  We report on the demonstration of the first axial AlInN ultraviolet
core-shell nanowire light-emitting diodes with highly stable emission in the UV
wavelength range. During the epitaxial growth of AlInN layer, an AlInN shell is
spontaneously formed, resulted in the reduced nonradiative recombination on
nanowire surface. The AlInN nanowires exhibit high internal quantum efficiency
of ~ 52% at room temperature for emission at 295nm. The peak emission
wavelength can be varied from 290 nm to 355 nm by changing the growth
condition. Moreover, significantly strong transverse magnetic (TM) polarized
emission is recorded which is ~ 4 times stronger compared to the transverse
electric (TE) polarized light at 295 nm. This study provides alternative
approach for the fabrication of new type of high-performance ultraviolet
light-emitters.


  We prove the Cauchy type identities for the universal double Schubert
polynomials, introduced recently by W. Fulton. As a corollary, the
determinantal formulae for some specializations of the universal double
Schubert polynomials corresponding to the Grassmannian permutations are
obtained. We also introduce and study the universal Schur functions and
multiparameter deformation of Schubert polynomials.


  Managing the transactions in real time distributed computing system is not
easy, as it has heterogeneously networked computers to solve a single problem.
If a transaction runs across some different sites, it may commit at some sites
and may failure at another site, leading to an inconsistent transaction. The
complexity is increase in real time applications by placing deadlines on the
response time of the database system and transactions processing. Such a system
needs to process Transactions before these deadlines expired. A series of
simulation study have been performed to analyze the performance under different
transaction management under conditions such as different workloads,
distribution methods, execution mode-distribution and parallel etc. The
scheduling of data accesses are done in order to meet their deadlines and to
minimize the number of transactions that missed deadlines. A new concept is
introduced to manage the transactions in dynamic ways rather than setting
computing parameters in static ways. With this approach, the system gives a
significant improvement in performance.


  The existence of spin-triplet superconductivity in non-collinear magnetic
heterostructures with superconductors is by now well established. This
observation lays the foundation of superconducting spintronics with the aim to
create a low-power consuming devices in order to replace the conventional
electronics limited by heating. From a fundamental point of view the
investigation of the structure and properties of spin-triplet Cooper pairs
continues. Recently, spectroscopic evidence has shown to offer more detailed
insights in the structure of triplet Cooper pairs than the supercurrent. Hence,
we study here the structure of spin-triplet Cooper pairs through the density of
states in bilayers of a textured magnetic insulator in proximity to a
superconductor. Using quasiclassical Green function methods, we study the local
density of states, both spin-resolved and spin-independent. We show that the
equal-spin and mixed-spin triplet Cooper pairs leads to different spectroscopic
signatures, which can be further enhanced by spin-polarized spectroscopy. Our
results show the huge potential spin-polarized tunneling methods offer in
characterizing unconventional superconductivity in heterostructures.


  Given a poset $P$ we say a family $\mathcal{F}\subseteq P$ is centered if it
is obtained by `taking sets as close to the middle layer as possible'. A poset
$P$ is said to have the centeredness property if for any $M$, among all
families of size $M$ in $P$, centered families contain the minimum number of
comparable pairs. Kleitman showed that the Boolean lattice $\{0,1\}^n$ has the
centeredness property. It was conjectured by Noel, Scott, and Sudakov, and by
Balogh and Wagner, that the poset $\{0,1,\ldots,k\}^n$ also has the
centeredness property, provided $n$ is sufficiently large compared to $k$. We
show that this conjecture is false for all $k\geq 2$ and investigate the range
of $M$ for which it holds. Further, we improve a result of Noel, Scott, and
Sudakov by showing that the poset of subspaces of $\mathbb{F}_q^n$ has the
centeredness property. Several open questions are also given.


  We investigate the possibility of boiling instability of nuclear liquid in
the inner core of the proto-neutron star formed in the core collapse of a type
II supernova. We derive a simple criterion for boiling to occur. Using this
criterion for one of best described equations of state of supernova matter, we
find that boiling is quite possible under the conditions realized inside the
proto-neutron star. We discuss consequences of this process such as the
increase of heat transfer rate and pressure in the boiling region. We expect
that taking this effect into account in the conventional neutrino-driven
delayed-shock mechanism of type II supernova explosions can increase the
explosion energy and reduce the mass of the neutron-star remnant.


  We consider a registration-based approach for localizing sensor networks from
range measurements. This is based on the assumption that one can find
overlapping cliques spanning the network. That is, for each sensor, one can
identify geometric neighbors for which all inter-sensor ranges are known. Such
cliques can be efficiently localized using multidimensional scaling. However,
since each clique is localized in some local coordinate system, we are required
to register them in a global coordinate system. In other words, our approach is
based on transforming the localization problem into a problem of registration.
In this context, the main contributions are as follows. First, we describe an
efficient method for partitioning the network into overlapping cliques. Second,
we study the problem of registering the localized cliques, and formulate a
necessary rigidity condition for uniquely recovering the global sensor
coordinates. In particular, we present a method for efficiently testing
rigidity, and a proposal for augmenting the partitioned network to enforce
rigidity. A recently proposed semidefinite relaxation of global registration is
used for registering the cliques. We present simulation results on random and
structured sensor networks to demonstrate that the proposed method compares
favourably with state-of-the-art methods in terms of run-time, accuracy, and
scalability.


  We review recent results in heavy quark hadron spectroscopy at the Tevatron.
With increasing data samples the Tevatron experiments start to uncover
information on the b-hadrons. Most important are the first observations of the
narrow Bs** as well as Sigma_b, Sigma_b* and Xi_b- baryons. In addition we
present updated results on the narrow B** and Bc mesons.


  While oscillations of solar neutrinos are usually studied using the
single-particle quantum-mechanical approach, flavor conversions of supernovae
neutrinos are typically analyzed using the kinetic equation for the matrix of
densities due to the necessity of including also the scattering processes.
Using the Wigner formulation of quantum mechanics we show the equivalence of
the quantum-mechanical and kinetic approaches in the limit of collisionless
neutrino propagation (in a background medium). Based on this observation we
also argue that solutions of the kinetic equation account for the Heisenberg
uncertainty principle and the related effect of wave packet separation (for
single neutrinos), as well as the Pauli exclusion principle, if the initial
conditions are consistent with these fundamental quantum principles. Such
initial conditions can be constructed e.g. by identifying the matrix of
densities with the (reduced) single-particle Wigner function computed using
initial conditions for the neutrino wave function. Hence the neutrino momentum
uncertainty is an integral part of the initial conditions for the matrix of
densities, that may have an impact on the phenomenology of supernovae neutrinos
via the effect of wave packet separation.


  Prospects of measuring polarized photons emitted from a quark-gluon plasma
(QGP) are discussed. In particular, the detection of a possible quark spin
polarization in a QGP using circularly polarized photons emitted from the
plasma is studied. Photons leave the QGP without further interaction and thus
provide a primary probe for quark polarization within the QGP. We find that
photon polarization cannot solely arise due to a possible QGP momentum space
anisotropy, but may be enhanced due to it. In particular, for oblate momentum
distributions and high photon energies, quark polarization is efficiently
transfered to photon polarization. The role of competing sources of polarized
photons in heavy-ion collisions is discussed.


  The idea of using multi-task learning approaches to address the joint
extraction of entity and relation is motivated by the relatedness between the
entity recognition task and the relation classification task. Existing methods
using multi-task learning techniques to address the problem learn interactions
among the two tasks through a shared network, where the shared information is
passed into the task-specific networks for prediction. However, such an
approach hinders the model from learning explicit interactions between the two
tasks to improve the performance on the individual tasks. As a solution, we
design a multi-task learning model which we refer to as recurrent interaction
network which allows the learning of interactions dynamically, to effectively
model task-specific features for classification. Empirical studies on two
real-world datasets confirm the superiority of the proposed model.


  This thesis gives a comprehensive analysis of hadron production in
$e^{+}e^{-}$ collisions at different center of mass energies in the framework
of the Statistical Model of the hadron resonance gas. The model used for the
analysis is formulated in the canonical ensemble with exact conservation of
five quantum numbers. The corresponding canonical partition function in quantum
statistics is derived and the canonical factor as the striking feature of the
canonical framework will be investigated in detail. The parameters of the
underlying model were determined using a fit to the average multiplicities of
the latest measurements at $\sqrt{s}$=10, 29-35, 91 and 130-200 GeV. The
measurements are compared to the pure thermal production like in heavy ion
collisions as well as to the production in a second scenario accounting for
hard collisions and the resulting enhancement of heavy quark. The results
demonstrate that, within the accuracy of the experiments, none of the data sets
is satisfactorily described with this approach, calling into question the
notion that particle production in $e^{+}e^{-}$ collisions is thermal in
origin. Nevertheless it is observed that the charm and bottom particle yields
produced in hard collisions can be explained by large canonical enhancements of
such particles in heavy jets, almost independent of the model parameters.


  Conical intersections are common in molecular physics and photochemistry, and
are often invoked to explain observed reaction products. A conical intersection
can occur when an excited electronic potential energy surface intersects with
the ground electronic potential energy surface in the coordinate space of the
nuclear positions. Theory predicts that the conical intersection will result in
a geometric phase for a wavepacket on the ground potential energy surface.
Although conical intersections have been observed experimentally, the geometric
phase has not been observed in a molecular system. Here we use a trapped atomic
ion system to perform a quantum simulation of a conical intersection. The
internal state of a trapped atomic ion serves as the electronic state and the
motion of the atomic nuclei are encoded into the normal modes of motion of the
ions. The simulated electronic potential is constructed by applying
state-dependent forces to the ion with a near-resonant laser. We experimentally
observe the geometric phase on the ground-state surface using adiabatic state
preparation followed by motional state measurement. Our experiment shows the
advantage of combining spin and motion degrees of freedom in a quantum
simulator.


This talk offers some comments and perspectives on Lorentz and CPT violation.


