  We calculate the electroweak-like one-loop supersymmetric contributions to
the rare and flavor-violating decay of the top quark into a charm quark and a
gauge boson: $t->c V$, with $V=\gamma,Z,g$. We consider loops of both charginos
and down-like squarks (where we identify and correct an error in the
literature) and neutralinos and up-like squarks (which have not been calculated
before). We also account for left-right and generational squark mixing. Our
numerical results indicate that supersymmetric contributions to $t->cV$ can be
upto 5 orders of magnitude larger than their Standard Model counterparts.
However, they still fall short of the sensitivity expected at the
next-generation top-quark factories.


  With regard to the wider area of AI/LT platform interoperability, we
concentrate on two core aspects: (1) cross-platform search and discovery of
resources and services; (2) composition of cross-platform service workflows. We
devise five different levels (of increasing complexity) of platform
interoperability that we suggest to implement in a wider federation of AI/LT
platforms. We illustrate the approach using the five emerging AI/LT platforms
AI4EU, ELG, Lynx, QURATOR and SPEAKER.


  In a test experiment at the Final Focus Test Beam of the Stanford Linear
Accelerator Center, the fluorescence yield of 28.5 GeV electrons in air and
nitrogen was measured. The measured photon yields between 300 and 400 nm at 1
atm and 29 deg C are Y(760 Torr, air) = 4.42 +/- 0.73 and Y(760 Torr, nitrogen)
= 29.2 +/- 4.8 photons per electron per meter. Assuming that the fluorescence
yield is proportional to the energy deposition of a charged particle traveling
through air, good agreement with measurements at lower particle energies is
observed.


  In this work we develop on the recently suggested concept of superstatistics
[C. Beck and E.G.D. Cohen, Physica A {\bf 322}, 267 (2003)], face the problem
of devising a viable way for estimating the correct statistics for a system in
absence of sufficient knowledge of its microscopical dynamics, and suggest to
solve it through the Maximum Entropy Principle. As an example, we deduce the
Probability Distribution Function for velocity fluctuations in turbulent
fluids, which is slightly different from the form suggested in [C. Beck, Phys.
Rev. Lett. {\bf 87}, 180601 (2001)].


  We consider the mirror-prox algorithm for solving monotone Variational
Inequality (VI) problems. As the mirror-prox algorithm is not practically
implementable, except in special instances of VIs (such as affine VIs), we
consider its implementation with Popov method updates. We provide convergence
rate analysis of our proposed method for a monotone VI with a Lipschitz
continuous mapping. We establish a convergence rate of $O(1/t)$, in terms of
the number $t$ of iterations, for the dual gap function. Simulations on a two
player matrix game corroborate our findings.


  We investigate the impact of supervised prediction models on the strength and
efficiency of artificial agents that use the Monte-Carlo Tree Search (MCTS)
algorithm to play a popular video game Hearthstone: Heroes of Warcraft. We
overview our custom implementation of the MCTS that is well-suited for games
with partially hidden information and random effects. We also describe
experiments which we designed to quantify the performance of our Hearthstone
agent's decision making. We show that even simple neural networks can be
trained and successfully used for the evaluation of game states. Moreover, we
demonstrate that by providing a guidance to the game state search heuristic, it
is possible to substantially improve the win rate, and at the same time reduce
the required computations.


  We model collective disk flow though a square array of obstacles as the flow
direction is changed relative to the symmetry directions of the array. At lower
disk densities there is no clogging for any driving direction, but as the disk
density increases, the average disk velocity decreases and develops a drive
angle dependence. For certain driving angles, the flow is reduced or drops to
zero when the system forms a heterogeneous clogged state consisting of high
density clogged regions coexisting with empty regions. The clogged states are
fragile and can be unclogged by changing the driving angle. For large obstacle
sizes, we find a uniform clogged state that is distinct from the collective
clogging regime. Within the clogged phases, depinning transitions can occur as
a function of increasing driving force, with strongly intermittent motion
appearing just above the depinning threshold. The clogging is robust against
the random removal or dilution of the obstacle sites, and the disks are able to
form system-spanning clogged clusters even under increasing dilution. If the
dilution becomes too large, however, the clogging behavior is lost.


  We prove a quantitative version of Gordon's Theorem concerning absence of
eigenvalues for Jacobi matrices and Sturm-Liouville operators with complex
coefficients.


  We propose a new domain adaptation method for Combinatory Categorial Grammar
(CCG) parsing, based on the idea of automatic generation of CCG corpora
exploiting cheaper resources of dependency trees. Our solution is conceptually
simple, and not relying on a specific parser architecture, making it applicable
to the current best-performing parsers. We conduct extensive parsing
experiments with detailed discussion; on top of existing benchmark datasets on
(1) biomedical texts and (2) question sentences, we create experimental
datasets of (3) speech conversation and (4) math problems. When applied to the
proposed method, an off-the-shelf CCG parser shows significant performance
gains, improving from 90.7% to 96.6% on speech conversation, and from 88.5% to
96.8% on math problems.


  We consider the ground state of the one-dimensional quantum Ising model with
transverse field $h_x$ in one dimension depending on the site $x \in \mathbb Z$
in a finite volume $\Lambda_{m}:=\{-m,-m+1,\ldots,m+L\}\ $. We make suitable
assumptions on the regions where the field is small and prove that if the field
is sufficiently large on the complementary set, then the entanglement of the
interval $\Lambda_{0}:=\left\{ 0,..,L\right\} $ relative to its complement
$\Lambda_{m}\backslash\Lambda_{0}$ is bounded uniformly in $m$ and $L$. The
result applies in particular to periodic transverse fields. The bound is
established by means of a suitable cluster expansion.


This paper has been withdrawn by the authors until some changes are made.


  We present our work on High Energy Material detection based on thin film of
Lithium using the phenomenon of Optical Pumping. The Li atoms present in the
thin film are optically pumped to one of the ground hyperfine energy levels so
that they can no more absorb light from the resonant light source. Now in
presence of a RF signal, which quantifies the ambient magnetic field, this
polarized atomic system is again randomized thus making it reabsorb the
resonant light. This gives a quantified measurement of the magnetic field
surrounding the thin film detector. This is then mapped to the presence of
magnetic HEM and hence the HEM are detected. Our approach in this regard starts
with verifying the stability of Lithium atoms in various solvents so as to get
a suitable liquid medium to form a thin film. In this regard, various
UV-visible characterization spectra are presented to finally approach a stable
system for the detection. We have worked on around 10 polar and non- polar
solvents to see the stability criteria. UV-Vis probe has been found to be quite
effective in addressing issue of hosting Lithium atoms by various solvents.
Further results on the atomic system size as dispersed in the various solvents
are presented. Atomic system size gives us a good quantitative estimate of the
density of Lithium dispersed system in the thin film. Finally a separate atomic
system under Ultrahigh vacuum is presented to achieve optical pumping wherein
the polarization and depolarization of atomic system is seen using a 670 nm
diode laser system which is electronically locked using a double window Hollow
cathode lamp. These both systems will finally be combined together to give the
final form to the detection system. As this whole system measures relative
change in magnetic field due to the presence of HEM,it gives a more reliable
and accurate detection method compared to the absolute measurement detectors.


  Four-quark potentials for $SU(2)$ are evaluated in the static limit with the
quenched approximation -- using a lattice of $16^3\times 32$ and $\beta=2.4$.
The four quarks are restricted to the corners of rectangles with sides upto
seven lattice spacings long. The results are analysed in terms of a strategy
based on interquark two-body potentials -- as advocated earlier by the authors.
This shows that a standard two-body approach overestimates the four-quark
binding energy by upto a factor of about three for the largest rectangles.


  Using an effective Dirac model, we study the orbital Hall effect (OHE) in
bilayers of transition metal dichalcogenides with 2H stacking (2H-TMD). We use
first-order perturbation theory in the interlayer coupling of the bilayer
system to obtain analytical expressions for the orbital Hall conductivity in
the linear response regime. We use two distinct descriptions of the orbital
angular momentum (OAM) operator: The first one is the intra-atomic
approximation that considers only the intrasite contribution to the OAM [Cysne
et al. Phys. Rev. Lett. 126, 056601 (2021)]. The second one uses the
Berry-phase formula of the orbital (valley) magnetic moment to describe the OAM
operator [Bhowal and Vignale, Phys. Rev. B 103, 195309 (2021)]. This approach
includes both intersite and intrasite contributions to the OAM. Our results
suggest that the two approaches agree qualitatively in describing the OHE in
bilayers of 2H-TMDs, although they present some quantitative differences. We
also show that interlayer coupling plays an essential role in understanding the
OHE in the unbiased bilayer of 2H-TMD. This coupling causes the Bloch states to
become bonding (antibonding) combinations of states of individual layers,
demanding the consideration of the non-Abelian structure of the orbital
magnetic moment to the occurrence of OHE. As we discuss throughout the work,
the emerging picture of transport of OAM in the unbiased bilayer of 2H-TMDs
based on OHE is very different from the usual picture based on the valley Hall
effect, shedding new lights on previous experimental results. We also discuss
the effect of the inclusion of a gate-voltage bias in the bilayer system. Our
work gives support to recent theoretical predictions on OHE in two-dimensional
materials.


  This paper focuses on developing a real-time optimal operation model for a
new engineering system, wind-to-hydrogen-driven low-carbon critical
infrastructure (W2H-LCCI), that utilizes wind power to generate hydrogen
through electrolysis and combines it with carbon capture to reduce carbon
emissions from the power sector. First, a convex mathematical model for
W2H-LCCI is proposed, and then optimization models for its real-time
decision-making are developed, which are mixed-integer convex programs (MICPs).
Furthermore, since this large-scale MICP problem must be solved in real-time, a
fast solution method based on active constraint and integer variable prediction
(ACIVP) is presented. ACIVP method predicts the binary variable values and the
set of limited-number constraints, which most likely contain all of the active
constraints, based on historical optimization data. It results in only a
small-scale continuous convex optimization problem needing to be solved by
optimization solvers for W2H-LCCI real-time optimal operation. To increase the
accuracy of the ACIVP method, feature space expansion (FSE) is employed, and a
multi-stage ACIVP-FSE method is proposed. The effects of stage design and stage
ordering on ACIVP-FSE performance are also discussed. We validate the
effectiveness of the developed system and solution method using two
water-energy nexus case studies.


