  Using a hydrodynamics plus N-body simulation of galaxy cluster formation
within a large volume and mock Chandra X-ray observations, we study the form
and evolution of the intrinsic scatter about the best-fit X-ray
temperature-mass relation for clusters. We investigate the physical origin of
the scatter by correlating it with quantities that are closely related to
clusters' formation and merging histories. We also examine the distribution of
the scatter for merging and nonmerging populations, identified using halo
merger trees derived from the simulation as well as X-ray substructure
measures. We find a strong correlation between the scatter in the M-T_X
relation and the halo concentration, in the sense that more concentrated
clusters tend to be cooler than clusters with similar masses. No bias is found
between the merging and relaxed clusters, but merging clusters generally have
greater scatter, which is related to the properties of the distribution of halo
concentrations. We also detect a signature of non-lognormality in the
distribution of scatter for our simulated clusters both at z=0 and at z=1. A
detailed comparison of merging clusters identified by substructure measures and
by halo merger trees is given in the discussion. We conclude that, when
cooling-related effects are neglected, the variation in halo concentrations is
a more important factor for driving the intrinsic scatter in the M-T_X
relation, while departures from hydrostatic equilibrium due to cluster mergers
have a minor effect.


  With the recent surge in the research of vision transformers, they have
demonstrated remarkable potential for various challenging computer vision
applications, such as image recognition, point cloud classification as well as
video understanding. In this paper, we present empirical results for training a
stronger video vision transformer on the EPIC-KITCHENS-100 Action Recognition
dataset. Specifically, we explore training techniques for video vision
transformers, such as augmentations, resolutions as well as initialization,
etc. With our training recipe, a single ViViT model achieves the performance of
47.4\% on the validation set of EPIC-KITCHENS-100 dataset, outperforming what
is reported in the original paper by 3.4%. We found that video transformers are
especially good at predicting the noun in the verb-noun action prediction task.
This makes the overall action prediction accuracy of video transformers notably
higher than convolutional ones. Surprisingly, even the best video transformers
underperform the convolutional networks on the verb prediction. Therefore, we
combine the video vision transformers and some of the convolutional video
networks and present our solution to the EPIC-KITCHENS-100 Action Recognition
competition.


  Properties of time-periodic Hamiltonians can be exploited to increase the
dephasing time of qubits and to design protected one and two-qubit gates.
Recently, Huang et al. [Phys. Rev. Applied 15, 034065 (2021)] have shown that
time-dependent Floquet states offer a manifold of working points with dynamical
protection larger than the few usual static sweet spots. Here, we use the
framework of many-mode Floquet theory to describe approaches to robustly
control Floquet qubits in the presence of multiple drive tones. Following the
same approach, we introduce a longitudinal readout protocol to measure the
Floquet qubit without the need of first adiabatically mapping back the Floquet
states to the static qubit states, which results in a significant speedup in
the measurement time of the Floquet qubit. The analytical approach developed
here can be applied to any Hamiltonian involving a small number of distinct
drive tones, typically the study of standard parametric gates for qubits
outside of the rotating-wave approximation.


  This paper tackles the problem of disentangling the latent variables of style
and content in language models. We propose a simple yet effective approach,
which incorporates auxiliary multi-task and adversarial objectives, for label
prediction and bag-of-words prediction, respectively. We show, both
qualitatively and quantitatively, that the style and content are indeed
disentangled in the latent space. This disentangled latent representation
learning method is applied to style transfer on non-parallel corpora. We
achieve substantially better results in terms of transfer accuracy, content
preservation and language fluency, in comparison to previous state-of-the-art
approaches.


  In the 1980's, Almgren developed a theory of multi-valued Dirichlet energy
minimizing functions on $n$ dimensional domains and used it, in an essential
way, to bound the Hausdorff dimension of the singular sets of area minimizing
rectifiable currents of dimension $n$ and codimension $\geq 2$. Recent work of
the second author shows that two-valued $C^{1, \mu}$ harmonic functions on $n$
dimensional domains, which are typically-non-minimizing stationary points of
Dirichlet energy, play an essential role in the study of multiplicity 2 branch
points of stable codimension 1 rectifiable currents of dimension $n$. In all of
these cases (of multi-valued harmonic functions and minimal currents), it is
known that the branch sets have Hausdorff dimension $\leq n-2.$ In this paper
we initiate a study of the local structure of branch sets. We show that the
branch set of a two-valued Dirichlet energy minimizing function or a two-valued
$C^{1, \mu}$ harmonic function, in each closed ball of its domain, is either
empty or has positive $(n-2)$-dimensional Hausdorff measure and is equal to the
union of a finite number of locally compact, locally $(n-2)$-rectifiable sets.
Our method is inspired by the work of L. Simon on the structure of
singularities of minimal submanifolds in compact, multiplicity 1 classes.


  For any $H$ in (0,1/2), we construct complete, non-proper, stable,
simply-connected surfaces embedded in $H^2xR$ with constant mean curvature $H$.


  Controlling the pump beam transverse profile in multimode Hong-Ou-Mandel
interference, we generate a ''localized" two-photon singlet state, in which
both photons propagate in the same beam. This type of multi-photon singlet beam
may useful in quantum communication to avoid decoherence. We show that although
the photons are part of the same beam, they are never in the same plane wave
mode, which is characterized by spatial antibunching behavior in the plane
normal to the propagation direction.


  We report on the results of X-ray observations of 4XMM J111816.0-324910, a
transient ultra-luminous X-ray source located in the galaxy NGC 3621. This
system is characterised by a transient nature and marked variability with
characteristic time-scale of ~3500 s, differently from other ULXs, which in the
vast majority show limited intra-observation variability. Such a behaviour is
very reminiscent of the so-called heartbeats sometimes observed in the Galactic
black hole binary GRS 1915+105, where the variability time-scale is ~10-1000 s.
We study the spectral and timing properties of this object and find that
overall, once the differences in the variability time-scales are taken into
account, they match quite closely those of both GRS 1915+105, and of a number
of objects showing heartbeats in their light-curves, including a confirmed
neutron star and a super-massive black hole powering an active galactic
nucleus. We investigate the nature of the compact object in 4XMM
J111816.0-324910 by searching for typical neutron star signatures and by
attempting a mass estimate based on different methods and assumptions. Based on
the current available data, we are not able to unambiguously determine the
nature of the accreting compact object responsible for the observed
phenomenology.


  This work aims to develop a global quantization in the concrete settings of
two graded nilpotent Lie groups of 3-step; namely of the Engel group and the
Cartan group. We provide a preliminary analysis on the structure and the
representations of the aforementioned groups, and their corresponding Lie
algebras. In addition, the explicit formulas for the difference operators in
the two settings are derived, constituting the necessary prerequisites for the
constructions of the $\Psi^{m}_{\rho,\delta}$ classes of symbols in both cases.
In the case of the Engel group, the relation between the Kohn-Nirenberg
quantization and the representations of the Engel group enables us to express
operators in this setting in terms of quantization of symbols in the Euclidean
space. We illustrate the $L^p-L^q$ boundedness for spectral multipliers
theorems with particular examples of operators in both groups, that yield
appropriate Sobolev-type inequalities. As a further application of the above,
we get some consequences for the $L^p-L^q$ norm for the heat kernel of those
particular examples of operators in both settings.


  In this paper we apply the method of Lagrangian descriptors as an indicator
to study the chaotic and regular behavior of trajectories in the phase space of
the classical double pendulum system. In order to successfully quantify the
degree of chaos with this tool, we first derive Hamilton's equations of motion
for the problem in non-dimensional form, showing that they can be written
compactly using matrix algebra. Once the dynamical equations are obtained, we
carry out a parametric study in terms of the system's total energy and the
other model parameters (lengths and masses of the pendulums, and gravity), to
determine the extent of the chaotic and regular regions in the phase space. Our
numerical results show that for a given mass ratio, the maximum chaotic
fraction of phase space trajectories is attained when the pendulums have equal
lengths. Moreover, we give a characterization of the growth and decay of chaos
in the system in terms of the model parameters, and explore the hypothesis that
the chaotic fraction follows an exponential law over different energy regimes.


  The star Cyg OB2 No. 9 is a well-known non-thermal radio emitter. Recent
theoretical work suggests that all such O-stars should be in a binary or a
multiple system. However, there is no spectroscopic evidence of a binary
component. Re-analysis of radio observations from the VLA of this system over
25 years has revealed that the non-thermal emission varies with a period of
2.35+-0.02 yr. This is interpreted as a strong suggestion of a binary system,
with the non-thermal emission arising in a wind-collision region. We derived
some preliminary orbital parameters for this putative binary and revised the
mass-loss rate of the primary star downward from previous estimates.


  Given a diagram of small categories $F : J \rightarrow \textbf{Cat}$, we
provide a combinatorial description of its colimit in terms of the indexing
category $J$ and the categories and functors in the diagram $F$. We introduce
certain double categories of zig-zags in order to keep track of the necessary
identifications. We found these double categories necessary, but also
explanatory.
  When applied pointwise in the simplicially enriched setting, our
constructions offer a shorter proof of the necklace theorem of Dugger and
Spivak by direct computation.


  The practical implementation of Bayesian inference requires numerical
approximation when closed-form expressions are not available. What types of
accuracy (convergence) of the numerical approximations guarantee robustness and
what types do not? In particular, is the recursive application of Bayes' rule
robust when subsequent data or posteriors are approximated? When the prior is
the push forward of a distribution by the map induced by the solution of a PDE,
in which norm should that solution be approximated? Motivated by such
questions, we investigate the sensitivity of the distribution of posterior
distributions (i.e. posterior distribution-valued random variables, randomized
through the data) with respect to perturbations of the prior and data
generating distributions in the limit when the number of data points grows
towards infinity.