  Following recent interest in the qualitative analysis of some optimal control
and shape optimisation problems, we provide in this article a detailed study of
the optimisation of Robin boundary conditions in PDE constrained calculus of
variations. Our main model consists of an elliptic PDE of the form $-\Delta
u_\beta=f(x,u_\beta)$ endowed with the Robin boundary conditions $\partial_\nu
u_\beta+\beta(x)u_\beta=0$. The optimisation variable is the function $\beta$,
which is assumed to take values between 0 and 1 and to have a fixed integral.
Two types of criteria are under consideration: the first one is non-energetic
criteria. In other words, we aim at optimising functionals of the form
$\mathcal J(\beta)=\int_{\Omega\text{ or }\partial \Omega}j(u_\beta)$. We prove
that, depending on the monotonicity of the function $j$, the optimisers may be
of \emph{bang-bang} type (in other words, the optimisers write $1_\Gamma$ for
some measurable subset $\Gamma$ of $\partial \ Omega $) or, on the contrary,
that they may only take values strictly between 0 and 1. This has consequence
for a related shape optimisation problem, in which one tries to find where on
the boundary Neumann ($\partial_\nu u=0$ ) and constant Robin conditions
($\partial_\nu u+u=0$) should be placed in order to optimise criteria. The
proofs for this first case rely on new fine oscillatory techniques, used in
combination with optimality conditions. We then investigate the case of
compliance-type functionals. For such energetic functionals, we give an
in-depth analysis and even some explicit characterisation of optimal $\beta^*$.


  This talk reports on the status of an approach to the numerical study of
isolated systems with the conformal field equations. We first describe the
algorithms used in a code which has been developed at AEI in the last years,
and discuss a milestone result obtained by Huebner. Then we present more recent
results as examples to sketch the problems we face in the conformal approach to
numerical relativity and outline a possible roadmap toward making this approach
a practical tool.


  This work explores the task of synthesizing speech in nonexistent
human-sounding voices. We call this task "speaker generation", and present
TacoSpawn, a system that performs competitively at this task. TacoSpawn is a
recurrent attention-based text-to-speech model that learns a distribution over
a speaker embedding space, which enables sampling of novel and diverse
speakers. Our method is easy to implement, and does not require transfer
learning from speaker ID systems. We present objective and subjective metrics
for evaluating performance on this task, and demonstrate that our proposed
objective metrics correlate with human perception of speaker similarity. Audio
samples are available on our demo page.


  We obtain approximation ratio $2(2+\frac{1}{\ell})$ for the (undirected)
$k$-Connected Subgraph problem, where $\ell \approx \frac{1}{2} (\log_k n-1)$
is the largest integer such that $2^{\ell-1} k^{2\ell+1} \leq n$. For large
values of $n$ this improves the $6$-approximation of Cheriyan and V\'egh when
$n =\Omega(k^3)$, which is the case $\ell=1$. For $k$ bounded by a constant we
obtain ratio $4+\epsilon$. For large values of $n$ our ratio matches the best
known ratio $4$ for the augmentation version of the problem, as well as the
best known ratios for $k=6,7$. Similar results are shown for the problem of
covering an arbitrary crossing supermodular biset function.


  A brief summary of recent developments in mathematical diffraction theory is
given. Particular emphasis is placed on systems with aperiodic order and
continuous spectral components. We restrict ourselves to some key results and
refer to the literature for further details.


  There remain many open questions pertaining to the scaling behaviour of
Transformer architectures. These scaling decisions and findings can be
critical, as training runs often come with an associated computational cost
which have both financial and/or environmental impact. The goal of this paper
is to present scaling insights from pretraining and finetuning Transformers.
While Kaplan et al. presents a comprehensive study of the scaling behaviour of
Transformer language models, the scope is only on the upstream (pretraining)
loss. Therefore, it is still unclear if these set of findings transfer to
downstream task within the context of the pretrain-finetune paradigm. The key
findings of this paper are as follows: (1) we show that aside from only the
model size, model shape matters for downstream fine-tuning, (2) scaling
protocols operate differently at different compute regions, (3) widely adopted
T5-base and T5-large sizes are Pareto-inefficient. To this end, we present
improved scaling protocols whereby our redesigned models achieve similar
downstream fine-tuning quality while having 50\% fewer parameters and training
40\% faster compared to the widely adopted T5-base model. We publicly release
over 100 pretrained checkpoints of different T5 configurations to facilitate
future research and analysis.


  The relativistic six-quark equations including the u, d quarks and antiquarks
are found. The nonstrange baryonia B/bar B are contructed without the mixing of
the quarks and antiquarks. The relativistic six-quark amplitudes of the
baryonia are calculed. The poles of these amplitudes determine the masses of
baryonia. 16 masses of baryonia are predicted.


  We present an analysis of the main systematic effects that could impact the
measurement of CMB polarization with the proposed CORE space mission. We employ
timeline-to-map simulations to verify that the CORE instrumental set-up and
scanning strategy allow us to measure sky polarization to a level of accuracy
adequate to the mission science goals. We also show how the CORE observations
can be processed to mitigate the level of contamination by potentially worrying
systematics, including intensity-to-polarization leakage due to bandpass
mismatch, asymmetric main beams, pointing errors and correlated noise. We use
analysis techniques that are well validated on data from current missions such
as Planck to demonstrate how the residual contamination of the measurements by
these effects can be brought to a level low enough not to hamper the scientific
capability of the mission, nor significantly increase the overall error budget.
We also present a prototype of the CORE photometric calibration pipeline, based
on that used for Planck, and discuss its robustness to systematics, showing how
CORE can achieve its calibration requirements. While a fine-grained assessment
of the impact of systematics requires a level of knowledge of the system that
can only be achieved in a future study phase, the analysis presented here
strongly suggests that the main areas of concern for the CORE mission can be
addressed using existing knowledge, techniques and algorithms.


  We employ min-max techniques to show that the unit ball in $\mathbb{R}^3$
contains embedded free boundary minimal surfaces with connected boundary and
arbitrary genus.


  The Brans Class I solution in Brans-Dicke gravity is a staple in the study of
gravitational theories beyond General Relativity. Discovered in 1961, it
describes the exterior vacuum of a spherical Brans-Dicke star and is
characterized by two adjustable parameters. Surprisingly, the relationship
between these parameters and the properties of the star has not been rigorously
established. In this Proceeding, we bridge this gap by deriving $\textit{the}$
complete exterior solution of Brans Class I, expressed in terms of the total
energy and total pressure of the spherisymmetric gravity source. The solution
allows for the $\textit{exact}$ derivation of $\textit{all}$ post-Newtonian
parameters in Brans-Dicke gravity for far field regions of a spherical source.
Particularly for the $\gamma$ parameter, instead of the conventional result
$\gamma_{\,\text{PPN}}=\frac{\omega+1}{\omega+2}$, we obtain the analytical
expression
$\gamma_{\,\text{exact}}=\frac{\omega+1+(\omega+2)\,\Theta}{\omega+2+(\omega+1)\,\Theta}$
where $\Theta$ is the ratio of the total pressure
$P_{\parallel}^{*}+2P_{\perp}^{*}$ and total energy $E^{*}$ contained within
the mass source. Our $\textit{non-perturbative}$ $\gamma$ formula is valid for
all field strengths and types of matter comprising the mass source.
Consequently, observational constraints on $\gamma$ thus set $\textit{joint}$
bounds on $\omega$ and $\varTheta$, with the latter representing a global
characteristic of the mass source. More broadly, our formula highlights the
importance of pressure (when $\varTheta\neq0$) in spherical Brans-Dicke stars,
and potentially in stars within other modified theories of gravitation.


  We present a robust and flexible optimization approach for dynamic mode
decomposition analysis of data with complex dynamics and low signal-to-noise
ratios. The approach borrows techniques and insights from the field of deep
learning. Specifically, we employ automatic differentiation and stochastic
gradient descent to compute eigenvalues, modes, and mode amplitudes
simultaneously. The method allows embedding regularization or physical
constraints into the operator definition. The optimization approach is applied
to three examples of increasing complexity, the most challenging of which is an
experimental dataset of transonic shock buffet on a swept at realistic flight
conditions.


  In this work we discuss charged rotating black holes in $AdS_5 \times S^5$
that degenerate to extremal black holes with zero entropy. These black holes
have scaling properties between charge and angular momentum similar to those of
Fermi surface operators in a subsector of $\mathcal{N}=4$ SYM. We add a
massless uncharged scalar to the five dimensional supergravity theory, such
that it still forms a consistent truncation of the type IIB ten dimensional
supergravity and analyze its quasinormal modes. Separating the equation of
motion to a radial and angular part, we proceed to solve the radial equation
using the asymptotic matching expansion method applied to a Heun equation with
two nearby singularities. We use the continued fraction method for the angular
Heun equation and obtain numerical results for the quasinormal modes. In the
case of the supersymmetric black hole we present some analytic results for the
decay rates of the scalar perturbations. The spectrum of quasinormal modes
obtained is similar to that of a chiral 1+1 CFT, which is consistent with the
conjectured field-theoretic dual. In addition, some of the modes can be found
analytically.


  Experiments were performed to study how rocket exhaust blows soil in lunar
and Martian conditions. Jets of gas were blown downwardly at various granular
materials while a camera recorded the formation of scour holes as the material
was removed. The experiments were performed in a series of conditions ranging
from ambient 101 kPa pressure down to 0.3 kPa. This includes the range that is
relevant for lunar conditions, because it is not hard vacuum inside the rocket
exhaust where soil is being eroded. Prior work in ambient conditions showed
that erosion rate is proportional to the square of the densimetric Froude
number. However, the prior work was not performed at low pressures. In the new
work, preliminary results show that as the gas density is so low that gas flow
around the sand grains is no longer in the continuum regime, then erosion rate
is faster than predicted by the earlier results. The dependency of erosion rate
on various parameters is determined, but it turns out to be more complex than
expected so additional experimental work will be required to develop complete
scaling relationships.


