  Context. Pair programming (PP) has been found to increase student interest in
Computer Science, particularly so for women, and would therefore appear to be a
way to help remedy their under-representation, which could be partially
motivated by gender stereotypes applied to software engineers, assuming that
men perform better than their women peers. If this same bias is present in pair
programming, it could work against the goal of improving gender balance.
  Objective. In a remote setting in which students cannot directly observe
their peers, we aim to explore whether they behave differently when the
perceived gender of their remote PP partners changes, searching for differences
in (i) the perceived productivity compared to solo programming; (ii) the
partner's perceived technical competency compared to their own; (iii) the
partner's perceived skill level; (iv) the interaction behavior, such as the
frequency of source code additions, deletions, etc.; and (v) the type and
relative frequencies of dialog messages in a chat window.
  Method. Using the twincode platform, several behaviors are automatically
measured during the remote PP process, together with two questionnaires and a
semantic tagging of the pairs' chats. A series of experiments to identify the
effect, if any, of possible gender bias shall be performed. The control group
will have no information about their partner's gender, whereas the treatment
group will receive such information but will be selectively deceived about
their partner's gender. For each response variable we will (i) compare control
and experimental groups for the score distance between two in-pair tasks; then,
using the data from the experimental group only, we will (ii) compare scores
using the partner's perceived gender as a within-subjects variable; and (iii)
analyze the interaction between the partner's perceived gender and the
subject's gender.


  Any multiplicative quiver variety is endowed with a Poisson structure
constructed by Van den Bergh through reduction from a Hamiltonian quasi-Poisson
structure. The smooth locus carries a corresponding symplectic form defined by
Yamakama through quasi-Hamiltonian reduction. In this note, we include the
Poisson structure as part of a pencil of compatible Poisson structures on the
multiplicative quiver variety. The pencil is defined by reduction from a pencil
of Hamiltonian quasi-Poisson structures which has dimension $\ell(\ell-1)/2$,
where $\ell$ is the number of arrows in the underlying quiver. For each element
of the pencil, we exhibit the corresponding compatible symplectic or
quasi-Hamiltonian structure. We comment on analogous constructions for
character varieties and quiver varieties. This formalism is applied to the spin
Ruijsenaars-Schneider phase space in order to explain the compatibility of two
Poisson structures that have recently appeared in the literature.


  It has been known for many years that there is a CP-conserving component for
the decay mode K_L -> pi0 e+e- and that its magnitude can be obtained from a
measurement of the amplitudes in the K_L -> pi0 gamma gamma decay mode. We
point out that the usual description of the latter in terms of a single
parameter, a_V, is not sufficient to extract the former in a model independent
manner. We further show that there exist known physics contributions to K_L ->
pi0 gamma gamma that cannot be described in terms of the single parameter a_V.
We conclude that a model independent analysis requires the experimental
extraction of three parameters.


  Phase, microstructural, dielectric and ferroelectric properties investigation
clearly reveal that the charge imbalance created due to alio-valent Gd3+-ion
substitution at Pb2+ site in lead magnesium niobate should be compensated by
changing B-site cation ratio instead of creating A-site or B-site vacancies.
Microstructure analysis along with elemental mapping exhibit segregation of
magnesium oxide (MgO) and gadolinium niobate (GdNbO4) phase, which is observed
to remain invariant to the charge compensation mechanism. Fitting of frequency
dependent temperature of dielectric constant maximum Tm (temperature of emax)
and the Mydosh parameter K calculated from the emax (T) clearly reveals
critical slowing down of polar nano-regions (PNRs) dynamics leading to
super-dipolar glass state in Gd3+-ion substituted PMN, when the charge
imbalance is compensated by creating B-site vacancy or changing B-site cations
ratio. Precipitation of secondary (pyrochlore) phase is resulted in Gd3+-ion
substituted PMN when the charge imbalance is compensated by creating A-site
vacancies, which causes reduction in interaction among the PNRs and
circumventing the super-dipolar glass state.


  We demonstrate the possibility of detecting tidal stripping of dark matter
subhalos within galaxy groups using weak gravitational lensing. We have run
ray-tracing simulations on galaxy catalogues from the Millennium Simulation to
generate mock shape catalogues. The ray-tracing catalogues assume a halo model
for galaxies and groups, using various models with different distributions of
mass between galaxy and group halos to simulate different stages of group
evolution. Using these mock catalogues, we forecast the lensing signals that
will be detected around galaxy groups and satellite galaxies, as well as test
two different methods for isolating the satellites' lensing signals. A key
challenge is to determine the accuracy to which group centres can be
identified. We show that with current and ongoing surveys, it will possible to
detect stripping in groups of mass 10^12--10^15 Msun.


  Context: Analyzing digital pathology images is necessary to draw diagnostic
conclusions by investigating tissue patterns and cellular morphology. However,
manual evaluation can be time-consuming, expensive, and prone to inter- and
intra-observer variability. Objective: To assist pathologists using
computerized solutions, automated tissue structure detection and segmentation
must be proposed. Furthermore, generating pixel-level object annotations for
histopathology images is expensive and time-consuming. As a result, detection
models with bounding box labels may be a feasible solution. Design: This paper
studies. YOLO-v4 (You-Only-Look-Once), a real-time object detector for
microscopic images. YOLO uses a single neural network to predict several
bounding boxes and class probabilities for objects of interest. YOLO can
enhance detection performance by training on whole slide images. YOLO-v4 has
been used in this paper. for glomeruli detection in human kidney images.
Multiple experiments have been designed and conducted based on different
training data of two public datasets and a private dataset from the University
of Michigan for fine-tuning the model. The model was tested on the private
dataset from the University of Michigan, serving as an external validation of
two different stains, namely hematoxylin and eosin (H&E) and periodic
acid-Schiff (PAS). Results: Average specificity and sensitivity for all
experiments, and comparison of existing segmentation methods on the same
datasets are discussed. Conclusions: Automated glomeruli detection in human
kidney images is possible using modern AI models. The design and validation for
different stains still depends on variability of public multi-stain datasets.


  Deep Q Network (DQN) has several limitations when applied in planning a path
in environment with a number of dilemmas according to our experiment. The
reward function may be hard to model, and successful experience transitions are
difficult to find in experience replay. In this context, this paper proposes an
improved Double DQN (DDQN) to solve the problem by reference to A* and
Rapidly-Exploring Random Tree (RRT). In order to achieve the rich experiments
in experience replay, the initialization of robot in each training round is
redefined based on RRT strategy. In addition, reward for the free positions is
specially designed to accelerate the learning process according to the
definition of position cost in A*. The simulation experimental results validate
the efficiency of the improved DDQN, and robot could successfully learn the
ability of obstacle avoidance and optimal path planning in which DQN or DDQN
has no effect.


  Previous work in phonetically-grounded language generation has mainly focused
on domains such as lyrics and poetry. In this paper, we present work on the
generation of tongue twisters - a form of language that is required to be
phonetically conditioned to maximise sound overlap, whilst maintaining semantic
consistency with an input topic, and still being grammatically correct. We
present \textbf{TwistList}, a large annotated dataset of tongue twisters,
consisting of 2.1K+ human-authored examples. We additionally present several
benchmark systems (referred to as TwisterMisters) for the proposed task of
tongue twister generation, including models that both do and do not require
training on in-domain data. We present the results of automatic and human
evaluation to demonstrate the performance of existing mainstream pre-trained
models in this task with limited (or no) task specific training and data, and
no explicit phonetic knowledge. We find that the task of tongue twister
generation is challenging for models under these conditions, yet some models
are still capable of generating acceptable examples of this language type.


  Purpose: To evaluate the efficacy of a recently proposed method for
characterizing tear film dynamics using non-invasive high speed
videokeratoscopy in assessing the loss of homeostasis of tear film. Methods:
Thirty subjects, from a retrospective study, of which 11 were classified as dry
eye and 19 as normal, were included. High speed videokeratoscopy measurements
were performed using E300 videokeratoscope (Medmont Pty., Ltd, Melbourne,
Australia). Raw data was analyzed using a recently proposed method to estimate
the dynamics of the tear film, based on a fractal dimension approach. This
method provides three time-varying indicators related to the regularity of the
reflected rings: Tear Film Surface Quality (TFSQ) indicator, Breaks Feature
Indicator (BFI) and Distortions Feature Indicator (DFI). From each indicator
five parameters were extracted and analyzed, including non-invasive break up
time, mean value of the indicator in the stability phase, mean value of the
indicator in the whole inter blink interval, mean value of the indicator in the
levelling phase and the general trend of the time series. Receiver Operating
Characteristic were used to determine the sensitivity and specificity of each
parameter in dry eye detection. Results: The best discrimination performance
between dry eye and normal subjects was achieved with the BFI non-invasive
break up time parameter, with an area under the curve of 0.85. For a cut off
value of 10 s the sensitivity was 100% and the specificity 84%. Conclusions:
The analyzed method improves the assessment of tear film homeostasis in
comparison to previous high speed videokeratoscopy methods showing higher
potential in assisting dry eye diagnosis.


  We explore two generic hypotheses for tracing the sources of ultra-high
energy cosmic rays (UHECRs) in the Universe: star formation rate density or
stellar mass density. For each scenario, we infer a set of constraints for the
emission mechanisms in the accelerators, for their energetics and for the
abundances of elements at escape from their environments. From these
constraints, we generate sky maps above 40~EeV expected from a catalog that
comprises 410,761 galaxies out to 350 Mpc and provides a near-infrared
flux-limited sample to map both stellar mass and star formation rate over the
full sky. Considering a scenario of intermittent sources hosted in every
galaxy, we show that the main features observed in arrival directions of UHECRs
can in turn constrain the burst rate of the sources provided that
magnetic-horizon effects are at play in clusters of galaxies.


  Some multidimensional generalizations of the Fokker-Planck equation used by
R. Friedrich and J. Peinke for the description of a turbulent cascade as a
stochastic process of Markovian type, are considered. The exact solutions of
the Cauchy problems for these equations are found with the operator methods.