  In the present paper, we study a relation between the cohomology of moduli
stacks of smooth and proper curves $\mathcal M_{g,n}$ and the cohomology of
ribbon graph complexes. The main results of this work are proofs of T.
Willwacher's conjecture and A. C$\check{\mathrm a}$ld$\check{\mathrm a}$raru's
conjecture about the cohomology of the Bridgeland differential. We also discuss
the relation to string topology and the Chan-Galatius-Payne theorem about the
weight zero part of the compactly supported cohomology of $\mathcal M_{g,n}.$


  The causal representation of multi-loop scattering amplitudes, obtained from
the application of the loop-tree duality formalism, comprehensively elucidates,
at integrand level, the behaviour of only physical singularities. This
representation is found to manifest compact expressions for multi-loop
topologies that have the same number of \textit{vertices}. Interestingly,
integrands considered in former studies, with up-to six vertices and $L$
internal lines, display the same structure of up-to four-loop ones. The former
is an insight that there should be a correspondence between vertices and the
collection of internal lines, \textit{edges}, that characterise a multi-loop
topology. By virtue of this relation, in this paper, we embrace an approach to
properly classify multi-loop topologies according to vertices and edges.
Differently from former studies, we consider the most general topologies, by
connecting vertices and edges in all possible ways. Likewise, we provide a
procedure to generate causal representation of multi-loop topologies by
considering the structure of causal propagators. Explicit causal
representations of loop topologies with up-to nine vertices are provided.


  We study the solvability of a quadratic integral equation of fractional order
with linear modification of the argument. This equation is considered in the
Banach space of real functions defined, bounded and continuous on an unbounded
interval. Moreover, we will obtain some asymptotic characterization of
solutions.


  In standard potential-model descriptions of the Be7(p,gamma)B8 reaction the
Be7+p spectroscopic factors S appear in the cross section. We argue that the
microscopic substructure effects which are represented by S are short-ranged
and cannot affect the asymptotic normalization of the wave function. We believe
that the standard way of describing reactions in a potential model may be
incorrect and the low-energy cross section should not depend on S in the case
of external capture reactions, like Be7(p,gamma)B8


  Using electronic Raman spectroscopy, we report direct measurements of charge
nematic fluctuations in the tetragonal phase of strain-free
Ba(Fe$_{1-x}$Co$_{x})_{2}$As$_{2}$ single crystals. The strong enhancement of
the Raman response at low temperatures unveils an underlying charge nematic
state that extends to superconducting compositions and which has hitherto
remained unnoticed. Comparison between the extracted charge nematic
susceptibility and the elastic modulus allows us to disentangle the charge
contribution to the nematic instability, and to show that charge nematic
fluctuations are weakly coupled to the lattice.


  Artificial Intelligence (AI) and its associated applications are ubiquitous
in today's world, making it imperative that students and their teachers
understand how it works and the ramifications arising from its usage. In this
study, we investigate the experiences of seven teachers following their
implementation of modules from the MIT RAICA (Responsible AI for Computational
Action) curriculum. Through semi-structured interviews, we investigated their
instructional strategies as they engaged with the AI curriculum in their
classroom, how their teaching and learning beliefs about AI evolved with the
curriculum as well as how those beliefs impacted their implementation of the
curriculum. Our analysis suggests that the AI modules not only expanded our
teachers' knowledge in the field, but also prompted them to recognize its daily
applications and their ethical and societal implications, so that they could
better engage with the content they deliver to students. Teachers were able to
leverage their own interdisciplinary backgrounds to creatively introduce
foundational AI topics to students to maximize engagement and playful learning.
Our teachers advocated their need for better external support when navigating
technological resources, additional time for preparation given the novelty of
the curriculum, more flexibility within curriculum timelines, and additional
accommodations for students of determination. Our findings provide valuable
insights for enhancing future iterations of AI literacy curricula and teacher
professional development (PD) resources.


  We explore, in the MSSM context, an extension of the Anomaly Mediated
Supersymmetry Breaking solution for the soft scalar masses that is possible if
the underlying theory has a gauged R-symmetry. The slepton mass problem
characteristic of the scenario is resolved, and a context for the explanation
of the fermion mass hierarchy provided.


  We consider the problem of calculating learning curves (i.e., average
generalization performance) of Gaussian processes used for regression. On the
basis of a simple expression for the generalization error, in terms of the
eigenvalue decomposition of the covariance function, we derive a number of
approximation schemes. We identify where these become exact, and compare with
existing bounds on learning curves; the new approximations, which can be used
for any input space dimension, generally get substantially closer to the truth.
We also study possible improvements to our approximations. Finally, we use a
simple exactly solvable learning scenario to show that there are limits of
principle on the quality of approximations and bounds expressible solely in
terms of the eigenvalue spectrum of the covariance function.


  The effective rate is an important performance metric of real-time
applications in next generation wireless networks. In this paper, we present an
analysis of the effective rate of multiple-input single-output (MISO) systems
over $\alpha$-$\mu$ fading channels under a maximum delay constraint. More
specifically, novel and highly accurate closed-form approximate expressions of
the effective rate are derived for such systems assuming the generalized
$\alpha$-$\mu$ channel model. In order to examine the impact of system and
channel parameters on the effective rate, we also derive closed-form
expressions of the effective rate in asymptotically high and low
signal-to-noise ratio (SNR) regimes. Furthermore, connections between our
derived results and existing results from the literature are revealed for the
sake of completeness. Our results demonstrate that the effective rate is a
monotonically increasing function of channel fading parameters $\alpha$ and
$\mu$, as well as the number of transmit antennas, while it decreases to zero
when the delay constraint becomes stringent.


  Machine learning methods are becoming increasingly important for the
development of materials science. In spite of this, the use of image analysis
in the development of these systems is still recent and underexplored,
especially in materials often studied via optical imaging techniques such as
liquid crystals. Here we apply the recently proposed method of ordinal networks
to map optical textures obtained from experimental samples of liquid crystals
into complex networks and use this representation jointly with a simple
statistical learning algorithm to investigate different physical properties of
these materials. Our research demonstrates that ordinal networks formed by only
24 nodes encode crucial information about liquid crystal properties, thus
allowing us to train simple machine learning models capable of identifying and
classifying mesophase transitions, distinguishing among different doping
concentrations used to induce chiral mesophases, and predicting sample
temperatures with outstanding accuracy. The precision and scalability of our
approach indicate it can be used to probe properties of different materials in
situations involving large-scale datasets or real-time monitoring systems.


  In recent years, we constructed a microscopic optical potential (OP) for
elastic nucleon-nucleus ($NA$) scattering using modern approaches based on
chiral theories for the nucleon-nucleon ($NN$) interaction. The OP was derived
at first order of the spectator expansion in Watson multiple scattering theory
and its final expression was a folding integral between the $NN$ $t$ matrix and
the nuclear density of the target. Two- and three-body forces are consistently
included both in the target and in the projectile description. The purpose of
this work is to apply our microscopic OP to nuclei characterized by a ground
state of spin-parity quantum numbers $J^\pi \ne 0^+$. We extended our formalism
to include the spin of the target nucleus. The full amplitudes of the $NN$
reaction matrix are retained in the calculations starting from two- and
three-body chiral forces. We show a remarkable agreement with experimental data
for the available observables and, simultaneously, provide reliable estimates
for the theoretical uncertainties. This work paves the way toward a full
microscopic approach to inelastic $NA$ scattering, showing that the derivation
of optical potentials between states with $J^\pi \ne 0^+$ is completely under
control.


  We argue that fermion-boson mapping techniques represent a natural tool for
studying many-body supersymmetry in fermionic systems with pairing. In
particular, using the generalized Dyson mapping of a many-level fermion
superalgebra with the SU(2) type of pairing we investigate two kinds of
supersymmetry connecting excitations in the systems with even and odd particle
numbers: dynamical supersymmetry, which ensures a unified classification of
states for both even and odd populations, and invariant supersymmetry with
actual degeneracies of states within the same supermultiplet. Dynamical
supersymmetries based on the dynamical algebra of the type U(K/2\Omega) (where
K and 2\Omega denote the number of fermion-pair and single-fermion states,
respectively) naturally arise in the bosonized description of the system.
Conditions for invariant supersymmetry are determined in a restricted case of
bilinear supercharge operators.


  Multi-modal crowd counting involves estimating crowd density from both visual
and thermal/depth images. This task is challenging due to the significant gap
between these distinct modalities. In this paper, we propose a novel approach
by introducing an auxiliary broker modality and on this basis frame the task as
a triple-modal learning problem. We devise a fusion-based method to generate
this broker modality, leveraging a non-diffusion, lightweight counterpart of
modern denoising diffusion-based fusion models. Additionally, we identify and
address the ghosting effect caused by direct cross-modal image fusion in
multi-modal crowd counting. Through extensive experimental evaluations on
popular multi-modal crowd-counting datasets, we demonstrate the effectiveness
of our method, which introduces only 4 million additional parameters, yet
achieves promising results. The code is available at
https://github.com/HenryCilence/Broker-Modality-Crowd-Counting.


  Prior work has identified a resilient phenomenon that threatens the
performance of human-AI decision-making teams: overreliance, when people agree
with an AI, even when it is incorrect. Surprisingly, overreliance does not
reduce when the AI produces explanations for its predictions, compared to only
providing predictions. Some have argued that overreliance results from
cognitive biases or uncalibrated trust, attributing overreliance to an
inevitability of human cognition. By contrast, our paper argues that people
strategically choose whether or not to engage with an AI explanation,
demonstrating empirically that there are scenarios where AI explanations reduce
overreliance. To achieve this, we formalize this strategic choice in a
cost-benefit framework, where the costs and benefits of engaging with the task
are weighed against the costs and benefits of relying on the AI. We manipulate
the costs and benefits in a maze task, where participants collaborate with a
simulated AI to find the exit of a maze. Through 5 studies (N = 731), we find
that costs such as task difficulty (Study 1), explanation difficulty (Study 2,
3), and benefits such as monetary compensation (Study 4) affect overreliance.
Finally, Study 5 adapts the Cognitive Effort Discounting paradigm to quantify
the utility of different explanations, providing further support for our
framework. Our results suggest that some of the null effects found in
literature could be due in part to the explanation not sufficiently reducing
the costs of verifying the AI's prediction.


