  Emotional expressions form a key part of user behavior on today's digital
platforms. While multimodal emotion recognition techniques are gaining research
attention, there is a lack of deeper understanding on how visual and non-visual
features can be used to better recognize emotions in certain contexts, but not
others. This study analyzes the interplay between the effects of multimodal
emotion features derived from facial expressions, tone and text in conjunction
with two key contextual factors: i) gender of the speaker, and ii) duration of
the emotional episode. Using a large public dataset of 2,176 manually annotated
YouTube videos, we found that while multimodal features consistently
outperformed bimodal and unimodal features, their performance varied
significantly across different emotions, gender and duration contexts.
Multimodal features performed particularly better for male speakers in
recognizing most emotions. Furthermore, multimodal features performed
particularly better for shorter than for longer videos in recognizing neutral
and happiness, but not sadness and anger. These findings offer new insights
towards the development of more context-aware emotion recognition and
empathetic systems.


  We discuss the zero temperature phase diagram of a dilute gas with three
fermionic species. We make use of solvable limits to conjecture the behavior of
the system in the "unitary" regions. The physics of the Thomas-Efimov effect
plays a role in these considerations. We find a rich phase diagram with
superfluid, gapless superfluid and inhomogeneous phases with different symmetry
breaking patterns. We then discuss one particular possible experimental
implementation in a system of ^6Li atoms and the possible phases arising in
this system as an external magnetic field is varied across three overlaping
Feshbach resonances. We also suggest how to experimentally distinguish the
different phases.


  The extraordinarily long outburst of GRS 1915+105 makes it one of the most
remarkable low-mass X-ray binaries (LMXBs). It has been in a state of constant
outburst since its discovery in 1992, an eruption which has persisted ~100
times longer than those of more typical LXMBs. The long orbital period of GRS
1915+105 implies that it contains large and massive accretion disc which is
able to fuel its extreme outburst. In this paper, we address the longevity of
the outburst and quiescence phases of GRS 1915+105 using Smooth Particle
Hydrodynamics (SPH) simulations of its accretion disc through many outburst
cycles. Our model is set in the two-alpha framework and includes the effects of
the thermo-viscous instability, tidal torques, irradiation by central X-rays
and wind mass loss. We explore the model parameter space and the examine the
impact of the various ingredients. We predict that the outburst of GRS 1915+105
should last a minimum of 20 years and possibly up to ~100 years if X-ray
irradiation is very significant. The predicted recurrence times are of the
order of 10^4 years, making the X-ray duty cycle a few 0.1%. Such a low duty
cycle may mean that GRS 1915+105 is not an anomaly among the more standard
LMXBs and that many similar, but quiescent, systems could be present in the
Galaxy.


  We couple a free solute diffusion model to a model of crystal surface growth
represented by, but not limited to, a (2 + 1)-dimensional solid-on-solid (SOS)
model confined by a flat surface. We use kinetic Monte Carlo (KMC) with
dissolution rates based on nearest-neighbor interactions to solve the Master
equation for the surface dynamics, and we use an offlattice random walk to
model the Fickian diffusion of the solute particles. The two solvers are
coupled directly through deposition rates of the free particles calculated
using the mean first passage time (MFPT) of deposition that is found to scale
as $r^{-4}$. Two variants are studied: ignoring (radial) and not ignoring the
line of sight (pathfinding). Reference models such as uniform concentration
(random deposition) and lattice diffusion (crystal lattice extended into the
liquid) are used for comparison. We find that the macroscopic limit of the
surface dynamics is reproduced by all models. The free diffusion models produce
a lower equilibrium roughness and a smaller height autocorrelation length than
the reference models, and are found to behave very well in tight confinements.
It is also demonstrated that lattice diffusion does not work well in tight
confinements. The two MFPT models behave very similarly close to equilibrium
and for dissolution, but becomes increasingly different with increasing surface
growth speed. The model is put to use by simulating a cavity with a flux
boundary condition at one side. The conclusion is that the new model excels in
confinement, and line of sight can in practice be ignored since the dominant
deposition sites likely are in line of sight, which minimizes the CPU-time
needed in the coupling.


  Classes of (p,q)-deformations of the Jaynes-Cummings model in the rotating
wave approximation are considered. Diagonalization of the Hamiltonian is
performed exactly, leading to useful spectral decompositions of a series of
relevant operators. The latter include ladder operators acting between adjacent
energy eigenstates within two separate infinite discrete towers, except for a
singleton state. These ladder operators allow for the construction of
(p,q)-deformed vector coherent states. Using (p,q)-arithmetics, explicit and
exact solutions to the associated moment problem are displayed, providing new
classes of coherent states for such models. Finally, in the limit of decoupled
spin sectors, our analysis translates into (p,q)-deformations of the
supersymmetric harmonic oscillator, such that the two supersymmetric sectors
get intertwined through the action of the ladder operators as well as in the
associated coherent states.


  Free energy calculations in molecular simulations have a variety of
applications including determining the strength of molecular processes such as
solvation and binding. It has been recently shown that when removing the VDW
and Coulomb potential terms of a group of atoms in a molecule by performing a
transformation, the molecule can be treated as non interacting systems in the
free energy calculation. This treatment is applicable both when the molecule is
in vacuum and in liquid and enables a very simple calculation of the free
energies associated with the potentials that depend on the relative spherical
coordinates of these atoms. Here we demonstrate the method in the free energy
calculation of a Methanethiol molecule and compare the results to these
obtained by MD simulations in vacuum and in water. The comparison shows
agreement between the results and faster computation when using the method by
factors varying between 5000 and 10^(12) for the same computational resources.


  Future asymmetric e+e- --> Upsilon(4S) B factories are being constructed to
search for CP violation in B0 decays. It is hoped that a CP asymmetry may be
observed in B0-B0bar mixing in analogy with that found in neutral kaons. In
this mixing measurement, B0 decays to CP eigenstates, such as the rare decay B0
psi Kshort, may provide information on the CKM angle sin(2beta). I discuss
decay additional CP eigenstate branching ratios measured by the CLEO experiment
that may be used by future B factories to measure sin(2beta).


  In the modeling of biological systems by Boolean networks a key problem is
finding the set of fixed points of a given network. Some constructed algorithms
consider certain structural properties of the interaction graph like those
proposed by Akutsu et al. in \cite{akutsu1998system,zhang2007algorithms} which
consider a feedback vertex set of the graph. However, these methods do not take
into account the type of action (activation, inhibition) between its
components.
  In this paper we propose a new algorithm for finding the set of fixed points
of a Boolean network, based on a positive feedback vertex set $P$ of its
interaction graph and which works, by applying a sequential update schedule, in
time $O(2^{|P|} \cdot n^2)$, where $n$ is the number of components. The
theoretical foundation of this algorithm is due a nice characterization, that
we give, of the dynamical behavior of the Boolean networks without positive
cycles and with a fixed point.
  An executable file of \Afp made in Java and some examples of input files are
available at:
\href{http://www.inf.udec.cl/~lilian/FPCollector/}{\url{www.inf.udec.cl/~lilian/FPCollector/}}


  In this two part series, we present a contact model able to capture the
response of interacting adhesive elastic-perfectly plastic particles under a
variety of loadings. In Part I, we focus on elastic through fully-plastic
contact with and without adhesion. For these contact regimes the model is built
upon the method of dimensionality reduction which allows the problem of a 3D
axisymmetric contact to be mapped to a semi-equivalent problem of a 1D rigid
indenter penetrating a bed of independent Hookean springs. Plasticity is
accounted for by continuously varying the 1D indenter profile subject to a
constraint on the contact pressure. Unloading falls out naturally, and simply
requires lifting the 1D indenter out of the springs and tracking the force. By
accounting for the incompressible nature of this plastic deformation, the
contact model is able to capture multi-neighbor dependent effects such as
increased force and formation of new contacts. JKR type adhesion is recovered
seamlessly within the method of dimensionality reduction by simply allowing the
springs to stick to the 1D indenter's surface. Because of the mechanics-focused
formulation of the contact model, only a few physical inputs describing the
interacting particles are needed: particle radius, Young's modulus, Poisson
ratio, yield stress, and effective surface energy. The contact model is
validated against finite element simulations and analytic theory, including
Hertz's contact law and the JKR theory of adhesion. These comparisons show that
the proposed contact model is able to accurately capture plastic displacement,
average contact pressure, contact area, and force as a function of displacement
for contacts as well as particle volume within the elastic to fully-plastic
regimes.


  We conjecture that there exists a relationship between Lyapunov exponents and
black hole phase transitions. To support our conjecture, Lyapunov exponents of
the motion of particles and ring strings are calculated for
Reissner-Nordstr\"{o}m-AdS black holes. When a phase transition occurs, the
Lyapunov exponents become multivalued, and branches of the Lyapunov exponents
coincide with black hole phases. Moreover, the discontinuous change in the
Lyapunov exponents can be treated as an order parameter, and has a critical
exponent of $1/2$ near the critical point. Our findings reveal that Lyapunov
exponents can be an efficient tool to study phase structure of black holes.


  We consider reaction-diffusion equations and Korteweg-de Vries-Burgers (KdVB)
equations, i.e. scalar conservation laws with diffusive-dispersive
regularization. We review the existence of traveling wave solutions for these
two classes of evolution equations. For classical equations the traveling wave
problem (TWP) for a local KdVB equation can be identified with the TWP for a
reaction-diffusion equation. In this article we study this relationship for
these two classes of evolution equations with nonlocal diffusion/dispersion.
This connection is especially useful, if the TW equation is not studied
directly, but the existence of a TWS is proven using one of the evolution
equations instead. Finally, we present three models from fluid dynamics and
discuss the TWP via its link to associated reaction-diffusion equations.


  We have studied the effect of gravitational lensing on the Cosmic Microwave
Background (CMB) anisotropy in flat and open universes. We develop a formalism
to calculate the changes on the radiation power spectrum induced by lensing in
the Newtonian and synchronous-comoving gauges. The previously considered
negligible contribution to the CMB radiation power spectrum of the anisotropic
term of the lensing correlation is shown to be appreciable. However,
considering the nonlinear evolution of the matter power spectrum produces only
slight differences on the results based on linear evolution. The general
conclusion for flat as well as open universes is that lensing slightly smoothes
the radiation power spectrum. For a given range of multipoles the effect of
lensing increases with Omega but for the same acoustic peak it decreases with
$\Omega$. The maximum contribution of lensing to the radiation power spectrum
for $l\leq 2000$ is $\sim$ 5% for $\Omega$ values in the range 0.1-1.


