  A nationwide interoperable public safety broadband network is being planned
by the United States government. The network will be based on long term
evolution (LTE) standards and use recently designated spectrum in the 700 MHz
band. The public safety network has different objectives and traffic patterns
than commercial wireless networks. In particular, the public safety network
puts more emphasis on coverage, reliability and latency in the worst case
scenario. Moreover, the routine public safety traffic is relatively light,
whereas when a major incident occurs, the traffic demand at the incident scene
can be significantly heavier than that in a commercial network. Hence it is
prohibitively costly to build the public safety network using conventional
cellular network architecture consisting of an infrastructure of stationary
base transceiver stations. A novel architecture is proposed in this paper for
the public safety broadband network. The architecture deploys stationary base
stations sparsely to serve light routine traffic and dispatches mobile base
stations to incident scenes along with public safety personnel to support heavy
traffic. The analysis shows that the proposed architecture can potentially
offer more than 75% reduction in terms of the total number of base stations
needed.


  More than a dozen binary systems are now established as sources of variable,
high energy (HE, 0.1-100 GeV) gamma rays. Five are also established sources of
very high energy (VHE, >100 GeV) gamma rays. The mechanisms behind gamma-ray
emission in binaries are very diverse. My current understanding is that they
divide up into four types of systems: gamma-ray binaries, powered by pulsar
rotation; microquasars, powered by accretion onto a black hole or neutron star;
novae, powered by thermonuclear runaway on a white dwarf; colliding wind
binaries, powered by stellar winds from massive stars. Some of these types had
long been suspected to emit gamma rays (microquasars), others have taken the
community by surprise (novae). My purpose here is to provide a brief review of
the current status of gamma-ray emission from binaries, in the context of
related objects where similar mechanisms are at work (pulsar wind nebulae,
active galactic nuclei, supernova remnants).


  We extend the use of holography to investigate the scrambling properties of
various physical systems. Specifically, we consider: (i) non-conformal
backgrounds of black $Dp$ branes, (ii) asymptotically Lifshitz black holes, and
(iii) black $AdS$ solutions of Gauss-Bonnet gravity. We use the disruption of
the entanglement entropy as a probe of the chaotic features of such systems.
Our analysis shows that these theories share the same type of behavior as
conformal theories as they undergo chaos; however, in the case of Gauss-Bonnet
gravity, we find a stark difference in the evolution of the mutual information
for negative Gauss-Bonnet coupling. This may signal an inconsistency of the
latter.


  We study the asymptotic behavior of the Max $\kappa$-cut on a family of
sparse, inhomogeneous random graphs. In the large degree limit, the leading
term is a variational problem, involving the ground state of a constrained
inhomogeneous Potts spin glass. We derive a Parisi type formula for the free
energy of this model, with possible constraints on the proportions, and derive
the limiting ground state energy by a suitable zero temperature limit.


  The Arecibo L-Band Feed Array Zone of Avoidance (ALFA ZOA) Survey has
discovered a nearby galaxy, ALFA ZOA J1952+1428, at a heliocentric velocity of
+279 km s-1. The galaxy was discovered at low Galactic latitude by 21-cm
emission from neutral hydrogen (Hi). We have obtained follow-up observations
with the EVLA and the 0.9-m SARA optical telescope. The Hi distribution
overlaps an uncataloged, potential optical counterpart. The Hi linear size is
1.4 kpc at our adopted distance of D = 7 Mpc, but the distance estimate is
uncertain as Hubble's law is unreliable at low recessional velocities. The
optical counterpart has mB = 16.9 mag and B - R = 0.1 mag. These
characteristics, including MHI = 107.0 M\odot and LB = 107.5 L\odot, if at 7
Mpc, indicate that this galaxy is a blue compact dwarf, but this remains
uncertain until further follow-up observations are complete. Optical follow-up
observations are ongoing and near infrared follow-up observations have been
scheduled.


  We report on theoretical analysis of large amplitude current dynamics due to
Bragg reflections in carbon nanotubes exposed to an external electric field.
Using the kinetic equation with constant relaxation time, an analytical
expression for the current density is obtained. Our results suggest that Bloch
gain exists up to frequencies on the order of the Bloch frequency. We noted
that due to the high density of states of conduction electrons in metallic
carbon nanotubes and the specific dispersion law inherent in hexagonal
crystalline structure result in a uniquely high frequency gain than the
corresponding values for semiconducting ones. We suggest that this phenomenon
can be used for domainless multiplication of the frequency of an
electromagnetic signal at room temperature


  We provide a novel construction of quantized universal enveloping
$*$-algebras of real semisimple Lie algebras, based on Letzter's theory of
quantum symmetric pairs. We show that these structures can be `integrated',
leading to a quantization of the group C$^*$-algebra of an arbitrary semisimple
algebraic real Lie group.


  We consider a trapped atomic ensemble of interacting bosons in the presence
of a single trapped ion in a quasi one dimensional geometry. Our study is
carried out by means of the newly developed multilayer-multiconfiguration
time-dependent Hartree method for bosons, a numerical exact approach to
simulate quantum many-body dynamics. In particular, we are interested in the
scenario by which the ion is so strongly trapped that its motion can be
effectively neglected. This enables us to focus on the atomic ensemble only.
With the development of a model potential for the atom-ion interaction, we are
able to numerically obtain the exact many-body ground state of the atomic
ensemble in the presence of an ion. We analyse the influence of the atom number
and the atom-atom interaction on the ground state properties. Interestingly,
for weakly interacting atoms, we find that the ion impedes the transition from
the ideal gas behaviour to the Thomas-Fermi limit. Furthermore, we show that
this effect can be exploited to infer the presence of the ion both in the
momentum distribution of the atomic cloud and by observing the interference
fringes occurring during an expansion of the quantum gas. In the strong
interacting regime, the ion modifies the fragmentation process in dependence of
the atom number parity which allows a clear identification of the latter in
expansion experiments. Hence, we propose in both regimes experimentally viable
strategies to assess the impact of the ion on the many-body state of the atomic
gas. This study serves as the first building block for systematically
investigate many-body physics of such hybrid system.


  It is known that for a topologically ordered state the area law for the
entanglement entropy shows a negative universal additive constant contribution,
$-\gamma$, called the topological entanglement entropy. We theoretically study
the entanglement entropy of the two-dimensional Shastry-Sutherland quantum
antiferromagnet using exact diagonalization on clusters of 16 and 24 spins. By
utilizing the Kitaev-Preskill construction [A. Kitaev and J. Preskill, Phys.
Rev. Lett. {\bf 96}, 110404 (2006)] we extract a finite topological term,
$-\gamma$, in the region of bond-strength parameter space corresponding to high
geometrical frustration. Thus, we provide strong evidence for the existence of
an exotic topologically ordered state and shed light on the nature of this
model's strongly frustrated, and long controversial, intermediate phase.


  We investigate the focal plane wavefront sensing technique, known as Phase
Diversity, at the scientific focal plane of a segmented mirror telescope with
an adaptive optics (AO) system. We specifically consider an optical system
imaging a point source in the context of (i) an artificial source within the
telescope structure and (ii) from AO-corrected images of a bright star. From
our simulations, we reliably disentangle segmented telescope phasing errors
from non-common path aberrations (NCPA) for both a theoretical source and
on-sky, AO-corrected images where we have simulated the Keck/NIRC2 system. This
quantification from on-sky images is appealing, as it's sensitive to the
cumulative wavefront perturbations of the entire optical train; disentanglement
of phasing errors and NCPA is therefore critical, where any potential
correction to the primary mirror from an estimate must contain minimal NCPA
contributions. Our estimates require a one-minute sequence of short-exposure,
AO-corrected images; by exploiting a slight modification to the AO-loop, we
find that 75 defocused images produces reliable estimates. We demonstrate a
correction from our estimates to the primary and deformable mirror results in a
wavefront error reduction of up to 67% and 65% for phasing errors and NCPA,
respectively. If the segment phasing errors on the Keck primary are on the
order of ~130 nm RMS, we show we can improve the H-band Strehl ratio by up to
10% by using our algorithm. We conclude our technique works well to estimate
NCPA alone from on-sky images, suggesting it is a promising method for any
AO-system.


  In 1940, Luis Santal\'o proved a Helly-type theorem for line transversals to
boxes in R^d. An analysis of his proof reveals a convexity structure for
ascending lines in R^d that is isomorphic to the ordinary notion of convexity
in a convex subset of R^{2d-2}. This isomorphism is through a Cremona
transformation on the Grassmannian of lines in P^d, which enables a precise
description of the convex hull and affine span of up to d ascending lines: the
lines in such an affine span turn out to be the rulings of certain classical
determinantal varieties. Finally, we relate Cremona convexity to a new
convexity structure that we call frame convexity, which extends to
arbitrary-dimensional flats.


  In this work, we consider a rotating charged black hole surrounded by perfect
fluid dark matter. We consider the system to be immersed in non-magnetised,
pressureless plasma. First, we evaluate the null geodesics in order to study
the co-rotating and counter rotating photon orbits. Further, we analyse the
null geodesics to calculate the celestial coordinates ($\alpha, \beta$). The
celestial coordinates are used to determine the black hole shadow radius
($R_s$). Thereafter, we observe and analyse the effects of black hole
spacetime, perfect fluid dark matter and plasma parameters ($a$, $Q$, $\chi$,
$k$) on the black hole shadow in detail. Finally, we study the effect of plasma
distribution on the effective potential ($V_{eff}$) of the black hole spacetime
as encountered by the photons. We also present bounds on the plasma parameter
from the observational data from $M87^{*}$ central supermassive black hole.


  We prove the existence of the invariant subspaces of some operators in a real
Banach space. For example, linear isometries have invariant subspaces


  This work derives explicit series reversions for the solution of Calder\'on's
problem. The governing elliptic partial differential equation is
$\nabla\cdot(A\nabla u)=0$ in a bounded Lipschitz domain and with a
matrix-valued coefficient. The corresponding forward map sends $A$ to a
projected version of a local Neumann-to-Dirichlet operator, allowing for the
use of partial boundary data and finitely many measurements. It is first shown
that the forward map is analytic, and subsequently reversions of its Taylor
series up to specified orders lead to a family of numerical methods for solving
the inverse problem with increasing accuracy. The convergence of these methods
is shown under conditions that ensure the invertibility of the Fr\'echet
derivative of the forward map. The introduced numerical methods are of the same
computational complexity as solving the linearised inverse problem. The
analogous results are also presented for the smoothened complete electrode
model.