  This paper presents the Sokoto Coventry Fingerprint Dataset (SOCOFing), a
biometric fingerprint database designed for academic research purposes.
SOCOFing is made up of 6,000 fingerprint images from 600 African subjects.
SOCOFing contains unique attributes such as labels for gender, hand and finger
name as well as synthetically altered versions with three different levels of
alteration for obliteration, central rotation, and z-cut. The dataset is freely
available for noncommercial research purposes at:
https://www.kaggle.com/ruizgara/socofing


  Magnetic rare-earth orthoferrites $R$FeO$_3$ host a variety of functional
properties from multiferroicity and strong magnetostriction, to
spin-reorientation transitions and ultrafast light-driven manipulation of
magnetism, which can be exploited in spintronics and next-generation devices.
Among these systems, SmFeO$_3$ is attracting a particular interest for its rich
phase diagram and the high temperature Fe-spin magnetic transitions, which
combines with a very low temperature and as yet unclear Sm-spin ordering.
Various experiments suggest that the interaction between the Sm and Fe magnetic
moments (further supported by the magnetic anisotropy), is at the origin of the
complex cascade of transitions, but a conclusive and clear picture has not yet
been reached. In this work, by means of comprehensive first-principles
calculations, we unravel the role of the magnetic Sm ions in the Fe-spin
reorientation transition and in the detected anomalies in the lattice
vibrational spectrum, which are a signature of a relevant spin-phonon coupling.
By including both Sm-$f$ electrons and non-collinear magnetism, we find
frustrated and anisotropic Sm interactions, and a large magnetocrystalline
anisotropy mediated by the SOC of the Sm-$4f$ electrons, which drive the
complex magnetic properties and phase diagram of SmFeO$_3$.


  We provide the full asymptotic description of the quasi-normal modes
(resonances) in any strip of fixed width for Dirac fields in slowly rotating
Kerr-Newman-de Sitter black holes. The resonances split in a way similar to the
Zeeman effect. The method is based on the extension to Dirac operators of
techniques applied by Dyatlov 2011, 2012 to the (uncharged) Kerr-de Sitter
black holes. We show that the mass of the Dirac field does not have effect on
the two leading terms in the expansions of resonances.


  We prove that the connected sum of two links is quasipositive if and onlyif
each summand is quasipositive. The prove is based on the filling disk technique


  This article provides entropic inequalities for binomial-Poisson
distributions, derived from the two point space. They appear as local
inequalities of the M/M/$\infty$ queue. They describe in particular the
exponential dissipation of $\Phi$-entropies along this process. This simple
queueing process appears as a model of ``constant curvature'', and plays for
the simple Poisson process the role played by the Ornstein-Uhlenbeck process
for Brownian Motion. Some of the inequalities are recovered by semi-group
interpolation. Additionally, we explore the behaviour of these entropic
inequalities under a particular scaling, which sees the Ornstein-Uhlenbeck
process as a fluid limit of M/M/$\infty$ queues. Proofs are elementary and rely
essentially on the development of a ``$\Phi$-calculus''.


  We introduce LaViLa, a new approach to learning video-language
representations by leveraging Large Language Models (LLMs). We repurpose
pre-trained LLMs to be conditioned on visual input, and finetune them to create
automatic video narrators. Our auto-generated narrations offer a number of
advantages, including dense coverage of long videos, better temporal
synchronization of the visual information and text, and much higher diversity
of text. The video-text embedding learned contrastively with these additional
auto-generated narrations outperforms the previous state-of-the-art on multiple
first-person and third-person video tasks, both in zero-shot and finetuned
setups. Most notably, LaViLa obtains an absolute gain of 10.1% on EGTEA
classification and 5.9% Epic-Kitchens-100 multi-instance retrieval benchmarks.
Furthermore, LaViLa trained with only half the narrations from the Ego4D
dataset outperforms baseline models trained on the full set, and shows positive
scaling behavior on increasing pre-training data and model size.


  This review summarizes all known results (up to this date) about methods of
integration of the classical Lotka-Volterra systems with diffusion and presents
a wide range of exact solutions, which are the most important from
applicability point of view. It is the first attempt in this direction. Because
the diffusive Lotka-Volterra systems are used for mathematical modeling
enormous variety of processes in ecology, biology, medicine, physics and
chemistry, the review should be interesting not only for specialists from
Applied Mathematics but also those from other branches of Science. The obtained
exact solutions can also be used as test problems for estimating the accuracy
of approximate analytical and numerical methods for solving relevant boundary
value problems.


  A special class of quantum channels, named subspace local (SL), are defined
and investigated. The proposed definition of subspace locality of quantum
channels is an attempt to answer the question of what kind of restriction
should be put on a channel, if it is to act `locally' with respect to two
`locations', when these naturally correspond to a separation of the total
Hilbert space in an orthogonal sum of subspaces, rather than a tensor product
decomposition. It is shown that the set of SL channels decomposes into four
disjoint families of channels. Explicit expressions to generate all channels in
each family is presented. It is shown that one of these four families, the
local subspace preserving (LSP) channels, is precisely the intersection between
the set of subspace preserving channels and the SL channels. For a subclass of
the LSP channels, a special type of unitary representation using ancilla
systems is presented.


  We present the first measurement of two-particle momentum correlations in
jets produced in $p\bar p$ collisions at $\sqrt{s}=1.96$ TeV. Results are
obtained for charged particles within a restricted cone with an opening angle
of 0.5 radians around the jet axis and for events with dijet masses between 66
and 563 GeV/c$^{2}$. A comparison of the experimental data to theoretical
predictions obtained for partons within the framework of resummed perturbative
QCD in the next-to-leading log approximation (NLLA) shows that the parton
momentum correlations survive the hadronization stage of jet fragmentation,
giving further support to the hypothesis of local parton-hadron duality. The
extracted value of the NLLA parton shower cutoff scale $Q_\mathit{eff}$ set
equal to $\Lambda_\mathit{QCD}$ is found to be $(1.4^{+0.9}_{-0.7})\times 100$
MeV.


  (Abridged) While the gravitational-wave (GW) signal GW170817 was accompanied
by a variety of electromagnetic (EM) counterparts, sufficiently high-mass
binary neutron star (BNS) mergers are expected to be unable to power bright EM
counterparts. The putative high-mass binary BNS merger GW190425, for which no
confirmed EM counterpart has been identified, may be an example of such a
system. It is thus important to understand how well we will be able to
distinguish high-mass BNSs and low-mass binary black holes (BBHs) solely from
their GW signals. To do this, we consider the imprint of the tidal
deformability of the neutron stars on the GW signal for systems undergoing
prompt black hole formation after merger. We model the BNS signals using hybrid
numerical relativity -- tidal effective-one-body waveforms. Specifically, we
consider a set of five nonspinning equal-mass BNS signals with masses of 2.7,
3.0, 3.2 Msun and with three different equations of state, as well as the
analogous BBH signals. We perform parameter estimation on these signals in
three networks: Advanced LIGO-Advanced Virgo and Advanced LIGO-Advanced
Virgo-KAGRA with sensitivities similar to O3 and O4, respectively, and a 3G
network of two Cosmic Explorers (CEs) and one Einstein Telescope, with a CE
sensitivity similar to Stage 2. Our analysis suggests that we cannot
distinguish the signals from high-mass BNSs and BBHs at a 90% credible level
with the O3-like network even at 40 Mpc. However, we can distinguish all but
the most compact BNSs that we consider in our study from BBHs at 40 Mpc at a >=
95% credible level using the O4-like network, and can even distinguish them at
a > 99.2% (>= 97%) credible level at 369 (835) Mpc using the 3G network.
Additionally, we present a simple method to compute the leading effect of the
Earth's rotation on the response of a gravitational wave detector in the
frequency domain.


  In this paper we have criticized the so-called Landau damping theory. We have
analyzed solutions of the standard dispersion equations for longitudinal
(electric) and transversal (electromagnetic and electron) waves in
half-infinite slab of the uniform collisionless plasmas with non-Maxwellian and
Maxwellian-like electron energy distribution functions. One considered the most
typical cases of both the delta-function type distribution function (the plasma
stream with monochromatic electrons) and distribution functions, different from
Maxwellian ones as with a surplus as well as with a shortage in the Maxwellian
distribution function tail. It is shown that there are present for the
considered cases both collisionless damping and also non-damping electron waves
even in the case of non-Maxwellian distribution function.


  We address the question of the degree of spatial non-locality of the self
energy in the iron-based superconductors, a subject which is receiving
considerable attention. Using LiFeAs as a prototypical example, we extract the
self energy from angular-resolved photoemission spectroscopy (ARPES) data. We
use two distinct electronic structure references: density functional theory in
the local density approximation and linearized quasiparticle self consistent GW
(LQSGW). We find that with the LQSGW reference, spatially local dynamical
correlations provide a consistent description of the experimental data, and
account for some surprising aspects of the data such as the substantial out of
plan dispersion of the electron Fermi surface having dominant xz/yz character.
Hence, correlations effects can be separated into static non-local
contributions well described by LQSGW and dynamical local contributions. Hall
effect and resistivity data are shown to be consistent with this description.


  (accepted for publication in the Ap.J.) I present a general classification of
self-similar solutions to the equations of gravitational hydrodynamics that
contain many previous results as special cases. For cold flows with spherical
symmetry, the solution space can be classified into several regions of behavior
similar to the Bondi solutions for steady flow. A full description of these
solution is possible, which serves as the asymptotic limit for the general
problem. By applying a shock jump condition, exact general solutions can be
constructed. The isothermal case allows an extra exact integral, and can be
asymptotically analyzed in the presence of finite pressure. These solutions
serve as analytic models for problems such as spherical accretion for star
formation, infall or outflow of gas into galaxies, Lyman alpha cloud dynamics,
etc. Most previous self-similar results are obtained as special cases. The
critical values for a cosmological flow with $\Omega=1$ and $\gamma=4/3$ turn
out to play a special role.