  Based on a recently developed relativistic density functional approach to
color-superconducting quark matter and a novel quark-hadron transition
construction which phenomenologically accounts for the effects of inhomogeneous
pasta phases and quark-hadron continuity, we construct a class of hybrid
equations of state applicable at the regimes typical for compact star
astrophysics and heavy ion collisions. We outline that early quark
deconfinement is a notable consequence of strong diquark pairing providing a
good agreement with the observational data and driving the trajectories of the
matter evolution during the supernovae explosions toward the regimes typical
for the compact star mergers and heavy-ion collisions.


  The temporal activity of many biological systems, including neural circuits,
exhibits fluctuations simultaneously varying over a large range of timescales.
The mechanisms leading to this temporal heterogeneity are yet unknown. Here we
show that random neural networks endowed with a distribution of self-couplings,
representing functional neural clusters of different sizes, generate multiple
timescales activity spanning several orders of magnitude. When driven by a
time-dependent broadband input, slow and fast neural clusters preferentially
entrain slow and fast spectral components of the input, respectively,
suggesting a potential mechanism for spectral demixing in cortical circuits.


  In this paper, we study the existence and multiplicity results of nontrivial
positive solutions to the following quasilinear elliptic equation on $\RN$,
when $N\geq2$, \begin{equation} \Lp
u=\lambda\hspace{0.2mm}K(x)u^{r-1}-V(x)u^{q-1}.\nonumber \end{equation} Here,
$K(x),V(x)>0$ are suitable potentials, $1<p<r<q<\infty$, and $\lambda>0$ is a
parameter. To study this problem, some compact embedding results regarding
$\MVR\hookrightarrow\LKR$ are proved that unify and extend some recent results
of the author \cite{Ha1,Ha2,Ha3}.


  We evaluate the continuum limit of the valence (quenched) approximation to
the mass of the lightest scalar quarkonium state, for a range of different
quark masses, and to the mixing energy between these states and the lightest
scalar glueball. Our results support the interpretation of $f_0(1710)$ as
composed mainly of the lightest scalar glueball.


  A basic problem of interest in connection with the study of Schauder frames
in Banach spaces is that of characterizing those Schauder frames which can
essentially be regarded as Schauder bases. In this paper, we give a solution to
this problem using the notion of the minimal-associated sequence spaces and the
minimal-associated reconstruction operators for Schauder frames. We prove that
a Schauder frame is a near-Schauder basis if and only if the kernel of the
minimal-associated reconstruction operator contains no copy of $c_0$. In
particular, a Schauder frame of a Banach space with no copy of $c_0$ is a
near-Schauder basis if and only if the minimal-associated sequence space
contains no copy of $c_0$. In these cases, the minimal-associated
reconstruction operator has a finite dimensional kernel and the dimension of
the kernel is exactly the excess of the near-Schauder basis. Using these
results, we make related applications on Besselian frames and near-Riesz bases.


  Existing salient instance detection (SID) methods typically learn from
pixel-level annotated datasets. In this paper, we present the first
weakly-supervised approach to the SID problem. Although weak supervision has
been considered in general saliency detection, it is mainly based on using
class labels for object localization. However, it is non-trivial to use only
class labels to learn instance-aware saliency information, as salient instances
with high semantic affinities may not be easily separated by the labels. As the
subitizing information provides an instant judgement on the number of salient
items, it is naturally related to detecting salient instances and may help
separate instances of the same class while grouping different parts of the same
instance. Inspired by this observation, we propose to use class and subitizing
labels as weak supervision for the SID problem. We propose a novel
weakly-supervised network with three branches: a Saliency Detection Branch
leveraging class consistency information to locate candidate objects; a
Boundary Detection Branch exploiting class discrepancy information to delineate
object boundaries; and a Centroid Detection Branch using subitizing information
to detect salient instance centroids. This complementary information is then
fused to produce a salient instance map. To facilitate the learning process, we
further propose a progressive training scheme to reduce label noise and the
corresponding noise learned by the model, via reciprocating the model with
progressive salient instance prediction and model refreshing. Our extensive
evaluations show that the proposed method plays favorably against carefully
designed baseline methods adapted from related tasks.


  We consider a misspecified optimization problem that requires minimizing a
function f(x;q*) over a closed and convex set X where q* is an unknown vector
of parameters that may be learnt by a parallel learning process. In this
context, We examine the development of coupled schemes that generate iterates
{x_k,q_k} as k goes to infinity, then {x_k} converges x*, a minimizer of
f(x;q*) over X and {q_k} converges to q*. In the first part of the paper, we
consider the solution of problems where f is either smooth or nonsmooth under
various convexity assumptions on function f. In addition, rate statements are
also provided to quantify the degradation in rate resulted from learning
process. In the second part of the paper, we consider the solution of
misspecified monotone variational inequality problems to contend with more
general equilibrium problems as well as the possibility of misspecification in
the constraints. We first present a constant steplength misspecified
extragradient scheme and prove its asymptotic convergence. This scheme is
reliant on problem parameters (such as Lipschitz constants)and leads us to
present a misspecified variant of iterative Tikhonov regularization. Numerics
support the asymptotic and rate statements.


  Boron doped amorphous conducting carbon films show MI transition induced by
doping. In this paper we discuss the ac conductivity of the films, which lie on
the insulating side of MI transition. The ac conductivity data is analyzed as a
function of frequency as well as temperature for both real as well as imaginary
part of the conductivity data. The ac conductivity of these samples shows
enhanced interaction effect at low temperature. The conduction mechanism at
high frequency and at low temperature is due to the tunneling mechanism. At
intermediate temperatures and at moderate frequencies, the conductivity data is
in good agreement with extended pair approximation modified for interaction
effect. At high temperature and at low frequencies, the mechanism of ac
conductivity is similar to that of dc conduction. The conductivity data for the
insulating samples near the boundary of MI transition were analyzed using an RC
element model. While fitting the impedance data, a new type of constant phase
element (inductive instead of capacitive) was found to be appropriate. At
present the origin of this inductive CPE is not is clear but we believe its
origin is in the electron-electron interaction, which makes any attempt of
applied frequency a slower response. Our ac conductivity results are well
explained by tunneling models rather than hopping models.


  The connection of the Callen-Welton and Nyquist fluctuation-dissipation
relations is considered for plasma-like classical and quantum systems. The
conditions for appearance of the dissipative parameters in the equilibrium
current-current correlation function are investigated. The paper presents the
arguments for the restrictions of the Nyquist theorem and against violation of
the Callen-Welton theorem in the quantum case.


  Deep learning based neural networks have gained popularity for a variety of
biomedical imaging applications. In the last few years several works have shown
the use of these methods for colon cancer detection and the early results have
been promising. These methods can potentially be utilized to assist doctor's
and may help in identifying the number of lesions or abnormalities in a
diagnosis session. From our literature survey we found out that there is a lack
of publicly available labeled data. Thus, as part of this work, we have aimed
at open sourcing a dataset which contains annotations of polyps and ulcers.
This is the first dataset that's coming from India containing polyp and ulcer
images. The dataset can be used for detection and classification tasks. We also
evaluated our dataset with several popular deep learning object detection
models that's trained on large publicly available datasets and found out
empirically that the model trained on one dataset works well on our dataset
that has data being captured in a different acquisition device.


  After revival of the concept of minimal length, many investigations have been
devoted, in literature, to estimate upper-bound on minimal length for systems
like hydrogen atom, deuteron etc. We report here a possible origin of minimal
length for atomic and nuclear systems which is connected with the fundamental
interaction strength and the Compton wavelength. The formula we appear at is
numerically close to the upper-bounds found in literature.


  We introduce two independent quantifications for 3-mode and 4-mode
entanglement. We investigate the conversion of one type of nonclassicality,
i.e. single-mode nonclassicality, into another type of nonclassicality, i.e.
multi-mode entanglement, in beam-splitters. We observe parallel behavior of the
two quantifications. The methods can be generalized to the quantification of
any multi-mode entanglement.


  This paper presents "Predictive Pipelined Decoding (PPD)," an approach that
speeds up greedy decoding in Large Language Models (LLMs) while maintaining the
exact same output as the original decoding. Unlike conventional strategies, PPD
employs additional compute resources to parallelize the initiation of
subsequent token decoding during the current token decoding. This method
reduces decoding latency and reshapes the understanding of trade-offs in LLM
decoding strategies. We have developed a theoretical framework that allows us
to analyze the trade-off between computation and latency. Using this framework,
we can analytically estimate the potential reduction in latency associated with
our proposed method, achieved through the assessment of the match rate,
represented as p_correct. The results demonstrate that the use of extra
computational resources has the potential to accelerate LLM decoding.
Additionally, we implement PPD and conduct preliminary experiments to
empirically validate its efficacy, addressing potential practical overheads not
covered by theoretical analysis.


  The recent availability of wideband receivers and high sensitivity
instruments in the mm and submm wavelengths has opened the possibility of
studying in detail the chemistry of the interstellar medium in extragalactic
objects. Within the central few hundred parsec of galaxies, we find enormous
amounts of molecular material fueling a wide variety of highly energetic events
observed in starbursts (galaxies undergoing an intense burst of star formation)
and active galactic nuclei (AGN, where activity is driven by the accretion of
material onto the nuclear black hole).
  Here it is presented a brief summary of both the history and the latest
results in observational chemistry in distant galaxies. It will be shown how
the molecular emission, is a powerful tool to explore the physics of the
dust-enshrouded, buried nuclei of distant ultraluminous galaxies, which are
heavily obscured at other wavelengths. Special attention will be given to the
possibilities offered by next generationinstruments such as ALMA (Atacama Large
Millimeter Array), expected to have a vast impact on the field of Extragalactic
Chemistry. Molecular studies in the early Universe will become available at
unprecedented sensitivity and resolution.


  Let $T_1^{(\mu)}$ be the first hitting time of the point 1 by the Bessel
process with index $\mu\in \R$ starting from $x>1$. Using an integral formula
for the density $q_x^{(\mu)}(t)$ of $T_1^{(\mu)}$, obtained in Byczkowski,
Ryznar (Studia Math., 173(1):19-38, 2006), we prove sharp estimates of the
density of $T_1^{(\mu)}$ which exibit the dependence both on time and space
variables. Our result provides optimal estimates for the density of the hitting
time of the unit ball by the Brownian motion in $\mathbb{R}^n$, which improve
existing bounds. Another application is to provide sharp estimates for the
Poisson kernel for half-spaces for hyperbolic Brownian motion in real
hyperbolic spaces.