  The article describes prime intervals into the prime factorization of the
middle binomial coefficient. Prime factors and prime powers are distributed in
layers. Each layer consists of non-repeated prime numbers which are chosen (not
calculated) from the noncrossing prime intervals. Repeated factors are formed
when primes are duplicated among different layers.


  Unsupervised domain adaptation aims to transfer knowledge from a source
domain to a target domain so that the target domain data can be recognized
without any explicit labelling information for this domain. One limitation of
the problem setting is that testing data, despite having no labels, from the
target domain is needed during training, which prevents the trained model being
directly applied to classify unseen test instances. We formulate a new
cross-domain classification problem arising from real-world scenarios where
labelled data is available for a subset of classes (known classes) in the
target domain, and we expect to recognize new samples belonging to any class
(known and unseen classes) once the model is learned. This is a generalized
zero-shot learning problem where the side information comes from the source
domain in the form of labelled samples instead of class-level semantic
representations commonly used in traditional zero-shot learning. We present a
unified domain adaptation framework for both unsupervised and zero-shot
learning conditions. Our approach learns a joint subspace from source and
target domains so that the projections of both data in the subspace can be
domain invariant and easily separable. We use the supervised locality
preserving projection (SLPP) as the enabling technique and conduct experiments
under both unsupervised and zero-shot learning conditions, achieving
state-of-the-art results on three domain adaptation benchmark datasets:
Office-Caltech, Office31 and Office-Home.


  Neural Machine Translation (NMT) has achieved significant breakthrough in
performance but is known to suffer vulnerability to input perturbations. As
real input noise is difficult to predict during training, robustness is a big
issue for system deployment. In this paper, we improve the robustness of NMT
models by reducing the effect of noisy words through a Context-Enhanced
Reconstruction (CER) approach. CER trains the model to resist noise in two
steps: (1) perturbation step that breaks the naturalness of input sequence with
made-up words; (2) reconstruction step that defends the noise propagation by
generating better and more robust contextual representation. Experimental
results on Chinese-English (ZH-EN) and French-English (FR-EN) translation tasks
demonstrate robustness improvement on both news and social media text. Further
fine-tuning experiments on social media text show our approach can converge at
a higher position and provide a better adaptation.


  Representations of the quantum q-oscillator algebra are studied with
particular attention to local Hamiltonian representations of the Schroedinger
type. In contrast to the standard harmonic oscillators such systems exhibit a
continuous spectrum. The general scheme of realization of the q-oscillator
algebra on the space of wave functions for a one-dimensional Schroedinger
Hamiltonian shows the existence of non-Fock irreducible representations
associated to the continuous part of the spectrum and directly related to the
deformation. An algorithm for the mapping of energy levels is described.


  Let $F$ be a $\delta-$field (differential field) of characteristic zero with
an algebraically closed field of constants $F^\delta$, $A$ be a
$\delta-F-$central simple algebra, $K$ be a Picard-Vessiot extension for the
$\delta-F-$module $A$ and $\mathscr G(K|F)$ be the $\delta-$Galois group of $K$
over $F.$ We prove that a $\delta-$field extension $L$ of $F,$ having
$F^\delta$ as its field of constants, splits the $\delta-F-$central simple
algebra $A$ if and only if the $\delta-$field $K$ embeds in $L.$
  We then extend the theory of $\delta-F-$matrix algebras over a $\delta-$field
$F,$ put forward by Magid & Juan (2008), to arbitrary $\delta-F-$central simple
algebras. In particular, we establish a natural bijective correspondence
between the isomorphism classes of $\delta-F-$central simple algebras of
dimension $n^2$ over $F$ that are split by the $\delta-$field $K$ and the
classes of inequivalent representations of the algebraic group $\mathscr
G(K|F)$ in $\mathrm{PGL}_n(F^\delta).$ We show that $\mathscr G(K|F)$ is a
reductive or a solvable algebraic group if and only if $A$ has certain kinds of
$\delta-$right ideals.


  We investigate spin effects in four-quark systems consisting of two heavy
anti-bottom quarks and two light up/down quarks. To this end we use the
Born-Oppenheimer approximation. We utilize potentials of two static antiquarks
in the presence of two quarks of finite mass computed via lattice QCD and solve
a coupled-channel Schr\"odinger equation for the anti-bottom-anti-bottom
separation. Without taking heavy quark spins into account this approach
predicted a $u d \bar b \bar b$ tetraquark bound state with quantum numbers
$I(J^P) = 0(1^+)$. We now extend this Born-Oppenheimer approach with coupled
channel Schr\"odinger equations allowing us to incorporate effects due to the
heavy $\bar b$ spins. We confirm the existence of the $u d \bar b \bar b$
tetraquark.


  Simulation results showing the comparison between the performance of
different km3 detector geometries are reported. Effective neutrino areas and
angular resolutions are reported for three different geometries based on
NEMO-towers and strings. The results show that the NEMO-tower based detector
has the best performance concerning both the effective area and the angular
resolution isotropy


  Motor babbling and goal babbling has been used for sensorimotor learning of
highly redundant systems in soft robotics. Recent works in goal babbling has
demonstrated successful learning of inverse kinematics (IK) on such systems,
and suggests that babbling in the goal space better resolves motor redundancy
by learning as few sensorimotor mapping as possible. However, for
musculoskeletal robot systems, motor redundancy can be of useful information to
explain muscle activation patterns, thus the term motor abundance. In this
work, we introduce some simple heuristics to empirically define the unknown
goal space, and learn the inverse kinematics of a 10 DoF musculoskeletal robot
arm using directed goal babbling. We then further propose local online motor
babbling using Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which
bootstraps on the collected samples in goal babbling for initialization, such
that motor abundance can be queried for any static goal within the defined goal
space. The result shows that our motor babbling approach can efficiently
explore motor abundance, and gives useful insights in terms of muscle stiffness
and synergy.


  We consider arbitrary trajectories subject to a coordinate-wise energy
decrease: the sign of the derivative of each entry is never the same as that of
the corresponding entry of the gradient of some energy function. We show that
this simple condition guarantees convergence to a point, to the minimum of the
energy functions, or to a set where its Hessian has very specific properties.
This extends and strengthens recent results that were restricted to convex
quadratic energy functions. We demonstrate the application of our result by
using it to prove the convergence of a class of multi-agent systems subject to
multiple uncertainties.


  In the human brain, sequences of language input are processed within a
distributed and hierarchical architecture, in which higher stages of processing
encode contextual information over longer timescales. In contrast, in recurrent
neural networks which perform natural language processing, we know little about
how the multiple timescales of contextual information are functionally
organized. Therefore, we applied tools developed in neuroscience to map the
"processing timescales" of individual units within a word-level LSTM language
model. This timescale-mapping method assigned long timescales to units
previously found to track long-range syntactic dependencies. Additionally, the
mapping revealed a small subset of the network (less than 15% of units) with
long timescales and whose function had not previously been explored. We next
probed the functional organization of the network by examining the relationship
between the processing timescale of units and their network connectivity. We
identified two classes of long-timescale units: "controller" units composed a
densely interconnected subnetwork and strongly projected to the rest of the
network, while "integrator" units showed the longest timescales in the network,
and expressed projection profiles closer to the mean projection profile.
Ablating integrator and controller units affected model performance at
different positions within a sentence, suggesting distinctive functions of
these two sets of units. Finally, we tested the generalization of these results
to a character-level LSTM model and models with different architectures. In
summary, we demonstrated a model-free technique for mapping the timescale
organization in recurrent neural networks, and we applied this method to reveal
the timescale and functional organization of neural language models.


  We prove the necessity of the UMD condition, with a quantitative estimate of
the UMD constant, for any inequality in a family of $L^p$ bounds between
different partial derivatives $\partial^\beta u$ of $u\in
C^\infty_c(\mathbb{R}^n,X)$. In particular, we show that the estimate
$\|u_{xy}\|_p\leq K(\|u_{xx}\|_p+\|u_{yy}\|_p)$ characterizes the UMD property,
and the best constant $K$ is equal to one half of the UMD constant. This
precise value of $K$ seems to be new even for scalar-valued functions.


  One of the historical suggestions to tackle the strong CP problem is to take
the up quark mass to zero while keeping $m_d$ finite. The $\theta$ angle is
then supposed to become irrelevant, i.e. the topological susceptibility
vanishes. However, the definition of the quark mass is scheme-dependent and
identifying the $m_u=0$ point is not trivial, in particular with Wilson-like
fermions. More specifically, up to our knowledge there is no theoretical
argument guaranteeing that the topological susceptibility exactly vanishes when
the PCAC mass does.
  We will present our recent progresses on the empirical check of this property
using $N_f=1+2$ flavours of clover fermions, where the lightest fermion is
tuned very close to $m^{PCAC}_u$=0 and the mass of the other two is kept of the
order of magnitude of the physical $m_s$. This choice is indeed expected to
amplify any unknown non-perturbative effect caused by $m_u\not=m_d$. The
simulation is repeated for several $\beta$s and those results, although
preliminary, give a hint about what happens in the continuum limit.


  By using random multilinear maps, we provide new lower bounds for the
Erd\H{o}s box problem, the problem of estimating the extremal number of the
complete $d$-partite $d$-uniform hypergraph with two vertices in each part,
thereby improving on work of Gunderson, R\"{o}dl and Sidorenko.


  Angular momentum $J=3/2$ holes in semiconductor heterostructures are showed
to accumulate nonabelian geometric phases as a consequence of their motion. We
provide a general framework for analyzing such a system and compute conductance
oscillations for a simple ring geometry. We also analyze a figure-8 geometry
which captures intrinsically nonabelian interference effects.


