  Most large organizations have dedicated departments that monitor the media to
keep up-to-date with relevant developments and to keep an eye on how they are
represented in the news. Part of this media monitoring work can be automated.
In the European Union with its 23 official languages, it is particularly
important to cover media reports in many languages in order to capture the
complementary news content published in the different countries. It is also
important to be able to access the news content across languages and to merge
the extracted information. We present here the four publicly accessible systems
of the Europe Media Monitor (EMM) family of applications, which cover between
19 and 50 languages (see http://press.jrc.it/overview.html). We give an
overview of their functionality and discuss some of the implications of the
fact that they cover quite so many languages. We discuss design issues
necessary to be able to achieve this high multilinguality, as well as the
benefits of this multilinguality.


  Supernova 2004dj in the nearby spiral galaxy NGC2403 was detected optically
in July 2004. Peaking at a magnitude of 11.2, this is the brightest supernova
detected for several years. Here we present Multi-Element Radio Linked
Interferometer Network (MERLIN) observations of this source, made over a four
month period, which give a position of R.A. = 07h37m17.044s, Dec
=+65deg35'57.84" (J2000.0). We also present a well-sampled 5 GHz light curve
covering the period from 5 August to 2 December 2004. With the exception of the
unusual and very close SN 1987A, these observations represent the first
detailed radio light curve for the prompt emission from a Type II-P supernova.


  We introduce a Reinforcement Learning Psychotherapy AI Companion that
generates topic recommendations for therapists based on patient responses. The
system uses Deep Reinforcement Learning (DRL) to generate multi-objective
policies for four different psychiatric conditions: anxiety, depression,
schizophrenia, and suicidal cases. We present our experimental results on the
accuracy of recommended topics using three different scales of working alliance
ratings: task, bond, and goal. We show that the system is able to capture the
real data (historical topics discussed by the therapists) relatively well, and
that the best performing models vary by disorder and rating scale. To gain
interpretable insights into the learned policies, we visualize policy
trajectories in a 2D principal component analysis space and transition
matrices. These visualizations reveal distinct patterns in the policies trained
with different reward signals and trained on different clinical diagnoses. Our
system's success in generating DIsorder-Specific Multi-Objective Policies
(DISMOP) and interpretable policy dynamics demonstrates the potential of DRL in
providing personalized and efficient therapeutic recommendations.


  A model is proposed to explain the observed correlation between monthly
fluctuations in atmospheric CO2 concentrations and temperatures. The model
relies on the oceans being in a temperature-dependent equilibrium with the
atmosphere. When temperature changes, the system attempts to restore the
equilibrium, with a time constant constrained by the dynamic fluxes between the
two. The model is used to reconstruct both the historic evolution of CO2
concentrations since 1885, as well as the monthly fluctuations since 1959. The
best fit is obtained with the southern hemisphere sea surface temperature data.
In this new picture, human emissions play no role in the build-up of
atmospheric CO2, but do contribute to the fluxes that restore the equilibrium.


  The cellular automaton is a widely known model of both reversible and
irreversible computations. The family of reversible second-order cellular
automata considered in this work is appropriate both for construction of logic
gates and analysis of damage distribution. The quantities such as formal
dimension of damage patterns can be used only for rough estimation of
consequences of particular faults and numerical experiments are provided for
illustration of some subtleties. Such analysis demonstrates high sensitivity to
errors from defects, lack of synchronization and too short intervals between
signals.


  Artificial learning systems aspire to mimic human intelligence by continually
learning from a stream of tasks without forgetting past knowledge. One way to
enable such learning is to store past experiences in the form of input examples
in episodic memory and replay them when learning new tasks. However,
performance of such method suffers as the size of the memory becomes smaller.
In this paper, we propose a new approach for experience replay, where we select
the past experiences by looking at the saliency maps which provide visual
explanations for the model's decision. Guided by these saliency maps, we pack
the memory with only the parts or patches of the input images important for the
model's prediction. While learning a new task, we replay these memory patches
with appropriate zero-padding to remind the model about its past decisions. We
evaluate our algorithm on CIFAR-100, miniImageNet and CUB datasets and report
better performance than the state-of-the-art approaches. With qualitative and
quantitative analyses we show that our method captures richer summaries of past
experiences without any memory increase, and hence performs well with small
episodic memory.


  The dynamics of physical systems is often constrained to lower dimensional
sub-spaces due to the presence of conserved quantities. Here we propose a
method to learn and exploit such symmetry constraints building upon Hamiltonian
Neural Networks. By enforcing cyclic coordinates with appropriate loss
functions, we find that we can achieve improved accuracy on simple classical
dynamics tasks. By fitting analytic formulae to the latent variables in our
network we recover that our networks are utilizing conserved quantities such as
(angular) momentum.


  Prior works have demonstrated that a low-resource language pair can benefit
from multilingual machine translation (MT) systems, which rely on many language
pairs' joint training. This paper proposes two simple strategies to address the
rare word issue in multilingual MT systems for two low-resource language pairs:
French-Vietnamese and English-Vietnamese. The first strategy is about dynamical
learning word similarity of tokens in the shared space among source languages
while another one attempts to augment the translation ability of rare words
through updating their embeddings during the training. Besides, we leverage
monolingual data for multilingual MT systems to increase the amount of
synthetic parallel corpora while dealing with the data sparsity problem. We
have shown significant improvements of up to +1.62 and +2.54 BLEU points over
the bilingual baseline systems for both language pairs and released our
datasets for the research community.


  We propose a flexible framework for clustering hypergraph-structured data
based on recently proposed random walks utilizing edge-dependent vertex
weights. When incorporating edge-dependent vertex weights (EDVW), a weight is
associated with each vertex-hyperedge pair, yielding a weighted incidence
matrix of the hypergraph. Such weightings have been utilized in term-document
representations of text data sets. We explain how random walks with EDVW serve
to construct different hypergraph Laplacian matrices, and then develop a suite
of clustering methods that use these incidence matrices and Laplacians for
hypergraph clustering. Using several data sets from real-life applications, we
compare the performance of these clustering algorithms experimentally against a
variety of existing hypergraph clustering methods. We show that the proposed
methods produce higher-quality clusters and conclude by highlighting avenues
for future work.


  The upcoming high energy experiments at the LHC are one of the most
outstanding efforts for a better understanding of nature. It is associated with
great hopes in the physics community. But there is also some fear in the
public, that the conjectured production of mini black holes might lead to a
dangerous chain reaction. In this paper we summarize the most straight forward
arguments that are necessary to rule out such doomsday scenarios.


  Suppose that L is a null--homologous Legendrian knot in a contact
3--manifold. We determine the connection between the sutured invariant of the
complement of L and the Legendrian invariant defined by Lisca, Ozsvath,
Stipsicz and Szabo. In particular, we derive a vanishing theorem for the
Legendrian invariant in the presence of Giroux torsion in the complement of the
knot, and reprove several known properties of the Legendrian invariant from
this perspective.


  The performance of automatic speech recognition systems(ASR) degrades in the
presence of noisy speech. This paper demonstrates that using
electroencephalography (EEG) can help automatic speech recognition systems
overcome performance loss in the presence of noise. The paper also shows that
distillation training of automatic speech recognition systems using EEG
features will increase their performance. Finally, we demonstrate the ability
to recognize words from EEG with no speech signal on a limited English
vocabulary with high accuracy.


  We prove that Berwald spaces whose flag curvature is nowhere vanishing are in
fact Riemannian spaces. This means that any Berwald space with flag curvature
bounded below by a positive number must be also Riemannian. This rigidity
result shows the importance of non-Riemannian examples when imposing flag
curvature bounds on Finsler spaces.


  Robustness has been extensively studied in reinforcement learning (RL) to
handle various forms of uncertainty such as random perturbations, rare events,
and malicious attacks. In this work, we consider one critical type of
robustness against spurious correlation, where different portions of the state
do not have correlations induced by unobserved confounders. These spurious
correlations are ubiquitous in real-world tasks, for instance, a self-driving
car usually observes heavy traffic in the daytime and light traffic at night
due to unobservable human activity. A model that learns such useless or even
harmful correlation could catastrophically fail when the confounder in the test
case deviates from the training one. Although motivated, enabling robustness
against spurious correlation poses significant challenges since the uncertainty
set, shaped by the unobserved confounder and causal structure, is difficult to
characterize and identify. Existing robust algorithms that assume simple and
unstructured uncertainty sets are therefore inadequate to address this
challenge. To solve this issue, we propose Robust State-Confounded Markov
Decision Processes (RSC-MDPs) and theoretically demonstrate its superiority in
avoiding learning spurious correlations compared with other robust RL
counterparts. We also design an empirical algorithm to learn the robust optimal
policy for RSC-MDPs, which outperforms all baselines in eight realistic
self-driving and manipulation tasks.


  We are considering the geometric amoebot model where a set of $n$ amoebots is
placed on the triangular grid. An amoebot is able to send information to its
neighbors, and to move via expansions and contractions. Since amoebots and
information can only travel node by node, most problems have a natural lower
bound of $\Omega(D)$ where $D$ denotes the diameter of the structure. Inspired
by the nervous and muscular system, Feldmann et al. have proposed the
reconfigurable circuit extension and the joint movement extension of the
amoebot model with the goal of breaking this lower bound.
  In the joint movement extension, the way amoebots move is altered. Amoebots
become able to push and pull other amoebots. Feldmann et al. demonstrated the
power of joint movements by transforming a line of amoebots into a rhombus
within $O(\log n)$ rounds. However, they left the details of the extension
open. The goal of this paper is therefore to formalize and extend the joint
movement extension. In order to provide a proof of concept for the extension,
we consider two fundamental problems of modular robot systems: shape formation
and locomotion.
  We approach these problems by defining meta-modules of rhombical and
hexagonal shape, respectively. The meta-modules are capable of movement
primitives like sliding, rotating, and tunneling. This allows us to simulate
shape formation algorithms of various modular robot systems. Finally, we
construct three amoebot structures capable of locomotion by rolling, crawling,
and walking, respectively.


  This report introduces the Transfer Waveguide (TRANSGUIDE); an ultra-thin
flat technology that promises light emitting applications a practical solution
to total internal reflection light trapping and diverging emission. By invoking
reciprocity, light can be temporarily stored in the form of a virtual-dipole
and recovered back again.