  Let $\mathscr{L}$ be a recursive language. Let $S(\mathscr{L})$ be the set of
$\mathscr{L}$-structures with domain $\omega$. Let $\Phi : {}^\omega 2
\rightarrow S(\mathscr{L})$ be a $\Delta_1^1$ function with the property that
for all $x,y \in {}^\omega 2$, $\omega_1^x = \omega_1^y$ if and only if
$\Phi(x) \approx_{\mathscr{L}} \Phi(y)$. Then there is some $x \in {}^\omega 2$
so that $\mathrm{SR}(\Phi(x)) = \omega_1^x + 1$.


  In this work we propose simple algorithms for signal detection in a
single-carrier transmission corrupted by a strong phase noise. The proposed
phase tracking algorithms are formulated within the framework of a parametric
message passing (MP) which reduces the complexity of the Bayesian inference by
using distributions from a predefined family; here, of Tikhonov distributions.
This stays in line with previous works mainly inspired by the well-known
Colavolpe-Barbieri-Caire (CBC) algorithm which gained popularity due to its
simplicity and possibility for decoder-aided operation. In our work we mainly
focus on practically relevant case of one-shot phase tracking that does not
require decoder's feedback. Applying the principles of the expectation
propagation (EP), we notably improve the performance of the phase tracking
before the decoder's feedback can be even considered. The EP algorithms can be
also integrated in the decoding loop in the spirit of joint decoding and phase
tracking.


  Let $H$ be a fixed undirected graph on $k$ vertices. The $H$-hitting set
problem asks for deleting a minimum number of vertices from a given graph $G$
in such a way that the resulting graph has no copies of $H$ as a subgraph. This
problem is a special case of the hypergraph vertex cover problem on $k$-uniform
hypergraphs, and thus admits an efficient $k$-factor approximation algorithm.
The purpose of this article is to investigate the question that for which
graphs $H$ this trivial approximation factor $k$ can be improved.


  Large pre-trained code generation models, such as OpenAI Codex, can generate
syntax- and function-correct code, making the coding of programmers more
productive and our pursuit of artificial general intelligence closer. In this
paper, we introduce CodeGeeX, a multilingual model with 13 billion parameters
for code generation. CodeGeeX is pre-trained on 850 billion tokens of 23
programming languages as of June 2022. Our extensive experiments suggest that
CodeGeeX outperforms multilingual code models of similar scale for both the
tasks of code generation and translation on HumanEval-X. Building upon
HumanEval (Python only), we develop the HumanEval-X benchmark for evaluating
multilingual models by hand-writing the solutions in C++, Java, JavaScript, and
Go. In addition, we build CodeGeeX-based extensions on Visual Studio Code,
JetBrains, and Cloud Studio, generating 4.7 billion tokens for tens of
thousands of active users per week. Our user study demonstrates that CodeGeeX
can help to increase coding efficiency for 83.4% of its users. Finally,
CodeGeeX is publicly accessible and in Sep. 2022, we open-sourced its code,
model weights (the version of 850B tokens), API, extensions, and HumanEval-X at
https://github.com/THUDM/CodeGeeX.


  Commutativity of the diagram of the maps connecting three one--particle
state, implied by the Equivalence Postulate (EP), gives a cocycle condition
which unequivocally leads to the quantum Hamilton--Jacobi equation. Energy
quantization is a direct consequences of the local homeomorphicity of the
trivializing map. We review the EP and show that the quantum potential for two
free particles, which depends on constants which may have a geometrical
interpretation, plays the role of interaction term that admits solutions which
do not vanish in the classical limit.


  We continue our study of finite BRST-antiBRST transformations for general
gauge theories in Lagrangian formalism, initiated in [arXiv:1405.0790[hep-th]
and arXiv:1406.0179[hep-th]], with a doublet $\lambda_{a}$, $a=1,2$, of
anticommuting Grassmann parameters and prove the correctness of the explicit
Jacobian in the partition function announced in [arXiv:1406.0179[hep-th]],
which corresponds to a change of variables with functionally-dependent
parameters $\lambda_{a}=U_{a}\Lambda$ induced by a finite Bosonic functional
$\Lambda(\phi,\pi,\lambda)$ and by the anticommuting generators $U_{a}$ of
BRST-antiBRST transformations in the space of fields $\phi$ and auxiliary
variables $\pi^{a},\lambda$. We obtain a Ward identity depending on the
field-dependent parameters $\lambda_{a}$ and study the problem of gauge
dependence, including the case of Yang--Mills theories. We examine a
formulation with BRST-antiBRST symmetry breaking terms, additively introduced
to the quantum action constructed by the Sp(2)-covariant Lagrangian rules,
obtain the Ward identity and investigate the gauge-independence of the
corresponding generating functional of Green's functions. A formulation with
BRST symmetry breaking terms is developed. It is argued that the gauge
independence of the above generating functionals is fulfilled in the BRST and
BRST-antiBRST settings. These concepts are applied to the average effective
action in Yang--Mills theories within the functional renormalization group
approach.


  Traditional wisdom in generative modeling literature is that spurious samples
that a model can generate are errors and they should be avoided. Recent
research, however, has shown interest in studying or even exploiting such
samples instead of eliminating them. In this paper, we ask the question whether
such samples can be eliminated all together without sacrificing coverage of the
generating distribution. For the class of models we consider, we experimentally
demonstrate that this is not possible without losing the ability to model some
of the test samples. While our results need to be confirmed on a broader set of
model families, these initial findings provide partial evidence that spurious
samples share structural properties with the learned dataset, which, in turn,
suggests they are not simply errors but a feature of deep generative nets.


  This paper is concerned with power concavity properties of the solution to
the parabolic boundary value problem \begin{equation} \tag{$P$}
\left\{\begin{array}{ll} \partial_t u=\Delta u +f(x,t,u,\nabla u) &
\mbox{in}\quad\Omega\times(0,\infty),\vspace{3pt}\\ u(x,t)=0 &
\mbox{on}\quad\partial \Omega\times(0,\infty),\vspace{3pt}\\ u(x,0)=0 &
\mbox{in}\quad\Omega, \end{array} \right. \end{equation} where $\Omega$ is a
bounded convex domain in ${\bf R}^n$ and $f$ is a nonnegative continuous
function in $\Omega\times(0,\infty)\times{\bf R}\times{\bf R}^n$. We give a
sufficient condition for the solution of $(P)$ to be parabolically power
concave in $\bar{\Omega}\times[0,\infty)$.


  We study a family of functions defined in a very simple way as sums of powers
of binary Hermitian forms with coefficients in the ring of integers of an
Euclidean imaginary quadratic field $K$ with discriminant $d_K$. Using these
functions we construct a nontrivial cocycle belonging to the space of parabolic
cocycles on Euclidean Bianchi groups. We also show that the average value of
these functions is related to the special values of $L(\chi_{d_K},s)$. Using
the properties of these functions we give new and computationally efficient
formulas for computing some special values of $L(\chi_{d_K},s)$.


  We have developed a compact cryogenic system with a pulse tube refrigerator
and a coaxial heat exchanger. This liquefaction-purification system not only
saves the cooling power used to reach high gaseous recirculation rate, but also
reduces the impurity level with high speed. The heat exchanger operates with an
efficiency of 99%, which indicates the possibility for fast xenon gas
recirculation in a highpressurized large-scale xenon storage with much less
thermal losses.


  We classify the growth of a $k$-regular sequence based on information from
its $k$-kernel. In order to provide such a classification, we introduce the
notion of a growth exponent for $k$-regular sequences and show that this
exponent is equal to the joint spectral radius of any set of a special class of
matrices determined by the $k$-kernel.


  Microscopes and various forms of interferometers have been used for decades
in optical metrology of objects that are typically larger than the wavelength
of light {\lambda}. However, metrology of subwavelength objects was deemed
impossible due to the diffraction limit. We report that measurement of the
physical size of sub-wavelength objects with accuracy exceeding {\lambda}/800
by analyzing the diffraction pattern of coherent light scattered by the objects
with deep learning enabled analysis. With a 633nm laser, we show that the width
of sub-wavelength slits in opaque screen can be measured with accuracy of
0.77nm, challenging the accuracy of electron beam and ion beam lithographies.
The technique is suitable for high-rate non-contact measurements of nanometric
sizes in smart manufacturing applications with integrated metrology and
processing tools.


  We describe the non-relativistic time evolution of an ultra-cold degenerate
quantum gas (bosons/fermions) falling in Earth's gravity during long times (10
sec) and over large distances (100 m). This models a drop tower experiment that
is currently performed by the QUANTUS collaboration at ZARM (Bremen, Germany).
Starting from the classical mechanics of the drop capsule and a single particle
trapped within, we develop the quantum field theoretical description for this
experimental situation in an inertial frame, the corotating frame of the Earth,
as well as the comoving frame of the drop capsule. Suitable transformations
eliminate non-inertial forces, provided all external potentials (trap, gravity)
can be approximated with a second order Taylor expansion around the
instantaneous trap center. This is an excellent assumption and the harmonic
potential theorem applies. As an application, we study the quantum dynamics of
a cigar-shaped Bose-Einstein condensate in the Gross-Pitaevskii mean-field
approximation. Due to the instantaneous transformation to the rest-frame of the
superfluid wave packet, the long-distance drop (100m) can be studied easily on
a numerical grid.


  We analyze the simultaneous time-optimal control of two-spin systems. The two
non coupled spins which differ in the value of their chemical offsets are
controlled by the same magnetic fields. Using an appropriate rotating frame, we
restrict the study to the case of opposite shifts. We then show that the
optimal solution of the inversion problem in a rotating frame is composed of a
pulse sequence of maximum intensity and is similar to the optimal solution for
inverting only one spin by using a non-resonant control field in the laboratory
frame. An example is implemented experimentally using techniques of Nuclear
Magnetic Resonance.