  We have studied the shadow of a disformal Kerr black hole with an extra
deformation parameter, which belongs to non-stealth rotating solutions in
quadratic Degenerate Higher Order Scalar Tensor (DHOST) theory. Our result show
that the size of the shadow increases with the deformation parameter for the
black hole with arbitrary spin parameter. However, the effect of the
deformation parameter on the shadow shape depends heavily on the spin parameter
of black hole and the sign of the deformation parameter. The change of the
shadow shape becomes more distinct for the black hole with the more quickly
rotation and the more negative deformation parameter. Especially, for the
near-extreme black hole with negative deformation parameter, there exist a
"pedicel"-like structure appeared in the shadow, which increases with the
absolute value of deformation parameter. The eyebrow-like shadow and the
self-similar fractal structures also appear in the shadow for the disformal
Kerr black hole in DHOST theory. These features in the black hole shadow
originating from the scalar field could help us to understand the non-stealth
disformal Kerr black hole and quadratic DHOST theory.


  Tolerance to image variations (e.g. translation, scale, pose, illumination)
is an important desired property of any object recognition system, be it human
or machine. Moving towards increasingly bigger datasets has been trending in
computer vision specially with the emergence of highly popular deep learning
models. While being very useful for learning invariance to object inter- and
intra-class shape variability, these large-scale wild datasets are not very
useful for learning invariance to other parameters forcing researchers to
resort to other tricks for training a model. In this work, we introduce a
large-scale synthetic dataset, which is freely and publicly available, and use
it to answer several fundamental questions regarding invariance and selectivity
properties of convolutional neural networks. Our dataset contains two parts: a)
objects shot on a turntable: 16 categories, 8 rotation angles, 11 cameras on a
semicircular arch, 5 lighting conditions, 3 focus levels, variety of
backgrounds (23.4 per instance) generating 1320 images per instance (over 20
million images in total), and b) scenes: in which a robot arm takes pictures of
objects on a 1:160 scale scene. We study: 1) invariance and selectivity of
different CNN layers, 2) knowledge transfer from one object category to
another, 3) systematic or random sampling of images to build a train set, 4)
domain adaptation from synthetic to natural scenes, and 5) order of knowledge
delivery to CNNs. We also explore how our analyses can lead the field to
develop more efficient CNNs.


  Particle swam optimization (PSO) is a popular stochastic optimization method
that has found wide applications in diverse fields. However, PSO suffers from
high computational complexity and slow convergence speed. High computational
complexity hinders its use in applications that have limited power resources
while slow convergence speed makes it unsuitable for time critical
applications. In this paper, we propose two techniques to overcome these
limitations. The first technique reduces the computational complexity of PSO
while the second technique speeds up its convergence. These techniques can be
applied, either separately or in conjunction, to any existing PSO variant. The
proposed techniques are robust to the number of dimensions of the optimization
problem. Simulation results are presented for the proposed techniques applied
to the standard PSO as well as to several PSO variants. The results show that
the use of both these techniques in conjunction results in a reduction in the
number of computations required as well as faster convergence speed while
maintaining an acceptable error performance for time-critical applications.


  Early in the Moon's history volcanic outgassing may have produced a periodic
millibar level atmosphere (Needham and Kring, 2017). We examined the relevant
atmospheric escape processes and lifetime of such an atmosphere. Thermal escape
rates were calculated as a function of atmospheric mass for a range of
temperatures including the effect of the presence of a light constituent such
as H2. Photochemical escape and atmospheric sputtering were calculated using
estimates of the higher EUV and plasma fluxes consistent with the early Sun.
The often used surface Jeans calculation carried out in Vondrak (1974) is not
applicable for the scale and composition of the atmosphere considered. We show
that solar driven non-thermal escape can remove an early CO millibar level
atmosphere on the order of 1 Myr if the average exobase temperature is below
350 - 400 K. However, if solar UV/EUV absorption heats the upper atmosphere to
temperatures > 400 K thermal escape increasingly dominates the loss rate, and
we estimated a minimum lifetime of 100's of years considering energy limited
escape.


  We discuss computing with hierarchies of families of (potentially weighted)
semiclassical Jacobi polynomials which arise in the construction of
multivariate orthogonal polynomials. In particular, we outline how to build
connection and differentiation matrices with optimal complexity and compute
analysis and synthesis operations in quasi-optimal complexity. We investigate a
particular application of these results to constructing orthogonal polynomials
in annuli, called the generalised Zernike annular polynomials, which lead to
sparse discretisations of partial differential equations. We compare against a
scaled-and-shifted Chebyshev--Fourier series showing that in general the
annular polynomials converge faster when approximating smooth functions and
have better conditioning. We also construct a sparse spectral element method by
combining disk and annulus cells, which is highly effective for solving PDEs
with radially discontinuous variable coefficients and data.


  We prove that if a framework of a graph is neighborhood affine rigid in
$d$-dimensions (or has the stronger property of having an equilibrium stress
matrix of rank $n-d-1$) then it has an affine flex (an affine, but non
Euclidean, transform of space that preserves all of the edge lengths) if and
only if the framework is ruled on a single quadric. This strengthens and also
simplifies a related result by Alfakih. It also allows us to prove that the
property of super stability is invariant with respect to projective transforms
and also to the coning and slicing operations. Finally this allows us to unify
some previous results on the Strong Arnold Property of matrices.


  We investigate analytically the low temperature behavior of the specific heat
$C_v(T)$ for a large class of quantum disordered models within Mean Field
approximation. This includes the vibrational modes of a lattice pinned by
impurity disorder in the quantum regime, the quantum spherical $p$-spin-glass
and a quantum Heisenberg spin glass. We exhibit a general mechanism, common to
all these models, arising from the so-called marginality condition, responsible
for the cancellation of the linear and quadratic contributions in $T$ in the
specific heat. We thus find for all these models the Mean Field result $C_v(T)
\propto T^3$.


  We provide criteria for deciding whether a given planar curve is an image of
a given spatial curve, obtained by a central or a parallel projection with
unknown parameters. These criteria reduce the projection problem to a certain
modification of the equivalence problem of planar curves under affine and
projective transformations. The latter problem can be addressed using Cartan's
moving frame method. This leads to a novel algorithmic solution of the
projection problem for curves. The computational advantage of the algorithms
presented here, in comparison to algorithms based on a straightforward
solution, lies in a significant reduction of a number of real parameters that
has to be eliminated in order to establish existence or non-existence of a
projection that maps a given spatial curve to a given planar curve. The same
approach can be used to decide whether a given finite set of ordered points on
a plane is an image of a given finite set of ordered points in R^3. The
motivation comes from the problem of establishing a correspondence between an
object and an image, taken by a camera with unknown position and parameters.


  Prediction and quantification of future volatility and returns play an
important role in financial modelling, both in portfolio optimization and risk
management. Natural language processing today allows to process news and social
media comments to detect signals of investors' confidence. We have explored the
relationship between sentiment extracted from financial news and tweets and
FTSE100 movements. We investigated the strength of the correlation between
sentiment measures on a given day and market volatility and returns observed
the next day. The findings suggest that there is evidence of correlation
between sentiment and stock market movements: the sentiment captured from news
headlines could be used as a signal to predict market returns; the same does
not apply for volatility. Also, in a surprising finding, for the sentiment
found in Twitter comments we obtained a correlation coefficient of -0.7, and
p-value below 0.05, which indicates a strong negative correlation between
positive sentiment captured from the tweets on a given day and the volatility
observed the next day. We developed an accurate classifier for the prediction
of market volatility in response to the arrival of new information by deploying
topic modelling, based on Latent Dirichlet Allocation, to extract feature
vectors from a collection of tweets and financial news. The obtained features
were used as additional input to the classifier. Thanks to the combination of
sentiment and topic modelling our classifier achieved a directional prediction
accuracy for volatility of 63%.


  This paper studies the performance of single-input multiple-output (SIMO)
systems under receive antenna selection (RAS) and BPSK/QPSK modulations. At the
receiver, a subset of branches are selected and combined using maximal-ratio
combining (MRC) to maximize the instantaneous Signal to Noise Ratio (SNR). By
assuming independent and identical distributed (i.i.d.) Rayleigh flat fading, a
closed-form expression, with considerably high precision, is developed to
approximate the average input-output mutual information, also termed as
symmetric capacity, of the whole system. Later, this approximated expression is
further utilized to investigate the efficient capacity and energy efficiency of
the SIMO system under BPSK/QPSK modulations and RAS. Besides analytical
derivations, simulations are provided to demonstrate the approximation
precision, feasibility and validity of the derived results.


  The stochastic multi-arm bandit problem has been extensively studied under
standard assumptions on the arm's distribution (e.g bounded with known support,
exponential family, etc). These assumptions are suitable for many real-world
problems but sometimes they require knowledge (on tails for instance) that may
not be precisely accessible to the practitioner, raising the question of the
robustness of bandit algorithms to model misspecification. In this paper we
study a generic Dirichlet Sampling (DS) algorithm, based on pairwise
comparisons of empirical indices computed with re-sampling of the arms'
observations and a data-dependent exploration bonus. We show that different
variants of this strategy achieve provably optimal regret guarantees when the
distributions are bounded and logarithmic regret for semi-bounded distributions
with a mild quantile condition. We also show that a simple tuning achieve
robustness with respect to a large class of unbounded distributions, at the
cost of slightly worse than logarithmic asymptotic regret. We finally provide
numerical experiments showing the merits of DS in a decision-making problem on
synthetic agriculture data.


  We present MOTLEE, a distributed mobile multi-object tracking algorithm that
enables a team of robots to collaboratively track moving objects in the
presence of localization error. Existing approaches to distributed tracking
make limiting assumptions regarding the relative spatial relationship of
sensors, including assuming a static sensor network or that perfect
localization is available. Instead, we develop an algorithm based on the
Kalman-Consensus filter for distributed tracking that properly leverages
localization uncertainty in collaborative tracking. Further, our method allows
the team to maintain an accurate understanding of dynamic objects in the
environment by realigning robot frames and incorporating frame alignment
uncertainty into our object tracking formulation. We evaluate our method in
hardware on a team of three mobile ground robots tracking four people. Compared
to previous works that do not account for localization error, we show that
MOTLEE is resilient to localization uncertainties, enabling accurate tracking
in distributed, dynamic settings with mobile tracking sensors.