  Let $(Z_i)_{i\geq 1}$ be an independent, identically distributed sequence of
random variables on $\RRR^d$. Under mild conditions on the density of $Z_1$, we
provide a nonstandard uniform functional limit law for the following processes
on $[0,1)^d$: $$\Delta_n(z,h_n,\cdot):=s\mapsto \frac{\sliin
1_{[0,s_1]\times...\times[0,s_d]}\poo\frac{Z_i-z}{h_n^{1/d}}\pff}{c\log
n},\;s\in [0,1)^d,$$ along a sequence $(h_n)\suite$ fulfilling $h_n\downarrow
0,\;nh_n\uparrow,\;nh_n/\log c\rar c>0$. Here $z$ ranges through a compact set
of $\RRR^d$. This result is an extension of a theorem of Deheuvels and Mason
(1992) to the multivariate, non uniform case.\lb


  The solar atmosphere was traditionally represented with a simple
one-dimensional model. Over the past few decades, this paradigm shifted for the
chromosphere and corona that constitute the outer atmosphere, which is now
considered a dynamic structured envelope. Recent observations by IRIS
(Interface Region Imaging Spectrograph) reveal that it is difficult to
determine what is up and down even in the cool 6000-K photosphere just above
the solar surface: this region hosts pockets of hot plasma transiently heated
to almost 100,000 K. The energy to heat and accelerate the plasma requires a
considerable fraction of the energy from flares, the largest solar disruptions.
These IRIS observations not only confirm that the photosphere is more complex
than conventionally thought, but also provide insight into the energy
conversion in the process of magnetic reconnection.


  We introduce the notion of "special superpolynomials" by putting q=1 in the
formulas for reduced superpolynomials. In this way we obtain a generalization
of special HOMFLY polynomials depending on one extra parameter t. Special
HOMFLY are known to depend on representation R in especially simple way: as
|R|-th power of the fundamental ones. We show that the same dependence persists
for our special superpolynomials in the case of symmetric representations, at
least for the 2-strand torus and figure-eight knots. For antisymmetric
representations the same is true, but for t=1 and arbitrary q. It would be
interesting to find an interpolation between these two relations for arbitrary
representations, but no superpolynomails are yet available in this case.


  We consider the mechanism design problem of a principal allocating a single
good to one of several agents without monetary transfers. Each agent desires
the good and uses it to create value for the principal. We designate this value
as the agent's private type. Even though the principal does not know the
agents' types, she can verify them at a cost. The allocation of the good thus
depends on the agents' self-declared types and the results of any verification
performed, and the principal's payoff matches her value of the allocation minus
the costs of verification. It is known that if the agents' types are
independent, then a favored-agent mechanism maximizes her expected payoff.
However, this result relies on the unrealistic assumptions that the agents'
types follow known independent probability distributions. In contrast, we
assume here that the agents' types are governed by an ambiguous joint
probability distribution belonging to a commonly known ambiguity set and that
the principal maximizes her worst-case expected payoff. We study support-only
ambiguity sets, which contain all distributions supported on a rectangle,
Markov ambiguity sets, which contain all distributions in a support-only
ambiguity set satisfying some first-order moment bounds, and Markov ambiguity
sets with independent types, which contain all distributions in a Markov
ambiguity set under which the agents' types are mutually independent. In all
cases we construct explicit favored-agent mechanisms that are not only optimal
but also Pareto-robustly optimal.


  We use geometric algebra techniques to give a synthetic and computationally
efficient approach to Fierz identities in arbitrary dimensions and signatures,
thus generalizing previous work. Our approach leads to a formulation which
displays the underlying real, complex or quaternionic structure in an explicit
and conceptually clear manner and is amenable to implementation in various
symbolic computation systems. We illustrate our methods and results with a few
examples which display the basic features of the three classes of pin
representations governing the structure of such identities in various
dimensions and signatures.


  From bird flocking to neural dynamics, complex systems generate fascinating
structures and correlations. Often, seemingly simple dynamics lead to intricate
emergent properties. Despite their visceral appeal, defining complex systems
lacks universal agreement. In this paper, I will debunk three prevalent myths
in complex systems and propose resolutions. This work contributes by offering a
contemporary interpretation of complex systems, presenting essential
definitions that benefit complexity scientists.


  We revisit the Heaviest Induced Ancestors (HIA) problem that was introduced
by Gagie, Gawrychowski, and Nekrich [CCCG 2013] and has a number of
applications in string algorithms. Let $T_1$ and $T_2$ be two rooted trees
whose nodes have weights that are increasing in all root-to-leaf paths, and
labels on the leaves, such that no two leaves of a tree have the same label. A
pair of nodes $(u, v)\in T_1 \times T_2$ is \emph{induced} if and only if there
is a label shared by leaf-descendants of $u$ and $v$. In an HIA query, given
nodes $x \in T_1$ and $y \in T_2$, the goal is to find an induced pair of nodes
$(u, v)$ of the maximum total weight such that $u$ is an ancestor of~$x$ and
$v$ is an ancestor of $y$.
  Let $n$ be the upper bound on the sizes of the two trees. It is known that no
data structure of size $\tilde{\mathcal{O}}(n)$ can answer HIA queries in
$o(\log n / \log \log n)$ time [Charalampopoulos, Gawrychowski, Pokorski; ICALP
2020]. This (unconditional) lower bound is a $\operatorname{polyloglog} n$
factor away from the query time of the fastest $\tilde{\mathcal{O}}(n)$-size
data structure known to date for the HIA problem [Abedin, Hooshmand, Ganguly,
Thankachan; Algorithmica 2022]. In this work, we resolve the query-time
complexity of the HIA problem for the near-linear space regime by presenting a
data structure that can be built in $\tilde{\mathcal{O}}(n)$ time and answers
HIA queries in $\mathcal{O}(\log n/\log\log n)$ time. As a direct corollary, we
obtain an $\tilde{\mathcal{O}}(n)$-size data structure that maintains the LCS
of a static string and a dynamic string, both of length at most $n$, in time
optimal for this space regime.
  The main ingredients of our approach are fractional cascading and the
utilization of an $\mathcal{O}(\log n/ \log\log n)$-depth tree decomposition.


  We study the ground-state entanglement in systems of spins forming the
boundary of a quantum spin network in arbitrary geometries and dimensionality.
We show that as long as they are weakly coupled to the bulk of the network, the
surface spins are strongly entangled, even when distant and non directly
interacting, thereby generalizing the phenomenon of long-distance entanglement
occurring in quantum spin chains. Depending on the structure of the couplings
between surface and bulk spins, we discuss in detail how the patterns of
surface entanglement can range from multi-pair bipartite to fully multipartite.
In the context of quantum information and communication, these results find
immediate application to the implementation of quantum routers, that is devices
able to distribute quantum correlations on demand among multiple network nodes.


  Let $(P, Q)$ be a pair of Jacobian polynomials. We can show that $ <P,
Q>+l+2g(P)-2= 0= <P, [P,Q]>$, where $<f, g>$ is the intersection number of $f,
g\in \CC[x, y]$ in the affine plane, $l$ is the number of branch at point at
infinity and $g(P)$ is the geometric genus of affine curve defined by $P$.
Hence we can show that every Jacobian polynomial defines a smooth rational
curve with one point at infinity. It is sufficient to fix the Jacobian
conjecture in two dimension by the Abhyankar theorem or the
Abhyankar-Moh-Suzuki theorem.


  Competitive accretion occurs when stars in a cluster accrete from a shared
reservoir of gas. The competition arises due to the relative attraction of
stars as a function of their mass and location in the cluster.
  The low relative motions of the stars and gas in young, gas dominated
clusters results in a tidal limit to the accretion whereas in the stellar
dominated cluster cores, the high relative velocities results in Bondi-Hoyle
accretion.
  The combination of these two accretion processes produces a two power-law IMF
with $\gamma \approx -1.5$, for low-mass stars which accrue their mass in the
gas dominated regime, and a steeper, $\gamma\approx -2.5$, IMF for higher-mass
stars that form in the core of a cluster. Simulations of the fragmentation and
formation of a stellar cluster show that the final stellar masses, and IMF, are
due to competitive accretion. Competitive accretion also naturally results in a
mass segregated cluster and in a direct correlation between the richness of a
cluster and the mass of the most massive star therein. The {\sl knee} where the
IMF slope changes occurs near the Jeans mass of the system.


  Non-intrusive load monitoring (NILM) helps meet energy conservation goals by
estimating individual appliance power usage from a single aggregate
measurement. Deep neural networks have become increasingly popular in
attempting to solve NILM problems; however, many of them are not causal which
is important for real-time application. We present a causal 1-D convolutional
neural network inspired by WaveNet for NILM on low-frequency data. We also
study using various components of the complex power signal for NILM, and
demonstrate that using all four components available in a popular NILM dataset
(current, active power, reactive power, and apparent power) we achieve faster
convergence and higher performance than state-of-the-art results for the same
dataset.


  We propose a novel neural attention architecture to tackle machine
comprehension tasks, such as answering Cloze-style queries with respect to a
document. Unlike previous models, we do not collapse the query into a single
vector, instead we deploy an iterative alternating attention mechanism that
allows a fine-grained exploration of both the query and the document. Our model
outperforms state-of-the-art baselines in standard machine comprehension
benchmarks such as CNN news articles and the Children's Book Test (CBT)
dataset.


  Visual inspection tasks often require humans to cooperate with AI-based image
classifiers. To enhance this cooperation, explainable artificial intelligence
(XAI) can highlight those image areas that have contributed to an AI decision.
However, the literature on visual cueing suggests that such XAI support might
come with costs of its own. To better understand how the benefits and cost of
XAI depend on the accuracy of AI classifications and XAI highlights, we
conducted two experiments that simulated visual quality control in a chocolate
factory. Participants had to decide whether chocolate moulds contained faulty
bars or not, and were always informed whether the AI had classified the mould
as faulty or not. In half of the experiment, they saw additional XAI highlights
that justified this classification. While XAI speeded up performance, its
effects on error rates were highly dependent on (X)AI accuracy. XAI benefits
were observed when the system correctly detected and highlighted the fault, but
XAI costs were evident for misplaced highlights that marked an intact area
while the actual fault was located elsewhere. Eye movement analyses indicated
that participants spent less time searching the rest of the mould and thus
looked at the fault less often. However, we also observed large interindividual
differences. Taken together, the results suggest that despite its potentials,
XAI can discourage people from investing effort into their own information
analysis.


