  The classical deflection function is a valuable computational tool to
investigate reaction mechanisms. It provides, at a glance, detailed information
about how the reaction is affected by changes in reactant properties (impact
parameter) and products properties (scattering angle), and, more importantly,
it also shows how they are correlated. It is also useful to predict the
presence of quantum phenomena such as interferences. However, rigorously
speaking, there is not a quantum analogue as the differential cross section
depends on the coherences between the different values of $J$ caused by the
cross terms in the expansion of partial waves. Therefore, the classical
deflection function has a limited use whenever quantum effects become
important. In this article, we present a method to calculate a quantum
deflection function that can shed light onto reaction mechanism using just
quantum mechanical results. Our results show that there is a very good
agreement between the quantum and classical deflection function as long as
quantum effects are not all relevant. When this is not the case, it will be
also shown that the quantum deflection function is most useful to observe the
extent of quantum effects such as interferences. The present results are
compared with other proposed quantum deflection functions, and the advantages
and disadvantages of the different formulations will be discussed.


  In this article, we derive an asymptotic formula for the second moment of
$S(t)$ which includes the lower order terms using a prediction from the ratios
conjecture of Conrey, Farmer and Zirnbauer. The formula matches very well with
actual values of the second moment.


  Dental implant stability influences the decision on the determination of the
duration between implant insertion and loading, This work investigates the
resonant frequency analysis by means of a numerical model. The investigation is
done numerically through the determination of the eigenfrequencies and
performing a steady state response analyses using a commercial finite element
package. A peri-implant interface, of simultaneously varying stiffness
(density) and layer thickness is introduced in the numerical 3D model in order
to probe the sensitivity of the eigenfrequencies and steady state response to
an evolving weakened layer, in an attempt to identify the bone reconstruction
around the implant. For the first two modes, the resonant frequency is somewhat
insensitive to the healing process, unless the weakened layer is rather large
and compliant, like in the very early stages of the implantation. A "Normalized
Healing Factor" is devised in the spirit of the Implant Stability Quotient,
which can identify the healing process especially at the early stages after
implantation. The sensitivity of the RFA to changes of mechanical properties of
periprosthetic bone tissue seems relatively weak. Another indicator considering
the amplitude as well as the resonance frequency might be more adapted to bone
healing estimations. However, these results need to be verified experimentally
as well as clinically.


  Sample contrastive methods, typically referred to simply as contrastive are
the foundation of most unsupervised methods to learn text and sentence
embeddings. On the other hand, a different class of self-supervised loss
functions and methods have been considered in the computer vision community and
referred to as dimension contrastive. In this paper, we thoroughly compare this
class of methods with the standard baseline for contrastive sentence
embeddings, SimCSE. We find that self-supervised embeddings trained using
dimension contrastive objectives can outperform SimCSE on downstream tasks
without needing auxiliary loss functions.


  We study a system of hard-core particles sliding downwards on a fluctuating
one-dimensional surface which is characterized by a dynamical exponent $z$. In
numerical simulations, an initially random particle density is found to coarsen
and obey scaling with a growing length scale $\sim t^{1/z}$. The structure
factor deviates from the Porod law in some cases. The steady state is unusual
in that the density-segregation order parameter shows strong fluctuations. The
two-point correlation function has a scaling form with a cusp at small argument
which we relate to a power law distribution of particle cluster sizes. Exact
results on a related model of surface depths provides insight into the origin
of this behaviour.


  In this paper, we propose hybrid building/floor classification and
floor-level two-dimensional location coordinates regression using a
single-input and multi-output (SIMO) deep neural network (DNN) for large-scale
indoor localization based on Wi-Fi fingerprinting. The proposed scheme exploits
the different nature of the estimation of building/floor and floor-level
location coordinates and uses a different estimation framework for each task
with a dedicated output and hidden layers enabled by SIMO DNN architecture. We
carry out preliminary evaluation of the performance of the hybrid floor
classification and floor-level two-dimensional location coordinates regression
using new Wi-Fi crowdsourced fingerprinting datasets provided by Tampere
University of Technology (TUT), Finland, covering a single building with five
floors. Experimental results demonstrate that the proposed SIMO-DNN-based
hybrid classification/regression scheme outperforms existing schemes in terms
of both floor detection rate and mean positioning errors.


  We give a partial description of the (s,t)-p-path polytope of a directed
graph D which is the convex hull of the incidence vectors of simple directed
(s,t)-paths in D of length p. First, we point out how the (s,t)-p-path polytope
is located in the family of path and cycle polyhedra. Next, we give some
classes of valid inequalities which are very similar to inequalities which are
valid for the p-cycle polytope, that is, the convex hull of the incidence
vectors of simple cycles of length p in D. We give necessary and sufficient
conditions for these inequalities to be facet defining. Furthermore, we
consider a class of inequalities that has been identified to be valid for
(s,t)-paths of cardinality at most p. Finally, we transfer the results to
related polytopes, in particular, the undirected counterpart of the
(s,t)-p-path polytope.


  A procedure to obtain single-electron wavefunctions within the tight-binding
formalism is proposed. It is based on linear combinations of Slater-type
orbitals whose screening coefficients are extracted from the optical matrix
elements of the tight-binding Hamiltonian. Bloch functions obtained for
zinc-blende semiconductors in the extended-basis spds* tight-binding model
demonstrate very good agreement with first-principles wavefunctions. We apply
this method to the calculation of electron-hole exchange interaction, and
obtain the dispersion of excitonic fine structure of bulk GaAs. Beyond
semiconductor nanostructures, this work is a fundamental step toward modeling
many-body effects from post-processing single particle wavefunctions within the
tight-binding theory.


  It has recently been shown that nonlinear skew product dynamical systems with
invariant subspaces which are capable of displaying on-off intermittency can
show supersensitivity to small input signals.
  Here we show that this supersensitivity is absent for more general dynamical
systems with non-skew product structure, capable of displaying a generalized
form of on-off intermittency, and is therefore in this sense fragile. This
absence of supersensitivity is of importance in view of the fact that dynamical
systems are generically expected to be of non-skew product nature.


  We probe spin transport in Cu_{2}O by measuring spin valve effect in
La_{0.7}Sr_{0.3}MnO_{3}/Cu_{2}O/Co and
La_{0.7}Sr_{0.3}MnO_{3}/Cu_{2}O/La_{0.7}Sr_{0.3}MnO_{3} epitaxial
heterostructures. In La_{0.7}Sr_{0.3}MnO_{3}/Cu_{2}O/Co systems we find that a
fraction of out-of-equilibrium spin polarized carrier actually travel across
the Cu_{2}O layer up to distances of almost 100 nm at low temperature. The
corresponding spin diffusion length dspin is estimated around 40 nm.
Furthermore, we find that the insertion of a SrTiO_{3} tunneling barrier does
not improve spin injection, likely due to the matching of resistances at the
interfaces. Our result on dspin may be likely improved, both in terms of
Cu_{2}O crystalline quality and sub-micrometric morphology and in terms of
device geometry, indicating that Cu_{2}O is a potential material for efficient
spin transport in devices based on crystalline oxides.


  An intermediate inflationary universe model within the context of
non-minimally coupled to the scalar curvature is analyzed. We will conduct our
analysis under the slow roll approximation of the inflationary dynamics and the
cosmological perturbations considering a coupling of the form
$F(\varphi)=\kappa+\xi_n\varphi^n$. Considering the trajectories in the $r-n_s$
plane from Planck data, we find the constraints on the parameter-space in our
model.


  The properties of nuclei in the ``island of inversion'' (IOI) around Z=10 and
N=20 are the focus of current nuclear physics research. Recent studies showed
that $^{28}$F has a negative-parity ground state (g.s.) and thus lies within
the southern shore of the IOI, and $^{29}$F presents a halo structure in its
g.s., but it is unclear which effects, such as deformation, shell evolution due
to tensor forces, or couplings to the continuum, lead to this situation. We
investigate the role of quadrupole deformation and continuum effects on the
single-particle (s.p.) structure of $^{28,29,31}$F from a relativistic
mean-field (RMF) approach, and show how both phenomena can lead to a
negative-parity g.s. in $^{28}$F and halo structures in $^{29,31}$F. We solve
the Dirac equation in the complex-momentum (Berggren) representation for a
potential with quadrupole deformation at the first order obtained from RMF
calculations using the NL3 interaction, and calculate the continuum level
densities using the Green's function method. We extract s.p. energies and
widths from the continuum level densities to construct Nilsson diagrams, and
analyse the evolution of both the widths and occupation probabilities of
relevant Nilsson orbitals in $^{28}$F and find that some amount of prolate
deformation must be present. In addition, we calculate the density
distributions for bound Nilsson orbitals near the Fermi surface in $^{29,31}$F
and reveal that for a quadrupole deformation $0.3 \leq \beta_2 \leq 0.45$
(prolate), halo tails appear at large distances. We also demonstrate that while
in the spherical case the $pf$ shells are already inverted and close to the
neutron emission threshold, a small amount of quadrupole deformation can reduce
the gap between $fp$ shells and increase the role of the continuum, ultimately
leading to the negative parity in the g.s. of $^{28}$F and the halo structures
in $^{29,31}$F.


  We wish to show that the root lattice of B\"acklund transformations of the
$q$-analogue of the third and fourth Painlev\'e equations, which is of type
$(A_2+ A_1)^{(1)}$, may be expressed as a quotient of the lattice of connection
preserving deformations. Furthermore, we will show various directions in the
lattice of connection preserving deformations present equivalent evolution
equations under suitable transformations. These transformations correspond to
the Dynkin diagram automorphisms.


  We establish a criterion for the completeness of an exponential system in the
spaces of functions continuous on a convex compact set and holomorphic in the
interior of this compact set, as well as in the spaces of holomorphic functions
in the convex domain in terms of the breadth of the compact set or the domain
in the direction. The main results are formulated exclusively through the
relations between the breadth in the direction, width or diameter of the
compact set or domain on the one hand and the logarithmic submeasures or
logarithmic block densities of the distribution of exponents of exponential
system on the other.


