
simulated data. In a benchmark against the Laplace approximation on real data
consisting of over $670,000$ impressions from autotrader.co.uk, FAB-COST
demonstrates at one point increase of over $16\%$ in user clicks. On the basis
of these results, it is argued that FAB-COST is likely to be an attractive
approach to cold-start recommendation systems in a variety of contexts.


  EPG graphs, introduced by Golumbic et al. in 2009, are edge-intersection
graphs of paths on an orthogonal grid. The class $B_k$-EPG is the subclass of
EPG graphs where the path on the grid associated to each vertex has at most $k$
bends. Epstein et al. showed in 2013 that computing a maximum clique in
$B_1$-EPG graphs is polynomial. As remarked in [Heldt et al., 2014], when the
number of bends is at least $4$, the class contains $2$-interval graphs for
which computing a maximum clique is an NP-hard problem. The complexity status
of the Maximum Clique problem remains open for $B_2$ and $B_3$-EPG graphs. In
this paper, we show that we can compute a maximum clique in polynomial time in
$B_2$-EPG graphs given a representation of the graph.
  Moreover, we show that a simple counting argument provides a
${2(k+1)}$-approximation for the coloring problem on $B_k$-EPG graphs without
knowing the representation of the graph. It generalizes a result of [Epstein et
al, 2013] on $B_1$-EPG graphs (where the representation was needed).


  Model-based diagnosis reasons backwards from a functional schematic of a
system to isolate faults given observations of anomalous behavior. We develop a
fully probabilistic approach to model based diagnosis and extend it to support
hierarchical models. Our scheme translates the functional schematic into a
Bayesian network and diagnostic inference takes place in the Bayesian network.
A Bayesian network diagnostic inference algorithm is modified to take advantage
of the hierarchy to give computational gains.


  Emotion detection is a critical technology extensively employed in diverse
fields. While the incorporation of commonsense knowledge has proven beneficial
for existing emotion detection methods, dialogue-based emotion detection
encounters numerous difficulties and challenges due to human agency and the
variability of dialogue content.In dialogues, human emotions tend to accumulate
in bursts. However, they are often implicitly expressed. This implies that many
genuine emotions remain concealed within a plethora of unrelated words and
dialogues.In this paper, we propose a Dynamic Causal Disentanglement Model
based on hidden variable separation, which is founded on the separation of
hidden variables. This model effectively decomposes the content of dialogues
and investigates the temporal accumulation of emotions, thereby enabling more
precise emotion recognition. First, we introduce a novel Causal Directed
Acyclic Graph (DAG) to establish the correlation between hidden emotional
information and other observed elements. Subsequently, our approach utilizes
pre-extracted personal attributes and utterance topics as guiding factors for
the distribution of hidden variables, aiming to separate irrelevant ones.
Specifically, we propose a dynamic temporal disentanglement model to infer the
propagation of utterances and hidden variables, enabling the accumulation of
emotion-related information throughout the conversation. To guide this
disentanglement process, we leverage the ChatGPT-4.0 and LSTM networks to
extract utterance topics and personal attributes as observed
information.Finally, we test our approach on two popular datasets in dialogue
emotion detection and relevant experimental results verified the model's
superiority.


  There are two fundamental obstructions to representing noncommutative rings
via sheaves. First, there is no subcanonical coverage on the opposite of the
category of rings that includes all covering families in the big Zariski site.
Second, there is no contravariant functor F from the category of rings to the
category of ringed categories whose composite with the global sections functor
is naturally isomorphic to the identity, such that F restricts to the Zariski
spectrum functor Spec on the category of commutative rings (in a compatible way
with the natural isomorphism). Both of these no-go results are proved by
restricting attention to matrix rings.


