  This work proposes and analyzes a family of spatially inhomogeneous epidemic
models. This is our first effort to use stochastic partial differential
equations (SPDEs) to model epidemic dynamics with spatial variations and
environmental noise. After setting up the problem, existence and uniqueness of
solutions of the underlying SPDEs are examined. Then definitions of permanence
and extinction are given. Certain sufficient conditions are provided for the
permanence and extinction. Our hope is that this paper will open up windows for
investigation of epidemic models from a new angle.


  We investigate the behaviour of $r$-neighbourhood bootstrap percolation on
the binomial $k$-uniform random hypergraph $H_k(n,p)$ for given integers $k\geq
2$ and $r\geq 2$. In $r$-neighbourhood bootstrap percolation, infection spreads
through the hypergraph, starting from a set of initially infected vertices, and
in each subsequent step of the process every vertex with at least $r$ infected
neighbours becomes infected. For our analysis the set of initially infected
vertices is chosen uniformly at random from all sets of given size. In the
regime $n^{-1}\ll n^{k-2}p \ll n^{-1/r}$ we establish a threshold such that if
the number of initially infected vertices remains below the threshold, then
with high probability only a few additional vertices become infected, while if
the number of initially infected vertices exceeds the threshold then with high
probability almost every vertex becomes infected. In fact we show that the
probability of failure decreases exponentially.


  The circular photogalvanic effect (CPGE) has been observed in (100)-oriented
$p$-GaAs/AlGaAs quantum wells at normal incidence of far-infrared radiation. It
is shown that monopolar optical spin orientation of free carriers causes an
electric current which reverses its direction upon changing from left to right
circularly polarized radiation. CPGE at normal incidence and the occurence of
the linear photogalvanic effect indicate a reduced point symmetry of studied
multi-layered heterostructures. As proposed, CPGE can be utilized to
investigate separately spin polarization of electrons and holes and the
symmetry of quantum wells.


  In this paper, we study a non-linear numerical scheme arising from the
implicit time discretization of the Ba\v{z}ant-Thonguthai model for
hygro-thermal behavior of concrete at high temperatures. Existence and
uniqueness of the time-discrete solution in two dimensions is established using
the theory of pseudomonotone operators in Banach spaces. Next, the spatial
discretization is accomplished by the conforming finite element method. An
illustrative numerical example shows that the numerical model reproduces well
the rapid increase of pore pressure in wet concrete due to extreme heating.
Such phenomenon is of particular interest for the safety assessment of concrete
structures prone to thermally-induced spalling.


  We show how to optimally protect quantum states and quantum entanglement
under non-zero temperature based on measurement reversal from weak measurement.
In particular, we present explicit formulas of the protection.


  We present the results of the analysis of 8 years of Fermi-LAT data of the
pulsar/pulsar wind nebula complex PSR J0205+6449/3C 58. Using a contemporaneous
ephemeris, we carried out a detailed analysis of PSR J0205+6449 both during its
off-peak and on-peak phase intervals. 3C 58 is significantly detected during
the off-peak phase interval. We show that the spectral energy distribution at
high energies is the same disregarding the phases considered, and thus that
this part of the spectrum is most likely dominated by the nebula radiation. We
present results of theoretical models of the nebula and the magnetospheric
emission that confirm this interpretation. Possible high-energy flares from 3C
58 were searched for, but none was unambiguously identified.


  We count orientations of $G(n,p)$ avoiding certain classes of oriented
graphs. In particular, we study $T_r(n,p)$, the number of orientations of the
binomial random graph $G(n,p)$ in which every copy of $K_r$ is transitive, and
$S_r(n,p)$, the number of orientations of $G(n,p)$ containing no strongly
connected copy of $K_r$. We give the correct order of growth of $\log T_r(n,p)$
and $\log S_r(n,p)$ up to polylogarithmic factors; for orientations with no
cyclic triangle, this significantly improves a result of Allen, Kohayakawa,
Mota and Parente. We also discuss the problem for a single forbidden oriented
graph, and state a number of open problems and conjectures.


  In this paper, we present a variational treatment of the linear dependence
for a non-orthogonal time-dependent basis set in solving the Schr\"odinger
equation. The method is based on: i) the definition of a linearly independent
working space, and ii) a variational construction of the propagator over finite
time-steps. The second point allows the method to properly account for changes
in the dimensionality of the working space along the time evolution. In
particular, the time evolution is represented by a semi-unitary transformation.
Tests are done on a quartic double-well potential with Gaussian basis function
whose centers evolve according to classical equations of motion. We show that
the resulting dynamics converges to the exact one and is unitary by
construction.


  The human brain is adept at solving difficult high-level visual processing
problems such as image interpretation and object recognition in natural scenes.
Over the past few years neuroscientists have made remarkable progress in
understanding how the human brain represents categories of objects and actions
in natural scenes. However, all current models of high-level human vision
operate on hand annotated images in which the objects and actions have been
assigned semantic tags by a human operator. No current models can account for
high-level visual function directly in terms of low-level visual input (i.e.,
pixels). To overcome this fundamental limitation we sought to develop a new
class of models that can predict human brain activity directly from low-level
visual input (i.e., pixels). We explored two classes of models drawn from
computer vision and machine learning. The first class of models was based on
Fisher Vectors (FV) and the second was based on Convolutional Neural Networks
(ConvNets). We find that both classes of models accurately predict brain
activity in high-level visual areas, directly from pixels and without the need
for any semantic tags or hand annotation of images. This is the first time that
such a mapping has been obtained. The fit models provide a new platform for
exploring the functional principles of human vision, and they show that modern
methods of computer vision and machine learning provide important tools for
characterizing brain function.


  In [BEZ] the notion of a complete one-sided M-ideal for an operator space X
was introduced as a generalization of Alfsen and Effros' notion of an M-ideal
for a Banach space [AE72]. In particular, various equivalent formulations of
complete one-sided M-projections were given. In this paper, some sharper
equivalent formulations are given in the special situation that $X =
\mathcal{A}$, a $C^*$-algebra (in which case the complete left M-projections
are simply left multiplication on $\mathcal{A}$ by a fixed orthogonal
projection in $\mathcal{A}$ or its multiplier algebra). The proof of the first
equivalence makes use of a technique which is of interest in its own right--a
way of ``solving'' multi-linear equations in von Neumann algebras. This
technique is also applied to show that preduals of von Neumann algebras have no
nontrivial complete one-sided M-ideals. In addition, we show that in a
$C^*$-algebra, the intersection of finitely many complete one-sided M-summands
need not be a complete one-sided M-summand, unlike the classical situation.


  Sampling-based planning has become a de facto standard for complex robots
given its superior ability to rapidly explore high-dimensional configuration
spaces. Most existing optimal sampling-based planning algorithms are sequential
in nature and cannot take advantage of wide parallelism available on modern
computer hardware. Further, tight synchronization of exploration and
exploitation phases in these algorithms limits sample throughput and planner
performance. Policy Iteration RRT# (PI-RRT#) exposes fine-grained parallelism
during the exploitation phase, but this parallelism has not yet been evaluated
using a concrete implementation. We first present a novel GPU implementation of
PI-RRT#'s exploitation phase and discuss data structure considerations to
maximize parallel performance. Our implementation achieves 3-4x speedup over a
serial PI-RRT# implementation for a 77.9% decrease in overall planning time on
average. As a second contribution, we introduce the Batched-Extension RRT#
algorithm, which loosens the synchronization present in PI-RRT# to realize
independent 12.97x and 12.54x speedups under serial and parallel exploitation,
respectively.


  Modern reinforcement learning (RL) commonly engages practical problems with
large state spaces, where function approximation must be deployed to
approximate either the value function or the policy. While recent progresses in
RL theory address a rich set of RL problems with general function
approximation, such successes are mostly restricted to the single-agent
setting. It remains elusive how to extend these results to multi-agent RL,
especially due to the new challenges arising from its game-theoretical nature.
This paper considers two-player zero-sum Markov Games (MGs). We propose a new
algorithm that can provably find the Nash equilibrium policy using a polynomial
number of samples, for any MG with low multi-agent Bellman-Eluder dimension --
a new complexity measure adapted from its single-agent version (Jin et al.,
2021). A key component of our new algorithm is the exploiter, which facilitates
the learning of the main player by deliberately exploiting her weakness. Our
theoretical framework is generic, which applies to a wide range of models
including but not limited to tabular MGs, MGs with linear or kernel function
approximation, and MGs with rich observations.


  Let $M$ be $\CP#2\CPb$, $3\CP#4\CPb$ or $(2n-1)\CP#2n\CPb$ for any integer
$n\geq 3$.
  We construct an irreducible symplectic 4-manifold homeomorphic to $M$ and
also an infinite family of pairwise non-diffeomorphic irreducible
non-symplectic 4-manifolds homeomorphic to $M$. We also construct such exotic
smooth structures when $M$ is $\CP#4\CPb$ or $3\CP# k \CPb$ for $k=6,8,10$.


  Development of Automatic Speech Recognition system for Kazakh language is
very challenging due to a lack of data.Existing data of kazakh speech with its
corresponding transcriptions are heavily accessed and not enough to gain a
worth mentioning results.For this reason, speech recognition of Kazakh language
has not been explored well.There are only few works that investigate this area
with traditional methods Hidden Markov Model, Gaussian Mixture Model, but they
are suffering from poor outcome and lack of enough data.In our work we suggest
a new method that takes pre-trained model of Russian language and applies its
knowledge as a starting point to our neural network structure, which means that
we are transferring the weights of pre-trained model to our neural network.The
main reason we chose Russian model is that pronunciation of kazakh and russian
languages are quite similar because they share 78 percent letters and there are
quite large corpus of russian speech dataset. We have collected a dataset of
Kazakh speech with transcriptions in the base of Suleyman Demirel University
with 50 native speakers each having around 400 sentences.Data have been chosen
from famous Kazakh books.
  We have considered 4 different scenarios in our experiment. First, we trained
our neural network without using a pre-trained Russian model with 2 LSTM layers
and 2 BiLSTM .Second, we have trained the same 2 LSTM layered and 2 BiLSTM
layered using a pre-trained model. As a result, we have improved our models
training cost and Label Error Rate by using external Russian speech recognition
model up to 24 percent and 32 percent respectively.Pre-trained Russian language
model has trained on 100 hours of data with the same neural network
architecture.