  Automated seizure detection and classification from electroencephalography
(EEG) can greatly improve seizure diagnosis and treatment. However, several
modeling challenges remain unaddressed in prior automated seizure detection and
classification studies: (1) representing non-Euclidean data structure in EEGs,
(2) accurately classifying rare seizure types, and (3) lacking a quantitative
interpretability approach to measure model ability to localize seizures. In
this study, we address these challenges by (1) representing the spatiotemporal
dependencies in EEGs using a graph neural network (GNN) and proposing two EEG
graph structures that capture the electrode geometry or dynamic brain
connectivity, (2) proposing a self-supervised pre-training method that predicts
preprocessed signals for the next time period to further improve model
performance, particularly on rare seizure types, and (3) proposing a
quantitative model interpretability approach to assess a model's ability to
localize seizures within EEGs. When evaluating our approach on seizure
detection and classification on a large public dataset, we find that our GNN
with self-supervised pre-training achieves 0.875 Area Under the Receiver
Operating Characteristic Curve on seizure detection and 0.749 weighted F1-score
on seizure classification, outperforming previous methods for both seizure
detection and classification. Moreover, our self-supervised pre-training
strategy significantly improves classification of rare seizure types.
Furthermore, quantitative interpretability analysis shows that our GNN with
self-supervised pre-training precisely localizes 25.4% focal seizures, a 21.9
point improvement over existing CNNs. Finally, by superimposing the identified
seizure locations on both raw EEG signals and EEG graphs, our approach could
provide clinicians with an intuitive visualization of localized seizure
regions.


  A new framework for solving the hierarchy problem was recently proposed which
does not rely on low energy supersymmetry or technicolor. The fundamental
Planck mass is at a $\tev$ and the observed weakness of gravity at long
distances is due the existence of new sub-millimeter spatial dimensions. In
this picture the standard model fields are localized to a $(3+1)$-dimensional
wall or ``3-brane''. The hierarchy problem becomes isomorphic to the problem of
the largeness of the extra dimensions. This is in turn inextricably linked to
the cosmological constant problem, suggesting the possibility of a common
solution. The radii of the extra dimensions must be prevented from both
expanding to too great a size, and collapsing to the fundamental Planck length
$\tev^{-1}$. In this paper we propose a number of mechanisms addressing this
question. We argue that a positive bulk cosmological constant $\bar\Lambda$ can
stabilize the internal manifold against expansion, and that the value of
$\bar\Lambda$ is not unstable to radiative corrections provided that the
supersymmetries of string theory are broken by dynamics on our 3-brane. We
further argue that the extra dimensions can be stabilized against collapse in a
phenomenologically successful way by either of two methods: 1) Large,
topologically conserved quantum numbers associated with higher-form bulk U(1)
gauge fields, such as the naturally occurring Ramond-Ramond gauge fields, or
the winding number of bulk scalar fields. 2) The brane-lattice-crystallization
of a large number of 3-branes in the bulk. These mechanisms are consistent with
theoretical, laboratory, and cosmological considerations such as the absence of
large time variations in Newton's constant during and after primordial
nucleosynthesis, and millimeter-scale tests of gravity.


  In this paper we study the behaviour in time of the trace (the partition
function) of the heat semigroup associated with symmetric stable processes in
domains of $\Rd$. In particular, we show that for domains with the so called
{\it{$R$-smoothness}} property the second terms in the asymptotic as $t\to 0$
involves the surface area of the domain, just as in the case of Brownian
motion.


  Experimental data of the DNA cyclization (J-factor) at short length scales,
as a way to study the elastic behavior of tightly bent DNA, exceed the
theoretical expectation based on the wormlike chain (WLC) model by several
orders of magnitude. Here, we propose that asymmetric bending rigidity of the
double helix in the groove direction can be responsible for extreme bendability
of DNA at short length scales and it also facilitates DNA loop formation at
these lengths. To account for the bending asymmetry, we consider the asymmetric
elastic rod (AER) model which has been introduced and parametrized in an
earlier study (B. Eslami-Mossallam and M. Ejtehadi, Phys. Rev. E 80, 011919
(2009)). Exploiting a coarse grained representation of DNA molecule at base
pair (bp) level, and using the Monte Carlo simulation method in combination
with the umbrella sampling technique, we calculate the loop formation
probability of DNA in the AER model. We show that, for DNA molecule has a
larger J-factor compared to the WLC model which is in excellent agreement with
recent experimental data.


  As for the Fourier transforms of positive and integrable functions supported
in the unit interval, we make a list of improvements for P\'olya's results on
the distribution of their positive zeros and give new sufficient conditions
under which those zeros are simple and regularly distributed. As an
application, we take the two-parameter family of beta probability density
functions defined by \begin{equation*} f(t)= \frac{1}{B(\alpha, \beta)}\,
(1-t)^{\alpha-1} t^{\beta-1},\quad 0<t<1, \end{equation*} where
$\,\alpha>0,\,\beta>0,$ and specify the distribution of zeros of the associated
Fourier transforms for some region of $(\alpha, \beta)$ in the first quadrant
which turns out to be much larger than the region where P\'olya's results are
applicable.


  An ongoing debate in evolutionary biology is whether phenotypic change occurs
predominantly around the time of speciation or whether it instead accumulates
gradually over time. In this work I propose a general framework incorporating
both types of change, quantify the effects of speciational change via the
correlation between species and attribute the proportion of change to each
type. I discuss results of parameter estimation of Hominoid body size in this
light. I derive mathematical formulae related to this problem, the probability
generating functions of the number of speciation events along a randomly drawn
lineage and from the most recent common ancestor of two randomly chosen tip
species for a conditioned Yule tree. Additionally I obtain in closed form the
variance of the distance from the root to the most recent common ancestor of
two randomly chosen tip species.


  Cryptocurrencies are increasingly popular. Even people who are not experts
have started to invest in these securities, and nowadays, cryptocurrency
exchanges process transactions for over 100 billion US dollars per month. In
spite of this, many cryptocurrencies have low liquidity, and therefore, they
are highly prone to market manipulation. This paper performs an in-depth
analysis of two market manipulations organized by communities over the
Internet: The pump and dump and the crowd pump. The pump and dump scheme is a
fraud as old as the stock market. Now, it got new vitality in the loosely
regulated market of cryptocurrencies. Groups of highly coordinated people
arrange this scam, usually on Telegram and Discord. We monitored these groups
for more than 3 years detecting around 900 individual events. We analyze how
these communities are organized and how they carry out the fraud. We report on
three case studies of pump and dump. Then, we leverage our unique dataset of
the verified pump and dumps to build a machine learning model able to detect a
pump and dump in 25 seconds from the moment it starts, achieving the results of
94.5% of F1-score. Then, we move on to the crowd pump, a new phenomenon that
hit the news in the first months of 2021, when a Reddit community inflates the
price of the GameStop stocks (GME) of over 1,900% on Wall Street, the world's
largest stock exchange. Later, other Reddit communities replicate the operation
on the cryptocurrency markets. The targets were Dogecoin (DOGE) and Ripple
(XRP). We reconstruct how these operations developed, and we discuss
differences and analogies with the standard pump and dump. Lastly, we
illustrate how it is possible to leverage our classifier to detect this kind of
operation too.


  We present a new method for matching the two twin-processes gb->H+/-t and
gg->H+/-tb in Monte Carlo event generators. The matching is done by defining a
double-counting term, which is used to generate events that are subtracted from
the sum of these two twin-processes. In this way we get a smooth transition
between the collinear region of phase space, which is best described by
gb->H+/-t, and the hard region, which requires the use of the gg->H+/-tb
process. The resulting differential distributions show large differences
compared to both the gb-> H+/-t and gg->H+/-tb processes illustrating the
necessity to use matching when tagging the accompanying b-jet.


  We examine the four-quark structure of the recently discovered charged
$Z_c(3900)$, $Z(4430)$, and $X_b(5568)$ states. We calculate the widths of the
strong decays $Z_c^+ \to J/\psi \pi^+$ ($\eta_c\rho^+$, $\bar D^0D^{\ast\,+}$,
$\bar D^{\ast\,0}D^+$), $Z(4430)^+ \to J/\psi \pi^+$ ($\psi(2s) \pi^+$), and
$X^+_b\to B_s\pi^+$ within a covariant quark model previously developed by us.
We find that the tetraquark-type current widely used in the literature for the
$Z_c(3900)$ leads to a significant suppression of the $\bar D D^\ast$ and $\bar
D^\ast D$ modes. Contrary to this a molecular-type current provides an
enhancement by a factor of 6-7 for the $\bar D D^\ast$ modes compared with the
$Z_c^+\to J/\psi\pi^+$, $\eta_c\rho^+$ modes in agreement with recent
experimental data from the BESIII Collaboration. In case of the $Z(4430)$ state
we test a sensitivity of the ratio $R_Z$ of the $Z(4430)^+ \to \psi(2s) \pi^+$
and $Z(4430)^+ \to J/\psi \pi^+$ decay rates to a choice of the size parameter
$\Lambda_{Z(4430)}$ of the $Z(4430)$. Using upper constraint for the sum of
these two modes deduced from the LHCb Collaboration data we find that $R_Z$
varies from 4.64 to 4.08 when $\Lambda_{Z(4430)}$ changes from 2.2 to 3.2 GeV.
Also we make the prediction for the $Z(4430)^+ \to D^{\ast\,+} \bar
D^{\ast\,0}$ decay rate.


  We analyze the origin of separability for rotating black holes in string
theory, considering both massless and massive geodesic equations as well as the
corresponding wave equations. We construct a conformal Killing-Stackel tensor
for a general class of black holes with four independent charges, then identify
two-charge configurations where enhancement to an exact Killing-Stackel tensor
is possible. We show that further enhancement to a conserved Killing-Yano
tensor is possible only for the special case of Kerr-Newman black holes. We
construct natural null congruences for all these black holes and use the
results to show that only the Kerr-Newman black holes are algebraically special
in the sense of Petrov. Modifying the asymptotic behavior by the subtraction
procedure that induces an exact SL(2)^2 also preserves only the conformal
Killing-Stackel tensor. Similarly, we find that a rotating Kaluza-Klein black
hole possesses a conformal Killing-Stackel tensor but has no further
enhancements.