  The task of Few-shot learning (FSL) aims to transfer the knowledge learned
from base categories with sufficient labelled data to novel categories with
scarce known information. It is currently an important research question and
has great practical values in the real-world applications. Despite extensive
previous efforts are made on few-shot learning tasks, we emphasize that most
existing methods did not take into account the distributional shift caused by
sample selection bias in the FSL scenario. Such a selection bias can induce
spurious correlation between the semantic causal features, that are causally
and semantically related to the class label, and the other non-causal features.
Critically, the former ones should be invariant across changes in
distributions, highly related to the classes of interest, and thus well
generalizable to novel classes, while the latter ones are not stable to changes
in the distribution. To resolve this problem, we propose a novel data
augmentation strategy dubbed as PatchMix that can break this spurious
dependency by replacing the patch-level information and supervision of the
query images with random gallery images from different classes from the query
ones. We theoretically show that such an augmentation mechanism, different from
existing ones, is able to identify the causal features. To further make these
features to be discriminative enough for classification, we propose
Correlation-guided Reconstruction (CGR) and Hardness-Aware module for instance
discrimination and easier discrimination between similar classes. Moreover,
such a framework can be adapted to the unsupervised FSL scenario.


  The search for a theory of quantum gravity is the most fundamental problem in
all of theoretical physics, but there are as yet no experimental results at all
to guide this endeavor. What seems to be needed is a pragmatic way to test if
gravitation really occurs between quantum objects or not. In this article we
suggest such a potential way out of this deadlock, utilizing macroscopic
quantum systems; superfluid helium, gaseous Bose-Einstein condensates and
"macroscopic" molecules. It turns out that true quantum gravity effects - here
defined as observable gravitational interactions between truly quantum objects
- could and should be seen (if they occur in nature) using existing technology.
A falsification of the low-energy limit, in the accessible weak-field regime,
would also falsify the full theory of quantum gravity, making it enter the
realm of testable, potentially falsifiable theories, i.e. becoming real physics
after almost a century of pure theorizing. If weak-field gravity between
quantum objects is shown to be absent (in the regime where the approximation
should apply), we know that gravity then is a strictly classical phenomenon
absent at the quantum level.


  The diversity of expressed genes plays a critical role in cellular
specialization, adaptation to environmental changes, and overall cell
functionality. This diversity varies dramatically across cell types and is
orchestrated by intricate, dynamic, and cell type-specific gene regulatory
networks (GRNs). Despite extensive research on GRNs, their governing
principles, as well as the underlying forces that have shaped them, remain
largely unknown. Here, we investigated whether there is a tradeoff between the
diversity of expressed genes and the intensity of GRN interactions. We have
developed a computational framework that evaluates GRN interaction intensity
from scRNA-seq data and used it to analyze simulated and real scRNA-seq data
collected from different tissues in humans, mice, fruit flies, and C. elegans.
We find a significant tradeoff between diversity and interaction intensity,
driven by stability constraints, where the GRN could be stable up to a critical
level of complexity - a product of gene expression diversity and interaction
intensity. Furthermore, we analyzed hematopoietic stem cell differentiation
data and find that the overall complexity of unstable transition states cells
is higher than that of stem cells and fully differentiated cells. Our results
suggest that GRNs are shaped by stability constraints which limit the diversity
of gene expression.


  An electromagnetic wave can be uniquely characterized by the four Stokes
parameters: I, Q, U, and V. Typical observations in astronomy rely solely on
total intensity measurements of the incoming radiation (Stokes I). However, a
significant amount of information both about the emitting region and the
propagation path is carried in the remaining Stokes parameters. These data
provide a means to observe parts of the interstellar medium which remain
invisible in Stokes I, at any wavelength. For example, when an electromagnetic
wave propagates through a region containing free electrons and a magnetic
field, the plane of polarisation of the wave will rotate - an effect recorded
only in Stokes Q and U. The interstellar medium of the Galaxy is such a region,
containing free electrons (observed as HII) and a magnetic field of a few
microgauss. By imaging in Stokes Q and U we are able to observe signatures of
magnetic field perturbations from the small scale (tens of pc) to the large
scale (kpc). In this paper, we review the status of Canadian polarisation
studies of cosmic magnetic fields and discuss the leading role Canada is
playing in polarsation studies around the world.


  We study ground states and excited states in semiconductor quantum dots
containing 1 to 12 electrons. For the first time, it is possible to identify
the quantum numbers of the states in the excitation spectra and make a direct
comparison to exact calculations. A magnetic field induces transitions between
excited states and ground state. These transitions are discussed in terms of
crossings between single-particle states, singlet-triplet transitions, spin
polarization, and Hund's rule. Our impurity-free quantum dots allow for atomic
physics experiments in magnetic field regimes not accessible for atoms.


  The classical Bertini theorem on generic intersection of an algebraic set
with hyperplanes states the following: \emph{Let X be a nonsingular closed
subvariety of $\mathbb{P}^n_k$, where $k$ is an algebraically closed field.
Then there exists a hyperplane $H\subset \mathbb{P}^n_k$ not containing $X$ and
such that the scheme $H\cap X$ is regular at every point. Furthermore, the set
of hyperplanes with this property forms an open dense subset of the complete
linear system $|H|$ considered as a projective space. } We will show that one
can effectively indicate a finite family of hyperplanes $H$ such that at least
one of them satisfies the assertion of the Bertini theorem. As an application
of the method used in the proof we will give effective formulas for the
multiplicity and the {\L}ojasiewicz exponent of polynomial mappings.


  The existence of phase-separated states is an essential feature of
infinite-volume systems with a thermal, first-order phase transition. At
energies between those at which the phase transition takes place, equilibrium
homogeneous states are either metastable or suffer from a spinodal instability.
In this range the stable states are inhomogeneous, phase-separated states. We
use holography to investigate how this picture is modified at finite volume in
a strongly coupled, four-dimensional gauge theory. We work in the planar limit,
$N \to \infty$, which ensures that we remain in the thermodynamic limit. We
uncover a rich set of inhomogeneous states dual to lumpy black branes on the
gravity side, as well as first- and second-order phase transitions between
them. We establish their local (in)stability properties and show that fully
non-linear time evolution in the bulk takes unstable states to stable ones.


  Training unsupervised speech recognition systems presents challenges due to
GAN-associated instability, misalignment between speech and text, and
significant memory demands. To tackle these challenges, we introduce a novel
ASR system, ESPUM. This system harnesses the power of lower-order N-skipgrams
(up to N=3) combined with positional unigram statistics gathered from a small
batch of samples. Evaluated on the TIMIT benchmark, our model showcases
competitive performance in ASR and phoneme segmentation tasks. Access our
publicly available code at https://github.com/lwang114/GraphUnsupASR.


  We demonstrate that coulomb interaction between a static charge source and K+
and K- from phi-meson decay could introduce systematic shift in the phi
invariant mass reconstructed from the K+K- pair.


  We show that a complete set of one-bit gates can be realized by coupling the
two logical states of a phase qubit to a third level (at higher energy) using
microwave pulses. Thus, one can achieve coherent control without invoking any
tunneling between the qubit levels. We propose two implementations, using
rf-SQUIDs and d-wave Josephson junctions.


  Optical microcavities support Whispering Gallery Modes (WGMs) with a very
high quality factor Q. However, WGMs typically display a far-field isotropic
emission profile and modifying this far-field profile without spoiling the
associated high Q remains a challenge. Using a 2D annular cavity, we present a
procedure capable to achieve these two apparently conflicting goals. With the
correspondence between the classical and the wave picture, properties of the
classical phase space shed some light on the characteristics of the wave
dynamics. Specifically, the annular cavity has a well separated mixed phase
space, a characteristic that proves to be of crucial importance in the emission
properties of WGMs. While the onset of directionality in the far-field may be
achieved through parametric deformation of the distance cavity-hole centers,
this contribution presents a method to control the emission profile via a
second parameter, the hole radius. The influence of the classical dynamics to
control and predict the field emission will be demonstrated.


  Physically-realistic simulated environments are powerful platforms for
enabling measurable, replicable and statistically-robust investigation of
complex robotic systems. Such environments are epitomised by the RoboCup
simulation leagues, which have been successfully utilised to conduct
massively-parallel experiments in topics including: optimisation of bipedal
locomotion, self-localisation from noisy perception data and planning complex
multi-agent strategies without direct agent-to-agent communication. Many of
these systems are later transferred to physical robots, making the simulation
leagues invaluable well-beyond the scope of simulated soccer matches. In this
study, we provide an overview of the RoboCup simulation leagues and describe
their properties as they pertain to replicable and robust robotics research. To
demonstrate their utility directly, we leverage the ability to run parallelised
experiments to evaluate different competition formats (e.g. round robin) for
the RoboCup 2D simulation league. Our results demonstrate that a
previously-proposed hybrid format minimises fluctuations from 'true'
(statistically-significant) team performance rankings within the time
constraints of the RoboCup world finals. Our experimental analysis would be
impossible with physical robots alone, and we encourage other researchers to
explore the potential for enriching their experimental pipelines with simulated
components, both to minimise experimental costsand enable others to replicate
and expand upon their results in a hardware-independent manner.


  In this paper we prove an optimal error estimate for the H(curl)-conforming
projection based p-interpolation operator introduced in [L. Demkowicz and I.
Babuska, p interpolation error estimates for edge finite elements of variable
order in two dimensions, SIAM J. Numer. Anal., 41 (2003), pp. 1195-1208]. This
result is proved on the reference element (either triangle or square) K for
regular vector fields in H^r(curl,K) with arbitrary r>0. The formulation of the
result in the H(div)-conforming setting, which is relevant for the analysis of
high-order boundary element approximations for Maxwell's equations, is provided
as well.


  Calibration and higher order statistics (HOS) are standard components of many
image steganalysis systems. These techniques have not yet found adequate
attention in audio steganalysis context. Specifically, most of current works
are either non-calibrated or only based on noise removal approach. This paper
aims to fill these gaps by proposing a new set of calibrated features based on
re-embedding technique. Additionally, we show that least significant bit (LSB)
is the most sensitive bit-plane to data hiding algorithms and therefore it can
be employed as a universal embedding method. Furthermore, the proposed features
are based on a model that has the maximum deviation from human auditory system
(HAS), and therefore are more suitable for the purpose of steganalysis.
Performance of the proposed method is evaluated on a wide range of data hiding
algorithms in both targeted and universal paradigms. Simulation results show
that the proposed method can detect the finest traces of data hiding algorithms
and in very low embedding rates. The system detects steghide at capacity of
0.06 bit per symbol (BPS) with sensitivity of 98.6% (music) and 78.5% (speech).
These figures are respectively 7.1% and 27.5% higher than state-of-the-art
results based on RMFCC.