  When heated, micro-resonators present a shift of their resonance frequencies.
We study specifically silicon cantilevers heated locally by laser absorption,
and evaluate theoretically and experimentally their temperature profile and its
interplay with the mechanical resonances. We present a enhanced version of our
earlier model [F. Aguilar Sandoval et al., J. Appl. Phys. 117, 234503 (2015)]
including both elasticity and geometry temperature dependency, showing that the
latter can account for 20% of the observed shift for the first flexural mode.
The temperature profile description takes into account thermal clamping
conditions, radiation at high temperature, and lower conductivity than bulk
silicon due to phonon confinement. Thanks to a space-power equivalence in the
heat equation, scanning the heating point along the cantilever directly reveals
the temperature profile. Finally, frequency shift measurement can be used to
infer the temperature field with a few percent precision.


  A general procedure is presented how to improve the effective potential by
using the renormalization group equation (RGE) in MS bar scheme. If one knows
the L-loop effective potential and the RGE coefficient functions up to
(L+1)-loop level, this procedure gives an improved potential which satisfies
the RGE and contains all of the leading, next-to-leading,... , and
L-th-to-leading log terms.


  This paper introduces Gnowee, a modular, Python-based, open-source hybrid
metaheuristic optimization algorithm (Available from
https://github.com/SlaybaughLab/Gnowee). Gnowee is designed for rapid
convergence to nearly globally optimum solutions for complex, constrained
nuclear engineering problems with mixed-integer and combinatorial design
vectors and high-cost, noisy, discontinuous, black box objective function
evaluations. Gnowee's hybrid metaheuristic framework is a new combination of a
set of diverse, robust heuristics that appropriately balance diversification
and intensification strategies across a wide range of optimization problems.
  This novel algorithm was specifically developed to optimize complex nuclear
design problems; the motivating research problem was the design of material
stack-ups to modify neutron energy spectra to specific targeted spectra for
applications in nuclear medicine, technical nuclear forensics, nuclear physics,
etc. However, there are a wider range of potential applications for this
algorithm both within the nuclear community and beyond. To demonstrate Gnowee's
behavior for a variety of problem types, comparisons between Gnowee and several
well-established metaheuristic algorithms are made for a set of eighteen
continuous, mixed-integer, and combinatorial benchmarks. These results
demonstrate Gnoweee to have superior flexibility and convergence
characteristics over a wide range of design spaces. We anticipate this wide
range of applicability will make this algorithm desirable for many complex
engineering applications.


  A topological defect in the form of the Abrikosov-Nielsen-Olesen vortex is
considered as a gauge-flux-carrying tube that is impenetrable for quantum
matter. The relativistic spinor matter field is quantized in the vortex
background in $2+1$-dimensional conical space-time which is a section
orthogonal to the vortex axis; the most general set of boundary conditions
ensuring the impenetrability of the vortex core is employed. We find the
induced vacuum current circulating around the vortex and the induced vacuum
magnetic field strength pointing along the vortex axis. The requirement of
finiteness and physical plausibility for the total induced vacuum magnetic flux
allows us to restrict the variety of admissible boundary conditions. The
dependence of the results on the transverse size of the vortex, as well as on
the vortex flux and the parameter of conicity, is elucidated.


  Existence and uniqueness in ${\Bbb R}^{n,1}$ of entire spacelike
hypersurfaces contained in the future of the origin $O$ and asymptotic to the
light-cone, with scalar curvature prescribed at their generic point $M$ as a
negative function of the unit vector $\overrightarrow{Om}$ pointing in the
direction of $\overrightarrow{OM}$, divided by the square of the norm of
$\overrightarrow{OM}$ (a dilation invariant problem). The solutions are seeked
as graphs over the future unit-hyperboloid emanating from $O$ (the hyperbolic
space); radial upper and lower solutions are constructed which, relying on a
previous result in the Cartesian setting, imply their existence.


  The necessary information for specifying a complex system may not be
completely accessible to us, i.e., to mathematical treatments. This is not to
be confounded with the incompleteness of our knowledge about whatever systems
or nature, since here information is our ignorance. In conventional statistics
and information theories, this information or ignorance is supposed completely
accessible to theoretical treatments connected with complete probability
distributions. However, the hypothesis of incomplete information supposes that
the information of certain systems can be incomplete as calculated in the usual
way as in the conventional information theories. This hypothesis has been used
in order to generalize the conventional statistics theory. The generalized
statistics and information theory characterized by an empirical parameter has
been proved useful for the formulation of the nonextensive statistical
mechanics based on Tsallis entropy, for the description of some correlated
quantum systems and for the derivation of the stationary probability
distributions of nonequilibrium complex systems evolving in hierarchical or
fractal phase space. In this paper, the incompleteness of the information will
be discussed from mathematical, physical and epistemological considerations
with an example of information calculation in fractal phase space with
stationary probability distribution.


  In this paper we classify symplectic leaves of the regular part of the
projectivization of the space of meromorphic endomorphisms of a stable vector
bundle on an elliptic curve, using the study of shifted Poisson structures on
the moduli of complexes from our previous work \cite{HP17}. This Poisson
ind-scheme is closely related to the ind Poisson-Lie group associated to
Belavin's elliptic $r$-matrix, studied by Sklyanin, Cherednik and Reyman and
Semenov-Tian-Shansky. Our result leads to a classification of symplectic leaves
on the regular part of meromorphic matrix algebras over an elliptic curve,
which can be viewed as the Lie algebra of the above-mentioned ind Poisson-Lie
group. We also describe the decomposition of the product of leaves under the
multiplication morphism and show the invariance of Poisson structures under
autoequivalences of the derived category of coherent sheaves on an elliptic
curve.


  In Quantum Illumination (QI), a signal beam initially entangled with an idler
beam held at the receiver interrogates a target region bathed in thermal
background light. The returned beam is measured jointly with the idler in order
to determine whether a weakly reflecting target is present. Using tools from
quantum information theory, we derive lower bounds on the average error
probability of detecting both specular and fading targets and on the mean
squared error of estimating the reflectance of a detected target, which are
obeyed by any QI transmitter satisfying a signal energy constraint. For bright
thermal backgrounds, we show that the QI system using multiple copies of
low-brightness two-mode squeezed vacuum states is nearly optimal. More
generally, our results place limits on the best possible performance achievable
using QI systems at all wavelengths, and at all signal and background noise
levels.


  We study the Glauber dynamics for the zero-temperature Ising model in
dimension d=4 with "plus" boundary condition.Let T+ be the time needed for an
hypercube of size L entirely filled with "minus" spins to become entirely
"plus". We prove that T+ is O(L^2(log L)^c) for some constant c, not depending
on the dimension. This brings further rigorous justification for the so-called
"Lifshitz law" T+ = O(L^2) [5, 3] conjectured on heuristic grounds. The key
point of our proof is to use the detail knowledge that we have on the
three-dimensional problem: results for fluctuation of monotone interfaces at
equilibrium and mixing time for monotone interfaces dynamics extracted from
[2], to get the result in higher dimension.


  It is well known that static wormhole configurations in general relativity
(GR) are possible only if matter threading the wormhole throat is
"exotic"---i.e., violates a number of energy conditions. For this reason, it is
impossible to construct {static} wormholes supported only by dust-like matter
which satisfies all usual energy conditions. However, this is not the case for
non-static configurations. In 1934, Tolman found a general solution describing
the evolution of a spherical dust shell in GR. In this particular case,
Tolman's solution describes the collapsing dust ball; the inner space--time
structure of the ball corresponds to the Friedmann universe filled by a dust.
In the present work we use the general Tolman's solution in order to construct
a dynamic spherically symmetric wormhole solution in GR with dust-like matter.
The solution constructed represents the collapsing dust ball with the inner
wormhole space--time structure. It is worth noting that, with the dust-like
matter, the ball is made of satisfies the usual energy conditions and cannot
prevent the collapse. We discuss in detail the properties of the collapsing
dust wormhole.


  Automatic Chemical Design is a framework for generating novel molecules with
optimized properties. The original scheme, featuring Bayesian optimization over
the latent space of a variational autoencoder, suffers from the pathology that
it tends to produce invalid molecular structures. First, we demonstrate
empirically that this pathology arises when the Bayesian optimization scheme
queries latent points far away from the data on which the variational
autoencoder has been trained. Secondly, by reformulating the search procedure
as a constrained Bayesian optimization problem, we show that the effects of
this pathology can be mitigated, yielding marked improvements in the validity
of the generated molecules. We posit that constrained Bayesian optimization is
a good approach for solving this class of training set mismatch in many
generative tasks involving Bayesian optimization over the latent space of a
variational autoencoder.


  We study the number $P(n)$ of partitions of an integer $n$ into sums of
distinct squares and derive an integral representation of the function $P(n)$.
Using semi-classical and quantum statistical methods, we determine its
asymptotic average part $P_{as}(n)$, deriving higher-order contributions to the
known leading-order expression [M. Tran {\it et al.}, Ann.\ Phys.\ (N.Y.) {\bf
311}, 204 (2004)], which yield a faster convergence to the average values of
the exact $P(n)$. From the Fourier spectrum of $P(n)$ we obtain hints that
integer-valued frequencies belonging to the smallest Pythagorean triples
$(m,p,q)$ of integers with $m^2+p^2=q^2$ play an important role in the
oscillations of $P(n)$. Finally we analyze the oscillating part $\delta
P(n)=P(n)-P_{as}(n)$ in the spirit of semi-classical periodic orbit theory [M.
Brack and R. K. Bhaduri: {\it Semiclassical Physics} (Bolder, Westview Press,
2003)]. A semi-classical trace formula is derived which accurately reproduces
the exact $\delta P(n)$ for $n > \sim 500$ using 10 pairs of `orbits'. For $n >
\sim 4000$ only two pairs of orbits with the frequencies 4 and 5 -- belonging
to the lowest Pythagorean triple (3,4,5) -- are relevant and create the
prominent beating pattern in the oscillations. For $n > \sim 100,000$ the beat
fades away and the oscillations are given by just one pair of orbits with
frequency 4.