  We study synchronization dynamics in networks of coupled oscillators with
bimodal distribution of natural frequencies. This setup can be interpreted as a
simple model of frequency synchronization dynamics among generators and loads
working in a power network. We derive the minimum coupling strength required to
ensure global frequency synchronization. This threshold value can be
efficiently found by solving a binary optimization problem, even for large
networks. In order to validate our procedure, we compare its results with
numerical simulations on a realistic network describing the European
interconnected high-voltage electricity system, finding a very good agreement.
Our synchronization threshold can be used to test the stability of frequency
synchronization to link removals. As the threshold value changes only in very
few cases when aplied to the European realistic network, we conclude that
network is resilient in this regard. Since the threshold calculation depends on
the local connectivity, it can also be used to identify critical network
partitions acting as synchronization bottlenecks. In our stability experiments
we observe that when a link removal triggers a change in the critical
partition, its limits tend to converge to national borders. This phenomenon,
which can have important consequences to synchronization dynamics in case of
cascading failure, signals the influence of the uncomplete topological
integration of national power grids at the European scale.


  Given a graph $G=(V,E)$ and an integer $k$, the Minimum Membership Dominating
Set (MMDS) problem seeks to find a dominating set $S \subseteq V$ of $G$ such
that for each $v \in V$, $|N[v] \cap S|$ is at most $k$. We investigate the
parameterized complexity of the problem and obtain the following results about
MMDS:
  W[1]-hardness of the problem parameterized by the pathwidth (and thus,
treewidth) of the input graph.
  W[1]-hardness parameterized by $k$ on split graphs.
  An algorithm running in time $2^{\mathcal{O}(\textbf{vc})}
|V|^{\mathcal{O}(1)}$, where $\textbf{vc}$ is the size of a minimum-sized
vertex cover of the input graph.
  An ETH-based lower bound showing that the algorithm mentioned in the previous
item is optimal.


  This paper studies direct limits of full upper triangular matrix algebras
with embeddings which are not *-extendible. A representation of the limit
algebra is found so that the generated C*-algebra is the C*-envelope. Some
examples are described.


  It is generally believed that the non-Hermitian effect (NHSE), due to its
non-reciprocal nature, creates barriers for the appearance of impurity bound
states. In this paper, we find that in two and higher dimensions, the presence
of geometry-dependent skin effect eliminates this barrier such that even an
infinitesimal impurity potential can confine bound states in this type of
non-Hermitian systems. By examining bound states around Bloch saddle points, we
find that non-Hermiticity can disrupt the isotropy of bound states, resulting
in concave dumbbell-shaped bound states. Our work reveals a geometry transition
of bound state between concavity and convexity in high-dimensional
non-Hermitian systems.


  The Babar and CLEO collaborations have recently observed states decaying to
D_s^+\pi^0 and D_s^{*+}\pi^0 respectively and suggest the possible explanation
that they are the missing P-wave c\bar{s} states with J^P=0^+ and 1^+. In this
note we compare the properties of the D_{sJ}^*(2317)^+ and D_{sJ}(2463)^+
states to those expected of the c\bar{s} D_{s0}^* and D_{s1} states. We expect
the D_{s0}^* and D_{s1} with the reported masses to be extremely narrow, \Gamma
\sim {\cal O}(10\hbox{keV}), with large branching ratios to D^*_s\gamma for the
D_{s0}^* and to D^*_s\gamma and D_s\gamma for the D_{s1}. Crucial to this
interpretation of the Babar and CLEO observations is the measurement of the
radiative transitions. We note that it may be possible to observe the
$D_{s1}(2536)$ in radiative transitions to the $D_s^*$.


  Let $E$ be a rearrangement invariant (r.i.) function space on $[0,1]$, and
let $Z_E$ consist of all measurable functions $f$ on $(0,\infty)$ such that
$f^*\chi_{[0,1]}\in E$ and $f^*\chi_{[1,\infty)}\in L^2$. We reveal close
connections between properties of the generalized Rosenthal's space,
corresponding to the space $Z_E$, and the behaviour of independent
symmetrically distributed random variables in $E$. The results obtained are
applied to consider the problem of the existence of isomorphisms between r.i.\
spaces on $[0,1]$ and $(0,\infty)$. Exploiting particular properties of
disjoint sequences, we identify a rather wide new class of r.i.\ spaces on
$[0,1]$ ``close'' to $L^\infty$, which fail to be isomorphic to r.i.\ spaces on
$(0,\infty)$. In particular, this property is shared by the Lorentz spaces
$\Lambda_2(\log^{-\alpha}(e/u))$, with $0<\alpha\le 1$.


  One of the most unusual aspects of the X-ray binary LSI +61 303 is that at
each orbit (P1=26.4960 \pm 0.0028 d) one radio outburst occurs whose amplitude
is modulated with Plong, a long-term period of more than 4 yr. It is still not
clear whether the compact object of the system or the companion Be star is
responsible for the long-term modulation. We study here the stability of Plong.
Such a stability is expected if Plong is due to periodic (P2) Doppler boosting
of periodic (P1) ejections from the accreting compact object of the system. On
the contrary it is not expected if Plong is related to variations in the mass
loss of the companion Be star. We built a database of 36.8 yr of radio
observations of LSI +61 303 covering more than 8 long-term cycles. We performed
timing and correlation analysis. In addition to the two dominant features at P1
and P2, the timing analysis gives a feature at Plong=1628 \pm 48 days. The
determined value of Plong agrees with the beat of the two dominant features,
i.e. Pbeat=1/(\nu1 -\nu2)=1626 \pm 68 d. The correlation coefficient of the
radio data oscillates at multiples of Pbeat. Cycles in varying Be stars change
in length and disappear after 2-3 cycles following the well-studied case of the
binary system zeta Tau. On the contrary, in LSI +61 303 the long-term period is
quite stable and repeats itself over the available 8 cycles. The long-term
modulation in LSI +61 303 accurately reflects the beat of periodical Doppler
boosting (induced by precession) with the periodicity of the ejecta. The peak
of the long-term modulation occurs at the coincidence of the maximum number of
ejected particles with the maximum Doppler boosting of their emission; this
coincidence occurs every 1/(\nu1 - \nu2) and creates the long-term modulation
observed in LSI +61 303.


  As computability implies value definiteness, certain sequences of quantum
outcomes cannot be computable.


  We consider the quasi-periodic Schr\"odinger operator with the non-degenerate
Gevrey potential for the Diophantine frequency. We prove that if the coupling
number of the potential is large, then the spectrum is homogeneous.


  In order to achieve better performance for point cloud analysis, many
researchers apply deeper neural networks using stacked Multi-Layer-Perceptron
(MLP) convolutions over irregular point cloud. However, applying dense MLP
convolutions over large amount of points (e.g. autonomous driving application)
leads to inefficiency in memory and computation. To achieve high performance
but less complexity, we propose a deep-wide neural network, called
ShufflePointNet, to exploit fine-grained local features and reduce redundancy
in parallel using group convolution and channel shuffle operation. Unlike
conventional operation that directly applies MLPs on high-dimensional features
of point cloud, our model goes wider by splitting features into groups in
advance, and each group with certain smaller depth is only responsible for
respective MLP operation, which can reduce complexity and allows to encode more
useful information. Meanwhile, we connect communication between groups by
shuffling groups in feature channel to capture fine-grained features. We claim
that, multi-branch method for wider neural networks is also beneficial to
feature extraction for point cloud. We present extensive experiments for shape
classification task on ModelNet40 dataset and semantic segmentation task on
large scale datasets ShapeNet part, S3DIS and KITTI. We further perform
ablation study and compare our model to other state-of-the-art algorithms in
terms of complexity and accuracy.


  The present work is devoted to proving that the boundary regularity of the
three dimensional density patch persists by time evolution for inhomogeneous
incompressible viscous flow, with some smallness condition on the initial
velocity.


  A minimal system of homogeneous generating elements of the invariants algebra
for the binary form of degree 7 is calculated.


  Many biological high-throughput data sets, such as targeted amplicon-based
and metagenomic sequencing data, are compositional in nature. A common
exploratory data analysis task is to infer statistical associations between the
high-dimensional microbial compositions and habitat- or host-related
covariates. We propose a general robust statistical regression framework,
RobRegCC (Robust Regression with Compositional Covariates), which extends the
linear log-contrast model by a mean shift formulation for capturing outliers.
RobRegCC includes sparsity-promoting convex and non-convex penalties for
parsimonious model estimation, a data-driven robust initialization procedure,
and a novel robust cross-validation model selection scheme. We show RobRegCC's
ability to perform simultaneous sparse log-contrast regression and outlier
detection over a wide range of simulation settings and provide theoretical
non-asymptotic guarantees for the underlying estimators. To demonstrate the
seamless applicability of the workflow on real data, we consider a gut
microbiome data set from HIV patients and infer robust associations between a
sparse set of microbial species and host immune response from soluble CD14
measurements. All experiments are fully reproducible and available on GitHub at
https://github.com/amishra-stats/robregcc.


  The diagonal case of the $sl(2)$ Richardson-Gaudin quantum pairing model
\cite{Richardson1,Richardson2,Richardson3,Richardson4,Richardson5,Richardson6,G
audin76} is known to be solvable as an Abel-Jacobi inversion problem
\cite{SOV,Kuznetzov,Kuz1,Kuz2,Kuz3,Kuz4,Kuz5,YAKE04}. This is an isospectral
(stationary) solution to a more general integrable hierarchy, in which the full
time evolution can be written as isomonodromic deformations. Physically, the
more general solution is appropriate when the single-particle electronic
spectrum is subject to external perturbations. The asymptotic behavior of the
nonlinear oscillations in the case of elliptic solutions is derived.


  We suggest using the max-norm as a convex surrogate constraint for
clustering. We show how this yields a better exact cluster recovery guarantee
than previously suggested nuclear-norm relaxation, and study the effectiveness
of our method, and other related convex relaxations, compared to other
clustering approaches.


  We are interested in describing the infected size of the SIS Epidemic model
using Birth-Death Markov process. The Susceptible-Infected-Susceptible (SIS)
model is defined within a population of constant size $M$; the size is kept
constant by replacing each death with a newborn healthy individual. The life
span of each individual in the population is modelled by an exponential
distribution with parameter $\alpha$; and the disease spreads within the
population is modelled by a Poisson process with a rate $\lambda_{I}$.
$\lambda_{I}=\beta I(1-\frac{I}{M}) $ is similar to the instantaneous rate in
the logistic population growth model. The analysis is focused on the disease
outbreak, where the reproduction number $R=\frac{\beta}{\alpha} $ is greater
than one. As methodology, we use both numerical and analytical approaches. The
analysis relies on the stationary distribution for Birth and Death Markov
process. The numerical approach creates sample path simulations into order show
the infected size dynamics, and the relationship between infected size and $R$.
As $M$ becomes large, some stable statistical characteristics of the infected
size distribution can be deduced. And the infected size is shown analytically
to follow a normal distribution with mean $(1-\frac{1}{R}) M$ and Variance
$\frac{M}{R} $.