  Principal component analysis (PCA) has been widely applied to dimensionality
reduction and data pre-processing for different applications in engineering,
biology and social science. Classical PCA and its variants seek for linear
projections of the original variables to obtain a low dimensional feature
representation with maximal variance. One limitation is that it is very
difficult to interpret the results of PCA. In addition, the classical PCA is
vulnerable to certain noisy data. In this paper, we propose a convex sparse
principal component analysis (CSPCA) algorithm and apply it to feature
analysis. First we show that PCA can be formulated as a low-rank regression
optimization problem. Based on the discussion, the l 2 , 1 -norm minimization
is incorporated into the objective function to make the regression coefficients
sparse, thereby robust to the outliers. In addition, based on the sparse model
used in CSPCA, an optimal weight is assigned to each of the original feature,
which in turn provides the output with good interpretability. With the output
of our CSPCA, we can effectively analyze the importance of each feature under
the PCA criteria. The objective function is convex, and we propose an iterative
algorithm to optimize it. We apply the CSPCA algorithm to feature selection and
conduct extensive experiments on six different benchmark datasets. Experimental
results demonstrate that the proposed algorithm outperforms state-of-the-art
unsupervised feature selection algorithms.


  There is a lot of research interest in encoding variable length sentences
into fixed length vectors, in a way that preserves the sentence meanings. Two
common methods include representations based on averaging word vectors, and
representations based on the hidden states of recurrent neural networks such as
LSTMs. The sentence vectors are used as features for subsequent machine
learning tasks or for pre-training in the context of deep learning. However,
not much is known about the properties that are encoded in these sentence
representations and about the language information they capture. We propose a
framework that facilitates better understanding of the encoded representations.
We define prediction tasks around isolated aspects of sentence structure
(namely sentence length, word content, and word order), and score
representations by the ability to train a classifier to solve each prediction
task when using the representation as input. We demonstrate the potential
contribution of the approach by analyzing different sentence representation
mechanisms. The analysis sheds light on the relative strengths of different
sentence embedding methods with respect to these low level prediction tasks,
and on the effect of the encoded vector's dimensionality on the resulting
representations.


  We realize a spin-orbit interaction between the collective spin precession
and center-of-mass motion of a trapped ultracold atomic gas, mediated by spin-
and position-dependent dispersive coupling to a driven optical cavity. The
collective spin, precessing near its highest-energy state in an applied
magnetic field, can be approximated as a negative-mass harmonic oscillator.
When the Larmor precession and mechanical motion are nearly resonant, cavity
mediated coupling leads to a negative-mass instability, driving exponential
growth of a correlated mode of the hybrid system. We observe this growth
imprinted on modulations of the cavity field and estimate the full covariance
of the resulting two-mode state by observing its transient decay during
subsequent free evolution.


  The Gauge Connection at qcd.nersc.gov is one of the most popular repositories
of QCD lattice ensembles. It is used to access 16TB of archived QCD data from
the High Performance Storage System (HPSS) at the National Energy Research
Scientific Computing Center (NERSC). Here, we present a new web interface for
qcd.nersc.gov which allows physicists to browse and search the data, as well as
download individual files or entire ensembles in batch. Our system
distinguishes itself from others because of its ease of use and web based
workflow.


  The anthropocentric cultural idea that humans are active agents exerting
control over their environments has been largely normalized and inscribed in
practices, policies, and products of contemporary industrialized societies.
This view underlies a human-ecology relationship based on resource and
knowledge extraction. To create a more sustainable and equitable future, it is
essential to consider alternative cultural ideas rooted in ecological thinking.
This perspective underscores the interconnectedness between humans and
more-than-human worlds. We propose a path to reshape the human-ecology
relationship by advocating for alternative human-AI interactions. In this
paper, we undertake a critical comparison between anthropocentrism and
ecological thinking, using storytelling to illustrate various human-AI
interactions that embody ecological thinking. We also delineate a set of design
principles aimed at guiding AI developments toward fostering a more caring
human-ecology relationship.


  The principal aim of this article is to establish an iteration method on the
space of resurgent functions. We discuss endless continuability of iterated
convolution products of resurgent functions and derive their estimates
developing the method in arXiv:1610.05453. Using the estimates, we show the
resurgence of formal series solutions of nonlinear differential and difference
equations.


  The following inequalities are established, improving a former inequality due
to Kojima. For any closed arithmetic hyperbolic $3$--manifold fibering over a
circle, the entropy of the pseudo-Anosov monodromy is bounded by the hyperbolic
volume of the $3$--manifold, up to a universal constant factor. For any closed
hyperbolic $3$--manifold fibering over a circle with systole
$\geq\varepsilon>0$, the entropy is bounded by the hyperbolic volume times
$\log(3+1/\varepsilon)$, up to a universal constant factor. The proof relies on
Heegaard Floer homology and hyperbolic geometry.


  Microlocal analysis techniques are extended and applied to stochastic partial
differential equations (SPDEs). In particular, the H\"ormander propagation of
singularities theorem is shown to be valid for hyperbolic SPDEs driven by a
standard Brownian motion. In this case the wave front set of the solution is
invariant under the stochastic Hamiltonian flow associated to the principal
symbol of the SPDE. This study leads to the introduction of a class of random
pseudodifferential operators.


  We study the spaces of locally-finite stability conditions on the derived
categories of coherent sheaves on the minimal resolutions of
$A_n$-singularities supported at the exceptional sets. Our main theorem is that
they are connected and simply-connected. The proof is based on the study of
spherical objects in math.AG/0409151 and the homological mirror symmetry for
$A_n$-singularities.


  Loop Quantum Cosmology (LQC) is a theory which renders the Big Bang initial
singularity into a quantum bounce, by means of short range repulsive quantum
effects at the Planck scale. In this work, we are interested in reproducing the
effective Friedmann equation of LQC, by considering a generic $f(R,P,Q)$ theory
of gravity, where $R=g^{\mu\nu}R_{\mu\nu}$ is the Ricci scalar,
$P=R_{\mu\nu}R^{\mu\nu}$, and $Q=R_{\alpha\beta\mu\nu}R^{\alpha\beta\mu\nu}$ is
the Kretschmann scalar. An order reduction technique allows us to work in
$f(R,P,Q)$ theories which are perturbatively close to General Relativity, and
to deduce a modified Friedmann equation in the reduced theory. Requiring that
the modified Friedmann equation mimics the effective Friedmann equation of LQC,
we are able to derive several functional forms of $f(R,P,Q)$. We discuss the
necessary conditions to obtain viable bouncing cosmologies for the proposed
effective actions of $f(R,P,Q)$ theory of gravity.


  In this paper we present a comprehensive treatment of function spaces with
logarithmic smoothness (Besov, Sobolev, Triebel-Lizorkin). We establish the
following results: Sharp embeddings between the Besov spaces defined by
differences and by Fourier-analytical decompositions as well as between Besov
and Sobolev/Triebel-Lizorkin spaces; Various new characterizations for Besov
norms in terms of different K-functionals. For instance, we derive
characterizations via ball averages, approximation methods, heat kernels, and
Bianchini-type norms; Sharp estimates for Besov norms of derivatives and
potential operators (Riesz and Bessel potentials) in terms of norms of
functions themselves. We also obtain quantitative estimates of regularity
properties of the fractional Laplacian. The key tools behind our results are
limiting interpolation techniques and new characterizations of Besov and
Sobolev norms in terms of the behavior of the Fourier transforms for functions
such that their Fourier transforms are of monotone type or lacunary series.


  In the present work we are concerned with the following
Kirchhoff-Choquard-type equation $$-M(||\nabla u||_{2}^{2})\Delta u +Q(x)u +
\mu(V(|\cdot|)\ast u^2)u = f(u) \mbox{ in } \mathbb{R}^2 , $$ for $M:
\mathbb{R} \rightarrow \mathbb{R}$ given by $M(t)=a+bt$, $ \mu >0 $, $ V $ a
sign-changing and possible unbounded potential, $ Q $ a continuous external
potential and a nonlinearity $f$ with exponential critical growth. We prove
existence and multiplicity of solutions in the nondegenerate case and guarantee
the existence of solutions in the degenerate case.


  Superconducting properties of metallic nano-wires may strongly depend on
specific experimental conditions. Here we consider a setup where
superconducting phase fluctuations are restricted at one point inside the wire
and equilibrium supercurrent flows along the wire segment of an arbitrary
length $L$. Low temperature physics of this structure is essentially
determined, on one hand, by smooth phase fluctuations and, on the other hand,
by quantum phase slips. The zero temperature phase diagram is controlled by the
wire cross section and consists of a truly superconducting phase and two
different phases where superconductivity can be observed only at shorter length
scales. One of the latter phases exhibits more robust short-scale
superconductivity whereas another one demonstrates a power-law decay of the
supercurrent with increasing $L$ already at relatively short scales.


  Determinations of the chemical composition of red giants in a large sample of
open clusters show that the abundances of the heavy elements La, Ce, Nd and Sm
but not so obviously Y and Eu vary from one cluster to another across a sample
all having about the solar metallicity. For La, Ce, Nd and Sm the amplitudes of
the variations at solar metallicity scale approximately with the main s-process
contribution to solar system material. Consideration of published abundances of
field stars suggest that such a spread in heavy element abundances is present
for the thin and thick disk stars of different metallicity. This new result
provides an opportunity to chemically tag stars by their heavy elements and to
reconstruct dissolved open clusters from the field star population.


  Cell membranes are a key element of life because they keep the genetic
material and metabolic machinery together. All present cell membranes are made
of phospholipids, yet the nature of the first membranes and the origin of
phospholipids are still under debate. We report here the first detection in
space of ethanolamine, NH$_2$CH$_2$CH$_2$OH, which forms the hydrophilic head
of the simplest and second most abundant phospholipid in membranes. The
molecular column density of ethanolamine in interstellar space is
$N$=(1.51$\pm$0.07)$\times$10$^{13}$ cm$^{-2}$, implying a molecular abundance
with respect to H$_2$ of (0.9-1.4)$\times$10$^{-10}$. Previous studies reported
its presence in meteoritic material but they suggested that it is synthesized
in the meteorite itself by decomposition of amino acids. However, we find that
the proportion of the molecule with respect to water in the interstellar medium
is similar to the one found in the meteorite (10$^{-6}$). These results
indicate that ethanolamine forms efficiently in space and, if delivered onto
early Earth, it could have contributed to the assembling and early evolution of
primitive membranes.