  Fluid flows between rotating concentric cylinders exhibit two distinct routes
to turbulence. In flows dominated by inner-cylinder rotation, a sequence of
linear instabilities leads to temporally chaotic dynamics as the rotation speed
is increased. The resulting flow patterns occupy the whole system and
sequentially lose spatial symmetry and coherence in the transition process. In
flows dominated by outer-cylinder rotation, the transition is abrupt and leads
directly to turbulent flow regions that compete with laminar ones. We here
review the main features of these two routes to turbulence. Bifurcation theory
rationalises the origin of temporal chaos in both cases. However, the
catastrophic transition of flows dominated by outer-cylinder rotation can only
be understood by accounting for the spatial proliferation of turbulent regions
with a statistical approach. We stress the role of the rotation number (the
ratio of Coriolis to inertial forces) and show that it determines the lower
border for the existence of intermittent laminar-turbulent patterns.


  In this work we present a novel strategy to evaluate multi-variable integrals
with quantum circuits. The procedure first encodes the integration variables
into a parametric circuit. The obtained circuit is then derived with respect to
the integration variables using the parameter shift rule technique. The
observable representing the derivative is then used as the predictor of the
target integrand function following a quantum machine learning approach. The
integral is then estimated using the fundamental theorem of integral calculus
by evaluating the original circuit. Embedding data according to a reuploading
strategy, multi-dimensional variables can be easily encoded into the circuit's
gates and then individually taken as targets while deriving the circuit. These
techniques can be exploited to partially integrate a function or to quickly
compute parametric integrands within the training hyperspace.


  In this paper we show that a recent hydrodynamical model of superfluid
turbulence describes vortex density waves and their effects on the speed of
high-frequency second sound. In this frequency regime, the vortex dynamics is
not purely diffusive, as for low frequencies, but exhibits ondulatory features,
whose influence on the second sound is here explored.


  Despite mounting evidence that materials imperfections are a major obstacle
to practical applications of superconducting qubits, connections between
microscopic material properties and qubit coherence are poorly understood.
Here, we perform measurements of transmon qubit relaxation times $T_1$ in
parallel with spectroscopy and microscopy of the thin polycrystalline niobium
films used in qubit fabrication. By comparing results for films deposited using
three techniques, we reveal correlations between $T_1$ and grain size, enhanced
oxygen diffusion along grain boundaries, and the concentration of suboxides
near the surface. Physical mechanisms connect these microscopic properties to
residual surface resistance and $T_1$ through losses arising from the grain
boundaries and from defects in the suboxides. Further, experiments show that
the residual resistance ratio can be used as a figure of merit for qubit
lifetime. This comprehensive approach to understanding qubit decoherence charts
a pathway for materials-driven improvements of superconducting qubit
performance.


This paper is withdrawn. We found a mistake in Lemma 4.1


  This manuscript studies the Minkowski-Bellman equation, which is the Bellman
equation arising from finite or infinite horizon optimal control of
unconstrained linear discrete time systems with stage and terminal cost
functions specified as Minkowski functions of proper C-sets. In regards to the
finite horizon optimal control, it is established that, under natural
conditions, the Minkowski-Bellman equation and its iteration are well posed.
The characterization of the value functions and optimizer maps is derived. In
regards to the infinite horizon optimal control, it is demonstrated that, under
the same natural conditions, the fixed point of the Minkowski-Bellman equation
is unique, in terms of the value function, over the space of Minkowski
functions of proper C-sets. The characterization of the fixed point value
function and optimizer map is reported.


  We study the problem of agent-based negotiation in combinatorial domains. It
is difficult to reach optimal agreements in bilateral or multi-lateral
negotiations when the agents' preferences for the possible alternatives are not
common knowledge. Self-interested agents often end up negotiating inefficient
agreements in such situations. In this paper, we present a protocol for
negotiation in combinatorial domains which can lead rational agents to reach
optimal agreements under incomplete information setting. Our proposed protocol
enables the negotiating agents to identify efficient solutions using
distributed search that visits only a small subspace of the whole outcome
space. Moreover, the proposed protocol is sufficiently general that it is
applicable to most preference representation models in combinatorial domains.
We also present results of experiments that demonstrate the feasibility and
computational efficiency of our approach.


  This paper seeks to construct a representation of the algebra of angular
momentum (SU(2) algebra) in terms of the operator relations corresponding to
Gentile statistics in which one quantum state can be occupied by $n$ particles.
First, we present an operator realization of Gentile statistics. Then, we
propose a representation of angular momenta. The result shows that there exist
certain underlying connections between the operator realization of Gentile
statistics and the angular momentum (SU(2)) algebra.


  We have studied the feedback influence that the central engine of the Seyfert
2 galaxy NGC 1068 may have on the chemistry of the 200 pc circumnuclear gas
disk (CND). With this purpose, we have conducted a
multi-species/multi-transition survey of molecular gas in the CND of NGC 1068
using the IRAM 30m telescope. Abundances of several molecular species have been
estimated, including HCN, CN, CS, HCO+, SiO and HOC+. We report on the
detection of significant SiO emission in this galaxy, as well as on the first
extragalactic detection of the active radical HOC+. We conclude that the
chemistry of the molecular gas reservoir in the CND can be best explained in
the framework of X-rays Dominated Regions (XDR) models.


  We present a detailed analysis of the intrinsic absorption in the Seyfert 1
galaxy NGC 4151 using UV spectra from the HST/STIS and FUSE, obtained 2002 May
as part of a set of contemporaneous observations that included Chandra/HETGS
spectra. In our analysis of the Chandra spectra, we determined that the soft
X-ray absorber was the source of the saturated UV lines of O VI, C IV, and N V
associated with the absorption feature at a radial velocity of ~ -500 km/sec,
which we referred to as component D+E. In the present work, we have derived
tighter constrains on the the line-of-sight covering factors, densities, and
radial distances of the absorbers. We find that the Equivalent Widths (EWs) of
the low-ionization lines associated with D+E varied over the period from 1999
July to 2002 May. The drop in the EWs of these lines between 2001 April and
2002 May are suggestive of bulk motion of gas out of our line-of-sight. If
these lines from these two epochs arose in the same sub-component, the
transverse velocity of the gas is ~ 2100 km/sec. Transverse velocities of this
order are consistent with an origin in a rotating disk, at the roughly radial
distance we derived for D+E.


  We investigate the connection between the geometry of Favoured Local
Structures (FLS) in liquids and the associated liquid and solid properties. We
introduce a lattice spin model - the FLS model on a face-centered cubic lattice
- where this geometry can be arbitrarily chosen among a discrete set of 115
possible FLS. We find crystalline groundstates for all choices of a single FLS.
Sampling all possible FLS's, we identify the following trends: i) low symmetry
FLS's produce larger crystal unit cells but not necessarily higher energy
groundstates, ii) chiral FLS's exhibit to peculiarly poor packing properties,
iii) accumulation of FLS's in supercooled liquids is linked to large crystal
unit cells, and iv) low symmetry FLS's tend to find metastable structures on
cooling.


  This paper considers reference free quality assessment of distorted and noisy
images. Specifically, it considers the first and second order statistics of
stem noise that can be evaluated given any image. In the research field of
Image quality Assessment (IQA), the stem noise is defined as the input of an
Auto Regressive (AR) process, from which a low-energy and de-correlated version
of the image can be recovered. To estimate the AR model parameters and
associated stem noise energy, the Yule-walker equations are used such that the
accompanying Auto Correlation Function (ACF) coefficients can be treated as
model parameters for image reconstruction. To characterize systematic signal
dependent and signal independent distortions, the mean and variance of stem
noise can be evaluated over the image. Crucially, this paper shows that these
statistics have a predictive validity in relation to human ratings of image
quality. Furthermore, under certain kinds of image distortion, stem noise
statistics show very significant correlations with established measures of
image quality.


  We derive a number of estimates for the probability that a chordal SLE path
in the upper half plane H intersects a semicircle centred on the real line. We
prove that if 0<kappa<8 and gamma:[0,infinity) to closure(H) is a chordal SLE
in H from 0 to infinity, then P(gamma[0,infinity) cap C(x;rx) neq emptyset)
asymp r^(4a-1) where a=2/kappa and C(x;rx) denotes the semicircle centred at
x>0 of radius rx, 0<r<1/3, in the upper half plane. As an application of our
results, for 0<kappa<8, we derive an estimate for the diameter of a chordal SLE
path in H between two real boundary points 0 and x>0. For 4<kappa<8, we also
estimate the probability that an entire semicircle on the real line is
swallowed at once by a chordal SLE path in H from 0 to infinity.


  The conventional cluster-robust (CR) standard errors may not be robust. They
are vulnerable to data that contain a small number of large clusters. When a
researcher uses the 51 states in the U.S. as clusters, the largest cluster
(California) consists of about 10% of the total sample. Such a case in fact
violates the assumptions under which the widely used CR methods are guaranteed
to work. We formally show that the conventional CR methods fail if the
distribution of cluster sizes follows a power law with exponent less than two.
Besides the example of 51 state clusters, some examples are drawn from a list
of recent original research articles published in a top journal. In light of
these negative results about the existing CR methods, we propose a weighted CR
(WCR) method as a simple fix. Simulation studies support our arguments that the
WCR method is robust while the conventional CR methods are not.


  Thompson sampling is a heuristic algorithm for the multi-armed bandit problem
which has a long tradition in machine learning. The algorithm has a Bayesian
spirit in the sense that it selects arms based on posterior samples of reward
probabilities of each arm. By forging a connection between combinatorial binary
bandits and spike-and-slab variable selection, we propose a stochastic
optimization approach to subset selection called Thompson Variable Selection
(TVS). TVS is a framework for interpretable machine learning which does not
rely on the underlying model to be linear. TVS brings together Bayesian
reinforcement and machine learning in order to extend the reach of Bayesian
subset selection to non-parametric models and large datasets with very many
predictors and/or very many observations. Depending on the choice of a reward,
TVS can be deployed in offline as well as online setups with streaming data
batches. Tailoring multiplay bandits to variable selection, we provide regret
bounds without necessarily assuming that the arm mean rewards be unrelated. We
show a very strong empirical performance on both simulated and real data.
Unlike deterministic optimization methods for spike-and-slab variable
selection, the stochastic nature makes TVS less prone to local convergence and
thereby more robust.