  This paper deals with a cosmological model in which the universe is filled
with tachyon dark energy in order to describe current and future accelerating
expansion. We obtain that the simplest condition for the regime of phantom
energy to occur in this scenario is that the scalar field be Wick rotated to
imaginary values which correspond to an axionic field classically. By
introducing analytical expressions for the scale factor or the Hubble parameter
that satisfy all constraint equations of the used models we show that such
models describe universes which may develop a big rip singularity in the finite
future. It is argued that, contrary to a recent claim, the entropy for a
universe filled with dark energy is definite positive even on the phantom
regime where the universe would instead acquire a negative temperature. It is
also seen that, whichever the fate of the tachyonic accelerating universe, it
will be stable to any fluctuations of the scalar field, and that since the
considered models have all an imaginary sound speed, any overdense regions will
undergo an accelerate collapse leading rapidly to formation of giant black
holes. Finally the conjecture is advanced that these black holes may be the
supermassive black holes that most galaxies harbor at their center.


  A new formula for the conserved charges in 3+1 gravity for spacetimes with
local AdS asymptotic geometry is proposed. It is shown that requiring the
action to have an extremum for this class of asymptotia sets the boundary term
that must be added to the Lagrangian as the Euler density with a fixed weight
factor. The resulting action gives rise to the mass and angular momentum as
Noether charges associated to the asymptotic Killing vectors without requiring
specification of a reference background in order to have a convergent
expression. A consequence of this definition is that any negative constant
curvature spacetime has vanishing Noether charges. These results remain valid
in the limit of vanishing cosmological constant.


  We investigate theoretically a one-dimensional semiconductor-superconductor
(SM-SC) heterostructure with Rashba spin-orbit coupling and parallel Zeeman
field in the presence of disorder generated by random charged impurities and
identify the optimal regimes for realizing topological superconductivity and
Majorana zero modes. Using a Green's function approach, we show that upon
increasing the disorder strength the stable topological superconducting phase
characterized by robust end-to-end Majorana correlations "migrates" toward
larger values of the Zeeman field and can be stabilized by increasing the
effective SM-SC coupling. Based on these findings, we propose a strategy for
accessing a regime characterized by well-separated Majorana zero modes that is
based on (a) enhancing the strength of the effective SM-SC coupling (e.g.,
through interface engineering) and (b) expanding the range of accessible Zeeman
fields (e.g., by enhancing the gyromagnetic ratio or optimizing the parent
superconductor, to enable the application of larger magnetic fields). While
this strategy may still require some reduction of the disorder strength, this
requirement is significantly less strict than the corresponding requirement in
a strategy that focuses exclusively on disorder reduction.


  It is well known that the moduli space of all deformations of a compact
special Lagrangian submanifold $X$ in a Calabi-Yau manifold $Y$ within the
class of special Lagrangian submanifolds is isomorphic to the first de Rham
cohomology group of $X$. Reinterpreting the embedding data $X\subset Y$ within
the mathematical framework of the Batalin-Vilkovisky quantization, we find a
natural deformation problem which extends the above moduli space to the full de
Rham cohomology group of $X$.


  Recent advances in reconstruction methods for inverse problems leverage
powerful data-driven models, e.g., deep neural networks. These techniques have
demonstrated state-of-the-art performances for several imaging tasks, but they
often do not provide uncertainty on the obtained reconstruction. In this work,
we develop a scalable, data-driven, knowledge-aided computational framework to
quantify the model uncertainty via Bayesian neural networks. The approach
builds on, and extends deep gradient descent, a recently developed greedy
iterative training scheme, and recasts it within a probabilistic framework.
Scalability is achieved by being hybrid in the architecture: only the last
layer of each block is Bayesian, while the others remain deterministic, and by
being greedy in training. The framework is showcased on one representative
medical imaging modality, viz. computed tomography with either sparse view or
limited view data, and exhibits competitive performance with respect to
state-of-the-art benchmarks, e.g., total variation, deep gradient descent and
learned primal-dual.


  We know SGAN may have a risk of gradient vanishing. A significant improvement
is WGAN, with the help of 1-Lipschitz constraint on discriminator to prevent
from gradient vanishing. Is there any GAN having no gradient vanishing and no
1-Lipschitz constraint on discriminator? We do find one, called GAN-QP.
  To construct a new framework of Generative Adversarial Network (GAN) usually
includes three steps: 1. choose a probability divergence; 2. convert it into a
dual form; 3. play a min-max game. In this articles, we demonstrate that the
first step is not necessary. We can analyse the property of divergence and even
construct new divergence in dual space directly. As a reward, we obtain a
simpler alternative of WGAN: GAN-QP. We demonstrate that GAN-QP have a better
performance than WGAN in theory and practice.


  We investigate a generic dynamical theory to characterize topological quantum
phases by quantum quenches, and study the emergent topology of quantum dynamics
when the quenches start from a deep or shallow trivial phase to topological
regimes. Two dynamical schemes are examined: One is to characterize topological
phases via quantum dynamics induced by a single quench along an arbitrary axis,
and the other applies a sequence of quenches with respect to all (pseudo)spin
axes. These two schemes are both built on the so-called dynamical bulk-surface
correspondence, which shows that the $d$-dimensional ($d$D) topological phases
with integer invariants can be characterized by the dynamical topological
pattern emerging on $(d-1)$D band inversion surfaces (BISs). We show that the
first dynamical scheme works for both deep and shallow quenches, the latter of
which is initialized in an incompletely polarized trivial phase. For the second
scheme, however, when the initial phase for the quench study varies from the
deep trivial (fully polarized) to shallow trivial (incompletely polarized)
regime, a new dynamical topological transition, associated with topological
charges crossing BISs, is predicted in quench dynamics. A generic criterion of
the dynamical topological transition is precisely obtained. Above the
criterion, quantum dynamics on BISs well characterizes the topology of the
post-quench Hamiltonian. Below the criterion, the quench dynamics may depict a
new dynamical topology; the post-quench topology can be characterized by the
emergent topological invariant plus the total charges moving outside the region
enclosed by BISs. We illustrate our results by numerically calculating the 2D
quantum anomalous Hall model. This work broadens the way to classify
topological phases by non-equilibrium quantum dynamics, and has feasibility for
experimental realization.


  It is commonly accepted that Commodities futures and forward prices, in
principle, agree under some simplifying assumptions. One of the most relevant
assumptions is the absence of counterparty risk. Indeed, due to margining,
futures have practically no counterparty risk. Forwards, instead, may bear the
full risk of default for the counterparty when traded with brokers or outside
clearing houses, or when embedded in other contracts such as swaps. In this
paper we focus on energy commodities and on Oil in particular. We use a hybrid
commodities-credit model to asses impact of counterparty risk in pricing
formulas, both in the gross effect of default probabilities and on the subtler
effects of credit spread volatility, commodities volatility and
credit-commodities correlation. We illustrate our general approach with a case
study based on an oil swap, showing that an accurate valuation of counterparty
risk depends on volatilities and correlation and cannot be accounted for
precisely through a pre-defined multiplier.


  We propose a scheme for the spatial exciton energy control and exciton
routing in a transition metal dichalcogenide (TMD) monolayer which lies on a
quantum paraelectric substrate. It relies on the ultrasensitive response of the
substrate dielectric permittivity to temperature changes, allowing for
spatially inhomogeneous screening of Coulomb interaction in a monolayer. As an
example, we consider the heterostructure of TMD and strontium titanate oxide
SrTiO$_3$, where large dielectric screening can be attained. We study the
impact of substrate temperature on the characteristic electronic features of
TMD monolayers such as the particle bandgap and exciton binding energy, Bohr
radius and nonlinearity (an exciton-exciton interaction). The combination of
particle bandgap and exciton binding energy modulation results in the shift of
the exciton resonance energy. Applying local heating, we create spatial
patterns with varying exciton resonant energy and an exciton flow towards the
energetically lower region of the sample.


  A search for quantum black holes in electron+jet and muon+jet invariant mass
spectra is performed with 140 fb$^{-1}$ of data collected by the ATLAS detector
in proton-proton collisions at $\sqrt{s}$ = 13 TeV at the Large Hadron
Collider. The observed invariant mass spectrum of lepton+jet pairs is
consistent with Standard Model expectations. Upper limits are set at 95%
confidence level on the production cross-sections times branching fractions for
quantum black holes decaying into a lepton and a quark in a search region with
invariant mass above 2.0 TeV. The resulting quantum black hole lower mass
threshold limit is 9.2 TeV in the Arkani-Hamed-Dimopoulos-Dvali model, and 6.8
TeV in the Randall-Sundrum model.


  Through solution of the multielectron, semi-relativistic, time-dependent
Schr\"{o}dinger equation, we show that angular streaking produces strongly
spin-polarized electrons in a noble gas. The degree of spin polarization
increases with the Keldysh parameter, so that angular streaking -- ordinarily
applied to investigate tunneling -- may be repurposed to generate strongly
spin-polarized electron bunches. Additionally, we explore modifications of the
angular streaking scheme that also enhance spin polarization.


  This paper addresses the issue of efficient turbo packet combining techniques
for coded transmission with a Chase-type automatic repeat request (ARQ)
protocol operating over a multiple-input--multiple-output (MIMO) channel with
intersymbol interference (ISI). First of all, we investigate the outage
probability and the outage-based power loss of the MIMO-ISI ARQ channel when
optimal maximum a posteriori (MAP) turbo packet combining is used at the
receiver. We show that the ARQ delay (i.e., the maximum number of ARQ rounds)
does not completely translate into a diversity gain. We then introduce two
efficient turbo packet combining algorithms that are inspired by minimum mean
square error (MMSE)-based turbo equalization techniques. Both schemes can be
viewed as low-complexity versions of the optimal MAP turbo combiner. The first
scheme is called signal-level turbo combining and performs packet combining and
multiple transmission ISI cancellation jointly at the signal-level. The second
scheme, called symbol-level turbo combining, allows ARQ rounds to be separately
turbo equalized, while combining is performed at the filter output. We conduct
a complexity analysis where we demonstrate that both algorithms have almost the
same computational cost as the conventional log-likelihood ratio (LLR)-level
combiner. Simulation results show that both proposed techniques outperform
LLR-level combining, while for some representative MIMO configurations,
signal-level combining has better ISI cancellation capability and achievable
diversity order than that of symbol-level combining.