  Systems at finite temperature make up the vast majority of realistic physical
scenarios. Indeed, although zero temperature is often accompanied by simpler
mathematics, the richness in physical results is evident when one considers the
system to have temperature and even more so if the background geometry is
curved. This thesis will be dedicated to the study of this type of physical
systems, where thermodynamics and general relativity equally contribute to the
dynamics. The first part will be devoted to the study of classical
thermodynamic systems in curved spacetime, namely thin matter shells at finite
temperature. These objects partition spacetime into separate pieces, and their
very existence is conditioned by the so-called junctions conditions. The latter
conditions allow us to carefully study both the mechanical and thermodynamics
of the shell and, in particular, they give rise to a well-defined notion of
entropy. The shell can then be taken to its black hole limit, providing an
alternative way to study black hole thermodynamics. We will do this for
different geometries, obtaining as byproduct a plausible answer for the debated
value of the entropy of an extremal black hole. In the second part we shall
review the standard formalisms to study QFT in curved spacetimes, in order to
explore quantum properties of thermodynamic systems in the presence of gravity.
Massive quantum scalar fields at finite temperature will be the systems of
choice, whereby various instances of vacuum polarisation will be calculated in
a variety of black hole geometries. Both numerical and analytic results will be
obtained, and new addition formulas for a certain class of transcendental
functions will be derived. This part will culminate with a careful numerical
study of symmetry restoration of a self-interacting scalar field around a
charged black hole, where we verify insights present in the literature.


  Automatic speech recognition (ASR) systems are known to be sensitive to the
sociolinguistic variability of speech data, in which gender plays a crucial
role. This can result in disparities in recognition accuracy between male and
female speakers, primarily due to the under-representation of the latter group
in the training data. While in the context of hybrid ASR models several
solutions have been proposed, the gender bias issue has not been explicitly
addressed in end-to-end neural architectures. To fill this gap, we propose a
data augmentation technique that manipulates the fundamental frequency (f0) and
formants. This technique reduces the data unbalance among genders by simulating
voices of the under-represented female speakers and increases the variability
within each gender group. Experiments on spontaneous English speech show that
our technique yields a relative WER improvement up to 9.87% for utterances by
female speakers, with larger gains for the least-represented f0 ranges.


  Hardware accelerators such as Graphics Processing Units (GPUs), Intel Xeon
Phi co-processors (PHIs), and Field-Programmable Gate Arrays (FPGAs) are now
ubiquitous in extreme-scale high performance computing (HPC), cloud, and Big
data platforms to facilitate execution of workloads that demand high energy
efficiency. They present unique interfaces and programming models therefore
posing several limitations, which must be addressed to facilitate execution of
large workloads. There is no library providing a unifying interface that allows
programmers to write reusable out-of-core implementations of their
data-parallel kernels that can run efficiently on different mainstream
accelerators such as GPUs, PHIs, and FPGAs. We address this shortage in this
paper. We present a library called libhclooc, which provides a unifying
interface facilitating out-of-core implementations for data parallel kernels on
the three different mainstream accelerators (GPUs, Intel Xeon Phis, FPGAs). We
implement out-of-core matrix-matrix multiplication (MMOOC) using the libhclooc
API and demonstrate its superior performance over vendor implementations. We
show that it suffers from a maximum overhead of 10%, 4%, and 8% (due to
abstraction) compared to the state-of-the-art optimised implementations for
Nvidia K40c GPU, Nvidia P100 PCIe GPU, and Intel Xeon Phi 3120P respectively.
We also show that using libhclooc API reduces the number of lines of code (LOC)
by 75% thereby drastically improving programmer productivity.


  A non-technical discussion of the naturalness criterion and its implications
for new physics searches at the LHC. To be published in the book "LHC
Perspectives", edited by G. Kane and A. Pierce.


  We introduce several spatially adaptive model order reduction approaches
tailored to non-coercive elliptic boundary value problems, specifically,
parametric-in-frequency Helmholtz problems. The offline information is computed
by means of adaptive finite elements, so that each snapshot lives in a
different discrete space that resolves the local singularities of the
analytical solution and is adjusted to the considered frequency value. A
rational surrogate is then assembled adopting either a least-squares or an
interpolatory approach, yielding function-valued version of the standard
rational interpolation method ($\mathcal{V}$-SRI) and the minimal rational
interpolation method (MRI). In the context of building an approximation for
linear or quadratic functionals of the Helmholtz solution, we perform several
numerical experiments to compare the proposed methodologies. Our simulations
show that, for interior resonant problems (whose singularities are encoded by
poles on the real axis), the spatially adaptive $\mathcal{V}$-SRI and MRI work
comparably well. Instead, when dealing with exterior scattering problems, whose
frequency response is mostly smooth, the $\mathcal{V}$-SRI method seems to be
the best-performing one.


  The question of CP violating phases in supersymmetry and electric dipole
moments (EDMs) is considered within the framework of supergravity grand
unification (GUT) models with a light ($\stackrel{<}{\sim}$1 TeV) mass
spectrum. In the minimal model, the nearness of the t-quark Landau pole
automatically suppresses the t-quark cubic soft breaking phase at the
electroweak scale. However, current EDM data require the quadratic soft
breaking phase to be small at the electroweak scale unless tan$\beta$ is small
(tan$\beta\stackrel{<}{\sim}$3), and the EDM data combined with the requirement
of electroweak symmetry breaking require this phase to be both large and highly
fine tuned at the GUT scale unless tan$\beta$ is small. Non minimal models are
also examined, and generally show the same behavior.


  Many network information theory problems face the similar difficulty of
single-letterization. We argue that this is due to the lack of a geometric
structure on the space of probability distribution. In this paper, we develop
such a structure by assuming that the distributions of interest are close to
each other. Under this assumption, the K-L divergence is reduced to the squared
Euclidean metric in an Euclidean space. In addition, we construct the notion of
coordinate and inner product, which will facilitate solving communication
problems. We will present the application of this approach to the
point-to-point channel, general broadcast channel, and the multiple access
channel (MAC) with the common source. It can be shown that with this approach,
information theory problems, such as the single-letterization, can be reduced
to some linear algebra problems. Moreover, we show that for the general
broadcast channel, transmitting the common message to receivers can be
formulated as the trade-off between linear systems. We also provide an example
to visualize this trade-off in a geometric way. Finally, for the MAC with the
common source, we observe a coherent combining gain due to the cooperation
between transmitters, and this gain can be quantified by applying our
technique.


  In the present work we investigate the photocatalytic hydrogen production
(water splitting) activity of different Pt loaded TiO2 nanotube layers.
Therefore, we fabricate free standing membranes and fix them in four different
configurations: top up (with initiation layer grass or without) and bottom up
(bottom closed or open), then decorate the tubes with various amounts of Pt and
measure the open-circuit photocatalytic H2 production rate. We find a strong
influence of the configuration with the open top morphology showing the highest
photocatalytic hydrogen production efficiency, these nanotubes yield 3.5 times
more H2 than the least efficient structure (bottom closed). The work therefore
provides valuable guidelines for optimizing TiO2 nanotube layers for
photocatalytic applications.


  Faraday tomography is a novel method to probe 3-dimensional structure of
magnetic fields of polarized radio sources. In this paper, we investigate
Faraday dispersion function (FDF) of disk galaxies extending a simple analytic
model of galactic magnetic fields developed in Ideguchi et al. (2017). The
model consists of axisymmetric coherent fields and turbulent fields and we
consider the effects of inclination, relative amplitude of coherent and
turbulent magnetic fields and pitch angle of coherent fields. Our simple model
makes it easy to obtain physical interpretation of FDFs and helps understanding
observational results. We find FDFs have two peaks when galaxies are observed
with non zero inclination. The gap and relative height of two peaks are
dependent on the inclination angle and pitch angle but are not affected by
relative amplitude of coherent and turbulent magnetic fields so much. These
findings give us an important caution that two peaks in observed FDFs do not
necessarily imply the presence of two separate radio sources within a beam.


  We calculate the Pauli-limited upper critical field and the
Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) instability for {\it dirty} d-wave
superconductors within the quasiclassical theory using the self-consistent
$\hat{t}$-matrix approximation for impurities. We find that the phase diagram
depends sensitively on the scattering rate and phase shift of nonmagnetic
impurities. The transition into the superconducting state is always second
order for weak (Born) scattering, while in the unitarity (strong) scattering
limit a first-order transition into both uniform and spatially modulated
superconducting states is stabilized. Contrary to general belief, we find that
the FFLO phase is robust against disorder and survives impurity scattering
equivalent to a $T_c$ suppression of roughly 40%. Our results bear on the
search of FFLO states in heavy-fermion and layered organic superconductors.


  In this article we study resonances and surface waves in $\pi^+$--p
scattering. We focus on the sequence whose spin-parity values are given by $J^p
= {3/2}^+,{7/2}^+, {11/2}^+, {15/2}^+,{19/2}^+$. A widely-held belief takes for
granted that this sequence can be connected by a moving pole in the complex
angular momentum (CAM) plane, which gives rise to a linear trajectory of the
form $J = \alpha_0+\alpha' m^2$, $\alpha'\sim 1/(\mathrm{GeV})^2$, which is the
standard expression of the Regge pole trajectory. But the phenomenology shows
that only the first few resonances lie on a trajectory of this type. For higher
$J^p$ this rule is violated and is substituted by the relation $J\sim kR$,
where $k$ is the pion--nucleon c.m.s.-momentum, and $R\sim 1$ fm. In this
article we prove: (a) Starting from a non-relativistic model of the proton,
regarded as composed by three quarks confined by harmonic potentials, we prove
that the first three members of this $\pi^+$-p resonance sequence can be
associated with a vibrational spectrum of the proton generated by an algebra
$Sp(3,R)$. Accordingly, these first three members of the sequence can be
described by Regge poles and lie on a standard linear trajectory. (b) At higher
energies the amplitudes are dominated by diffractive scattering, and the
creeping waves play a dominant role. They can be described by a second class of
poles, which can be called Sommerfeld's poles, and lie on a line nearly
parallel to the imaginary axis of the CAM-plane. (c) The Sommerfeld pole which
is closest to the real axis of the CAM-plane is dominant at large angles, and
describes in a proper way the backward diffractive peak in both the following
cases: at fixed $k$, as a function of the scattering angle, and at fixed
scattering angle $\theta=\pi$, as a function of $k$. (d) The evolution of this
pole, as a function of $k$, is given in first approximation by $J\simeq kR$.