  Transformer-based methods have demonstrated excellent performance on
super-resolution visual tasks, surpassing conventional convolutional neural
networks. However, existing work typically restricts self-attention computation
to non-overlapping windows to save computational costs. This means that
Transformer-based networks can only use input information from a limited
spatial range. Therefore, a novel Hybrid Multi-Axis Aggregation network (HMA)
is proposed in this paper to exploit feature potential information better. HMA
is constructed by stacking Residual Hybrid Transformer Blocks(RHTB) and Grid
Attention Blocks(GAB). On the one side, RHTB combines channel attention and
self-attention to enhance non-local feature fusion and produce more attractive
visual results. Conversely, GAB is used in cross-domain information interaction
to jointly model similar features and obtain a larger perceptual field. For the
super-resolution task in the training phase, a novel pre-training method is
designed to enhance the model representation capabilities further and validate
the proposed model's effectiveness through many experiments. The experimental
results show that HMA outperforms the state-of-the-art methods on the benchmark
dataset. We provide code and models at https://github.com/korouuuuu/HMA.


  Preliminary results for the spectra of excited and exotic bottom mesons are
presented. The calculation on a dynamical anisotropic ensemble exploits
distillation, enabling the use of a large basis of interpolating operators
including those proportional to the gluonic field strength which are relevant
for hybrid states. A comparison of results with similar calculations in the
light and charm sectors is discussed.


  A numerical calculation of properties of finite absorbers and of
intergalactic medium based on photoionization equilibrium is performed to
confront alternative UV sources in addition to quasars. It is seen that a
spectrum including a large peak around the HI ionization energy due to decaying
neutrinos is too soft in the region up to the HeI edge to explain the
relatively small observed ratio of neutral He and H densities in Lyman-limit
systems if their size is of the kpc order. The recently proposed decrease of
the contribution from unstable neutrinos solves this problem but tends to spoil
the consistence between IGM and Lyman-$\alpha$ clouds, which requires a large
ratio of fluxes for the HI and HeII ionization frequencies, unless there is a
very fast decline of quasars above $z = 3$. On the other hand, the addition of
stars to quasars may produce a spectrum sufficiently hard between HI and HeI
and thereafter soft up to HeII to allow a reasonable agreement of the
properties of denser absorbers with those of IGM. This model seems to favour
cold dark matter with additional cosmological constant.


  We propose an experimental realization of a time crystal using an atomic
Bose-Einstein condensate in a high finesse optical cavity pumped with laser
light detuned to the blue side of the relevant atomic resonance. By mapping out
the dynamical phase diagram, we identify regions in parameter space showing
stable limit cycle dynamics. Since the model describing the system is
time-independent, the emergence of a limit cycle phase indicates the breaking
of continuous time translation symmetry. Employing a semiclassical analysis to
demonstrate the robustness of the limit cycles against perturbations and
quantum fluctuations, we establish the emergence of a time crystal.


  In this study, we investigate interstellar absorption lines along the line of
sight toward the galactic low-mass X-ray binary Cygnus X-2. We combine
absorption line data obtained from high-resolution X-ray spectra collected with
Chandra and XMM-Newton satellites, along with Far-UV absorption lines observed
by the Hubble Space Telescope's (HST) Cosmic Origins Spectrograph (COS)
Instrument. Our primary objective is to understand the abundance and depletion
of oxygen, iron, sulfur, and carbon. To achieve this, we have developed an
analysis pipeline that simultaneously fits both the UV and X-ray datasets. This
novel approach takes into account the line spread function (LSF) of HST/COS,
enhancing the precision of our results. We examine the absorption lines of
FeII, SII, CII, and CI present in the FUV spectrum of Cygnus X-2, revealing the
presence of at least two distinct absorbers characterized by different
velocities. Additionally, we employ Cloudy simulations to compare our findings
concerning the ionic ratios for the studied elements. We find that gaseous iron
and sulfur exist in their singly ionized forms, Fe II and S II, respectively,
while the abundances of CII and CI do not agree with the Cloudy simulations of
the neutral ISM. Finally, we explore discrepancies in the X-ray atomic data of
iron and discuss their impact on the overall abundance and depletion of iron.


  Recent advances in diffusion-based generative modeling have led to the
development of text-to-video (T2V) models that can generate high-quality videos
conditioned on a text prompt. Most of these T2V models often produce
single-scene video clips that depict an entity performing a particular action
(e.g., 'a red panda climbing a tree'). However, it is pertinent to generate
multi-scene videos since they are ubiquitous in the real-world (e.g., 'a red
panda climbing a tree' followed by 'the red panda sleeps on the top of the
tree'). To generate multi-scene videos from a pretrained T2V model, we
introduce Time-Aligned Captions (TALC) framework. Specifically, we enhance the
text-conditioning mechanism in the T2V architecture to recognize the temporal
alignment between the video scenes and scene descriptions. As a result, we show
that the pretrained T2V model can generate multi-scene videos that adhere to
the multi-scene text descriptions and be visually consistent (e.g., w.r.t
entity and background). Our TALC-finetuned model outperforms the baseline
methods on multi-scene video-text data by 15.5 points on aggregated score,
averaging visual consistency and text adherence using human evaluation. The
project website is https://talc-mst2v.github.io/.


  It was recently shown [7, 9] that "properly built" linear and polyhedral
estimates nearly attain minimax accuracy bounds in the problem of recovery of
unknown signal from noisy observations of linear images of the signal when the
signal set is an ellitope. However, design of nearly optimal estimates relies
upon solving semidefinite optimization problems with matrix variables, what
puts the synthesis of such estimates beyond the rich of the standard Interior
Point algorithms of semidefinite optimization even for moderate size recovery
problems. Our goal is to develop First Order Optimization algorithms for the
computationally efficient design of linear and polyhedral estimates. In this
paper we (a) explain how to eliminate matrix variables, thus reducing
dramatically the design dimension when passing from Interior Point to First
Order optimization algorithms and (2) develop and analyse a dedicated algorithm
of the latter type -- Composite Truncated Level method.


  Physics-informed machine learning (PIML) as a means of solving partial
differential equations (PDE) has garnered much attention in the Computational
Science and Engineering (CS&E) world. This topic encompasses a broad array of
methods and models aimed at solving a single or a collection of PDE problems,
called multitask learning. PIML is characterized by the incorporation of
physical laws into the training process of machine learning models in lieu of
large data when solving PDE problems. Despite the overall success of this
collection of methods, it remains incredibly difficult to analyze, benchmark,
and generally compare one approach to another. Using Kolmogorov n-widths as a
measure of effectiveness of approximating functions, we judiciously apply this
metric in the comparison of various multitask PIML architectures. We compute
lower accuracy bounds and analyze the model's learned basis functions on
various PDE problems. This is the first objective metric for comparing
multitask PIML architectures and helps remove uncertainty in model validation
from selective sampling and overfitting. We also identify avenues of
improvement for model architectures, such as the choice of activation function,
which can drastically affect model generalization to "worst-case" scenarios,
which is not observed when reporting task-specific errors. We also incorporate
this metric into the optimization process through regularization, which
improves the models' generalizability over the multitask PDE problem.


  We consider the kinematics of bi-partite quantum states as determined by
observable quantities, in particular the Bloch vectors of the subsystems. In
examining the simplest case of a pair of two-level systems, there is a
remarkable connection between the presence of non-classical correlations and
the chirality of the two bases generated by the singular value decomposition of
the correlation matrix of the Bloch vectors. We investigate the limits imposed
by quantum mechanics of this effect and it relationship with other methods on
quantifying the system's non-classical behaviour.


  In pervasive computing environments, Location- Based Services (LBSs) are
becoming increasingly important due to continuous advances in mobile networks
and positioning technologies. Nevertheless, the wide deployment of LBSs can
jeopardize the location privacy of mobile users. Consequently, providing
safeguards for location privacy of mobile users against being attacked is an
important research issue. In this paper a new scheme for safeguarding location
privacy is proposed. Our approach supports location K-anonymity for a wide
range of mobile users with their own desired anonymity levels by clustering.
The whole area of all users is divided into clusters recursively in order to
get the Minimum Bounding Rectangle (MBR). The exact location information of a
user is replaced by his MBR. Privacy analysis shows that our approach can
achieve high resilience to location privacy threats and provide more privacy
than users expect. Complexity analysis shows clusters can be adjusted in real
time as mobile users join or leave. Moreover, the clustering algorithms possess
strong robustness.


  We propose a framework of genetic algorithms which use multi-level
hierarchies to solve an optimization problem by searching over the space of
simpler objective functions. We solve a variant of Travelling Salesman Problem
called \texttt{soft-TSP} and show that when the constraints on the overall
objective function are changed the algorithm adapts to churn out solutions for
the changed objective. We use this idea to speed up learning by systematically
altering the constraints to find a more globally optimal solution. We also use
this framework to solve polynomial regression where the actual objective
function is unknown but searching over space of available objective functions
yields a good approximate solution.


  We study Majorana zero energy modes (MZEM) that occur in a s-wave
superconducting surface, at the ends of a ferromagnetic (FM) chain of adatoms,
in the presence of Rashba spin-orbit interaction (SOI) considering both non
self-consistent and self-consistent superconducting order. We find that in the
self-consistent solution the average superconducting gap function over the
adatom sites has a discontinuous drop with increasing exchange interaction at
the same critical value where the topological phase transition occurs. We also
study the MZEM for both treatments of superconducting order and find that the
decay length is a linear function of the exchange coupling strength, chemical
potential and superconducting order. For wider FM chains the MZEM occur at
smaller exchange couplings and the slope of the decay length as a function of
exchange coupling grows with chain width. Thus we suggest experimental
detection of different delocalization of MZEM in chains of varying widths. We
discuss similarities and differences between the MZEM for the two treatments of
the superconducting order.


  We investigate general frameworks for calculating transport coefficients for
quasiparticle theories at finite temperature. Hadronic transport coefficients
are then computed using the linear sigma model (LSM). The bulk viscosity over
entropy density ($\zeta/s$) is evaluated in the relaxation time approximation
(RTA) and the specific shear viscosity ($\eta/s$) and static electrical
conductivity ($\sigma_{el}/T$) are both obtained in the RTA and using a
functional variational approach. Results are shown for different values of the
scalar-isoscalar hadron vacuum mass with in-medium masses for the interacting
fields. The advantages and limitations of the LSM for studies of strongly
interacting matter out of equilibrium are discussed and results are compared
with others in the literature.