  Thermal radiation transport (TRT) is a time dependent, high dimensional
partial integro-differential equation. In practical applications such as
inertial confinement fusion, TRT is coupled to other physics such as
hydrodynamics, plasmas, etc., and the timescales one is interested in capturing
are often much slower than the radiation timescale. As a result, TRT is treated
implicitly, and due to its stiffness and high dimensionality, is often a
dominant computational cost in multiphysics simulations. Here we develop a new
approach for implicit-explicit (IMEX) integration of gray TRT in the
deterministic S$_N$ setting, which requires only one sweep per stage, with the
simplest first-order method requiring only one sweep per time step. The
partitioning of equations is done via a moment-based high-order low-order
formulation of TRT, where the streaming operator and first two moments are used
to capture the asymptotic stiff regimes of the streaming limit and diffusion
limit. Absorption-reemission is treated explicitly, and although stiff, is
sufficiently damped by the implicit solve that we achieve stable accurate time
integration without incorporating the coupling of the high order and low order
equations implicitly. Due to nonlinear coupling of the high-order and low-order
equations through temperature-dependent opacities, to facilitate IMEX
partitioning and higher-order methods, we use a semi-implicit integration
approach amenable to nonlinear partitions. Results are demonstrated on thick
Marshak and crooked pipe benchmark problems, demonstrating orders of magnitude
improvement in accuracy and wallclock compared with the standard first-order
implicit integration typically used.


  Data often are formed of multiple modalities, which jointly describe the
observed phenomena. Modeling the joint distribution of multimodal data requires
larger expressive power to capture high-level concepts and provide better data
representations. However, multimodal generative models based on variational
inference are limited due to the lack of flexibility of the approximate
posterior, which is obtained by searching within a known parametric family of
distributions. We introduce a method that improves the representational
capacity of multimodal variational methods using normalizing flows. It
approximates the joint posterior with a simple parametric distribution and
subsequently transforms into a more complex one. Through several experiments,
we demonstrate that the model improves on state-of-the-art multimodal methods
based on variational inference on various computer vision tasks such as
colorization, edge and mask detection, and weakly supervised learning. We also
show that learning more powerful approximate joint distributions improves the
quality of the generated samples. The code of our model is publicly available
at https://github.com/SashoNedelkoski/BPFDMVM.


  We discuss the decay rates of chaotic quantum systems coupled to noise. We
model both the Hamiltonian and the system-noise coupling by random $N \times N$
Hermitian matrices, and study the spectral properties of the resulting Lindblad
superoperator. We consider various random-matrix ensembles, and find that for
all of them the asymptotic decay rate remains nonzero in the thermodynamic
limit, i.e., the spectrum of the superoperator is gapped as $N \rightarrow
\infty$. A sharp spectral transition takes place as the dissipation strength is
increased: for weak dissipation, the non-zero eigenvalues of the master
equation form a continuum; whereas for strong dissipation, the asymptotic decay
rate is an \emph{isolated eigenvalue}, i.e., a `mid-gap state' that is sharply
separated from the continuous spectrum of the master equation. For finite $N$,
the probability of finding a very small gap vanishes algebraically with a
scaling exponent that is extensive in system size, and depends only on the
symmetry class of the random matrices and the number of independent decay
channels. We comment on experimental implications of our results.


  We study the spectral function $A_k (\omega)$ and the optical conductivity
$\sigma_1 (\omega)$ for a system of fermions interacting with their collective
spin fluctuations (the spin-fermion model), and apply the results to optimally
doped cuprates in the normal state. We show that there is no qualitative
distinction between hot and cold regions in the
  Brillouin zone - in both cases, there exists a wide range of frequencies
where the width of the peak in $A_k (\omega)$ scales linearly with $\omega$. We
demonstrate that $\sigma_1 (\omega)$ is inversely linear in $\omega$ up to very
high frequencies. We argue that these results agree {\it quantitatively} with
the photoemission and optical data for $Bi2212$.


  The partially observable constrained optimization problems (POCOPs) impede
data-driven optimization techniques since an infeasible solution of POCOPs can
provide little information about the objective as well as the constraints. We
endeavor to design an efficient and provable method for expensive POCOPs under
the framework of constrained Bayesian optimization. Our method consists of two
key components. Firstly, we present an improved design of the acquisition
functions that introduces balanced exploration during optimization. We
rigorously study the convergence properties of this design to demonstrate its
effectiveness. Secondly, we propose a Gaussian process embedding different
likelihoods as the surrogate model for a partially observable constraint. This
model leads to a more accurate representation of the feasible regions compared
to traditional classification-based models. Our proposed method is empirically
studied on both synthetic and real-world problems. The results demonstrate the
competitiveness of our method for solving POCOPs.


  This paper investigates the utility of Neural Radiance Fields (NeRF) models
in extending the regions of operation of a mobile robot, controlled by
Image-Based Visual Servoing (IBVS) via static CCTV cameras. Using NeRF as a
3D-representation prior, the robot's footprint may be extrapolated
geometrically and used to train a CNN-based network to extract it online from
the robot's appearance alone. The resulting footprint results in a tighter
bound than a robot-wide bounding box, allowing the robot's controller to
prescribe more optimal trajectories and expand its safe operational floor area.


  Neural Text-to-Speech (TTS) systems find broad applications in voice
assistants, e-learning, and audiobook creation. The pursuit of modern models,
like Diffusion Models (DMs), holds promise for achieving high-fidelity,
real-time speech synthesis. Yet, the efficiency of multi-step sampling in
Diffusion Models presents challenges. Efforts have been made to integrate GANs
with DMs, speeding up inference by approximating denoising distributions, but
this introduces issues with model convergence due to adversarial training. To
overcome this, we introduce CM-TTS, a novel architecture grounded in
consistency models (CMs). Drawing inspiration from continuous-time diffusion
models, CM-TTS achieves top-quality speech synthesis in fewer steps without
adversarial training or pre-trained model dependencies. We further design
weighted samplers to incorporate different sampling positions into model
training with dynamic probabilities, ensuring unbiased learning throughout the
entire training process. We present a real-time mel-spectrogram generation
consistency model, validated through comprehensive evaluations. Experimental
results underscore CM-TTS's superiority over existing single-step speech
synthesis systems, representing a significant advancement in the field.


  We show that a recently proposed extension of the MSSM can provide a scenario
where both the cold and hot dark matter of the universe owe their origin to a
single scale connected with the breakdown of the global B-L symmetry. The susy
partner of the majoron and the light Majorana neutrinos are the cold and hot
dark matter candidates respectively in this model and their desired relative
abundances emerge when the scale of B-L symmetry breaking is in the TeV range.


  The equation of state (EoS) of the neutron star (NS) matter remains an
enigma. In this work we perform the Bayesian parameter inference with the
gravitational wave data (GW170817) and mass-radius observations of some NSs
(PSR J0030+0451, PSR J0437-4715, and 4U 1702-429) using the phenomenologically
constructed EoS models to search for a potential first-order phase transition.
Our phenomenological EoS models take the advantages of current widely used
parametrizing methods, which are flexible enough to resemble various
theoretical EoS models. We find that the current observation data are still not
informative enough to support/rule out phase transition, due to the comparable
evidences for models with and without phase transition. However, the bulk
properties of the canonical $1.4\,M_\odot$ NS and the pressure at around
$2\rho_{\rm sat}$ are well constrained by the data, where $\rho_{\rm sat}$ is
the nuclear saturation density. Moreover, strong phase transition at low
densities is disfavored, and the $1\sigma$ lower bound of transition density is
constrained to $1.84\rho_{\rm sat}$.


  The main goal of this paper is to provide an alternative proof of the
following theorem of Petty: in the normed space of dimension at least three,
every 3-element equilateral set can be extended to a 4-element equilateral set.
Our approach is based on the result of Kramer and N\'emeth about inscribing a
simplex into a convex body. To prove the theorem of Petty, we shall also
establish that for every 3 points in the normed plane, forming an equilateral
set of the common distance $p$, there exists a fourth point, which is
equidistant to the given points with the distance not larger than $p$. We will
also improve the example given by Petty and obtain the existence of a smooth
and strictly convex norm in $\mathbb{R}^n$, which contain a maximal 4-element
equilateral set. This shows that the theorem of Petty cannot be generalized to
higher dimensions, even for smooth and strictly convex norms.


  In this paper we consider Riemannian manifolds $(M^n,g)$ of dimension $n \geq
5$, with semi-positive $Q$-curvature and non-negative scalar curvature. Under
these assumptions we prove $(i)$ the Paneitz operator satisfies a strong
maximum principle; $(ii)$ the Paneitz operator is a positive operator; and
$(iii)$ its Green's function is strictly positive. We then introduce a
non-local flow whose stationary points are metrics of constant positive
$Q$-curvature. Modifying the test function construction of Esposito-Robert, we
show that it is possible to choose an initial conformal metric so that the flow
has a sequential limit which is smooth and positive, and defines a conformal
metric of constant positive $Q$-curvature.


  The existence of leptoquarks (LQ), color-triplets of bosons with lepton and
quark quantum numbers, is predicted by various theories beyond the Standard
Model. This article summarizes the D-Zero Run I searches for the three
different LQ generations in ppbar collisions at a center-of-mass energy of 1.8
TeV and presents ongoing studies at Run II.


  The flow around a cylinder oscillating in the streamwise direction with a
frequency, $f_f$, much lower than the shedding frequency, $f_s$, has been
relatively less studied than the case when these frequencies have the same
order of magnitude, or the transverse oscillation configuration. In this study,
Particle Image Velocimetry and Koopman Mode Decomposition are used to
investigate the streamwise-oscillating cylinder wake for forcing frequencies
$f_f/f_s \sim 0.04-0.2$ and mean Reynolds number, $Re_0 = 900$. The amplitude
of oscillation is such that the instantaneous Reynolds number remains above the
critical value for vortex shedding at all times. Characterization of the wake
reveals a range of phenomena associated with the interaction of the two
frequencies, including modulation of both the amplitude and frequency of the
wake structure by the forcing. Koopman analysis reveals a frequency spreading
of Koopman modes. A scaling parameter and associated transformation are
developed to relate the unsteady, or forced, dynamics of a system to that of a
quasi-steady, or unforced, system. For the streamwise-oscillating cylinder, it
is shown that this transformation leads to a Koopman Mode Decomposition similar
to that of the unforced system.