  Hazard assessment of storm tide is addressed for Southeast China coast in
this study. In particular, we pay attention to the scarcely discussed issue of
storm tide inundation. The main procedures of hazard assessment are: 1)
non-stationary tropical cyclone intensification (TCI) and sea level rise (SLR)
for the study area are analyzed based on the long-term historical database; 2)
four typical scenarios of storm tide are examined using the surge-tide-wave
coupled hydrodynamic model; 3) the potential inundated regions are identified
on the GIS platform. The distributions of water elevation show that high water
elevation tends to occur in the bays and around the estuaries. Without
considering the impacts of TCI and SLR, the maximal water elevations caused by
the typhoon wind of 100-year recurrence period can reach as high as 6.06 m,
5.82 m and 5.67 m around Aojiang, Feiyunjiang and Oujiang river estuaries,
respectively. In this circumstance, about 533 km2 area is under the threat of
storm tide inundation. TCI and SLR due to climate change can further
deteriorate the situation and enhance the risk of inundation there. The maximal
water elevations at the aforementioned three estuaries considering the
potential non-stationary TCI and SLR in 2100s can be as high as 7.02m, 6.67 m,
6.44m, respectively. The corresponding potential inundation area expands by 50%
compared with the situation without considering TCI and SLR. In addition, the
remotely sensed maps and inundation durations of the hardest hit regions are
provided as well.


  This is a contribution to MG9 session BHT4. Certain geometrically
distinguished frame on a non-expanding horizon and in its space-time
neighborhood, as well as the Bondi-like coordinates are constructed. The
construction provides free degrees of freedom, invariants, and the existence
conditions for a Killing vector field. The reported results come from the joint
works with Ashtekar and Beetle.


  We prove that for any contact 3-manifold supported by a spinal open book
decomposition with planar pages, there is a universal bound on the Euler
characteristic and signature of its minimal symplectic fillings. The proof is
an application of the spine removal surgery operation recently introduced in
joint work of the authors with Van Horn-Morris.


  Every irreducible component of the variety of semi-simple n-dimensional
representations of the modular group has a Zariski dense subset contained in
the image of an etale map from a rational quotient variety of representations
of a fixed quiver Q. As an application we prove that there is a unique
component of 6-dimensional representations of the three string braid group B(3)
able to detect braid-reversion. Further, we give explicit rational
parametrizations of dense families of simple B(3)-representations in all
dimensions < 12.


  Chemists and material scientists have often focused on the properties of
previously reported compounds, leaving out numerous unreported but chemically
plausible compounds that could have interesting properties. For example, the
18-valence electron ABX family of compounds includes the half-Heusler subgroup,
and features examples of topological insulators, thermoelectrics and
superconductors, but only 83 out of 483 of these possible compounds have been
made. Using first-principles thermodynamics we have examined the theoretical
stability of 400 unreported members and predict that 54 should be stable. 15
previously missing materials, now predicted stable, were grown in this study;
X-ray studies agreed with the predicted crystal structure in all 15 cases.
Among the characterized properties of the missing compounds are potential
transparent conductors, thermoelectric materials and topological semimetals.
This integrated process-prediction of functionality in unreported compounds
followed by laboratory synthesis and characterization-could be a route to the
systematic discovery of hitherto missing, realizable functional materials.


  The creativity of scientists often manifests as localized hot streaks of
significant success. Understanding the underlying mechanisms of these
influential phases can enhance the effectiveness of support systems and funding
allocation, fostering groundbreaking discoveries worthy of accolades.
Historically, analyses have suggested that hot streaks occur randomly over
time. However, our research, through meticulous examination, reveals that these
phases are not flatly distributed but are more frequent at the early and late
stages of scientists' careers. Notably, both early and late hot streaks are
marked by dense tie collaborations, with the former typically involving close
partnerships with particular authors and the latter being characterized by
involvement in large-scale projects compared with single-top or ordinary
papers. This pattern indicates that mid-career researchers lack both intimate
relations and resources to keep big projects, leading to``mid-career pitfal''
of consecutive success. This insight holds profound implications for the
development of policies and initiatives aimed at bolstering innovative research
and discovery.