  Recently text-to-image models have gained widespread attention in the
community due to their controllable and high-quality generation ability.
However, the robustness of such models and their potential ethical issues have
not been fully explored. In this paper, we introduce Universal Semantic
Trigger, a meaningless token sequence that can be added at any location within
the input text yet can induce generated images towards a preset semantic
target.To thoroughly investigate it, we propose Semantic Gradient-based Search
(SGS) framework. SGS automatically discovers the potential universal semantic
triggers based on the given semantic targets. Furthermore, we design evaluation
metrics to comprehensively evaluate semantic shift of images caused by these
triggers. And our empirical analyses reveal that the mainstream open-source
text-to-image models are vulnerable to our triggers, which could pose
significant ethical threats. Our work contributes to a further understanding of
text-to-image synthesis and helps users to automatically auditing their models
before deployment.


  Recent time-resolved spectral studies of a few Active Galactic Nuclei in hard
X-rays revealed occultations of the X-ray primary source probably by Broad Line
Region (BLR) clouds. An important open question on the structure of the
circumnuclear medium of AGN is whether this phenomenon is common, i.e. whether
a significant fraction of the X-ray absorption in AGN is due to BLR clouds.
Here we present the first attempt to perform this kind of analysis in a
homogeneous way, on a statistically representative sample of AGN, consisting of
the ~40 brightest sources with long XMM-Newton and/or Suzaku observations. We
describe our method, based on a simple analysis of hardness-ratio light curves,
and its validation through a complete spectroscopic analysis of a few cases. We
find that X-ray eclipses, most probably due to clouds at the distance of the
BLR, are common in sources where the expected occultation time is compatible
with the observation time, while they are not found in sources with longer
estimated occultation times. Overall, our results show that occultations by BLR
clouds may be responsible for most of the observed X-ray spectral variability
at energies higher than 2 keV, on time scales longer than a few ks.


  A real-valued function $\varphi$ that is defined over all Borel sets of a
topological space is \emph{regular} if for every Borel set $W$, $\varphi(W)$ is
the supremum of $\varphi(C)$, over all closed sets $C$ that are contained in
$W$, and the infimum of $\varphi(O)$, over all open sets $O$ that contain $W$.
  We study Blackwell games with finitely many players. We show that when each
player has a countable set of actions and the objective of a certain player is
represented by a Borel winning set, that player's minmax value is regular.
  We then use the regularity of the minmax value to establish the existence of
$\varepsilon$-equilibria in two distinct classes of Blackwell games. One is the
class of $n$-player Blackwell games where each player has a finite action space
and an analytic winning set, and the sum of the minmax values over the players
exceeds $n-1$. The other class is that of Blackwell games with bounded upper
semi-analytic payoff functions, history-independent finite action spaces, and
history-independent minmax values.
  For the latter class, we obtain a characterization of the set of equilibrium
payoffs.


  If the stellar halos of disk galaxies are built up from the disruption of
dwarf galaxies, models predict highly structured variations in the stellar
populations within these halos. We test this prediction by studying the ratio
of blue horizontal branch stars (BHB stars; more abundant in old, metal-poor
populations) to main-sequence turn-off stars (MSTO stars; a feature of all
populations) in the stellar halo of the Milky Way using data from the Sloan
Digital Sky Survey. We develop and apply an improved technique to select BHB
stars using ugr color information alone, yielding a sample of ~9000 g<18
candidates where ~70% of them are BHB stars. We map the BHB/MSTO ratio across
~1/4 of the sky at the distance resolution permitted by the absolute magnitude
distribution of MSTO stars. We find large variations of BHB/MSTO star ratio in
the stellar halo. Previously identified, stream-like halo structures have
distinctive BHB/MSTO ratios, indicating different ages/metallicities. Some halo
features, e.g., the low-latitude structure, appear to be almost completely
devoid of BHB stars, whereas other structures appear to be rich in BHB stars.
The Sagittarius tidal stream shows an apparent variation in BHB/MSTO ratio
along its extent, which we interpret in terms of population gradients within
the progenitor dwarf galaxy. Our detection of coherent stellar population
variations between different stellar halo substructures provides yet more
support to cosmologically motivated models for stellar halo growth.


  Small-angle coronagraphy is technically and scientifically appealing because
it enables the use of smaller telescopes, allows covering wider wavelength
ranges, and potentially increases the yield and completeness of circumstellar
environment - exoplanets and disks - detection and characterization campaigns.
However, opening up this new parameter space is challenging. Here we will
review the four posts of high contrast imaging and their intricate interactions
at very small angles (within the first 4 resolution elements from the star).
The four posts are: choice of coronagraph, optimized wavefront control,
observing strategy, and post-processing methods. After detailing each of the
four foundations, we will present the lessons learned from the 10+ years of
operations of zeroth and first-generation adaptive optics systems. We will then
tentatively show how informative the current integration of second-generation
adaptive optics system is, and which lessons can already be drawn from this
fresh experience. Then, we will review the current state of the art, by
presenting world record contrasts obtained in the framework of technological
demonstrations for space-based exoplanet imaging and characterization mission
concepts. Finally, we will conclude by emphasizing the importance of the
cross-breeding between techniques developed for both ground-based and
space-based projects, which is relevant for future high contrast imaging
instruments and facilities in space or on the ground.


  There has been mounting observational evidence in favour of Luminous Blue
Variables (LBVs) being the direct progenitors of supernovae. Here we present
possibly the most convincing evidence yet for such progenitors. We find
multiple absorption component P-Cygni profiles of hydrogen and helium in the
spectrum of SN 2005gj, which we interpret as being an imprint of the
progenitors mass-loss history. Such profiles have previously only been detected
in Luminous Blue Variables. This striking resemblance of the profiles, along
with wind velocities and periods consistent with LBV's leads us to connect SN
2005gj to an LBV progenitor.


  With deep reinforcement learning (RL) methods achieving results that exceed
human capabilities in games, robotics, and simulated environments, continued
scaling of RL training is crucial to its deployment in solving complex
real-world problems. However, improving the performance scalability and power
efficiency of RL training through understanding the architectural implications
of CPU-GPU systems remains an open problem. In this work we investigate and
improve the performance and power efficiency of distributed RL training on
CPU-GPU systems by approaching the problem not solely from the GPU
microarchitecture perspective but following a holistic system-level analysis
approach. We quantify the overall hardware utilization on a state-of-the-art
distributed RL training framework and empirically identify the bottlenecks
caused by GPU microarchitectural, algorithmic, and system-level design choices.
We show that the GPU microarchitecture itself is well-balanced for
state-of-the-art RL frameworks, but further investigation reveals that the
number of actors running the environment interactions and the amount of
hardware resources available to them are the primary performance and power
efficiency limiters. To this end, we introduce a new system design metric,
CPU/GPU ratio, and show how to find the optimal balance between CPU and GPU
resources when designing scalable and efficient CPU-GPU systems for RL
training.


  The $\text{PSL}(4,\mathbb{R})$ Hitchin component of a closed surface group
$\pi_1(S)$ consists of holonomies of properly convex foliated projective
structures on the unit tangent bundle of $S$. We prove that the leaves of the
codimension-$1$ foliation of any such projective structure are all projectively
equivalent if and only if its holonomy is Fuchsian. This implies constraints on
the symmetries and shapes of these leaves.
  We also give an application to the topology of the non-${\rm T}_0$ space
$\mathfrak{C}(\mathbb{RP}^n)$ of projective classes of properly convex domains
in $\mathbb{RP}^n$. Namely, Benz\'ecri asked in 1960 if every closed subset of
$\mathfrak{C}(\mathbb{RP}^n)$ that contains no proper nonempty closed subset is
a point. Our results imply a negative resolution for $n \geq 2$.


  We describe the effect of vibration on a confined volume of fluid which is
density stratified due to its compressibility. We show that internal
gravity-acoustic waves can be parametrically destabilized by the vibration. The
resulting instability is similar to the classic Faraday instability of surface
waves, albeit modified by the compressible nature of the fluid. It may be
possible to observe experimentally near a gas-liquid critical point.


  The magnetic properties of a variety of materials with promise for
technological applications have been described by models in which fermions are
coupled to local moment spins. Monte Carlo studies of such models usually
ignore electron-electron interactions, even though the energy scale
corresponding to these interactions may be comparable to or larger than other
relevant energy scales. In this work we add on-site interactions between
fermions to the double exchange model which we study with a Monte Carlo scheme
in which temporal fluctuations of local moment spins are fully accounted for
and electron-electron interactions are treated at a mean field level. We show
that when the number of fermions is considerably less than the number of local
moments even moderate interactions can lead to significant enhancement of
ferromagnetism and the Curie temperature.


  In this brief review we introduce the methods of quantum field theory out of
equilibrium and study the non-equilibrium aspects of phase transitions.
Specifically we critically study the picture of the ``slow-roll'' phase
transition in the new inflationary models, we show that the instabilities that
are the hallmark of the phase transition, that is the formation of correlated
domains, dramatically change this picture. We analyze in detail the dynamics of
phase separation in strongly supercooled phase transitions in Minkowski space.
We argue that this is typically the situation in weakly coupled scalar
theories. The effective evolution equations for the expectation value and the
fluctuations of an inflaton field in a FRW cosmology are derived both in the
loop expansion and in a self-consistent non-perturbative scheme. Finally we use
these non-equilibrium techniques and concepts to study the influence of quantum
and thermal fluctuations on the dynamics of a proposed mechanism for the
formation of disoriented chiral condensates during a rapid phase transition out
of equilibrium. This last topic may prove to be experimentally relevant at
present accelerator energies. To appear in the Proceedings of the `2nd.
Journ\'ee Cosmologie', Observatoire de Paris, 2-4, June 1994. H J de Vega and
N. S\'anchez, Editors, World Scientific.


  Text classification is the process of classifying documents into predefined
categories based on their content. Existing supervised learning algorithms to
automatically classify text need sufficient documents to learn accurately. This
paper presents a new algorithm for text classification that requires fewer
documents for training. Instead of using words, word relation i.e association
rules from these words is used to derive feature set from preclassified text
documents. The concept of Naive Bayes classifier is then used on derived
features and finally only a single concept of Genetic Algorithm has been added
for final classification. Experimental results show that the classifier build
this way is more accurate than the existing text classification systems.