  Prior works for reconstructing hand-held objects from a single image rely on
direct 3D shape supervision which is challenging to gather in real world at
scale. Consequently, these approaches do not generalize well when presented
with novel objects in in-the-wild settings. While 3D supervision is a major
bottleneck, there is an abundance of in-the-wild raw video data showing
hand-object interactions. In this paper, we automatically extract 3D
supervision (via multiview 2D supervision) from such raw video data to scale up
the learning of models for hand-held object reconstruction. This requires
tackling two key challenges: unknown camera pose and occlusion. For the former,
we use hand pose (predicted from existing techniques, e.g. FrankMocap) as a
proxy for object pose. For the latter, we learn data-driven 3D shape priors
using synthetic objects from the ObMan dataset. We use these indirect 3D cues
to train occupancy networks that predict the 3D shape of objects from a single
RGB image. Our experiments on the MOW and HO3D datasets show the effectiveness
of these supervisory signals at predicting the 3D shape for real-world
hand-held objects without any direct real-world 3D supervision.


  The security development lifecycle (SDL) is becoming an industry standard.
Dynamic symbolic execution (DSE) has enormous amount of applications in
computer security (fuzzing, vulnerability discovery, reverse-engineering,
etc.). We propose several performance and accuracy improvements for dynamic
symbolic execution. Skipping non-symbolic instructions allows to build a path
predicate 1.2--3.5 times faster. Symbolic engine simplifies formulas during
symbolic execution. Path predicate slicing eliminates irrelevant conjuncts from
solver queries. We handle each jump table (switch statement) as multiple
branches and describe the method for symbolic execution of multi-threaded
programs. The proposed solutions were implemented in Sydr tool. Sydr performs
inversion of branches in path predicate. Sydr combines DynamoRIO dynamic binary
instrumentation tool with Triton symbolic engine. We evaluated Sydr features on
64-bit Linux executables.


  Based on the supersymmetric quantum mechanical approach, we have
systematically studied both the $U\left(1\right)$ gauge anomaly and the
diffeomorphism anomaly in Weyl systems with torsion, curvature and external
electromagnetic fields. These anomalies relate to the chiral current (or
current) non-conservation and chiral energy-momentum (or energy-momentum)
non-conservation, respectively, which can be applied to the $^{3}\text{He-A}$
phase, the chiral superconductors and the Weyl semimetals with dislocations and
disclinations. In sharp difference with other anomalies, there exist torsional
anomalies depending on the position of Weyl nodes in the energy-momentum space.
These anomalies originate from particles pumped up through the Weyl nodes and
they are thus insensitive to the ultra-violet physics, while the Nieh-Yan
anomaly is from the particle inflow through the ultra-violet cut-off. The
current non-conservation as well as the energy-momentum non-conservation are
found, which stem from the zero modes trapped in the dislocations and they can
be understood from the Callan-Harvey mechanism. Finally, by comparing our
results with the well-established momentum anomaly in the $^{3}\text{He-A}$
phase, the Nieh-Yan term as well as other cut-off dependent terms are shown to
be negligible, because the ratio between the Lorentz symmetry breaking scale
and the chemical potential is of order $10^{-5}$.


  We represent transport between different regions of a fluid domain by flow
networks, constructed from the discrete representation of the Perron-Frobenius
or transfer operator associated to the fluid advection dynamics. The procedure
is useful to analyze fluid dynamics in geophysical contexts, as illustrated by
the construction of a flow network associated to the surface circulation in the
Mediterranean sea. We use network-theory tools to analyze the flow network and
gain insights into transport processes. In particular we quantitatively relate
dispersion and mixing characteristics, classically quantified by Lyapunov
exponents, to the degree of the network nodes. A family of network entropies is
defined from the network adjacency matrix, and related to the statistics of
stretching in the fluid, in particular to the Lyapunov exponent field. Finally
we use a network community detection algorithm, Infomap, to partition the
Mediterranean network into coherent regions, i.e. areas internally well mixed,
but with little fluid interchange between them.


  Mutual information (MI) is a fundamental quantity in information theory and
machine learning. However, direct estimation of MI is intractable, even if the
true joint probability density for the variables of interest is known, as it
involves estimating a potentially high-dimensional log partition function. In
this work, we present a unifying view of existing MI bounds from the
perspective of importance sampling, and propose three novel bounds based on
this approach. Since accurate estimation of MI without density information
requires a sample size exponential in the true MI, we assume either a single
marginal or the full joint density information is known. In settings where the
full joint density is available, we propose Multi-Sample Annealed Importance
Sampling (AIS) bounds on MI, which we demonstrate can tightly estimate large
values of MI in our experiments. In settings where only a single marginal
distribution is known, we propose Generalized IWAE (GIWAE) and MINE-AIS bounds.
Our GIWAE bound unifies variational and contrastive bounds in a single
framework that generalizes InfoNCE, IWAE, and Barber-Agakov bounds. Our
MINE-AIS method improves upon existing energy-based methods such as MINE-DV and
MINE-F by directly optimizing a tighter lower bound on MI. MINE-AIS uses MCMC
sampling to estimate gradients for training and Multi-Sample AIS for evaluating
the bound. Our methods are particularly suitable for evaluating MI in deep
generative models, since explicit forms of the marginal or joint densities are
often available. We evaluate our bounds on estimating the MI of VAEs and GANs
trained on the MNIST and CIFAR datasets, and showcase significant gains over
existing bounds in these challenging settings with high ground truth MI.


  The heterogeneity in cellular networks that comprise multiple base stations
types imposes new challenges in network planning and deployment. The Radio
Resource Management (RRM) techniques, such as dynamic sharing of the available
resources and advanced user association strategies determine the overall
network capacity and the network/spectrum efficiency. This paper evaluates the
downlink performance of a two-tier heterogeneous LTE network (consisting of
macro and femto tiers) in terms of rate distribution, i.e. the percentage of
users that achieve certain rate in the system. The paper specifically
addresses: (1) the femto tier RRM by randomization of the allocated resources;
(2) the user association process by introducing novel proactive offloading
scheme and (3) femto tier access control. System level simulation results show
that an optimal RRM strategy can be designed for different scenarios (e.g.
congested and uncongested networks). The proposed proactive offloading scheme
in the association phase improves the performance of congested networks by
efficiently utilizing the available femto tier resources. Finally, the
introduced hybrid access in femto tier is shown to perform nearly identical as
the open access.


  Alkali superoxide CsO2 is one of candidates for the spin-1/2 one-dimensional
(1D) antiferromagnet, which may be sequentially caused by an ordering of the
pi-orbital of O2- molecule below TS ~ 70 K. Here, we report on the magnetism in
powder CsO2 and high-magnetic field magnetization measurements in
pulsed-magnetic fields of up to 60 T. We obtained the low temperature phase
diagram around the antiferromagnetic ordering temperature TN = 9.6 K under the
magnetic field. At 1.3 K, remarkable up-turn curvature in the magnetization
around a saturation field of ~ 60 T is found, which corresponds to the
spin-1/2. We will compare it with the theoretical calculation.


  The main result of this paper is the computation of the Lie superalgebras of
holomorphic vector fields on complex flag supermanifolds, introduced by
Yu.I.Manin. We prove that with several exceptions any holomorphic vector field
is fundamental with respect to the natural action of the Lie superalgebra
$\mathfrak {gl}_{m|n}(\mathbb C)$.


  Investigations of natural variation among cells within a population are
essential for understanding the stochastic nature of tissue cell deformation
under applied load. In the existing literature, the population variation of
single-cell creep compliance has so far been modeled universally by using a
log-normal distribution. Here we use optical stretching, a non-contact and
relatively high-throughput technique for probing cell mechanics, to accumulate
a sufficient data set that demonstrates robustly that cell compliance varies
according to the similar but distinct gamma distribution. Additionally, we
re-examine existing simulations that were originally proposed to justify a
log-normal fit, and show that in fact these simulation data also correspond to
the gamma distribution. Finally, we propose a general stochastic differential
equation that analytically predicts a gamma distribution of creep compliance
during cell stretching, as well as the Gaussian distribution of cell recovery
that we observe experimentally upon removal of applied load. The population
variation is well characterized by just a single parameter in each of the creep
and recovery regimes. We expect our correction of a phenomenological
distribution fit, enabled by an expansive data set for mesenchymal stem cells,
to enable the development of more accurate constitutive laws to describe
cytoskeletal deformation. These findings thus serve to replace an empirical
distribution with a better-fitting model that rests on a more solid
experimental and theoretical foundation, and also provides a basis to predict
and understand the stochastic nature of the mechanical response of individual
cells within populations.


  We discuss U(1) lattice gauge theory models based on a modified Villain
formulation of the gauge action, which allows coupling to bosonic electric and
magnetic matter. The formulation enjoys a duality which maps electric and
magnetic sectors into each other. We propose several generalizations of the
model and discuss their 't~Hooft anomalies. A particularly interesting class of
theories is the one where electric and magnetic matter fields are coupled with
identical actions, such that for a particular value of the gauge coupling the
theory has a self-dual symmetry. The self-dual symmetry turns out to be a
generator of a group which is a central extension of $\mathbb Z_4$ by the
lattice translation symmetry group. The simplest case amenable to numerical
simulations is the case when there is exactly one electrically and one
magnetically charged boson. We discuss the phase structure of this theory and
the nature of the self-dual symmetry in detail. Using a suitable worldline
representation of the system we present the results of numerical simulations
that support the conjectured phase diagram.


  The SYK model consists of $N\gg 1$ fermions in $0+1$ dimensions with a
random, all-to-all quartic interaction. Recently, Kitaev has found that the SYK
model is maximally chaotic and has proposed it as a model of holography. We
solve the Schwinger-Dyson equation and compute the spectrum of two-particle
states in SYK, finding both a continuous and discrete tower. The four-point
function is expressed as a sum over the spectrum. The sum over the discrete
tower is evaluated.