  Single crystals of the frustrated S=1/2 ferro-antiferromagnetic proximate
square lattice material SrZnZnVO(PO$_4$)$_2$ are studied in magnetometric,
calorimetric, neutron diffraction and inelastic neutron scattering experiments.
The measured spin wave spectrum reveals a substantial degree of magnetic
frustration and a large quantum renormalization of the exchange constants. The
H-T magnetic phase diagram is established. It features a novel pre-saturation
phase, which appears for only one particular field orientation. The results are
discussed noting the similarities and differences with the previously studied
and similarly structured Pb$_2$VO(PO$_4$)$_2$ compound.


  This file summarizes the plenary talk on laboratory experiments on logic at
the TARK 2013 - 14th Conference on Theoretical Aspects of Rationality and
Knowledge.


  The pursuit of room temperature quantum optomechanics with tethered
nanomechanical resonators faces stringent challenges owing to extraneous
mechanical degrees of freedom. An important example is thermal intermodulation
noise (TIN), a form of excess optical noise produced by mixing of thermal noise
peaks. While TIN can be decoupled from the phase of the optical field, it
remains indirectly coupled via radiation pressure, implying a hidden source of
backaction that might overwhelm shot noise. Here we report observation of TIN
backaction in a high-cooperativity, room temperature cavity optomechanical
system consisting of an acoustic-frequency Si$_3$N$_4$ trampoline coupled to a
Fabry-P\'{e}rot cavity. The backaction we observe exceeds thermal noise by 20
dB and radiation pressure shot noise by 40 dB, despite the thermal motion being
10 times smaller than the cavity linewidth. Our results suggest that mitigating
TIN may be critical to reaching the quantum regime from room temperature in a
variety of contemporary optomechanical systems.


  Spectral properties of the spin-1/2 antiferromagnetic Heisenberg chain in a
magnetic field are investigated by using exact Bethe-ansatz solutions. We argue
that not only quasiparticles called psinon and antipsinon but also a
quasiparticle representing a 2-string in the Bethe ansatz plays an important
role for dynamical properties in a magnetic field. Combined with psinon and
antipsinon, the quasiparticle for a 2-string forms a continuum in the
high-energy regime for transverse dynamical structure factor
$S^{+-}(k,\omega)$. In the zero-field limit, the continuum is located on the
mode of the lowest excited states in zero field called the des
Cloizeaux-Pearson mode. In a magnetic field, the continuum separates from other
low-energy continua, and reduces to the mode of bound states of overturned
spins from the fully polarized state near the saturation field. We confirm the
relevance through comparisons with available experimental results on the
quasi-one-dimensional antiferromagnet CuCl$_2\cdot$2N(C$_5$D$_5$).


  We discuss a class of gauge mediated supersymmetry breaking models with
conformal invariance above the messenger mass scale (conformal gauge
mediation). The spectrum of the supersymmetric particles including the
gravitino is uniquely determined by the messenger mass. When the conformal
fixed point is strongly interacting, it predicts a light gravitino of mass
m_{3/2}<O(10) eV, which is attractive since such a light gravitino causes no
problem in cosmology.


  We present a fast algorithm for global rigid symmetry detection with
approximation guarantees. The algorithm is guaranteed to find the best
approximate symmetry of a given shape, to within a user-specified threshold,
with very high probability. Our method uses a carefully designed sampling of
the transformation space, where each transformation is efficiently evaluated
using a sub-linear algorithm. We prove that the density of the sampling depends
on the total variation of the shape, allowing us to derive formal bounds on the
algorithm's complexity and approximation quality. We further investigate
different volumetric shape representations (in the form of truncated distance
transforms), and in such a way control the total variation of the shape and
hence the sampling density and the runtime of the algorithm. A comprehensive
set of experiments assesses the proposed method, including an evaluation on the
eight categories of the COSEG data-set. This is the first large-scale
evaluation of any symmetry detection technique that we are aware of.


  Luminosity profiles of galaxies acting as strong gravitational lenses can be
tricky to study. Indeed, strong gravitational lensing images display several
lensed components, both point-like and diffuse, around the lensing galaxy.
Those objects limit the study of the galaxy luminosity to its inner parts.
Therefore, the usual fitting methods perform rather badly on such images.
Previous studies of strong lenses luminosity profiles using software such as
GALFIT or IMFITFITS and various PSF-determining methods have resulted in
discrepant results. The present work aims at investigating the causes of those
discrepancies, as well as at designing more robust techniques for studying the
morphology of early-type lensing galaxies with the ability to subtract a lensed
signal from their luminosity profiles. We design a new method to independently
measure each shape parameter, namely, the position angle, ellipticity, and
half-light radius of the galaxy. Our half-light radius measurement method is
based on an innovative scheme for computing isophotes that is well suited to
measuring the morphological properties of gravitational lensing galaxies. Its
robustness regarding various specific aspects of gravitational lensing image
processing is analysed and tested against GALFIT. It is applied to a sample of
systems from the CASTLES database. Simulations show that when restricted to
small, inner parts of the lensing galaxy, the technique presented here is more
trustworthy than GALFIT. It gives more robust results than GALFIT, which shows
instabilities regarding the fitting region, the value of the Sersic index, and
the signal-to-noise ratio. It is therefore better suited than GALFIT for
gravitational lensing galaxies. It is also able to study lensing galaxies that
are not much larger than the PSF. New values for the half-light radius of the
objects in our sample are presented and compared to previous works.


  We investigate the nonlinear stability of the superposition of a viscous
contact wave and two rarefaction waves for one-dimensional bipolar
Vlasov-Poisson-Boltzmann (VPB) system, which can be used to describe the
transportation of charged particles under the additional electrostatic
potential force. Based on a new micro-macro type decomposition around the local
Maxwellian related to the bipolar VPB system in our previous work [26], we
prove that the superposition of a viscous contact wave and two rarefaction
waves is time-asymptotically stable to 1D bipolar VPB system under some
smallness conditions on the initial perturbations and wave strength, which
implies that this typical composite wave pattern is nonlinearly stable under
the combined effects of the binary collisions, the electrostatic potential
force, and the mutual interactions of different charged particles. Note that
this is the first result about the nonlinear stability of the combination of
two different wave patterns for the Vlasov-Poisson-Boltzmann system.


  The short distance asymptotics of the Ising Model scaling functions are
computed for the scaling functions that arise as continuum limits of lattice
correlations from below the critical temperature.


  To address increasing societal concerns regarding privacy and climate, the EU
adopted the General Data Protection Regulation (GDPR) and committed to the
Green Deal. Considerable research studied the energy efficiency of software and
the accuracy of machine learning models trained on anonymised data sets. Recent
work began exploring the impact of privacy-enhancing techniques (PET) on both
the energy consumption and accuracy of the machine learning models, focusing on
k-anonymity. As synthetic data is becoming an increasingly popular PET, this
paper analyses the energy consumption and accuracy of two phases: a) applying
privacy-enhancing techniques to the concerned data set, b) training the models
on the concerned privacy-enhanced data set. We use two privacy-enhancing
techniques: k-anonymisation (using generalisation and suppression) and
synthetic data, and three machine-learning models. Each model is trained on
each privacy-enhanced data set. Our results show that models trained on
k-anonymised data consume less energy than models trained on the original data,
with a similar performance regarding accuracy. Models trained on synthetic data
have a similar energy consumption and a similar to lower accuracy compared to
models trained on the original data.


  A holographic dark energy model characterized by the conformal-age-like
length scale $L= \frac{1}{a^4(t)}\int_0^tdt' a^3(t') $ is motivated from the
four dimensional spacetime volume at cosmic time $t$ in the flat
Friedmann-Robertson-Walker universe. It is shown that when the background
constituent with constant equation of state $w_m$ dominates the universe in the
early time, the fractional energy density of the dark energy scales as
$\Omega_{de}\simeq \frac94(3+w_m)^2d^2a^2$ with the equation of state given by
$w_{de}\simeq-\frac23 +w_m$. The value of $w_m$ is taken to be $w_m\simeq-1$
during inflation, $w_m=\frac13$ in radiation-dominated epoch and $w_m=0$ in
matter-dominated epoch respectively. When the model parameter $d$ takes the
normal value at order one, the fractional density of dark energy is naturally
negligible in the early universe, $\Omega_{de} \ll 1$ at $a \ll 1$. With such
an analytic feature, the model can be regarded as a single-parameter model like
the $\Lambda$CDM model, so that the present fractional energy density
$\Omega_{de}(a=1)$ can solely be determined by solving the differential
equation of $\Omega_{de}$ once $d$ is given. We further extend the model to the
general case in which both matter and radiation are present. The scenario
involving possible interaction between the dark energy and the background
constituent is also discussed.


  We present H-alpha velocity fields for a sample of nearly face--on spiral
galaxies observed with DensePak on the WIYN telescope. We combine kinematic
inclinations and position angles measured from these data with photometric
inclinations and position angles measured from I-band images to show that
spiral disks are intrinsically non-circular.


  Secure compilation prevents all low-level attacks on compiled code and allows
for sound reasoning about security in the source language. In this work we
propose a new attacker model for secure compilation that extends the well-known
notion of full abstraction to ensure protection for mutually distrustful
components. We devise a compiler chain (compiler, linker, and loader) and a
novel security monitor that together defend against this strong attacker model.
The monitor is implemented using a recently proposed, generic tag-based
protection framework called micro-policies, which comes with hardware support
for efficient caching and with a formal verification methodology. Our monitor
protects the abstractions of a simple object-oriented language---class
isolation, the method call discipline, and type safety---against arbitrary
low-level attackers.