  Spin current generators are critical components for spintronics-based
information processing. In this work, we theoretically and computationally
investigate the bulk spin photovoltaic (BSPV) effect for creating DC spin
current under light illumination. The only requirement for BPSV is inversion
symmetry breaking, thus it applies to a broad range of materials and can be
readily integrated with existing semiconductor technologies. The BSPV effect is
a cousin of the bulk photovoltaic (BPV) effect, whereby a DC charge current is
generated under light. Thanks to the different selection rules on spin and
charge currents, a pure spin current can be realized if the system possesses
mirror symmetry or inversion-mirror symmetry. The mechanism of BPSV and the
role of the electronic relaxation time $\tau$ are also elucidated. We apply our
theory to several distinct material systems, including transition metal
dichalcogenides, anti-ferromagnetic $\rm MnBi_2Te_4$, and the surface of
topological crystalline insulator cubic $\rm SnTe$.


  It is an outstanding goal to unveil the key features of quantum dynamics at
eigenstate transitions. Focusing on quadratic fermionic Hamiltonians that
exhibit localization transitions, we identify physical observables that exhibit
scale-invariant critical dynamics at the transition when quenched from the
initially localized charge density-wave states. The identification is based on
two ingredients: (a) A relationship between the time evolution of observables
in a many-body state and the transition probabilities of single-particle
states, and (b) scale invariance of transition probabilities, which generalizes
the corresponding recent result for survival probabilities [M. Hopjan and L.
Vidmar, Phys. Rev. Lett. 131, 060404 (2023); Phys. Rev. Res. 5, 043301 (2023)].
These properties suggest that the scale-invariant critical dynamics in the
quantum-quench dynamics is also exhibited by the observables, which share the
common eigenbasis with the Hamiltonian before the quench. Focusing on
experimentally relevant observables such as site occupations and the particle
imbalance, we numerically demonstrate their critical behavior at the eigenstate
transitions in the three-dimensional Anderson model and the one-dimensional
Aubry-Andr\'e model model.


  The paper treats the issue of the length of a rotating circumference as seen
from on board the moving disk and from an inertial reference frame. It is shown
that, properly defining a measuring process, the result is in both cases 2piR
thus dissolving the Ehrenfest paradox. The same holds good when considering
that, for the rotating observer, the perceived radius coincides with the
curvature radius of a space-time helix and a complete round trip corresponds to
an angle which differs from the one seen by the inertial observer. The apparent
contradiction with the Lorentz contraction is discussed.


  We employ a topological approach to investigate the nature of
quasi-stationary states of the Mean Field XY Hamiltonian model that arise when
the system is initially prepared in a fully magnetized configuration. By means
of numerical simulations and analytical considerations, we show that, along the
quasi-stationary trajectories, the system evolves in a manifold of critical
points of the potential energy function. Although these critical points are
maxima, the large number of directions with marginal stability may be
responsible for the slow relaxation dynamics and the trapping of the system in
such trajectories.


  One challenge in condensed-matter physics is to unravel the interplay between
magnetism and superconductivity in copper oxides with a high critical
temperature (Tc). Kang et al. claim to have revealed a quantum phase transition
from the superconducting to an antiferromagnetic state in the electron-doped
material Nd2-xCexCuO4 (NCCO) based on the observation of magnetic-field-induced
neutron scattering intensity at (1/2,1/2,0), (1/2,0,0), and related
reflections. Here we argue that the observed magnetic intensity is due to a
secondary phase of (Nd,Ce)2O3. We therefore contend that the effect is spurious
and not intrinsic to superconducting NCCO.


  A graph $G$ is $d$-degenerate if every non-null subgraph of $G$ has a vertex
of degree at most $d$.
  We prove that every $n$-vertex planar graph has a $3$-degenerate induced
subgraph of order at least $3n/4$.


  Many motile microorganisms react to environmental light cues with a variety
of motility responses guiding cells towards better conditions for survival and
growth. The use of spatial light modulators could help to elucidate the
mechanisms of photo-movements while, at the same time, providing an efficient
strategy to achieve spatial and temporal control of cell concentration. Here we
demonstrate that millions of bacteria, genetically modified to swim smoothly
with a light controllable speed, can be arranged into complex and
reconfigurable density patterns using a digital light projector. We show that a
homogeneous sea of freely swimming bacteria can be made to morph between
complex shapes. We model non-local effects arising from memory in light
response and show how these can be mitigated by a feedback control strategy
resulting in the detailed reproduction of grayscale density images.


  Quantum electrodynamics coupled-cluster (QED-CC) theory is used to model
vacuum-field-induced changes to ground-state properties of a series of sodium
halide compounds (NaX, X = F, Cl, Br, I) strongly coupled to an optical cavity.
Ionization potentials (IPs) and electron affinities (EAs) are presented, and it
is demonstrated that EAs are easily modulated by cavity interactions, while IPs
for these compounds are far less sensitive to the presence of the cavity. EAs
predicted by QED-CC can be reduced by as much 0.22 eV (or ~50%) when
considering experimentally-accessible coupling parameters.


  This work focuses on the development of a self adjusting multirate strategy
based on an implicit time discretization for the numerical solution of
hyperbolic equations, that could benefit from different time steps in different
areas of the spatial domain. We propose a novel mass conservative multirate
approach, that can be generalized to various implicit time discretization
methods. It is based on flux partitioning, so that flux exchanges between a
cell and its neighbors are balanced. A number of numerical experiments on both
non-linear scalar problems and systems of hyperbolic equations have been
carried out to test the efficiency and accuracy of the proposed approach.


  For the past decade, convolutional networks have been used for 3D
reconstruction of neurons from electron microscopic (EM) brain images. Recent
years have seen great improvements in accuracy, as evidenced by submissions to
the SNEMI3D benchmark challenge. Here we report the first submission to surpass
the estimate of human accuracy provided by the SNEMI3D leaderboard. A variant
of 3D U-Net is trained on a primary task of predicting affinities between
nearest neighbor voxels, and an auxiliary task of predicting long-range
affinities. The training data is augmented by simulated image defects. The
nearest neighbor affinities are used to create an oversegmentation, and then
supervoxels are greedily agglomerated based on mean affinity. The resulting
SNEMI3D score exceeds the estimate of human accuracy by a large margin. While
one should be cautious about extrapolating from the SNEMI3D benchmark to
real-world accuracy of large-scale neural circuit reconstruction, our result
inspires optimism that the goal of full automation may be realizable in the
future.


  Orthogonal frequency-division multiplexing (OFD-M) and multi-cell
architecture are widely adopted in current high speed train (HST) systems for
providing high data rate wireless communications. In this paper, a typical
multi-antenna OFDM HST communication system with multi-cell architecture is
considered, where the inter-carrier interference (ICI) caused by high mobility
and multi-cell interference (MCI) are both taken into consideration. By
exploiting the train position information, a new position-based interference
elimination method is proposed to eliminate both the MCI and ICI for a general
basis expansion model (BEM). We show that the MCI and ICI can be completely
eliminated by the proposed method to get the ICI-free pilots at each receive
antenna. In addition, for the considered multi-cell HST system, we develop a
low-complexity compressed channel estimation method and consider the optimal
pilot pattern design. Both the proposed interference elimination method and the
optimal pilot pattern are robust to the train speed and position,as well as the
multi-cell multi-antenna system. Simulation results demonstrate the benefits
and robustness of the proposed method in the multi-cell HST system.


  Finding the optimal design of a hydrodynamic or aerodynamic surface is often
impossible due to the expense of evaluating the cost functions (say, with
computational fluid dynamics) needed to determine the performances of the flows
that the surface controls. In addition, inherent limitations of the design
space itself due to imposed geometric constraints, conventional
parameterization methods, and user bias can restrict {\it all} of the designs
within a chosen design space regardless of whether traditional optimization
methods or newer, data-driven design algorithms with machine learning are used
to search the design space. We present a 2-pronged attack to address these
difficulties: we propose (1) a methodology to create the design space using
morphing that we call {\it Design-by-Morphing} (DbM); and (2) an optimization
algorithm to search that space that uses a novel Bayesian Optimization (BO)
strategy that we call {\it Mixed variable, Multi-Objective Bayesian
Optimization} (MixMOBO). We apply this shape optimization strategy to maximize
the power output of a hydrokinetic turbine. Applying these two strategies in
tandem, we demonstrate that we can create a novel, geometrically-unconstrained,
design space of a draft tube and hub shape and then optimize them
simultaneously with a {\it minimum} number of cost function calls. Our
framework is versatile and can be applied to the shape optimization of a
variety of fluid problems.


  Smart contract is one of the core features of Ethereum and has inspired many
blockchain descendants. Since its advent, the verification paradigm of smart
contract has been improving toward high scalability. It shifts from the
expensive on-chain verification to the orchestration of off-chain VM (virtual
machine) execution and on-chain arbitration with the pinpoint protocol. The
representative projects are TrueBit, Arbitrum, YODA, ACE, and Optimism.
Inspired by visionaries in academia and industry, we consider the DNN
computation to be promising but on the next level of complexity for the
verification paradigm of smart contract. Unfortunately, even for the
state-of-the-art verification paradigm, off-chain VM execution of DNN
computation has an orders-of-magnitude slowdown compared to the native
off-chain execution.
  To enable the native off-chain execution of verifiable DNN computation, we
present Agatha system, which solves the significant challenges of misalignment
and inconsistency: (1) Native DNN computation has a graph-based computation
paradigm misaligned with previous VM-based execution and arbitration; (2)
Native DNN computation may be inconsistent cross platforms which invalidates
the verification paradigm. In response, we propose the graph-based pinpoint
protocol (GPP) which enables the pinpoint protocol on computational graphs, and
bridges the native off-chain execution and the contract arbitration. We also
develop a technique named Cross-evaluator Consistent Execution (XCE), which
guarantees cross-platform consistency and forms the correctness foundation of
GPP. We showcase Agatha for the DNN computation of popular models (MobileNet,
ResNet50 and VGG16) on Ethereum. Agatha achieves a negligible on-chain
overhead, and an off-chain execution overhead of 3.0%, which represents an
off-chain latency reduction of at least 602x compared to the state-of-the-art
verification paradigm.