  We propose currently feasible experiments using small, isolated systems of
ultracold atoms to investigate the effects of dynamical chaos in the
microscopic onset of irreversibility. A control parameter is tuned past a
critical value, then back to its initial value; hysteresis appears as a finite
probability that the atoms fail to return to their initial state even when the
parameter sweep is arbitrarily slow. We show that an episode of chaotic
dynamics during part of the sweep time produces distinctive features in the
distribution of final states that will be clearly observable in experiments.


  The role of electron correlation on different pairing symmetries are
discussed in details where the electron correlation has been treated within the
slave boson formalism. It is shown that for a pure $s$ or pure $d$ wave pairing
symmetry, the electronic correlation suppresses the $s$ wave gap magnitude (as
well as the $T_c$) at a faster rate than that for the $d$ wave gap. On the
otherhand, a complex order parameter of the form ($s+id$) shows anomalous
temperature dependence. For example, if the temperature ($T_{c}^d$) at which
the $d$ wave component of the complex order parameter vanishes happens to be
larger than that for the $s$ wave component ($T_{c}^s$) then the growth of the
$d$ wave component is arrested with the onset of the $s$ wave component of the
order parameter. In this mixed phase however, we find that the suppression in
different components of the gap as well as the corresponding $T_c$ due to
coulomb correlation are very sensitive to the relative pairing strengths of $s$
and $d$ channels as well as the underlying lattice. Interestingly enough, in
such a scenario (for a case of $T_{c}^s > T_{c}^d$) the gap magnitude of the
$d$ wave component increases with electron correlation but not $T_{c}^d$ for
certain values of electron correlation. However, this never happens in case of
the $s$ wave component. We also calculate the temperature dependence of the
superconducting gap along both the high symmetry directions ($\Gamma$ - M and
$\Gamma $ - X) in a mixed $s+id$ symmetry pairing state and the thermal
variation of the gap anisotropy ($\frac{\Delta_{\Gamma - M}}{\Delta_{\Gamma -
X}}$) with electron correlation. The results are discussed with reference to
experimental observations.


  The general problem of a perfect incompressible fluid motion with vortex
areas and variant constant vorticities is formulated. The M.A. Goldshtik's
variational approach is considered on research of dual problems for flows with
vortex and potential areas that describe detached flow and a motion model of a
perfect incompressible fluid in field of Coriolis forces.


  Fast data acquisition in Magnetic Resonance Imaging (MRI) is vastly in demand
and scan time directly depends on the number of acquired k-space samples. The
data-driven methods based on deep neural networks have resulted in promising
improvements, compared to the conventional methods, in image reconstruction
algorithms. The connection between deep neural network and Ordinary
Differential Equation (ODE) has been observed and studied recently. The studies
show that different residual networks can be interpreted as Euler
discretization of an ODE. In this paper, we propose an ODE-based deep network
for MRI reconstruction to enable the rapid acquisition of MR images with
improved image quality. Our results with undersampled data demonstrate that our
method can deliver higher quality images in comparison to the reconstruction
methods based on the standard UNet network and Residual network.


  This paper studies the trajectory optimization problem in a scenario where a
single rotary-wing UAV acts as a relay of data payloads for downlink
transmission requests generated randomly by two ground nodes (GNs) in a
wireless network. The goal is to optimize the UAV trajectory in order to
minimize the expected average communication delay to serve these random
requests. It is shown that the problem can be cast as a semi-Markov decision
process (SMDP), and the resulting minimization problem is solved via
multi-chain policy iteration. The optimality of a two-scale optimization
approach is proved: the optimal trajectory in the communication phase greedily
minimizes the communication delay of the current request while moving between
the current start position and a target end position (inner optimization); the
end positions are selected to minimize the expected average long-term delay in
the SMDP (outer optimization). Numerical simulations show that the expected
average delay is minimized when the UAV moves towards the geometric center of
the GNs during phases in which it is not actively servicing transmission
requests, and demonstrate significant improvements over sensible heuristics.
Finally, it is revealed that the optimal end positions of communication phases
become increasingly independent of the data payload, for large data payload
values.


  We establish the irreversibility of renormalization group flows on a
pointlike defect inserted in a $d$-dimensional Lorentzian conformal field
theory. We identify the impurity entropy $g$ with the quantum relative entropy
in two equivalent ways. One involves a null deformation of the Cauchy surface,
and the other is given in terms of a local quench protocol. Positivity and
monotonicity of the relative entropy imply that $g$ decreases monotonically
along renormalization group flows, and provides a clear information-theoretic
meaning for this irreversibility.


  We consider Higgs production through bottom-quark annihilation at hadron
colliders and we calculate next-to-next-to-leading-order (NNLO) corrections in
QCD perturbation theory matched to parton showers (NNLO+PS). To this end, we
have adapted the MINNLO$_{\text{PS}}$ method to account for the extra scale
dependence induced by an overall Yukawa coupling that is $\overline{\rm MS}$
renormalized. We compare our results against state-of-the-art fixed-order
predictions at NNLO as well as resummed predictions at
next-to-next-to-leading-logarithmic (NNLL) accuracy.


  In order to protect devices from physical manipulations, protective security
enclosures were developed. However, these battery-backed solutions come with a
reduced lifetime, and have to be actively and continuously monitored. In order
to overcome these drawbacks, batteryless capacitive enclosures based on
Physical Unclonable Functions (PUFs) have been developed that generate a
key-encryption-key (KEK) for decryption of the key chain. In order to reproduce
the PUF-key reliably and to compensate the effect of noise and environmental
influences, the key generation includes error correction codes. However,
drilling attacks that aim at partially destroying the enclosure also alter the
PUF-response and are subjected to the same error correction procedures.
Correcting attack effects, however, is highly undesirable as it would destroy
the security concept of the enclosure. In general, designing error correction
codes such that they provide tamper-sensitivity to attacks, while still
correcting noise and environmental effects is a challenging task. We tackle
this problem by first analyzing the behavior of the PUF-response under external
influences and different post-processing parameters. From this, we derive a
system model of the PUF-based enclosure, and construct a wiretap channel
implementation from q-ary polar codes. We verify the obtained error correction
scheme in a Monte Carlo simulation and demonstrate that our wiretap channel
implementation achieves a physical layer security of 100 bits for 306 bits of
entropy for the PUF-secret. Through this, we further develop capacitive
PUF-based security enclosures and bring them one step closer to their
commercial deployment.


  A relation giving a minimum for the irreversible work in quasi-equilibrium
processes was derived by Sekimoto et al. (K. Sekimoto and S. Sasa, J. Phys.
Soc. Jpn. {\bf 66} (1997), 3326) in the framework of stochastic energetics.
This relation can also be written as a type of "uncertainty principle" in such
a way that the precise determination of the Helmholtz free energy through the
observation of the work $<W>$ requires an indefinitely large experimental time
$\Delta t$. In the present article, we extend this relation to the case of
quasi-steady processes by using the concept of non-equilibrium Helmholtz free
energy. We give a formulation of the second law for these processes that
extends that presented by Sekimoto (K. Sekimoto, Prog. Theo. Phys. Suppl. No.
{\bf 130} (1998), 17) by a term of the first order in the inverse of the
experimental time. As application of our results, two possible experimental
situations are considered: stretching of a RNA molecule and the drag of a
dipolar particle in the presence of a gradient of electric force.


  Image Dehazing aims to remove atmospheric fog or haze from an image. Although
the Dehazing models have evolved a lot in recent years, few have precisely
tackled the problem of High-Resolution hazy images. For this kind of image, the
model needs to work on a downscaled version of the image or on cropped patches
from it. In both cases, the accuracy will drop. This is primarily due to the
inherent failure to combine global and local features when the image size
increases. The Dehazing model requires global features to understand the
general scene peculiarities and the local features to work better with fine and
pixel details. In this study, we propose the Streamlined Global and Local
Features Combinator (SGLC) to solve these issues and to optimize the
application of any Dehazing model to High-Resolution images. The SGLC contains
two successive blocks. The first is the Global Features Generator (GFG) which
generates the first version of the Dehazed image containing strong global
features. The second block is the Local Features Enhancer (LFE) which improves
the local feature details inside the previously generated image. When tested on
the Uformer architecture for Dehazing, SGLC increased the PSNR metric by a
significant margin. Any other model can be incorporated inside the SGLC process
to improve its efficiency on High-Resolution input data.


  Using a set of model equations of state satisfying the latest constraints
from both terrestrial nuclear experiments and astrophysical observations as
well as state-of-the-art nuclear many-body calculations of the pure neutron
matter equation of state, the tidal polarizability of canonical neutron stars
in coalescing binaries is found to be a very sensitive probe of the
high-density behavior of nuclear symmetry energy which is among the most
uncertain properties of dense neutron-rich nucleonic matter. Moreover, it
changes less than $\pm 10%$ by varying various properties of symmetric nuclear
matter and symmetry energy around the saturation density within their
respective ranges of remaining uncertainty.


  High-dimensional data are routinely collected in many areas. We are
particularly interested in Bayesian classification models in which one or more
variables are imbalanced. Current Markov chain Monte Carlo algorithms for
posterior computation are inefficient as $n$ and/or $p$ increase due to
worsening time per step and mixing rates. One strategy is to use a
gradient-based sampler to improve mixing while using data sub-samples to reduce
per-step computational complexity. However, usual sub-sampling breaks down when
applied to imbalanced data. Instead, we generalize piece-wise deterministic
Markov chain Monte Carlo algorithms to include importance-weighted and
mini-batch sub-sampling. These approaches maintain the correct stationary
distribution with arbitrarily small sub-samples, and substantially outperform
current competitors. We provide theoretical support and illustrate gains in
simulated and real data applications.


  Jet measurements in heavy ion collisions at low jet momentum can provide
constraints on the properties of the quark gluon plasma but are overwhelmed by
a significant, fluctuating background. We build upon our previous work which
demonstrated the ability of the jet multiplicity method to extend jet
measurements into the domain of low jet momentum [1, Mengel:2023]. We extend
this method to a wide range of jet resolution parameters. We investigate the
over-complexity of non-interpretable machine learning used to tackle the
problem of jet background subtraction through network optimization. Finally, we
show that the resulting shallow neural network is able to learn the underlying
relationship between jet multiplicity and background fluctuations, with a
lesser complexity, reinforcing the utility of interpretable methods.