  One of the most promising probes to study deconfined matter created in high
energy nuclear collisions is the energy loss of (heavy) quarks. It has been
shown in experiments at the Relativistic Heavy Ion Collider (RHIC) that even
charm and bottom quarks, despite their high mass, experience a remarkable
medium suppression in the Quark Gluon Plasma. In this exploratory investigation
we study the energy loss of heavy quarks in high multiplicity proton-proton
collisions at LHC energies. Although the colliding systems are smaller than
compared to those at RHIC (p+p vs. Au+Au) the higher energy might lead to
multiplicities comparable to Cu+Cu collisions at RHIC. Recently the CMS
collaboration has shown that these particles likely interact among each other.
The interaction of charm quarks with this environment gives rise to a
non-negligible suppression of high momentum heavy quarks in elementary
collisions.


  The Delaunay tessellation of a locally finite subset of hyperbolic space is
constructed using convex hulls in Euclidean space of one higher dimension. For
finite and lattice-invariant sets it is proven to be a polyhedral
decomposition, and versions (necessarily modified from the Euclidean setting)
of the empty circumspheres condition and geometric duality with the Voronoi
tessellation are proved. Some pathological examples of infinite, non
lattice-invariant sets are exhibited.


  Let $p\geq 2$ and $q\neq 0$ an integer. A knot $K$ in the three-sphere is
said to be a $(p,q)$-lens knot if and only if it covers a link in the lens
space $L(p,q)$. In this paper, we use the second coefficient of the HOMFLY
polynomial to provide a necessary condition for a knot to be a $(p,q)$-lens
knot. As an application, it is shown that this criterion rules out the
possibility of being $(5,1)$-lens for 80 among the 84 knots with less than 9
crossings.


  We have performed, in October 2005, a deep Chandra X-ray observation of HH
154. Comparison with the previous (2001) Chandra observation allows to detect
proper motion down to the level predicted by models of X-ray emitting shocks in
the jet. The 2005 Chandra observation of HH 154 shows unexpected morphological
changes of the X-ray emission in comparison with the 2001 data. Two components
are present: a stronger, point-like component with no detectable motion and a
weaker component which has expanded in size by approximately 300 AU over the 4
years time base of the two observations. This expansion corresponds to
approximately 500 km/s, very close to the velocity of the X-ray emitting shock
in the simple theoretical models. The 2005 data show a more complex system than
initially thought (and modeled), with multiple components with different
properties. The observed morphology is possibly indicating a pulsed jet
propagating through a non-homogeneous medium, likely with medium density
decreasing with distance from the driving source. Detailed theoretical modeling
and deeper X-ray observations will be needed to understand the physics of this
fascinating class of sources.


  Bilateral teleoperation provides humanoid robots with human planning
intelligence while enabling the human to feel what the robot feels. It has the
potential to transform physically capable humanoid robots into dynamically
intelligent ones. However, dynamic bilateral locomotion teleoperation remains
as a challenge due to the complex dynamics it involves. This work presents our
initial step to tackle this challenge via the concept of wheeled humanoid robot
locomotion teleoperation by body tilt. Specifically, we developed a
force-feedback-capable whole-body human-machine interface (HMI), and designed a
force feedback mapping and two teleoperation mappings that map the human's body
tilt to the robot's velocity or acceleration. We compared the two mappings and
studied the force feedback's effect via an experiment, where seven human
subjects teleoperated a simulated robot with the HMI to perform dynamic target
tracking tasks. The experimental results suggest that all subjects accomplished
the tasks with both mappings after practice, and the force feedback improved
their performances. However, the subjects exhibited two distinct teleoperation
styles, which benefited from the force feedback differently. Moreover, the
force feedback affected the subjects' preferences on the teleoperation
mappings, though most subjects performed better with the velocity mapping.


  Standard contextual bandit problem assumes that all the relevant contexts are
observed before the algorithm chooses an arm. This modeling paradigm, while
useful, often falls short when dealing with problems in which valuable
additional context can be observed after arm selection. For example, content
recommendation platforms like Youtube, Instagram, Tiktok also observe valuable
follow-up information pertinent to the user's reward after recommendation
(e.g., how long the user stayed, what is the user's watch speed, etc.). To
improve online learning efficiency in these applications, we study a novel
contextual bandit problem with post-serving contexts and design a new
algorithm, poLinUCB, that achieves tight regret under standard assumptions.
Core to our technical proof is a robustified and generalized version of the
well-known Elliptical Potential Lemma (EPL), which can accommodate noise in
data. Such robustification is necessary for tackling our problem, and we
believe it could also be of general interest. Extensive empirical tests on both
synthetic and real-world datasets demonstrate the significant benefit of
utilizing post-serving contexts as well as the superior performance of our
algorithm over the state-of-the-art approaches.


  The Large Area Telescope (LAT) onboard the {\it Fermi} Gamma Ray Space
Telescope has detected \gamma-ray emission from some Narrow Line Seyfert
1(NLSy1) galaxies. This indicates the presence of relativistic jets in these
sources similar to blazars and radio galaxies. In an attempt to have an idea of
the intranight optical variability (INOV) characteristics of these \gamma-ray
loud NLSy1 galaxies, we have carried out optical flux monitoring observations
of three NLSy1 galaxies detected by {\it Fermi}/LAT: 1H 0323+342, PMN
J0948+0022 and PKS 1502+036. These optical monitoring observations showed the
presence of rapid optical flux variations in these sources. The intranight
differential light curves of these sources have revealed flux variations on
time scales of hours with amplitudes of variability > 3 percent for most of the
time. On using the {\it F}-statistics to classify the variability nature of
these sources, we obtained a duty cycle (DC) of INOV of ~85 percent.
Alternatively, the more commonly used {\it C}-statistics gave a DC of INOV of
~57 percent. Such high DC of INOV are characteristics of the BL Lac class of
AGN. The results of our monitoring observations thus indicate that there is
similarity in the INOV nature of \gamma-ray loud NLSy1 galaxies and BL Lac
objects, arguing strongly for the presence of relativistic jets aligned closely
to the observers line of sight. Moreover, our dense monitoring observations on
some of the nights have led to the clear detection of some mini-flares
superimposed on the flux variations during the night over timescales as short
as 12 minutes. The detection of short timescale flux variability in the sources
studied here is clearly due to stronger time compression leading to the jets in
these sources having large Doppler factors, similar to that of the inner jets
of TeV blazars.


  We present novel techniques for neuro-symbolic concurrent stochastic games, a
recently proposed modelling formalism to represent a set of probabilistic
agents operating in a continuous-space environment using a combination of
neural network based perception mechanisms and traditional symbolic methods. To
date, only zero-sum variants of the model were studied, which is too
restrictive when agents have distinct objectives. We formalise notions of
equilibria for these models and present algorithms to synthesise them. Focusing
on the finite-horizon setting, and (global) social welfare subgame-perfect
optimality, we consider two distinct types: Nash equilibria and correlated
equilibria. We first show that an exact solution based on backward induction
may yield arbitrarily bad equilibria. We then propose an approximation
algorithm called frozen subgame improvement, which proceeds through iterative
solution of nonlinear programs. We develop a prototype implementation and
demonstrate the benefits of our approach on two case studies: an automated
car-parking system and an aircraft collision avoidance system.


  Numerous numerical studies indicate that dark matter halos show an almost
universal radial density profile. The origin of the profile is still under
debate. We investigate this topic and pay particular attention to the velocity
dispersion profile. To this end we have performed high-resolution simulations
with two independent codes, ART and {\sc Gadget}. The radial velocity
dispersion can be approximated as function of the potential by $\sigma_r^2 = a
(\Phi / \Phi_{\rm{out}})^\kappa (\Phi_{\rm{out}} - \Phi)$, where
$\Phi_{\rm{out}}$ is the outer potential of the halo. For the parameters $a$
and $\kappa$ we find $a=0.29\pm0.04$ and $\kappa=0.41\pm0.03$. We find that the
power-law asymptote $\sigma^2 \propto \Phi^\kappa$ is valid out to much larger
distances from the halo center than any power asymptote for the density profile
$\rho \propto r^{-n}$. The asymptotic slope $n(r \to 0)$ of the density profile
is related to the exponent $\kappa$ via $n=2\kappa/(1+\kappa)$. Thus the value
obtained for $\kappa$ from the available simulation data can be used to obtain
an estimate of the density profile below presently resolved scales. We predict
a continuously decreasing $n$ towards the halo center with the asymptotic value
$n \lesssim 0.58$ at $r=0$.


  Let $\rho:\Gamma\rightarrow PSL_d(\mathbb{K})$ be a Zariski dense
Borel-Anosov representation, for $\mathbb{K}$ equal to $\mathbb{R}$ or
$\mathbb{C}$. Let $o$ be a form of signature $(p,d-p)$ on $\mathbb{K}^d$ (where
$0<p<d)$. Let $\mathsf{S}^o$ be the corresponding geodesic copy of the
Riemannian symmetric space of $PSO(o)$, inside the Riemannian symmetric space
of $PSL_d(\mathbb{K})$. For certain choices of $o$ and every $t$ large enough,
we show exponential bounds for the number of $\gamma\in\Gamma$ for which the
distance between $\mathsf{S}^o$ and $\rho\gamma\cdot\mathsf{S}^o$ is smaller
than $t$. Under an extra assumption, satisfied for instance when the boundary
of $\Gamma$ is connected, we show an asymptotic as $t\rightarrow\infty$ for the
counting function relative to a functional in the interior of the dual limit
cone.