  On the 19th June 2020 at 20:05:07 UTC, a fireball lasting 5.5 s was observed
above Western Australia by three Desert Fireball Network observatories. The
meteoroid entered the atmosphere with a speed of $14.00 \pm 0.17$ km s$^{-1}$
and followed a $58^{\circ}$ slope trajectory from a height of 75 km down to
18.6 km. Despite the poor angle of triangulated planes between observatories
(29$^{\circ}$) and the large distance from the observatories, a well
constrained kilo-size main mass was predicted to have fallen just South of
Madura in Western Australia. However, the search area was predicted to be large
due to the trajectory uncertainties. Fortunately, the rock was rapidly
recovered along the access track during a reconnaissance trip. The 1.072 kg
meteorite called Madura Cave was classified as an L5 ordinary chondrite. The
calculated orbit is of Aten type (mostly contained within the Earth's orbit),
the second time only a meteorite is observed on such an orbit after Bunburra
Rockhole. Dynamical modelling shows that Madura Cave has been in near-Earth
space for a very long time. The NEO dynamical lifetime for the progenitor
meteoroid is predicted to be $\sim87$ Myr. This peculiar orbit also points to a
delivery from the main asteroid belt via the $\nu6$ resonance, and therefore an
origin in the inner belt. This result contributes to drawing a picture for the
existence of a present-day L chondrite parent body in the inner belt.


  The development of solid-state photonic quantum technologies is of great
interest for fundamental studies of light-matter interactions and quantum
information science. Diamond has turned out to be an attractive material for
integrated quantum information processing due to the extraordinary properties
of its colour centres enabling e.g. bright single photon emission and spin
quantum bits. To control emitted photons and to interconnect distant quantum
bits, micro-cavities directly fabricated in the diamond material are desired.
However, the production of photonic devices in high-quality diamond has been a
challenge so far. Here we present a method to fabricate one- and
two-dimensional photonic crystal micro-cavities in single-crystal diamond,
yielding quality factors up to 700. Using a post-processing etching technique,
we tune the cavity modes into resonance with the zero phonon line of an
ensemble of silicon-vacancy centres and measure an intensity enhancement by a
factor of 2.8. The controlled coupling to small mode volume photonic crystal
cavities paves the way to larger scale photonic quantum devices based on
single-crystal diamond.


  This chapter provides a comprehensive overview of the Bohmian formulation of
quantum mechanics. It starts with a historical review of the difficulties found
by Louis de Broglie, David Bohm, and John S. Bell to convince the scientific
community about the validity and utility of Bohmian mechanics. Then, a formal
explanation of Bohmian mechanics for nonrelativistic, single-particle quantum
systems is presented. The generalization to many-particle systems, where the
exchange interaction and the spin play an important role, is also presented.
After that, the measurement process in Bohmian mechanics is discussed. It is
emphasized that Bohmian mechanics exactly reproduces the mean value and
temporal and spatial correlations obtained from the standard, that is the
Copenhagen or orthodox, formulation. The ontological characteristics of Bohmian
mechanics provide a description of measurements as another type of interaction
without the need for introducing the wave function collapse. Several solved
problems are presented at the end of the chapter, giving additional
mathematical support to some particular issues. A detailed description of
computational algorithms to obtain Bohmian trajectories from the numerical
solution of the Schrodinger or the Hamilton-Jacobi equations are presented in
an appendix. The motivation of this chapter is twofold: first, as a didactic
introduction to Bohmian formalism, which is used in the subsequent chapters,
and second, as a self-contained summary for any newcomer interested in using
Bohmian mechanics in his or her daily research activity.


  Machine learning algorithms aim at minimizing the number of false decisions
and increasing the accuracy of predictions. However, the high predictive power
of advanced algorithms comes at the costs of transparency. State-of-the-art
methods, such as neural networks and ensemble methods, often result in highly
complex models that offer little transparency.
  We propose shallow model trees as a way to combine simple and highly
transparent predictive models for higher predictive power without losing the
transparency of the original models. We present a novel split criterion for
model trees that allows for significantly higher predictive power than
state-of-the-art model trees while maintaining the same level of simplicity.
This novel approach finds split points which allow the underlying simple models
to make better predictions on the corresponding data. In addition, we introduce
multiple mechanisms to increase the transparency of the resulting trees.


  This short technical note addresses a number of issues regarding the
estimates of the 2004 paper by Ericson, Loiseau, and Wycech for the corrections
$\delta_\epsilon$ and $\delta_\Gamma$, which aim at the removal of the effects
of electromagnetic origin from the measurements of the strong-interaction shift
$\epsilon_{1s}$ and of the total decay width $\Gamma_{1s}$ of the ground state
in pionic hydrogen.


  We study a particular realization of the cosmological bounce scenario
proposed recently by Ijjas and Steinhardt. First, we find that their bouncing
solution starts from a divergent sound speed and ends with its vanishing. Thus,
the solution connects two strongly coupled configurations. These pathologies
are separated from the bouncing regime by only a few Planck times. We then
reveal the exact structure of the Lagrangian, which reproduces this bouncing
solution. This reconstruction allowed us to consider other cosmological
solutions of the theory and analyze the phase space. In particular, we find
other bouncing solutions and solutions with superluminal sound speed. These
stable superluminal states can be continuously transformed into the solution
constructed by Ijjas and Steinhardt. We discuss the consequences of this
feature for a possible UV-completion.


  Electrical conductivity measurements on a 2D packing of metallic beads have
been performed to study internal rearrangements in weakly pertubed granular
materials. Small thermal perturbations lead to large non gaussian conductance
fluctuations. These fluctuations are found to be intermittent and gathered in
bursts. The distributions of the waiting time between to peaks is found to be a
power law inside bursts. The exponent is independent of the bead network, the
intensity of the perturbation and external stress. these bursts are interpreted
as the signature of individual bead creep rather than collective vaults
reorganisations. We propose a simple model linking the exponent of the waiting
time distribution to the roughness exponent of the surface of the beads.


  3D simulated environments play a critical role in Embodied AI, but their
creation requires expertise and extensive manual effort, restricting their
diversity and scope. To mitigate this limitation, we present Holodeck, a system
that generates 3D environments to match a user-supplied prompt fully
automatedly. Holodeck can generate diverse scenes, e.g., arcades, spas, and
museums, adjust the designs for styles, and can capture the semantics of
complex queries such as "apartment for a researcher with a cat" and "office of
a professor who is a fan of Star Wars". Holodeck leverages a large language
model (i.e., GPT-4) for common sense knowledge about what the scene might look
like and uses a large collection of 3D assets from Objaverse to populate the
scene with diverse objects. To address the challenge of positioning objects
correctly, we prompt GPT-4 to generate spatial relational constraints between
objects and then optimize the layout to satisfy those constraints. Our
large-scale human evaluation shows that annotators prefer Holodeck over
manually designed procedural baselines in residential scenes and that Holodeck
can produce high-quality outputs for diverse scene types. We also demonstrate
an exciting application of Holodeck in Embodied AI, training agents to navigate
in novel scenes like music rooms and daycares without human-constructed data,
which is a significant step forward in developing general-purpose embodied
agents.


  Supermassive black hole binaries are one of the primary targets for
gravitational wave searches using pulsar timing arrays. Gravitational wave
signals from such systems are well represented by parametrized models, allowing
the standard Generalized Likelihood Ratio Test (GLRT) to be used for their
detection and estimation. However, there is a dichotomy in how the GLRT can be
implemented for pulsar timing arrays: there are two possible ways in which one
can split the set of signal parameters for semi-analytical and numerical
extremization. The straightforward extension of the method used for continuous
signals in ground-based gravitational wave searches, where the so-called pulsar
phase parameters are maximized numerically, was addressed in an earlier paper
(Wang et al. 2014). In this paper, we report the first study of the performance
of the second approach where the pulsar phases are maximized semi-analytically.
This approach is scalable since the number of parameters left over for
numerical optimization does not depend on the size of the pulsar timing array.
Our results show that, for the same array size (9 pulsars), the new method
performs somewhat worse in parameter estimation, but not in detection, than the
previous method where the pulsar phases were maximized numerically. The origin
of the performance discrepancy is likely to be in the ill-posedness that is
intrinsic to any network analysis method. However, scalability of the new
method allows the ill-posedness to be mitigated by simply adding more pulsars
to the array. This is shown explicitly by taking a larger array of pulsars.


  The integrability of N=(2,2) dilaton supergravity in two dimensions is
studied by the use of the graded Poisson Sigma model approach. Though important
differences compared to the purely bosonic models are found, the general
analytic solutions are obtained. The latter include minimally gauged models as
well as an ungauged version. BPS solutions are an especially interesting
subclass.


  Phylogenetic trees can be reconstructed from the matrix which contains the
distances between all pairs of languages in a family. Recently, we proposed a
new method which uses normalized Levenshtein distances among words with same
meaning and averages on all the items of a given list. Decisions about the
number of items in the input lists for language comparison have been debated
since the beginning of glottochronology. The point is that words associated to
some of the meanings have a rapid lexical evolution. Therefore, a large
vocabulary comparison is only apparently more accurate then a smaller one since
many of the words do not carry any useful information. In principle, one should
find the optimal length of the input lists studying the stability of the
different items. In this paper we tackle the problem with an automated
methodology only based on our normalized Levenshtein distance. With this
approach, the program of an automated reconstruction of languages relationships
is completed.


  We study the tunneling magneto thermopower and tunneling magneto
thermocurrent of CoFeB/MgO/CoFeB magnetic tunnel junctions (MTJ). The devices
show a clear change of the thermoelectric properties upon reversal of the
magnetisation of the CoFeB layers from parallel to the antiparallel
orientation. When switching from parallel to antiparallel the thermopower
increases by up to 55% where as the thermocurrent drops by 45%. These
observations can be well explained by the Onsager relations taking into account
the tunneling magneto resistance of the MTJ. These findings contrast previous
studies on AlO based MTJ systems, revealing tunneling magneto thermo power but
no tunneling magneto thermocurrent.


  I review the recent progress in understanding the complete gauge invariant
decomposition of the nucleon spin with particular emphasis on its twist
structure.


  Doors are important landmarks for indoor mobile robot navigation and also
assist blind people to independently access unfamiliar buildings. Most existing
algorithms of door detection are limited to work for familiar environments
because of restricted assumptions about color, texture and shape. In this paper
we propose a novel approach which employs feature based classification and uses
the Kohonen Self-Organizing Map (SOM) for the purpose of door detection.
Generic and stable features are used for the training of SOM that increase the
performance significantly: concavity, bottom-edge intensity profile and door
edges. To validate the robustness and generalizability of our method, we
collected a large dataset of real world door images from a variety of
environments and different lighting conditions. The algorithm achieves more
than 95% detection which demonstrates that our door detection method is generic
and robust with variations of color, texture, occlusions, lighting condition,
scales, and viewpoints.