  Reliably determining system trajectories is essential in many analysis and
control design approaches. To this end, an initial value problem has to be
usually solved via numerical algorithms which rely on a certain software
realization. Because software realizations can be error-prone, proof assistants
may be used to verify the underlying mathematical concepts and corresponding
algorithms. In this work we present a computer-certified formalization of the
solution of the initial value problem of ordinary differential equations. The
concepts are performed in the framework of constructive analysis and the proofs
are written in the \texttt{Minlog} proof system. We show the extraction of a
program, which solves an ODE numerically and provide some possible optimization
regarding the efficiency. Finally, we provide numerical experiments to
demonstrate how programs of a certain high level of abstraction can be obtained
efficiently. The presented concepts may also be viewed as a part of preliminary
work for the development of formalized nonlinear control theory, hence offering
the possibility of computer-assisted controller design and program extraction
for the controller implementation.


  We study the $\Lambda_{b}\rightarrow J/\psi K^{0}\Lambda$ reaction
considering both the $K^{0}\Lambda$ interaction with its coupled channels and
the $J/\psi\Lambda$ interaction. The latter is described by taking into account
the fact that there are predictions for a hidden-charm state with strangeness
that couples to $J/\psi\Lambda$. By using the coupling of the resonance to
$J/\psi\Lambda$ from these predictions we show that a neat peak can be observed
in the $J/\psi\Lambda$ invariant mass distribution, rather stable under changes
of unknown magnitudes. In some cases, one finds a dip structure associated to
that state, but a signal of the state shows up in the $J/\psi$ spectrum.


  In the report he submitted to the Acad\'emie des Sciences, Poisson imagined a
set of concentric spheres at the origin of the Earth's magnetic field. It may
come as a surprise to many that Poisson as well as Gauss both considered the
magnetic field to be constant. We propose in this study to test this surprising
assertion for the first time evoked by Poisson (1826). First, we will present a
development of Maxwell's equations in the framework of a static electric field
and a static magnetic field in order to draw the necessary consequences for the
Poisson hypothesis. In a second step, we will see if the observations can be in
agreement with Poisson (1826). To do so, we have chosen to compare 1) the polar
motion drift and the secular variation of the Earth's magnetic field, 2) the
seasonal pseudo-cycles of day length together with those of the sea level
recorded by different tide gauges around the globe and those of the Earth's
magnetic field recorded in different magnetic observatories. We then propose a
mechanism, in the spirit of Poisson, to explain the presence of the 11-year in
the magnetic field. We test this mechanism with observations and finally we
study closely the evolution of the g10 coefficient of the IGFR over time.


  Solvent-mediated solid-solid phase transformations often result in the
formation of a porous medium, which may be stable on long time scales or
undergo ripening and consolidation. We have studied replace- ment processes in
the KBr-KCl-H2O system using both in situ and ex situ experiments. The
replacement of a KBr crystal by a K(Br,Cl) solid solution in the presence of an
aqueous solution is facilitated by the gen- eration of a surprisingly stable,
highly anisotropic and connected pore structure that pervades the product
phase. This pore structure ensures efficient solute transport from the bulk
solution to the reacting KBr and K(Br,Cl) surfaces. The compositional profile
of the K(Br,Cl) solid solu- tion exhibits striking discontinuities across
disc-like cavities in the product phase. Similar transformation mechanisms are
probably important in con- trolling phase transformation processes and rates in
a variety of natural and man-made systems.


  The COVID-19 pandemic has significantly impacted the tourism and hospitality
sector. Public policies such as travel restrictions and stay-at-home orders had
significantly affected tourist activities and service businesses' operations
and profitability. To this end, it is essential to develop an interpretable
forecast model that supports managerial and organizational decision-making. We
developed DemandNet, a novel deep learning framework for predicting time series
data under the influence of the COVID-19 pandemic. The framework starts by
selecting the top static and dynamic features embedded in the time series data.
Then, it includes a nonlinear model which can provide interpretable insight
into the previously seen data. Lastly, a prediction model is developed to
leverage the above characteristics to make robust long-term forecasts. We
evaluated the framework using daily hotel demand and revenue data from eight
cities in the US. Our findings reveal that DemandNet outperforms the
state-of-art models and can accurately predict the impact of the COVID-19
pandemic on hotel demand and revenues.


  We report on the relation between the mass of supermassive black holes
(SMBHs; M_BH) and that of hosting dark matter halos (M_h) for 49 z ~ 6
quasi-stellar objects (QSOs) with [CII]158um velocity-width measurements. Here,
we estimate M_h assuming that the rotation velocity from FWHM_CII is equal to
the circular velocity of the halo; we have tested this procedure using z ~ 3
QSOs that also have clustering-based M_h estimates. We find that a vast
majority of the z ~ 6 SMBHs are more massive than expected from the local M_BH
- M_h relation, with one-third of the sample by factors >~ 10^2. The median
mass ratio of the sample, M_BH/M_h = 6 x 10^{-4}, means that 0.4% of the
baryons in halos are locked up in SMBHs. The mass growth rates of our SMBHs
amount to ~ 10% of the SFRs, or ~ 1% of the mean baryon accretion rates, of the
hosting galaxies. A large fraction of the hosting galaxies are consistent with
average galaxies in terms of SFR and perhaps of stellar mass and size. Our
study indicates that the growth of SMBHs (M_BH ~ 10^{8-10} Msun) in luminous z
~ 6 QSOs greatly precedes that of hosting halos owing to efficient gas
accretion even under normal star formation activities, although we cannot rule
out the possibility that undetected SMBHs have local M_BH/M_h ratios. This
preceding growth is in contrast to much milder evolution of the stellar-to-halo
mass ratio.


  We introduce a general version of singular compactness theorem which makes it
possible to show that being a $\Sigma$-cotorsion module is a property of the
complete theory of the module. As an application of the powerful tools
developed along the way, we give a new description of Gorenstein flat modules
which implies that, regardless of the ring, the class of all Gorenstein flat
modules forms the left-hand class of a perfect cotorsion pair. We also prove
the dual result for Gorenstein injective modules.


  The production of black holes in large extra dimensions is studied for
Tevatron energies. We find that black holes may have already been created in
small abundance in $\bar p p$ collisions at $\sqrt s=1.8$ TeV. For the next
Tevatron run ($\sqrt s=2.0$ TeV) large production rates for black holes are
predicted.


  An intermediate-mass black hole (IMBH) was recently reported to reside in the
centre of the Galactic globular cluster (GC) NGC 6624, based on timing
observations of a millisecond pulsar (MSP) located near the cluster centre in
projection. We present dynamical models with multiple mass components of NGC
6624 - without an IMBH - which successfully describe the surface brightness
profile and proper motion kinematics from the Hubble Space Telescope (HST) and
the stellar mass function at different distances from the cluster centre. The
maximum line-of-sight acceleration at the position of the MSP accommodates the
inferred acceleration of the MSP, as derived from its first period derivative.
With discrete realizations of the models we show that the higher-order period
derivatives - which were previously used to derive the IMBH mass - are due to
passing stars and stellar remnants, as previously shown analytically in
literature. We conclude that there is no need for an IMBH to explain the timing
observations of this MSP.


  A recent phase transition in the relational interpretation of quantum
mechanics (RQM) is situated in its historical context, and the novelty of the
post-transition viewpoint is questioned.


  Today's mechanical tools for bone cutting (osteotomy) cause mechanical trauma
that prolongs the healing process. Medical device manufacturers aim to minimize
this trauma, with minimally invasive surgery using laser cutting as one
innovation. This method ablates tissue using laser light instead of mechanical
tools, reducing post-surgery healing time. A reliable feedback system is
crucial during laser surgery to prevent damage to surrounding tissues. We
propose a tissue classification method analyzing acoustic waves generated
during laser ablation, demonstrating its applicability in an ex-vivo
experiment. The ablation process with a microsecond pulsed Er:YAG laser
produces acoustic waves, acquired with an air-coupled transducer. These waves
were used to classify five porcine tissue types: hard bone, soft bone, muscle,
fat, and skin. For automated tissue classification, we compared five Neural
Network (NN) approaches: a one-dimensional Convolutional Neural Network (CNN)
with time-dependent input, a Fully-connected Neural Network (FcNN) with either
the frequency spectrum or principal components of the frequency spectrum as
input, and a combination of a CNN and an FcNN with time-dependent data and its
frequency spectrum as input. Consecutive acoustic waves were used to improve
classification accuracy. Grad-Cam identified the activation map of the
frequencies, showing low frequencies as the most important for this task. Our
results indicated that combining time-dependent data with its frequency
spectrum achieved the highest classification accuracy (65.5%-75.5%). We also
found that using the frequency spectrum alone was sufficient, with no
additional benefit from applying Principal Components Analysis (PCA).


  We consider the gravitational clustering of a multicomponent fluid in an
expanding Newtonian universe, taking into account the mutual gravitational
interactions of the medium. We obtain a set of exact and approximate solutions
for two fluid components and for several fluid components: primordial hydrogen
and helium or cosmological massive neutrinos of different mass and/or velocity
distribution may be described by these solutions. The ratio between the two
fluid perturbations, even if originally equal to unity, changes monotonically
with time; this phenomenon leads to different epochs for nonlinear clustering
that may be reflected into different epochs of galaxy formation.
  Additional note: see also D. Fargion (Nuovo Cimento 65,1981,316), L.V.
Solov'eva & I.S. Nurgaliev (Sov. Astron. 29(3),1985,267-272), L.V. Solov'eva &
A.A. Starobinskiy (Sov. Astron. 29(4),1985,367-371), I.S. Nurgaliev (Sov.
Astron. Lett. 12(2),1986,73-76) and compare with astro-ph/9605133.