  We construct a set of post-Newtonian reference frames for a comprehensive
study of the orbital dynamics and rotational motion of the Moon and Earth by
means of lunar laser ranging (LLR) with the precision of one millimeter. We
also derive the post-Newtonian coordinate transformations between the frames
and analyze the residual gauge freedom, which is used for removing spurious
post-Newtonian effects from the equations of motion of the solar system bodies.


  The band structure of noninteracting fermions in the honeycomb lattice
exhibits the Dirac cones at the corners of the Brillouin zone. As a
consequence, fermions in this lattice manifest a semiconducting behavior below
some critical value of the onsite attraction, $U_{c}$. However, above $U_{c}$,
the superconducting phase can occur. We discuss an interplay between the
semiconductor--superconductor transition and the possibility of realization of
the spin-polarized superconductivity (the so-called Sarma phase). We show that
the critical interaction can be tuned by the next-nearest-neighbor (NNN)
hopping in the absence of the magnetic field. Moreover, a critical value of the
NNN hopping exists, defining a range of parameters for which the semiconducting
phase can emerge. In the weak coupling limit case, this quantum phase
transition occurs for the absolute value of the NNN hopping equal to one third
of the hopping between the nearest neighbors. Similarly, in the presence of the
magnetic field, the Sarma phase can appear, but only in a range of parameters
for which initially the semiconducting state is observed. Both of these aspects
are attributed to the Lifshitz transition, which is induced by the NNN hopping
as well as the external magnetic field.


  We study within the many-body Green's function GW and Bethe-Salpeter
approaches the neutral singlet excitations of the zinctetraphenylporphyrin and
C70 fullerene donor-acceptor complex. The lowest transition is a
charge-transfer excitation between the donor and the acceptor with an energy in
excellent agreement with recent constrained density functional theory
calculations. Beyond the lowest charge-transfer state, of which the energy can
be determined with simple electrostatic models that we validate, the
Bethe-Salpeter approach provides the full excitation spectrum. We evidence the
existence of hot electron-hole states which are resonant in energy with the
lowest donor intramolecular excitation and show an hybrid intramolecular and
charge-transfer character, favouring the transition towards charge separation.
These findings support the recently proposed scenario for charge separation at
donor-acceptor interfaces through delocalized hot charge-transfer states.


  U-Net has been widely used for segmenting abdominal organs, achieving
promising performance. However, when it is used for multi-organ segmentation,
first, it may be limited in exploiting global long-range contextual information
due to the implementation of standard convolutions. Second, the use of
spatial-wise downsampling (e.g., max pooling or strided convolutions) in the
encoding path may lead to the loss of deformable or discriminative details.
Third, features upsampled from the higher level are concatenated with those
that persevered via skip connections. However, repeated downsampling and
upsampling operations lead to misalignments between them and their
concatenation degrades segmentation performance. To address these limitations,
we propose Dynamically Calibrated Convolution (DCC), Dynamically Calibrated
Downsampling (DCD), and Dynamically Calibrated Upsampling (DCU) modules,
respectively. The DCC module can utilize global inter-dependencies between
spatial and channel features to calibrate these features adaptively. The DCD
module enables networks to adaptively preserve deformable or discriminative
features during downsampling. The DCU module can dynamically align and
calibrate upsampled features to eliminate misalignments before concatenations.
We integrated the proposed modules into a standard U-Net, resulting in a new
architecture, termed Dynamic U-Net. This architectural design enables U-Net to
dynamically adjust features for different organs. We evaluated Dynamic U-Net in
two abdominal multi-organ segmentation benchmarks. Dynamic U-Net achieved
statistically improved segmentation accuracy compared with standard U-Net. Our
code is available at https://github.com/sotiraslab/DynamicUNet.


  We consider the 6d (2,0) theory on a fibration by genus g curves, and
dimensionally reduce along the fiber to 4d theories with duality defects. This
generalizes class S theories, for which the fibration is trivial. The
non-trivial fibration in the present setup implies that the gauge couplings of
the 4d theory, which are encoded in the complex structures of the curve, vary
and can undergo S-duality transformations. These monodromies occur around 2d
loci in space-time, the duality defects, above which the fiber is singular. The
key role that the fibration plays here motivates refering to this setup as
theories of class F. In the simplest instance this gives rise to 4d N=4
Super-Yang-Mills with space-time dependent coupling that undergoes SL(2, Z)
monodromies. We determine the anomaly polynomial for these theories by pushing
forward the anomaly polynomial of the 6d (2,0) theory along the fiber. This
gives rise to corrections to the anomaly polynomials of 4d N=4 SYM and theories
of class S. For the torus case, this analysis is complemented with a field
theoretic derivation of a U(1) anomaly in 4d N=4 SYM. The corresponding anomaly
polynomial is tested against known expressions of anomalies for wrapped
D3-branes with varying coupling, which are known field theoretically and from
holography. Extensions of the construction to 4d N = 0 and 1, and 2d theories
with varying coupling, are also discussed.


  Our recently developed 2+1 (boost-invariant) hydrodynamic model has been
presented and used to i) describe the soft hadronic data collected in the
central region of the relativistic heavy-ion collisions at RHIC and ii) to make
predictions for the heavy-ion collisions at the LHC energies. We have addressed
both the one- and two-particle observables: the transverse momentum spectra,
the elliptic flow coefficient v2, and the pion HBT radii. The realistic
equation of state for strongly interacting matter has been constructed that
interpolates between the hadron gas model and the results of the QCD lattice
simulations. The computational platform has been constructed, which combines
the results of our hydrodynamic code with the statistical hadronization model
THERMINATOR. The satisfactory description of the soft hadronic RHIC data has
been achieved with the standard initial conditions obtained from the optical
limit of the Glauber model. Predictions for the future heavy-ion collisions at
LHC have been formulated. The solution of the RHIC HBT puzzle has been
proposed. It suggests the use of the modified Gaussian-type initial conditions
for the energy density in the transverse plane. Finally, the processes of the
free streaming of partons followed by the sudden equilibration have been
incorporated in the model. The inclusion of the free-streaming stage allows for
the delayed start of the hydrodynamic evolution, which is a desirable effect in
the context of the early thermalization problem.


  We develop an improved bound for the chromatic number of graphs of maximum
degree $\Delta$ under the assumption that the number of edges spanning any
neighbourhood is at most $(1-\sigma)\binom{\Delta}{2}$ for some fixed
$0<\sigma<1$. The leading term in the reduction of colours achieved through
this bound is best possible as $\sigma\to0$. As two consequences, we advance
the state of the art in two longstanding and well-studied graph colouring
conjectures, the Erd\H{o}s-Ne\v{s}et\v{r}il conjecture and Reed's conjecture.
We prove that the strong chromatic index is at most $1.772\Delta^2$ for any
graph $G$ with sufficiently large maximum degree $\Delta$. We prove that the
chromatic number is at most $\lceil 0.881(\Delta+1)+0.119\omega\rceil$ for any
graph $G$ with clique number $\omega$ and sufficiently large maximum degree
$\Delta$. Additionally, we show how our methods can be adapted under the
additional assumption that the codegree is at most $(1-\sigma)\Delta$, and
establish what may be considered first progress towards a conjecture of Vu.


  By using an extended Regge parametrization and taking into account the
discrepancies in the high-energy pp and p(bar)p total cross section data, in
both accelerator and cosmic-ray regions, we estimate extrema bounds for the
soft Pomeron intercept. First we consider two ensembles of data with either the
CDF or the E710 and E811 results for the p(bar)p total cross section at 1.8
TeV, from which we obtain the bounds 1.102 and 1.081, respectively. These
ensembles are then combined with the highest and lowest estimations for the pp
total cross section from cosmic-ray experiments (6-40 TeV), leading to the
upper and lower bounds 1.109 and 1.082, respectively. The effects of
simultaneous fits to total cross sections and the rho parameter, individual
fits to total cross sections, and the influence of the subtraction constant in
the dispersion relations are also presented. Our global results favor the E710
and E811 data.


  We report the most accurate X-ray position of the X-ray source in the giant
globular cluster G1 in M31 by using the Chandra X-ray Observatory, Hubble Space
Telescope (HST), and Canada-France-Hawaii Telescope (CFHT). G1 is clearly
detected with Chandra and by cross-registering with HST and CFHT images, we
derive a 1-sigma error radius of 0.15 arcsec, significantly smaller than the
previous measurement by XMM-Newton. We conclude that the X-ray emission of G1
is likely to come from within the core radius of the cluster. We have
considered a number of possibilities for the origin of the X-ray emission but
can rule all but two scenarios out: it could be due to either accretion onto a
central intermediate-mass black hole (IMBH), or an ordinary low-mass X-ray
binary (LMXB). Based on the X-ray luminosity and the Bondi accretion rate, an
IMBH accreting from the cluster gas seems unlikely and we suggest that the
X-rays are due to accretion from a companion. Alternatively, the probability
that a 1.5 solar masses cluster LMXB lies within the 95 per cent X-ray error
circle is about 0.7. Therefore we cannot rule out a single LMXB as the origin
of the X-ray emission. While we cannot distinguish between different models
with current observations, future high-resolution and high-sensitivity radio
imaging observations will reveal whether there is an IMBH at the centre of G1.


  The augmented Lagrangian (AL) method that solves convex optimization problems
with linear constraints has drawn more attention recently in imaging
applications due to its decomposable structure for composite cost functions and
empirical fast convergence rate under weak conditions. However, for problems
such as X-ray computed tomography (CT) image reconstruction and large-scale
sparse regression with "big data", where there is no efficient way to solve the
inner least-squares problem, the AL method can be slow due to the inevitable
iterative inner updates. In this paper, we focus on solving regularized
(weighted) least-squares problems using a linearized variant of the AL method
that replaces the quadratic AL penalty term in the scaled augmented Lagrangian
with its separable quadratic surrogate (SQS) function, thus leading to a much
simpler ordered-subsets (OS) accelerable splitting-based algorithm, OS-LALM,
for X-ray CT image reconstruction. To further accelerate the proposed
algorithm, we use a second-order recursive system analysis to design a
deterministic downward continuation approach that avoids tedious parameter
tuning and provides fast convergence. Experimental results show that the
proposed algorithm significantly accelerates the "convergence" of X-ray CT
image reconstruction with negligible overhead and greatly reduces the OS
artifacts in the reconstructed image when using many subsets for OS
acceleration.