  The main objective of this paper is to propose an alternative procedure to
carry out one of the key steps of immersion and invariance stabilising
controller design. Namely, the one that ensures attractivity of the manifold
whose internal dynamics contains a copy of the desired system behaviour.
Towards this end we invoke contraction theory principles and ensure the
attractivity of the manifold rendering it horizontally contractive. The main
advantage of adopting this alternative approach is to make more systematic the
last step of the design with more explicit degrees of freedom to accomplish the
task. The classical case of systems in feedback form is used to illustrate the
proposed controller design.


  Data centers handle impressive high figures in terms of energy consumption,
and the growing popularity of Cloud applications is intensifying their
computational demand. Moreover, the cooling needed to keep the servers within
reliable thermal operating conditions also has an impact on the thermal
distribution of the data room, thus affecting to servers' power leakage.
Optimizing the energy consumption of these infrastructures is a major challenge
to place data centers on a more scalable scenario. Thus, understanding the
relationship between power, temperature, consolidation and performance is
crucial to enable an energy-efficient management at the data center level. In
this research, we propose novel power and thermal-aware strategies and models
to provide joint cooling and computing optimizations from a local perspective
based on the global energy consumption of metaheuristic-based optimizations.
Our results show that the combined awareness from both metaheuristic and best
fit decreasing algorithms allow us to describe the global energy into faster
and lighter optimization strategies that may be used during runtime. This
approach allows us to improve the energy efficiency of the data center,
considering both computing and cooling infrastructures, in up to a 21.74\%
while maintaining quality of service.


  Large Language Models (LLMs) such as GPTs and LLaMa have ushered in a
revolution in machine intelligence, owing to their exceptional capabilities in
a wide range of machine learning tasks. However, the transition of LLMs from
data centers to edge devices presents a set of challenges and opportunities.
While this shift can enhance privacy and availability, it is hampered by the
enormous parameter sizes of these models, leading to impractical runtime costs.
In light of these considerations, we introduce EdgeMoE, the first on-device
inference engine tailored for mixture-of-expert (MoE) LLMs, a popular variant
of sparse LLMs that exhibit nearly constant computational complexity as their
parameter size scales. EdgeMoE achieves both memory and computational
efficiency by strategically partitioning the model across the storage
hierarchy. Specifically, non-expert weights are stored in the device's memory,
while expert weights are kept in external storage and are fetched into memory
only when they are activated. This design is underpinned by a crucial insight
that expert weights, though voluminous, are infrequently accessed due to sparse
activation patterns. To further mitigate the overhead associated with expert
I/O swapping, EdgeMoE incorporates two innovative techniques: (1) Expert-wise
bitwidth adaptation: This method reduces the size of expert weights with an
acceptable level of accuracy loss. (2) Expert management: It predicts the
experts that will be activated in advance and preloads them into the
compute-I/O pipeline, thus further optimizing the process. In empirical
evaluations conducted on well-established MoE LLMs and various edge devices,
EdgeMoE demonstrates substantial memory savings and performance improvements
when compared to competitive baseline solutions.


  Development of nuclear energy applications requires data for neutron-induced
reactions for actinides in a wide neutron energy range. Here we describe
measurements of pre-neutron emission fission fragment mass yields of 232Th and
238U at incident neutron energies from 10 to 33 MeV. The measurements were done
at the quasi-monoenergetic neutron beam of the Louvain-la-Neuve cyclotron
facility CYCLONE; a multi-section twin Frisch-gridded ionization chamber was
used to detect fission fragments. For the peak neutron energies at 33, 45 and
60 MeV, the details of the data analysis and the experimental results have been
published before and in this work we present data analysis in the low-energy
tail of the neutron energy spectra. The preliminary measurement results are
compared with available experimental data and theoretical predictions.


  In this paper, we derive a B\"{a}cklund transformation for the supersymmetric
Kortweg-de Vries equation. We also construct a nonlinear superposition formula,
which allows us to rebuild systematically for the supersymmetric KdV equation
the soliton solutions of Carstea, Ramani and Grammaticos.


  Graph Neural Network (GNN) has achieved state-of-the-art performance in
various high-stake prediction tasks, but multiple layers of aggregations on
graphs with irregular structures make GNN a less interpretable model. Prior
methods use simpler subgraphs to simulate the full model, or counterfactuals to
identify the causes of a prediction. The two families of approaches aim at two
distinct objectives, "simulatability" and "counterfactual relevance", but it is
not clear how the objectives can jointly influence the human understanding of
an explanation. We design a user study to investigate such joint effects and
use the findings to design a multi-objective optimization (MOO) algorithm to
find Pareto optimal explanations that are well-balanced in simulatability and
counterfactual. Since the target model can be of any GNN variants and may not
be accessible due to privacy concerns, we design a search algorithm using
zeroth-order information without accessing the architecture and parameters of
the target model. Quantitative experiments on nine graphs from four
applications demonstrate that the Pareto efficient explanations dominate
single-objective baselines that use first-order continuous optimization or
discrete combinatorial search. The explanations are further evaluated in
robustness and sensitivity to show their capability of revealing convincing
causes while being cautious about the possible confounders. The diverse
dominating counterfactuals can certify the feasibility of algorithmic recourse,
that can potentially promote algorithmic fairness where humans are
participating in the decision-making using GNN.


  The nonlinear Schrodinger (NLS) equation is considered on a periodic metric
graph subject to the Kirchhoff boundary conditions. Bifurcations of standing
localized waves for frequencies lying below the bottom of the linear spectrum
of the associated stationary Schrodinger equation are considered by using
analysis of two-dimensional discrete maps near hyperbolic fixed points. We
prove existence of two distinct families of small-amplitude standing localized
waves, which are symmetric about the two symmetry points of the periodic
graphs. We also prove properties of the two families, in particular, positivity
and exponential decay. The asymptotic reduction of the two-dimensional discrete
map to the stationary NLS equation on an infinite line is discussed in the
context of the homogenization of the NLS equation on the periodic metric graph.


  Signal processing and communication communities have witnessed the rise of
many exciting communication technologies in recent years. Notable examples
include alternative waveforms, massive multiple-input multiple-output (MIMO)
signaling, non-orthogonal multiple access (NOMA), joint communications and
sensing, sparse vector coding, index modulation, and so on. It is inevitable
that 6G wireless networks will require a rethinking of wireless communication
systems and technologies, particularly at the physical layer (PHY), considering
the fact that the cellular industry reached another important milestone with
the development of 5G wireless networks with diverse applications. Within this
perspective, this article aims to shed light on the rising concept of
reconfigurable intelligent surface (RIS)-empowered communications towards 6G
wireless networks. Software-defined RISs can manipulate their impinging signals
in an effective way to boost certain key performance indicators. We discuss the
recent developments in the field and put forward promising candidates for
future research and development. Specifically, we put our emphasis on active,
transmitter-type, transmissive-reflective, and standalone RISs, by discussing
their advantages and disadvantages compared to reflective RIS designs. Finally,
we also envision an ultimate RIS architecture, which is able to adjust its
operation modes dynamically, and introduce the new concept of PHY slicing over
RISs towards 6G wireless networks.


  As Internet-based commerce becomes increasingly widespread, large data sets
about the demand for and pricing of a wide variety of products become
available. These present exciting new opportunities for empirical economic and
business research, but also raise new statistical issues and challenges. In
this article, we summarize research that aims to assess the optimality of price
discrimination in the software industry using a large e-commerce panel data set
gathered from Amazon.com. We describe the key parameters that relate to demand
and cost that must be reliably estimated to accomplish this research
successfully, and we outline our approach to estimating these parameters. This
includes a method for ``reverse engineering'' actual demand levels from the
sales ranks reported by Amazon, and approaches to estimating demand elasticity,
variable costs and the optimality of pricing choices directly from publicly
available e-commerce data. Our analysis raises many new challenges to the
reliable statistical analysis of e-commerce data and we conclude with a brief
summary of some salient ones.


  Segmentation of fetal brain tissue from magnetic resonance imaging (MRI)
plays a crucial role in the study of in utero neurodevelopment. However,
automated tools face substantial domain shift challenges as they must be robust
to highly heterogeneous clinical data, often limited in numbers and lacking
annotations. Indeed, high variability of the fetal brain morphology, MRI
acquisition parameters, and superresolution reconstruction (SR) algorithms
adversely affect the model's performance when evaluated out-of-domain. In this
work, we introduce FetalSynthSeg, a domain randomization method to segment
fetal brain MRI, inspired by SynthSeg. Our results show that models trained
solely on synthetic data outperform models trained on real data in out-ofdomain
settings, validated on a 120-subject cross-domain dataset. Furthermore, we
extend our evaluation to 40 subjects acquired using lowfield (0.55T) MRI and
reconstructed with novel SR models, showcasing robustness across different
magnetic field strengths and SR algorithms. Leveraging a generative synthetic
approach, we tackle the domain shift problem in fetal brain MRI and offer
compelling prospects for applications in fields with limited and highly
heterogeneous data.


  We present observational results of the [Fe II] 1.644 um emission from the
jets of L1551 IRS 5. The data sets were obtained through 13 fully sampled slits
aimed at the base of the jets. These sets are used to construct a
three-dimensional cube. The field of view was 5."8 X 4."2. We confirmed that
the position of the knot PHK1 coincides with a stationary, point-like x-ray
source within 0."3. The northern and southern jets are distinguished from each
other at a point 0."6 away from their driving sources. We also confirmed that
the northern jet consists of well-separated high- and low-velocity components
(HVC and LVC, respectively). The HVC has a terminal velocity of ~400 km/s and
shows a consistently narrow velocity width of 40 km/s. The LVC covers the
velocity range from V_LSR = 0 to -240 km/s and has broad velocity widths of
~150-180 km/s. These decrease with distance from the driving sources. The
spatial width of the LVC varies from 0."6-0."7 at V_LSR ~ -200 km/s to
0."8-0."9 at V_LSR ~ -30 km/s. These characteristics are well understood in
terms of the two types of outflow mechanisms that are working simultaneously:
one is the HVC, which is launched in a narrow, inner radial region at 0.04-0.05
AU, and the other is the LVC, which is launched in a wider, outer radial region
from within 0.1-4.5 AU of the accretion disk. Part of the LVC emission could
arise in the gas entrained or shocked by the HVC. We also discuss the
possibility that part of the HVC gas is thermalized at PHK1 to produce the
x-ray emission and LVC.