  By imposing certain combined inversion and rotation symmetries on the
rational maps for SU(2) BPS monopoles we construct geodesics in the monopole
moduli space. In the moduli space approximation these geodesics describe a
novel kind of monopole scattering. During these scattering processes axial
symmetry is instantaneously attained and, in some, monopoles with the
symmetries of the regular solids are formed. The simplest example corresponds
to a charge three monopole invariant under a combined inversion and 90 degree
rotation symmetry. In this example three well-separated collinear unit charge
monopoles coalesce to form first a tetrahedron, then a torus, then the dual
tetrahedron and finally separate again along the same axis of motion. We
explicitly construct the spectral curves in this case and use a numerical ADHMN
construction to compute the energy density at various times during the motion.
We find that the dynamics of the zeros of the Higgs field is extremely rich and
we discover a new phenomenon; there exist charge k SU(2) BPS monopoles with
more than k zeros of the Higgs field.


  Many exoplanets in close-in orbits are observed to have relatively high
eccentricities and large stellar obliquities. We explore the possibility that
these result from planet-planet scattering by studying the dynamical outcomes
from a large number of orbit integrations in systems with two and three
gas-giant planets in close-in orbits (0.05 AU < a < 0.15 AU). We find that at
these orbital separations, unstable systems starting with low eccentricities
and mutual inclinations ($e\lesssim0.1$, $i\lesssim0.1$) generally lead to
planet-planet collisions in which the collision product is a planet on a
low-eccentricity, low-inclination orbit. This result is inconsistent with the
observations. We conclude that eccentricity and inclination excitation from
planet-planet scattering must precede migration of planets into short-period
orbits. This result constrains theories of planet migration: the semi-major
axis must shrink by 1-2 orders of magnitude without damping the eccentricity
and inclination.


  We consider the following general scheduling problem: The input consists of n
jobs, each with an arbitrary release time, size, and a monotone function
specifying the cost incurred when the job is completed at a particular time.
The objective is to find a preemptive schedule of minimum aggregate cost. This
problem formulation is general enough to include many natural scheduling
objectives, such as weighted flow, weighted tardiness, and sum of flow squared.
Our main result is a randomized polynomial-time algorithm with an approximation
ratio O(log log nP), where P is the maximum job size. We also give an O(1)
approximation in the special case when all jobs have identical release times.
The main idea is to reduce this scheduling problem to a particular geometric
set-cover problem which is then solved using the local ratio technique and
Varadarajan's quasi-uniform sampling technique. This general algorithmic
approach improves the best known approximation ratios by at least an
exponential factor (and much more in some cases) for essentially all of the
nontrivial common special cases of this problem. Our geometric interpretation
of scheduling may be of independent interest.


  In this paper, we show that the monomial basis is generally as good as a
well-conditioned polynomial basis for interpolation, provided that the
condition number of the Vandermonde matrix is smaller than the reciprocal of
machine epsilon.


  The dense environment of neutron stars makes them an excellent target for
probing dark matter interactions with the Standard Model. We study neutron star
heating from capture of inelastic dark matter, which can evade direct detection
constraints. We investigate kinematics of the inelastic scattering process
between quasirelativistic dark matter particles and ultrarelativistic targets
in neutron stars, and derive analytical expressions for the maximal mass gap
allowed for the scattering to occur. We implement them into a fully
relativistic formalism for calculating the capture rate and apply it to various
scenarios of inelastic dark matter. The projected constraints from neutron
stars can systematically surpass those from terrestrial searches, including
direct detection and collider experiments. Neutron stars can also be sensitive
to the parameter space of inelastic self-interacting dark matter. Our results
indicate that extreme astrophysical environments, such as neutron stars, are an
important target for searching dark matter.


  In the paper "Observation of Fast Evolution in Parity-Time-Symmetric System"
the authors propose a physical apparatus for the realization of a faster than
Hermitian evolution. This last appears in contrast with the conclusions
obtained in our paper "Some Remarks on Quantum Brachistochrone". We will
clarify this apparent contradiction and some problematic aspects of the
treatment in [1].


  In a class of models designed to solve the cosmological constant problem by
coupling scalar or tensor classical fields to the space-time curvature, the
universal scale factor grows as a power law in the age, $a \propto t^\alpha$,
regardless of the matter content or cosmological epoch. We investigate
constraints on such "power-law cosmologies" from the present age of the
Universe, the magnitude-redshift relation, and from primordial nucleosynthesis.
Constraints from the current age of the Universe and from the high-redshift
supernovae data require "large" $\alpha$ ($\approx 1$), while consistency with
the inferred primordial abundances of deuterium and helium-4 forces $\alpha$ to
lie in a very narrow range around a lower value ($\approx 0.55$). Inconsistency
between these independent cosmological constraints suggests that such power-law
cosmologies are not viable.


  Modern deep neural network models are large and computationally intensive.
One typical solution to this issue is model pruning. However, most current
pruning algorithms depend on hand crafted rules or domain expertise. To
overcome this problem, we propose a learning based auto pruning algorithm for
deep neural network, which is inspired by recent automatic machine
learning(AutoML). A two objectives' problem that aims for the the weights and
the best channels for each layer is first formulated. An alternative
optimization approach is then proposed to derive the optimal channel numbers
and weights simultaneously. In the process of pruning, we utilize a searchable
hyperparameter, remaining ratio, to denote the number of channels in each
convolution layer, and then a dynamic masking process is proposed to describe
the corresponding channel evolution. To control the trade-off between the
accuracy of a model and the pruning ratio of floating point operations, a novel
loss function is further introduced. Preliminary experimental results on
benchmark datasets demonstrate that our scheme achieves competitive results for
neural network pruning.


  We compute the dispersion curves for neutrinos propagating in an extremely
dense electroweak plasma, in the presence of very strong magnetic fields of
order $B \le M_W^2/e$. The neutrino self-energy is calculated in the one-loop
approximation. We consider only contributions of the first Landau level to the
propagator of the W-bosons, and distinguish between motion parallel or
perpendicular to the external magnetic field. We find that the neutrino
dispersion curve for parallel propagation to the field suggests a superfluid
behavior. An interesting analogy with fractional QHE is pointed out. We obtain
a neutrino effective mass which increases with the magnetic field.


  This paper presents a deterministic linear time complexity IDE-SimRank method
to approximately compute SimRank with proved error bound. SimRank is a
well-known similarity measure between graph vertices which relies on graph
topology only and is built on intuition that "two objects are similar if they
are related to similar objects". The fixed point equation for direct SimRank
computation is the discrete Lyapunov equation with specific diagonal matrix in
the right hand side. The proposed method is based on estimation of this
diagonal matrix with GMRES and use this estimation to compute singe-source and
single pairs queries. These computations are executed with the part of series
converging to the discrete Lyapunov equation solution.


  Environmental changes greatly influence the evolution of populations. Here,
we study the dynamics of a population of two strains, one growing slightly
faster than the other, competing for resources in a time-varying binary
environment modeled by a carrying capacity switching either randomly or
periodically between states of abundance and scarcity. The population dynamics
is characterized by demographic noise (birth and death events) coupled to a
varying environment. We elucidate the similarities and differences of the
evolution subject to a stochastically- and periodically-varying environment.
Importantly, the population size distribution is generally found to be broader
under intermediate and fast random switching than under periodic variations,
which results in markedly different asymptotic behaviors between the fixation
probability of random and periodic switching. We also determine the detailed
conditions under which the fixation probability of the slow strain is maximal.


  We present an algorithm for receding-horizon motion planning using a finite
family of motion primitives for underactuated dynamic walking over uneven
terrain. The motion primitives are defined as virtual holonomic constraints,
and the special structure of underactuated mechanical systems operating subject
to virtual constraints is used to construct closed-form solutions and a special
binary search tree that dramatically speed up motion planning. We propose a
greedy depth-first search and discuss improvement using energy-based
heuristics. The resulting algorithm can plan several footsteps ahead in a
fraction of a second for both the compass-gait walker and a planar
7-Degree-of-freedom/five-link walker.


  Motivated by the need to solve open-loop optimal control problems with
computational efficiency and reliable constraint satisfaction, we introduce a
general framework that combines diffusion models and numerical optimization
solvers. Optimal control problems are rarely solvable in closed form, hence
they are often transcribed into numerical trajectory optimization problems,
which then require initial guesses. These initial guesses are supplied in our
framework by diffusion models. To mitigate the effect of samples that violate
the problem constraints, we develop a novel constrained diffusion model to
approximate the true distribution of locally optimal solutions with an
additional constraint violation loss in training. To further enhance the
robustness, the diffusion samples as initial guesses are fed to the numerical
solver to refine and derive final optimal (and hence feasible) solutions.
Experimental evaluations on three tasks verify the improved constraint
satisfaction and computational efficiency with 4$\times$ to 30$\times$
acceleration using our proposed framework, which generalizes across trajectory
optimization problems and scales well with problem complexity.


  The SU(2) collective coordinates expansion of the Born-Infeld\break Skyrmion
Lagrangian is performed. The classical Hamiltonian is computed from this
special Lagrangian in approximative way: it is derived from the expansion of
this non-polynomial Lagrangian up to second-order variable in the collective
coordinates. This second-class constrained model is quantized by Dirac
Hamiltonian method and symplectic formalism. Although it is not expected to
find symmetries on second-class systems, a hidden symmetry is disclosed by
formulating the Born-Infeld Skyrmion %model as a gauge theory. To this end we
developed a new constraint conversion technique based on the symplectic
formalism. Finally, a discussion on the role played by the hidden symmetry on
the computation of the energy spectrum is presented.


  This paper presents an existence theory for solitary waves at the interface
between a thin ice sheet (modelled using the Cosserat theory of hyperelastic
shells) and an ideal fluid (of finite depth and in irrotational motion) for
sufficiently large values of a dimensionless parameter $\gamma$. We establish
the existence of a minimiser of the wave energy ${\mathcal E}$ subject to the
constraint ${\mathcal I}=2\mu$, where ${\mathcal I}$ is the horizontal impulse
and $0< \mu \ll 1$, and show that the solitary waves detected by our
variational method converge (after an appropriate rescaling) to solutions of he
nonlinear Schr\"{o}dinger equation with cubic focussing nonlinearity as $\mu
\downarrow 0$.


  A distributed data collection algorithm to accurately store and forward
information obtained by wireless sensor networks is proposed. The proposed
algorithm does not depend on the sensor network topology, routing tables, or
geographic locations of sensor nodes, but rather makes use of uniformly
distributed storage nodes. Analytical and simulation results for this algorithm
show that, with high probability, the data disseminated by the sensor nodes can
be precisely collected by querying any small set of storage nodes.