  The unavoidable travel time variability in transportation networks, resulted
from the widespread supply side and demand side uncertainties, makes travel
time reliability (TTR) be a common and core interest of all the stakeholders in
transportation systems, including planners, travelers, service providers, and
managers. This common and core interest stimulates extensive studies on
modeling TTR. Researchers have developed a range of theories and models of TTR,
many of which have been incorporated into transportation models, policies, and
project appraisals. Adopting the network perspective, this paper aims to
provide an integrated framework for reviewing the methodological developments
of modeling TTR in transportation networks, including its characterization,
evaluation and valuation, and traffic assignment. Specifically, the TTR
characterization provides a whole picture of travel time distribution in
transportation networks. TTR evaluation and TTR valuation (known as the value
of reliability, VOR) simply and intuitively interpret abstract characterized
TTR to be well understood by different stakeholders of transportation systems.
TTR-based traffic assignment investigates the effects of TTR on the individual
users travel behavior and consequently the collective network flow pattern. As
the above three topics are mainly separately studied in different disciplines
and research areas, the integrated framework allows us to better understand
their relationships and may contribute to developing possible combinations of
TTR modeling philosophy. Also, the network perspective enables to focus on
common challenges of modeling TTR, especially the uncertainty propagation from
the uncertainty sources to the TTR at spatial levels including link, route, and
the entire network. Some directions for future research are discussed in the
era of new data environment, applications, and emerging technologies.


  The occurrence of quark matter at the center of neutron stars is still in
debate. This study defines some semi-empirical parameters that quantify the
occurrence and the amount of quark matter at star interiors. These parameters
show semi-universal relations across all the EoS. One parameter depends on the
shifting of the keplerian mass-radius curve from the static one and shows it is
a constant across all EoS. The Z-parameter shows how tidal deformability
depends on the quark content of the star and the stiffness of the EoS. The
quark content of the star also affects the compactness of the star, and its
dependence is almost universal. The empirical parameter gives a bound on the
quark content of the star and shows that if the amount of the quark content
increases, the stars are likely to collapse into a black hole. It is seen that
the change in the mass and radius after PT is linearly proportional to the mass
of the parent NS. Given a hadronic EoS, bag constant, and quark coupling
constant, one can have a critical mass of the neutron star and the maximum mass
of the hybrid star for phase transition without any baryonic mass loss.


  Recent years have witnessed rapid advances in learnt video coding. Most
algorithms have solely relied on the vector-based motion representation and
resampling (e.g., optical flow based bilinear sampling) for exploiting the
inter frame redundancy. In spite of the great success of adaptive kernel-based
resampling (e.g., adaptive convolutions and deformable convolutions) in video
prediction for uncompressed videos, integrating such approaches with
rate-distortion optimization for inter frame coding has been less successful.
Recognizing that each resampling solution offers unique advantages in regions
with different motion and texture characteristics, we propose a hybrid motion
compensation (HMC) method that adaptively combines the predictions generated by
these two approaches. Specifically, we generate a compound spatiotemporal
representation (CSTR) through a recurrent information aggregation (RIA) module
using information from the current and multiple past frames. We further design
a one-to-many decoder pipeline to generate multiple predictions from the CSTR,
including vector-based resampling, adaptive kernel-based resampling,
compensation mode selection maps and texture enhancements, and combines them
adaptively to achieve more accurate inter prediction. Experiments show that our
proposed inter coding system can provide better motion-compensated prediction
and is more robust to occlusions and complex motions. Together with jointly
trained intra coder and residual coder, the overall learnt hybrid coder yields
the state-of-the-art coding efficiency in low-delay scenario, compared to the
traditional H.264/AVC and H.265/HEVC, as well as recently published
learning-based methods, in terms of both PSNR and MS-SSIM metrics.


  The static friction coefficient between two materials is considered to be a
material constant. We present experiments demonstrating that the ratio of shear
to normal force needed to move contacting blocks can, instead, vary
systematically with controllable changes in the external loading configuration.
Large variations in both the friction coefficient and consequent stress drop
are tightly linked to changes in the rupture dynamics of the rough interface
separating the two blocks.


  In the context of the left--right symmetric model, the decay $b \rightarrow s
\gamma$ receives contributions from the gauge interactions mediated mainly by
the $W_L$, through $W_L$--$W_R$ mixing and also from the Yukawa interactions of
the charged and the neutral (flavour-changing) scalars (the latter type of
Yukawa interaction has been overlooked in the previous literature). Following
the recent CLEO measurement of the inclusive $b \rightarrow s \gamma$ process
and the measurement of the top-quark mass by the CDF and D0 collaborations, the
parameter space of the left--right symmetric model is more squeezed than
before.


  We designed and demonstrated a multi-spectral programmable perfect absorber
that exploits two different phase-change materials. This programmability is
possible by resonantly coupling two phase change materials, a Ge2Sb2Te5 layer
to vanadium dioxide nanoparticles (VO2 NPs). The perfect absorption is
attributed to the coalescence of gap plasmon modes excited between the NPs and
waveguide cavity-like modes excited between the film and the NPs. The
absorptance peak (>90%) can be tuned to four different infrared (IR)
wavelengths from 1906 to 2960 nm by heating the structure to different
temperatures. The perfect absorber is reconfigurable, lithography-free,
large-scale, polarization-insensitive omnidirectional. Our strategy opens a new
path for programmable infrared photonics.


  We present the geodesical completion of the Schwarzschild black hole in four
dimensions which covers the entire space in (u,v) Kruskal-Szekeres coordinates,
including the spacetime behind the black and white hole singularities. The
gravitational constant switches sign abruptly at the singularity, thus we
interpret the other side of the singularity as a region of antigravity. The
presence of such sign flips is a prediction of local (Weyl) scale invariant
geodesically complete spacetimes which improve classical general relativity and
string theory. We compute the geodesics for our new black hole and show that
all geodesics of a test particle are complete. Hence, an ideal observer, that
starts its journey in the usual space of gravity, can reach the other side of
the singularity in a finite amount of proper time. As usual, an observer
outside of the horizon cannot verify that such phenomena exist. However, the
fact that there exist proper observers that can see this, is of fundamental
significance for the construction of the correct theory and the interpretation
of phenomena pertaining to black holes and cosmology close to and beyond the
singularities.


  In this paper, we propose a new construction for the Mexican hat wavelets on
shapes with applications to partial shape matching. Our approach takes its main
inspiration from the well-established methodology of diffusion wavelets. This
novel construction allows us to rapidly compute a multiscale family of Mexican
hat wavelet functions, by approximating the derivative of the heat kernel. We
demonstrate that it leads to a family of functions that inherit many attractive
properties of the heat kernel (e.g., a local support, ability to recover
isometries from a single point, efficient computation). Due to its natural
ability to encode high-frequency details on a shape, the proposed method
reconstructs and transfers $\delta$-functions more accurately than the
Laplace-Beltrami eigenfunction basis and other related bases. Finally, we apply
our method to the challenging problems of partial and large-scale shape
matching. An extensive comparison to the state-of-the-art shows that it is
comparable in performance, while both simpler and much faster than competing
approaches.


  Ultrasound elasticity images which enable the visualization of quantitative
maps of tissue stiffness can be reconstructed by solving an inverse problem.
Classical model-based approaches for ultrasound elastography use deterministic
finite element methods (FEMs) to incorporate the governing physical laws
resulting in poor performance in noisy conditions. Moreover, these approaches
utilize fixed regularizers for various tissue patterns while appropriate
data-adaptive priors might be required for capturing the complex spatial
elasticity distribution. In this regard, we propose a joint model-based and
learning-based framework for estimating the elasticity distribution by solving
a regularized optimization problem. We present an integrated objective function
composed of a statistical physics-based forward model and a data-driven
regularizer to leverage deep neural networks for learning the underlying
elasticity prior. This constrained optimization problem is solved using the
gradient descent (GD) method and the gradient of regularizer is simply replaced
by the residual of the trained denoiser network for having an explicit
objective function with reduced computation time.


  A remarkable connection between optimal design and Monge transport was
initiated in the years 1997 in the context of the minimal elastic compliance
problem and where the euclidean metric cost was naturally involved. In this
paper we present different variants in optimal design of mechanical structures,
in particular focusing on the optimal pre-stressed elastic membrane problem. We
show that the underlying metric cost is associated with an unknown maximal
monotone map which maximizes the Monge-Kantorovich distance between two
measures. In parallel with the classical duality theory leading to existence
and (in a smooth case) to PDE optimality conditions, we present a general
geometrical approach arising from a two-point scheme in which geodesics with
respect to the optimal metric play a central role. These two aspects are
enlightened by several explicit examples and also by numerical solutions in
which optimal structures very often turn out to be truss-like i.e supported by
piecewise affine geodesics. In case of a discrete load, we are able to relate
the existence of such truss-like solutions to an extension property of maximal
monotone maps which is of independent interest and that we propose here as a
conjecture.


  We present massively parallel (MPC) algorithms and hardness of approximation
results for computing Single-Linkage Clustering of $n$ input $d$-dimensional
vectors under Hamming, $\ell_1, \ell_2$ and $\ell_\infty$ distances. All our
algorithms run in $O(\log n)$ rounds of MPC for any fixed $d$ and achieve
$(1+\epsilon)$-approximation for all distances (except Hamming for which we
show an exact algorithm). We also show constant-factor inapproximability
results for $o(\log n)$-round algorithms under standard MPC hardness
assumptions (for sufficiently large dimension depending on the distance used).
Efficiency of implementation of our algorithms in Apache Spark is demonstrated
through experiments on a variety of datasets exhibiting speedups of several
orders of magnitude.


  A comparative study of the electrical and optical properties has been done on
3d-doped TiO$_2$. Ti$_{1-x}$M$_x$O$_2$ (M= Sc, V, Cr, Mn, Fe, Co, Ni, Cu, Zn)
powder and its corresponding pellets, with doping concentration $x= 0.05$. The
samples were prepared using the solid-state route. Optical and electrical
measurements have been performed for all prepared samples and interestingly, it
is observed that due to external pressure (i.e. strain) both the properties
change significantly. A rigorous theoretical calculation has also been carried
out to verify the experimental band gap obtained from optical absorption
spectroscopy. In case of pellet sample band gap decreases as compared to the
powder sample due to variation of pressure inside the structures. Role of
doping has also been investigated both in pellet and powder forms and we found
that the band gap decreases as the atomic number of dopants increases. A
cross-over behavior is seen in pellet samples on doping with Ni, Cu and Zn
(i.e. band gap increases with an increase in the atomic number of dopant).
Electrical resistivity measurements have been carried out for both pellet and
powder samples and it is found that in the case of strained samples the value
of resistivity is smaller while in the case of strain-free samples it is quite
large. We believe that the present study suggests a novel approach for tuning
the electrical and optical properties of semiconducting oxides either from
doping or from applied pressure (or strain).