  Ultracold atoms in optical lattices have proven to provide an extremely clean
and controlled setting to explore quantum many-body phases of matter. Now,
imaging of atoms in such lattice structures has reached the level of
single-atom sensitive detection combined with the highest resolution down to
the level of individual lattice sites. This has opened up fundamentally new
opportunities for the characterization and the control of quantum many-body
systems. Here we give a brief overview of this field and explore the
opportunities offered for future research.


  Differentially private stochastic gradient descent (DP-SGD) refers to a
family of optimization algorithms that provide a guaranteed level of
differential privacy (DP) through DP accounting techniques. However, current
accounting techniques make assumptions that diverge significantly from
practical DP-SGD implementations. For example, they may assume the loss
function is Lipschitz continuous and convex, sample the batches randomly with
replacement, or omit the gradient clipping step.
  In this work, we analyze the most commonly used variant of DP-SGD, in which
we sample batches cyclically with replacement, perform gradient clipping, and
only release the last DP-SGD iterate. More specifically - without assuming
convexity, smoothness, or Lipschitz continuity of the loss function - we
establish new R\'enyi differential privacy (RDP) bounds for the last DP-SGD
iterate under the mild assumption that (i) the DP-SGD stepsize is small
relative to the topological constants in the loss function, and (ii) the loss
function is weakly-convex. Moreover, we show that our bounds converge to
previously established convex bounds when the weak-convexity parameter of the
objective function approaches zero. In the case of non-Lipschitz smooth loss
functions, we provide a weaker bound that scales well in terms of the number of
DP-SGD iterations.


  In this paper, we formulate a regular $q$-fractional Sturm--Liouville problem
(qFSLP) which includes the left-sided Riemann--Liouville and the right-sided
Caputo $q$-fractional derivatives of the same order $\alpha$, $\alpha\in
(0,1)$. We introduce the essential $q$-fractional variational analysis needed
in proving the existence of a countable set of real eigenvalues and associated
orthogonal eigenfunctions for the regular qFSLP when $\alpha>1/2$ associated
with the boundary condition $y(0)=y(a)=0$. A criteria for the first eigenvalue
is proved. Examples are included. These results are a generalization of the
integer regular $q$-Sturm--Liouville problem introduced by Annaby and Mansour
in [1].


  Open domain Question Answering (QA) systems must interact with external
knowledge sources, such as web pages, to find relevant information. Information
sources like Wikipedia, however, are not well structured and difficult to
utilize in comparison with Knowledge Bases (KBs). In this work we present a
two-step approach to question answering from unstructured text, consisting of a
retrieval step and a comprehension step. For comprehension, we present an RNN
based attention model with a novel mixture mechanism for selecting answers from
either retrieved articles or a fixed vocabulary. For retrieval we introduce a
hand-crafted model and a neural model for ranking relevant articles. We achieve
state-of-the-art performance on W IKI M OVIES dataset, reducing the error by
40%. Our experimental results further demonstrate the importance of each of the
introduced components.


  Eye movements during reading offer insights into both the reader's cognitive
processes and the characteristics of the text that is being read. Hence, the
analysis of scanpaths in reading have attracted increasing attention across
fields, ranging from cognitive science over linguistics to computer science. In
particular, eye-tracking-while-reading data has been argued to bear the
potential to make machine-learning-based language models exhibit a more
human-like linguistic behavior. However, one of the main challenges in modeling
human scanpaths in reading is their dual-sequence nature: the words are ordered
following the grammatical rules of the language, whereas the fixations are
chronologically ordered. As humans do not strictly read from left-to-right, but
rather skip or refixate words and regress to previous words, the alignment of
the linguistic and the temporal sequence is non-trivial. In this paper, we
develop Eyettention, the first dual-sequence model that simultaneously
processes the sequence of words and the chronological sequence of fixations.
The alignment of the two sequences is achieved by a cross-sequence attention
mechanism. We show that Eyettention outperforms state-of-the-art models in
predicting scanpaths. We provide an extensive within- and across-data set
evaluation on different languages. An ablation study and qualitative analysis
support an in-depth understanding of the model's behavior.


  We report measurements of the temperature dependence of the magnetic
penetration depth \lambda(T) in non-centrosymmetric superconductor Re_3W. We
employed two experimental techniques: extraction of \lambda(T) from magnetic
{\em dc}-susceptibility, measured on a powder sample, and the rf tunnel diode
resonator technique, where a bulk polycrystalline sample was used. The results
of both techniques agree: the temperature dependence of the penetration depth
can be well described by weak-coupling, dirty-limit, s-wave BCS theory where we
obtain $\Delta(0)/k_BT_C=1.76$. No evidence for unconventional pairing
resulting from the absence of the inversion symmetry is found.


  In this work, we consider an advanced noise reduction differential chaotic
shift keying (NR-DCSK) system in which a single antenna source communicates
with a single antenna destination under the attack of a single antenna jammer.
We devote our efforts to design a novel anti-jamming (AJ) receiver for the
considered system. Particularly, we propose a variational mode
decomposition-independent component analysis-wavelet packet decomposition-based
(VMD-ICA-WPD-based) structure, in which the VMD method is firstly exploited to
generate multiple signals from the single received one. Secondly, the ICA
method is applied to coarsely separate chaotic and jamming signals. After that,
the WPD method is used to finely estimate and mitigate jamming signals that
exist on all outputs of the ICA method. Finally, an inverse ICA procedure is
carried out, followed by a summation, and the outcome is passed through the
conventional correlation-based receiver for recovering the transmitted
information. Simulation results show that the proposed receiver provides
significant system performance enhancement compared to that given by the
conventional correlation-based receiver with WPD, i.e. 8 dB gain at BER =0.03
and Eb/N0 = 20 dB.


  Factor analysis acts a pivotal role in enhancing maritime safety. Most
previous studies conduct factor analysis within the framework of
incident-related label prediction, where the developed models can be
categorized into short-term and long-term prediction models. The long-term
models offer a more strategic approach, enabling more proactive risk
management, compared to the short-term ones. Nevertheless, few studies have
devoted to rigorously identifying the key factors for the long-term prediction
and undertaking comprehensive factor analysis. Hence, this study aims to delve
into the key factors for predicting the incident risk levels in the subsequent
year given a specific datestamp. The majority of candidate factors potentially
contributing to the incident risk are collected from vessels' historical safety
performance data spanning up to five years. An improved embedded feature
selection, which integrates Random Forest classifier with a feature filtering
process is proposed to identify key risk-contributing factors from the
candidate pool. The results demonstrate superior performance of the proposed
method in incident prediction and factor interpretability. Comprehensive
analysis is conducted upon the key factors, which could help maritime
stakeholders formulate management strategies for incident prevenion.


  Mechanical and shape memory properties of a polycrystalline Ni2.18Mn0.82Ga
alloy prepared by a PDS (Pulse Discharge Sintering) method were investigated.
It was found that the material demonstrates the two-way shape memory effect
after a loading - unloading cycle performed in the martensitic state, i. e.
essentially without special training. The samples exhibiting the two-way shape
memory effect show a significant enhancement in the magnitude of
magnetic-field-induced strain.


  Distance metric learning is a successful way to enhance the performance of
the nearest neighbor classifier. In most cases, however, the distribution of
data does not obey a regular form and may change in different parts of the
feature space. Regarding that, this paper proposes a novel local distance
metric learning method, namely Local Mahalanobis Distance Learning (LMDL), in
order to enhance the performance of the nearest neighbor classifier. LMDL
considers the neighborhood influence and learns multiple distance metrics for a
reduced set of input samples. The reduced set is called as prototypes which try
to preserve local discriminative information as much as possible. The proposed
LMDL can be kernelized very easily, which is significantly desirable in the
case of highly nonlinear data. The quality as well as the efficiency of the
proposed method assesses through a set of different experiments on various
datasets and the obtained results show that LDML as well as the kernelized
version is superior to the other related state-of-the-art methods.


  We model Alzheimer's disease (AD) progression by combining differential
equations (DEs) and reinforcement learning (RL) with domain knowledge. DEs
provide relationships between some, but not all, factors relevant to AD. We
assume that the missing relationships must satisfy general criteria about the
working of the brain, for e.g., maximizing cognition while minimizing the cost
of supporting cognition. This allows us to extract the missing relationships by
using RL to optimize an objective (reward) function that captures the above
criteria. We use our model consisting of DEs (as a simulator) and the trained
RL agent to predict individualized 10-year AD progression using baseline (year
0) features on synthetic and real data. The model was comparable or better at
predicting 10-year cognition trajectories than state-of-the-art learning-based
models. Our interpretable model demonstrated, and provided insights into,
"recovery/compensatory" processes that mitigate the effect of AD, even though
those processes were not explicitly encoded in the model. Our framework
combines DEs with RL for modelling AD progression and has broad applicability
for understanding other neurological disorders.


  Electronic fluids can display exciting dynamical properties. In particular,
due to Landau damping, the collective modes spectrum of an electronic system
with multipolar interactions is non-hermitian, and can present non-hermitian
degeneracies called $\textit{exceptional points}$. In this work, we want to
explore the dynamical properties of these degeneracies using laser control. We
show that by using a light pulse, we can control the collective mode spectrum
and tune a non-hermitian $\mathcal{PT}$ phase transition in which two
exceptional points anhilate each other. At this transition, the gap closes with
a cubic root signature, what defines a third order exceptional point.


  We present the analysis of baryonic and non-baryonic matter distributions in
a sample of eleven nearby clusters (0.03<z<0.09) with temperatures between 4.4
and 9.4 keV. These galaxy clusters have been studied in detail using X-ray data
and global physical properties have been determined. Correlations between these
quantities have been analysed and compared with the results for distant
clusters. We found an interesting dependence between the relative gas extent
(expressed as the ratio of gas mass fractions at different radii) and the total
cluster mass. The extent of the gas relative to the extent of the dark matter
tends to be larger in less massive clusters. This dependence might give us some
hints about non-gravitational processes in clusters.