  We extend basic results in $3$-manifold topology to general three-dimensional
Alexandrov spaces (or Alexandrov $3$-spaces for short), providing a unified
framework for manifold and non-manifold spaces. We generalize the connected sum
to non-manifold $3$-spaces and prove a prime decomposition theorem, exhibit an
infinite family of closed, prime non-manifold $3$-spaces which are not
irreducible, and establish a conjecture of Mitsuishi and Yamaguchi on the
structure of closed, simply-connected Alexandrov $3$-spaces with non-negative
curvature. Additionally, we define a notion of generalized Dehn surgery for
Alexandrov $3$-spaces and show that any closed Alexandrov $3$-space may be
obtained by performing generalized Dehn surgery on a link in $S^3$ or the
non-trivial $S^2$-bundle over $S^1$. As an application of this result, we show
that every closed Alexandrov $3$-space is homeomorphic to the boundary of a
$4$-dimensional Alexandrov space.


  We discuss examples of two dimensional metallic states with charge
fractionalization, and we will demonstrate that the mechanism of charge
fractionalization leads to exotic metallic behaviors at low and intermediate
temperature. The simplest example of such state is constructed by fermionic
partons at finite density coupled to a $Z_N$ gauge field, whose properties can
be studied through rudimentary methods. This simple state has the following
exotic features: (1) at low temperature this state is a "bad metal" whose
resistivity can exceed the Mott-Ioffe-Regel limit; (2) while increasing
temperature $T$ the resistivity $\rho(T)$ is a nonmonotonic function, and it
crosses over from a bad metal at low $T$ to a good metal at relatively high
$T$; (3) the optical conductivity $\sigma(\omega)$ has a small Drude weight at
low $T$, and a larger Drude weight at intermediate $T$; (4) at low temperature
the metallic state has a large Lorenz number, which strongly violates the
Wiedemann-Franz law. A more complex example with fermionic partons at finite
density coupled to a SU(N) gauge field will also be constructed.


  In this paper, we introduced a generalization of the derived category, which
is called the $n$-derived category and denoted by $\D_{n}(R)$, of a given ring
$R$ for each $n\in\mathbb{N}\cup\{\infty\}$. The $n$-derived category of a ring
is proved to be very closely connected with its left little finitistic
dimension. We also introduce and investigate the notions of $n$-exact
sequences, $n$-projective (resp., $n$-injective) modules and $n$-exact
complexes. In particular, we characterize the left little finitistic dimensions
in terms of all above notions. Finally, we build a connection of the classical
derived categories and $n$-derived categories.


  This paper presents a model that uses the information that sellers publish in
real estate market websites to predict whether a property has higher or lower
price than the average price of its similar properties. The model learns the
correlation between price and information (text descriptions and features) of
real estate properties through automatic identification of latent semantic
content given by a machine learning model based on doc2vec and xgboost. The
proposed model was evaluated with a data set of 57,516 publications of real
estate properties collected from 2016 to 2018 of Bogot\'a city. Results show
that the accuracy of a classifier that involves text descriptions is slightly
higher than a classifier that only uses features of the real estate properties,
as text descriptions tends to contain detailed information about the property.


  An important ingredient for the non-equilibrium evolution of partons at RHIC
and LHC is to have some physically reasonable initial conditions for the single
particle phase space distribution functions for the partons. We consider
several plausible parametrizations of initial conditions for the single
particle distribution function f_i(x,p) and fix the parameters by matching \int
f(x,p)p^\mu d \sigma_\mu to the invariant momentum space semi-hard parton
distributions obtained using perturbative QCD (pQCD), as well as fitting low
order moments of the distribution function. We consider parametrizations of
f_i(x,p) with both boost invariant and boost non-invariant assumptions. We
determine the initial number density, energy density and the corresponding
(effective) temperature of the minijet plasma at RHIC and LHC energies. For a
boost non-invariant minijet phase-space distribution function we obtain ~
30(140) /fm^3 as the initial number density, ~ 50(520) GeV/fm^3 as the initial
energy density and ~ 520(930) MeV as the corresponding initial effective
temperature at RHIC(LHC).


  We present a long term optical $R$ band light curve analysis of the
gravitationally lensed blazar AO 0235+164 in the time span 1982 - 2019. Several
methods of analysis lead to the result that there is a periodicity of ~8.13
years present in these data. In addition, each of these five major flares are
apparently double-peaked, with the secondary peak following the primary one by
~2 years. Along with the well known system, OJ 287, our finding constitutes one
of the most secure cases of long term quasi-periodic optical behaviour in a
blazar ever found. A binary supermassive black hole system appears to provide a
good explanation for these results.


  Graph neural network-based network intrusion detection systems have recently
demonstrated state-of-the-art performance on benchmark datasets. Nevertheless,
these methods suffer from a reliance on target encoding for data
pre-processing, limiting widespread adoption due to the associated need for
annotated labels--a cost-prohibitive requirement. In this work, we propose a
solution involving in-context pre-training and the utilization of dense
representations for categorical features to jointly overcome the
label-dependency limitation. Our approach exhibits remarkable data efficiency,
achieving over 98% of the performance of the supervised state-of-the-art with
less than 4% labeled data on the NF-UQ-NIDS-V2 dataset.


  In this paper we complete the results of Sullivant and Sturmfels proving that
many of the algebraic group-based models for Markov processes on trees are
pseudo-toric. We also show in which cases these varieties are normal. This is
done by the generalization of the discrete Fourier transform approach. In the
next step, following Sullivant and Sturmfels, we describe a fast algorithm
finding a polytope associated to these algebraic models. However in our case we
apply the notions of sockets and networks extending the work of Buczynska and
Wisniewski who introduced it for the binary case.


  In a recent letter, R.G. Brown and M. Ciftan (Phys. Rev. Lett. 76, 1352,
1996) reported high precision Monte Carlo (MC) estimates of the static critical
exponents of the classical 3D Heisenberg model, which stand in sharp contrast
to values obtained by four independent approaches, namely by other recent high
statistics MC simulations, high-temperature series analyses, field theoretical
methods, and experimental studies. In reply to the above cited work we
submitted this paper as a comment to Phys. Rev. Lett.


  Motivated by the experimental detection of superconductivity in the
low-carrier density half-Heusler compound YPtBi, we study the pairing
instabilities of three-dimensional strongly spin-orbit coupled semimetals with
a quadratic band touching point. In these semimetals the electronic structure
at the Fermi energy is described by spin j=3/2 quasiparticles, which are
fundamentally different from those in ordinary metals with spin j=1/2. We
develop a general approach to analyzing pairing instabilities in j=3/2
materials by decomposing the pair scattering interaction into irreducible
channels, projecting them to the Fermi surface and deriving the corresponding
Eliashberg theory. Applying our method to a generic density-density interaction
in YPtBi we establish the following results: (i) The pairing strength in each
channel uniquely encodes the j=3/2 nature of the Fermi surface band
structure--a manifestation of the fundamental difference with ordinary metals.
In particular, this implies that Anderson's theorem, which addresses the effect
of spin-orbit coupling and disorder on pairing states of spin-1/2 electrons,
cannot be applied in this case. (ii) The leading pairing instabilities are
different for electron and hole doping. This implies that superconductivity
depends on carrier type. (iii) In the case of hole doping--relevant to YPtBi,
we find two odd-parity channels in close competition with s-wave pairing. One
of these two channels is a multicomponent pairing channel, allowing for the
possibility of time-reversal symmetry breaking. (iv) In the case of Coulomb
interactions mediated by the long-ranged electric polarization of optical
phonon modes, a significant coupling strength is generated in spite of the
extremely low density of carriers. Furthermore, non-linear response and Fermi
liquid corrections can favor non-s-wave pairing and potentially account for the
experimentally-observed Tc.


  We describe software that simulates the hardware of a scanning force
microscope. The essential feature of the software is its real-time response,
which is critical for mimicking the behavior of real scanning probe hardware.
The simulator runs on an open-source real time Linux kernel, and can be used to
test scanning probe microscope control software as well as theoretical models
of different types of scanning probe microscopes. We describe the
implementation of a tuning-fork based atomic force microscope and a dc
electrostatic force microscope, and present representative images obtained from
these models.


  Anomalous metals are observed in numerous experiments on disordered
two-dimensional systems proximate to superconductivity. A characteristic
feature of an anomalous metal is that its low temperature conductivity has a
weakly temperature dependent value, significantly higher than that of a
disordered Fermi liquid. We propose a dynamical mean-field model of an
anomalous metal: interacting electrons similar in structure to that of the
well-studied universal Hamiltonian of mesoscopic metallic grains, but with
independent random interactions between pairs of sites, involving Cooper pair
hopping and spin exchange. We find evidence for critical anomalous phases or
points between a superconducting phase and a disordered Fermi liquid phase in
this model. Our results are obtained by a renormalization group analysis in a
weak coupling limit, and a complementary solution at large $M$ when the spin
symmetry is generalized to USp($M$). The large $M$ limit describes the
anomalous metal by fractionalization of the electron into spinons, holons, and
doublons, with these partons forming critical non-Fermi liquids in the
Sachdev-Ye-Kitaev class. We compute the low temperature conductivity in the
large $M$ limit, and find temperature-independent values moderately enhanced
from that in the disordered metal.