  For an integer $b\ge 0$, a $b$-matching in a graph $G=(V,E)$ is a set
$S\subseteq E$ such that each vertex $v\in V$ is incident to at most $b$ edges
in $S$. We design a fully polynomial-time approximation scheme (FPTAS) for
counting the number of $b$-matchings in graphs with bounded degrees. Our FPTAS
also applies to a broader family of counting problems, namely Holant problems
with log-concave signatures. Our algorithm is based on Moitra's linear
programming approach (JACM'19). Using a novel construction called the extended
coupling tree, we derandomize the coupling designed by Chen and Gu (SODA'24).


  Race conditions are a class of bugs in software where concurrent accesses to
shared resources are not protected from each other. Consequences of race
conditions include privilege escalation, denial of service, and memory
corruption which can potentially lead to arbitrary code execution. However, in
large code bases the exact rules as to which fields should be accessed under
which locks are not always clear. We propose a novel static technique that
infers rules for how field accesses should be locked, and then checks the code
against these rules. Traditional static analysers for detecting race conditions
are based on lockset analysis. Instead, we propose an outlier-based technique
enhanced with a context-sensitive mechanism that scales well. We have
implemented this analysis in LLIF, and evaluated it to find incorrectly
protected field accesses in Linux v5.14.11. We thoroughly evaluate its ability
to find race conditions, and study the causes for false positive reports. In
addition, we reported a subset of the issues and submitted patches. The
maintainers confirmed 24 bugs.


  The mechanism which discriminates the pattern classes at the same $\lambda$,
is found. It is closely related to the structure of the rule table and
expressed by the numbers of the rules which break the strings of the quiescent
states. It is shown that for the N-neighbor and K-state cellular automata, the
class I, class II, class III and class IV patterns coexist at least in the
range, $\frac{1}{K} \le \lambda \le 1-\frac{1}{K} $. The mechanism is studied
quantitatively by introducing a new parameter $F$, which we call quiescent
string dominance parameter. It is taken to be orthogonal to $\lambda$. Using
the parameter F and $\lambda$, the rule tables of one dimensional 5-neighbor
and 4-state cellular automata are classified. The distribution of the four
pattern classes in ($\lambda$,F) plane shows that the rule tables of class III
pattern class are distributed in larger $F$ region, while those of class II and
class I pattern classes are found in the smaller $F$ region and the class IV
behaviors are observed in the overlap region between them. These distributions
are almost independent of $\lambda$ at least in the range $0.25 \leq \lambda
\leq 0.75$, namely the overlapping region in $F$, where the class III and class
II patterns coexist, has quite gentle $\lambda$ dependence in this $\lambda$
region. Therefore the relation between the pattern classes and the $\lambda$
parameter is not observed. PACS: 89.75.-k Complex Systems


  This article offers a detailed analysis of pseudo-phase transitions of Ising
and Baxter-Wu models in two-dimensional finite-size lattices. We carry out Wang
Landau sampling to obtain the density of states. Using microcanonical
inflection point analysis with microcanonical entropy, we obtain the order of
the psuedo-phase transitions in the models. The microcanonical analysis results
of the second-order transition for the Ising model and the first-order
transition for the Baxter-Wu model are consistent with the traditional
canonical results. In addition, the third-order transitions are found in both
models, implying the universality of higher-order phase transitions.


  A large fraction of the luminous distant submm-wave galaxies recently
detected using SCUBA on the JCMT appear to be associated with interacting
optical counterparts. We investigate these systems using a simple hierarchical
clustering model of galaxy evolution, in which the large luminosity of the
SCUBA galaxies is assumed to be generated at the epoch of galaxy mergers in a
burst of either star formation activity or the fuelling of an active galactic
nucleus (AGN). The models are well constrained by the observed spectrum of the
FIR/submm background radiation and the 60-micron counts of low-z IRAS galaxies.
The ratio between the total amount of energy released during mergers and the
mass of dark matter involved must increase sharply with z at z<1, and then
decrease at higher z, independent of the fraction of the luminosity of mergers
that is produced by starbursts and AGN. One additional parameter - the
reciprocal of the product of the duration of the enhanced luminosity produced
by the merger and the fraction of mergers that induce an enhanced luminosity,
which we call the activity parameter - is introduced, to allow the relationship
between merging dark matter haloes and the observed counts of distant dusty
galaxies to be investigated. The observed counts can only be reproduced if the
activity parameter is greater by a factor of about 5 and 100 at redshifts of 1
and 3 respectively, compared with the present epoch. Hence, if mergers account
for the SCUBA galaxies, then the merger process must have been much more
violent at high redshifts. We discuss the counts of galaxies and the intensity
of background radiation in the optical/near-IR wavebands in the context of
these hierarchical models, and thus investigate the relationship between the
populations of SCUBA and Lyman-break galaxies.


  The localization of stellar-mass binary black hole mergers using
gravitational waves is critical in understanding the properties of the
binaries' host galaxies, observing possible electromagnetic emission from the
mergers, or using them as a cosmological distance ladder. The precision of this
localization can be substantially increased with prior astrophysical
information about the binary system. In particular, constraining the
inclination of the binary can reduce the distance uncertainty of the source.
Here we present the first realistic set of localizations for binary black hole
mergers, including different prior constraints on the binaries' inclinations.
We find that prior information on the inclination can reduce the localization
volume by a factor of 3. We discuss two astrophysical scenarios of interest:
(i) follow-up searches for beamed electromagnetic/neutrino counterparts and
(ii) mergers in the accretion disks of active galactic nuclei.


  Grand canonical simulations at various levels, $\zeta=5$-20, of fine- lattice
discretization are reported for the near-critical 1:1 hard-core electrolyte or
RPM. With the aid of finite-size scaling analyses it is shown convincingly
that, contrary to recent suggestions, the universal critical behavior is
independent of $\zeta$ $(\grtsim 4)$; thus the continuum $(\zeta\to\infty)$ RPM
exhibits Ising-type (as against classical, SAW, XY, etc.) criticality. A
general consideration of lattice discretization provides effective
extrapolation of the {\em intrinsically} erratic $\zeta$-dependence, yielding
$(\Tc^ {\ast},\rhoc^{\ast})\simeq (0.0493_{3},0.075)$ for the $\zeta=\infty$
RPM.


  This paper analyses a $(1,\lambda)$-Evolution Strategy, a randomised
comparison-based adaptive search algorithm, on a simple constraint optimisation
problem. The algorithm uses resampling to handle the constraint and optimizes a
linear function with a linear constraint. Two cases are investigated: first the
case where the step-size is constant, and second the case where the step-size
is adapted using path length control. We exhibit for each case a Markov chain
whose stability analysis would allow us to deduce the divergence of the
algorithm depending on its internal parameters. We show divergence at a
constant rate when the step-size is constant. We sketch that with step-size
adaptation geometric divergence takes place. Our results complement previous
studies where stability was assumed.


  We describe the process of stacking radio interferometry visibilities to form
a deep composite image and its application to the observation of transient
phenomena. We apply "visibility stacking" to 46 archival Very Large Array
observations of nearby type Ia supernovae (SNeIa). This new approach provides
an upper limit on the SNIa ensemble peak radio luminosity of
1.2x10^{25}erg/s/Hz at 5GHz, which is 5-10 times lower than previously
measured. This luminosity implies an upper limit on the average companion
stellar wind mass loss rate of 1.3x10^{-7}M_o/yr. This mass loss rate is
consistent with the double degenerate scenario for SNeIa and rules out
intermediate and high mass companions in the single degenerate scenario. In the
era of time domain astronomy, techniques such as visibility stacking will be
important in extracting the maximum amount of information from observations of
populations of short lived events.


  Advanced integration of logistics systems has been promoted for the sake of
competitiveness and sustainability. Such efforts will enable more globally
optimal and flexible operations by efficiently utilizing transportation
capacity. At the same time, interconnection of transport operations increases
complexity at a network level, which reduces the predictability of the response
of the system to disruptions. However, our understanding of the behavior of
such systems is still limited. In particular, the topology of the network,
which changes as the systems are integrated, is an important factor that
affects the performance of the entire system. Knowledge of such mechanisms
would be useful in the design and evaluation of integrated logistics. Here, we
developed a simple mathematical model that extracts the essence of the problem
and performed extensive numerical experiments by Monte Carlo simulations for
three scenarios that mimic changes in demand: (i) locally and temporally
increased traffic demand, (ii) globally and temporally increased traffic
demand, and (iii) permanent change in demand pattern, under various conditions
on the type of route-finding algorithm, network structure, and transportation
capacity. Adaptive route-finding algorithms were more effective in square
lattice and random networks, which contained many bypass routes, than in
hub-and-spoke networks. Furthermore, the square lattice and random networks
were robust to the change in the demand pattern and temporal blockage of
delivery paths (e.g., due to high demand). We suggest that such preferable
properties are only present in networks with redundancy and that the bypass
structure is an important criterion for designing network logistics.


  We present a new method of modelling time-series data based on the running
optimal average (ROA). By identifying the effective number of parameters for
the ROA model, in terms of the shape and width of its window function and the
times and accuracies of the data, we enable a Bayesian analysis, optimising the
ROA width, along with other model parameters, by minimising the Bayesian
Information Criterion (BIC) and sampling joint posterior parameter
distributions using MCMC methods. For analysis of quasar lightcurves, our
implementation of ROA modelling can measure time delays among lightcurves at
different wavelengths or from different images of a lensed quasar and, in
future work, be used to inter-calibrate lightcurve data from different
telescopes and estimate the shape and thus the power-density spectrum of the
lightcurve. Our noise model implements a robust treatment of outliers and
error-bar adjustments to account for additional variance or poorly-quantified
uncertainties. Tests with simulated data validate the parameter uncertainty
estimates. We compare ROA delay measurements with results from
cross-correlation and from JAVELIN, which models lightcurves with a prior on
the power-density spectrum. We analyse published COSMOGRAIL lightcurves of
multi-lensed quasar lightcurves and present the resulting measurements of the
inter-image time delays and detection of microlensing effects.