  Nova eruptions, thermonuclear explosions on the surfaces of white dwarfs
(WDs), are now recognized to be among the most common shock-powered
astrophysical transients. We present the early discovery and rapid ultraviolet
(UV), optical, and infrared (IR) temporal development of AT 2019qyl, a recent
nova in the nearby Sculptor Group galaxy NGC 300. The light curve shows a rapid
rise lasting $\lesssim 1$ day, reaching a peak absolute magnitude of $M_V =
-9.2$ mag, and a very fast decline, fading by 2 mag over 3.5 days. A steep
dropoff in the light curves after 71 days and the rapid decline timescale
suggest a low-mass ejection from a massive WD with $M_{\rm WD} \gtrsim
1.2~M_{\odot}$. We present an unprecedented view of the early spectroscopic
evolution of such an event. Three spectra prior to the peak reveal a complex,
multicomponent outflow giving rise to internal collisions and shocks in the
ejecta of an He/N-class nova. We identify a coincident IR-variable counterpart
in the extensive preeruption coverage of the transient location and infer the
presence of a symbiotic progenitor system with an O-rich
asymptotic-giant-branch donor star, as well as evidence for an earlier
UV-bright outburst in 2014. We suggest that AT 2019qyl is analogous to the
subset of Galactic recurrent novae with red-giant companions such as RS Oph and
other embedded nova systems like V407 Cyg. Our observations provide new
evidence that internal shocks between multiple, distinct outflow components
likely contribute to the generation of the shock-powered emission from such
systems.


  Local (shearing box) simulations of the nonlinear evolution of the
magnetorotational instability in a collisionless plasma show that angular
momentum transport by pressure anisotropy ($p_\perp \ne p_\parallel$, where the
directions are defined with respect to the local magnetic field) is comparable
to that due to the Maxwell and Reynolds stresses. Pressure anisotropy, which is
effectively a large-scale viscosity, arises because of adiabatic invariants
related to $p_\perp$ and $p_\parallel$ in a fluctuating magnetic field. In a
collisionless plasma, the magnitude of the pressure anisotropy, and thus the
viscosity, is determined by kinetic instabilities at the cyclotron frequency.
Our simulations show that $\sim 50$ % of the gravitational potential energy is
directly converted into heat at large scales by the viscous stress (the
remaining energy is lost to grid-scale numerical dissipation of kinetic and
magnetic energy). We show that electrons receive a significant fraction ($\sim
[T_e/T_i]^{1/2}$) of this dissipated energy. Employing this heating by an
anisotropic viscous stress in one dimensional models of radiatively inefficient
accretion flows, we find that the radiative efficiency of the flow is greater
than 0.5% for $\dot{M} \gtrsim 10^{-4} \dot{M}_{Edd}$. Thus a low accretion
rate, rather than just a low radiative efficiency, is necessary to explain the
low luminosity of many accreting black holes. For Sgr A* in the Galactic
Center, our predicted radiative efficiencies imply an accretion rate of
$\approx 3 \times 10^{-8} M_\odot {\rm yr^{-1}}$ and an electron temperature of
$\approx 3 \times 10^{10}$ K at $\approx 10$ Schwarzschild radii; the latter is
consistent with the brightness temperature inferred from VLBI observations.


  Starting from a two-scale description involving receptor binding dynamics and
a kinetic transport equation for the evolution of the cell density function
under velocity reorientations, we deduce macroscopic models for glioma invasion
featuring partial differential equations for the mass density and momentum of a
population of glioma cells migrating through the anisotropic brain tissue. The
proposed first and higher order moment closure methods enable numerical
simulations of the kinetic equation. Their performance is then compared to that
of the diffusion limit. The approach allows for DTI-based, patient-specific
predictions of the tumor extent and its dynamic behavior.


  We describe an experiment using the Parkes radio telescope in the 1.2-1.5 GHz
frequency range as part of the LUNASKA project, to search for nanosecond-scale
pulses from particle cascades in the Moon, which may be triggered by
ultra-high-energy astroparticles. Through the combination of a highly sensitive
multi-beam radio receiver, a purpose-built backend and sophisticated
signal-processing techniques, we achieve sensitivity to radio pulses with a
threshold electric field strength of 0.0053 $\mu$V/m/MHz, lower than previous
experiments by a factor of three. We observe no pulses in excess of this
threshold in observations with an effective duration of 127 hours. The
techniques we employ, including compensating for the phase, dispersion and
spectrum of the expected pulse, are relevant for future lunar radio
experiments.