  In this article, well-posedness of stochastic anisotropic $p$-Laplace
equation driven by L\'evy noise is shown. Such an equation in deterministic
setting was considered by Lions [7]. The results obtained in this article can
be applied to solve a large class of semilinear and quasilinear stochastic
partial differential equations.


  This paper presents an anthropomorphic robotic bear for the exploration of
human-robot interaction including verbal and non-verbal communications. This
robot is implemented with a hybrid face composed of a mechanical faceplate with
10 DOFs and an LCD-display-equipped mouth. The facial emotions of the bear are
designed based on the description of the Facial Action Coding System as well as
some animal-like gestures described by Darwin. The mouth movements are realized
by synthesizing emotions with speech. User acceptance investigations have been
conducted to evaluate the likability of these facial behaviors exhibited by the
eBear. Multiple Kernel Learning is proposed to fuse different features for
recognizing user's facial expressions. Our experimental results show that the
developed Bear-Like robot can perceive basic facial expressions and provide
emotive conveyance towards human beings.


  For ordinary modular forms, there are two constructions of a p-adic
L-function attached to the non-unit root of the Hecke polynomial, which are
conjectured but not known to coincide. We prove this conjecture for modular
forms of CM type, by calculating the the critical-slope L-function arising from
Kato's Euler system and comparing this with results of Bellaiche on the
critical-slope L-function defined using overconvergent modular symbols.


  Statistical analysis of Faraday Rotation Measure (RM) maps of the
intracluster medium (ICM) of galaxy clusters provides a unique tool to evaluate
some spatial features of the magnetic fields there. Its combination with
numerical simulations of magnetohydrodynamic (MHD) turbulence allows the
diagnosis of the ICM turbulence. Being the ICM plasma weakly collisional, the
thermal velocity distribution of the particles naturally develops anisotropies
as a consequence of the large scale motions and the conservation of the
magnetic moment of the charged particles. A previous study (Paper I) analyzed
the impact of large scale thermal anisotropy on the statistics of RM maps
synthesized from simulations of turbulence; these simulations employed a
collisionless MHD model which considered a tensor pressure with uniform
anisotropy. In the present work, we extend that analysis to a collisionless MHD
model in which the thermal anisotropy develops according to the conservation of
the magnetic moment of the thermal particles. We also consider the effect of
anisotropy relaxation caused by the micro-scale mirror and firehose
instabilities. We show that if the relaxation rate is fast enough to keep the
anisotropy limited by the threshold values of the instabilities, the dispersion
and power spectrum of the RM maps are indistinguishable from those obtained
from collisional MHD. Otherwise, there is a reduction in the dispersion and
steepening of the power spectrum of the RM maps (compared to the collisional
case). Considering the first scenario, the use of collisional MHD simulations
for modeling the RM statistics in the ICM becomes better justified.


  Preclinical magnetic resonance imaging often requires the entire body of an
animal to be imaged with sufficient quality. This is usually performed by
combining regions scanned with small coils with high sensitivity or long scans
using large coils with low sensitivity. Here, a metamaterial-inspired design
employing of a parallel array of wires operating on the principle of eigenmode
hybridization is used to produce a small animal whole-body imaging coil. The
coil field distribution responsible for the coil field of view and sensitivity
is simulated in an electromagnetic simulation package and the coil geometrical
parameters are optimized for the chosen application. A prototype coil is then
manufactured and assembled using brass telescopic tubes and copper plates as
distributed capacitance, its field distribution is measured experimentally
using B1+ mapping technique and found to be in close correspondence with
simulated results. The coil field distribution is found to be suitable for
whole-body small animal imaging and coil image quality is compared with a
number of commercially available coils by whole-body living mice scanning.
Signal to noise measurements in living mice show outstanding coil performance
compared to commercially available coils with large receptive fields, and
rivaling performance compared to small receptive field and high-sensitivity
coils. The coil is deemed suitable for whole-body small animal preclinical
applications.


  In this paper, we provide algorithms to rank and unrank certain
degree-restricted classes of Cayley trees (spanning trees of the n-vertex
complete graph). Specifically, we consider classes of trees that have a given
set of leaves or a fixed number k of leaves. For fixed k, the number of Cayley
trees with n vertices and k leaves grows roughly as n! and hence the ranks have
O(nlog_2(n)) bits. Our ranking and unranking algorithms require at most O(n^2)
comparisons of numbers less than or equal to n plus O(n) operations of
multiplication, division, addition, substraction and comparision on numbers of
length O(nlog(n)).


  We demonstrate that the spin-2 Affleck-Kennedy-Lieb-Tasaki (AKLT) state on
the square lattice is a universal resource for the measurement-based quantum
computation. Our proof is done by locally converting the AKLT to
two-dimensional random planar graph states and by certifying that with high
probability the resulting random graphs are in the supercritical phase of
percolation using Monte Carlo simulations. One key enabling point is the exact
weight formula that we derive for arbitrary measurement outcomes according to a
spin-2 POVM on all spins. We also argue that the spin-2 AKLT state on the
three-dimensional diamond lattice is a universal resource, the advantage of
which would be the possibility of implementing fault-tolerant quantum
computation with topological protection. In addition, as we deform the AKLT
Hamiltonian, there is a finite region that the ground state can still support a
universal resource before making a transition in its quantum computational
power.


  We present new cooling models for carbon-oxygen white dwarfs with both H- and
He-atmospheres, covering the whole relevant mass range, to extend our updated
BaSTI (a Bag of Stellar Tracks and Isochrones) stellar evolution archive. They
have been computed using core chemical stratifications obtained from new
progenitor calculations, adopting a semiempirical initial-final mass relation.
The physics inputs have been updated compared to our previous BaSTI
calculations: ^{22}Ne diffusion in the core is now included, together with an
updated CO phase diagram, and updated electron conduction opacities. We have
calculated models with various different neon abundances in the core, suitable
to study white dwarfs in populations with metallicities ranging from
super-solar to metal poor, and have performed various tests/comparisons of the
chemical stratification and cooling times of our models. Two complete sets of
calculations are provided, for two different choices of the electron conduction
opacities, to reflect the current uncertainty in the evaluation of the electron
thermal conductivity in the transition regime between moderate and strong
degeneracy, crucial for the H and He envelopes. We have also made a first,
preliminary estimate of the effect -- that turns out to be generally small --
of Fe sedimentation on the cooling times of white dwarf models, following
recent calculations of the phase diagrams of carbon-oxygen-iron mixtures.
  We make publicly available the evolutionary tracks from both sets of
calculations, including cooling times and magnitudes in the Johnson-Cousins,
Sloan, Pan-STARSS, Galex, Gaia-DR2, Gaia-eDR3, HST-ACS, HST-WFC3, and JWST
photometric systems.


  We are concerned with solutions of the following quasilinear Schr\"odinger
equations \begin{eqnarray*} -{\mathrm{div}}\left(\varphi^{2}(u) \nabla
u\right)+\varphi(u) \varphi^{\prime}(u)|\nabla u|^{2}+\lambda u=f(u), \quad x
\in \mathbb{R}^{N} \end{eqnarray*} with prescribed mass $$
\int_{\mathbb{R}^{N}} u^{2} \mathrm{d}x=c, $$ where $N\ge 3, c>0$, $\lambda \in
\mathbb{R}$ appears as the Lagrange multiplier and $\varphi\in C
^{1}(\mathbb{R} ,\mathbb{R}^{+})$. The nonlinearity $f \in C\left ( \mathbb{R},
\, \mathbb{R} \right )$ is allowed to be mass-subcritical, mass-critical and
mass-supercritical at origin and infinity. Via a dual approach, the fixed point
index and a global branch approach, we establish the existence of normalized
solutions to the problem above. The results extend previous results by L.
Jeanjean, J. J. Zhang and X.X. Zhong to the quasilinear case.


  At modern construction sites, utilizing GNSS (Global Navigation Satellite
System) to measure the real-time location and orientation (i.e. pose) of
construction machines and navigate them is very common. However, GNSS is not
always available. Replacing GNSS with on-board cameras and visual simultaneous
localization and mapping (visual SLAM) to navigate the machines is a
cost-effective solution. Nevertheless, at construction sites, multiple
construction machines will usually work together and side-by-side, causing
large dynamic occlusions in the cameras' view. Standard visual SLAM cannot
handle large dynamic occlusions well. In this work, we propose a motion
segmentation method to efficiently extract static parts from crowded dynamic
scenes to enable robust tracking of camera ego-motion. Our method utilizes
semantic information combined with object-level geometric constraints to
quickly detect the static parts of the scene. Then, we perform a two-step
coarse-to-fine ego-motion tracking with reference to the static parts. This
leads to a novel dynamic visual SLAM formation. We test our proposals through a
real implementation based on ORB-SLAM2, and datasets we collected from real
construction sites. The results show that when standard visual SLAM fails, our
method can still retain accurate camera ego-motion tracking in real-time.
Comparing to state-of-the-art dynamic visual SLAM methods, ours shows
outstanding efficiency and competitive result trajectory accuracy.


  Classical simplicial cohomology on a simplicial complex G deals with
functions on simplices x in G. Quadratic cohomology deals with functions on
pairs of simplices (x,y) in G x G that intersect. If K,U is a closed-open pair
in G, we prove here a quadratic version of the linear fusion inequality.
Additional to the quadratic cohomology of G there are five additional
interaction cohomology groups. Their Betti numbers are computed from functions
on pairs (x,y) of simplices that intersect. Define the Betti vector b(X)
computed from pairs (x,y) in X x X with x intersected y in X a and b(X,Y) with
pairs in X xY with x intersected y in K. We prove the fusion inequality b(G) <=
b(K)+b(U)+b(K,U)+b(U,K)+b(U,U) for cohomology groups linking all five possible
interaction cases. Counting shows f(G) = f(K)+f(U) + f(K,U)+f(U,K)+f(U,U) for
the f-vectors. Super counting gives Euler-Poincare sum_k (-1)^k f_k(X)=\sum_k
(-1)^k b_k(X) and sum_k (-1)^k f_k(X,Y)=sum_k (-1)^k b_k(X,Y) for X,Y in {U,K}.
As in the linear case, also the proof of the quadratic fusion inequality
follows from the fact that the spectra of all the involved Laplacians
L(X),L(X,Y) are bounded above by the spectrum of the quadratic Hodge Laplacian
L(G) of G.