  We present a facial landmark position correlation analysis as well as its
applications. Although numerous facial landmark detection methods have been
presented in the literature, few of them explicitly take into account the
inherent relationship among landmarks. To reveal and interpret this
relationship, we propose to analyze landmark correlation by using Canonical
Correlation Analysis~(CCA). We experimentally show that the dense facial
landmark annotations in current benchmarks are strongly correlated. We propose
two applications based on this analysis. First, by analyzing the landmark
correlation, we gain some interesting insights into the predictions of
different landmark detection models (including random forests model and CNN
models). We also demonstrate how CNNs progressively learn to predict facial
landmarks. Second, we propose a few-shot learning method that allows to
considerably reduce the manual effort for dense landmark annotation.


  In this paper we fix a set \Lambda^* of positive elements of the free group
F_n (e.g. the set of finite words occurring in a Markov subshift) as well as n
partial isometries on a Hilbert space H. Based on these we define a map S:F_n
--> L(H) which we prove to be a partial representation of F_n on H under
certain conditions studied by Matsumoto.


  While SNRs have been considered the most relevant Galactic CR accelerators
for decades, CCSNe could accelerate particles during the earliest stages of
their evolution and hence contribute to the CR energy budget in the Galaxy.
Some SNRs have indeed been associated with TeV gamma-rays, yet proton
acceleration efficiency during the early stages of an SN expansion remains
mostly unconstrained. The multi-wavelength observation of SN 2023ixf, a Type II
SN in the nearby galaxy M101, opens the possibility to constrain CR
acceleration within a few days after the collapse of the RSG stellar
progenitor. With this work, we intend to provide a phenomenological,
quasi-model-independent constraint on the CR acceleration efficiency during
this event at photon energies above 100 MeV. We performed a maximum-likelihood
analysis of gamma-ray data from the Fermi Large Area Telescope up to one month
after the SN explosion. We searched for high-energy emission from its expanding
shock, and estimated the underlying hadronic CR energy reservoir assuming a
power-law proton distribution consistent with standard diffusive shock
acceleration. We do not find significant gamma-ray emission from SN 2023ixf.
Nonetheless, our non-detection provides the first limit on the energy
transferred to the population of hadronic CRs during the very early expansion
of a CCSN. Under reasonable assumptions, our limits would imply a maximum
efficiency on the CR acceleration of as low as 1%, which is inconsistent with
the common estimate of 10% in generic SNe. However, this result is highly
dependent on the assumed geometry of the circumstellar medium, and could be
relaxed back to 10% by challenging spherical symmetry. A more sophisticated,
inhomogeneous characterisation of the shock and the progenitor's environment is
required before establishing whether or not Type II SNe are indeed efficient CR
accelerators at early times.


  Recently, it has been claimed by Frimer et al. (2015) that there is a linear
relationship between the level of prosocial language and the level of public
disapproval of US Congress. A re-analysis demonstrates that this relationship
is the result of a misspecified model that does not account for first-order
autocorrelated disturbances. A Stata script to reproduce all presented results
is available as an appendix.


  This note summarises the workshop LCTW09 held between the 3.11.2009 and
5.11.2009 at LAL Orsay. The workshop was dedicated to discuss the beam tests in
the years 2010 up to 2013 for detectors to be operated at a future linear
electron positron collider. The document underlines the rich R&D program on
these detectors in the coming years. Large synergies were identified in the DAQ
and software systems. Considerable consolidation of resources are expected from
the establishment of semi-permanent beam lines for linear collider detector R&D
at major centres like CERN and FNAL. Reproducing a beam structure as foreseen
for the International Linear Collider (ILC) would clearly enhance the value of
the obtained beam test results. Although not ultimately needed for every
research program, all groups would exploit such a feature if it is available.


  The structure and nature of water confined between hydrophobic molybdenum
disulfide (MoS2) and graphene (Gr) are investigated at room temperature by
means of atomic force microscopy. We find the formation of two-dimensional (2D)
crystalline ice layers. In contrast to the hexagonal ice 'bilayers' of bulk
ice, these 2D crystalline ice phases consist of two planar hexagonal layers.
Additional water condensation leads to either lateral expansion of the ice
layers or to the formation of three-dimensional water droplets on top or at the
edges of the two-layer ice, indicating that water does not wet these planar ice
films. The results presented here are in line with a recent theory suggesting
that water confined between hydrophobic walls forms 2D crystalline two-layer
ice with a nontetrahedral geometry and intrahydrogen bonding. The lack of
dangling bonds on either surface of the ice film gives rise to a hydrophobic
character. The unusual geometry of these ice films is of great potential
importance in biological systems with water in direct contact with hydrophobic
surfaces.


  Surface cracks on buildings, natural walls and underground mine tunnels can
indicate serious structural integrity issues that threaten the safety of the
structure and people in the environment. Timely detection and monitoring of
cracks are crucial to managing these risks, especially if the systems can be
made highly automated through robots. Vision-based crack detection algorithms
using deep neural networks have exhibited promise for structured surfaces such
as walls or civil engineering tunnels, but little work has addressed highly
unstructured environments such as rock cliffs and bare mining tunnels. To
address this challenge, this paper presents PointCrack3D, a new
3D-point-cloud-based crack detection algorithm for unstructured surfaces. The
method comprises three key components: an adaptive down-sampling method that
maintains sufficient crack point density, a DNN that classifies each point as
crack or non-crack, and a post-processing clustering method that groups crack
points into crack instances. The method was validated experimentally on a new
large natural rock dataset, comprising coloured LIDAR point clouds spanning
more than 900 m^2 and 412 individual cracks. Results demonstrate a crack
detection rate of 97% overall and 100% for cracks with a maximum width of more
than 3 cm, significantly outperforming the state of the art. Furthermore, for
cross-validation, PointCrack3D was applied to an entirely new dataset acquired
in different locations and not used at all in training and shown to detect 100%
of its crack instances. We also characterise the relationship between detection
performance, crack width and number of points per crack, providing a foundation
upon which to make decisions about both practical deployments and future
research directions.


  We have measured magnetic response of second-generation high temperature
superconductor YBa2Cu3Ox wire and Nb thin film in perpendicular ac field as a
function of temperature. We compare experimental complex ac susceptibility to
the calculated susceptibility based on the model of Bean's critical-state
response in two-dimensional (2D) disk in perpendicular field. The harmonic
analysis is needed for comparison between the model and the experimental data.
We present a method of linking model and experimental susceptibility. Good
agreement of experimental susceptibility with model susceptibility of 2D disk
allows contactless estimation of critical depinning current density and its
temperature dependence.


  With the advent of e-commerce platforms, reviews are crucial for customers to
assess the credibility of a product. The star ratings do not always match the
review text written by the customer. For example, a three star rating (out of
five) may be incongruous with the review text, which may be more suitable for a
five star review. A clustering approach can be used to relabel the correct star
ratings by grouping the text reviews into individual groups. In this work, we
explore the task of choosing different text embeddings to represent these
reviews and also explore the impact the embedding choice has on the performance
of various classes of clustering algorithms. We use contextual (BERT) and
non-contextual (Word2Vec) text embeddings to represent the text and measure
their impact of three classes on clustering algorithms - partitioning based
(KMeans), single linkage agglomerative hierarchical, and density based (DBSCAN
and HDBSCAN), each with various experimental settings. We use the silhouette
score, adjusted rand index score, and cluster purity score metrics to evaluate
the performance of the algorithms and discuss the impact of different
embeddings on the clustering performance. Our results indicate that the type of
embedding chosen drastically affects the performance of the algorithm, the
performance varies greatly across different types of clustering algorithms, no
embedding type is better than the other, and DBSCAN outperforms KMeans and
single linkage agglomerative clustering but also labels more data points as
outliers. We provide a thorough comparison of the performances of different
algorithms and provide numerous ideas to foster further research in the domain
of text clustering.


  Scalar fields play an important role in constructing modified gravity
theories. In the case of a single scalar field with timelike gradient, the
corresponding Lagrangian in the unitary gauge takes the form of spatially
covariant gravity (SCG), which is proved useful in analyzing and extending the
generally covariant theories. In this work, we apply the SCG method to the
scalar-nonmetricity theory, of which the Lagrangian is built of the
nonmetricity tensor and a scalar field. We derive the 3+1 decomposition of the
geometric quantities and especially covariant derivatives of the scalar field
up to the third order in the presence of a nonvanishing nonmetricity tensor. By
fixing the unitary gauge, the resulting Lagrangian takes the form of a SCG with
nonmetricity, in which all the ingredients are spatial tensors. We then exhaust
the scalar monomials of SCG with nonmetricity up to $d=3$ with $d$ the total
number of derivatives. Since the disformation tensor plays as an auxiliary
variable, we take the Lagrangian with $d=2$ as an example to show that after
solving the disformation tensor, we can get an effective SCG theory for the
metric variables but with modified coefficients. Our results provides a novel
approach to extending the scalar-nonmetricity theory.


  In earlier papers the second author and Charles Read have introduced and
studied a new notion of positivity for operator algebras, with an eye to
extending certain C*-algebraic results and theories to more general algebras.
The present paper consists of complements to some facts in the just mentioned
papers, concerning this notion of positivity. For example we prove a result on
the numerical range of products of the roots of commuting operators with
numerical range in a sector.


  Let $b > a > 0$. We prove the following asymptotic formula $$\sum_{n\ge 0}
\big\lvert\{x/(n+a)\} - \{x/(n+b)\}\big\rvert =
\frac{2}{\pi}\zeta(3/2)\sqrt{cx} + O(c^{2/9}x^{4/9}),$$ with $c=b-a$, uniformly
for $x \ge 40 c^{-5}(1+b)^{27/2}$.


  Sepsis poses a major global health threat, accounting for millions of deaths
annually and significant economic costs. Accurate predictions of mortality risk
in sepsis patients facilitate the efficient allocation of medical resources,
thereby enhancing patient survival and quality of life. Through precise risk
assessments, healthcare facilities can effectively distribute intensive care
beds, medical equipment, and staff, ensuring high-risk patients receive timely
and appropriate care. Early identification and intervention significantly
decrease mortality rates and improve patient outcomes. Current methods
typically utilize only one type of data--either constant, temporal, or ICD
codes. This study introduces the Time-Constant KAN Integrated Network(TCKIN),
an innovative model that enhances the accuracy of sepsis mortality risk
predictions by integrating both temporal and constant data from electronic
health records and ICD codes. Validated against the MIMIC-III and MIMIC-IV
datasets, TCKIN surpasses existing machine learning and deep learning methods
in accuracy, sensitivity, and specificity. Notably, TCKIN achieved AUCs of
87.76% and 88.07%, demonstrating superior capability in identifying high-risk
patients. Additionally, TCKIN effectively combats the prevalent issue of data
imbalance in clinical settings, improving the detection of patients at elevated
risk of mortality and facilitating timely interventions. These results confirm
the model's effectiveness and its potential to transform patient management and
treatment optimization in clinical practice. With this advanced risk assessment
tool, healthcare providers can devise more tailored treatment plans, optimize
resource utilization, and ultimately enhance survival rates and quality of life
for sepsis patients.


