  Active galactic nuclei, hosting supermassive black holes and launching
relativistic jets, are the most numerous objects on the gamma-ray sky. At the
other end of the mass scale, phenomena related to stellar mass black holes, in
particular gamma-ray bursts and microquasars, are also seen on the gamma-ray
sky. While all of them are thought to launch relativistic jets, the diversity
even within each of these classes is enormous. In this review, I will discuss
recent very high energy gamma-ray results that underline both the similarity of
the black hole systems, as well as their diversity.


  In this article, we study directed graphs (digraphs) with a coloring
constraint due to Von Neumann and related to Nim-type games. This is equivalent
to the notion of kernels of digraphs, which appears in numerous fields of
research such as game theory, complexity theory, artificial intelligence
(default logic, argumentation in multi-agent systems), 0-1 laws in monadic
second order logic, combinatorics (perfect graphs)... Kernels of digraphs lead
to numerous difficult questions (in the sense of NP-completeness,
#P-completeness). However, we show here that it is possible to use a generating
function approach to get new informations: we use technique of symbolic and
analytic combinatorics (generating functions and their singularities) in order
to get exact and asymptotic results, e.g. for the existence of a kernel in a
circuit or in a unicircuit digraph. This is a first step toward a
generatingfunctionology treatment of kernels, while using, e.g., an approach "a
la Wright". Our method could be applied to more general "local coloring
constraints" in decomposable combinatorial structures.


  We present optical and X-ray radiographical characterization of silica
aerogels with refractive index from 1.05 to 1.07 for a Cherenkov radiator. A
novel pin-drying method enables us to produce highly transparent hydrophobic
aerogels with high refractive index by shrinking wet-gels. In order to
investigate the uniformity in the density (i.e., refractive index) of an
individual aerogel monolith, we use the laser Fraunhofer method, an X-ray
absorption technique, and Cherenkov imaging by a ring imaging Cherenkov
detector in a beam test. We observed an increase in density at the edge of the
aerogel tiles, produced by pin-drying.


  To reach a cost-efficient 5G architecture, the use of remote radio heads
connected through a fronthaul to baseband controllers is a promising solution.
However, the fronthaul links must support high bit rates as 5G networks are
projected to use wide bandwidths and many antennas. Upgrading all of the
existing fronthaul connections would be cumbersome, while replacing the remote
radio head and upgrading the software in the baseband controllers is relatively
simple. In this paper, we consider the uplink and seek the answer to the
question: If we have a fixed fronthaul capacity and can deploy any technology
in the remote radio head, what is the optimal technology? In particular, we
optimize the number of antennas, quantization bits and bandwidth to maximize
the sum rate under a fronthaul capacity constraint. The analytical results
suggest that operating with many antennas equipped with low-resolution
analog-to-digital converters, while the interplay between number of antennas
and bandwidth depends on various parameters. The numerical analysis provides
further insights into the design of communication systems with limited
fronthaul capacity.


  This paper proposes a unified approach for studying global exponential
stability of a general class of switched systems described by time-varying
nonlinear functional differential equations. Some new delay-independent
criteria of global exponential stability are established for this class of
systems under arbitrary switching which satisfies some assumptions on the
average dwell time. The obtained criteria are shown to cover and improve many
previously known results, including, in particular, sufficient conditions for
absolute exponential stability of switched time-delay systems with sector
nonlinearities. Some simple examples are given to illustrate the proposed
method.


  Given the degree sequence $d$ of a graph, the realization graph of $d$ is the
graph having as its vertices the labeled realizations of $d$, with two vertices
adjacent if one realization may be obtained from the other via an
edge-switching operation. We describe a connection between Cartesian products
in realization graphs and the canonical decomposition of degree sequences
described by R.I. Tyshkevich and others. As applications, we characterize the
degree sequences whose realization graphs are triangle-free graphs or
hypercubes.


  This article deals with asynchronous server vacation and customer retrial
facility in a multi-server queueing-inventory system. The Poisson process
governs the arrival of a customer. The system is comprised of c identical
servers, a finite-size waiting area, and a storage area containing S items. The
service time is distributed exponentially. If each server finds that there are
an insufficient number of customers and items in the system after the busy
period, they start a vacation. Once the servers vacation is over and it
recognizes there is no chance of getting busy, it goes into an idle state if
the number of customers or items is not sufficient, otherwise, it will take
another vacation. Furthermore, each server's vacation period occurs
independently of the other servers. The system accepts a (s, Q) control policy
for inventory replenishment. For the steady state analysis, the Marcel F Neuts
and B Madhu Rao matrix geometric approximation approach is used owing to the
structure of an infinitesimal generator matrix. The necessary stability
condition and R matrix are to be computed and presented. After calculating the
sufficient system performance measures, an expected total cost of the system is
to be constructed and numerically incorporated with the parameters.
Additionally, numerical analyses will be conducted to examine the waiting time
of customers in the queue and in orbit, as well as the expected rate of
customer loss.