  Background: Ultra-low $Q$-value $\beta$-decays are interesting processes to
study with potential applications to nuclear $\beta$-decay theory and neutrino
physics. While a number of potential ultra-low $Q$-value $\beta$-decay
candidates exist, improved mass measurements are necessary to determine which
are energetically allowed. Method: Penning trap mass spectrometry was used to
determine the atomic mass of $^{89}$Y and $^{139}$La, from which $\beta$-decay
$Q$-values for $^{89}$Sr and $^{139}$Ba were obtained to determine if there
could be an ultra-low $Q$-value decay branch in the $\beta$-decay of $^{89}$Sr
$\rightarrow$ $^{89}$Y or $^{139}$Ba $\rightarrow$ $^{139}$La. Results: The
$^{89}$Sr $\rightarrow$ $^{89}$Y and $^{139}$Ba $\rightarrow$ $^{139}$La
$\beta$-decay $Q$-values were measured to be $Q_{\rm{Sr}}$ = 1502.20(0.35) keV
and $Q_{\rm{Ba}}$ = 2308.37(68) keV. These were compared to energies of excited
states in $^{89}$Y at 1507.4(1) keV, and in $^{139}$La at 2310(19) keV and
2313(1) keV to determine $Q$-values of -5.20(37) keV for the potential
ultra-low $\beta$-decay branch of $^{89}$Sr and -1.6(19.0) keV and -4.6(1.2)
keV for those of $^{139}$Ba. Conclusion: The potential ultra-low $Q$-value
decay branch of $^{89}$Sr to the $^{89}$Y (3/2$^-$, 1507.4 keV) state is
energetically forbidden and has been ruled out. The potential ultra-low
$Q$-value decay branch of $^{139}$Ba to the 2313 keV state in $^{139}$La with
unknown J$^{\pi}$ has also been ruled out at the 4$\sigma$ level, while more
precise energy level data is needed for the $^{139}$La (1/2$^+$, 2310 keV)
state to determine if an ultra-low $Q$-value $\beta$-decay branch to this state
is energetically allowed.


  We give a formulation of classical mechanics in the language of operators
acting on a Hilbert space. The formulation given comes from a unitary
irreducible representation of the Galilei group that is compatible with the
basic postulates of classical mechanics, particularly the absence of an
uncertainty principle between the position and momentum of a particle. It is
shown that the theory exposed in the article contains the Koopman-von Neumann
formalism of classical mechanics as a particular case.


  We generalize the array orthogonality property for perfect autocorrelation
sequences to $n$-dimensional arrays. The generalized array orthogonality
property is used to derive a number of $n$-dimensional perfect array
constructions.


  Modern scientific software stacks have become extremely complex, using many
programming models and libraries to exploit a growing variety of GPUs and
accelerators. Package managers can mitigate this complexity using dependency
solvers, but they are reaching their limits. Finding compatible dependency
versions is NP-complete, and modeling the semantics of package compatibility
modulo build-time options, GPU runtimes, flags, and other parameters is
extremely difficult. Within this enormous configuration space, defining a
"good" configuration is daunting.
  We tackle this problem using Answer Set Programming (ASP), a declarative
model for combinatorial search problems. We show, using the Spack package
manager, that ASP programs can concisely express the compatibility rules of HPC
software stacks and provide strong quality-of-solution guarantees. Using ASP,
we can mix new builds with preinstalled binaries, and solver performance is
acceptable even when considering tens of thousands of packages.


  In the near future, it is envisioned that cellular networks will have to cope
with extensive Internet of Things (IoT) devices. Therefore, a required feature
of cellular IoT will be the capability to serve simultaneously a large number
of devices with heterogeneous demands and qualities of Channel State
Information at the Transmitter (CSIT). In this paper, we focus on an overloaded
Multiple-Input Single-Output (MISO) Broadcast Channel (BC) with two groups of
CSIT qualities, namely one group of users (representative of high-end devices)
for which the transmitter has partial knowledge of the CSI, the other group of
users (representative of IoT devices) for which the transmitter only has
knowledge of the statistical CSI. We introduce Rate-Splitting Multiple Access
(RSMA), a new multiple access based on multi-antenna Rate-Splitting (RS) for
cellular IoT. Two strategies are proposed, namely, Time Partitioning-RSMA
(TP-RSMA) and Power Partitioning-RSMA (PP-RSMA). The former independently
serves the two groups of users over orthogonal time slots while the latter
jointly serves the two groups of users within the same time slot in a
non-orthogonal manner. We first show at high Signal-to-Noise Ratio (SNR) that
PP-RSMA achieves the optimum Degrees-of-Freedom (DoF) in an overloaded MISO BC
with heterogeneous CSIT qualities and then show at finite SNR that, by marrying
the benefits of PP and RSMA, PP-RSMA achieves explicit sum rate gain over
TP-RSMA and all baseline schemes. Furthermore, PP-RSMA is robust to CSIT
inaccuracy and flexible to cope with Quality of Service (QoS) rate constraints
of all users. The DoF and rate analysis helps us draw the conclusion that
PP-RSMA is a powerful framework for cellular IoT with a large number of
devices.


  We present an equilibrium statistical mechanical theory of collisionless
self-gravitational systems with isotropic velocity distributions. Compared to
existing standard theories, we introduce two changes: (1) the number of
possible microstates is computed in energy (orbit) space rather than phase
space and (2) low occupation numbers are treated more appropriately than using
Stirling's approximation. Combined, the two modifications predict that the
relaxed parts of collisionless self-gravitating systems, such as dark-matter
halos, have a differential energy distribution N(E) ~ [exp(phi_0 - E) - 1],
dubbed "DARKexp". Such systems have central power-law density cusps rho(r) ~
r^-1, which suggests a statistical mechanical origin of cusps in simulated
dark-matter halos.


  Semi-Global Matching (SGM) is a widely-used efficient stereo matching
technique. It works well for textured scenes, but fails on untextured slanted
surfaces due to its fronto-parallel smoothness assumption. To remedy this
problem, we propose a simple extension, termed SGM-P, to utilize precomputed
surface orientation priors. Such priors favor different surface slants in
different 2D image regions or 3D scene regions and can be derived in various
ways. In this paper we evaluate plane orientation priors derived from stereo
matching at a coarser resolution and show that such priors can yield
significant performance gains for difficult weakly-textured scenes. We also
explore surface normal priors derived from Manhattan-world assumptions, and we
analyze the potential performance gains using oracle priors derived from
ground-truth data. SGM-P only adds a minor computational overhead to SGM and is
an attractive alternative to more complex methods employing higher-order
smoothness terms.


  Weighted enumeration of reduced pipe dreams (or rc-graphs) results in a
combinatorial expression for Schubert polynomials. The duality between the set
of reduced pipe dreams and certain antidiagonals has important geometric
implications [A. Knutson and E. Miller, Gr\"obner geometry of Schubert
polynomials, Ann. Math. 161, 1245-1318]. The original proof of the duality was
roundabout, relying on the algebra of certain monomial ideals and a recursive
characterization of reduced pipe dreams. This paper provides a direct
combinatorial proof.


  Haptic upper limb exoskeletons are robots that assist human operators during
task execution while having the ability to render virtual or remote
environments. Therefore, the stability of such robots in physical
human-robot-environment interaction must be guaranteed, in addition to
performing well during task execution. Having a wide range of Z-width, which
shows the region of passively renderable impedance by a haptic display, is also
important to render a wide range of virtual environments. To address these
issues, in this study, subsystem-based adaptive impedance control is designed
for having a stable human-robot-environment interaction of 7 degrees of freedom
haptic exoskeleton. The presented control decomposes the entire system into
subsystems and designs the controller at the subsystem level. The stability of
the controller in the presence of contact with the virtual environment and
human arm force is proved by employing the virtual stability concept.
Additionally, the Z-width of the 7-DoF haptic exoskeleton is drawn using
experimental data and improved using varying virtual mass element for the
virtual environment. Finally, experimental results are provided to demonstrate
the perfect performance of the proposed controller in accomplishing the
predefined task.


  Ride-sourcing or transportation network companies (TNCs) provide on-demand
transportation service for compensation, connecting drivers of personal
vehicles with passengers through smartphone applications. In this study, we
consider the problem of estimating a spatiotemporally varying probability
distribution for the productivity of a TNC driver, using data on more than 1.2
million TNC trips in Austin, Texas. We propose a graph-based smoothing approach
that allows for distinct spatial and temporal dynamics, including different
degrees of smoothness, spatio-temporal interactions, and interpolation in
regions with little or no data. For such a goal, we introduce the Graph-fused
Elastic Net (GFEN) and use it in combination with a dyadic tree decomposition
for density estimation. In addition, we present an optimization-driven approach
for fast point estimates scalable to massive graphs. Bayesian inference and
uncertainty quantification with MCMC are also illustrated. The main results
demonstrate that the optimization strategy is an effective exploration tool for
selecting adequate regularization schemes using Bayesian optimization of the
cross-validation loss. Two key empirical findings made possible by our method
include: 1) the probability that a TNC driver can expect to earn a living wage
in Austin exhibits high variability in space and time, from as low as 25% to as
high as 85%; and 2) some drivers suffer considerable "tail risk", with the
bottom 10% of the earnings distribution falling below $10 per hour -- grossly
below a living wage in Austin for a single adult -- for specific times and
locations. All code and data for the paper are publicly available, as a Shiny
app for visualizing the results and a software package in Julia for
implementing the GFEN.


  Neutron reflectometry is a method for measuring of the spatial dependence
(profile) of the potential interaction between neutron and medium. At interface
of media the interaction potential is the sum of the elements potentials. For
definition of potentials of separate elements (isotopes) a secondary radiation
is recorded. Recording channels of secondary radiation are created on
spectrometer REMUR at pulsed reactor IBR-2 in Dubna (Russia). The results for
testing of the channels are reported and perspectives of reflectometry with
registration of secondary radiation are discussed


  The advent of Cloud Computing enabled the proliferation of IoT applications
for smart environments. However, the distance of these resources makes them
unsuitable for delay-sensitive applications. Hence, Fog Computing has emerged
to provide such capabilities in proximity to end devices through distributed
resources. These limited resources can collaborate to serve distributed IoT
application workflows using the concept of stateless micro Fog service
replicas, which provides resiliency and maintains service availability in the
face of failures. Load balancing supports this collaboration by optimally
assigning workloads to appropriate services, i.e., distributing the load among
Fog nodes to fairly utilize compute and network resources and minimize
execution delays. In this paper, we propose using ELECTRE, a Multi-Criteria
Decision Analysis (MCDA) approach, to efficiently balance the load in Fog
environments. We considered multiple objectives to make service selection
decisions, including compute and network load information. We evaluate our
approach in a realistic unbalanced topological setup with heterogeneous
workload requirements. To the best of our knowledge, this is the first time
ELECTRE-based methods are used to balance the load in Fog environments. Through
simulations, we compared the performance of our proposed approach with
traditional baseline methods that are commonly used in practice, namely random,
Round-Robin, nearest node, and fastest service selection algorithms. In terms
of the overall system performance, our approach outperforms these methods with
up to 67% improvement.