  An attractive class of materials for photo(electro)chemical reactions are
hybrids based on semiconducting metal oxides and nanocarbons (e.g. carbon
nanotubes (CNT), graphene), where the nanocarbon acts as a highly-stable
conductive scaffold onto which the nanostructured inorganic phase can be
immobilised; an architecture that maximises surface area and minimises charge
transport/transfer resistance. TiO$_{2}$/CNT photoanodes produced by atomic
layer deposition on CNT fabrics are shown to be efficient for H$_{2}$
production ($0.07 \mu mol/min$ $H_{2}$ at $0.2V$ $vs Ag/AgCl$), nearly doubling
the performance of TiO$_{2}$ deposited on planar substrates, with $100 \%$
Faradaic efficiency. The results are rationalised based on electrochemical
impedance spectroscopy measurements showing a large reduction in photoelectron
transport resistance compared to control samples and a higher surface area. The
low TiO$_{2}$/CNT interfacial charge transfer resistance ($10 \Omega$) is
consistent with the presence of an interfacial Ti-O-C bond and corresponding
electronic hybridisation determined by spatially-resolved Scanning
Photoelectron Microscopy (SPEM) using synchrotron radiation.


  We show how applying field conjugated to the order parameter, may act as a
very precise probe to explore the probability distribution function of the
order parameter. Using this `magnetic-field scanning' on large-scale numerical
simulations of the critical 2D XY-model, we are able to discard the conjectured
double-exponential form of the large-magnetization asymptote.


  Solitosynthesis of Q-balls in the false vacuum can result in a phase
transition of a new kind. Formation and subsequent growth of Q-balls via the
charge accretion proceeds until the solitons reach a critical charge, at which
point it becomes energetically favorable for the Q-ball interior to expand
filling space with the true vacuum phase. Solitosynthesis can destabilize a
false vacuum even when the tunneling rate is negligible. In models with
low-energy supersymmetry, where the Q-balls associated with baryon and lepton
number conservation are generically present, solitosynthesis can precipitate
transitions between the vacua with different VEV's of squarks and sleptons.


  Strangeness enhancement and collective flow are considered signatures of the
quark gluon plasma formation. These phenomena have been detected not only in
relativistic heavy ion collisions but also in high energy, high multiplicity
events of proton-proton and proton-nucleus (small systems) scatterings. A
universal behavior emerges by considering the parton density in the transverse
plane as the dynamical quantity to specify the initial condition of the
collisions, which in electron-positron annihilation at the available energies
is too low to expect collective effects.


  Evidential-EM (E2M) algorithm is an effective approach for computing maximum
likelihood estimations under finite mixture models, especially when there is
uncertain information about data. In this paper we present an extension of the
E2M method in a particular case of incom-plete data, where the loss of
information is due to both mixture models and censored observations. The prior
uncertain information is expressed by belief functions, while the
pseudo-likelihood function is derived based on imprecise observations and prior
knowledge. Then E2M method is evoked to maximize the generalized likelihood
function to obtain the optimal estimation of parameters. Numerical examples
show that the proposed method could effectively integrate the uncertain prior
infor-mation with the current imprecise knowledge conveyed by the observed
data.


  Hybrid beamforming (HBF) design is a crucial stage in millimeter wave
(mmWave) multi-user multi-input multi-output (MU-MIMO) systems. However,
conventional HBF methods are still with high complexity and strongly rely on
the quality of channel state information. We propose an extreme learning
machine (ELM) framework to jointly optimize transmitting and receiving
beamformers. Specifically, to provide accurate labels for training, we first
propose an factional-programming and majorization-minimization based HBF method
(FP-MM-HBF). Then, an ELM based HBF (ELM-HBF) framework is proposed to increase
the robustness of beamformers. Both FP-MM-HBF and ELM-HBF can provide higher
system sum-rate compared with existing methods. Moreover, ELM-HBF cannot only
provide robust HBF performance, but also consume very short computation time.


  A short informal survey on the topics listed in the title. For the
proceedings of the International Conference on Rings and Algebras X.


  We study the character of an Ising nematic quantum phase transition (QPT)
deep inside a d-wave superconducting state with nodal quasiparticles in a
two-dimensional tetragonal crystal. We find that, within a 1/N expansion, the
transition is continuous. To leading order in 1/N, quantum fluctuations enhance
the dispersion anisotropy of the nodal excitations, and cause strong scattering
which critically broadens the quasiparticle (qp) peaks in the spectral
function, except in a narrow wedge in momentum space near the Fermi surface
where the qp's remain sharp. We also consider the possible existence of a
nematic glass phase in the presence of weak disorder. Some possible
implications for cuprate physics are also discussed.


  Propositional logic serves as a fundamental cornerstone in mathematical
logic. This paper delves into a semiring characterization of propositional
logic, employing the Gr\"oebner-Shirshov basis theory to furnish an algebraic
framework for deduction and proof grounded in atoms of propositional logic. The
result is an algebraic approach to proving propositions in propositional logic.
To illustrate the effectiveness and constraints of this method, we conclude
with several specific examples.


  Comparisons on $L^{n\over 2}$-norms of scalar curvatures between Riemannian
metrics and standard metrics are obtained. The metrics are restricted to
conformal classes or under certain curvature conditions.


  We discuss how the electrons in relativistic GRB shocks can reach
near-equipartition in energy with the protons. We emphasize the non-Fermi
origin of such acceleration. We argue that the dynamics of the electrons in the
foreshock region and at the shock front plays an important role. We also
demonstrate that PIC simulations can directly probe this physics in the regimes
relevant to GRBs.


  Factor graphs are a very powerful graphical representation, used to model
many problems in robotics. They are widely spread in the areas of Simultaneous
Localization and Mapping (SLAM), computer vision, and localization. In this
paper we describe an approach to fill the gap with other areas, such as optimal
control, by presenting an extension of Factor Graph Solvers to constrained
optimization. The core idea of our method is to encapsulate the Augmented
Lagrangian (AL) method in factors of the graph that can be integrated
straightforwardly in existing factor graph solvers. We show the generality of
our approach by addressing three applications, arising from different areas:
pose estimation, rotation synchronization and Model Predictive Control (MPC) of
a pseudo-omnidirectional platform. We implemented our approach using C++ and
ROS. Besides the generality of the approach, application results show that we
can favorably compare against domain specific approaches.


  We discuss classical dynamics of electron spin in two-dimensional
semiconductors with a spin-split spectrum. We focus on a special case, when
spin-orbit induced random magnetic field is directed along a fixed axis. This
case is realized in III-V-based quantum wells grown in [110] direction and also
in [100]-grown quantum wells with equal strength of Dresselhaus and
Bychkov-Rashba spin-orbit couplings. We show that in such wells the long-time
spin dynamics is determined by non-Markovian memory effects. Due to these
effects the non-exponential tail $1/t^2$ appears in the spin polarization.


  More than one-fifth of the US population does not subscribe to a fixed
broadband service despite broadband being a recognized merit good. For example,
less than 4% of citizens earning more than US \$70k annually do not have
broadband, compared to 26% of those earning below US \$20k annually. To address
this, the Biden Administration has undertaken one of the largest broadband
investment programs ever via The Bipartisan Infrastructure Law, with the aim of
addressing this disparity and expanding broadband connectivity to all citizens.
We examine broadband availability, adoption, and need for each US state, and
then construct an Input-Output model to explore the potential macroeconomic
impacts of broadband spending to Gross Domestic Product (GDP) and supply chain
linkages. Our analysis indicates that higher funding allocations do appear to
be allocated to areas with poorer broadband. While this may be logical, as it
illustrates funding going to areas most in need, this could not have been
assumed a priori given politically-motivated funding is not always rationally
allocated. In terms of macroeconomic impact, the total direct contribution to
US GDP by the program could be as high as US \$84.8 billion, \$55.2 billion,
and \$5.99 billion for the BEAD program, ACP, and TBCP, respectively. Thus,
overall, the broadband allocations could expand US GDP by \$146 billion (0.13%
of annual US GDP over the next five years). We contribute one of the first
economic impact assessments of the US Bipartisan Infrastructure Law to the
literature.


  Results for the first test of the ``crude'' QCD continuum model, commonly
used in QCD Sum Rule analyses, are presented for baryon correlation functions.
The QCD continuum model is found to effectively account for excited state
contributions to the short-time regime of two-point correlation functions and
allows the isolation of ground state properties. Confusion in the literature
surrounding the physics represented in point-to-point correlation functions is
also addressed. These results justify the use of the ``crude'' QCD continuum
model and lend credence to the results of rigorous QCD Sum Rule analyses.


  Serverless computing has emerged as a new execution model which gained a lot
of attention in cloud computing thanks to the latest advances in
containerization technologies. Recently, serverless has been adopted at the
edge, where it can help overcome heterogeneity issues, constrained nature and
dynamicity of edge devices. Due to the distributed nature of edge devices,
however, the scaling of serverless functions presents a major challenge. We
address this challenge by studying the optimality of serverless function
scaling. To this end, we propose Semi-Markov Decision Process-based (SMDP)
theoretical model, which yields optimal solutions by solving the serverless
function scaling problem as a decision making problem. We compare the SMDP
solution with practical, monitoring-based heuristics. We show that SMDP can be
effectively used in edge computing networks, and in combination with
monitoring-based approaches also in real-world implementations.