  The OAuth 2.0 protocol is one of the most widely deployed
authorization/single sign-on (SSO) protocols and also serves as the foundation
for the new SSO standard OpenID Connect. Despite the popularity of OAuth, so
far analysis efforts were mostly targeted at finding bugs in specific
implementations and were based on formal models which abstract from many web
features or did not provide a formal treatment at all.
  In this paper, we carry out the first extensive formal analysis of the OAuth
2.0 standard in an expressive web model. Our analysis aims at establishing
strong authorization, authentication, and session integrity guarantees, for
which we provide formal definitions. In our formal analysis, all four OAuth
grant types (authorization code grant, implicit grant, resource owner password
credentials grant, and the client credentials grant) are covered. They may even
run simultaneously in the same and different relying parties and identity
providers, where malicious relying parties, identity providers, and browsers
are considered as well. Our modeling and analysis of the OAuth 2.0 standard
assumes that security recommendations and best practices are followed, in order
to avoid obvious and known attacks.
  When proving the security of OAuth in our model, we discovered four attacks
which break the security of OAuth. The vulnerabilities can be exploited in
practice and are present also in OpenID Connect.
  We propose fixes for the identified vulnerabilities, and then, for the first
time, actually prove the security of OAuth in an expressive web model. In
particular, we show that the fixed version of OAuth (with security
recommendations and best practices in place) provides the authorization,
authentication, and session integrity properties we specify.


  Forest wildfires represent one of the catastrophic events that, over the last
decades, caused huge environmental and humanitarian damages. In addition to a
significant amount of carbon dioxide emission, they are a source of risk to
society in both short-term (e.g., temporary city evacuation due to fire) and
long-term (e.g., higher risks of landslides) cases. Consequently, the
availability of tools to support local authorities in automatically identifying
burned areas plays an important role in the continuous monitoring requirement
to alleviate the aftereffects of such catastrophic events. The great
availability of satellite acquisitions coupled with computer vision techniques
represents an important step in developing such tools. This paper introduces a
novel open dataset that tackles the burned area delineation problem, a binary
segmentation problem applied to satellite imagery. The presented resource
consists of pre- and post-fire Sentinel-2 L2A acquisitions of California forest
fires that took place starting in 2015. Raster annotations were generated from
the data released by California's Department of Forestry and Fire Protection.
Moreover, in conjunction with the dataset, we release three different baselines
based on spectral indexes analyses, SegFormer, and U-Net models.


  The current crisis, at the time of writing, has had a profound impact on the
financial world, introducing the need for creative approaches to revitalising
the economy at the micro level as well as the macro level. In this informal
analysis and design proposal, we describe how infrastructure for digital assets
can serve as a useful monetary and fiscal policy tool and an enabler of
existing tools in the future, particularly during crises, while aligning the
trajectory of financial technology innovation toward a brighter future. We
propose an approach to digital currency that would allow people without banking
relationships to transact electronically and privately, including both internet
purchases and point-of-sale purchases that are required to be cashless. We also
propose an approach to digital currency that would allow for more efficient and
transparent clearing and settlement, implementation of monetary and fiscal
policy, and management of systemic risk. The digital currency could be
implemented as central bank digital currency (CBDC), or it could be issued by
the government and collateralised by public funds or Treasury assets. Our
proposed architecture allows both manifestations and would be operated by banks
and other money services businesses, operating within a framework overseen by
government regulators. We argue that now is the time for action to undertake
development of such a system, not only because of the current crisis but also
in anticipation of future crises resulting from geopolitical risks, the
continued globalisation of the digital economy, and the changing value and
risks that technology brings.


  We provide new examples of \'etale extensions of Green functors by
transferring classical examples of \'etale extensions to the equivariant
setting. Our examples are Tambara functors, and we prove Green \'etaleness for
them, which implies Tambara \'etaleness. We show that every $C_2$-Galois
extensions of fields gives rise to an \'etale extension of $C_2$-Green
functors. Here we associate the constant Tambara functor to the base field and
the fix-Tambara functor to the extension. We also prove that all $C_n$-Kummer
extensions give rise to \'etale extensions for arbitrary finite $n$. \'Etale
extensions of fields induce \'etale extension of $G$-Green functors for any
finite group $G$ by passing to the corresponding constant $G$-Tambara functors.


  We revisit the derivation of the nonlocal collision term in the Boltzmann
equation for spin-1/2 particles, using both the Wigner-function approach by de
Groot, van Leeuwen, and van Weert, and the Kadanoff-Baym equation in $T$-matrix
approximation. Contrary to previous calculations, our results maintain full
Lorentz covariance of the nonlocal collision term.


  We use Kaluza-Klein reduction to construct flat-space massive spin-2
Lagrangians based on a kinetic term that has local Weyl and
transverse-diffeomorphism gauge symmetries in the massless limit. This yields
Lagrangians describing a free massive spin 2 which differ from the usual
Fierz-Pauli theory, but are physically equivalent to it. These Lagrangians
require the use of auxiliary fields, which appear naturally from the
higher-dimensional construction. We discuss how these Lagrangians are related
to each other and to the Fierz-Pauli theory through St\"uckelberg
transformations, gauge fixings, and field replacements, and we use this to
generalize the construction to spin-2 fields on Einstein manifolds.


  Cosmic necklaces are hybrid topological defects consisting of monopoles and
strings, with two strings attached to each monopole. We argue that the
cosmological evolution of necklaces may significantly differ from that of
cosmic strings. The typical velocity of necklaces can be much smaller than the
speed of light, and the characteristic scale of the network much smaller than
the horizon. We estimate the flux of high-energy protons produced by monopole
annihilation in the decaying closed loops. For some reasonable values of the
parameters it is comparable to the observed flux of ultrahigh-energy cosmic
rays.


  A scenario is proposed that explains both the observed high pulsar velocities
and extragalactic gamma-ray bursts (GRBs). The model involves an ultra-
relativistic jet from a supernova (SN), that produces a GRB and its afterglow,
whose characteristics are similar to an isotropic fireball GRB perhaps with
some differences at late times in the afterglow once some significant
transverse diffusion has occurred. The time scales and many other properties of
GRBs and their afterglows in this model are consistent with observations.
  GRBs in this model have special intrinsic properties, that can either falsify
or prove this model unambiguously by observations. The most direct proof is the
detection of a SN about the same time as the luminous GRB event. Most GRBs and
SNe are expected occur at moderate redshift ($z\sim 1-3$), if they follow the
observed universal star formation history, as implied in this model. Searching
for GRB/SN associations is a challenge, because majority of the SNe will be
faint. Some additional, dramatic observable consequences are predicted, which
can also be utilized to test the model.


  We show that the interlayer tunneling I--V in double-layer quantum Hall
states displays a rich behavior which depends on the relative magnitude of
sample size, voltage length scale, current screening, disorder and thermal
lengths. For weak tunneling, we predict a negative differential conductance of
a power-law shape crossing over to a sharp zero-bias peak. An in-plane magnetic
field splits this zero-bias peak, leading instead to a ``derivative'' feature
at $V_B(B_{||})=2\pi\hbar v B_{||}d/e\phi_0$, which gives a direct measure of
the dispersion of the Goldstone mode corresponding to the spontaneous symmetry
breaking of the double-layer Hall state.


  Let $k$ be a positive integer. A {\em Roman $k$-dominating function} on a
graph $G$ is a labeling $f:V (G)\longrightarrow \{0, 1, 2\}$ such that every
vertex with label 0 has at least $k$ neighbors with label 2. A set
$\{f_1,f_2,\ldots,f_d\}$ of distinct Roman $k$-dominating functions on $G$ with
the property that $\sum_{i=1}^df_i(v)\le 2k$ for each $v\in V(G)$, is called a
{\em Roman $(k,k)$-dominating family} (of functions) on $G$. The maximum number
of functions in a Roman $(k,k)$-dominating family on $G$ is the {\em Roman
$(k,k)$-domatic number} of $G$, denoted by $d_{R}^k(G)$. Note that the Roman
$(1,1)$-domatic number $d_{R}^1(G)$ is the usual Roman domatic number
$d_{R}(G)$. In this paper we initiate the study of the Roman $(k,k)$-domatic
number in graphs and we present sharp bounds for $d_{R}^k(G)$. In addition, we
determine the Roman $(k,k)$-domatic number of some graphs. Some of our results
extend those given by Sheikholeslami and Volkmann in 2010 for the Roman domatic
number.


  In drug discovery, structure-based virtual high-throughput screening (vHTS)
campaigns aim to identify bioactive ligands or "hits" for therapeutic protein
targets from docked poses at specific binding sites. However, while generally
successful at this task, many deep learning methods are known to be insensitive
to protein-ligand interactions, decreasing the reliability of hit detection and
hindering discovery at novel binding sites. Here, we overcome this limitation
by introducing a class of models with two key features: 1) we condition
bioactivity on pose quality score, and 2) we present poor poses of true binders
to the model as negative examples. The conditioning forces the model to learn
details of physical interactions. We evaluate these models on a new benchmark
designed to detect pose-sensitivity.


  The preceding paper (Nissen 2017) shows multiphasic profiles, straight lines
separated by discontinuous transitions, for the ion activities of NaCl and KCl
at 15, 25, 35 and 45oC based on very precise tabular data of Lee et al. (2002).
In addition, references are given to previous findings of multiphasic profiles
for a variety of processes and phenomena, starting with the uptake of sulfate
by roots and leaf slices of barley (Nissen 1971).
  In a continuation, the present paper shows multiphasic profiles for the ion
activities of NaBr and KBr for data also from Lee et al. (2002).


  Let $S$ be a finite set with $n$ elements in a real linear space. Let $\cJ_S$
be a set of $n$ intervals in $\nR$. We introduce a convex operator
$\co(S,\cJ_S)$ which generalizes the familiar concepts of the convex hull
$\conv S$ and the affine hull $\aff S$ of $S$. We establish basic properties of
this operator. It is proved that each homothet of $\conv S$ that is contained
in $\aff S$ can be obtained using this operator. A variety of convex subsets of
$\aff S$ can also be obtained. For example, this operator assigns a regular
dodecagon to the 4-element set consisting of the vertices and the orthocenter
of an equilateral triangle. For $\cJ_S$ which consists of bounded intervals, we
give the upper bound for the number of vertices of the polytope $\co(S,\cJ_S)$.