  Bicontinuous Pickering emulsions (bijels) are a physically interesting class
of soft materials with many potential applications including catalysis,
microfluidics and tissue engineering. They are created by arresting the
spinodal decomposition of a partially-miscible liquid with a (jammed) layer of
interfacial colloids. Porosity $L$ (average interfacial separation) of the
bijel is controlled by varying the radius ($r$) and volume fraction ($\phi$) of
the colloids ($L \propto r/\phi$). However, to optimize the bijel structure
with respect to other parameters, e.g. quench rate, characterizing by $L$ alone
is insufficient. Hence, we have used confocal microscopy and X-ray CT to
characterize a range of bijels in terms of local and area-averaged interfacial
curvatures. In addition, the curvatures of bijels have been monitored as a
function of time, which has revealed an intriguing evolution up to 60 minutes
after bijel formation, contrary to previous understanding.


  We investigate the effect of non-trivial spatial correlations between proton
constituents, considered in this work to be gluonic hot spots, on the initial
conditions of proton-proton collisions from ISR to LHC energies, i.e. $\sqrt
s\!=\!52.6,7000,13000$ GeV. The inclusion of these correlations is motivated by
their fundamental role in the description of a recently observed new feature of
$pp$ scattering at $\sqrt s\!=\!7$ TeV, the hollowness effect. Our analysis
relies on a Monte-Carlo Glauber approach including fluctuations in the hot spot
positions and their entropy deposition in the transverse plane. We explore both
the energy dependence and the effect of spatial correlations on the number of
wounded hot spots, their spatial distribution and the eccentricities,
$\varepsilon_n$, of the initial state geometry of the collision. In minimum
bias collisions we find that the inclusion of short range repulsive
correlations between the hot spots reduces the value of the eccentricity
($\varepsilon_2$) and the triangularity ($\varepsilon_3$). In turn, upon
considering only the events with the highest entropy deposition i.e. the
ultra-central ones, the probability of having larger $\varepsilon_{2,3}$
increases significantly in the correlated scenario. Finally, the eccentricities
show a quite mild energy dependence.


  Performing link prediction using knowledge graph embedding models has become
a popular approach for knowledge graph completion. Such models employ a
transformation function that maps nodes via edges into a vector space in order
to measure the likelihood of the links. While mapping the individual nodes, the
structure of subgraphs is also transformed. Most of the embedding models
designed in Euclidean geometry usually support a single transformation type -
often translation or rotation, which is suitable for learning on graphs with
small differences in neighboring subgraphs. However, multi-relational knowledge
graphs often include multiple sub-graph structures in a neighborhood (e.g.
combinations of path and loop structures), which current embedding models do
not capture well. To tackle this problem, we propose a novel KGE model (5*E) in
projective geometry, which supports multiple simultaneous transformations -
specifically inversion, reflection, translation, rotation, and homothety. The
model has several favorable theoretical properties and subsumes the existing
approaches. It outperforms them on the most widely used link prediction
benchmarks


  The main aim of the paper is to introduce the concept of metric duality in
the category of topological Abelian groups that extends the classical notion of
duality for normed vector spaces and behaves quite nicely for LCA groups
(equipped with nice metrics). In particular, it is shown that each Polish LCA
group admits a reflexive proper metric and, more generally, all LCA groups
possess reflexive (proper) metric structures.


  Some recent results of lattice QCD calculations which are relevant for the
phenomenology of the Standard Model are reviewed. They concern the lattice
determinations of quark masses, studies of K-Kbar and B-Bbar mixings, and a
prediction of the B_s-mesons lifetime difference. The results of a recent
analysis of the CKM unitarity triangle, which is mostly based on the lattice
calculations of the relevant hadronic matrix elements, are also presented.


  We prove the equivalence of two conjectural constructions of unramified
cuspidal automorphic functions on the adelic group GL_n(A) associated to an
irreducible l-adic local system of rank n on an algebraic curve X over a finite
field. The existence of such a function is predicted by the Langlands
conjecture.
  The first construction, which was proposed by Shalika and Piatetski-Shapiro
following Weil and Jacquet-Langlands (n=2), is based on considering the
Whittaker function. The second construction, which was proposed recently by
Laumon following Drinfeld (n=2) and Deligne (n=1), is geometric: the
automorphic function is obtained via Grothendieck's ``faisceaux-fonctions''
correspondence from a complex of sheaves on an algebraic stack.
  Our proof of their equivalence is based on a local result about the spherical
Hecke algebra, which we prove for an arbitrary reductive group. We also discuss
a geometric interpretation of this result.


  In this work we explore some mathematical physics aspects of the spherically
symmetric Lovelock black hole in high dimensions. Intended for this aim, we
thoroughly consider the metric corresponding to the five-dimensional Lovelock
black hole spacetime. We construct the strong retractions by the geodesic
equations on the background under consideration. As a result, from the
topological point of view, we construct the theory of strong homotopy retract,
which will allow us, in principle, to better understand some of its suitable
applications on astrophysics and cosmology, in particular, in the analysis of
the spacetime singularities. We find the solutions of the equation of motion
for both radial and angular coordinates, and then we describe the outer
("exterior") and lower ("interior") apparent horizons. Indeed, the outer
apparent horizon is the last surface from which the light waves could still
escape from the black hole. Thus, it is meaningful to analyze some physical
phenomena related to quantum particles propagating outside the exterior
apparent horizon, in particular, we discuss the quasistationary levels of
scalar fields and their radial wave functions, which are given in terms of the
general Heun functions. We also calculate the perihelion precession in this
background.


  We consider two ultrarelativistic shock waves propagating and colliding in
five-dimensional Anti-de-Sitter spacetime. By transforming to Rosen
coordinates, we are able to find the form of the metric shortly after the
collision. Using holographic renormalization, we calculate the energy-momentum
tensor on the boundary of AdS space for early times after the collision. Via
the gauge-gravity duality, this gives some insights on bulk dynamics of systems
created by high energy scattering in strongly coupled gauge theories. We find
that Bjorken boost-invariance is explicitely violated at early times and we
obtain an estimate for the thermalization time in this simple system.


  Star clusters provide an excellent opportunity to study the role of
environment on determining the frequencies of short period planets. They
provide a large sample of stars which can be imaged simultaneously, with a
common distance, age and pre-determined physical parameters. This allows the
search to be tailor-made for each specific cluster. Several groups are
attempting to detect transiting planets in open clusters. Three previous
surveys have also targeted the two brightest globular clusters. No cluster
survey has yet detected a planet. This contribution presents a brief overview
of the field, highlighting the pros and cons of performing such a search, and
presents the expected and current results, with implications for planetary
frequencies in regions of high stellar density and low metallicity.


  Segment Anything Model (SAM) has recently shown its powerful effectiveness in
visual segmentation tasks. However, there is less exploration concerning how
SAM works on audio-visual tasks, such as visual sound localization and
segmentation. In this work, we propose a simple yet effective audio-visual
localization and segmentation framework based on the Segment Anything Model,
namely AV-SAM, that can generate sounding object masks corresponding to the
audio. Specifically, our AV-SAM simply leverages pixel-wise audio-visual fusion
across audio features and visual features from the pre-trained image encoder in
SAM to aggregate cross-modal representations. Then, the aggregated cross-modal
features are fed into the prompt encoder and mask decoder to generate the final
audio-visual segmentation masks. We conduct extensive experiments on
Flickr-SoundNet and AVSBench datasets. The results demonstrate that the
proposed AV-SAM can achieve competitive performance on sounding object
localization and segmentation.


  Multi-phase computed tomography (CT) scans use contrast agents to highlight
different anatomical structures within the body to improve the probability of
identifying and detecting anatomical structures of interest and abnormalities
such as liver lesions. Yet, detecting these lesions remains a challenging task
as these lesions vary significantly in their size, shape, texture, and contrast
with respect to surrounding tissue. Therefore, radiologists need to have an
extensive experience to be able to identify and detect these lesions.
Segmentation-based neural networks can assist radiologists with this task.
Current state-of-the-art lesion segmentation networks use the encoder-decoder
design paradigm based on the UNet architecture where the multi-phase CT scan
volume is fed to the network as a multi-channel input. Although this approach
utilizes information from all the phases and outperform single-phase
segmentation networks, we demonstrate that their performance is not optimal and
can be further improved by incorporating the learning from models trained on
each single-phase individually. Our approach comprises three stages. The first
stage identifies the regions within the liver where there might be lesions at
three different scales (4, 8, and 16 mm). The second stage includes the main
segmentation model trained using all the phases as well as a segmentation model
trained on each of the phases individually. The third stage uses the
multi-phase CT volumes together with the predictions from each of the
segmentation models to generate the final segmentation map. Overall, our
approach improves relative liver lesion segmentation performance by 1.6% while
reducing performance variability across subjects by 8% when compared to the
current state-of-the-art models.


  For integer $k\geq2,$ a spanning $k$-ended-tree is a spanning tree with at
most $k$ leaves. Motivated by the closure theorem of Broersma and Tuinstra
[Independence trees and Hamilton cycles, J. Graph Theory 29 (1998) 227--237],
we provide tight spectral conditions to guarantee the existence of a spanning
$k$-ended-tree in a connected graph of order $n$ with extremal graphs being
characterized. Moreover, by adopting Kaneko's theorem [Spanning trees with
constraints on the leaf degree, Discrete Appl. Math. 115 (2001) 73--76], we
also present tight spectral conditions for the existence of a spanning tree
with leaf degree at most $k$ in a connected graph of order $n$ with extremal
graphs being determined, where $k\geq1$ is an integer.


  Starting with Gilmer et al. (2018), several works have demonstrated the
inevitability of adversarial examples based on different assumptions about the
underlying input probability space. It remains unclear, however, whether these
results apply to natural image distributions. In this work, we assume the
underlying data distribution is captured by some conditional generative model,
and prove intrinsic robustness bounds for a general class of classifiers, which
solves an open problem in Fawzi et al. (2018). Building upon the
state-of-the-art conditional generative models, we study the intrinsic
robustness of two common image benchmarks under $\ell_2$ perturbations, and
show the existence of a large gap between the robustness limits implied by our
theory and the adversarial robustness achieved by current state-of-the-art
robust models. Code for all our experiments is available at
https://github.com/xiaozhanguva/Intrinsic-Rob.