  We study the geometry and cohomology of Lefschetz pencils for semi-stable
schemes over a discrete valuation ring. We relate the global cohomological
properties of the Lefschetz pencil and the monodromy-weight conjecture, in
particular we show that if one assumes the monodromy-weight conjecture in
smaller dimensions then one can obtain a rather complete understanding of the
relative cohomology of the pencil. This reduces the monodromy-weight conjecture
to an arithmetic variant of a conjecture of Kashiwara for the projective line.


  We initiate the study of bribery problem in the context of gerrymandering and
reverse gerrymandering. In our most general problem, the input is a set of
voters having votes over a set of alternatives, a graph on the voters, a
partition of voters into connected districts, cost of every voter for changing
her district, a budget for the briber, and a favorite alternative of the
briber. The briber needs to compute if the given partition can be modified so
that (i) the favorite alternative of the briber wins the resulting election,
(ii) the modification is budget feasible, and (iii) every new district is
connected. We study four natural variants of the above problem -- the graph on
voter being arbitrary vs complete graph (corresponds to removing connectedness
requirement for districts) and the cost of bribing every voter being uniform vs
non-uniform. We show that all the four problems are NP-complete even under
quite restrictive scenarios. Hence our results show that district based
elections are quite resistant under this new kind of electoral attack. We
complement our hardness results with polynomial time algorithms in some other
cases.


  The growing cosmological interest of different entropy functions (like the
Tsallis entropy, the R\'{e}nyi entropy, the Barrow entropy, the Sharma-Mittal
entropy, the Kaniadakis entropy and the Loop Quantum gravity entropy) naturally
raises an important question: "Does there exist a generalized entropy that can
bring all the known entropies proposed so far within a single umbrella?" In
spirit of this, recently a four parameter generalized entropy has been
formulated that reduces to different known entropies for suitable limits of the
parameters. Based on such four parameter generalized entropy (symbolized by
$S_\mathrm{g}$), in the present paper, we examine the universe's evolution
during its early phase, particularly from inflation to reheating, in the
context of entropic cosmology where the entropic energy density acts as the
inflaton. It turns out that the entropic energy successfully drives an early
inflationary phase with a graceful exit, and moreover, the theoretical
expectations of the observable indices get consistent with the recent Planck
data for suitable ranges of the entropic parameters. After the inflation ends,
the universe enters to a reheating stage when the entropic energy decays to
relativistic particles with a certain decay rate. Actually the presence of the
entropic parameters in the $S_\mathrm{g}$ ensures a continuous evolution of the
Hubble parameter from a quasi de-Sitter phase during the inflation to a power
law phase during the reheating stage dominated by a constant EoS parameter.
Consequently we investigate the reheating phenomenology, and scan the entropic
parameters from both the inflation and reheating requirements. We further
address the possibility of instantaneous reheating in the present context of
generalized entropy.


  Large Language Models (LLMs) have shown their success in language
understanding and reasoning on general topics. However, their capability to
perform inference based on user-specified structured data and knowledge in
corpus-rare concepts, such as causal decision-making is still limited. In this
work, we explore the possibility of fine-tuning an open-sourced LLM into
LLM4Causal, which can identify the causal task, execute a corresponding
function, and interpret its numerical results based on users' queries and the
provided dataset. Meanwhile, we propose a data generation process for more
controllable GPT prompting and present two instruction-tuning datasets: (1)
Causal-Retrieval-Bench for causal problem identification and input parameter
extraction for causal function calling and (2) Causal-Interpret-Bench for
in-context causal interpretation. By conducting end-to-end evaluations and two
ablation studies, we showed that LLM4Causal can deliver end-to-end solutions
for causal problems and provide easy-to-understand answers, which significantly
outperforms the baselines.


  A statistical physical model of the two basic properties of clusters within a
hurricane--their convectivity and rotation--reveals a relationship for the time
evolution of a hurricane. Non-doppler data from NEXRAD surface radar imagery,
of Hurricane Irma transiting over Florida, is decomposed into unique clusters
on the basis of an annealing process using entropy and energy differences
between pixels. Application of the concept of entropic forces between a
cluster's pixels, provides an estimate of the radial velocity of each cluster
by application of Stokes' theorem. The ratio of the characteristic rotation and
convectivity, associated with radial flow, integrated over the extent of the
hurricane, closely tracks the hurricane's state, providing more time resolution
than aircraft sorties alone allow. It is concluded that monitoring the
rotational and convective state, in conjunction with the size of a cluster, is
capable of quickly providing forecasters and others with changes in a
hurricane's state. It is also shown that entropic tornado state can be
similarly described in terms of convectivity and rotation rate.


  We study the thermal evolution of primordial star-forming gas clouds using
three-dimensional cosmological simulations. We critically examine how
assumptions and approximations made in calculating radiative cooling rates
affect the dynamics of the collapsing gas clouds. We consider two important
molecular hydrogen cooling processes that operate in a dense primordial gas;
H_2 line cooling and continuum cooling by H_2 collision-induced emission. To
calculate the optically thick cooling rates, we follow the Sobolev method for
the former, whereas we perform ray-tracing for the latter. We also run the same
set of simulations using simplified fitting functions for the net cooling
rates. We compare the simulation results in detail. We show that the time- and
direction-dependence of hydrodynamic quantities such as gas temperature and
local velocity gradients significantly affects the optically thick cooling
rates. Gravitational collapse of the cloud core is accelerated when the cooling
rates are calculated by using the fitting functions. The structure and
evolution of the central pre-stellar disk are also affected. We conclude that
physically motivated implementations of radiative transfer are necessary to
follow accurately the thermal and chemical evolution of a primordial gas to
high densities.


  We investigate the 3.45-eV luminescence band of spontaneously formed GaN
nanowires on Si(111) by photoluminescence and cathodoluminescence spectroscopy.
This band is found to be particularly prominent for samples synthesized at
comparatively low temperatures. At the same time, these samples exhibit a
peculiar morphology, namely, isolated long nanowires are interspersed within a
dense matrix of short ones. Cathodoluminescence intensity maps reveal the
3.45-eV band to originate primarily from the long nanowires. Transmission
electron microscopy shows that these long nanowires are either Ga polar and are
joined by an inversion domain boundary with their short N-polar neighbors, or
exhibit a Ga-polar core surrounded by a N-polar shell with a tubular inversion
domain boundary at the core/shell interface. For samples grown at high
temperatures, which exhibit a uniform nanowire morphology, the 3.45-eV band is
also found to originate from particular nanowires in the ensemble and thus
presumably from inversion domain boundaries stemming from the coexistence of N-
and Ga-polar nanowires. For several of the investigated samples, the 3.45-eV
band splits into a doublet. We demonstrate that the higher-energy component of
this doublet arises from the recombination of two-dimensional excitons free to
move in the plane of the inversion domain boundary. In contrast, the
lower-energy component of the doublet originates from excitons localized in the
plane of the inversion domain boundary. We propose that this in-plane
localization is due to shallow donors in the vicinity of the inversion domain
boundaries.


  We present a new analysis of the interstellar protons toward the TeV
$\gamma$-ray SNR RX J0852.0$-$4622 (G266.2$-$1.2, Vela Jr.). We used the
NANTEN2 $^{12}$CO($J$ = 1-0) and ATCA & Parkes HI datasets in order to derive
the molecular and atomic gas associated with the TeV $\gamma$-ray shell of the
SNR. We find that atomic gas over a velocity range from $V_\mathrm{LSR}$ = $-4$
km s$^{-1}$ to 50 km s$^{-1}$ or 60 km s$^{-1}$ is associated with the entire
SNR, while molecular gas is associated with a limited portion of the SNR. The
large velocity dispersion of the HI is ascribed to the expanding motion of a
few HI shells overlapping toward the SNR but is not due to the Galactic
rotation. The total masses of the associated HI and molecular gases are
estimated to be $\sim2.5 \times 10^4 $ $M_{\odot}$ and $\sim10^3$ $M_{\odot}$,
respectively. A comparison with the H.E.S.S. TeV $\gamma$-rays indicates that
the interstellar protons have an average density around 100 cm$^{-3}$ and shows
a good spatial correspondence with the TeV $\gamma$-rays. The total cosmic ray
proton energy is estimated to be $\sim10^{48}$ erg for the hadronic
$\gamma$-ray production, which may still be an underestimate by a factor of a
few due to a small filling factor of the SNR volume by the interstellar
protons. This result presents a third case, after RX J1713.7$-$3946 and HESS
J1731$-$347, of the good spatial correspondence between the TeV $\gamma$-rays
and the interstellar protons, lending further support for a hadronic component
in the $\gamma$-rays from young TeV $\gamma$-ray SNRs.


  A study has been conducted into the mechanisms of evolution of clusters and
their subsequent fragmentation under energetic heavy ion bombardment of
amorphous graphite. The evolving clusters and their subsequent fragmentation
under continuing ion bombardment are revealed by detecting various clusters in
the energy spectra of the Direct Recoils emitted as a result of collisions
between ions and surface constituents. The successive Direct Recoil spectra
reveal that the energetics of Carbon Carbon bond formation as well as any
subsequent fragmentation can be related to the processes of energy dissipation
in a cylindrical volume of a few Angstrom surrounding the ion path. The
dependence of cluster with m carbon atoms formation or its subsequent
fragmentation into cluster with m-2 carbon atoms and diatomic carbon C2 is seen
to be a function of the ionic stopping powers in this cylindrical volume.