  We carried out a model-independent search for light scalar (s) and
pseudoscalar axionlike (a) particles that couple to two photons by using the
high-energy CERN SPS H4 electron beam. The new particles, if they exist, could
be produced through the Primakoff effect in interactions of hard bremsstrahlung
photons generated by 100 GeV electrons in the NA64 active dump with virtual
photons provided by the nuclei of the dump. The a(s) would penetrate the
downstream HCAL module, serving as shielding, and would be observed either
through their $a(s)\to\gamma \gamma$ decay in the rest of the HCAL detector or
as events with large missing energy if the a(s) decays downstream of the HCAL.
This method allows for the probing the a(s) parameter space, including those
from generic axion models, inaccessible to previous experiments. No evidence of
such processes has been found from the analysis of the data corresponding to
$2.84\times10^{11}$ electrons on target allowing to set new limits on the
$a(s)\gamma\gamma$-coupling strength for a(s) masses below 55 MeV.


  Let $(X_1 , \ldots , X_d)$ be random variables taking nonnegative integer
values and let $f(z_1, \ldots , z_d)$ be the probability generating function.
Suppose that $f$ is real stable; equivalently, suppose that the polarization of
this probability distribution is strong Rayleigh. In specific examples, such as
occupation counts of disjoint sets by a determinantal point process, it is
known~\cite{soshnikov02} that the joint distribution must approach a
multivariate Gaussian distribution. We show that this conclusion follows
already from stability of $f$.


  Many model compression techniques of Deep Neural Networks (DNNs) have been
investigated, including weight pruning, weight clustering and quantization,
etc. Weight pruning leverages the redundancy in the number of weights in DNNs,
while weight clustering/quantization leverages the redundancy in the number of
bit representations of weights. They can be effectively combined in order to
exploit the maximum degree of redundancy. However, there lacks a systematic
investigation in literature towards this direction.
  In this paper, we fill this void and develop a unified, systematic framework
of DNN weight pruning and clustering/quantization using Alternating Direction
Method of Multipliers (ADMM), a powerful technique in optimization theory to
deal with non-convex optimization problems. Both DNN weight pruning and
clustering/quantization, as well as their combinations, can be solved in a
unified manner. For further performance improvement in this framework, we adopt
multiple techniques including iterative weight quantization and retraining,
joint weight clustering training and centroid updating, weight clustering
retraining, etc. The proposed framework achieves significant improvements both
in individual weight pruning and clustering/quantization problems, as well as
their combinations. For weight pruning alone, we achieve 167x weight reduction
in LeNet-5, 24.7x in AlexNet, and 23.4x in VGGNet, without any accuracy loss.
For the combination of DNN weight pruning and clustering/quantization, we
achieve 1,910x and 210x storage reduction of weight data on LeNet-5 and
AlexNet, respectively, without accuracy loss. Our codes and models are released
at the link http://bit.ly/2D3F0np


  The Resistive Plate Chambers (RPC) detector of the CMS experiment at the LHC
proton collider (CERN, Switzerland) will employ an online gas analysis and
monitoring system of the freon-based gas mixture used. We give an overview of
the CMS RPC gas system, describe the project parameters and first results on
gas-chromatograph analysis. Finally, we report on preliminary results for a set
of monitor RPC.


  The goal of imitation learning is to mimic expert behavior from
demonstrations, without access to an explicit reward signal. A popular class of
approach infers the (unknown) reward function via inverse reinforcement
learning (IRL) followed by maximizing this reward function via reinforcement
learning (RL). The policies learned via these approaches are however very
brittle in practice and deteriorate quickly even with small test-time
perturbations due to compounding errors. We propose Imitation with Planning at
Test-time (IMPLANT), a new meta-algorithm for imitation learning that utilizes
decision-time planning to correct for compounding errors of any base imitation
policy. In contrast to existing approaches, we retain both the imitation policy
and the rewards model at decision-time, thereby benefiting from the learning
signal of the two components. Empirically, we demonstrate that IMPLANT
significantly outperforms benchmark imitation learning approaches on standard
control environments and excels at zero-shot generalization when subject to
challenging perturbations in test-time dynamics.


  Theory of laminated turbulnece includes continuous layer of turbulence
(statistical description, kinetic equations, Zakharov-Kolmogorov spectra, etc)
AND discrete layer of turbulence (isolated groups of interacting waves, no
statisticaldescription). This theory is presented, examples of possible
applications are given, important open questions are formulated.


  In this paper we present new constraints on velocity-independent cross
section of keV-scale mass annihilating Dark Matter particles obtained with
SRG/ART-XC after 4 full-sky surveys. These constraints are derived from
observations of the Milky Way Halo, 33 Local Group spheroidal dwarf (dSph)
galaxies and separately for the dSph galaxy Ursa Major III/UNIONS 1. The
constraints from the Milky Way Halo are the strongest among others and among
all available in literature for this class of Dark Matter models with particle
masses from 4 to 15 keV.


  Let $\ell \ne 3$ be a prime. We show that there are only finitely many cyclic
number fields $F$ of degree $\ell$ for which the unit equation $$\lambda + \mu
= 1, \qquad \lambda,~\mu \in \mathcal{O}_F^\times$$ has solutions. Our result
is effective. For example, we deduce that the only cyclic quintic number field
for which the unit equation has solutions is $\mathbb{Q}(\zeta_{11})^+$.


  We calculate the semileptonic form factors $f_+^{B\to \eta}(q^2)$ and
$f_+^{B\to \eta'}(q^2)$ from QCD sum rules on the light-cone (LCSRs), to NLO in
QCD, and for small to moderate q^2, $0\leq q^2\leq 16 {\rm
  GeV}^2$. We include in particular the so-called singlet contribution, i.e.\
weak annihilation of the B meson with the emission of two gluons which, thanks
to the U(1)$_{\rm A}$ anomaly, couple directly to $\etap$. This effect is
included to leading-twist accuracy. This contribution has been neglected in
previous calculations of the form factors from LCSRs. We find that the singlet
contribution to $f_+^{B\to \eta'}$ can be up to 20%, while that to $f_+^{B\to
\eta}$ is, as expected, much smaller and below 3%. We also suggest to measure
the ratio ${\cal B}(B\to\eta' e \nu)/{\cal
  B}(B\to \eta e \nu)$ to better constrain the size of the singlet
contribution.


  We study the \textit{stochastic heat equation} (SHE) on $\R^d$ subject to a
centered Gaussian noise that is white in time and colored in space.The drift
term is assumed to satisfy an Osgood-type condition and the diffusion
coefficient may have certain related growth. We show that there exists random
field solution which do not explode in finite time. This complements and
improves upon recent results on blow-up of solutions to stochastic partial
differential equations.


  A result of Akemann, Anderson, and Pedersen states that if a sequence of pure
states of a C*-algebra A approaches infinity in a certain sense, then there is
a MASA B such that each of the states has the unique extension property with
respect to B. We generalize this in two ways: We prove that B can be required
to contain an approximate identity of A, and we show that the discrete space
which underlies the result cited can be replaced with a totally disconnected
space.
  We consider two special kinds of type I closed faces, both related to the
above, atomic closed faces and closed faces with nearly closed extreme
boundary. One specific question is whether an atomic closed face always has an
"isolated point". We give a counterexample for this and also show that the
answer is yes if the the atomic face has nearly closed extreme boundary.
  We prove a complement to Glimm's theorem on type I C*-algebras which arises
from the theory of type I closed faces. One of our examples is a type I closed
face which is isomorphic to a closed face of every non-type I separable
C*-algebra and which is not isomorphic to a closed face of any type I
C*-algebra.


  This paper presents a novel solution paradigm of general optimization under
both exogenous and endogenous uncertainties. This solution paradigm consists of
a probability distribution (PD)-free method of obtaining deterministic
equivalents and an innovative approach of scenario reduction. First, dislike
the existing methods that use scenarios sampled from pre-known PD functions,
the PD-free method uses historical measurements of uncertain variables as input
to convert the logical models into a type of deterministic equivalents called
General Scenario Program (GSP). Our contributions to the PD-free deterministic
equivalent construction reside in generalization (making it applicable to
general optimization under uncertainty rather than just chance-constrained
optimization) and extension (enabling it to the problems under endogenous
uncertainty via developing an iterative and a non-iterative frameworks).
Second, this paper reveals some unknown properties of the PD-free deterministic
equivalent construction, such as the characteristics of active scenarios and
repeated scenarios. Base on this discoveries, we propose a concept and methods
of strategic scenario selection which can effectively reduce the required
number of scenarios as demonstrated in both mathematical analysis and numerical
experiments. Numerical experiments are conducted on two typical smart grid
optimization problems under exogenous and endogenous uncertainties.


  In this paper, we discuss the damping of density oscillations in dense
nuclear matter in the temperature range relevant to neutron star mergers. This
damping is due to bulk viscosity arising from the weak interaction ``Urca''
processes of neutron decay and electron capture. The nuclear matter is modelled
in the relativistic density functional approach. The bulk viscosity reaches a
resonant maximum close to the neutrino trapping temperature, then drops rapidly
as temperature rises into the range where neutrinos are trapped in neutron
stars. We investigate the bulk viscous dissipation timescales in a post-merger
object and identify regimes where these timescales are as short as the
characteristic timescale $\sim$10 ms, and, therefore, might affect the
evolution of the post-merger object. Our analysis indicates that bulk viscous
damping would be important at not too high temperatures of the order of a few
MeV and densities up to a few times saturation density.


  The heavy quark sector of Coulomb gauge QCD is investigated, by making a
heavy quark mass expansion of the QCD action and restricting to the leading
order. With the truncation of the Yang-Mills sector to include only dressed
two-point functions, we study the Dyson-Schwinger equations for the four-point
quark Green's functions (proper and amputated). In this limit, we provide an
exact solution for the four-point quark Green's functions and show that the
corresponding poles relate to the bound state energy of the heavy quark system.
Moreover, a natural separation between the physical and unphysical poles in the
Green's functions emerges.


  Trajectory classification tasks became more complex as large volumes of
mobility data are being generated every day and enriched with new sources of
information, such as social networks and IoT sensors. Fast classification
algorithms are essential for discovering knowledge in trajectory data for real
applications. In this work we propose a method for fast discovery of
subtrajectories with the reduction of the search space and the optimization of
the MASTERMovelets method, which has proven to be effective for discovering
interpretable patterns in classification problems.