  Hom-Lie algebras are non-associative algebras generalizing Lie algebras by
twisting the Jacobi identity by a linear map. In this paper, we mainly study
the irreducible representation of the twisted Heisenberg-Virasoro algebra of
Hom-type, which can be induced by the irreducible representations of its
induced Lie algebra. In particular, we construct some kinds of irreducible
representations of the twisted Heisenberg-Virasoro algebra of Hom-type.


  Prior methods for retrieval of nearest neighbors in high dimensions are fast
and approximate--providing probabilistic guarantees of returning the correct
answer--or slow and exact performing an exhaustive search. We present Certified
Cosine, a novel approach to nearest-neighbors which takes advantage of
structure present in the cosine similarity distance metric to offer
certificates. When a certificate is constructed, it guarantees that the nearest
neighbor set is correct, possibly avoiding an exhaustive search. Certified
Cosine's certificates work with high dimensional data and outperform previous
exact nearest neighbor methods on these datasets.


  In chiral magnetic materials, numerous intriguing phenomena such as built in
chiral magnetic domain walls (DWs) and skyrmions are generated by the
Dzyaloshinskii Moriya interaction (DMI). The DMI also results in asymmetric DW
speed under in plane magnetic field, which provides a useful scheme to measure
the DMI strengths. However, recent findings of additional asymmetries such as
chiral damping have disenabled unambiguous DMI determination and the underlying
mechanism of overall asymmetries becomes under debate. By extracting the
DMI-induced symmetric contribution, here we experimentally investigated the
nature of the additional asymmetry. The results revealed that the additional
asymmetry has a truly antisymmetric nature with the typical behavior governed
by the DW chirality. In addition, the antisymmetric contribution changes the DW
speed more than 100 times, which cannot be solely explained by the chiral
damping scenario. By calibrating such antisymmetric contributions, experimental
inaccuracies can be largely removed, enabling again the DMI measurement scheme.


  Study of stochastic differential equations on the field of p-adic numbers was
initiated by the second author and has been developed by the first author, who
proved several results for the p-adic case, similar to the theory of ordinary
stochastic integral with respect to Levy processes on the Euclidean spaces. In
this article, we present an improved definition of a stochastic integral on the
field and prove the joint (time and space) continuity of the local time for
p-adic stable processes. Then we use the method of random time change to obtain
sufficient conditions for the existence of a weak solution of a stochastic
differential equation on the field, driven by the p-adic stable process, with a
Borel measurable coefficient.


  We derive an expression for the phase shift of an atom interferometer in a
gravitational field taking into account both the finite duration of the light
pulses and the effect of a small perturbing potential added to a stronger
uniform gravitational field, extending the well-known results for rectangular
pulses and at most quadratic potentials. These refinements are necessary for a
correct analysis of present day high resolution interferometers.


  The prediction of the three-dimensional structures of the native state of
proteins from the sequences of their amino acids is one of the most important
challenges in molecular biology. An essential ingredient to solve this problem
within coarse-grained models is the task of deducing effective interaction
potentials between the amino acids. Over the years several techniques have been
developed to extract potentials that are able to discriminate satisfactorily
between the native and non-native folds of a pre-assigned protein sequence. In
general, when these potentials are used in actual dynamical folding
simulations, they lead to a drift of the native structure outside the
quasi-native basin. In this study, we present and validate an approach to
overcome this difficulty. By exploiting several numerical and analytical tools
we set up a rigorous iterative scheme to extract potentials satisfying a
pre-requisite of any viable potential: the stabilization of proteins within
their native basin (less than 3-4 \AA$ $ cRMS). The scheme is flexible and is
demonstrated to be applicable to a variety of parametrizations of the energy
function and provides, in each case, the optimal potentials.


  The project Massive Unseen Companions to Hot Faint Underluminous Stars from
SDSS (MUCHFUSS) aims at finding sdBs with compact companions like supermassive
white dwarfs (M>1.0 Msun), neutron stars or black holes. The existence of such
systems is predicted by binary evolution theory and recent discoveries indicate
that they are likely to exist in our Galaxy. A determination of the orbital
parameters is sufficient to put a lower limit on the companion mass by
calculating the binary mass function. If this lower limit exceeds the
Chandrasekhar mass and no sign of a companion is visible in the spectra, the
existence of a massive compact companion is proven without the need for any
additional assumptions. We identified about 1100 hot subdwarf stars from the
SDSS by colour selection and visual inspection of their spectra. Stars with
high velocities have been reobserved and individual SDSS spectra have been
analysed. In total 127 radial velocity variable subdwarfs have been discovered.
Binaries with high RV shifts and binaries with moderate shifts within short
timespans have the highest probability of hosting massive compact companions.
Atmospheric parameters of 69 hot subdwarfs in these binary systems have been
determined by means of a quantitative spectral analysis. The atmospheric
parameter distribution of the selected sample does not differ from previously
studied samples of hot subdwarfs. The systems are considered the best
candidates to search for massive compact companions by follow-up time resolved
spectroscopy.


  QCD-based thermodynamics at zero and finite quark chemical potential is
studied using an extended Nambu and Jona-Lasinio approach in which quarks
couple simultaneously to the chiral condensate and to a background temporal
gauge field representing Polyakov loop dynamics. This so-called PNJL model thus
includes features of both deconfinement and chiral symmetry restoration. We
discuss the phase diagram as it emerges from this approach in close comparison
with results from lattice QCD thermodynamics. The critical point, separating
crossover from first order phase transition, is investigated with special focus
on its quark mass dependence, starting from the relatively large masses
presently accessible by lattice simulations, down to the chiral limit.


  We give an explicit combinatorial Chevalley-type formula for the equivariant
K-theory of generalized flag varieties G/P which is a direct generalization of
the classical Chevalley formula. Our formula implies a simple combinatorial
model for the characters of the irreducible representations of G and, more
generally, for the Demazure characters. This model can be viewed as a discrete
counterpart of the Littelmann path model, and has several advantages. Our
construction is given in terms of a certain R-matrix, that is, a collection of
operators satisfying the Yang-Baxter equation. It reduces to combinatorics of
decompositions in the affine Weyl group and enumeration of saturated chains in
the Bruhat order on the (nonaffine) Weyl group. Our model easily implies
several symmetries of the coefficients in the Chevalley-type formula. We also
derive a simple formula for multiplying an arbitrary Schubert class by a
divisor class, as well as a dual Chevalley-type formula. The paper contains
other applications and examples.


  For $0<p<\infty $ and $\alpha >-1$ the space of Dirichlet type $\mathcal
D^p_\alpha $ consists of those functions $f$ which are analytic in the unit
disc $\mathbb D$ and satisfy $\int_{\mathbb D}(1-| z| )^\alpha| f^\prime (z)|
^p\,dA(z)<\infty $. The space $\Dp$ is the closest one to the Hardy space $H^p$
among all the $\mathcal D^p_\alpha $. Our main object in this paper is studying
similarities and differences between the spaces $H^p$ and $\Dp$ ($0<p<\infty $)
regarding superposition operators. Namely, for $0<p<\infty $ and $0<s<\infty $,
we characterize the entire functions $\varphi $ such that the superposition
operator $S_\varphi $ with symbol $\varphi $ maps the conformally invariant
space $Q_s$ into the space $\Dp$, and, also, those which map $\Dp$ into $Q_s$
and we compare these results with the corresponding ones with $H^p$ in the
place of $\Dp$. We also study the more general question of characterizing the
superposition operators mapping $\mathcal D^p_\alpha $ into $Q_s$ and $Q_s$
into $\mathcal D^p_\alpha $, for any admissible triplet of numbers $(p, \alpha
, s)$.


  Brane world models have shown to be promising to understand the late cosmic
acceleration, in particular because such acceleration can be naturally derived,
mimicking the dark energy behaviour just with a five dimensional geometry. In
this paper we present a strong lensing joint analysis using a compilation of
early-type galaxies acting as a lenses, united with the power of the well
studied strong lensing galaxy cluster Abell\,1689. We use the strong lensing
constraints to investigate a brane model with variable brane tension as a
function of the redshift. In our joint analysis we found a value $n =
7.8^{+0.9}_{-0.5}$, for the exponent related to the brane tension, showing that
$n$ deviates from a Cosmological Constant (CC) scenario (n=6). We obtain a
value for the deceleration parameter, $q(z)$ today, $q(0)=-1.2^{+0.6}_{-0.8}$,
and a transition redshift, $z_t=0.60\pm0.06$ (when the Universe change from an
decelerated phase to an accelerated one). These results are in contrast with
previous work that favors CC scenario, nevertheless our lensing analysis is in
agreement with a formerly reported conclusion suggesting that the variable
brane tension model is able to source a late cosmic acceleration without an
extra fluid as in the standard one.


  This work explores the use of constant-Q transform based modulation spectral
features (CQT-MSF) for speech emotion recognition (SER). The human perception
and analysis of sound comprise of two important cognitive parts: early auditory
analysis and cortex-based processing. The early auditory analysis considers
spectrogram-based representation whereas cortex-based analysis includes
extraction of temporal modulations from the spectrogram. This temporal
modulation representation of spectrogram is called modulation spectral feature
(MSF). As the constant-Q transform (CQT) provides higher resolution at emotion
salient low-frequency regions of speech, we find that CQT-based spectrogram,
together with its temporal modulations, provides a representation enriched with
emotion-specific information. We argue that CQT-MSF when used with a
2-dimensional convolutional network can provide a time-shift invariant and
deformation insensitive representation for SER. Our results show that CQT-MSF
outperforms standard mel-scale based spectrogram and its modulation features on
two popular SER databases, Berlin EmoDB and RAVDESS. We also show that our
proposed feature outperforms the shift and deformation invariant scattering
transform coefficients, hence, showing the importance of joint hand-crafted and
self-learned feature extraction instead of reliance on complete hand-crafted
features. Finally, we perform Grad-CAM analysis to visually inspect the
contribution of constant-Q modulation features over SER.