  A sufficient condition is given for a class of quantum birth and death chains
on the non-negative integers to possess invariant states. The result is applied
to generalised one-atom masers and to the Jaynes-Cummings one-atom maser with
random interaction time and not necessarily diagonal atomic states.


  We consider an inextensible, semiflexible polymer or worm-like chain which is
confined in the transverse direction by a parabolic potential and subject to a
longitudinal force at the ends, so that the polymer is stretched out and
backfolding is negligible. Simple analytic expressions for the partition
function, valid in this regime, are obtained for chains of arbitrary length
with a variety of boundary conditions at the ends. The spatial distribution of
the end points or radial distribution function is also analyzed.


  The idealized Kish-Sethuraman (KS) cipher is theoretically known to offer
perfect security through a classical information channel. However, realization
of the protocol is hitherto an open problem, as the required mathematical
operators have not been identified in the previous literature. A mechanical
analogy of this protocol can be seen as sending a message in a box using two
padlocks; one locked by the Sender and the other locked by the Receiver, so
that theoretically the message remains secure at all times. We seek a
mathematical representation of this process, considering that it would be very
unusual if there was a physical process with no mathematical description and
indeed we find a solution within a four dimensional Clifford algebra. The
significance of finding a mathematical description that describes the protocol,
is that it is a possible step toward a physical realization having benefits in
increased security with reduced complexity.


  Within Process mining, discovery techniques had made it possible to construct
business process models automatically from event logs. However, results often
do not achieve the balance between model complexity and its fitting accuracy,
so there is a need for manual model adjusting. The paper presents an approach
to process mining providing semi-automatic support to model optimization based
on the combined assessment of the model complexity and fitness. To balance
between the two ingredients, a model simplification approach is proposed, which
essentially abstracts the raw model at the desired granularity. Additionally,
we introduce a concept of meta-states, a cycle collapsing in the model, which
can potentially simplify the model and interpret it. We aim to demonstrate the
capabilities of the technological solution using three datasets from different
applications in the healthcare domain. They are remote monitoring process for
patients with arterial hypertension and workflows of healthcare workers during
the COVID-19 pandemic. A case study also investigates the use of various
complexity measures and different ways of solution application providing
insights on better practices in improving interpretability and
complexity/fitness balance in process models.


  Thermal properties in the metal/graphene (Gr) systems are analyzed by using
an atomistic phonon transport model based on Landauer formalism and
first-principles calculations. The specific structures under investigation
include chemisorbed Ni(111)/Gr, physisorbed Cu(111)/Gr and Au(111)/Gr, as well
as Pd(111)/Gr with intermediate characteristics. Calculated results illustrate
a strong dependence of thermal transfer on the details of interfacial
microstructures. In particular, it is shown that the chemisorbed case provides
a generally smaller interfacial thermal resistance than the physisorbed due to
the stronger bonding. However, our calculation also indicates that the weakly
chemisorbed interface of Pd/Gr may be an exception, with the largest thermal
resistance among the considered. Further examination of the electrostatic
potential and interatomic force constants reveal that the mixed bonding force
between the Pd and C atoms results in incomplete hybridization of Pd and
graphene orbital states at the junction, leading effectively to two phonon
interfaces and a larger than expected thermal resistance. Comparison with
available experimental data shows good agreement. The result clearly suggests
the feasibility of phonon engineering for thermal property optimization at the
interface.


  We examine the entanglement of cyclic spin 1/2 chains with anisotropic XY Z
Heisenberg couplings of arbitrary range at transverse factorizing magnetic
fields. At these fields the system exhibits a degenerate symmetry-breaking
separable ground state (GS). It is shown, however, that the side limits of the
GS pairwise entanglement at these fields are actually non-zero in finite
chains, corresponding such fields to a GS spin-parity transition. These limits
exhibit universal properties like being independent of the pair separation and
interaction range, and are directly related to the magnetization jump.
Illustrative exact results are shown for chains with I) full range and II)
nearest neighbor couplings. Global entanglement properties at such points are
also discussed.


  We consider misinformation games, i.e., multi-agent interactions where the
players are misinformed with regards to the game that they play, essentially
having an \emph{incorrect} understanding of the game setting, without being
aware of their misinformation. In this paper, we introduce and study a new
family of misinformation games, called Noisy games, where misinformation is due
to structured (white) noise that affects additively the payoff values of
players. We analyse the general properties of Noisy games and derive
theoretical formulas related to ``behavioural consistency'', i.e., the
probability that the players behaviour will not be significantly affected by
the noise. We show several properties of these formulas, and present an
experimental evaluation that validates and visualises these results.


  Unitary Fermi gases, where the scattering length is large compared to the
interparticle spacing, can have universal properties, which are independent of
the details of the interparticle interactions when the range of the scattering
potential is negligible. We prepare an optically-trapped, unitary Fermi gas of
$^6$Li, tuned just above the center of a broad Feshbach resonance. In agreement
with the universal hypothesis, we observe that this strongly-interacting
many-body system obeys the virial theorem for an ideal gas over a wide range of
temperatures. Based on this result, we suggest a simple volume thermometry
method for unitary gases. We also show that the observed breathing mode
frequency, which is close to the unitary hydrodynamic value over a wide range
of temperature, is consistent with a universal hydrodynamic gas with nearly
isentropic dynamics.


  DenseNets have been shown to be a competitive model among recent
convolutional network architectures. These networks utilize Dense Blocks, which
are groups of densely connected layers where the output of a hidden layer is
fed in as the input of every other layer following it. In this paper, we aim to
improve certain aspects of DenseNet, especially when it comes to practicality.
We introduce ParaNet, a new architecture that constructs three pipelines which
allow for early inference. We additionally introduce a cascading mechanism such
that different pipelines are able to share parameters, as well as logit
matching between the outputs of the pipelines. We separately evaluate each of
the newly introduced mechanisms of ParaNet, then evaluate our proposed
architecture on CIFAR-100.


  We give a survey of recent joint work with E. M. Stein (Princeton University)
concerning the application of suitable versions of the T(1)-theorem technique
to the study of orthogonal projections onto the Hardy and Bergman spaces of
holomorphic functions for domains with minimal boundary regularity.


  We consider the coupling between three dimensional gravity with zero
cosmological constant and massive spinning point particles. First, we study the
classical canonical analysis of the coupled system. Then, we go to the
Hamiltonian quantization generalizing loop quantum gravity techniques. We give
a complete description of the kinematical Hilbert space of the coupled system.
Finally, we define the physical Hilbert space of the system of self-gravitating
massive spinning point particles using Rovelli's generalized projection
operator which can be represented as a sum over spin foam amplitudes. In
addition we provide an explicit expression of the (physical) distance operator
between two particles which is defined as a Dirac observable.


  Traffic intersections are important scenes that can be seen almost everywhere
in the traffic system. Currently, most simulation methods perform well at
highways and urban traffic networks. In intersection scenarios, the challenge
lies in the lack of clearly defined lanes, where agents with various motion
plannings converge in the central area from different directions. Traditional
model-based methods are difficult to drive agents to move realistically at
intersections without enough predefined lanes, while data-driven methods often
require a large amount of high-quality input data. Simultaneously, tedious
parameter tuning is inevitable involved to obtain the desired simulation
results. In this paper, we present a novel adaptive and planning-aware
hybrid-driven method (TraInterSim) to simulate traffic intersection scenarios.
Our hybrid-driven method combines an optimization-based data-driven scheme with
a velocity continuity model. It guides the agent's movements using real-world
data and can generate those behaviors not present in the input data. Our
optimization method fully considers velocity continuity, desired speed,
direction guidance, and planning-aware collision avoidance. Agents can perceive
others' motion planning and relative distance to avoid possible collisions. To
preserve the individual flexibility of different agents, the parameters in our
method are automatically adjusted during the simulation. TraInterSim can
generate realistic behaviors of heterogeneous agents in different traffic
intersection scenarios in interactive rates. Through extensive experiments as
well as user studies, we validate the effectiveness and rationality of the
proposed simulation method.


  To probe the star formation process, we present an observational
investigation of the Pillar IV and an ionized knot HH 216 in the Eagle Nebula
(M16). Pillar IV is known to host a Class I protostar that drives a bipolar
outflow. The outflow has produced the bow shock, HH 216, which is associated
with the red-shifted outflow lobe. The James Webb Space Telescope's near- and
mid-infrared images (resolution $\sim$0.07 arcsec - 0.7 arcsec) reveal the
protostar as a single, isolated object (below 1000 AU). The outer boundary of
Pillar IV is depicted with the 3.3 $\mu$m Polycyclic aromatic hydrocarbon (PAH)
emission. HH 216 is traced with the 4.05 $\mu$m Br$\alpha$ and the radio
continuum emission, however it is undetected with 4.693 $\mu$m H$_{2}$
emission. HH 216 seems to be associated with both thermal and non-thermal radio
emissions. High-resolution images reveal entangled ionized structures (below
3000 AU) of HH 216, which appear to be located toward termination shocks. New
knots in 4.693 $\mu$m H$_{2}$ emission are detected, and are mainly found on
Pillar IV's northern side.
  This particular result supports the previously proposed episodic accretion in
the powering source of HH 216. One part of the ionized jet (extent $\sim$0.16
pc) is discovered on the southern side of the driving source.
  Using the $^{12}$CO($J$ = 1-0), $^{12}$CO($J$ = 3-2), and $^{13}$CO($J$ =
1-0) emission, observational signposts of Cloud-Cloud Collision (or interacting
clouds) toward Pillar IV are investigated. Overall, our results suggest that
the interaction of molecular cloud components around 23 and 26 km s$^{-1}$
might have influenced star formation activity in Pillar IV.


  This study explores fully leptonic WZ and WW production at the LHC within the
SMEFT framework at NLO in QCD, focusing on both CP-even and CP-odd
triple-gauge-coupling dimension-six operators. We investigate the off-shell
processes, contrasting our findings in inclusive setups with those in the
presence of realistic fiducial selections. Alongside the conventional kinematic
observables, we examine polarisation-sensitive observables and angular
coefficients. Moreover, we assess potential SMEFT effects on asymmetry
observables. Through a sensitivity analysis, we identify critical LHC
observables that are particularly sensitive to SMEFT-induced modifications,
thereby shedding light on potential avenues for new-physics searches in diboson
production at the LHC.