  Negative binomial regression is commonly employed to analyze overdispersed
count data. With small to moderate sample sizes, the maximum likelihood
estimator of the dispersion parameter may be subject to a significant bias,
that in turn affects inference on mean parameters. This paper proposes
inference for negative binomial regression based on adjustments of the score
function aimed at mean and median bias reduction. The resulting estimating
equations are similar to those available for improved inference in generalized
linear models and, in particular, can be solved using a suitable extension of
iterative weighted least squares. Simulation studies show a remarkable
performance of the new methods, which are also found to solve in many cases
numerical problems of maximum likelihood estimates. The methods are illustrated
and evaluated using two case studies: an Ames salmonella assay data set and
data on epileptic seizures. Inference based on adjusted scores turns out to be
generally preferable to explicit bias correction.


  We numerically investigate the migration of dust particles with initial
orbits close to those of the numbered asteroids, observed trans-Neptunian
objects, and Comet Encke. The fraction of silicate asteroidal particles that
collided with the Earth during their lifetime varied from 1.1% for 100 micron
particles to 0.008% for 1 micron particles. Almost all asteroidal particles
with diameter d>4 microns collided with the Sun. The peaks in the migrating
asteroidal dust particles' semi-major axis distribution at the n:(n+1)
resonances with Earth and Venus and the gaps associated with the 1:1 resonances
with these planets are more pronounced for larger particles. The probability of
collisions of cometary particles with the Earth is smaller than for asteroidal
particles, and this difference is greater for larger particles.


  In the spirit of the macroscopic crowd motion models with hard congestion
(i.e. a strong density constraint $\rho\leq 1$) introduced by Maury {\it et
al.} some years ago, we analyze a variant of the same models where diffusion of
the agents is also taken into account. From the modeling point of view, this
means that individuals try to follow a given spontaneous velocity, but are
subject to a Brownian diffusion, and have to adapt to a density constraint
which introduces a pressure term affecting the movement. From the PDE point of
view, this corresponds to a modified Fokker-Planck equation, with an additional
gradient of a pressure (only living in the saturated zone $\{\rho=1\}$) in the
drift. The paper proves existence and some estimates, based on optimal
transport techniques.


  At low metallicity the B-type stars rotate faster than at higher metallicity,
typically in the SMC. As a consequence, it was expected a larger number of fast
rotators in the SMC than in the Galaxy, in particular more Be/Oe stars. With
the ESO-WFI in its slitless mode, the SMC open clusters were examined and an
occurence of Be stars 3 to 5 times larger than in the Galaxy was found. The
evolution of the angular rotational velocity seems to be the main key on the
understanding of the specific behaviour and of the stellar evolution of such
stars at different metallicities. With the results of this WFI study and using
observational clues on the SMC WR stars and massive stars, as well as the
theoretical indications of long gamma-ray burst progenitors, we identify the
low metallicity massive Be and Oe stars as potential LGRB progenitors.
Therefore the expected rates and numbers of LGRB are calculated and compared to
the observed ones, leading to a good probability that low metallicity Be/Oe
stars are actually LGRB progenitors.


  Gesture typing is a method of typing words on a touch-based keyboard by
creating a continuous trace passing through the relevant keys. This work is
aimed at developing a keyboard that supports gesture typing in Indic languages.
We begin by noting that when dealing with Indic languages, one needs to cater
to two different sets of users: (i) users who prefer to type in the native
Indic script (Devanagari, Bengali, etc.) and (ii) users who prefer to type in
the English script but want the output transliterated into the native script.
In both cases, we need a model that takes a trace as input and maps it to the
intended word. To enable the development of these models, we create and release
two datasets. First, we create a dataset containing keyboard traces for 193,658
words from 7 Indic languages. Second, we curate 104,412 English-Indic
transliteration pairs from Wikidata across these languages. Using these
datasets we build a model that performs path decoding, transliteration, and
transliteration correction. Unlike prior approaches, our proposed model does
not make co-character independence assumptions during decoding. The overall
accuracy of our model across the 7 languages varies from 70-95%.


  Motivated by recent applications of superdiffusive transport models to
shock-accelerated particle distributions in the heliosphere, we solve
analytically a one-dimensional fractional diffusion-advection equation for the
particle density. We derive an exact Fourier transform solution, simplify it in
a weak diffusion approximation, and compare the new solution with previously
available analytical results and with a semi-numerical solution based on a
Fourier series expansion. We apply the results to the problem of describing the
transport of energetic particles, accelerated at a traveling heliospheric
shock. Our analysis shows that significant errors may result from assuming an
infinite initial distance between the shock and the observer. We argue that the
shock travel time should be a parameter of a realistic superdiffusive transport
model.


  Terahertz (THz) communication signals are susceptible to severe degradation
because of the molecular interaction with the atmosphere in the form of
subsequent absorption and re-radiation. Recently, reconfigurable intelligent
surface (RIS) has emerged as a potential technology to assist in THz
communications by boosting signal power or providing virtual line-of-sight
paths. However, the re-radiated energy has either been modeled as a
non-line-of-sight scattering component or as additive Gaussian noise in the
literature. Since the precise characterization is still a work in progress,
this paper presents the first comparative investigation of the performance of
an RIS-aided THz system under these two extreme re-radiation models. In
particular, we first develop a novel parametric channel model that encompasses
both models of the re-radiation through a simple parameter change, and then
utilize that to design a robust block-coordinate descent (BCD) algorithmic
framework which maximizes a lower bound on channel capacity considering
imperfect channel state information. In this framework, the original problem is
split into two sub-problems: a) receive beamformer optimization, and b) RIS
phase-shift optimization. We also analytically demonstrate the limited
interference suppression capability of a passive RIS. Our numerical results
also demonstrate that slightly better throughput is achieved when the
re-radiation manifests as scattering.


  In this paper, we introduce mean dimension and rate distortion dimension for
$\mathbb{Z}^{k}$-actions dynamical system $(\mathcal{X},\mathbb{Z}^k,T)$.
Suppose $(\mathcal{X},\mathbb{Z}^k,T)$ has the marker property. Taking these
two variables, the metric $d$ on $\mathcal{X}$ and $\mathbb{Z}^{k}$-invariant
measure $\mu$, into consideration, a minimax-type variational principle for
mean dimension of $\mathbb{Z}^{k}$-actions is established. This result extends
the double variational principle obtained recently by Lindenstrauss and
Tsukamoto \cite{LT19} from $\mathbb{Z}$-actions dynamical systems to
$\mathbb{Z}^{k}$-actions dynamical systems.


  The rapid and convenient provision of the available computing resources is a
crucial requirement in modern cloud computing environments. However, if only
the execution time is taken into account when the resources are scheduled, it
could lead to imbalanced workloads as well as to significant under-utilisation
of the involved Virtual Machines (VMs). In the present work a novel task
scheduling scheme is introduced, which is based on the proper adaptation of a
modern and quite effective evolutionary optimization method, the Gravitational
Search Algorithm (GSA). The proposed scheme aims at optimizing the entire
scheduling procedure, in terms of both the tasks execution time and the system
(VMs) resource utilisation. Moreover, the fitness function was properly
selected considering both the above factors in an appropriately weighted
function in order to obtain better results for large inputs. Sufficient
simulation experiments show the efficiency of the proposed scheme, as well as
its excellence over related approaches of the bibliography, with similar
objectives.


  A hyperbolic flux-tube configuration containing a null point below the flux
rope is considered as a pre-eruptive state of coronal mass ejections that start
simultaneously with flares. We demonstrate that this configuration is unstable
and cannot exist for a long time in the solar corona. The inference follows
from general equilibrium conditions and from analyzing simple models of the
flux-rope equilibrium. A direct consequence of the stable flux-rope equilibrium
in the corona are separatrices in the horizontal-field distribution in the
chromosphere. They can be recognized as specific "herring-bone structures" in a
chromospheric fibril pattern.


  The SKA's design is driven by the needs of cutting-edge radio astronomy for
sensitivity and spacial resolution. However its design is also driven by the
desire to explore the transient radio Universe. In addition to pulsars, the SKA
will be able to carry out high time resolution observations of several classes
of known and predicted transient sources. Here we consider a selection of them,
and describe how observational demands affect the instrument's design.


  The purpose of this paper is to indicate that the recently proposed Momentum
fractional least mean squares (mFLMS) algorithm has some serious flaws in its
design and analysis. Our apprehensions are based on the evidence we found in
the derivation and analysis in the paper titled: \textquotedblleft
\textit{Momentum fractional LMS for power signal parameter
estimation}\textquotedblright. In addition to the theoretical bases our claims
are also verified through extensive simulation results. The experiments clearly
show that the new method does not have any advantage over the classical least
mean square (LMS) method.


  Coronal mass ejections (CMEs) may disturb the solar wind either by overtaking
it, or by expanding into it, or both. CMEs whose front moves faster in the
solar wind frame than the fast magnetosonic speed, drive shocks. Such shocks
are important contributors to space weather, by triggering substorms,
compressing the magnetosphere and accelerating particles. In general, near 1
AU, CMEs with speed greater than about 500 km s$^{-1}$ drive shocks, whereas
slower CMEs do not. However, CMEs as slow as 350 km s$^{-1}$ may sometimes,
although rarely, drive shocks. Here, we study these slow CMEs with shocks and
investigate the importance of CME expansion in contributing to their ability to
drive shocks and in enhancing shock strength. Our focus is on CMEs with average
speeds under 375 km s$^{-1}$. From Wind measurements from 1996 to 2016, we find
22 cases of such shock-driving slow CMEs, and, for about half of them (11 out
of the 22), the existence of the shock appears to be strongly related to CME
expansion. We also investigate the proportion of all CMEs with speeds under 500
km s$^{-1}$ with and without shocks in solar cycles 23 and 24, depending on
their speed. We find no systematic difference, as might have been expected on
the basis of the lower solar wind and Alfv\'en speeds reported for solar cycle
24 vs. 23. The slower expansion speed of CMEs in solar cycle 24 might be an
explanation for this lack of increased frequency of shocks, but further studies
are required.