  In this work we study a family of bosonic lattice models that combine an
on-site repulsion term with a nearest-neighbor pairing term, $\sum_{< i,j>}
a^\dagger_i a^\dagger_j + \mathrm{H.c.}$ Like the original Bose-Hubbard model,
the nearest-neighbor term is responsible for the mobility of bosons and it
competes with the local interaction, inducing two-mode squeezing. However,
unlike a trivial hopping, the counter-rotating terms form pairing cannot be
studied with a simple mean-field theory and does not present a quantum phase
transition in phase space. Instead, we show that there is a cross-over from a
pure insulator to long-range correlations that start up as soon as the two-mode
squeezing is switched on. We also show how this model can be naturally
implemented using coupled microwave resonators and superconducting qubits.


  The Projection on Proper elements (PoPe) is a novel method of code control
dedicated to 1) checking the correct implementation of models, 2) determining
the convergence of numerical methods and 3) characterizing the residual errors
of any given solution at very low cost. The basic idea is to establish a
bijection between a simulation and a set of equations that generate it.
Recovering equations is direct and relies on a statistical measure of the
weight of the various operators. This method can be used in any dimensions and
any regime, including chaotic ones. This method also provides a procedure to
design reduced models and quantify the ratio costs to benefits. PoPe is applied
to a kinetic and a fluid code of plasma turbulence.


  We investigate the symplectic geometric and differential geometric aspects of
the moduli space of connections on a compact Riemann surface $X$. Fix a theta
characteristic $K^{1/2}_X$ on $X$; it defines a theta divisor on the moduli
space ${\mathcal M}$ of stable vector bundles on $X$ of rank $r$ degree zero.
Given a vector bundle $E \in {\mathcal M}$ lying outside the theta divisor, we
construct a natural holomorphic connection on $E$ that depends holomorphically
on $E$. Using this holomorphic connection, we construct a canonical holomorphic
isomorphism between the following two: \begin{enumerate} \item the moduli space
$\mathcal C$ of pairs $(E, D)$, where $E\in {\mathcal M}$ and $D$ is a
holomorphic connection on $E$, and
  \item the space ${\rm Conn}(\Theta)$ given by the sheaf of holomorphic
connections on the line bundle on $\mathcal M$ associated to the theta divisor.
\end{enumerate} The above isomorphism between $\mathcal C$ and ${\rm
Conn}(\Theta)$ is symplectic structure preserving, and it moves holomorphically
as $X$ runs over a holomorphic family of Riemann surfaces.


  The emergence of Large Language Models (LLMs) like ChatGPT has inspired the
development of LLM-based agents capable of addressing complex, real-world
tasks. However, these agents often struggle during task execution due to
methodological constraints, such as error propagation and limited adaptability.
To address this issue, we propose a multi-agent framework based on dynamic Task
Decomposition and Agent Generation (TDAG). This framework dynamically
decomposes complex tasks into smaller subtasks and assigns each to a
specifically generated subagent, thereby enhancing adaptability in diverse and
unpredictable real-world tasks. Simultaneously, existing benchmarks often lack
the granularity needed to evaluate incremental progress in complex, multi-step
tasks. In response, we introduce ItineraryBench in the context of travel
planning, featuring interconnected, progressively complex tasks with a
fine-grained evaluation system. ItineraryBench is designed to assess agents'
abilities in memory, planning, and tool usage across tasks of varying
complexity. Our experimental results reveal that TDAG significantly outperforms
established baselines, showcasing its superior adaptability and context
awareness in complex task scenarios.


  Explainable question answering systems predict an answer together with an
explanation showing why the answer has been selected. The goal is to enable
users to assess the correctness of the system and understand its reasoning
process. However, we show that current models and evaluation settings have
shortcomings regarding the coupling of answer and explanation which might cause
serious issues in user experience. As a remedy, we propose a hierarchical model
and a new regularization term to strengthen the answer-explanation coupling as
well as two evaluation scores to quantify the coupling. We conduct experiments
on the HOTPOTQA benchmark data set and perform a user study. The user study
shows that our models increase the ability of the users to judge the
correctness of the system and that scores like F1 are not enough to estimate
the usefulness of a model in a practical setting with human users. Our scores
are better aligned with user experience, making them promising candidates for
model selection.


  We analyze a solitaire game in which a demon rearranges some cards after each
move. The graph edge coloring theorems of K\H{o}nig (1931) and Vizing (1964)
follow from the winning strategies developed.


  The aerodynamic breakup of a polymeric droplet in the bag breakup regime is
investigated experimentally and compared with the result of the Newtonian
droplet. To understand the effect of liquid elasticity, the Weber number is
kept fixed ($\approx$ 12.5) while the elasticity number is varied in the range
of $\sim 10^{-4}-10^{-2}$. Experiments are performed by allowing a liquid
droplet to fall in a horizontal, continuously flowing air stream. It is
observed that the initial deformation dynamics of a polymeric droplet is
similar to the Newtonian droplet. However, in the later stages, the actual
fragmentation of liquid mass is resisted by the presence of polymers. Depending
upon the liquid elasticity, fragmentation can be completely inhibited in the
timescale of experimental observation. We provide a framework to study this
problem, identify the stages where the role of liquid elasticity can be
neglected and where it must be considered, and finally, establish a criterion
that governs the occurrence or the absence of fragmentation in a specified time
period.


  This paper presents our method for enabling a UAV quadrotor, equipped with a
monocular camera, to autonomously avoid collisions with obstacles in
unstructured and unknown indoor environments. When compared to obstacle
avoidance in ground vehicular robots, UAV navigation brings in additional
challenges because the UAV motion is no more constrained to a well-defined
indoor ground or street environment. Horizontal structures in indoor and
outdoor environments like decorative items, furnishings, ceiling fans,
sign-boards, tree branches etc., also become relevant obstacles unlike those
for ground vehicular robots. Thus, methods of obstacle avoidance developed for
ground robots are clearly inadequate for UAV navigation. Current control
methods using monocular images for UAV obstacle avoidance are heavily dependent
on environment information. These controllers do not fully retain and utilize
the extensively available information about the ambient environment for
decision making. We propose a deep reinforcement learning based method for UAV
obstacle avoidance (OA) and autonomous exploration which is capable of doing
exactly the same. The crucial idea in our method is the concept of partial
observability and how UAVs can retain relevant information about the
environment structure to make better future navigation decisions. Our OA
technique uses recurrent neural networks with temporal attention and provides
better results compared to prior works in terms of distance covered during
navigation without collisions. In addition, our technique has a high inference
rate (a key factor in robotic applications) and is energy-efficient as it
minimizes oscillatory motion of UAV and reduces power wastage.


  We combine a complete sample of 113 pointed observations taken with the Rossi
X-ray Timing Explorer between 1996-1999, monitoring observations taken with the
Ryle telescope and the Green Bank Interferometer, and selected observations
with the Very Large Array to study the radio and X-ray properties of GRS
1915+105 when its X-ray emission is hard and steady. We establish that radio
emission always accompanies the hard-steady state of GRS 1915+105, but that the
radio flux density at 15.2 GHz and the X-ray flux between 2-200 keV are not
correlated. Therefore we study the X-ray spectral and timing properties of GRS
1915+105 using three approaches: first, by describing in detail the properties
of three characteristic observations, then by displaying the time evolution of
the timing properties during periods of both faint and bright radio emission,
and lastly by plotting the timing properties as a function of the the radio
flux density. We find that as the radio emission becomes brighter and more
optically thick, 1) the frequency of a ubiquitous 0.5-10 Hz QPO decreases, 2)
the Fourier phase lags between hard (11.5-60 keV) and soft (2-4.3 keV) in the
frequency range of 0.01-10 Hz change sign from negative to positive, 3) the
coherence between hard and soft photons at low frequencies decreases, and 4)
the relative amount of low frequency power in hard photons compared to soft
photons decreases. We discuss how these results reflect upon basic models from
the literature describing the accretion flow around black holes and the
possible connection between Comptonizing electrons and compact radio jets.


  We propose a new topological characterization of RNA secondary structures
with pseudoknots based on two topological invariants. Starting from the classic
arc-representation of RNA secondary structures, we consider a model that
couples both I) the topological genus of the graph and II) the number of
crossing arcs of the corresponding primitive graph. We add a term proportional
to these topological invariants to the standard free energy of the RNA
molecule, thus obtaining a novel free energy parametrization which takes into
account the abundance of topologies of RNA pseudoknots observed in RNA
databases.


  A mechanism capable to provide a natural solution to two major cosmological
problems, i.e. the cosmic acceleration and the coincidence problem, is
proposed. A specific brane-bulk energy exchange mechanism produces a total dark
pressure, arising when adding all normal to the brane negative pressures in the
interior of galactic core black holes. This astrophysically produced negative
dark pressure explains cosmic acceleration and why the dark energy today is of
the same order to the matter density for a wide range of the involved
parameters. An exciting result of the analysis is that the recent rise of the
galactic core black hole mass density causes the recent passage from cosmic
deceleration to acceleration. Finally, it is worth mentioning that this work
corrects a wide spread fallacy among brane cosmologists, i.e. that escaping
gravitons result to positive dark pressure.


  Collaborative filtering recommenders provide effective personalization
services at the cost of sacrificing the privacy of their end users. Due to the
increasing concerns from the society and stricter privacy regulations, it is an
urgent research challenge to design privacy-preserving and yet robust
recommenders which offer recommendation services to privacy-aware users. Our
analysis shows that existing solutions fall short in several aspects, including
lacking attention to the precise output to end users and ignoring the
correlated robustness issues. In this paper, we provide a general system
structure for latent factor based collaborative filtering recommenders by
formulating them into model training and prediction computing stages, and also
describe a new security model. Aiming at pragmatic solutions, we first show how
to construct privacy-preserving and yet robust model training stage based on
existing solutions. Then, we propose two cryptographic protocols to realize a
privacy-preserving prediction computing stage, depending on whether or not an
extra proxy is involved. Different from standard Top-k recommendations, we
alternatively let the end user retrieve the unrated items whose predictions are
above a threshold, as a result of our privacy by design strategy. Experimental
results show that our new protocols are quite efficient.