  In this paper we show a deterministic parallel all-pairs shortest paths
algorithm for real-weighted directed graphs. The algorithm has
$\tilde{O}(nm+(n/d)^3)$ work and $\tilde{O}(d)$ depth for any depth parameter
$d\in [1,n]$. To the best of our knowledge, such a trade-off has only been
previously described for the real-weighted single-source shortest paths problem
using randomization [Bringmann et al., ICALP'17]. Moreover, our result improves
upon the parallelism of the state-of-the-art randomized parallel algorithm for
computing transitive closure, which has $\tilde{O}(nm+n^3/d^2)$ work and
$\tilde{O}(d)$ depth [Ullman and Yannakakis, SIAM J. Comput. '91]. Our APSP
algorithm turns out to be a powerful tool for designing efficient planar graph
algorithms in both parallel and sequential regimes.
  One notable ingredient of our parallel APSP algorithm is a simple
deterministic $\tilde{O}(nm)$-work $\tilde{O}(d)$-depth procedure for computing
$\tilde{O}(n/d)$-size hitting sets of shortest $d$-hop paths between all pairs
of vertices of a real-weighted digraph. Such hitting sets have also been called
$d$-hub sets. Hub sets have previously proved especially useful in designing
parallel or dynamic shortest paths algorithms and are typically obtained via
random sampling. Our procedure implies, for example, an $\tilde{O}(nm)$-time
deterministic algorithm for finding a shortest negative cycle of a
real-weighted digraph. Such a near-optimal bound for this problem has been so
far only achieved using a randomized algorithm [Orlin et al., Discret. Appl.
Math. '18].


  The molecular framework Ag(tcm) (tcm$^-$ = tricyanomethanide) expands
continuously in two orthogonal directions under hydrostatic compression. The
first of its kind, this negative area compressibility behaviour arises from the
flattening of honeycomb-like layers during rapid pressure-driven collapse of
the interlayer separation.


  The localized delivery of new long-lived species to Jupiter's stratosphere by
comet Shoemaker-Levy 9 in 1994 opened a window to constrain Jovian chemistry
and dynamics by monitoring the evolution of their vertical and horizontal
distributions. However, the spatial distributions of CO and HCN, two of these
long-lived species, had never been jointly observed at high latitudinal
resolution. Atacama large millimeter/submillimeter array observations of HCN
and CO in March 2017 show that CO was meridionally uniform and restricted to
pressures lower than 3 $\pm$ 1 mbar. HCN shared a similar vertical distribution
in the low- to mid-latitudes, but was depleted at pressures between 2$^{+2}_
{-1}$ and 0.04$^{+0.07}_{-0.03}$ mbar in the aurora and surrounding regions,
resulting in a drop by two orders of magnitude in column density. We propose
that heterogeneous chemistry bonds HCN on large aurora-produced aerosols at
these pressures in the Jovian auroral regions causing the observed depletion.


  Computations related to learning processes within an organizational social
network area require some network model preparation and specific algorithms in
order to implement human behaviors in simulated environments. The proposals in
this research model of collaborative learning in an organizational social
network are based on knowledge resource distribution through the establishment
of a knowledge flow. The nodes, which represent knowledge workers, contain
information about workers social and cognitive abilities. Moreover, the workers
are described by their set of competences, their skill level, and the
collaborative learning behavior that can be detected through knowledge flow
analysis. The proposed approach assumes that an increase in workers competence
is a result of collaborative learning. In other words, collaborative learning
can be analyzed as a process of knowledge flow that is being broadcast in a
network. In order to create a more effective organizational social network for
co-learning, the authors found the best strategies for knowledge facilitator,
knowledge collector, and expert roles allocation. Special attention is paid to
the process of knowledge flow in the community of practice. Acceleration within
the community of practice happens when knowledge flows more effectively between
community members. The presented procedure makes it possible to add new ties to
the community of practice in order to influence community members competences.
Both the proposed allocation and acceleration approaches were confirmed through
simulations.


  Disjoint sampling is critical for rigorous and unbiased evaluation of
state-of-the-art (SOTA) models. When training, validation, and test sets
overlap or share data, it introduces a bias that inflates performance metrics
and prevents accurate assessment of a model's true ability to generalize to new
examples. This paper presents an innovative disjoint sampling approach for
training SOTA models on Hyperspectral image classification (HSIC) tasks. By
separating training, validation, and test data without overlap, the proposed
method facilitates a fairer evaluation of how well a model can classify pixels
it was not exposed to during training or validation. Experiments demonstrate
the approach significantly improves a model's generalization compared to
alternatives that include training and validation data in test data. By
eliminating data leakage between sets, disjoint sampling provides reliable
metrics for benchmarking progress in HSIC. Researchers can have confidence that
reported performance truly reflects a model's capabilities for classifying new
scenes, not just memorized pixels. This rigorous methodology is critical for
advancing SOTA models and their real-world application to large-scale land
mapping with Hyperspectral sensors.
  The source code is available at
https://github.com/mahmad00/Disjoint-Sampling-for-Hyperspectral-Image-Classification.