  Reinforcement Learning (RL) has emerged as a highly effective technique for
addressing various scientific and applied problems. Despite its success,
certain complex tasks remain challenging to be addressed solely with a single
model and algorithm. In response, ensemble reinforcement learning (ERL), a
promising approach that combines the benefits of both RL and ensemble learning
(EL), has gained widespread popularity. ERL leverages multiple models or
training algorithms to comprehensively explore the problem space and possesses
strong generalization capabilities. In this study, we present a comprehensive
survey on ERL to provide readers with an overview of recent advances and
challenges in the field. Firstly, we provide an introduction to the background
and motivation for ERL. Secondly, we conduct a detailed analysis of strategies
such as model selection and combination that have been successfully implemented
in ERL. Subsequently, we explore the application of ERL, summarize the
datasets, and analyze the algorithms employed. Finally, we outline several open
questions and discuss future research directions of ERL. By offering guidance
for future scientific research and engineering applications, this survey
significantly contributes to the advancement of ERL.


  We investigate the possibility of a strongly correlated Fractional Quantum
Hall (FQH) state in bulk three dimensional isotropic (not layered) materials.
We find that a FQH state can exist at low densities only if it is accompanied
by a staging transition in which the electrons re-organize themselves in
layers, perpendicular to the magnetic field, at distances of order the magnetic
length apart. The Hartree energy associated to the staging transition is
off-set by the correlation Fock energy of the 3D FQH state. We obtain the phase
diagram of bulk electrons in a magnetic field subject to Coulomb interactions
as a function of carrier density and lattice constant. At very low densities,
the 3D FQH state exhibits a transition to a 3D Wigner crystal state stabilized
by phonon correlations.


  Ion-implantation of normally insulating polymers offers an alternative to
depositing conjugated organics onto plastic films to make electronic circuits.
We used a 50 keV nitrogen ion beam to mix a thin 10 nm Sn/Sb alloy film into
the sub-surface of polyetheretherketone (PEEK) and report the low temperature
properties of this material. We observed metallic behavior, and the onset of
superconductivity below 3 K. There are strong indications that the
superconductivity does not result from a residual thin-film of alloy, but
instead from a network of alloy grains coupled via a weakly conducting,
ion-beam carbonized polymer matrix.


  We present the properties of relativistic, inviscid, low angular momentum,
advective accretion flow in a $f(R)$ gravity theory that satisfactorily mimics
the asymptotically flat vacuum solutions of the Einstein's equations. With
this, we solve the governing equations describing the accretion flow and obtain
the global transonic accretion solutions in terms of flow energy (${\cal E}$),
angular momentum ($\lambda$) and gravity parameter ($A$) that determines the
effect of $f(R)$ gravity. We observe that depending on the model parameters,
flow may contain either single or multiple critical points. We separate the
effective domain of the parameter space in $\lambda-{\cal E}$ plane that admits
accretion solutions possessing multiple critical points and observe that
solution of this kind continues to form for wide range of the flow parameters.
We examine the modification of the parameter space and reveal that it gradually
shrinks with the decrease of $A$, and ultimately disappears for $A=-2.34$.
Finally, we calculate the disk luminosity ($L$) considering bremsstrahlung
emission process and find that global accretion solutions passing through the
inner critical point are more luminous compared to the outer critical point
solutions.


  We introduce a microscopic model which describes the dynamics of each dealer
in multiple foreign exchange markets, taking account of the triangular
arbitrage transaction. The model reproduces the interaction among the markets
well. We explore the relation between the parameters of the present microscopic
model and the spring constant of a macroscopic model that we proposed
previously.


  The Sivers parton distribution has been predicted to obey a particular
``universality relation'', namely to have opposite sign in semi-inclusive
deeply inelastic scattering (SIDIS) and the Drell-Yan process. We discuss how,
on the basis of present HERMES data, this remarkable prediction of the QCD
factorization approach to the description of single spin asymmetries related to
the Sivers effect could be checked experimentally in future experiments at PAX
and COMPASS.


  Many interesting natural phenomena are sparsely distributed and discrete.
Locating the hotspots of such sparsely distributed phenomena is often difficult
because their density gradient is likely to be very noisy. We present a novel
approach to this search problem, where we model the co-occurrence relations
between a robot's observations with a Bayesian nonparametric topic model. This
approach makes it possible to produce a robust estimate of the spatial
distribution of the target, even in the absence of direct target observations.
We apply the proposed approach to the problem of finding the spatial locations
of the hotspots of a specific phytoplankton taxon in the ocean. We use
classified image data from Imaging FlowCytobot (IFCB), which automatically
measures individual microscopic cells and colonies of cells. Given these
individual taxon-specific observations, we learn a phytoplankton community
model that characterizes the co-occurrence relations between taxa. We present
experiments with simulated robot missions drawn from real observation data
collected during a research cruise traversing the US Atlantic coast. Our
results show that the proposed approach outperforms nearest neighbor and
k-means based methods for predicting the spatial distribution of hotspots from
in-situ observations.


  This article identifies the process of crystallization of thin high-$\kappa$
dielectric films and an optimal range of annealing temperature in the field of
high-$\kappa$ dielectric-based metal-oxide-semiconductor (MOS) technology for
its improved electrical performances. Differential Scanning Calorimetry (DSC)
technique is employed to understand the thermal behaviour of thin high-$\kappa$
dielectric films of HfO$_2$, deposited by rf sputtering, on Si. The exothermic
trends of the DSC signal and Grazing Incidence X-ray diffraction (GIXRD) data
indicate an amorphous to crystalline transition in the high-$\kappa$ film at
higher temperature. The enthalpy-temperature variation shows a glass
temperature (T$_g$) at $\sim$ 590 $^o$C beyond which an amorphous to m-HfO$_2$
crystalline transition takes place. Further, the Hf-Silicate formation,
observed in DSC measurement and corroborated by Fourier transformed Infrared
Spectroscopy (FT-IR) studies, indicates that the process of formation of
Hf-Silicate begins at $\sim$ 717 $^oC$. High-frequency (HF) capacitance-voltage
$(C-V)$ and current density - voltage $(J-V)$ characteristics establish that
the crystallization of the film is not the root cause of degradation of the
electrical properties of the high-$\kappa$-based MOS devices, rather the device
degrades due to formation of interfacial Hf-Silicate.


  The cosmological consequences of a simple scalar field model for the
generation of Newton's constant through the spontaneous breaking of scale
invariance in a curved space-time are again presented and discussed. Such a
model leads to a consistent description wherein the introduction of matter
introduces a small perturbation on a de Sitter universe and a time dependence
of the gravitational coupling.


  This work is part of VESTALE, a project initiated within the Rubin-LSST
Cadence Strategy Optimization Process . Its goal is to explore the potential of
Rubin-LSST observations aimed at the Galaxy's bulge (Bulge) for studying RR
Lyrae stars (RRL). Observation and analysis of RR Lyrae stars in the Bulge are
crucial for tracing the old population of the central part of our galaxy and
reconstructing the history of Bulge formation. Based on observations conducted
with CTIO/DECam by Saha et al. 2019 towards the Baade Window, our simulations
demonstrate that early Rubin-LSST observations will enable the recovery of RR
Lyrae light curves at Galactic center distances with sufficient precision. This
will allow us to utilize theoretical relations from Marconi et al. 2022 to
determine their distances and/or metallicity, following the REDIME algorithm
introduced in Bono et al. 2019. We show how reddening and crowding affect our
simulations and highlight the importance of considering these effects when
deriving pulsation parameters (luminosity amplitudes, mean magnitudes) based on
the light curves especially if the goal is to explore the opposite side of the
Bulge through the observation of its RRL. The simulations discussed in this
investigation were conducted to support the SCOC's decision to observe this
important sky region since it has only recently been decided to include part of
the Bulge as a target within the LSST main survey.


  A spectral fitter based on the graphics processor unit (GPU) has been
developed for Borexino solar neutrino analysis. It is able to shorten the
fitting time to a superior level compared to the CPU fitting procedure. In
Borexino solar neutrino spectral analysis, fitting usually requires around one
hour to converge since it includes time-consuming convolutions in order to
account for the detector response and pile-up effects. Moreover, the
convergence time increases to more than two days when including extra
computations for the discrimination of $^{11}$C and external $\gamma$s. In
sharp contrast, with the GPU-based fitter it takes less than 10 seconds and
less than four minutes, respectively. This fitter is developed utilizing the
GooFit project with customized likelihoods, pdfs and infrastructures supporting
certain analysis methods. In this proceeding the design of the package,
developed features and the comparison with the original CPU fitter are
presented.


  Recently developed techniques have made it possible to quickly learn accurate
probability density functions from data in low-dimensional continuous space. In
particular, mixtures of Gaussians can be fitted to data very quickly using an
accelerated EM algorithm that employs multiresolution kd-trees (Moore, 1999).
In this paper, we propose a kind of Bayesian networks in which low-dimensional
mixtures of Gaussians over different subsets of the domain's variables are
combined into a coherent joint probability model over the entire domain. The
network is also capable of modeling complex dependencies between discrete
variables and continuous variables without requiring discretization of the
continuous variables. We present efficient heuristic algorithms for
automatically learning these networks from data, and perform comparative
experiments illustrated how well these networks model real scientific data and
synthetic data. We also briefly discuss some possible improvements to the
networks, as well as possible applications.


  The conventional beam management procedure mandates that the user equipment
(UE) periodically measure the received signal reference power (RSRP) and
transmit these measurements to the base station (BS). The challenge lies in
balancing the number of beams used: it should be large enough to identify
high-RSRP beams but small enough to minimize reporting overhead. This paper
investigates this essential performance-versus-overhead trade-off using
Bayesian optimization. The proposed approach represents the first application
of real-time beam tracking via Bayesian optimization in RIS-assisted
communication systems. Simulation results validate the effectiveness of this
scheme.


  For the case of evidence ordered in a complete directed acyclic graph this
paper presents a new algorithm with lower computational complexity for
Dempster's rule than that of step-by-step application of Dempster's rule. In
this problem, every original pair of evidences, has a corresponding evidence
against the simultaneous belief in both propositions. In this case, it is
uncertain whether the propositions of any two evidences are in logical
conflict. The original evidences are associated with the vertices and the
additional evidences are associated with the edges. The original evidences are
ordered, i.e., for every pair of evidences it is determinable which of the two
evidences is the earlier one. We are interested in finding the most probable
completely specified path through the graph, where transitions are possible
only from lower- to higher-ranked vertices. The path is here a representation
for a sequence of states, for instance a sequence of snapshots of a physical
object's track. A completely specified path means that the path includes no
other vertices than those stated in the path representation, as opposed to an
incompletely specified path that may also include other vertices than those
stated. In a hierarchical network of all subsets of the frame, i.e., of all
incompletely specified paths, the original and additional evidences support
subsets that are not disjoint, thus it is not possible to prune the network to
a tree. Instead of propagating belief, the new algorithm reasons about the
logical conditions of a completely specified path through the graph. The new
algorithm is O(|THETA| log |THETA|), compared to O(|THETA| ** log |THETA|) of
the classic brute force algorithm.