  Let $D$ be a domain in $R^d$ and $u$ be the solution to the stochastic heat
equation $$ du=\Delta u dt+ g\,dW_t, \quad t>0, x\in D, $$ with zero initial
and boundary data. Here $W_t$ is a one-dimensional Wiener process on a
probability space $\Omega$. It has been proved (see below for references) that
for any $p\geq 2$ the inequality $$ \|\nabla u\|_{L_p(\Omega\times [0,T]\times
D)} \leq c \|g\|_{L_p(\Omega\times [0,T]\times D)} $$ holds if $\partial D\in
C^1$. In this note we prove that if $p>4$ then this inequality fails in any
polygon in $R^2$ having an angle greater than or equal to
$\frac{p\pi}{2(p-2)}$. We also show that a similar statement holds in higher
dimensional polygons. The counterexample introduced here is based on personal
communication with N.V. Krylov.


  Product reviews often contain a large number of implicit aspects and
object-attribute co-existence cases. Unfortunately, many existing studies in
Aspect-Based Sentiment Analysis (ABSA) have overlooked this issue, which can
make it difficult to extract opinions comprehensively and fairly. In this
paper, we propose a new task called Entity-Aspect-Opinion-Sentiment Quadruple
Extraction (EASQE), which aims to hierarchically decompose aspect terms into
entities and aspects to avoid information loss, non-exclusive annotations, and
opinion misunderstandings in ABSA tasks. To facilitate research in this new
task, we have constructed four datasets (Res14-EASQE, Res15-EASQE, Res16-EASQE,
and Lap14-EASQE) based on the SemEval Restaurant and Laptop datasets. We have
also proposed a novel two-stage sequence-tagging based Trigger-Opinion
framework as the baseline for the EASQE task. Empirical evaluations show that
our Trigger-Opinion framework can generate satisfactory EASQE results and can
also be applied to other ABSA tasks, significantly outperforming
state-of-the-art methods. We have made the four datasets and source code of
Trigger-Opinion publicly available to facilitate further research in this area.


  Hate speech is a form of online harassment that involves the use of abusive
language, and it is commonly seen in social media posts. This sort of
harassment mainly focuses on specific group characteristics such as religion,
gender, ethnicity, etc and it has both societal and economic consequences
nowadays. The automatic detection of abusive language in text postings has
always been a difficult task, but it is lately receiving much interest from the
scientific community. This paper addresses the important problem of discerning
hateful content in social media. The model we propose in this work is an
extension of an existing approach based on LSTM neural network architectures,
which we appropriately enhanced and fine-tuned to detect certain forms of
hatred language, such as racism or sexism, in a short text. The most
significant enhancement is the conversion to a two-stage scheme consisting of
Recurrent Neural Network (RNN) classifiers. The output of all One-vs-Rest (OvR)
classifiers from the first stage are combined and used to train the second
stage classifier, which finally determines the type of harassment. Our study
includes a performance comparison of several proposed alternative methods for
the second stage evaluated on a public corpus of 16k tweets, followed by a
generalization study on another dataset. The reported results show the superior
classification quality of the proposed scheme in the task of hate speech
detection as compared to the current state-of-the-art.


  JWST secondary eclipse observations of Trappist-1b seemingly disfavor
atmospheres >~1 bar since heat redistribution is expected to yield dayside
emission temperature below the ~500 K observed. Given the similar densities of
Trappist-1 planets, and the theoretical potential for atmospheric erosion
around late M-dwarfs, this observation might be assumed to imply substantial
atmospheres are also unlikely for the outer planets. However, the processes
governing atmosphere erosion and replenishment are fundamentally different for
inner and outer planets. Here, an atmosphere-interior evolution model is used
to show that an airless Trappist-1b (and c) only weakly constrains stellar
evolution, and that the odds of outer planets e and f retaining substantial
atmospheres remain largely unchanged. This is true even if the initial volatile
inventories of planets in the Trappist-1 system are highly correlated. The
reason for this result is that b and c sit unambiguously interior to the
runaway greenhouse limit, and so have potentially experienced ~8 Gyr of
XUV-driven hydrodynamic escape; complete atmospheric erosion in this
environment only weakly constrains stellar evolution and escape
parameterizations. In contrast, e and f reside within the habitable zone, and
likely experienced a comparatively short steam atmosphere during Trappist-1's
pre-main sequence, and consequently complete atmospheric erosion remains
unlikely across a broad swath of parameter space (e and f retain atmospheres in
~98% of model runs). Naturally, it is still possible that all Trappist-1
planets formed volatile-poor and are all airless today. But the airlessness of
b (and c) does not require this, and as such, JWST transit spectroscopy of e
and f remains the best near-term opportunity to characterize the atmospheres of
habitable zone terrestrial planets.


  Cross-modal video-text retrieval, a challenging task in the field of vision
and language, aims at retrieving corresponding instance giving sample from
either modality. Existing approaches for this task all focus on how to design
encoding model through a hard negative ranking loss, leaving two key problems
unaddressed during this procedure. First, in the training stage, only a
mini-batch of instance pairs is available in each iteration. Therefore, this
kind of hard negatives is locally mined inside a mini-batch while ignoring the
global negative samples among the dataset. Second, there are many text
descriptions for one video and each text only describes certain local features
of a video. Previous works for this task did not consider to fuse the multiply
texts corresponding to a video during the training. In this paper, to solve the
above two problems, we propose a novel memory enhanced embedding learning
(MEEL) method for videotext retrieval. To be specific, we construct two kinds
of memory banks respectively: cross-modal memory module and text center memory
module. The cross-modal memory module is employed to record the instance
embeddings of all the datasets for global negative mining. To avoid the fast
evolving of the embedding in the memory bank during training, we utilize a
momentum encoder to update the features by a moving-averaging strategy. The
text center memory module is designed to record the center information of the
multiple textual instances corresponding to a video, and aims at bridging these
textual instances together. Extensive experimental results on two challenging
benchmarks, i.e., MSR-VTT and VATEX, demonstrate the effectiveness of the
proposed method.


  I present two recent pieces of work on semileptonic $B \to \pi, \rho$ decays
where it is shown how lattice QCD calculations can be used to test heavy-quark
symmetry and determine phenomenologically relevant quantities despite the
limits on these calculations' kinematical reach. The study of semileptonic $B
\to \rho$ decays was performed with the UKQCD Collaboration.


  Zeros of the moment of the partition function $[Z^n]_{\bm{J}}$ with respect
to complex $n$ are investigated in the zero temperature limit $\beta \to
\infty$, $n\to 0$ keeping $y=\beta n \approx O(1)$. We numerically investigate
the zeros of the $\pm J$ Ising spin glass models on several Cayley trees and
hierarchical lattices and compare those results. In both lattices, the
calculations are carried out with feasible computational costs by using
recursion relations originated from the structures of those lattices. The
results for Cayley trees show that a sequence of the zeros approaches the real
axis of $y$ implying that a certain type of analyticity breaking actually
occurs, although it is irrelevant for any known replica symmetry breaking. The
result of hierarchical lattices also shows the presence of analyticity
breaking, even in the two dimensional case in which there is no
finite-temperature spin-glass transition, which implies the existence of the
zero-temperature phase transition in the system. A notable tendency of
hierarchical lattices is that the zeros spread in a wide region of the complex
$y$ plane in comparison with the case of Cayley trees, which may reflect the
difference between the mean-field and finite-dimensional systems.


  The dramatic appearance of luminescence rings with radius of several hundred
microns in quantum well structures can be understood through a fairly simple
nonlinear model of the diffusion and recombination of electrons and holes in a
driven nonequilibrium system. The ring corresponds to the boundary between a
positive hole gas and a negative electron gas in steady state. While this basic
effect is now well understood, we discuss several other experimental results
which can not be explained by this simple model.


  I present a calculation of the interfacial width within the capillary wave
(Gaussian) approximation. The calculation is done on rectangular lattices of
size L_1 times L_2, with periodic boundary conditions.


  We report how strategic evolution can stabilize topological states in a
network of FitzHugh-Nagumo systems. The evolution follows a repeated process of
adding or deleting of links between two nodes that is decided based on a
threshold set for the closeness between them. This results in a need based
rewiring that keeps the systems in synchrony in a single cluster or in phase
locked states in different clusters. We analyse in detail the occurrence of
such multi stable topological fixed points with corresponding emergent
dynamical states using the frequency of occurrence of each state when evolved
from a large number of initial states. We find that there is an optimum range
for the threshold used in the strategy that results in maximum frequency for
the stable topological states.


  In this paper, we will show that Hausdorff convergence and varifold
convergence coincide on the class of almost minimal sets.


  Many autonomous control systems are frequently exposed to attacks, so methods
for attack identification are crucial for a safe operation. To preserve the
privacy of the subsystems and achieve scalability in large-scale systems,
identification algorithms should not require global model knowledge. We analyze
a previously presented method for hierarchical attack identification, that is
embedded in a distributed control setup for systems of systems with coupled
nonlinear dynamics. It is based on the exchange of local sensitivity
information and ideas from sparse signal recovery. In this paper, we prove
sufficient conditions under which the method is guaranteed to identify all
components affected by some unknown attack. Even though a general class of
nonlinear dynamic systems is considered, our rigorous theoretical guarantees
are applicable to practically relevant examples, which is underlined by
numerical experiments with the IEEE~30 bus power system.


  Matter accreting onto black holes has long been known to have standing or
oscillating shock waves. The post-shock matter puffs up in the form of a torus,
which intercepts soft photons from the outer Keplerian disc and inverse
Comptonizes to produce hard photons. The post-shock region also produces jets.
We study the interaction of both hard photons and soft photons, with on-axis
electron-positron jets. We show that the radiation from post-shock torus
accelerates the flow to relativistic velocities, while that from the Keplerian
disc has marginal effect. We also show that, the velocity at infinity or
terminal velocity ${\vartheta}$, depends on the shock location in the disc.