  We study Euclidean lattice formulations of non-gauge supersymmetric models
with up to four supercharges in various dimensions. We formulate the conditions
under which the interacting lattice theory can exactly preserve one or more
nilpotent anticommuting supersymmetries. We introduce a superfield formalism,
which allows the enumeration of all possible lattice supersymmetry invariants.
We use it to discuss the formulation of Q-exact lattice actions and their
renormalization in a general manner. In some examples, one exact supersymmetry
guarantees finiteness of the continuum limit of the lattice theory. As a
consequence, we show that the desired quantum continuum limit is obtained
without fine tuning for these models. Finally, we discuss the implications and
possible further applications of our results to the study of gauge and
non-gauge models.


  We obtain the Plancherel theorem for the quotient of a simple Lie group of
real rank one by a convex-cocompact discrete subgroup and its consequences for
the spectrum of locally invariant differential operators on bundles over
Kleinian manifolds. We develop a geometric version of scattering theory. The
paper is an update of dg-ga/9609011, which is completely rewritten from the
point of view of representation theory. In particular, we are now able to show
the meromorphy of Eisenstein series for all parameters.


  Semantic Segmentation is essential to make self-driving vehicles autonomous,
enabling them to understand their surroundings by assigning individual pixels
to known categories. However, it operates on sensible data collected from the
users' cars; thus, protecting the clients' privacy becomes a primary concern.
For similar reasons, Federated Learning has been recently introduced as a new
machine learning paradigm aiming to learn a global model while preserving
privacy and leveraging data on millions of remote devices. Despite several
efforts on this topic, no work has explicitly addressed the challenges of
federated learning in semantic segmentation for driving so far. To fill this
gap, we propose FedDrive, a new benchmark consisting of three settings and two
datasets, incorporating the real-world challenges of statistical heterogeneity
and domain generalization. We benchmark state-of-the-art algorithms from the
federated learning literature through an in-depth analysis, combining them with
style transfer methods to improve their generalization ability. We demonstrate
that correctly handling normalization statistics is crucial to deal with the
aforementioned challenges. Furthermore, style transfer improves performance
when dealing with significant appearance shifts. Official website:
https://feddrive.github.io.


  Rare-earth doped nanocrystals are emerging light sources used for many
applications in nanotechnology enabled by human ability to control their
various optical properties with chemistry and material science. However, one
important optical problem -- polarisation of photoluminescence -- remains
largely out of control by chemistry methods. Control over photoluminescence
polarisation can be gained via coupling of emitters to resonant nanostructures
such as optical antennas and metasurfaces. However, the resulting polarization
is typically sensitive to position disorder of emitters, which is difficult to
mitigate. Recently, new classes of disorder-immune optical systems have been
explored within the framework of topological photonics. Here we explore
disorder-robust topological arrays of Mie-resonant nanoparticles for
polarisation control of photoluminescence of nanocrystals. We demonstrate
polarized emission from rare-earth-doped nanocrystals governed by photonic
topological edge states supported by zigzag arrays of dielectric resonators. We
verify the topological origin of polarised photoluminescence by comparing
emission from nanoparticles coupled to topologically trivial and nontrivial
arrays of nanoresonators.


  Long-form clinical summarization of hospital admissions has real-world
significance because of its potential to help both clinicians and patients. The
faithfulness of summaries is critical to their safe usage in clinical settings.
To better understand the limitations of abstractive systems, as well as the
suitability of existing evaluation metrics, we benchmark faithfulness metrics
against fine-grained human annotations for model-generated summaries of a
patient's Brief Hospital Course. We create a corpus of patient hospital
admissions and summaries for a cohort of HIV patients, each with complex
medical histories. Annotators are presented with summaries and source notes,
and asked to categorize manually highlighted summary elements (clinical
entities like conditions and medications as well as actions like "following
up") into one of three categories: ``Incorrect,'' ``Missing,'' and ``Not in
Notes.'' We meta-evaluate a broad set of proposed faithfulness metrics and,
across metrics, explore the importance of domain adaptation (e.g. the impact of
in-domain pre-training and metric fine-tuning), the use of source-summary
alignments, and the effects of distilling a single metric from an ensemble of
pre-existing metrics. Off-the-shelf metrics with no exposure to clinical text
correlate well yet overly rely on summary extractiveness. As a practical guide
to long-form clinical narrative summarization, we find that most metrics
correlate best to human judgments when provided with one summary sentence at a
time and a minimal set of relevant source context.