  We compare the galaxy populations in superclusters of different morphology in
the nearby Universe (180 < d < 270 Mpc) to see whether the inner structure and
overall morphology of superclusters are important in shaping galaxy properties
in superclusters. Supercluster morphology has been found with Minkowski
functionals. We analyse the probability density distributions of colours,
morphological types, stellar masses, star formation rates (SFR) of galaxies,
and the peculiar velocities of the main galaxies in groups in superclusters of
filament and spider types, and in the field. We show that the fraction of red,
early-type, low SFR galaxies in filament-type superclusters is higher than in
spider-type superclusters; in low-density global environments their fraction is
lower than in superclusters. In all environments the fraction of red, high
stellar mass, and low SFR galaxies in rich groups is higher than in poor
groups. In superclusters of spider morphology red, high SFR galaxies have
higher stellar masses than in filament-type superclusters. Groups of equal
richness host galaxies with larger stellar masses, a larger fraction of
early-type and red galaxies, and a higher fraction of low SFR galaxies, if they
are located in superclusters of filament morphology. The peculiar velocities of
the main galaxies in groups from superclusters of filament morphology are
higher than in those of spider morphology. Groups with higher peculiar
velocities of their main galaxies in filament-type superclusters are located in
higher density environment than those with low peculiar velocities. There are
significant differences between galaxy populations of the individual richest
superclusters. Therefore both local (group) and global (supercluster)
environments and even supercluster morphology play an important role in the
formation and evolution of galaxies.


  We present a 7 yr timing study of the 2.5 ms X-ray pulsar SAX J1808.4-3658,
an X-ray transient with a recurrence time of ~2 yr, using data from the Rossi
X-ray Timing Explorer covering 4 transient outbursts (1998-2005). We verify
that the 401 Hz pulsation traces the spin frequency fundamental and not a
harmonic. Substantial pulse shape variability, both stochastic and systematic,
was observed during each outburst. Analysis of the systematic pulse shape
changes suggests that, as an outburst dims, the X-ray "hot spot" on the pulsar
surface drifts longitudinally and a second hot spot may appear. The overall
pulse shape variability limits the ability to measure spin frequency evolution
within a given X-ray outburst (and calls previous nudot measurements of this
source into question), with typical upper limits of |nudot| < 2.5x10^{-14} Hz/s
(2 sigma). However, combining data from all the outbursts shows with high (6
sigma) significance that the pulsar is undergoing long-term spin down at a rate
nudot = (-5.6+/-2.0)x10^{-16} Hz/s, with most of the spin evolution occurring
during X-ray quiescence. We discuss the possible contributions of magnetic
propeller torques, magnetic dipole radiation, and gravitational radiation to
the measured spin down, setting an upper limit of B < 1.5x10^8 G for the
pulsar's surface dipole magnetic field and and Q/I < 5x10^{-9} for the
fractional mass quadrupole moment. We also measured an orbital period
derivative of Pdot = (3.5+/-0.2)x10^{-12} s/s. This surprising large Pdot is
reminiscent of the large and quasi-cyclic orbital period variation observed in
the so-called "black widow" millisecond radio pulsars, supporting speculation
that SAX J1808.4-3658 may turn on as a radio pulsar during quiescence. In an
appendix we derive an improved (0.15 arcsec) source position from optical data.


  Cold-atom interferometers with optical splitting and recombination use
off-resonant laser beams to split a cloud of Bose-Einstein condensate (BEC)
into two clouds that travel along different paths and are then recombined again
using optical beams. After the recombination, the BEC in general populates both
the cloud at rest and the moving clouds. Measuring relative number of atoms in
each of these clouds yields information about the relative phase shift
accumulated by the atoms in the two moving clouds during the interferometric
cycle. We derive the expression for the probability of finding any given number
of atoms in each of the clouds, discuss features of the probability density
distribution, analyze its dependence on the relative accumulated phase shift as
a function of the strength of the interatomic interactions, and compare our
results with experiment.


  The Epps effect is key phenomenology relating to high frequency correlation
dynamics in financial markets. We argue that it can be used to provide insight
into whether tick data is best represented as samples from Brownian diffusions,
or as samples from truly discrete events represented as connected point
processes. We derive the Epps effect arising from asynchrony and provide a
refined method to correct for the effect. We then propose three experiments
which show how to discriminate between possible underlying representations.
These in turn demonstrate how a simple Hawkes representation recovers
phenomenology reported in the literature that cannot be recovered using a
Brownian representation without additional ad hoc model complexity. However,
complex ad hoc noise models built on Brownian motions cannot in general be
discriminated relative to a Hawkes representation. Nevertheless, we argue that
high frequency correlation dynamics are most faithfully recovered when tick
data is represented as a web of interconnected discrete events rather than
being samples from continuous Brownian diffusions even when combined with
noise.


  In this note, we study the relation between Fontaine-Laffaille modules and
strongly divisible modules, without assuming the main theorem of
Fontaine-Laffaille (but we need to assume the main results concerning strongly
divisible modules). This in particular gives a new proof for the main theorem
of Fontaine-Laffaille (for $p>2$).


  Search results personalization has become an effective way to improve the
quality of search engines. Previous studies extracted information such as past
clicks, user topical interests, query click entropy and so on to tailor the
original ranking. However, few studies have taken into account the sequential
information underlying previous queries and sessions. Intuitively, the order of
issued queries is important in inferring the real user interests. And more
recent sessions should provide more reliable personal signals than older
sessions. In addition, the previous search history and user behaviors should
influence the personalization of the current query depending on their
relatedness. To implement these intuitions, in this paper we employ a
hierarchical recurrent neural network to exploit such sequential information
and automatically generate user profile from historical data. We propose a
query-aware attention model to generate a dynamic user profile based on the
input query. Significant improvement is observed in the experiment with data
from a commercial search engine when compared with several traditional
personalization models. Our analysis reveals that the attention model is able
to attribute higher weights to more related past sessions after fine training.


  Vision Transformer (ViT) has become a leading tool in various computer vision
tasks, owing to its unique self-attention mechanism that learns visual
representations explicitly through cross-patch information interactions.
Despite having good success, the literature seldom explores the explainability
of vision transformer, and there is no clear picture of how the attention
mechanism with respect to the correlation across comprehensive patches will
impact the performance and what is the further potential. In this work, we
propose a novel explainable visualization approach to analyze and interpret the
crucial attention interactions among patches for vision transformer.
Specifically, we first introduce a quantification indicator to measure the
impact of patch interaction and verify such quantification on attention window
design and indiscriminative patches removal. Then, we exploit the effective
responsive field of each patch in ViT and devise a window-free transformer
architecture accordingly. Extensive experiments on ImageNet demonstrate that
the exquisitely designed quantitative method is shown able to facilitate ViT
model learning, leading the top-1 accuracy by 4.28% at most. Moreover, the
results on downstream fine-grained recognition tasks further validate the
generalization of our proposal.


  Wireless local area networks (WLANs) based on IEEE 802.11 standards are
becoming ubiquitous today and typically support multiple data rates. In such
multi-rate WLANs, distributed medium access and rate adaptation are two key
elements to achieve efficient radio resource utilization, especially in
non-cooperative environments. In this paper, we present an analytical study on
the non-cooperative multi-rate WLANs composed of selfish users jointly
adjusting their data rate and contention window size at the medium access level
to maximize their own throughput, irrespective of the impact of their selfish
behaviors on overall system performance. Specifically, we develop an adapted
Tit-For-Tat (TFT) strategy to guide the system to an efficient equilibrium in
non-cooperative environments. We model the interactions among selfish users
under the adapted TFT framework as a non-cooperative joint medium access and
rate adaptation game. A systematic analysis is conducted on the structural
properties of the game to provide insights on the interaction between rate
adaptation and 802.11 medium access control in a competitive setting. We show
that the game has multiple equilibria, which, after the equilibrium refinement
process that we develop, reduce to a unique efficient equilibrium. We further
develop a distributed algorithm to achieve this equilibrium and demonstrate
that the equilibrium achieves the performance very close to the system optimum
in a social perspective.


  In this paper we study the gradient estimate for positive solutions of
Schrodinger equations on locally finite graph. Then we derive Harnack's
inequality for positive solutions of the Schrodinger equations. We also set up
some results about Green functions of the Laplacian equation on locally finite
graph. Interesting properties of Schrodinger equation are derived.


  We review some recent results on discrete symmetries and topological order in
spinor Bose-Einstein condensates (BECs) of $^{23}Na$. For spin one bosons with
two-body scatterings dominated by a total spin equal to two channel, the BECs
are in quantum spin nematic states at a low density limit. We study spin
correlations in condensates at different limits and analyze hidde$ symmetries
using a non-perturbative approach developed recently. We further more
investigate the influence of hidden $Z_2$ symmetries and U(1) quantum orders in
symmetry partially retored states, particularly the effects on topological
excitations.


  We derive large deviations type (LDT) estimates for linear cocycles over an
ergodic multifrequency torus translation. These models are called
quasi-periodic cocycles. We make the following assumptions on the model: the
translation vector satisfies a generic Diophantine condition, and the fiber
action is given by a matrix valued analytic function of several variables which
is not identically singular. The LDT estimates obtained here depend on some
uniform measurements on the cocycle.
  Our general results derived in [9] regarding the continuity properties of the
Lyapunov exponents (LE) and of the Oseledets filtration and decompositions are
then applicable, and we obtain local weak-Holder continuity of these quantities
in the presence of gaps in the Lyapunov spectrum. The main new feature of this
work is allowing a cocycle depending on several variables to have
singularities, i.e. points of non invertibility. This requires a careful
analysis of the set of zeros of certain analytic functions of several variables
and of the singularities (i.e. negative infinity values) of pluri-subharmonic
functions related to the iterates of the cocycle.
  A refinement of this method in the one variable case leads to a stronger LDT
estimate and in turn to a stronger, nearly-Holder modulus of continuity of the
LE, Oseledets filtration and Oseledets decomposition.
  This is a draft of a chapter in our forthcoming research monograph [9].