  Elongated magnetic polarities are observed during the emergence phase of
bipolar active regions (ARs). These extended features, called magnetic tongues,
are interpreted as a consequence of the azimuthal component of the magnetic
flux in the toroidal flux-tubes that form ARs. We develop a new systematic and
user-independent method to identify AR tongues. Our method is based on
determining and analyzing the evolution of the AR main polarity inversion line
(PIL). The effect of the tongues is quantified by measuring the acute angle [
tau] between the orientation of the PIL and the direction orthogonal to the AR
main bipolar axis. We apply a simple model to simulate the emergence of a
bipolar AR. This model lets us interpret the effect of magnetic tongues on
parameters that characterize ARs ( e.g. the PIL inclination and the tilt
angles, and their evolution). In this idealized kinematic emergence model, tau
is a monotonically increasing function of the twist and has the same sign as
the magnetic helicity. We systematically apply our procedure to a set of
bipolar ARs that were observed emerging in line-of-sight magnetograms over
eight years. For most of the cases studied, the tongues only have a small
influence on the AR tilt angle since tongues have a much lower magnetic flux
than the more concentrated main polarities. From the observed evolution of tau,
corrected for the temporal evolution of the tilt angle and its final value when
the AR is fully emerged, we estimate the average number of turns in the
subphotospherically emerging flux-rope. These values for the 41 observed ARs
are below unity, except for one. This indicates that subphotospheric flux-ropes
typically have a low amount of twist, i.e. highly twisted flux-tubes are rare.
Our results demonstrate that the evolution of the PIL is a robust indicator of
the presence of tongues and constrains the amount of twist in emerging
flux-tubes


  We generalize the optimal coupling theorem to multiple random variables:
Given a collection of random variables, it is possible to couple all of them so
that any two differ with probability comparable to the total-variation distance
between them. In a number of cases we show that the disagreement probability we
achieve is the best possible. The proofs of sharpness rely on new results in
extremal combinatorics, which may be of independent interest.


  We study the transient dynamics that arise during the formation of an atom
laser beam in a tight waveguide. During the time evolution the density profile
develops a series of wiggles which are related to the diffraction in time
phenomenon. The apodization of matter waves, which relies on the use of smooth
aperture functions, allows to suppress such oscillations in a time interval,
after which there is a revival of the diffraction in time. The revival time
scale is directly related to the inverse of the harmonic trap frequency for the
atom reservoir.


  We report temperature dependent measurements of ambient pressure specific
heat, magnetic susceptibility, anisotropic resistivity and thermal expansion as
well as in-plane resistivity under pressure up to 20.8 kbar on single crystals
of EuAg$_4$As$_2$. Based on thermal expansion and in-plane electrical transport
measurements at ambient pressure this compound has two, first order, structural
transitions in 80 - 120 K temperature range. Ambient pressure specific heat,
magnetization and thermal expansion measurements show a cascade of up to seven
transitions between 8 and 16 K associated with the ordering of the Eu$^{2+}$
moments. In-plane electrical transport is able to detect more prominent of
these transitions: at 15.5, 9.9, and 8.7 K as well as a weak feature at 11.8 K
at ambient pressure. Pressure dependent electrical transport data show that the
magnetic transitions shift to higher temperatures under pressure, as does the
upper structural transition, whereas the lower structural transition is
suppressed and ultimately vanishes. A jump in resistivity, associated with the
upper structural transition, decreases under pressure with an extrapolated
disappearance (or a change of sign) by 30-35 kbar. In the 10 - 15 kbar range a
kink in the pressure dependence of the upper structural transition temperature
as well as the high and low temperature in-plane resistivities suggest that a
change in the electronic structure may occur in this pressure range. The
results are compared with the literature data for SrAg$_4$As$_2$.


  In this short note we show that the existence of bilaterally symmetric
extremal K\"ahler metrics on $\mathbb{CP}^2\sharp 2\bar{\mathbb{CP}^2}$.


  The Dirac-Moshinsky oscillator is an elegant example of an exactly solvable
quantum relativistic model that under certain circumstances can be mapped onto
the Jaynes-Cummings model in quantum optics. In this work we show, how to do
this in detail. Then we extend it by considering its coupling with an external
(isospin) field and find the conditions that maintain solvability. We use this
extended system to explore entanglement in relativistic systems and then
identify its quantum optical analog: two different atoms interacting with an
electromagnetic mode. We show different aspects of entanglement which gain
relevance in this last system, which can be used to emulate the former.


  A method for the nonintrusive and structure-preserving model reduction of
canonical and noncanonical Hamiltonian systems is presented. Based on the idea
of operator inference, this technique is provably convergent and reduces to a
straightforward linear solve given snapshot data and gray-box knowledge of the
system Hamiltonian. Examples involving several hyperbolic partial differential
equations show that the proposed method yields reduced models which, in
addition to being accurate and stable with respect to the addition of basis
modes, preserve conserved quantities well outside the range of their training
data.


  Event Causality Identification (ECI) aims at determining whether there is a
causal relation between two event mentions. Conventional prompt learning
designs a prompt template to first predict an answer word and then maps it to
the final decision. Unlike conventional prompts, we argue that predicting an
answer word may not be a necessary prerequisite for the ECI task. Instead, we
can first make a deterministic assumption on the existence of causal relation
between two events and then evaluate its rationality to either accept or reject
the assumption. The design motivation is to try the most utilization of the
encyclopedia-like knowledge embedded in a pre-trained language model. In light
of such considerations, we propose a deterministic assumption prompt learning
model, called DAPrompt, for the ECI task. In particular, we design a simple
deterministic assumption template concatenating with the input event pair,
which includes two masks as predicted events' tokens. We use the probabilities
of predicted events to evaluate the assumption rationality for the final event
causality decision. Experiments on the EventStoryLine corpus and
Causal-TimeBank corpus validate our design objective in terms of significant
performance improvements over the state-of-the-art algorithms.


  We study the magnetic analogue of Myers' Dielectric Effect and, in some
cases, relate it to the blowing up of particles into branes, first investigated
by Greevy, Susskind and Toumbas. We show that $D0$ branes or gravitons in M
theory, moving in a magnetic four-form field strength background expand into a
non-commutative two sphere. Both examples of constant magnetic field and
non-constant fields in curved backgrounds generated by branes are considered.
We find, in all cases, another solution, consisting of a two-brane wrapping a
classical two-sphere, which has all the quantum numbers of the $D0$ branes.
Motivated by this, we investigate the blowing up of gravitons into branes in
backgrounds different from $AdS_m \times S^n$. We find the phenomenon is quite
general. In many cases with less or even no supersymmetry we find a brane
configuration which has the same quantum numbers and the same energy as a
massless particle in supergravity.


  Accretion disk theory was first developed as a theory with the local heat
balance, where the whole energy produced by a viscous heating was emitted to
the sides of the disk.
  One of the most important new invention of this theory was the
phenomenological treatment of the turbulent viscosity, known the `` alpha''
prescription, where the (r phi) component of the stress tensor was approximated
by (alpha p) with a unknown constant alpha.
  This prescription played the role in the accretion disk theory as well
important as the mixing-length theory of convection for stellar evolution.
Sources of turbulence in the accretion disk are discussed, including
hydrodynamical turbulence, convection and magnetic field role. In parallel to
the optically thick geometrically thin accretion disk models, a new branch of
the optically thin accretion disk models was discovered, with a larger
thickness for the same total luminosity. The choice between these solutions
should be done of the base of a stability analysis.
  The ideas underlying the necessity to include advection into the accretion
disk theory are presented and first models with advection are reviewed.
  The present status of the solution for a low-luminous optically thin
accretion disk model with advection is discussed and the limits for an
advection dominated accretion flows (ADAF) imposed by the presence of magnetic
field are analysed. Related problems of mass ejection from accretion disks and
jet formation are discussed.


  We derive relations between type 0 and type II D-brane configurations under
the T-duality suggested by Bergman and Gaberdiel and confirm that the massless
fields on D-branes are identical to those on the dual D-brane configurations.
Furthermore, we discuss dualities of type 0 and type II NS5-branes and find
that the dual of an unwrapped type 0 NS5-brane is a Kaluza-Klein monopole with
non-supersymmetric blow up modes.


  Social reviews have dominated the web and become a plausible source of
product information. People and businesses use such information for
decision-making. Businesses also make use of social information to spread fake
information using a single user, groups of users, or a bot trained to generate
fraudulent content. Many studies proposed approaches based on user behaviors
and review text to address the challenges of fraud detection. To provide an
exhaustive literature review, social fraud detection is reviewed using a
framework that considers three key components: the review itself, the user who
carries out the review, and the item being reviewed. As features are extracted
for the component representation, a feature-wise review is provided based on
behavioral, text-based features and their combination. With this framework, a
comprehensive overview of approaches is presented including supervised,
semi-supervised, and unsupervised learning. The supervised approaches for fraud
detection are introduced and categorized into two sub-categories; classical,
and deep learning. The lack of labeled datasets is explained and potential
solutions are suggested. To help new researchers in the area develop a better
understanding, a topic analysis and an overview of future directions is
provided in each step of the proposed systematic framework.


  Kinetically constrained models (KCM) are reversible interacting particle
systems on $\mathbb{Z}^d$ with continuous-time constrained Glauber dynamics.
They are a natural non-monotone stochastic version of the family of cellular
automata with random initial state known as $\mathcal{U}$-bootstrap
percolation. KCM have an interest in their own right, owing to their use for
modelling the liquid-glass transition in condensed matter physics.
  In two dimensions there are three classes of models with qualitatively
different scaling of the infection time of the origin as the density of
infected sites vanishes. Here we study in full generality the class termed
`critical'. Together with the companion paper by Martinelli and two of the
authors we establish the universality classes of critical KCM and determine
within each class the critical exponent of the infection time as well as of the
spectral gap. In this work we prove that for critical models with an infinite
number of stable directions this exponent is twice the one of their bootstrap
percolation counterpart. This is due to the occurrence of `energy barriers',
which determine the dominant behaviour for these KCM but which do not matter
for the monotone bootstrap dynamics. Our result confirms the conjecture of
Martinelli, Morris and the last author, who proved a matching upper bound.


  We study the Partial Degree Bounded Edge Packing (PDBEP) problem introduced
in [5] by Zhang. They have shown that this problem is NP-Hard even for uniform
degree constraint. They also presented approximation algorithms for the case
when all the vertices have degree constraint of 1 and 2 with approximation
ratio of 2 and 32=11 respectively. In this work we study general degree
constraint case (arbitrary degree constraint for each vertex) and present two
combinatorial approximation algorithms with approximation factors 4 and 2. We
also study integer program based solution and present an iterative rounding
algorithm with approximation factor 3/(1 - \epsilon)^2 for any positive
\epsilon. Next we study the same problem with weighted edges. In this case we
present an O(log n) approximation algorithm. Zhang has given an exact O(n^2)
complexity algorithm for trees in case of uniform degree constraint. We improve
their result by giving O(nlog n) complexity exact algorithm for trees with
general degree constraint.