  In this paper, we study spaceability of subsets of generalized Orlicz and
Lebesgue spaces associated to Banach function space. Also, we give some
sufficient conditions for spaceability of subsets of a general Banach space
which improves an important result on this topic. As an application, it is
shown that the set of all bounded linear operators which are not positive
semidefinite on a separable Hilbert space is spaceable.


  Crystalline topological insulators owe their topological character to the
protection that certain boundary states acquire because of certain point-group
symmetries. We first show that a Hermitian operator obeying supersymmetric
quantum mechanisms (SUSY QM) delivers the entanglement spectrum. We then show
that such an entanglement spectrum that is compatible with a certain
point-group symmetry obeys a certain local spectral symmetry. The latter result
is applied to the stability analysis of four fermionic non-interacting
Hamiltonians, the last of which describes graphene with a Kekule distortion.
All examples have the remarkable property that their entanglement spectra
inherit a local spectral symmetry from either an inversion or reflection
symmetry that guarantees the stability of gapless boundary entangling states,
even though all examples fail to support protected gapless boundary states at
their physical boundaries.


  We investigate the microwave electrodynamic properties of a single
superconducting thin film split-ring resonator (SRR). The experiments were
performed in an all-Nb waveguide, with Nb wires and Nb SRRs. Transmission data
showed a high-Q stopband for a single Nb SRR ($Q \sim 4.5\times10^4$ at 4.2 K)
below $T_c$, and no such feature for a Cu SRR, or closed Nb loops, of similar
dimensions. Adding SRRs increased the bandwidth, but decreased the insertion
loss of the features. Placing the Nb SRR into an array of wires produced a
single, elementary negative-index passband ($Q \sim 2.26\times10^4$ at 4.2 K).
Changes in the features due to the superconducting kinetic inductance were
observed. Models for the SRR permeability, and the wire dielectric response,
were used to fit the data.


  We bound the dimension of the fiber of a Riemannian submersion from a
positively curved manifold in terms of the dimension of the base of the
submersion and either its conjugate radius or the length of its shortest closed
geodesic.


  A graph is $\ell$-reconstructible if it is determined by its multiset of
induced subgraphs obtained by deleting $\ell$ vertices. We prove that strongly
regular graphs with at least six vertices are $2$-reconstructible.


  A longstanding belief has been that the semimajor axes, in the Newtonian
planetary problem, are stable. In the course of the XIX century, Laplace,
Lagrange and others gave stronger and stronger arguments in this direction,
thus culminating in what has commonly been referred to as the first
Laplace-Lagrange stability theorem. In the problem with 3 planets, we prove the
existence of orbits along which the semimajor axis of the outer planet
undergoes large random variations thus disproving the theorem of
Laplace-Lagrange. The time of instability varies as a negative power of the
masses of the planets. The orbits we have found fall outside the scope of the
theory of Nekhoroshev-Niederman because they are not confined by the
conservation of angular momentum and because the Hamiltonian is not (uniformly)
convex with respect to the Keplerian actions.


  The article presents simple analysis of cones which are used to generate a
given conic curve by section by a plane. It was found that if the given curve
is an ellipse, then the locus of vertexes of the cones is a hyperbola. The
hyperbola has focuses which coincidence with the ellipse vertexes. Similarly,
if the given curve is the hyperbola, the locus of vertex of the cones is the
ellipse. In the second case, the focuses of the ellipse are located in the
hyperbola's vertexes. These two relationships create kind of conjunction
between the ellipse and the hyperbola which originate from the cones used for
generation of these curves. The presented conjunction of the ellipse and
hyperbola is perfect example of mathematical beauty which may be shown by use
of very simple geometry. As in the past the conic curves appear to be very
interesting and fruitful mathematical beings.


  One predominant challenge in additive manufacturing (AM) is to achieve
specific material properties by manipulating manufacturing process parameters
during the runtime. Such manipulation tends to increase the computational load
imposed on existing simulation tools employed in AM. The goal of the present
work is to construct a fast and accurate reduced-order model (ROM) for an AM
model developed within the Multiphysics Object-Oriented Simulation Environment
(MOOSE) framework, ultimately reducing the time/cost of AM control and
optimization processes. Our adoption of the operator learning (OL) approach
enabled us to learn a family of differential equations produced by altering
process variables in the laser's Gaussian point heat source. More specifically,
we used the Fourier neural operator (FNO) and deep operator network (DeepONet)
to develop ROMs for time-dependent responses. Furthermore, we benchmarked the
performance of these OL methods against a conventional deep neural network
(DNN)-based ROM. Ultimately, we found that OL methods offer comparable
performance and, in terms of accuracy and generalizability, even outperform DNN
at predicting scalar model responses. The DNN-based ROM afforded the fastest
training time. Furthermore, all the ROMs were faster than the original MOOSE
model yet still provided accurate predictions. FNO had a smaller mean
prediction error than DeepONet, with a larger variance for time-dependent
responses. Unlike DNN, both FNO and DeepONet were able to simulate time series
data without the need for dimensionality reduction techniques. The present work
can help facilitate the AM optimization process by enabling faster execution of
simulation tools while still preserving evaluation accuracy.


  In this paper, we propose new basis functions defined on curved sides or
faces of curvilinear elements (polygons or polyhedrons with curved sides or
faces) for the weak Galerkin finite element method. Those basis functions are
constructed by collecting linearly independent traces of polynomials on the
curved sides/faces. We then analyze the modified weak Galerkin method for the
elliptic equation and the interface problem on curvilinear polytopal meshes
with Lipschitz continuous edges or faces. The method is designed to deal with
less smooth complex boundaries or interfaces. Optimal convergence rates for
$H^1$ and $L^2$ errors are obtained, and arbitrary high orders can be achieved
for sufficiently smooth solutions. The numerical algorithm is discussed and
tests are provided to verify theoretical findings.


  We continue monitoring supernova remnant (SNR) 1987A with the {\it Chandra
X-ray Observatory}. As of 2004 January, bright X-ray spots in the northwest and
the southwest are now evident in addition to the bright eastern ring. The
overall X-ray spectrum, Since 2002 December, can be described by a planar shock
with an electron temperature of $\sim$2.1 keV. The soft X-ray flux is now 8
$\times$ 10$^{-13}$ ergs cm$^{-2}$ s$^{-1}$, which is about five times higher
than four years ago. This flux increase rate is consistent with our prediction
based on an exponential density distribution along the radius of the SNR
between the H{\small II} region and the inner ring. We still have no direct
evidence of a central point source, and place an upper limit of $L_X$ = 1.3
$\times$ 10$^{34}$ ergs s$^{-1}$ on the 3$-$10 keV band X-ray luminosity.


  We present a proximal gradient method for solving convex multiobjective
optimization problems, where each objective function is the sum of two convex
functions, with one assumed to be continuously differentiable. The algorithm
incorporates a backtracking line search procedure that requires solving only
one proximal subproblem per iteration, and is exclusively applied to the
differentiable part of the objective functions. Under mild assumptions, we show
that the sequence generated by the method convergences to a weakly Pareto
optimal point of the problem. Additionally, we establish an iteration
complexity bound by showing that the method finds an $\varepsilon$-approximate
weakly Pareto point in at most ${\cal O}(1/\varepsilon)$ iterations. Numerical
experiments illustrating the practical behavior of the method is presented.


  In this paper we propose an iterative algorithm to find out the spectral
radius of nonnegative tensors. This algorithm is an extension of the smoothing
method for finding the largest eigenvalue of a nonnegative matrix \cite{s14}.
For nonnegative irreducible tensors, we establish the converges of the
algorithm. Finally we report some numerical results and conclude this paper
with some remarks.


  We consider the question whether, given a countable system of lattices
$(\Gamma_j)_{j \in J}$ in a locally compact abelian group $G$, there exists a
sequence of functions $(g_j)_{j \in J}$ such that the resulting generalized
shift-invariant system $(g_j(\cdot - \gamma))_{j \in J, \gamma \in \Gamma_j}$
is a tight frame of $L^2(G)$. This paper develops a new approach to the study
of almost periodic functions for generalized shift-invariant systems based on
an \emph{unconditionally convergence property}, replacing previously used local
integrability conditions. From this theory, we derive characterizing relations
for tight and dual frame generators, we introduce the \emph{system bandwidth}
as a measure of the total bandwidth a generalized shift-invariant system can
carry, and we show that the so-called Calder\'on sum is uniformly bounded from
below for generalized shift-invariant frames. We exhibit a condition on the
lattice system for which the unconditionally convergence property is guaranteed
to hold. Without the unconditionally convergence property, we show, counter
intuitively, that even orthonormal bases can have arbitrary small system
bandwidth. Our results show that the question of existence of frame generators
for a general lattice system can be rather subtle, depending on analytical
properties, such as the system bandwidth, as well as on algebraic properties of
the lattice system.


  We introduce and study an abstract class of semirings, which we call exact
semirings, defined by a Hahn-Banach-type separation property on modules. Our
motivation comes from the tropical semiring, and in particular a desire to
understand the often surprising extent to which it behaves like a field. The
definition of exactness abstracts an elementary property of fields and the
tropical semiring, which we believe is fundamental to explaining this
similarity. The class of exact semirings turns out to include many other
important examples of both rings (proper quotients of principal ideal domains,
matrix rings and finite group rings over these and over fields), and semirings
(the Boolean semiring, generalisations of the tropical semiring, matrix
semirings and group semirings over these).


  Nonrelativistic (NR) string theory was discovered as a framework that
underlies and unifies the various noncommutative open string (NCOS) theories,
which were originally envisioned as surprising exceptions to the maxim that all
string theories are gravitational in nature. In that view, the fact that NCOS
has a gravitational dual was believed to be directly analogous to the AdS/CFT
correspondence. When NCOS theories were understood to be simply the particular
classes of states of the underlying NR theory that include longitudinal
D-branes, it was suggested that the duality between NCOS and the corresponding
gravitational theory is not an instance of gauge/gravity-type duality, but of
open-string/closed-string duality between D-branes and black branes. The
present paper provides direct evidence in support of this perspective, by
starting from a stack of D-branes in NR string theory and deriving the
long-distance profile of the curved geometry in the corresponding black brane.