  Opinion formation and persuasion in argumentation are affected by three major
factors: the argument itself, the source of the argument, and the properties of
the audience. Understanding the role of each and the interplay between them is
crucial for obtaining insights regarding argument interpretation and
generation. It is particularly important for building effective argument
generation systems that can take both the discourse and the audience
characteristics into account. Having such personalized argument generation
systems would be helpful to expose individuals to different viewpoints and help
them make a more fair and informed decision on an issue. Even though studies in
Social Sciences and Psychology have shown that source and audience effects are
essential components of the persuasion process, most research in computational
persuasion has focused solely on understanding the characteristics of
persuasive language. In this thesis, we make several contributions to
understand the relative effect of the source, audience, and language in
computational persuasion. We first introduce a large-scale dataset with
extensive user information to study these factors' effects simultaneously.
Then, we propose models to understand the role of the audience's prior beliefs
on their perception of arguments. We also investigate the role of social
interactions and engagement in understanding users' success in online debating
over time. We find that the users' prior beliefs and social interactions play
an essential role in predicting their success in persuasion. Finally, we
explore the importance of incorporating contextual information to predict
argument impact and show improvements compared to encoding only the text of the
arguments.


  Continual learning in online scenario aims to learn a sequence of new tasks
from data stream using each data only once for training, which is more
realistic than in offline mode assuming data from new task are all available.
However, this problem is still under-explored for the challenging
class-incremental setting in which the model classifies all classes seen so far
during inference. Particularly, performance struggles with increased number of
tasks or additional classes to learn for each task. In addition, most existing
methods require storing original data as exemplars for knowledge replay, which
may not be feasible for certain applications with limited memory budget or
privacy concerns. In this work, we introduce an effective and memory-efficient
method for online continual learning under class-incremental setting through
candidates selection from each learned task together with prior incorporation
using stored feature embeddings instead of original data as exemplars. Our
proposed method implemented for image classification task achieves the best
results under different benchmark datasets for online continual learning
including CIFAR-10, CIFAR-100 and CORE-50 while requiring much less memory
resource compared with existing works.


  Solar flares are known to leave imprints on the magnetic field at the
photosphere, often manifested as an abrupt and permanent change in the
downward-directed Lorentz force in localized areas inside the active region.
Our study aims to differentiate eruptive and confined solar flares based on the
vertical Lorentz force variations. We select 26 eruptive and 11 confined major
solar flares (stronger than the GOES M5 class) observed during 2011-2017. We
analyze these flaring regions using SHARP vector-magnetograms obtained from the
NASA's Helioseismic and Magnetic Imager (HMI). We also compare data
corresponding to 2 synthetic flares from a $\delta$--sunspot simulation
reported in Chatterjee et al. [Phys. Rev. Lett. 116, 101101 (2016)]. We
estimate the change in the horizontal magnetic field and the total Lorentz
force integrated over an area around the polarity inversion line (PIL) that
encompasses the location of the flare. Our results indicate a rapid increase of
the horizontal magnetic field along the flaring PIL, accompanied by a
significant change in the downward-directed Lorentz force in the same vicinity.
Notably, we find that all the confined events under study exhibit a total
change in Lorentz force of < $1.8 \times 10^{22}$ dyne. This threshold plays an
important factor in effectively distinguishing eruptive and confined flares.
Further, our analysis suggests that the change in total Lorentz force also
depends on the reconnection height in the solar corona during the associated
flare onset. The results provide significant implications for understanding the
flare-related upward impulse transmission for the associated coronal mass
ejection.


  The conversion of the read-out from the anti-symmetric port of the LIGO
interferometers into gravitational strain has thus far been performed in the
frequency domain. Here we describe a conversion in the time domain which is
based on the method developed by GEO. We illustrate the method using the
Hanford 4km interferometer during the second LIGO science run (S2).


  In this paper, we use the hardness of quantization over general lattices as
the basis of developing a physical layer secrecy system. Assuming that the
channel state observed by the legitimate receiver and the eavesdropper are
distinct, this asymmetry is used to develop a cryptosystem that resembles the
McEliece cryptosystem, designed to be implemented at the physical layer. We
ensure that the legitimate receiver observes a specific lattice over which
decoding is known to be possible in polynomial-time. while the eavesdropper
observes a lattice over which decoding will prove to have the complexity of
lattice quantization over a general lattice


  We study the observational signatures of magnetically arrested black hole
accretion with non-rotating inflow onto a rotating black hole; we consider a
range of angles between the black hole spin and the initial magnetic field
orientation. We compare the results of our General Relativistic
Magneto-Hydrodynamic simulations to more commonly used rotating initial
conditions and to the Event Horizon Telescope (EHT) observations of M87. We
find that the mm intensity images, polarization images, and synchrotron
emission spectra are very similar among the different simulations when
post-processed with the same electron temperature model; observational
differences due to different electron temperature models are significantly
larger than those due to the different realizations of magnetically arrested
accretion. The orientation of the mm synchrotron polarization is particularly
insensitive to the initial magnetic field orientation, the electron temperature
model, and the rotation of the inflowing plasma. The largest difference among
the simulations with different initial rotation and magnetic tilt is in the
strength and stability of the jet; spherical inflow leads to kink-unstable
jets. We discuss the implications of our results for current and future EHT
observations and for theoretical models of event-horizon-scale black hole
accretion.


  The strong long-range interaction leads to localization in the closed quantum
system without disorders. Employing the exact diagonalization method, the
author numerically investigates thermalization and many-body localization in
translational invariant quantum chains with finite Coulomb interactions. In the
computational basis, excluding all trivial degeneracies, the
interaction-induced localization is well demonstrated in aspects of level
statistics, eigenstate expectation values, and the Anderson localization on
graphs constructed of the many-body basis. The nature of localization for
generic eigenstates is attributed to the quasi-disorder from the power-law
interactions. However, due to the real-space symmetries, the long-time dynamics
is dominated by the degenerate eigenstates and eventually reach homogeneity in
real space. On the other hand, the entanglement entropy exhibits the
size-dependence beyond the area law for the same reason, even deep in the
localized state, indicating an incomplete localization in real space.


  We estimate differential rapidity cross sections for $J/\Psi$, $\Psi(2S)$,
$\Upsilon(1S)$, $\Upsilon(2S)$, and $\Upsilon(3S)$ production; and $\Psi(2S)$
to $J/\Psi(1S)$, $\Upsilon(3S)$ to $\Upsilon(1S)$ suppression via Xe-Xe
collisions at proton-proton energy $\equiv \sqrt{s_{pp}}$ =5.44 TeV. For the
$\Psi(2S)$, $\Upsilon(3S)$ states we use the mixed heavy quark hybrid theory,
with these states being approximately 50\% standard and 50\% hybrid charmonium,
bottomonium meson states.


  I describe the long-standing search for a "smoking-gun" signal for the
manifestation of (scale-)chiral symmetry in nuclear interactions. It is
prompted by Gerry Brown's last unpublished note, reproduced verbatim below, on
the preeminent role of pions and vector ($\rho$,$\omega$) mesons in providing a
simple and elegant description of strongly correlated nuclear interactions. In
this note written in tribute to Gerry Brown, I first describe a case of an
unambiguous signal in axial-charge transitions in nuclei and then combine his
ideas with the more recent development on the role of hidden symmetries in
nuclear physics. What transpires is the surprising conclusion that the
Landau-Migdal fixed point interaction $G_0^\prime$, the nuclear tensor forces
and Brown-Rho scaling, all encoded in scale-invariant hidden local symmetry, as
Gerry put, "run the show and make all forces equal."


  We investigated in detail the M5.8 class solar flare that occurred on
2023-03-06. This flare was one of the first strong flares observed by the
Siberian Radioheliograph in the microwave range and the Advanced Space-based
Solar Observatory in the X-ray range. The flare consisted of two separate
flaring events (a "thermal" and a "cooler" ones), and was associated with (and
probably triggered by) a filament eruption. During the first part of the flare,
the microwave emission was produced in an arcade of relatively short and low
flaring loops. During the second part of the flare, the microwave emission was
produced by energetic electrons trapped near the top of a large-scale flaring
loop; the evolution of the trapped electrons was mostly affected by the Coulomb
collisions. Using the available observations and the GX Simulator tool, we
created a 3D model of the flare, and estimated the parameters of the energetic
electrons in it.


  Diffusion models have shown remarkable performance on many generative tasks.
Despite recent success, most diffusion models are restricted in that they only
allow linear transformation of the data distribution. In contrast, broader
family of transformations can potentially help train generative distributions
more efficiently, simplifying the reverse process and closing the gap between
the true negative log-likelihood and the variational approximation. In this
paper, we present Neural Diffusion Models (NDMs), a generalization of
conventional diffusion models that enables defining and learning time-dependent
non-linear transformations of data. We show how to optimise NDMs using a
variational bound in a simulation-free setting. Moreover, we derive a
time-continuous formulation of NDMs, which allows fast and reliable inference
using off-the-shelf numerical ODE and SDE solvers. Finally, we demonstrate the
utility of NDMs with learnable transformations through experiments on standard
image generation benchmarks, including CIFAR-10, downsampled versions of
ImageNet and CelebA-HQ. NDMs outperform conventional diffusion models in terms
of likelihood and produce high-quality samples.


  Regression bugs occur whenever software functionality that previously worked
as desired stops working, or no longer works as expected. Code changes, such as
bug fixes or new feature work, may result in a regression bug. Regression bugs
are an annoying and painful phenomena in the software development process,
requiring a great deal of effort to localize, effectively hindering team
progress. In this paper we present Regression Detective, a method which assists
the developer locating source code segments that caused a given regression bug.
Unlike some of the existing tools, our approach doesn't require an automated
test suite or executing past versions of the system. It is highly scalable to
millions of loc systems. The developer, who has no prior knowledge of the code
or the bug, reproduces the bug according to the steps described in the bug
database. We evaluated our approach with bugs from leading open source projects
(Eclipse, Tomcat, Ant). In over 90% of the cases, the developer only has to
examine 10-20 lines of code in order to locate the bug, regardless of the code
base size.


  A Bochner integral formula is derived that represents a function in terms of
weights and a parametrized family of functions. Comparison is made to pointwise
formulations, norm inequalities relating pointwise and Bochner integrals are
established, variation-spaces and tensor products are studied, and examples are
presented. The paper develops a functional analytic theory of neural networks
and shows that variation spaces are Banach spaces.


  We will show that if a dynamical system has enough constants of motion then a
Moser-Weinstein type theorem can be applied for proving the existence of
periodic orbits in the case when the linearized system is degenerate.