  It is shown that Einstein gravity tends to modify the electric and magnetic
fields appreciably at distances of the order of the Compton wavelength. At that
distance the gravitational field becomes spin dominated rather than mass
dominated. The gravitational field couples to the electromagnetic field via the
Einstein-Maxwell equations which in the simplest model causes the electrostatic
field of charged spinning particles to acquire an oblate structure relative to
the spin direction. For electrons and protons, a pure Coulomb field is
therefore likely to be incompatible with general relativity at the Compton
scale. In the simplest model, the magnetic dipole corresponds to the Dirac
g-factor, g=2. Also, it follows from the form of the electric field that the
electric dipole moment vanishes, in agreement with current experimental limits
for the electron. Quantitatively, the classical Einstein-Maxwell theory
predicts the magnetic and electric dipoles of the electron to an accuracy of
about one part in 10^{-3} or better. Going to the next multipole order, one
finds that the first non-vanishing higher multipole is the electric quadrupole
moment which is predicted to be -124 barn for the electron. Any non-zero value
of the electric quadrupole moment for the electron or the proton would be a
clear sign of curvature due to the implied violation of rotation invariance.
There is also a possible spherical modification of the Coulomb force
proportional to r^{-4}. However, the size of this effect is well below current
experimental limits. The corrections to the hydrogen spectrum are expected to
be small but possibly detectable.


  The paper presents a hybrid system controller, incorporating a neural and an
LQG controller. The neural controller has been optimized by genetic algorithms
directly on the inverted pendulum system. The failure free optimization process
stipulated a relatively small region of the asymptotic stability of the neural
controller, which is concentrated around the regulation point. The presented
hybrid controller combines benefits of a genetically optimized neural
controller and an LQG controller in a single system controller. High quality of
the regulation process is achieved through utilization of the neural
controller, while stability of the system during transient processes and a wide
range of operation are assured through application of the LQG controller. The
hybrid controller has been validated by applying it to a simulation model of an
inherently unstable system of inverted pendulum.


  We discuss the collapsing and expanding solutions of anisotropic charged
cylinder in the context of $f(R,T)$ theory ($R$ represents the Ricci scalar and
$T$ denotes the trace of energy-momentum tensor). For this purpose, we take an
auxiliary solution of Einstein-Maxwell field equations and evaluate expansion
scalar whose negative values lead to collapse and positive values give
expansion. For both cases, the behavior of density, pressure, anisotropic
parameter as well as mass is explored and the effects of charge as well as
model parameter on these quantities are examined. The energy conditions are
found to be satisfied for both solutions.


  We determine some composition laws related to the Fricke surface and the
"double" Fricke surface. This latter surface admits the squares of Markov
triples as its solutions.


  We study lattice embeddings for the class of countable groups $\Gamma$
defined by the property that the largest amenable uniformly recurrent subgroup
$A_\Gamma$ is continuous. When $A_\Gamma$ comes from an extremely proximal
action and the envelope of $A_\Gamma$ is co-amenable in $\Gamma$, we obtain
restrictions on the locally compact groups $G$ that contain a copy of $\Gamma$
as a lattice, notably regarding normal subgroups of $G$, product decompositions
of $G$, and more generally dense mappings from $G$ to a product of locally
compact groups.


  Across the life sciences, an ongoing effort over the last 50 years has made
data and methods more reproducible and transparent. This openness has led to
transformative insights and vastly accelerated scientific progress. For
example, structural biology and genomics have undertaken systematic collection
and publication of protein sequences and structures over the past half-century,
and these data have led to scientific breakthroughs that were unthinkable when
data collection first began. We believe that neuroscience is poised to follow
the same path, and that principles of open data and open science will transform
our understanding of the nervous system in ways that are impossible to predict
at the moment.
  To this end, new social structures along with active and open scientific
communities are essential to facilitate and expand the still limited adoption
of open science practices in our field. Unified by shared values of openness,
we set out to organize a symposium for Open Data in Neuroscience (ODIN) to
strengthen our community and facilitate transformative neuroscience research at
large. In this report, we share what we learned during this first ODIN event.
We also lay out plans for how to grow this movement, document emerging
conversations, and propose a path toward a better and more transparent science
of tomorrow.


  We suppose that there is new physics (NP) in b -> s transitions, and examine
its effect on the angular distribution of B_q^0 -> V_1 V_2 (q=d,s), where
V_{1,2} are vector mesons. We find that, in the presence of such NP, the
formulae relating the parameters of the untagged, time-integrated angular
distribution to certain observables (polarization fractions, CP-violating
triple-product asymmetries, CP-conserving interference term) must be modified
from their standard-model forms. This modification is due in part to a nonzero
B^0_q-Bbar^0_q width difference, which is significant only for B^0_s decays. We
re-analyze the B^0_s -> phi phi data to see the effect of these modifications.
As (Delta Gamma_s/2)/Gamma_s ~ 10%, there are O(10%) changes in the derived
observables. These are not large, but may be important given that one is
looking for signals of NP. In addition, if the NP contributes to the b -> s
decay, the measurement of the untagged time-dependent angular distribution
provides enough information to extract all the NP parameters.


  Volumetric biological imaging often involves compromising high temporal
resolution at the expense of high spatial resolution when popular scanning
methods are used to capture 3D information. We introduce an integrated
experimental and image reconstruction method for capturing dynamic 3D
fluorescent extended objects as a series of synchronously measured 3D snapshots
taken at the frame rate of the imaging camera. We employ multifocal microscopy
(MFM) to simultaneously image at 25 focal planes and process this depth-encoded
image to recover the 3D structure of extended objects, such as bacteria, using
a sparsity-based reconstruction approach. The combined experimental and
computational method produces image quality similar to confocal microscopy in a
fraction of the acquisition time. In addition, our computational image
reconstruction approach allows a simplified MFM optical design by correcting
aberrations using the measured response to point sources. This "compressive"
MFM acquisition and reconstruction method, where an image volume with roughly 8
million voxels is recovered from a single 1-megapixel captured image, enables
straightforward study of dynamic processes in 3D, and as a simultaneous
snapshot advances the state of the art in dynamic 3D microscopy.


  End-to-end (E2E) approach is gradually replacing hybrid models for automatic
speech recognition (ASR) tasks. However, the optimization of E2E models lacks
an intuitive method for handling decoding shifts, especially in scenarios with
a large number of domain-specific rare words that hold specific important
meanings. Furthermore, the absence of knowledge-intensive speech datasets in
academia has been a significant limiting factor, and the commonly used speech
corpora exhibit significant disparities with realistic conversation. To address
these challenges, we present Medical Interview (MED-IT), a multi-turn
consultation speech dataset that contains a substantial number of
knowledge-intensive named entities. We also explore methods to enhance the
recognition performance of rare words for E2E models. We propose a novel
approach, post-decoder biasing, which constructs a transform probability matrix
based on the distribution of training transcriptions. This guides the model to
prioritize recognizing words in the biasing list. In our experiments, for
subsets of rare words appearing in the training speech between 10 and 20 times,
and between 1 and 5 times, the proposed method achieves a relative improvement
of 9.3% and 5.1%, respectively.


  In the latest developments in the theory of skew lattices, distributivity has
been one of the main topics of study. The largest classes of examples of such
algebras are distributive. Unlike what happens in lattices, the properties of
cancellation and distributivity are independent for skew lattices. In this
paper we will discuss several aspects of distributivity in the absence of
commutativity, review the recent results by Kinyon and Leech on these matters
and have an insight on the coset structure of those algebras that satisfy this
property. We will also discuss combinatorial implications of these results.


  We present nucleon observables - primarily isovector vector form factors -
from calculations using 2+1 flavors of Wilson quarks. One ensemble is used for
a dedicated high-precision study of excited-state effects using five
source-sink separations between 0.7 and 1.6 fm. We also present results from a
larger set of calculations that include an ensemble with pion mass 149 MeV and
box size 5.6 fm, which nearly eliminates the uncertainty associated with
extrapolation to the physical pion mass. The results show agreement with
experiment for the vector form factors, which occurs only when excited-state
contributions are reduced. Finally, we show results from a subset of ensembles
that have pion mass 254 MeV with varying temporal and spatial box sizes, which
we use for a controlled study of finite-volume effects and a test of the
"$m_\pi L=4$" rule of thumb.


  The non-equidistant fast Fourier transform (NFFT) is an extension of the
famous fast Fourier transform (FFT), which can be applied to non-equidistantly
sampled data in time/space or frequency domain. It is an approximative
algorithm that allows to control the approximation error in such a way that
machine precision is reached while keeping the algorithmic complexity in the
same order as a regular FFT. The NFFT plays a major role in many signal
processing applications and has been intensively studied from a theoretical and
computational perspective. The fastest CPU implementations of the NFFT are
implemented in the low-level programming languages C and C++ and require a
compromise between code generalizability, code readability, and code
efficiency. The programming language Julia promises new opportunities in
optimizing these three conflicting goals. In this work we show that Julia
indeed allows to develop an NFFT implementation, which is completely generic,
dimension-agnostic and requires about 2-3 times less code than other famous
libraries while still being one of the fastest NFFT implementations developed
to date.


  The development and performance of quantum technologies heavily relies on the
properties of the quantum states, which often require careful optimization of
the driving conditions of all underlying components. In quantum key
distribution (QKD), optical injection locking (OIL) of pulsed lasers has
recently been shown as a promising technique to realize high-speed quantum
transmitters with efficient system design. However, due to the complex
underlying laser dynamics, tuning such laser system is both a challenging and
time-consuming task. Here, we experimentally demonstrate an OIL-based QKD
transmitter that can be automatically tuned to its optimum operating state by
employing a genetic algorithm. Starting with minimal knowledge of the laser
operating parameters, the phase coherence and the quantum bit error rate of the
system are optimized autonomously to a level matching the state of the art.


  State Space Model (SSM) is a mathematical model used to describe and analyze
the behavior of dynamic systems. This model has witnessed numerous applications
in several fields, including control theory, signal processing, economics and
machine learning. In the field of deep learning, state space models are used to
process sequence data, such as time series analysis, natural language
processing (NLP) and video understanding. By mapping sequence data to state
space, long-term dependencies in the data can be better captured. In
particular, modern SSMs have shown strong representational capabilities in NLP,
especially in long sequence modeling, while maintaining linear time complexity.
Notably, based on the latest state-space models, Mamba merges time-varying
parameters into SSMs and formulates a hardware-aware algorithm for efficient
training and inference. Given its impressive efficiency and strong long-range
dependency modeling capability, Mamba is expected to become a new AI
architecture that may outperform Transformer. Recently, a number of works have
attempted to study the potential of Mamba in various fields, such as general
vision, multi-modal, medical image analysis and remote sensing image analysis,
by extending Mamba from natural language domain to visual domain. To fully
understand Mamba in the visual domain, we conduct a comprehensive survey and
present a taxonomy study. This survey focuses on Mamba's application to a
variety of visual tasks and data types, and discusses its predecessors, recent
advances and far-reaching impact on a wide range of domains. Since Mamba is now
on an upward trend, please actively notice us if you have new findings, and new
progress on Mamba will be included in this survey in a timely manner and
updated on the Mamba project at
https://github.com/lx6c78/Vision-Mamba-A-Comprehensive-Survey-and-Taxonomy.