  We study adaptive mesh selection for the solution of systems of initial value
problems. The goal is a rigorous theoretical analysis of potential advantages
of adaption. For an optimal method in the sense of the speed of convergence, we
propose an algorithm for successive selection of the mesh points. The selection
is based on an upper bound on the local error, and it (asymptotically)
guarantees the local errors not exceeding a prescribed level. The mesh
selection algorithm can be applied to a general class of methods, not only to
the chosen one.
  We rigorously discuss the cost of the proposed algorithm, comparing it to
other algorithms equipped with different mesh selection procedures. We specify
a quantitative advantage of the adaptive mesh over the uniform one. Adjustment
of the mesh points to a local behavior of the solution yields improved
efficiency of the algorithm.
  Some numerical results illustrating theoretical findings are reported.


  This article is concerned with the time-harmonic electromagnetic (EM)
scattering from a generic inhomogeneous medium. It is shown that if there is a
right corner on the support of the medium, then it scatters every pair of
incident EM fields, excluding a possible class of EM fields which are of very
particular forms. That is, for every pair of admissible incident EM fields, the
corresponding scattered wave fields associated to the medium scatterer cannot
be identically vanishing outside the support of the medium. Indeed, we achieve
the corner scattering result by establishing a stronger result, that shows the
failure of the analytic extension across the corner of certain EM fields
satisfying the so-called interior transmission eigenvalue problem. This extends
the relevant study in [3] for the acoustic scattering governed by the Helmholtz
equation to the electromagnetic case governed by the Maxwell system.
Substantial new challenges arise from the corresponding extension from the
scalar PDE to the system of PDEs. Our mathematical arguments combine the
analysis for interior transmission eigenvalue problems associated to the
Maxwell system; the derivation of novel orthogonality relation for the
solutions of Maxwell systems; the construction of complex-geometrical-optics
(CGO) solutions for the Maxwell system with new Lp-estimates (p > 6) on the
remainder terms; and the proof of the non-vanishing property for the Laplace
transform of vectorial homogeneous harmonic polynomials.


  This paper modifies an adaptive multicopter autopilot to mitigate
instabilities caused by adaptive parameter drift and presents simulation and
experimental results to validate the modified autopilot. The modified adaptive
controller is obtained by including a static nonlinearity in the adaptive loop,
updated by the retrospective cost adaptive control algorithm. It is shown in
simulation and physical test experiments that the adaptive autopilot with
proposed modifications can continually improve the fixed-gain autopilot as well
as prevent the drift of the adaptive parameters, thus improving the robustness
of the adaptive autopilot.


  The reduction of dynamical systems has a rich history, with many important
applications related to stability, control and verification. Reduction of
nonlinear systems is typically performed in an exact manner - as is the case
with mechanical systems with symmetry--which, unfortunately, limits the type of
systems to which it can be applied. The goal of this paper is to consider a
more general form of reduction, termed approximate reduction, in order to
extend the class of systems that can be reduced. Using notions related to
incremental stability, we give conditions on when a dynamical system can be
projected to a lower dimensional space while providing hard bounds on the
induced errors, i.e., when it is behaviorally similar to a dynamical system on
a lower dimensional space. These concepts are illustrated on a series of
examples.


  This talk is a survey of the question of joint measurability of coexistent
observables and its is based on the monograph Operational Quantum Physics [1]
and on the papers [2,3,4].


  We present the results of the analysis of a large database of X-ray
observations of 22 galactic black-hole transients with the Rossi X-Ray timing
explorer throughout its operative life for a total exposure time of ~12 Ms. We
excluded persistent systems and the peculiar source GRS 1915+105, as well as
the most recently discovered sources. The semi-automatic homogeneous analysis
was aimed at the detection of high-frequency (100-1000 Hz) quasi-periodic
oscillations (QPO), of which several cases were previously reported in the
literature. After taking into account the number of independent trials, we
obtained 11 detections from two sources only: XTE J1550-564 and GRO J1655-40.
For the former, the detected frequencies are clustered around 180 Hz and 280
Hz, as previously found. For the latter, the previously-reported dichotomy
300-450 Hz is found to be less sharp. We discuss our results in comparison with
kHz QPO in neutron-star X-ray binaries and the prospects for future timing
X-ray missions.


  We explore the equilibrium properties of a system composed of dipolar hard
spheres. A new theory based on the ideas derived from the work of Debye and
H\"uckel, Bjerrum, and Onsager is proposed to explain the absence of the
anticipated critical point in this system.


  Using the multipolar post-Minkowskian and matching formalism we compute the
gravitational waveform of inspiralling compact binaries moving in
quasi-circular orbits at the second and a half post-Newtonian (2.5PN)
approximation to general relativity. The inputs we use include notably the
mass-type quadrupole at the 2.5PN order, the mass octupole and current
quadrupole at the 2PN order, the mass $2^5$-pole and current $2^4$-pole at 1PN.
The non-linear hereditary terms come from the monopole-quadrupole multipole
interactions or tails, present at the 1.5PN, 2PN and 2.5PN orders, and the
quadrupole-quadrupole interaction arising at the 2.5PN level. In particular,
the specific effect of non-linear memory is computed using a simplified model
of binary evolution in the past. The ``plus'' and ``cross'' wave polarisations
at the 2.5PN order are obtained in ready-to-use form, extending the 2PN results
calculated earlier by Blanchet, Iyer, Will and Wiseman.


  On R^n endowed with a riemannian metric of bounded nonpositive curvature, the
weakly convex closed subsets are topologically trivial. The stability of such
subsets under intersection characterizes the euclidean spaces.


  Assessing the fairness of a decision making system with respect to a
protected class, such as gender or race, is challenging when class membership
labels are unavailable. Probabilistic models for predicting the protected class
based on observable proxies, such as surname and geolocation for race, are
sometimes used to impute these missing labels for compliance assessments.
Empirically, these methods are observed to exaggerate disparities, but the
reason why is unknown. In this paper, we decompose the biases in estimating
outcome disparity via threshold-based imputation into multiple interpretable
bias sources, allowing us to explain when over- or underestimation occurs. We
also propose an alternative weighted estimator that uses soft classification,
and show that its bias arises simply from the conditional covariance of the
outcome with the true class membership. Finally, we illustrate our results with
numerical simulations and a public dataset of mortgage applications, using
geolocation as a proxy for race. We confirm that the bias of threshold-based
imputation is generally upward, but its magnitude varies strongly with the
threshold chosen. Our new weighted estimator tends to have a negative bias that
is much simpler to analyze and reason about.


  It is generally assumed that on-off detectors with single-photon sensitivity
cannot distinguish coherent states from phase-mixed coherent states without
some form of quadrature-based tomography. Here, we show that it is
theoretically possible to distinguish these states without quadrature-based
methods by comparing the vacuum probability of one output port of a variable
beam splitter (VBS) that has two different phase-mixed coherent states as its
inputs to the well-known case of dual coherent-state inputs. As an application,
a method is proposed to test whether a laser field is in a coherent state over
a given time by simply measuring the VBS output populations with an on-off
single-photon detector. Unfortunately, timing limitations of present technology
prohibit such simple tests, but the technique may become practical in the
future.


  We observed new effect which we called photonic flame effect (PFE). Several
3-dimensional photonic crystals (artificial opals) were posed on Cu plate at
the temperature of liquid nitrogen (77K). Typical distance between them was 1-5
centimeters. Long-continued optical luminescence was excited in one of them by
the ruby laser pulse. Analogous visible luminescence manifesting time delay
appeared in other samples of the crystals. Experiments were realized for opal
crystals and for nanocomposites (opals filled with nonlinear liquids).


  We consider the initial-boundary value problem for the coupled
Navier-Stokes-Keller-Segel-Fisher-Kolmogorov-Petrovskii-Piskunov system in two-
and three-dimensional domains. The problem describes oxytaxis and growth of
Bacillus subtilis in moving water. We prove existence of global weak solutions
to the problem. We distinguish between two cases determined by the cell
diffusion term and the space dimension, which are referred as the supercritical
and subcritical ones. At the first case, the choice of the growth function
enjoys wide range of possibilities: in particular, it can be zero. Our results
are new even at the absence of the growth term. At the second case, the
restrictions on the growth function are less relaxed: for instance, it cannot
be zero but can be Fisher-like. In the case of linear cell diffusion, the
solution is regular and unique provided the domain is the whole plane. In
addition, we study the long-time behaviour of the problem, find dissipative
estimates, and construct attractors.


  Using the dynamic renormalization group (DRG) technique, we analyze general
nonlinearities in a conservative nonlinear growth equation with non-conserved
gaussian white noise. We show that they fall in two classes only: the
Edwards-Wilkinson and Lai-Das Sarma types, by explicitly computing the
associated amputated two and three point functions at the first order in
perturbation parameter(s). We further generalize this analysis to higher order
nonlinearities and also suggest a physically meaningful geometric
interpretation of the same.


  We present a model independent study of the chiral condensate evolution in a
hadronic gas, in terms of temperature and baryon chemical potential. The
meson-meson interactions are described within Chiral Perturbation Theory and
the pion-nucleon interaction by means of Heavy Baryon Chiral Perturbation
Theory, both at one loop. Together with the virial expansion, this provides a
model independent systematic expansion at low temperatures and chemical
potentials, which includes the physical quark masses. This can serve as a
guideline for further studies on the lattice. We also obtain estimates of the
critical line of temperature and chemical potential where the chiral condensate
melts, which systematically lie somewhat higher than recent lattice
calculations but are consistent with several hadronic models.


  Unlike in many other semiconductors, the band gap of PbTe increases
considerably with temperature. We compute the thermoelectric transport
properties of n-type PbTe from first principles including the temperature
variation of the electronic band structure. The calculated temperature
dependence of the thermoelectric quantities of PbTe is in good agreement with
previous experiments when the temperature changes of the band structure are
accounted for. We also calculate the optimum band gap values which would
maximize the thermoelectric figure of merit of n-type PbTe at various
temperatures. We show that the actual gap values in PbTe closely follow the
optimum ones between 300 K and 900 K, resulting in the high figure of merit.
Our results indicate that an appreciable increase of the band gap with
temperature in direct narrow-gap semiconductors is very beneficial for
achieving high thermoelectric performance.