  Recent advances in space-qualified atomic clocks (low-mass, low
power-consumption, frequency stability comparable to that of ground-based
clocks) can enable interplanetary spacecraft radio science experiments at
unprecedented Doppler sensitivities. The addition of an on-board digital
receiver would allow the up- and down-link Doppler frequencies to be measured
separately. Such separate, high-quality measurements allow optimal data
combinations that suppress the currently-leading noise sources: phase
scintillation noise from the Earth's atmosphere and Doppler noise caused by
mechanical vibrations of the ground antenna. Here we provide a general
expression for the optimal combination of ground and on-board Doppler data and
compute the sensitivity such a system would have to low-frequency gravitational
waves (GWs). Assuming a plasma scintillation noise calibration comparable to
that already demonstrated with the multi-link CASSINI radio system, the
space-clock/digital-receiver instrumentation enhancements would give GW strain
sensitivity of $2.0 \times 10^{-17}$ for randomly polarized, monochromatic GW
signals over a two-decade ($\sim0.0001-0.01$ Hz) region of the low-frequency
band. This is about an order of magnitude better than currently achieved with
traditional two-way coherent Doppler experiments. The utility of optimally
combining simultaneous up- and down-link observations is not limited to GW
searches. The Doppler tracking technique discussed here could be performed at
minimal incremental cost to also improve other radio science experiments (i.e.
tests of relativistic gravity, planetary and satellite gravity field
measurements, atmospheric and ring occultations) on future interplanetary
missions.


  Lines are interesting geometrical features commonly seen in indoor and urban
environments. There is missing a complete benchmark where one can evaluate
lines from a sequential stream of images in all its stages: Line detection,
Line Association and Pose error. To do so, we present a complete and exhaustive
benchmark for visual lines in a SLAM front-end, both for RGB and RGBD, by
providing a plethora of complementary metrics. We have also labelled data from
well-known SLAM datasets in order to have all in one poses and accurately
annotated lines. In particular, we have evaluated 17 line detection algorithms,
5 line associations methods and the resultant pose error for aligning a pair of
frames with several combinations of detector-association. We have packaged all
methods and evaluations metrics and made them publicly available on web-page
https://prime-slam.github.io/evolin/.


  A careful reduction of the three-dimensional gravity to the Liouville
description is performed, where all gauge fixing and on-shell conditions come
from the definition of asymptotic de Sitter spaces. The roles of both past and
future infinities are discussed and the conditions space-time evolution imposes
on both Liouville fields are explicited. Space-times which correspond to
non-equivalent profiles of the Liouville field at past and future infinities
are shown to exist. The qualitative implications of this for any tentative dual
theory are presented.


  We establish new approximation results, in the sense of Lusin, of Sobolev
functions by Lipschitz ones, in some classes of non-doubling metric measure
structures. Our proof technique relies upon estimates for heat semigroups and
applies to Gaussian and $RCD(K, \infty)$ spaces. As a consequence, we obtain
quantitative stability for regular Lagrangian flows in Gaussian settings.


  The universal Witham hierarchy is considered from the point of view of
topological field theories. The $\tau$-function for this hierarchy is defined.
It is proved that the algebraic orbits of Whitham hierarchy can be identified
with various topological matter models coupled with topological gravity.


  We discuss a new class of quantum phase transitions -- Deconfined Mott
Transition (DMT) -- that describe a continuous transition between a Fermi
liquid metal with a generic electronic Fermi surface and an electrical
insulator without Fermi surfaces of emergent neutral excitations. We construct
a unified $U(2)$ gauge theory to describe a variety of metallic and insulating
phases, which include Fermi liquids, fractionalized Fermi liquids (FL*),
conventional insulators and quantum spin liquids, as well as the quantum phase
transitions between them. Using the DMT as a basic building block, we propose a
distinct quantum phase transition -- Deconfined Metal-Metal Transition
(DM$^2$T) -- that describes a continuous transition between two metallic
phases, accompanied by a jump in the size of their electronic Fermi surfaces
(also dubbed a 'Fermi transition'). We study these new classes of deconfined
metallic quantum critical points using a renormalization group framework at the
leading nontrivial order in a controlled expansion, and comment on the various
interesting scenarios that can emerge going beyond this leading order
calculation. We also study a $U(1)\times U(1)$ gauge theory that shares a
number of similarities with the $U(2)$ gauge theory and sheds important light
on many phenomena related to DMT, DM$^2$T and quantum spin liquids.


  These notes concern aspects of various graphs whose vertex set is a group $G$
and whose edges reflect group structure in some way (so that they are invariant
under the action of the automorphism group of $G$). The graphs I will discuss
are the power graph, enhanced power graph, deep commuting graph, commuting
graph, and non-generating graph, though I give a briefer discussion of the
nilpotence and solvability graphs, and make some remarks on more general
graphs. Aspects to be discussed include induced subgraphs, forbidden subgraphs,
connectedness, and automorphism groups. We can also ask about the graphs formed
by the edges in one graph but not in an earlier graph in the hierarchy. I have
included some results on intersection graphs of subgroups of various types,
which are often in a dual relation to one of the other graphs considered.
Another actor is the Gruenberg--Kegel graph, or prime graph, of a group: this
very small graph influences various graphs defined on the group. I say little
about Cayley graphs, since (except in special cases) these are not invariant
under the automorphism group of $G$.
  The graphs all have the property that they contain \emph{twins}, pairs of
vertices with the same neighbours (save possibly one another). Being equal or
twins is an equivalence relation, and the automorphism group of the graph has a
normal subgroup inducing the symmetric group on each twin class. For some
purposes, we can merge twin vertices and get a smaller graph. Continuing until
no further twins occur, the result is independent of the reduction, and is the
$1$-vertex graph if and only if the original graph is a \emph{cograph}. So I
devote a section to cographs and twin reduction, and another to consequences
for automorphism groups.
  There are briefer discussions of related matters.


  The most scalable approaches to certifying neural network robustness depend
on computing sound linear lower and upper bounds for the network's activation
functions. Current approaches are limited in that the linear bounds must be
handcrafted by an expert, and can be sub-optimal, especially when the network's
architecture composes operations using, for example, multiplication such as in
LSTMs and the recently popular Swish activation. The dependence on an expert
prevents the application of robustness certification to developments in the
state-of-the-art of activation functions, and furthermore the lack of tightness
guarantees may give a false sense of insecurity about a particular model. To
the best of our knowledge, we are the first to consider the problem of
automatically computing tight linear bounds for arbitrary n-dimensional
activation functions. We propose LinSyn, the first approach that achieves tight
bounds for any arbitrary activation function, while only leveraging the
mathematical definition of the activation function itself. Our approach
leverages an efficient heuristic approach to synthesize bounds that are tight
and usually sound, and then verifies the soundness (and adjusts the bounds if
necessary) using the highly optimized branch-and-bound SMT solver, dReal. Even
though our approach depends on an SMT solver, we show that the runtime is
reasonable in practice, and, compared with state of the art, our approach often
achieves 2-5X tighter final output bounds and more than quadruple certified
robustness.


This talk is a brief overview of the present status of neutrino mass physics.


  Quantitative computational models play an increasingly important role in
modern biology. Such models typically involve many free parameters, and
assigning their values is often a substantial obstacle to model development.
Directly measuring \emph{in vivo} biochemical parameters is difficult, and
collectively fitting them to other data often yields large parameter
uncertainties. Nevertheless, in earlier work we showed in a
growth-factor-signaling model that collective fitting could yield
well-constrained predictions, even when it left individual parameters very
poorly constrained. We also showed that the model had a `sloppy' spectrum of
parameter sensitivities, with eigenvalues roughly evenly distributed over many
decades. Here we use a collection of models from the literature to test whether
such sloppy spectra are common in systems biology. Strikingly, we find that
every model we examine has a sloppy spectrum of sensitivities. We also test
several consequences of this sloppiness for building predictive models. In
particular, sloppiness suggests that collective fits to even large amounts of
ideal time-series data will often leave many parameters poorly constrained.
Tests over our model collection are consistent with this suggestion. This
difficulty with collective fits may seem to argue for direct parameter
measurements, but sloppiness also implies that such measurements must be
formidably precise and complete to usefully constrain many model predictions.
We confirm this implication in our signaling model. Our results suggest that
sloppy sensitivity spectra are universal in systems biology models. The
prevalence of sloppiness highlights the power of collective fits and suggests
that modelers should focus on predictions rather than on parameters.


  Asymptotic expansion of the distribution of a perturbation $Z_n$ of a
Skorohod integral jointly with a reference variable $X_n$ is derived. We
introduce a second-order interpolation formula in frequency domain to expand a
characteristic functional and combine it with the scheme developed in the
martingale expansion. The second-order interpolation and Fourier inversion give
asymptotic expansion of the expectation $E[f(Z_n,X_n)]$ for differentiable
functions $f$ and also measurable functions $f$. In the latter case, the
interpolation method connects the two non-degeneracies of variables for finite
$n$ and $\infty$. Random symbols are used for expressing the asymptotic
expansion formula. Quasi tangent, quasi torsion and modified quasi torsion are
introduced in this paper. We identify these random symbols for a certain
quadratic form of a fractional Brownian motion and for a quadratic from of a
fractional Brownian motion with random weights. For a quadratic form of a
Brownian motion with random weights, we observe that our formula reproduces the
formula originally obtained by the martingale expansion.


  To control structural responses under various actions, the growing use of
supplementary damping systems in modern civil engineering structures
necessitates inspecting and evaluating their operational performance
postinstallation. However, due to the dispersed placement and complex
nonlinearities of these devices, difficulties arise in determining minimal
sensor configuration. This is inherently connected to a pivotal challenge:
establishing a reliable input-output mapping, which comprises both the
mathematical model and sensor arrangements. Prior work indicates this can be
achieved through theoretical observability analysis or Lie symmetries analysis,
both of which provide different perspectives on the existence of a way to
access the solutions of a system identification problem uniquely (at least
locally). The present study introduces a unified framework, enhanced by
algorithm realization as an application guide, for analyzing the observability
and Lie symmetries of a given input-output mapping. We demonstrate its
implementation via examples of a building structure with various damping
systems under different conditions such as seismic loads, wind loads, and
operational vibrations. Finally, we present a case study for an isolation
building with an inerter damper and minimal sensor arrangement under seismic
action. The results demonstrate that the unscented Kalman filter, a system
identification method, can precisely estimate structural responses and assess
damping device performance once a reliable input-output mapping is established.