  We calculate the anisotropy energy of a single-domain ferromagnetic particle
in which the only source of anisotropy is the presence of non-magnetic
impurities. We find that such anisotropy takes the form of combined easy-axis
and easy-plane anisotropies, with random orientations of the axes. Typically
the anisotropy energy is of order $N^{1/2} \hbar/tau_{so}$, where $N$ is the
number of electrons in the ferromagnetic particle and $\tau_{so}$ is the
spin-orbit time.


  Intel and AMD support the Carry-less Multiplication (CLMUL) instruction set
in their x64 processors. We use CLMUL to implement an almost universal 64-bit
hash family (CLHASH). We compare this new family with what might be the fastest
almost universal family on x64 processors (VHASH). We find that CLHASH is at
least 60% faster. We also compare CLHASH with a popular hash function designed
for speed (Google's CityHash). We find that CLHASH is 40% faster than CityHash
on inputs larger than 64 bytes and just as fast otherwise.


  In this study, thin elastic films supported on a rigid substrate are brought
into contact with a spherical glass indenter. Upon contact, adhesive fingers
emerge at the periphery of the contact patch with a characteristic wavelength.
Elastic films are also pre-strained along one axis before initiation of
contact, causing the fingering pattern to become anisotropic and align with the
axis along which the strain was applied. This transition from isotropic to
anisotropic patterning is characterized quantitatively and a simple model is
developed to understand the origin of the anisotropy.


  We report the discovery of two supernovae (SN 1999gw and SN 2001db) obtained
within the framework of an infrared monitoring campaign of Luminous Infrared
Galaxies, aimed at detecting obscured supernovae. SN 2001db, extinguished by
Av~5.5 mag, is the first supernova discovered in the infrared which has
received the spectroscopic confirmation. This result highlights the power of
infrared monitoring in detecting obscured SNe and indicates that optical
surveys are probably missing a significant fraction of SNe, especially in
obscured systems such as starburst galaxies. The preliminary estimate of SN
rate in LIRG galaxies is about an order of magnitude higher than that expected
from optical surveys.


  The proliferation of fake news and its serious negative social influence push
fake news detection methods to become necessary tools for web managers.
Meanwhile, the multi-media nature of social media makes multi-modal fake news
detection popular for its ability to capture more modal features than uni-modal
detection methods. However, current literature on multi-modal detection is more
likely to pursue the detection accuracy but ignore the robustness of the
detector. To address this problem, we propose a comprehensive robustness
evaluation of multi-modal fake news detectors. In this work, we simulate the
attack methods of malicious users and developers, i.e., posting fake news and
injecting backdoors. Specifically, we evaluate multi-modal detectors with five
adversarial and two backdoor attack methods. Experiment results imply that: (1)
The detection performance of the state-of-the-art detectors degrades
significantly under adversarial attacks, even worse than general detectors; (2)
Most multi-modal detectors are more vulnerable when subjected to attacks on
visual modality than textual modality; (3) Popular events' images will cause
significant degradation to the detectors when they are subjected to backdoor
attacks; (4) The performance of these detectors under multi-modal attacks is
worse than under uni-modal attacks; (5) Defensive methods will improve the
robustness of the multi-modal detectors.


  This paper reports state-of-the-art electronic structure calculations of La
adsorption on the Si(001) surface. We predict La chains in the low coverage
limit, which condense in a stable phase at a coverage of 1/5 monolayer. At 1/3
monolayer we predict a chemically rather inert, stable phase. La changes its
oxidation state from La(3+) at lower coverages to La(2+) at coverages beyond
1/3 monolayer. In the latter oxidation state, one electron resides in a state
with a considerable contribution from La-d and f states.


  In this paper, we are interested in studying the set
$\mathcal{A}_{\|\cdot\|}(X, Y)$ of all norm-attaining operators $T$ from $X$
into $Y$ satisfying the following: given $\epsilon>0$, there exists $\eta$ such
that if $\|Tx\| > 1 - \eta$, then there is $x_0$ such that $\| x_0 - x\| <
\epsilon$ and $T$ itself attains its norm at $x_0$. We show that every norm one
functional on $c_0$ which attains its norm belongs to
$\mathcal{A}_{\|\cdot\|}(c_0, \mathbb{K})$. Also, we prove that the analogous
result holds neither for $\mathcal{A}_{\|\cdot\|}(\ell_1, \mathbb{K})$ nor
$\mathcal{A}_{\|\cdot\|}(\ell_{\infty}, \mathbb{K})$. Under some assumptions,
we show that the sphere of the compact operators belongs to
$\mathcal{A}_{\|\cdot\|}(X, Y)$ and that this is no longer true when some of
these hypotheses are dropped. The analogous set $\mathcal{A}_{nu}(X)$ for
numerical radius of an operator instead of its norm is also defined and
studied. We present a complete characterization for the diagonal operators
which belong to the sets $\mathcal{A}_{\| \cdot \|}(X, X)$ and
$\mathcal{A}_{nu}(X)$ when $X=c_0$ or $\ell_p$. As a consequence, we get that
the canonical projections $P_N$ on these spaces belong to our sets. We give
examples of operators on infinite dimensional Banach spaces which belong to
$\mathcal{A}_{\| \cdot \|}(X, X)$ but not to $\mathcal{A}_{nu}(X)$ and
vice-versa. Finally, we establish some techniques which allow us to connect
both sets by using direct sums.


  The differential-geometric structure of the set of positive densities on a
given measure space has raised the interest of many mathematicians after the
discovery by C.R. Rao of the geometric meaning of the Fisher information. Most
of the research is focused on parametric statistical models. In series of
papers by author and coworkers a particular version of the nonparametric case
has been discussed. It consists of a minimalistic structure modeled according
the theory of exponential families: given a reference density other densities
are represented by the centered log likelihood which is an element of an Orlicz
space. This mappings give a system of charts of a Banach manifold. It has been
observed that, while the construction is natural, the practical applicability
is limited by the technical difficulty to deal with such a class of Banach
spaces. It has been suggested recently to replace the exponential function with
other functions with similar behavior but polynomial growth at infinity in
order to obtain more tractable Banach spaces, e.g. Hilbert spaces. We give
first a review of our theory with special emphasis on the specific issues of
the infinite dimensional setting. In a second part we discuss two specific
topics, differential equations and the metric connection. The position of this
line of research with respect to other approaches is briefly discussed.


  To accommodate diverse Quality-of-Service (QoS) requirements in the 5th
generation cellular networks, base stations need real-time optimization of
radio resources in time-varying network conditions. This brings high computing
overheads and long processing delays. In this work, we develop a deep learning
framework to approximate the optimal resource allocation policy that minimizes
the total power consumption of a base station by optimizing bandwidth and
transmit power allocation. We find that a fully-connected neural network (NN)
cannot fully guarantee the QoS requirements due to the approximation errors and
quantization errors of the numbers of subcarriers. To tackle this problem, we
propose a cascaded structure of NNs, where the first NN approximates the
optimal bandwidth allocation, and the second NN outputs the transmit power
required to satisfy the QoS requirement with given bandwidth allocation.
Considering that the distribution of wireless channels and the types of
services in the wireless networks are non-stationary, we apply deep transfer
learning to update NNs in non-stationary wireless networks. Simulation results
validate that the cascaded NNs outperform the fully connected NN in terms of
QoS guarantee. In addition, deep transfer learning can reduce the number of
training samples required to train the NNs remarkably.


  Safety is a desirable property that can immensely increase the applicability
of learning algorithms in real-world decision-making problems. It is much
easier for a company to deploy an algorithm that is safe, i.e., guaranteed to
perform at least as well as a baseline. In this paper, we study the issue of
safety in contextual linear bandits that have application in many different
fields including personalized ad recommendation in online marketing. We
formulate a notion of safety for this class of algorithms. We develop a safe
contextual linear bandit algorithm, called conservative linear UCB (CLUCB),
that simultaneously minimizes its regret and satisfies the safety constraint,
i.e., maintains its performance above a fixed percentage of the performance of
a baseline strategy, uniformly over time. We prove an upper-bound on the regret
of CLUCB and show that it can be decomposed into two terms: 1) an upper-bound
for the regret of the standard linear UCB algorithm that grows with the time
horizon and 2) a constant (does not grow with the time horizon) term that
accounts for the loss of being conservative in order to satisfy the safety
constraint. We empirically show that our algorithm is safe and validate our
theoretical analysis.


  Corrective interventions while a robot is learning to automate a task provide
an intuitive method for a human supervisor to assist the robot and convey
information about desired behavior. However, these interventions can impose
significant burden on a human supervisor, as each intervention interrupts other
work the human is doing, incurs latency with each context switch between
supervisor and autonomous control, and requires time to perform. We present
LazyDAgger, which extends the interactive imitation learning (IL) algorithm
SafeDAgger to reduce context switches between supervisor and autonomous
control. We find that LazyDAgger improves the performance and robustness of the
learned policy during both learning and execution while limiting burden on the
supervisor. Simulation experiments suggest that LazyDAgger can reduce context
switches by an average of 60% over SafeDAgger on 3 continuous control tasks
while maintaining state-of-the-art policy performance. In physical fabric
manipulation experiments with an ABB YuMi robot, LazyDAgger reduces context
switches by 60% while achieving a 60% higher success rate than SafeDAgger at
execution time.


  R Aqr is one of the closest symbiotic binaries and the only D-type system
with radial velocity data suitable for orbital parameters estimation. The aims
of our study are to derive a reliable spectroscopic orbit of the Mira
component, and to establish connections between the orbital motion and other
phenomena shown by R Aqr. We reanalize velocity data recently published by
McIntosh & Rustan complemented by additional velocities. We find an eccentric
orbit (e=0.25) with a period 43.6 yr. This solution is in agreement with a
resolved VLA observation of this system. We demonstrate that the last 1974-1981
increase of extinction towards the Mira occured during its superior
spectroscopic conjunction, and can be due to obscuration by a neutral material
in the accreting stream. We also show that jet ejection is not connected with
the orbital position.