  We have studied the lower critical fields H_{c1} of superconducting iron
oxipnictide PrFeAsO_{1-y} single crystals for H parallel and perpendicular to
the ab-planes. Measurements of the local magnetic induction at positions
straddling the sample edge by using a miniature Hall-sensor array clearly
resolve the first flux penetration from the Meissner state. The temperature
dependence of H_{c1} for H || c is well scaled by the in-plane penetration
depth without showing any unusual behavior, in contrast to previous reports.
The anisotropy of penetration lengths at low temperatures is estimated to be ~
2.5, which is much smaller than the anisotropy of the coherence lengths. This
is indicative of multiband superconductivity in this system, in which the
active band for superconductivity is more anisotropic. We also point out that
the local induction measured at a position near the center of the crystal,
which has been used in a number of reports for the determination of H_{c1},
might seriously overestimate the obtained H_{c1}-value.


  Typically, Hall coefficient of materials near magnetic instabilities exhibits
pronounced temperature dependence. To explore the reasons involved, we studied
the temperature dependence of Hall coefficient in $Cr_{1-x}V_x$, $V_{2-y}O_3$
and some high-$T_c$ superconducting cuprates. We argue that it can be
rationalized using the following unifying principle:\textit{ When a system is
near a magnetic instability and temperature is reduced towards the instability,
there is a progressive "loss" of carriers (progressive "tying down" of
electrons) as they participate in long-lived and long-ranged magnetic
correlations.} In other words, magnetic correlations grow in space and are
longer-lived as temperature is reduced towards the magnetic instability. This
is the mechanism behind reduced carrier density with reducing temperature and
leads to an enhancement of the Hall coefficient. This unifying principle is
implemented and quantitative analysis is done using the Gor'kov Teitel'baum
Thermal Activation (GTTA) model. We also show that the Hall angle data can be
understood using one relaxation time (in contrast to the "two-relaxation" times
idea of Anderson) by taking into consideration of temperature dependence of
carrier density. This unifying principle is shown to be working in above
studied systems, but authors believe that it is of much more general validity.


  We construct optimal protocols for verifying qubit and qudit GHZ states using
local projective measurements. When the local dimension is a prime, an optimal
protocol is constructed from Pauli measurements only. Our protocols provide a
highly efficient way for estimating the fidelity and certifying genuine
multipartite entanglement. In particular, they enable the certification of
genuine multipartite entanglement using only one test when the local dimension
is sufficiently large. By virtue of adaptive local projective measurements, we
then construct protocols for verifying GHZ-like states that are optimal over
all protocols based on one-way communication. The efficiency can be improved
further if additional communications are allowed. Finally, we construct optimal
protocols for verifying GHZ states and nearly optimal protocols for GHZ-like
states in the adversarial scenario.


  Hypomimia is a condition in the early stages of the progression of Parkinsons
disease that limits the movement of facial muscles, restricting the accurate
depiction of facial expressions. Also known as facial masking, this condition
is an early symptom of Parkinson's disease, a neurodegenerative disorder that
affects movement. To date, no specific test exists to diagnose the disease.
Instead, doctors rely on the patient medical history and symptoms to confirm
the onslaught of the disease, delaying treatment. This study aims to develop a
diagnostic tool for Parkinsons disease utilizing the Facial Action Coding
System, a comprehensive system describing all facially discernible movement. In
addition, this project generates image datasets by simulating faces or action
unit sets for both Parkinsons patients and non-affected individuals through
coding. Accordingly, the model is trained using supervised learning and neural
networks. The model efficiency is compared using Convolutional Neural Network
and Vision Transformer models and later tested with a test program by passing a
Parkinsons disease-affected image as input. The future goal is to develop a
self-determining mobile application (PDiagnosis) by utilizing the model to
determine the probability of Parkinsons progression.