  This paper describes continuously phase-modulated standing surface acoustic
waves (CPM-SSAW) and its application for particle separation in multiple
pressure nodes. A linear change of phase in CPM-SSAW applies a force to
particles whose magnitude depends on their size and contrast factors. During
continuous phase modulation, we demonstrate that particles with the target
dimension are translated in the direction of moving pressure nodes, whereas
smaller particles show oscillatory movements. The rate of phase modulation is
optimized for separation of target particles from the relationship between mean
particle velocity and period of oscillation. The developed technique is applied
to separate particles of the target dimension from the particle mixture.
Furthermore, we also demonstrate human keratinocyte cells can be separated in
the cell and bead mixture. The separation technique is incorporated with a
microfluidic channel spanning multiple pressure nodes, which is advantageous
over separation in a single pressure node in terms of high throughput.


  We analyze the opportunity to observe thermal dileptons emitted off
deconfined matter resulting in heavy-ion collisions at RHIC and LHC energies.
Special kinematical conditions provided by the detector systems PHENIX and
ALICE, and the so-called $M_\perp$ scaling behavior of thermal dilepton spectra
are taken into account. Our considerations include energy loss effects of the
fast heavy quarks in deconfined matter, which for themselves can help to
identify the creation of a hot and dense parton medium. Due to a threshold like
effect for decay leptons we find a window at large transverse pair momentum and
fixed transverse mass where the thermal signal can exceed the background of
dileptons from correlated semileptonic decays of charm and bottom mesons.


  We give the first {\sl reconstruction algorithm} for decision trees: given
queries to a function $f$ that is $\mathrm{opt}$-close to a size-$s$ decision
tree, our algorithm provides query access to a decision tree $T$ where:
  $\circ$ $T$ has size $S = s^{O((\log s)^2/\varepsilon^3)}$;
  $\circ$ $\mathrm{dist}(f,T)\le O(\mathrm{opt})+\varepsilon$;
  $\circ$ Every query to $T$ is answered with $\mathrm{poly}((\log
s)/\varepsilon)\cdot \log n$ queries to $f$ and in $\mathrm{poly}((\log
s)/\varepsilon)\cdot n\log n$ time.
  This yields a {\sl tolerant tester} that distinguishes functions that are
close to size-$s$ decision trees from those that are far from size-$S$ decision
trees. The polylogarithmic dependence on $s$ in the efficiency of our tester is
exponentially smaller than that of existing testers.
  Since decision tree complexity is well known to be related to numerous other
boolean function properties, our results also provide a new algorithms for
reconstructing and testing these properties.


  This work describes an automatic news chatbot that draws content from a
diverse set of news articles and creates conversations with a user about the
news. Key components of the system include the automatic organization of news
articles into topical chatrooms, integration of automatically generated
questions into the conversation, and a novel method for choosing which
questions to present which avoids repetitive suggestions. We describe the
algorithmic framework and present the results of a usability study that shows
that news readers using the system successfully engage in multi-turn
conversations about specific news stories.


  In this paper, we introduce and study the $S$-weak global dimension
$S$-w.gl.dim$(R)$ of a commutative ring $R$ for some multiplicative subset $S$
of $R$. Moreover, commutative rings with $S$-weak global dimension at most $1$
are studied. Finally, we investigated the $S$-weak global dimension of factor
rings and polynomial rings.


  As convolution has empowered many smart applications, dynamic convolution
further equips it with the ability to adapt to diverse inputs. However, the
static and dynamic convolutions are either layout-agnostic or
computation-heavy, making it inappropriate for layout-specific applications,
e.g., face recognition and medical image segmentation. We observe that these
applications naturally exhibit the characteristics of large intra-image
(spatial) variance and small cross-image variance. This observation motivates
our efficient translation variant convolution (TVConv) for layout-aware visual
processing. Technically, TVConv is composed of affinity maps and a
weight-generating block. While affinity maps depict pixel-paired relationships
gracefully, the weight-generating block can be explicitly overparameterized for
better training while maintaining efficient inference. Although conceptually
simple, TVConv significantly improves the efficiency of the convolution and can
be readily plugged into various network architectures. Extensive experiments on
face recognition show that TVConv reduces the computational cost by up to 3.1x
and improves the corresponding throughput by 2.3x while maintaining a high
accuracy compared to the depthwise convolution. Moreover, for the same
computation cost, we boost the mean accuracy by up to 4.21%. We also conduct
experiments on the optic disc/cup segmentation task and obtain better
generalization performance, which helps mitigate the critical data scarcity
issue. Code is available at https://github.com/JierunChen/TVConv.


  In graphene nanoribbons (GNRs), the lateral confinement of charge carriers
opens a band gap, the key feature to enable novel graphene-based electronics.
Successful synthesis of GNRs has triggered efforts to realize field-effect
transistors (FETs) based on single ribbons. Despite great progress, reliable
and reproducible fabrication of single-ribbon FETs is still a challenge that
impedes applications and the understanding of the charge transport. Here, we
present reproducible fabrication of armchair GNR-FETs based on a network of
nanoribbons and analyze the charge transport mechanism using nine-atom wide
and, in particular, five-atom-wide GNRs with unprecedented conductivity. We
show formation of reliable Ohmic contacts and a yield of functional FETs close
to unity by lamination of GNRs on the electrodes. Modeling the charge carrier
transport in the networks reveals that this process is governed by inter-ribbon
hopping mediated by nuclear tunneling, with a hopping length comparable to the
physical length of the GNRs. Furthermore, we demonstrate that nuclear tunneling
is a general charge transport characteristic of the GNR networks by using two
different GNRs. Overcoming the challenge of low-yield single-ribbon transistors
by the networks and identifying the corresponding charge transport mechanism
puts GNR-based electronics in a new perspective.


  Image retrieval is a complex task that differs according to the context and
the user requirements in any specific field, for example in a medical
environment. Search by text is often not possible or optimal and retrieval by
the visual content does not always succeed in modelling high-level concepts
that a user is looking for. Modern image retrieval techniques consist of
multiple steps and aim to retrieve information from large--scale datasets and
not only based on global image appearance but local features and if possible in
a connection between visual features and text or semantics. This paper presents
the Parallel Distributed Image Search Engine (ParaDISE), an image retrieval
system that combines visual search with text--based retrieval and that is
available as open source and free of charge. The main design concepts of
ParaDISE are flexibility, expandability, scalability and interoperability.
These concepts constitute the system, able to be used both in real-world
applications and as an image retrieval research platform. Apart from the
architecture and the implementation of the system, two use cases are described,
an application of ParaDISE in retrieval of images from the medical literature
and a visual feature evaluation for medical image retrieval. Future steps
include the creation of an open source community that will contribute and
expand this platform based on the existing parts.


  In this paper, a technique for the Berlekamp-Massey(BM) algorithm is provided
to reduce the latency of decoding and save decoding power by early termination
or early-stopped checking. We investigate the consecutive zero discrepancies
during the decoding iteration and decide to early stop the decoding process.
This technique is subject to decoding failure in exchange for the decoding
latency. We analyze our proposed technique by considering the weight
distribution of BCH code and estimating the bounds of undetected error
probability as the event of enormous stop checking. The proposed method is
effective in numerical results and the probability of decoding failure is lower
than $10^{-119}$ for decoding 16383 code length of BCH codes. Furthermore, the
complexity compared the conventional early termination method with the proposed
approach for decoding the long BCH code. The proposed approach reduces the
complexity of the conventional approach by up to 80\%. As a result, the FPGA
testing on a USB device validates the reliability of the proposed method.


  It was conjectured some time ago that an effective description of the
Coulomb-confinement transition in compact U(1) lattice gauge field theory could
be described by scalar QED obtained by soft breaking of the N=2 Seiberg-Witten
model down to N=0 in the strong coupling region where monopoles are light. In
two previous works this idea was presented at a qualitative level. In this work
we analyze in detail the conjecture and obtain encouraging quantitative
agreement with the numerical determination of the monopole mass and the dual
photon mass in the vicinity of the Coulomb to confining phase transition.


  Accurate prediction of pedestrians' future motions is critical for
intelligent driving systems. Developing models for this task requires rich
datasets containing diverse sets of samples. However, the existing naturalistic
trajectory prediction datasets are generally imbalanced in favor of simpler
samples and lack challenging scenarios. Such a long-tail effect causes
prediction models to underperform on the tail portion of the data distribution
containing safety-critical scenarios. Previous methods tackle the long-tail
problem using methods such as contrastive learning and class-conditioned
hypernetworks. These approaches, however, are not modular and cannot be applied
to many machine learning architectures. In this work, we propose a modular
model-agnostic framework for trajectory prediction that leverages a specialized
mixture of experts. In our approach, each expert is trained with a specialized
skill with respect to a particular part of the data. To produce predictions, we
utilise a router network that selects the best expert by generating relative
confidence scores. We conduct experimentation on common pedestrian trajectory
prediction datasets and show that our method improves performance on long-tail
scenarios. We further conduct ablation studies to highlight the contribution of
different proposed components.


  We investigated the in-plane resistivity anisotropy for underdoped
Ba(Fe$_{1-x}$Co$_x$)$_2$As$_2$ single crystals with improved quality. We
demonstrate that the anisotropy in resistivity in the magnetostructural ordered
phase arises from the anisotropy in the residual component which increases in
proportion to the Co concentration $x$. This gives evidence that the anisotropy
originates from the impurity scattering by Co atoms substituted for the Fe
sites, rather than so far proposed mechanism such as the anisotropy of Fermi
velocities of reconstructed Fermi surface pockets. As doping proceeds to the
paramagnetic-tetragonal phase, a Co impurity transforms to a weak and isotropic
scattering center.


  We propose a simple optomechanical model in which a mechanical oscillator
quadrature could be "cooled" well below its equilibrium temperature by applying
a suitable feedback to drive the orthogonal quadrature by means of the homodyne
current of the radiation field used to probe its position.