  We construct the low-energy effective lagrangian for a light gravitino
coupled to the minimal supersymmetric standard model under the assumption that
supersymmetry breaking is communicated to the observable sector dominantly
through soft terms. Our effective lagrangian is written in terms of the
spin-1/2 Goldstino (the longitudinal component of the gravitino) transforming
under a non-linear realization of supersymmetry. In this lagrangian, the
Goldstino is derivatively coupled and all couplings of the Goldstino to light
fields are determined uniquely by the supersymmetry-breaking scale \sqrt{F}.
This lagrangian is therefore a useful starting point for further investigation
of the light gravitino in gauge-mediated supersymmetry breaking models. We show
that the invisible width of the Z into Goldstinos gives the constraint \sqrt{F}
> 140 GeV.


  In vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic
Resonance Imaging (MRI) technique for evaluating the micro-structure of
myocardial tissue in the living heart, providing insights into cardiac function
and enabling the development of innovative therapeutic strategies. However, the
integration of cDTI into routine clinical practice is challenging due to the
technical obstacles involved in the acquisition, such as low signal-to-noise
ratio and long scanning times. In this paper, we investigate and implement
three different types of deep learning-based MRI reconstruction models for cDTI
reconstruction. We evaluate the performance of these models based on
reconstruction quality assessment and diffusion tensor parameter assessment.
Our results indicate that the models we discussed in this study can be applied
for clinical use at an acceleration factor (AF) of $\times 2$ and $\times 4$,
with the D5C5 model showing superior fidelity for reconstruction and the SwinMR
model providing higher perceptual scores. There is no statistical difference
with the reference for all diffusion tensor parameters at AF $\times 2$ or most
DT parameters at AF $\times 4$, and the quality of most diffusion tensor
parameter maps are visually acceptable. SwinMR is recommended as the optimal
approach for reconstruction at AF $\times 2$ and AF $\times 4$. However, we
believed the models discussed in this studies are not prepared for clinical use
at a higher AF. At AF $\times 8$, the performance of all models discussed
remains limited, with only half of the diffusion tensor parameters being
recovered to a level with no statistical difference from the reference. Some
diffusion tensor parameter maps even provide wrong and misleading information.


  High altitude clouds and hazes are integral to understanding exoplanet
observations, and are proposed to explain observed featureless transit spectra.
However, it is difficult to make inferences from these data because of the need
to disentangle effects of gas absorption from haze extinction. Here, we turn to
the quintessential hazy world -- Titan -- to clarify how high altitude hazes
influence transit spectra. We use solar occultation observations of Titan's
atmosphere from the Visual and Infrared Mapping Spectrometer (VIMS) aboard
NASA's Cassini spacecraft to generate transit spectra. Data span 0.88-5 microns
at a resolution of 12-18 nm, with uncertainties typically smaller than 1%. Our
approach exploits symmetry between occultations and transits, producing transit
radius spectra that inherently include the effects of haze multiple scattering,
refraction, and gas absorption. We use a simple model of haze extinction to
explore how Titan's haze affects its transit spectrum. Our spectra show strong
methane absorption features, and weaker features due to other gases. Most
importantly, the data demonstrate that high altitude hazes can severely limit
the atmospheric depths probed by transit spectra, bounding observations to
pressures smaller than 0.1-10 mbar, depending on wavelength. Unlike the usual
assumption made when modeling and interpreting transit observations of
potentially hazy worlds, the slope set by haze in our spectra is not flat, and
creates a variation in transit height whose magnitude is comparable to those
from the strongest gaseous absorption features. These findings have important
consequences for interpreting future exoplanet observations, including those
from NASA's James Webb Space Telescope.


  In this paper, the isoperimetric problem in Randers planes,
$(\mathbb{R}^2,F=\alpha +\beta)$, which are slight deformation of the Euclidean
plane $(\mathbb{R}^2,\alpha)$ by suitable one forms $\beta$, have been studied.
We prove that the circles centred at the origin achieves the local maximum area
of the isoperimetric problem with respect to well known volume forms in Finsler
geometry.


  This short note discusses open problems and partial solutions related to
Sheldon M Ross's second conjecture about the classical single server queue
subject to a randomly time-varying rate arrival rate.


  In the present day, AES is one the most widely used and most secure
Encryption Systems prevailing. So, naturally lots of research work is going on
to mount a significant attack on AES. Many different forms of Linear and
differential cryptanalysis have been performed on AES. Of late, an active area
of research has been Algebraic Cryptanalysis of AES, where although fast
progress is being made, there are still numerous scopes for research and
improvement. One of the major reasons behind this being that algebraic
cryptanalysis mainly depends on I/O relations of the AES S- Box (a major
component of the AES). As, already known, that the key recovery algorithm of
AES can be broken down as an MQ problem which is itself considered hard.
Solving these equations depends on our ability reduce them into linear forms
which are easily solvable under our current computational prowess. The lower
the degree of these equations, the easier it is for us to linearlize hence the
attack complexity reduces. The aim of this paper is to analyze the various
relations involving small number of monomials of the AES S- Box and to answer
the question whether it is actually possible to have such monomial equations
for the S- Box if we restrict the degree of the monomials. In other words this
paper aims to study such equations and see if they can be applicable for AES.


  We present preliminary results from a lattice calculation of tetraquark
states in the charm and bottom sector of the type $ud\bar{b}\bar{b}$,
$us\bar{b}\bar{b}$, $ud\bar{c}\bar{c}$ and $sc\bar{b}\bar{b}$. These
calculations are performed on $N_f = 2 + 1 + 1$ MILC ensembles with lattice
spacing of $a = 0.12~\mathrm{fm} $ and $a=0.06~\mathrm{fm} $. A relativistic
action with overlap fermions is employed for the light and charm quarks while a
non-relativistic action with non-perturbatively improved coefficients is used
in the bottom sector. Preliminary results provide a clear indication of
presence of energy levels below the relevant thresholds of different tetraquark
states. While in double charm sector we find shallow bound levels, our results
suggest deeply bound levels with double bottom tetraquarks.


  We study nonstationary intermittent dynamical systems, such as compositions
of a (deterministic) sequence of Pomeau-Manneville maps. We prove two main
results: sharp bounds on memory loss, including the "unexpected" faster rate
for a large class of measures, and sharp moment bounds for Birkhoff sums and,
more generally, "separately H\"older" observables.


  In this paper a new variational approach concerning functions (continuous)
over Hilbert spaces is presented.


  Causal set theory is a discrete model of spacetime that retains a notion of
causal structure. We understand how to construct causal sets that approximate a
given spacetime, but most causal sets are not at all manifold-like, and must be
dynamically excluded if something like our universe is to emerge from the
theory. Here we show that the most common of these "bad" causal sets, the
Kleitman-Rothschild orders, are strongly suppressed in the gravitational path
integral, and we provide evidence that a large class of other "bad" causal sets
are similarly suppressed. It thus becomes plausible that continuum behavior
could emerge naturally from causal set quantum theory.


  Several pulsars show sudden cessation of pulsed emission, which is known as
nulling. The number of known nulling pulsars has not been significantly
enhanced in the last decade, although the pulsar population has more than
doubled following the Parkes multi-beam pulsar survey. A systematic follow-up
study of the new pulsars, discovered in this survey, is being carried out by us
at 325-MHz with GMRT. The peculiar nulling behaviour of PSR J1738-2330,
observed as a part of this 325-MHz GMRT survey, is reported in this paper. The
pulsar appears to show a periodic null-burst cycle with an upper limit to
nulling fraction, of about 90 percent. The pulsed flux density declines by a
factor 94 during the nulled pulses in this pulsar.


  We investigate the rephasing efficiency of sequences of phased pulses for
spin echoes and light storage by electromagnetically induced transparency
(EIT). We derive a simple theoretical model and show that the rephasing
efficiency is very sensitive to the phases of the imperfect rephasing pulses.
The obtained efficiency differs substantially for spin echoes and EIT light
storage, which is due to the spatially retarded coherence phases after EIT
light storage. Similar behavior is also expected for other light-storage
protocols with spatial retardation or for rephasing of collective quantum
states with an unknown or undefined phase, e.g., as relevant in single-photon
storage. We confirm the predictions of our theoretical model by experiments in
a Pr$^{3+}$:Y$_{2}$SiO$_{5}$ crystal.


  It is well known that Sturmian sequences are the aperiodic sequences that are
balanced over a 2-letter alphabet. They are also characterized by their
complexity: they have exactly $(n+1)$ factors of length $n$. One possible
generalization of Sturmian sequences is the set of infinite sequences over a
$k$-letter alphabet, $k \geq 3$, which are closed under reversal and have at
most one right special factor for each length. This is the set of episturmian
sequences. These are not necessarily balanced over a $k$-letter alphabet, nor
are they necessarily aperiodic. In this paper, we characterize balanced
episturmian sequences, periodic or not, and prove Fraenkel's conjecture for the
class of episturmian sequences. This conjecture was first introduced in number
theory and has remained unsolved for more than 30 years. It states that for a
fixed $k> 2$, there is only one way to cover $\Z$ by $k$ Beatty sequences. The
problem can be translated to combinatorics on words: for a $k$-letter alphabet,
there exists only one balanced sequence up to letter permutation that has
different letter frequencies.


  We study the electronic states of narrow graphene ribbons (``nanoribbons'')
with zigzag and armchair edges. The finite width of these systems breaks the
spectrum into an infinite set of bands, which we demonstrate can be
quantitatively understood using the Dirac equation with appropriate boundary
conditions. For the zigzag nanoribbon we demonstrate that the boundary
condition allows a particle- and a hole-like band with evanescent wavefunctions
confined to the surfaces, which continuously turn into the well-known zero
energy surface states as the width gets large. For armchair edges, we show that
the boundary condition leads to admixing of valley states, and the band
structure is metallic when the width of the sample in lattice constant units is
divisible by 3, and insulating otherwise. A comparison of the wavefunctions and
energies from tight-binding calculations and solutions of the Dirac equations
yields quantitative agreement for all but the narrowest ribbons.