  Given a represented space (in the sense of TTE theory), an appropriate
representation is constructed for the Moschovakis extension of its carrier
(with paying attention to the cases of effective topological spaces and
effective metric spaces). Some results are presented about TTE computability in
the represented space obtained in this way. For single-valued functions, we
prove, roughly speaking, the computability of any function which is absolutely
prime computable in some computable functions. A similar result holds for
multi-valued functions, but with an analog of absolute prime computability. The
formulation of this result makes use of the notion of computability in
iterative combinatory spaces - a notion studied by the author in other
publications.


  We continue our previous study on what are the allowed forms of quark mass
matrices in the Randall-Sundrum (RS) framework that can reproduce the
experimentally observed quark mass spectrum and the CKM mixing pattern. We
study the constraints the $\Delta F = 2$ processes in the neutral meson sector
placed on the admissible forms found there, and we found only the asymmetrical
type of quark mass matrices arising from anarchical Yukawa structures stay
viable at the few TeV scale reachable at the LHC. We study also the decay of
the first Kaluza-Klein (KK) excitation of the gluon. We give the decay
branching ratios into quark pairs, and we point out that measurements of the
decay width and just one of the quark spins in the dominant $\bar{t}t$ decays
can be used to extract the effective coupling of the first KK gluon to top
quarks for both chiralities. This provides further probe to the flavour
structure of the RS framework.


  The most recognizable feature of graphene's electronic spectrum is its Dirac
point around which interesting phenomena tend to cluster. At low temperatures,
the intrinsic behavior in this regime is often obscured by charge inhomogeneity
but thermal excitations can overcome the disorder at elevated temperatures and
create electron-hole plasma of Dirac fermions. The Dirac plasma has been found
to exhibit unusual properties including quantum critical scattering and
hydrodynamic flow. However, little is known about the plasma's behavior in
magnetic fields. Here we report magnetotransport in this quantum-critical
regime. In low fields, the plasma exhibits giant parabolic magnetoresistivity
reaching >100% in 0.1 T even at room temperature. This is orders of magnitude
higher than magnetoresistivity found in any other system at such temperatures.
We show that this behavior is unique to monolayer graphene, being underpinned
by its massless spectrum and ultrahigh mobility, despite frequent
(Planckian-limit) scattering. With the onset of Landau quantization in a few T,
where the electron-hole plasma resides entirely on the zeroth Landau level,
giant linear magnetoresistivity emerges. It is nearly independent of
temperature and can be suppressed by proximity screening, indicating a
many-body origin. Clear parallels with magnetotransport in strange metals and
so-called quantum linear magnetoresistance predicted for Weyl metals offer an
interesting playground to further explore relevant physics using this
well-defined quantum-critical 2D system.


  Helical CT has been widely used in clinical diagnosis. Sparsely spaced
multidetector in z direction can increase the coverage of the detector provided
limited detector rows. It can speed up volumetric CT scan, lower the radiation
dose and reduce motion artifacts. However, it leads to insufficient data for
reconstruction. That means reconstructions from general analytical methods will
have severe artifacts. Iterative reconstruction methods might be able to deal
with this situation but with the cost of huge computational load. In this work,
we propose a cascaded dual-domain deep learning method that completes both data
transformation in projection domain and error reduction in image domain. First,
a convolutional neural network (CNN) in projection domain is constructed to
estimate missing helical projection data and converting helical projection data
to 2D fan-beam projection data. This step is to suppress helical artifacts and
reduce the following computational cost. Then, an analytical linear operator is
followed to transfer the data from projection domain to image domain. Finally,
an image domain CNN is added to improve image quality further. These three
steps work as an entirety and can be trained end to end. The overall network is
trained using a simulated lung CT dataset with Poisson noise from 25 patients.
We evaluate the trained network on another three patients and obtain very
encouraging results with both visual examination and quantitative comparison.
The resulting RRMSE is 6.56% and the SSIM is 99.60%. In addition, we test the
trained network on the lung CT dataset with different noise level and a new
dental CT dataset to demonstrate the generalization and robustness of our
method.


  Multi-person pose understanding from RGB videos involves three complex tasks:
pose estimation, tracking and motion forecasting. Intuitively, accurate
multi-person pose estimation facilitates robust tracking, and robust tracking
builds crucial history for correct motion forecasting. Most existing works
either focus on a single task or employ multi-stage approaches to solving
multiple tasks separately, which tends to make sub-optimal decision at each
stage and also fail to exploit correlations among the three tasks. In this
paper, we propose Snipper, a unified framework to perform multi-person 3D pose
estimation, tracking, and motion forecasting simultaneously in a single stage.
We propose an efficient yet powerful deformable attention mechanism to
aggregate spatiotemporal information from the video snippet. Building upon this
deformable attention, a video transformer is learned to encode the
spatiotemporal features from the multi-frame snippet and to decode informative
pose features for multi-person pose queries. Finally, these pose queries are
regressed to predict multi-person pose trajectories and future motions in a
single shot. In the experiments, we show the effectiveness of Snipper on three
challenging public datasets where our generic model rivals specialized
state-of-art baselines for pose estimation, tracking, and forecasting.


  There are strong phenomenological arguments favoring the existence of
vector-like leptons and quarks in nature. In spite of extensive studies
conducted in search of vector-like quarks, there are only a limited number of
experimental studies on vector-like leptons. Moreover, these searches do not
include all possible decay modes of vector-like leptons. Therefore, the
analyses done so far are incomplete. In this letter, we highlight decay
channels that are not covered by different experimental analyses, with a focus
on L3, ATLAS, and CMS results. We argue that experimental analyses should be
redone considering these shortcomings.


  We introduce a lattice model of protein conformations which is able to
reproduce second structures of proteins (alpha--helices and beta--sheets). This
model is based on the following two main ideas. First, we model backbone parts
of amino acid residues in a peptide chain by edges in the cubic lattice which
are not parallel to the coordinate axes. Second, we describe possible contacts
of amino acid residues using a discrete model of the Ramachandran plot.
  This model allows to describe hydrogen bonds between the residues in the
backbone of the peptide chain. In particular the lattice secondary structures
have the correct structure of hydrogen bonds. We also take into account the
side chains of amino acid residues and their interaction.
  The expression for the energy of conformation of a lattice protein which
contains contributions from hydrogen bonds in the backbone of the peptide chain
and from interaction of the side chains is proposed. The lattice secondary
structures are local minima of the introduced energy.


  The hypothesis is rapidly gaining popularity that the dark energy pervading
our universe is extra-repulsive ($-p>\rho$). The density of such a
substance(usually called phantom energy) grows with the cosmological expansion
and may become infinite in a finite time producing a Big Rip. In this Letter we
analyze the late stages of the universe evolution and demonstrate that the
presence of the phantom energy in the universe is not enough in itself to
produce the Big Rip. This singularity occurrence requires the fulfillment of
some additional, rather strong conditions. A more probable outcome of the
cosmological evolution is the decay of the phantom field into 'normal' matter.
  The second, more intriguing consequence of the presence of the phantom field
is the possibility to introduce a cosmological scenario that does not contain a
Big Bang. In the framework of this model the universe eternally expands, while
its density and other physical parameters oscillate over a wide range, never
reaching the Plank values. Thus, the universe evolution has no singularities at
all.


  We study the Whittaker coefficients of the minimal parabolic Eisenstein
series on the $n$-fold cover of the split odd orthogonal group $SO_{2r+1}$. If
the degree of the cover is odd, then Beineke, Brubaker and Frechette have
conjectured that the $p$-power contributions to the Whittaker coefficients may
be computed using the theory of crystal graphs of type C, by attaching to each
path component a Gauss sum or a degenerate Gauss sum depending on the fine
structure of the path. We establish their conjecture using a combination of
automorphic and combinatorial-representation-theoretic methods. Surprisingly,
we must make use of the type A theory, and the two different crystal graph
descriptions of Brubaker, Bump and Friedberg available for type A based on
different factorizations of the long word into simple reflections. We also
establish a formula for the Whittaker coefficients in the even degree cover
case, again based on crystal graphs of type C. As a further consequence, we
establish a Lie-theoretic description of the coefficients for $n$ sufficiently
large, thereby confirming a conjecture of Brubaker, Bump and Friedberg.


  We discuss consequences of finite plasma formation time at RHIC. Nuclear
modification factor, azimuthal assymetry, di-jet correlations can
quantitatively be described by particle production in the early stage of the
nuclear collision. A possible impact on interpretation of the non-photonic
electrons and J/psi data is also considered.


  Reverse Mathematics (RM for short) is a program in the foundations of
mathematics where the aim is to find the minimal axioms needed to prove a given
theorem of ordinary mathematics. Generally, the minimal axioms are equivalent
to the theorem at hand, assuming a weak logical system called the base theory.
Moreover, many (most?) theorems are either provable in the base theory or
equivalent to one of four logical systems, together called the Big Five. For
instance, the Weierstrass approximation theorem, i.e. that a continuous
function can be approximated uniformly by a sequence of polynomials, has been
classified in RM as being equivalent to weak Koenig's lemma, the second Big
Five system. In this paper, we study approximation theorems for discontinuous
functions via Bernstein polynomials from the literature. We obtain (many)
equivalences between the latter and weak Koenig's lemma. We also show that
slight variations of these approximation theorems fall far outside of the Big
Five but fit in the recently developed RM of new 'big' systems, namely the
uncountability of R, the enumeration principle for countable sets, the
pigeon-hole principle for measure, and the Baire category theorem. In
conclusion, one equivalence in second-order RM, namely for the Weierstrass
approximation theorem, gives rise to many equivalences in higher-order RM, and
we hope the case study in this paper can serve as a kind of template.


  We study the finite temperature (FT) effects on integer quantum Hall effect
(IQHE) and fractional quantum Hall effect (FQHE) as predicted by the composite
fermion model. We find that at $T\neq 0$, universality is lost, as is
quantization because of a new scale $T_0=\pi\rho /m^\ast p$. We find that this
loss is not inconsistent with the experimentally observed accuracies. While the
model seems to work very well for IQHE, it agrees with the bulk results of FQHE
but is shown to require refinement in its account of microscopic properties
such as the effective mass. Our analysis also gives a qualitative account of
the threshold temperatures at which the FQHE states are seen experimentally.
Finally, we extract model independent features of quantum Hall effect at FT,
common to all Chern-Simons theories that employ mean field ansatz.