  This paper explores the relationship amongst the various simplicial and
pseudo-simplicial objects characteristically associated to any bicategory C. It
proves the fact that the geometric realizations of all of these possible
candidate `nerves of C' are homotopy equivalent. Any one of these realizations
could therefore be taken as the classifying space BC of the bicategory. Its
other major result proves a direct extension of Thomason's `Homotopy Colimit
Theorem' to bicategories: When the homotopy colimit construction is carried out
on a diagram of spaces obtained by applying the classifying space functor to a
diagram of bicategories, the resulting space has the homotopy type of a certain
bicategory, called the `Grothendieck construction on the diagram'. Our results
provide coherence for all reasonable extensions to bicategories of Quillen's
definition of the `classifying space' of a category as the geometric
realization of the category's Grothendieck nerve, and they are applied to
monoidal (tensor) categories through the elemental `delooping' construction.


  Within a light-cone quantum-chromodynamics dipole formalism based on the
Green function technique, we study nuclear shadowing in deep-inelastic
scattering at small Bjorken xB < 0.01. Such a formalism incorporates naturally
color transparency and coherence length effects. Calculations of the nuclear
shadowing for the \bar{q}q Fock component of the photon are based on an exact
numerical solution of the evolution equation for the Green function, using a
realistic form of the dipole cross section and nuclear density function. Such
an exact numerical solution is unavoidable for xB > 0.0001, when a variation of
the transverse size of the \bar{q}q Fock component must be taken into account.
The eikonal approximation, used so far in most other models, can be applied
only at high energies, when xB < 0.0001 and the transverse size of the \bar{q}q
Fock component is "frozen" during propagation through the nuclear matter. At xB
< 0.01 we find quite a large contribution of gluon suppression to nuclear
shadowing, as a shadowing correction for the higher Fock states containing
gluons. Numerical results for nuclear shadowing are compared with the available
data from the E665 and NMC collaborations. Nuclear shadowing is also predicted
at very small xB corresponding to LHC kinematical range. Finally the model
predictions are compared and discussed with the results obtained from other
models.


  We give an asymptotic for the number of prime solutions to $Q(x_1,\dots, x_8)
= N$, subject to a mild non-degeneracy condition on the homogeneous quadratic
form $Q$.
  The argument initially proceeds via the circle method, but this does not
suffice by itself. To obtain a nontrivial bound on certain averages of
exponential sums, we interpret these sums as matrix coefficients for the Weil
representation of the symplectic group
$\operatorname{Sp}_8(\mathbf{Z}/q\mathbf{Z})$. Averages of such matrix
coefficients are then bounded using an amplification argument and a convergence
result for convolutions of measures, which reduces matters to understanding the
action of certain 12-dimensional subgroups in the Weil representation.
Sufficient understanding can be gained by using the basic represention theory
of $\operatorname{SL}_2(k)$, $k$ a finite field.


  The spectrum of N=1 supersymmetric Yang-Mills theory, calculated on the
lattice, is presented. The masses have been determined on three different
lattice spacings and extrapolated towards vanishing gluino mass. We present the
extrapolation to the continuum limit which is consistent with the formation of
degenerate supermultiplets.


  We consider the universal sector of a $d$-dimensional large-$N$
strongly-interacting holographic CFT on a black hole spacetime background $B$.
When our CFT$_d$ is coupled to dynamical Einstein-Hilbert gravity with Newton
constant $G_{d}$, the combined system can be shown to satisfy a version of the
thermodynamic Generalized Second Law (GSL) at leading order in $G_{d}$. The
quantity $S_{CFT} + \frac{A(H_{B, \text{perturbed}})}{4G_{d}}$ is
non-decreasing, where $A(H_{B, \text{perturbed}})$ is the (time-dependent) area
of the new event horizon in the coupled theory. Our $S_{CFT}$ is the notion of
(coarse-grained) CFT entropy outside the black hole given by causal holographic
information -- a quantity in turn defined in the AdS$_{d+1}$ dual by the
renormalized area $A_{ren}(H_{\rm bulk})$ of a corresponding bulk causal
horizon. A corollary is that the fine-grained GSL must hold for finite
processes taken as a whole, though local decreases of the fine-grained
generalized entropy are not obviously forbidden. Another corollary, given by
setting $G_{d} = 0$, states that no finite process taken as a whole can
increase the renormalized free energy $F = E_{out} - T S_{CFT} - \Omega J -
\Phi Q$, with $T, \Omega, \Phi$ constants set by ${H}_B$. This latter corollary
constitutes a 2nd law for appropriate non-compact AdS event horizons.


  Linear rate equations are used to describe the cascading decay of an initial
heavy cluster into fragments. Using a procedure inspired by the similar, but
continuous case of jet fragmentation in QCD, this discretized process may be
analyzed into eigenmodes, corresponding to moments of the distribution of
multiplicities. The orders of these moments are usually noninteger numbers. The
resulting analysis can be made time independent and is applicable to various
phenomenological multifragmentation processes, in which case it leads to new
approximate finite-size scaling relations for the spectrum of fragments.


  Scene flow represents the motion of points in the 3D space, which is the
counterpart of the optical flow that represents the motion of pixels in the 2D
image. However, it is difficult to obtain the ground truth of scene flow in the
real scenes, and recent studies are based on synthetic data for training.
Therefore, how to train a scene flow network with unsupervised methods based on
real-world data shows crucial significance. A novel unsupervised learning
method for scene flow is proposed in this paper, which utilizes the images of
two consecutive frames taken by monocular camera without the ground truth of
scene flow for training. Our method realizes the goal that training scene flow
network with real-world data, which bridges the gap between training data and
test data and broadens the scope of available data for training. Unsupervised
learning of scene flow in this paper mainly consists of two parts: (i) depth
estimation and camera pose estimation, and (ii) scene flow estimation based on
four different loss functions. Depth estimation and camera pose estimation
obtain the depth maps and camera pose between two consecutive frames, which
provide further information for the next scene flow estimation. After that, we
used depth consistency loss, dynamic-static consistency loss, Chamfer loss, and
Laplacian regularization loss to carry out unsupervised training of the scene
flow network. To our knowledge, this is the first paper that realizes the
unsupervised learning of 3D scene flow from monocular camera. The experiment
results on KITTI show that our method for unsupervised learning of scene flow
meets great performance compared to traditional methods Iterative Closest Point
(ICP) and Fast Global Registration (FGR). The source code is available at:
https://github.com/IRMVLab/3DUnMonoFlow.


  We present a non-local Poisson bracket defined on the phase space $G^{u,v}/H$
of a Coxeter--Toda lattice, where $G^{u,v}$ is a Coxeter double Bruhat cell of
$GL_n$ and $H$ is the subgroup of diagonal matrices. This non-local Poisson
bracket is given in an appropriate set of coordinates of $G^{u,v}/H$ derived
from the so-called {\it factorization parameters}. We prove that the
generalized B\"acklund--Darboux transformations $\sigma_{u,v}^{u',v'}:
G^{u,v}/H\to G^{u',v'}/H$ are Poisson maps. We exploit that fact to show that
the non-local Poisson bracket corresponds to the Atiyah--Hitchin bracket under
the Moser map.


  We present a formal translation of an actor-based language with cooperative
scheduling to the functional language Haskell. The translation is proven
correct with respect to a formal semantics of the source language and a
high-level operational semantics of the target, i.e. a subset of Haskell. The
main correctness theorem is expressed in terms of a simulation relation between
the operational semantics of actor programs and their translation. This allows
us to then prove that the resource consumption is preserved over this
translation, as we establish an equivalence of the cost of the original and
Haskell-translated execution traces.


  Achieving a balance between lightweight design and high performance remains a
challenging task for speech enhancement. In this paper, we introduce Multi-path
Enhanced Taylor (MET) Transformer based U-net for Speech Enhancement (MUSE), a
lightweight speech enhancement network built upon the Unet architecture. Our
approach incorporates a novel Multi-path Enhanced Taylor (MET) Transformer
block, which integrates Deformable Embedding (DE) to enable flexible receptive
fields for voiceprints. The MET Transformer is uniquely designed to fuse
Channel and Spatial Attention (CSA) branches, facilitating channel information
exchange and addressing spatial attention deficits within the
Taylor-Transformer framework. Through extensive experiments conducted on the
VoiceBank+DEMAND dataset, we demonstrate that MUSE achieves competitive
performance while significantly reducing both training and deployment costs,
boasting a mere 0.51M parameters.


  Utilizing common resources is always a dilemma for community members. While
cooperator players restrain themselves and consider the proper state of
resources, defectors demand more than their supposed share for a higher payoff.
To avoid the tragedy of the common state, punishing the latter group seems to
be an adequate reaction. This conclusion, however, is less straightforward when
we acknowledge the fact that resources are finite and even a renewable resource
has limited growing capacity. To clarify the possible consequences, we consider
a coevolutionary model where beside the payoff-driven competition of cooperator
and defector players the level of a renewable resource depends sensitively on
the fraction of cooperators and the total consumption of all players. The
applied feedback-evolving game reveals that beside a delicately adjusted
punishment it is also fundamental that cooperators should pay special attention
to the growing capacity of renewable resources. Otherwise, even the usage of
tough punishment cannot save the community from an undesired end.


  We describe the observable features of the recently proposed Extended
Quintessence scenarios on the Cosmic Microwave Background (CMB) anisotropy
spectra. In this class of models a scalar field $\phi$, assumed to provide most
of the cosmic energy density today, is non-minimally coupled to the Ricci
curvature scalar $R$. We implement the linear theory of cosmological
perturbations in scalar tensor gravitational theories to compute CMB
temperature and polarization spectra. All the interesting spectral features are
affected: on sub-degree angular scales, the acoustic peaks change both in
amplitude and position; on larger scales the low redshift dynamics enhances the
Integrated Sachs Wolfe effect. These results show how the future CMB
experiments could give information on the vacuum energy as well as on the
structure of the gravitational Lagrangian term.