  In this paper we prove the conjecture of Alexander and Currier that states,
except for covering maps of equidistant surfaces in hyperbolic 3-space, a
complete, nonnegatively curved immersed hypersurface in hyperbolic space is
necessarily properly embedded.


  Agent-based models (ABMs) often rely on psychometric constructs such as
'opinions', 'stubbornness', 'happiness', etc. The measurement process for these
constructs is quite different from the one used in physics as there is no
standardized unit of measurement for opinion or happiness. Consequently,
measurements are usually affected by 'psychometric distortions,' which can
substantially impact models' predictions. Even if distortions are well known in
psychometrics, their existence and nature is obscure to many researchers
outside this field. In this paper, we introduce distortions to the ABM
community. Initially, we show where distortions come from and how to observe
them in real-world data. We then show how they can strongly impact predictions,
qualitative comparison with data and the problem they pose for validation of
models. We conclude our analysis by discussing how researchers may mitigate
this problem and highlight possible future modelling trends that will address
this problem.


  We examine the SU(3) symmetry breaking in hyperon semileptonic decays (HSD)
by considering two typical sets of quark contributions to the spin content of
the octet baryons: Set-1 with SU(3) flavor symmetry and Set-2 with SU(3) flavor
symmetry breaking in HSD. The quark distributions of the octet baryons are
calculated with a successful statistical model. Using an approximate relation
between the quark fragmentation functions and the quark distributions, we
predict polarizations of the octet baryons produced in $e^+e^-$ annihilation
and semi-inclusive deeply lepton-nucleon scattering in order to reveal the
SU(3) symmetry breaking effect on the spin structure of the octet baryons. We
find that the SU(3) symmetry breaking significantly affects the hyperon
polarization. The available experimental data on the $\Lambda$ polarization
seem to favor the theoretical predictions with SU(3) symmetry breaking. We
conclude that there is a possibility to get a collateral evidence for SU(3)
symmetry breaking from hyperon production. The theoretical errors for our
predictions are discussed.


  Disk accretion onto stars leads to the formation of a Boundary Layer (BL)
near the stellar surface where the disk makes contact with the star. Albeit a
large fraction of the total luminosity of the system originates from this tiny
layer connecting the accretion disk and the accreting object, its structure has
not been fully understood yet. It is the aim of this work, to obtain more
insight into the Boundary Layer around the white dwarf in compact binary
systems. There are still many uncertainties concerning the extent and
temperature of the BL and the rotation rate of the white dwarf. We perform
numerical hydrodynamical simulations, where the problem is treated in a
one-dimensional, radial approximation (slim disk). The turbulence is described
by the alpha parameter viscosity. We include both cooling from the disk
surfaces and radial radiation transport. The radiation energy is treated in the
one-temperature approximation. For a given M_dot our results show a strong
dependence on the stellar mass and rotation rate. Both, the midplane and the
effective temperature rise considerably with increasing stellar mass or
decreasing stellar rotation rate. Our simulations further show, that the
radiation energy and pressure are indeed important in the BL. However, some
models show a low optical depth in the BL, making it necessary to find a better
representation for optically thin regions. The combination of a high mass and a
small radius, such as in white dwarfs, can lead to an enormous energy release
in the BL, provided the WD rotates slowly. Since the radial extent of BLs is
typically very small (about 0.02 to 0.05 R_star), this leads to surface
temperatures of a few hundred thousand Kelvin. All of our models showed
subsonic infall velocities with Mach numbers of < 0.4 at most.


  A method for generating multiple quantum systems has been suggested, which
can easily be tested against any PT-symmetry operators. IN the present, we have
generated six model Iso spectral systems. Method has been tested against few
matrix models and inverted quartic(logarithmic) model potentials. Graphically,
it can be represented.


  Two fundamental problems for extraterrestrial intelligences (ETIs) attempting
to establish interstellar communication are timing and energy consumption.
Humanity's study of exoplanets via their transit across the host star
highlights a means of solving both problems. An ETI 'A' can communicate with
ETI 'B' if B is observing transiting planets in A's star system, either by
building structures to produce artificial transits observable by B, or by
emitting signals at B during transit, at significantly lower energy consumption
than typical electromagnetic transmission schemes.
  This can produce a network of interconnected civilisations, establishing
contact via observing each other's transits. Assuming that civilisations reside
in a Galactic Habitable Zone (GHZ), I conduct Monte Carlo Realisation
simulations of the establishment and growth of this network, and analyse its
properties in the context of graph theory.
  I find that at any instant, only a few civilisations are correctly aligned to
communicate via transits. However, we should expect the true network to be
cumulative, where a "handshake" connection at any time guarantees connection in
the future via e.g. electromagnetic signals. In all our simulations, the
cumulative network connects all civilisations together in a complete network.
If civilisations share knowledge of their network connections, the network can
be fully complete on timescales of order a hundred thousand years. Once
established, this network can connect any two civilisations either directly, or
via intermediate civilisations, with a path much less than the dimensions of
the GHZ.


  Negative sampling is a limiting factor w.r.t. the generalization of
metric-learned neural networks. We show that uniform negative sampling provides
little information about the class boundaries and thus propose three novel
techniques for efficient negative sampling: drawing negative samples from (1)
the top-$k$ most semantically similar classes, (2) the top-$k$ most
semantically similar samples and (3) interpolating between contrastive latent
representations to create pseudo negatives. Our experiments on CIFAR-10,
CIFAR-100 and Tiny-ImageNet-200 show that our proposed \textit{Semantically
Conditioned Negative Sampling} and Latent Mixup lead to consistent performance
improvements. In the standard supervised learning setting, on average we
increase test accuracy by 1.52\% percentage points on CIFAR-10 across various
network architectures. In the knowledge distillation setting, (1) the
performance of student networks increase by 4.56\% percentage points on
Tiny-ImageNet-200 and 3.29\% on CIFAR-100 over student networks trained with no
teacher and (2) 1.23\% and 1.72\% respectively over a \textit{hard-to-beat}
baseline (Hinton et al., 2015).


  This contribution to the CPT'19 meeting provides a brief overview of recent
theoretical studies of nonminimal Lorentz violation in linearized gravity.
Signatures in gravitational waves from coalescing compact binaries are
discussed.


  Let $D$ be an effective divisor on a smooth projective variety $X$ over an
algebraically closed field $k$ of characteristic $0$. We show that there is a
one-to-one correspondence between the class of orthogonal (respectively,
symplectic) parabolic vector bundles on $X$ with parabolic structure along $D$
and having rational weights and the class of orthogonal (respectively,
symplectic) vector bundles on certain root stacks associated to this data.
Using this, we describe the orthogonal (respectively, symplectic) vector
bundles on the root stack as reductions of the structure group to orthogonal
(respectively, symplectic) groups. When $D$ is a divisor with strict normal
crossings, we prove a one-to-one correspondence between the class of orthogonal
(respectively, symplectic) parabolic connections on $X$ with rational weights,
and the class of orthogonal (respectively, symplectic) logarithmic connections
on certain fiber product of root stacks with poles along a divisor with strict
normal crossings.


  Lasso and Ridge are important minimization problems in machine learning and
statistics. They are versions of linear regression with squared loss where the
vector $\theta\in\mathbb{R}^d$ of coefficients is constrained in either
$\ell_1$-norm (for Lasso) or in $\ell_2$-norm (for Ridge). We study the
complexity of quantum algorithms for finding $\varepsilon$-minimizers for these
minimization problems. We show that for Lasso we can get a quadratic quantum
speedup in terms of $d$ by speeding up the cost-per-iteration of the
Frank-Wolfe algorithm, while for Ridge the best quantum algorithms are linear
in $d$, as are the best classical algorithms. As a byproduct of our quantum
lower bound for Lasso, we also prove the first classical lower bound for Lasso
that is tight up to polylog-factors.


  A determination of the initial mass function (IMF) of the current, incomplete
census of the 10 Myr-old TW Hya association (TWA) is presented. This census is
built from a literature compilation supplemented with new spectra and 17 new
radial velocities, as well as a re-analysis of Hipparcos data that confirmed HR
4334 (A2Vn) as a member. Though the dominant uncertainty in the IMF remains
census incompleteness, a detailed statistical treatment is carried out to make
the IMF determination independent of binning, while accounting for small number
statistics. The currently known high-likelihood members are fitted by a
log-normal distribution with a central mass of $0.21^{+0.11}_{-0.06}$
$M_{\odot}$ and a characteristic width of $0.8^{+0.2}_{-0.1}$ dex in the 12
$M_{\rm Jup}$-2 $M_{\odot}$ range, whereas a Salpeter power law with $\alpha =
2.2^{+1.1}_{-0.5}$ best describes the IMF slope in the $0.1-2$ $M_{\odot}$
range. This characteristic width is higher than other young associations, which
may be due to incompleteness in the current census of low-mass TWA stars. A
tentative overpopulation of isolated planetary-mass members similar to 2MASS
J11472421-2040204 and 2MASS J11193254-1137466 is identified: this indicates
that there might be as many as $10^{+13}_{-5}$ similar members of TWA with
hot-start model-dependent masses estimated at $\sim$ 5-7 $M_{\rm Jup}$, most of
which would be too faint to be detected in 2MASS. Our new radial velocity
measurements corroborate the membership of 2MASS J11472421-2040204, and secure
TWA 28 (M8.5$\gamma$), TWA 29 (M9.5$\gamma$) and TWA 33 (M4.5e) as members. The
discovery of 2MASS J09553336-0208403, a young L7-type interloper unrelated to
TWA, is also presented.


  Empirical evidence shows that ensembles, such as bagging, boosting, random
and rotation forests, generally perform better in terms of their generalization
error than individual classifiers. To explain this performance, Schapire et al.
(1998) developed an upper bound on the generalization error of an ensemble
based on the margins of the training data, from which it was concluded that
larger margins should lead to lower generalization error, everything else being
equal. Many other researchers have backed this assumption and presented tighter
bounds on the generalization error based on either the margins or functions of
the margins. For instance, Shen and Li (2010) provide evidence suggesting that
the generalization error of a voting classifier might be reduced by increasing
the mean and decreasing the variance of the margins. In this article we propose
several techniques and empirically test whether the current state of research
in explaining ensemble performance holds. We evaluate the proposed methods
through experiments with real and simulated data sets.