  Bielliptic and quasi-bielliptic surfaces form one of the four classes of
minimal smooth projective surfaces of Kodaira dimension $0$. In this article,
we determine the automorphism schemes of these surfaces over algebraically
closed fields of arbitrary characteristic, generalizing work of Bennett and
Miranda over the complex numbers; we also find some cases that are missing from
the classification of automorphism groups of bielliptic surfaces in
characteristic $0$.


  In this paper, we present a new generalized Hughes model designed to
intelligently depict pedestrian congestion dynamics, allowing pedestrian groups
to either navigate through or circumvent high-density regions. First, we
describe the microscopic settings of the model. The corresponding optimization
problems are deterministic and can be formulated by a closed-loop model
predictive control strategy. This microscopic setup leads in the mean-field
limit to the Generalized Hughes model which is a class of non-separable mean
field games system, i.e., Fokker-Planck equation and viscous Hamilton-Jacobi
Bellman equation are coupled in a forward-backward structure. We give an
overview on the mean field games in connection to our intelligent fluid model.
Therefore, we show the existence of weak solutions to the Generalized Hughes
model and analyze the vanishing viscosity limit of weak solutions. Finally, we
illustrate the generalized Hughes model with various numerical experiments.


  In-vivo optical microscopy is advancing into routine clinical practice for
non-invasively guiding diagnosis and treatment of cancer and other diseases,
and thus beginning to reduce the need for traditional biopsy. However, reading
and analysis of the optical microscopic images are generally still qualitative,
relying mainly on visual examination. Here we present an automated semantic
segmentation method called "Multiscale Encoder-Decoder Network (MED-Net)" that
provides pixel-wise labeling into classes of patterns in a quantitative manner.
The novelty in our approach is the modeling of textural patterns at multiple
scales. This mimics the procedure for examining pathology images, which
routinely starts with low magnification (low resolution, large field of view)
followed by closer inspection of suspicious areas with higher magnification
(higher resolution, smaller fields of view). We trained and tested our model on
non-overlapping partitions of 117 reflectance confocal microscopy (RCM) mosaics
of melanocytic lesions, an extensive dataset for this application, collected at
four clinics in the US, and two in Italy. With patient-wise cross-validation,
we achieved pixel-wise mean sensitivity and specificity of $70\pm11\%$ and
$95\pm2\%$, respectively, with $0.71\pm0.09$ Dice coefficient over six classes.
In the scenario, we partitioned the data clinic-wise and tested the
generalizability of the model over multiple clinics. In this setting, we
achieved pixel-wise mean sensitivity and specificity of $74\%$ and $95\%$,
respectively, with $0.75$ Dice coefficient. We compared MED-Net against the
state-of-the-art semantic segmentation models and achieved better quantitative
segmentation performance. Our results also suggest that, due to its nested
multiscale architecture, the MED-Net model annotated RCM mosaics more
coherently, avoiding unrealistic-fragmented annotations.


  Let R be a commutative noetherian ring. Denote by mod R the category of
finitely generated R-modules. In the present paper, we first provide various
sufficient (and necessary) conditions for a full subcategory of mod R to be a
Serre subcategory, which include several refinements of theorems of Stanley and
Wang and of Takahashi with simpler proofs. Next we consider when an IKE-closed
subcategory of mod R is a torsionfree class. We investigate certain modules out
of which all modules of finite length can be built by taking direct summands
and extensions, and then we apply it to show that the IKE-closed subcategories
of mod R are torsionfree classes in the case where R is a certain numerical
semigroup ring.


  Star scientists are highly influential researchers who have made significant
contributions to their field, gained widespread recognition, and often
attracted substantial research funding. They are critical for the advancement
of science and innovation, and they have a significant influence on the
transfer of knowledge and technology to industry. Identifying potential star
scientists before their performance becomes outstanding is important for
recruitment, collaboration, networking, or research funding decisions. Using
machine learning techniques, this study proposes a model to predict star
scientists in the field of artificial intelligence while highlighting features
related to their success. Our results confirm that rising stars follow
different patterns compared to their non-rising stars counterparts in almost
all the early-career features. We also found that certain features such as
gender and ethnic diversity play important roles in scientific collaboration
and that they can significantly impact an author's career development and
success. The most important features in predicting star scientists in the field
of artificial intelligence were the number of articles, group discipline
diversity, and weighted degree centrality. The proposed approach offers
valuable insights for researchers, practitioners, and funding agencies
interested in identifying and supporting talented researchers.


  In the presence of symmetry, entanglement measures of quantum many-body
states can be decomposed into contributions arising from distinct symmetry
sectors. Here we investigate the decomposability of negativity, a measure of
entanglement between two parts of a generally open system in a mixed state.
While the entanglement entropy of a subsystem within a closed system can be
resolved according to its total preserved charge, we find that negativity of
two subsystems may be decomposed into contributions associated with their
charge imbalance. We show that this charge-imbalance decomposition of the
negativity may be measured by employing existing techniques based on creation
and manipulation of many-body twin or triple states in cold atomic setups.
Next, using a geometrical construction in terms of an Aharonov-Bohm-like flux
inserted in a Riemann geometry, we compute this decomposed negativity in
critical one-dimensional systems described by conformal field theory. We show
that it shares the same distribution as the charge-imbalance between the two
subsystems. We numerically confirm our field theory results via an exact
calculations for non-interacting particles based on a double-gaussian
representation of the partially transposed density matrix.


  Westerlund 1 is the most important starburst cluster in the Galaxy due to its
massive star content. We have performed BVIc and JKs photometry to investigate
the initial mass function (IMF). By comparing the observed color with the
spectral type - intrinsic color relation, we obtain the mean interstellar
reddening of <E(B-V)>=4.19+/-0.23 and <E(J-Ks)>=1.70+/-0.21. Due to the heavy
extinction toward the cluster, the zero-age main sequence fitting method based
on optical photometry proved to be inappropriate for the distance
determination, while the near-infrared photometry gave a reliable distance to
the cluster -- 3.8 kpc from the empirical relation. Using the recent
theoretical stellar evolution models with rotation, the age of the cluster is
estimated to be 5.0+/-1.0 Myr. We derived the IMF in the massive part and
obtained a fairly shallow slope of {\Gamma} = -0.8 +/- 0.1. The integration of
the IMF gave a total mass for the cluster in excess of 5.0 x 10^4 solar mass.
The IMF shows a clear radial variation indicating the presence of mass
segregation. We also discuss the possible star formation history of Westerlund
1 from the presence of red supergiants and relatively low-luminosity yellow
hypergiants.


  Candidate retrieval is a fundamental issue in recommendation system. Given
user's recommendation request, relevant candidates need to be retrieved in
realtime for subsequent ranking operations. Considering that the retrieval
operation is conducted over considerable items, it has to be both precise and
scalable so that high-quality candidates can be acquired within tolerable
latency. Unfortunately, conventional methods would trade off precision for high
running efficiency, which leads to inferior retrieval quality. In contrast,
those deep learning-based approaches can be highly accurate in identifying
relevant items; yet, they are unsuitable for candidate retrieval due to their
inherent limitation on scalability.
  In this work, a novel framework is proposed to address the above challenges.
The underlying intuition is to rely on a well-trained ranking model for the
supervision of an efficient retrieval model, such that it will unify the
scalability and precision as a whole. We have implemented our conceptual
framework and made comprehensive evaluation for it, where promising results are
achieved against representative baselines.
  Our work is undergoing a anonymous review, and it will soon be released after
the notification. If you're also interested in this problem, please feel free
to contact us.


  We consider sequences of large sparse random graphs whose degree distribution
approaches a limit with finite mean. This model includes both the random
regular graphs and the Erd\"os-Renyi graphs of constant average degree. We
prove that the maximum bisection ratio of such a graph sequence converges
almost surely to a deterministic limit. We extend this result to so-called
2-spin spin glasses in the paramagnetic to ferromagnetic regime. Our work
generalizes the graph interpolation method to some non-additive graph
parameters.


  In current work, we investigate the density and temperature dependence of
polarization parameter; using the relativistic formalism for the
electron-electron interaction within the Fermi liquid model. we calculate the
spin dependent scattering matrix elements in relativistic region, and then
obtain the non-relativistic behavior to study the magnetic properties of an
electron gas. By varying the polarization parameter, we minimized the free
energy and then obtain the polarization of the system as a function of density
and temperature. At zero temperature the exact results for polarization and
magnetic susceptibility have obtained. It has been shown that for a given
temperature (density) there is critical density (temperature) that the
ferromagnetic phase can appears in electron gas. Our results show that at
nonzero temperatures and in very low and very high densities the ferromagnetism
phase can not be exist.


  The turbulent boundary layer over a flat plate is computed by direct
numerical simulation (DNS) of the incompressible Navier-Stokes equations as a
test bed for a synthetic turbulence generator (STG) inflow boundary condition.
The inlet momentum thickness Reynolds number is approximately 1,000. The study
provides validation of the ability of the STG to develop accurate turbulence in
5 to 7 boundary layer thicknesses downstream of the boundary condition. Also
tested was the effect of changes in the stabilization scheme on the development
of the boundary layer. Moreover, the grid resolution required for both the
development region and the downstream flow is investigated when using a
stabilized finite element method.


  Spectrum of the Laplacian on spherical domains is analyzed from the point of
view of the heat equation on the cone. The series solution to the heat equation
on the cone is known to lead to a study of the Laplacian eigenvalue problem on
domains on the sphere in higher dimensions. It is found that the solution leads
naturally to a spectral function, a `generating function' for the eigenvalues
and multiplicities of the Laplacian, expressible in closed form for certain
domains on the sphere. Analytical properties of the spectral function suggest a
simple scaling procedure for estimating the eigenvalues. Comparison of the
first eigenvalue estimate with the available theoretical and numerical results
for some specific domains shows remarkable agreement.