  Planar metamaterials have been recently proposed for thin dielectric film
sensing in the terahertz frequency range. Although the thickness of the
dielectric film can be very small compared with the wavelength, the required
area of sensed material is still determined by the diffraction-limited spot
size of the terahertz beam excitation. In this article, terahertz near-field
sensing is utilized to reduce the spot size. By positioning the metamaterial
sensing platform close to the sub-diffraction terahertz source, the number of
excited resonators, and hence minimal film area, are significantly reduced. As
an additional advantage, a reduction in the number of excited resonators
decreases the inter-cell coupling strength, and consequently the resonance Q
factor is remarkably increased. The experimental results show that the
resonance Q factor is improved by 113%. Moreover, for a film with a thickness
of \lambda/375 the minimal area can be as small as 0.2\lambda by 0.2\lambda.
The success of this work provides a platform for future metamaterial-based
sensors for biomolecular detection.


  We consider dynamic risk measures induced by Backward Stochastic Differential
Equations (BSDEs) in enlargement of filtration setting. On a fixed probability
space, we are given a standard Brownian motion and a pair of random variables
$(\tau, \zeta) \in (0,+\infty) \times E$, with $E \subset \mathbb{R}^m$, that
enlarge the reference filtration, i.e., the one generated by the Brownian
motion. These random variables can be interpreted financially as a default time
and an associated mark. After introducing a BSDE driven by the Brownian motion
and the random measure associated to $(\tau, \zeta)$, we define the dynamic
risk measure $(\rho_t)_{t \in [0,T]}$, for a fixed time $T > 0$, induced by its
solution. We prove that $(\rho_t)_{t \in [0,T]}$ can be decomposed in a pair of
risk measures, acting before and after $\tau$ and we characterize its
properties giving suitable assumptions on the driver of the BSDE. Furthermore,
we prove an inequality satisfied by the penalty term associated to the robust
representation of $(\rho_t)_{t \in [0,T]}$ and we discuss the dynamic entropic
risk measure case, providing examples where it is possible to write explicitly
its decomposition and simulate it numerically.


  The normal form theorem, proved in R. Laver, On the left distributive law and
the freeness of an algebra of elementary embeddings, Advances in Mathematics 91
(1992), 209-231, for the free algebra $\Cal A$ on one generator $x$ satisfying
the left distributive law $a(bc) = (ab)(ac)$ is extended by showing that
members of $\Cal A$ can be put into a "division form."


  This paper presents a small-gain theorem for networks composed of a countably
infinite number of finite-dimensional subsystems. Assuming that each subsystem
is exponentially input-to-state stable, we show that if the gain operator,
collecting all the information about the internal Lyapunov gains, has a
spectral radius less than one, the overall infinite network is exponentially
input-to-state stable. The effectiveness of our result is illustrated through
several examples including nonlinear spatially invariant systems with sector
nonlinearities and a road traffic network.


  We investigate the formation process of planetesimals from the dust layer by
the gravitational instability in the gas disk using local $N$-body simulations.
The gas is modeled as a background laminar flow. We study the formation process
of planetesimals and its dependence on the strength of the gas drag. Our
simulation results show that the formation process is divided into three stages
qualitatively: the formation of wake-like density structures, the creation of
planetesimal seeds, and their collisional growth. The linear analysis of the
dissipative gravitational instability shows that the dust layer is secularly
unstable although Toomre's $Q$ value is larger than unity. However, in the
initial stage, the growth time of the gravitational instability is longer than
that of the dust sedimentation and the decrease in the velocity dispersion.
Thus, the velocity dispersion decreases and the disk shrinks vertically. As the
velocity dispersion becomes sufficiently small, the gravitational instability
finally becomes dominant. Then wake-like density structures are formed by the
gravitational instability. These structures fragment into planetesimal seeds.
The seeds grow rapidly owing to mutual collisions.


  The macroscopic elastic-plastic response of a metal polycrystal is analyzed
here by homogenizing the response of a representative volume element. With this
purpose, the theory developed in Hill(1967)(The essential structure of
constitutive laws for metal composites and polycrystals. J. Mech. Phys. Solids,
15, 79-95) is extended so that the structural changes caused by plastic
deformation are taken into account. It is found that the type of evolution
assigned to the slip systems of a constituent crystal influences significantly
the structure of the constitutive system characterizing the overall response.
In particular, the classical normality structure is obtained only if slip
systems are assumed to convect with the crystal lattice. Any other type of slip
system evolution induces a deviation of the macro-rate of plastic deformation
from the exterior normal to the yield surface. This provides a physical
explanation and also a rigorous definition for the much debated concept of
plastic spin.


  By using a very simple and general model to describe the dynamics of disk
galaxies, we estimate the $\lambda$ spin parameter for a sample of observed
galaxies and present a study in which we show that several important physical
properties are intrinsically related to the dynamics of the systems. Although
correlations between average metallicity with magnitude or Hubble type are
evident, we obtain equally strong correlations with the spin parameter, where
galaxies with low $\lambda $ values present higher abundances and galaxies with
high $\lambda$ values are poor in metals. Also, the gas content of the galaxies
correlates with $\lambda$, with high $\lambda$ systems showing higher gas mass
fractions than low $\lambda$ galaxies, highlighting the important role this
parameter plays in the structure of disk galaxies and the proposal of $\lambda$
as a robust and objective physical measure of galactic morphology.


  In this paper, new probability estimates are derived for ideal lattice codes
from totally real number fields using ideal class Dedekind zeta functions. In
contrast to previous work on the subject, it is not assumed that the ideal in
question is principal. In particular, it is shown that the corresponding
inverse norm sum depends not only on the regulator and discriminant of the
number field, but also on the values of the ideal class Dedekind zeta
functions. Along the way, we derive an estimate of the number of elements in a
given ideal with a certain algebraic norm within a finite hypercube. We provide
several examples which measure the accuracy and predictive ability of our
theorems.


  The $ B_3 - L_2$ $ Z' $ model may explain certain features of the fermion
mass spectrum as well as the $b \rightarrow s \mu^+ \mu^-$ anomalies. The $ Z'
$ acquires its mass via a TeV-scale scalar field, the flavon, whose vacuum
expectation value spontaneously breaks the family non-universal gauged $
U(1)_{B_3 - L_2} $ symmetry. We review the key features of the model, with an
emphasis on its scalar potential and the flavon field, and use experimental
data and perturbativity arguments to place bounds upon the Higgs-flavon mixing
angle. Finally, we discuss flavonstrahlung as a means to discover the flavon
experimentally and compute flavonstrahlung cross-sections at current and future
colliders.


  Filtering multi-dimensional images such as color images, color videos,
multispectral images and magnetic resonance images is challenging in terms of
both effectiveness and efficiency. Leveraging the nonlocal self-similarity
(NLSS) characteristic of images and sparse representation in the transform
domain, the block-matching and 3D filtering (BM3D) based methods show powerful
denoising performance. Recently, numerous new approaches with different
regularization terms, transforms and advanced deep neural network (DNN)
architectures are proposed to improve denoising quality. In this paper, we
extensively compare over 60 methods on both synthetic and real-world datasets.
We also introduce a new color image and video dataset for benchmarking, and our
evaluations are performed from four different perspectives including
quantitative metrics, visual effects, human ratings and computational cost.
Comprehensive experiments demonstrate: (i) the effectiveness and efficiency of
the BM3D family for various denoising tasks, (ii) a simple matrix-based
algorithm could produce similar results compared with its tensor counterparts,
and (iii) several DNN models trained with synthetic Gaussian noise show
state-of-the-art performance on real-world color image and video datasets.
Despite the progress in recent years, we discuss shortcomings and possible
extensions of existing techniques. Datasets and codes for evaluation are made
publicly available at https://github.com/ZhaomingKong/Denoising-Comparison.


  The possible role of magneto-rotational instability (MRI) and its driven MHD
turbulence in the solar interior is studied on the basis of the linear and
nonlinear theories coupling with physical parameters, providing solar rotation
profile inverted from the helioseismic observation and a standard model for the
internal structure of the sun. We find that the MRI venue is confined to the
higher-latitude tachocline and lower-latitude near-surface shear layer. It is
especially interesting that the MRI-active region around the tachocline closely
overlaps with the area indicating a steep entropy rise which is required from
the thermal wind balance in the sun. This suggests that the MRI-driven
turbulence plays a crucial role in maintaining the thermal wind balance in the
sun via the exceptional turbulent heating and equatorward angular momentum
transports. The warm pole existing around the tachocline might be a natural
outcome of the turbulent activities energized by the MRI.


  Current methods to interpret deep learning models by generating saliency maps
generally rely on two key assumptions. First, they use first-order
approximations of the loss function neglecting higher-order terms such as the
loss curvatures. Second, they evaluate each feature's importance in isolation,
ignoring their inter-dependencies. In this work, we study the effect of
relaxing these two assumptions. First, by characterizing a closed-form formula
for the Hessian matrix of a deep ReLU network, we prove that, for a
classification problem with a large number of classes, if an input has a high
confidence classification score, the inclusion of the Hessian term has small
impacts in the final solution. We prove this result by showing that in this
case the Hessian matrix is approximately of rank one and its leading
eigenvector is almost parallel to the gradient of the loss function. Our
empirical experiments on ImageNet samples are consistent with our theory. This
result can have implications in other related problems such as adversarial
examples as well. Second, we compute the importance of group-features in deep
learning interpretation by introducing a sparsity regularization term. We use
the $L_0-L_1$ relaxation technique along with the proximal gradient descent to
have an efficient computation of group feature importance scores. Our empirical
results indicate that considering group features can improve deep learning
interpretation significantly.


  A general question behind this paper is to explore a good notion for
intrinsic curvature in the framework of noncommutative geometry started by
Alain Connes in the 80s. It has only recently begun (2014) to be comprehended
via the intensive study of modular geometry on the noncommutative two tori. In
this paper, we extend recent results on the modular geometry on noncommutative
two tori to a larger class of noncommutative manifolds: toric noncommutative
manifolds. The first contribution of this work is a pseudo differential
calculus which is suitable for spectral geometry on toric noncommutative
manifolds. As the main application, we derive a general expression for the
modular curvature with respect to a conformal change of metric on toric
noncommutative manifolds. By specializing our results to the noncommutative two
and four tori, we recovered the modular curvature functions found in the
previous works. An important technical aspect of the computation is that it is
free of computer assistance.