  Iterative parallel-in-time algorithms like Parareal can extend scaling beyond
the saturation of purely spatial parallelization when solving initial value
problems. However, they require the user to build coarse models to handle the
inevitably serial transport of information in time.This is a time consuming and
difficult process since there is still only limited theoretical insight into
what constitutes a good and efficient coarse model. Novel approaches from
machine learning to solve differential equations could provide a more generic
way to find coarse level models for parallel-in-time algorithms. This paper
demonstrates that a physics-informed Fourier Neural Operator (PINO) is an
effective coarse model for the parallelization in time of the two-asset
Black-Scholes equation using Parareal. We demonstrate that PINO-Parareal
converges as fast as a bespoke numerical coarse model and that, in combination
with spatial parallelization by domain decomposition, it provides better
overall speedup than both purely spatial parallelization and space-time
parallelizaton with a numerical coarse propagator.


  We study a set of linear transformations on the Fourier series representation
of a sequence that can be used as the basis for similarity queries on
time-series data. We show that our set of transformations is rich enough to
formulate operations such as moving average and time warping. We present a
query processing algorithm that uses the underlying R-tree index of a
multidimensional data set to answer similarity queries efficiently. Our
experiments show that the performance of this algorithm is competitive to that
of processing ordinary (exact match) queries using the index, and much faster
than sequential scanning. We relate our transformations to the general
framework for similarity queries of Jagadish et al.


  Different variants of the code-based McEliece cryptosystem were pro- posed to
reduce the size of the public key. All these variants use very structured
codes, which open the door to new attacks exploiting the underlying structure.
In this paper, we show that the dyadic variant can be designed to resist all
known attacks. In light of a new study on list decoding algorithms for binary
Goppa codes, we explain how to increase the security level for given public
keysizes. Using the state-of-the-art list decoding algorithm instead of unique
decoding, we exhibit a keysize gain of about 4% for the standard McEliece
cryptosystem and up to 21% for the adjusted dyadic variant.


  We determine the di-electron widths of the Upsilon(1S), Upsilon(2S), and
Upsilon(3S) resonances with better than 2% precision by integrating the
cross-section of e+e- -> Upsilon over the e+e- center-of-mass energy. Using
e+e- energy scans of the Upsilon resonances at the Cornell Electron Storage
Ring and measuring Upsilon production with the CLEO detector, we find
di-electron widths of 1.354 +- 0.004 (stat) +- 0.020 (syst) keV, 0.619 +- 0.004
+- 0.010 keV, and 0.446 +- 0.004 +- 0.007 keV for the Upsilon(1S), Upsilon(2S),
and Upsilon(3S), respectively.


  We consider the Eschenauer-Gligor key predistribution scheme under the
condition of partial visibility with i.i.d. on-off links between pairs of
nodes. This situation is modeled as the intersection of two random graphs,
namely a random key graph and an Erd\H{o}s-R\'enyi (ER) graph. For this class
of composite random graphs we give various improvements on a recent result by
Ya\u{g}an [IEEE Transactions on Information Theory, 58(6):3821-3835, 2012]
concerning zero-one laws for the absence of isolated nodes.


  We describe cyclic vectors for a Pommiez operator on a weighted (LF)-space E
of entire functions. The full description is obtained where $E$ is the Laplace
transform of the strong dual of the space of all germs of holomorphic functions
on a convex locally closed set in the complex plane.


  Arborescent knots are the ones which can be represented in terms of double
fat graphs or equivalently as tree Feynman diagrams. This is the class of knots
for which the present knowledge is enough for lifting topological description
to the level of effective analytical formulas. The paper describes the origin
and structure of the new tables of colored knot polynomials, which will be
posted at the dedicated site. Even if formal expressions are known in terms of
modular transformation matrices, the computation in finite time requires
additional ideas. We use the "family" approach, and apply it to arborescent
knots in the Rolfsen table by developing a Feynman diagram technique associated
with an auxiliary matrix model field theory. Gauge invariance in this theory
helps to provide meaning to Racah matrices in the case of non-trivial
multiplicities and explains the need for peculiar sign prescriptions in the
calculation of [21]-colored HOMFLY polynomials.