  The design of acoustic features is important for speech separation. It can be
roughly categorized into three classes: handcrafted, parameterized, and
learnable features. Among them, learnable features, which are trained with
separation networks jointly in an end-to-end fashion, become a new trend of
modern speech separation research, e.g. convolutional time domain audio
separation network (Conv-Tasnet), while handcrafted and parameterized features
are also shown competitive in very recent studies. However, a systematic
comparison across the three kinds of acoustic features has not been conducted
yet. In this paper, we compare them in the framework of Conv-Tasnet by setting
its encoder and decoder with different acoustic features. We also generalize
the handcrafted multi-phase gammatone filterbank (MPGTF) to a new parameterized
multi-phase gammatone filterbank (ParaMPGTF). Experimental results on the
WSJ0-2mix corpus show that (i) if the decoder is learnable, then setting the
encoder to STFT, MPGTF, ParaMPGTF, and learnable features lead to similar
performance; and (ii) when the pseudo-inverse transforms of STFT, MPGTF, and
ParaMPGTF are used as the decoders, the proposed ParaMPGTF performs better than
the other two handcrafted features.


  Ultrathin (~3 quintuple layer) field-effect transistors (FETs) of topological
insulator Bi2Se3 are prepared by mechanical exfoliation on 300nm SiO2/Si
susbtrates. Temperature- and gate-voltage dependent conductance measurements
show that ultrathin Bi2Se3 FETs are n-type, and have a clear OFF state at
negative gate voltage, with activated temperature-dependent conductance and
energy barriers up to 250 meV.


  A diffeomorphism $f$ of a compact manifold $X$ is pseudo-isotopic to the
identity if there is a diffeomorphism $F$ of $X\times I$ which restricts to $f$
on $X\times 1$, and which restricts to the identity on $X\times 0$ and
$\partial X\times I$. We construct examples of diffeomorphisms of 4-manifolds
which are pseudo-isotopic but not isotopic to the identity. To do so, we
further understanding of which elements of the "second pseudo-isotopy
obstruction", defined by Hatcher and Wagoner, can be realised by
pseudo-isotopies of 4-manifolds. We also prove that all elements of the first
and second pseudo-isotopy obstructions can be realised after connected sums
with copies of $S^2\times S^2$.


  We study homeomorphisms of compact metric spaces whose restriction to the
nonwandering set has the pseudo-orbit tracing property. We prove that if there
are positively expansive measures, then the topological entropy is positive.
Some short applications of this result are included.


  We discuss the production of multi-photons squeezed states induced by the
time variation of the (Abelian) gauge coupling constant in a string
cosmological context. Within a fully quantum mechanical approach we solve the
time evolution of the mean number of produced photons in terms of the squeezing
parameters and in terms of the gauge coupling. We compute the first (amplitude
interference) and second order (intensity interference) correlation functions
of the magnetic part of the photon background. The photons produced thanks to
the variation of the dilaton coupling are strongly bunched for the realistic
case where the growth of the dilaton coupling is required to explain the
presence of large scale magnetic fields and, possibly of a Faraday rotation of
the Cosmic Microwave Background.


  We review vibrational and electronic properties of single and a few layer
MoS$_2$ relevant to understand their resonant and non-resonant Raman scattering
results. In particular, the optical modes and low frequency shear and layer
breathing modes show significant dependence on the number of MoS$_2$ layers.
Further, the electron doping of the MoS$_2$ single layer achieved using
top-gating in a field effect transistor renormalizes the two optical modes
A$_{1g}$ and E$^1_{2g}$ differently due to symmetry-dependent electron-phonon
coupling. The issues related to carrier mobility, the Schottky barrier at the
MoS$_2$-metal contact pads and the modifications of the dielectric environment
are addressed. The direct optical transitions for single layer-MoS$_2$ involve
two excitons at K-point in the Brillouin zone and their stability with
temperature and pressure has been reviewed. Finally, the Fermi-level dependence
of spectral shift for a quasiparticle, called trion, has been discussed.


  The mid-wave infrared (MWIR) spectral region (3-5 {\mu}m) is important to a
vast variety of applications in imaging, sensing, spectroscopy, surgery, and
optical communications. Efficient third-harmonic generation (THG), converting
light from the MWIR range into the near-infrared, a region with mature optical
detection and manipulation technologies, offers the opportunity to mitigate a
commonly recognized limitation of current MWIR systems. In this work, we
present the possibility of boosting THG in the MWIR through a metasurface
design. Specifically, we demonstrate a 30-fold enhancement in a highly
nonlinear phase change material Ge2Sb2Se4Te1 (GSST), by patterning arrays of
subwavelength cylinders supporting a magnetic dipolar resonance. The
unprecedented broadband transparency, large refractive index, and remarkably
high nonlinear response, together with unique phase-change properties, make
GSST-based metasurfaces an appealing solution for reconfigurable and
ultra-compact nonlinear devices operating in the MWIR.


  The purpose of this paper is to study families of Artinian or one dimensional
quotients of a polynomial ring $R$ with a special look to level algebras. Let
$\GradAlg^H(R)$ be the scheme parametrizing graded quotients of $R$ with
Hilbert function $H$. Let $B \to A$ be any graded surjection of quotients of
$R$ with Hilbert function $H_B$ and $H_A$, and h-vectors
$h_B=(1,h_1,...,h_j,...)$ and $h_A$, respectively. If $\depth A = \dim A \leq
1$ and $A$ is a ``truncation'' of $B$ in the sense that
$h_A=(1,h_1,...,h_{j-1},\alpha,0,0,...)$ for some $\alpha \leq h_j$, then we
show there is a close relationship between $\GradAlg^{H_A}(R)$ and
$\GradAlg^{H_B}(R)$ concerning e.g. smoothness and dimension at the points
$(A)$ and $(B)$ respectively, provided $B$ is a complete intersection or
provided the Castelnuovo-Mumford regularity of $A$ is at least 3 (sometimes 2)
larger than the regularity of $B$. In the complete intersection case we
generalize this relationship to ``non-truncated'' Artinian algebras $A$ which
are compressed or close to being compressed. For more general Artinian algebras
we describe the dual of the tangent and obstruction space of deformations in a
manageable form which we make rather explicit for level algebras of
Cohen-Macaulay type 2. This description and a linkage theorem for families
allow us to prove a conjecture of Iarrobino on the existence of at least two
irreducible components of $\GradAlg^H(R)$, $H=(1,3,6,10,14,10,6,2)$, whose
general elements are Artinian level algebras of type 2.


  We propose and theoretically study a new hollow-core fiber-based curvature
sensor with the capability of detecting both bending magnitude and angle
(direction). The new sensor relies on a tubular-lattice fiber that encompasses,
in its microstructure, tubes with three different thicknesses. By adequately
choosing the placement of the tubes within the fiber cross-section, and by
exploring the spectral shifts of the fiber transmitted spectrum due to the
curvature-induced mode field distributions' displacements, we demonstrate a
multi-axis bending sensor. In the proposed sensor, curvature radii and angles
are retrieved via a suitable calibration routine, which is based on
conveniently adjusting empirical functions to the sensor response. Evaluation
of the sensor performance for selected cases allowed to determine the curvature
radii and angles with percentual errors of less than 7%. The approach proposed
herein provides a promising path for the accomplishment of new curvature
sensors able to resolve both the curvature magnitude and angle.


  RBS 1032 is a supersoft ($\Gamma\sim5$), luminous ($\sim10^{43}$ erg/s) ROSAT
PSPC source which has been associated with an inactive dwarf galaxy at
$z=0.026$, SDSS J114726.69+494257.8. We have analyzed an XMM-Newton observation
which confirms that RBS 1032 is indeed associated with the dwarf galaxy.
Moreover, RBS 1032 has undergone a factor of $\sim100-300$ decay since November
1990. This variability suggests that RBS 1032 may not be a steadily accreting
intermediate-mass black hole, but rather an accretion flare from the tidal
disruption of a star by the central black hole (which may or may not be
intermediate-mass). We suggest that additional tidal disruption events may
remain unidentified in archival ROSAT data, such that disruption rate estimates
based upon ROSAT All-Sky Survey data may need reconsideration.


  The problem of computing the vertex expansion of a graph is an NP-hard
problem. The current best worst-case approximation guarantees for computing the
vertex expansion of a graph are a $O(\sqrt{\log n})$-approximation algorithm
due to Feige, Hajiaghayi and Lee [SIAM J. Comp., 2008], and $O(\sqrt{OPT \log
d})$ bound in graphs having vertex degrees at most $d$, due to Louis,
Raghavendra and Vempala [FOCS 2013].
  We study a natural semi-random model of graphs with sparse vertex cuts. For
certain ranges of parameters, we give an algorithm to recover the planted
sparse vertex cut exactly. For a larger range of parameters, we give a constant
factor bi-criteria approximation algorithm to compute the graph's balanced
vertex expansion. Our algorithms are based on studying a semidefinite
programming relaxation for the balanced vertex expansion of the graph.
  In addition to being a family of instances that will help us to better
understand the complexity of the computation of vertex expansion, our model can
also be used in the study of community detection where only a few nodes from
each community interact with nodes from other communities. There has been a lot
of work on studying random and semi-random graphs with planted sparse edge
cuts. To the best of our knowledge, our model of semi-random graphs with
planted sparse vertex cuts has not been studied before.


  There are many instances in genetics in which we wish to determine whether
two candidate populations are distinguishable on the basis of their genetic
structure. Examples include populations which are geographically separated,
case--control studies and quality control (when participants in a study have
been genotyped at different laboratories). This latter application is of
particular importance in the era of large scale genome wide association
studies, when collections of individuals genotyped at different locations are
being merged to provide increased power. The traditional method for detecting
structure within a population is some form of exploratory technique such as
principal components analysis. Such methods, which do not utilise our prior
knowledge of the membership of the candidate populations. are termed
\emph{unsupervised}. Supervised methods, on the other hand are able to utilise
this prior knowledge when it is available.
  In this paper we demonstrate that in such cases modern supervised approaches
are a more appropriate tool for detecting genetic differences between
populations. We apply two such methods, (neural networks and support vector
machines) to the classification of three populations (two from Scotland and one
from Bulgaria). The sensitivity exhibited by both these methods is considerably
higher than that attained by principal components analysis and in fact
comfortably exceeds a recently conjectured theoretical limit on the sensitivity
of unsupervised methods. In particular, our methods can distinguish between the
two Scottish populations, where principal components analysis cannot. We
suggest, on the basis of our results that a supervised learning approach should
be the method of choice when classifying individuals into pre-defined
populations, particularly in quality control for large scale genome wide
association studies.