  Non-negative Matrix Factorization (NMF) is one of the most popular techniques
for data representation and clustering, and has been widely used in machine
learning and data analysis. NMF concentrates the features of each sample into a
vector, and approximates it by the linear combination of basis vectors, such
that the low-dimensional representations are achieved. However, in real-world
applications, the features are usually with different importances. To exploit
the discriminative features, some methods project the samples into the subspace
with a transformation matrix, which disturbs the original feature attributes
and neglects the diversity of samples. To alleviate the above problems, we
propose the Feature weighted Non-negative Matrix Factorization (FNMF) in this
paper. The salient properties of FNMF can be summarized as threefold: 1) it
learns the weights of features adaptively according to their importances; 2) it
utilizes multiple feature weighting components to preserve the diversity; 3) it
can be solved efficiently with the suggested optimization algorithm.
Performance on synthetic and real-world datasets demonstrate that the proposed
method obtains the state-of-the-art performance.


  The threshold degree of a Boolean function $f\colon\{0,1\}^n\to\{0,1\}$ is
the minimum degree of a real polynomial $p$ that represents $f$ in sign:
$\mathrm{sgn}\; p(x)=(-1)^{f(x)}.$ A related notion is sign-rank, defined for a
Boolean matrix $F=[F_{ij}]$ as the minimum rank of a real matrix $M$ with
$\mathrm{sgn}\; M_{ij}=(-1)^{F_{ij}}$. Determining the maximum threshold degree
and sign-rank achievable by constant-depth circuits ($\text{AC}^{0}$) is a
well-known and extensively studied open problem, with complexity-theoretic and
algorithmic applications.
  We give an essentially optimal solution to this problem. For any
$\epsilon>0,$ we construct an $\text{AC}^{0}$ circuit in $n$ variables that has
threshold degree $\Omega(n^{1-\epsilon})$ and sign-rank
$\exp(\Omega(n^{1-\epsilon})),$ improving on the previous best lower bounds of
$\Omega(\sqrt{n})$ and $\exp(\tilde{\Omega}(\sqrt{n}))$, respectively. Our
results subsume all previous lower bounds on the threshold degree and sign-rank
of $\text{AC}^{0}$ circuits of any given depth, with a strict improvement
starting at depth $4$. As a corollary, we also obtain near-optimal bounds on
the discrepancy, threshold weight, and threshold density of $\text{AC}^{0}$,
strictly subsuming previous work on these quantities. Our work gives some of
the strongest lower bounds to date on the communication complexity of
$\text{AC}^{0}$.


  An effective nucleon-nucleon interaction calculated in nuclear matter from
the Bonn potential has been parametrized in terms of a local density- and
energy-dependent two-body interaction. This allows to calculate the real part
of the nucleus-nucleus scattering potential and to test this effective
interaction over a wide region of densities ($\rho \leq 3\rho_0$) produced
dynamically in scattering experiments. Comparing our calculations with
empirical potentials extracted from data on light and heavy ion scattering by
model-unrestricted analysis methods, we find quantitative agreement with the
exception of proton scattering. The failure in this case may be traced back to
the properties of the effective interaction at low densities, for which the
nuclear matter results are not reliable. The success of the interaction at high
overlap densities confirms the empirical evidence for a soft equation of state
for cold nuclear matter.


  Topological insulators provide a new platform for spintronics due to the spin
texture of the surface states that are topologically robust against elastic
backscattering. Here, we report on an investigation of the measured voltage
obtained from efforts to electrically probe spin-momentum locking in the
topological insulator Bi$_2$Se$_3$ using ferromagnetic contacts. Upon inverting
the magnetization of the ferromagnetic contacts, we find a reversal of the
measured voltage. Extensive analysis of the bias and temperature dependence of
this voltage was done, considering the orientation of the magnetization
relative to the current. Our findings indicate that the measured voltage can
arise due to fringe-field-induced Hall voltages, different from current-induced
spin polarization of the surface state charge carriers, as reported recently.
Understanding the nontrivial origin of the measured voltage is important for
realizing spintronic devices with topological insulators.


  Fluctuations are one of the main probes of the physics of the new state of
hot and dense nuclear matter called the Quark Gluon Plasma (QGP) which is
created in the ultra-relativistic heavy ion collisions. In this dissertation we
extend and improve upon the existing descriptions of heavy ion collisions in
three different directions: we study the new signatures of initial state
fluctuations, the propagation of perturbations in the early stages of the
collision, and the effect of thermal fluctuations on the hydrodynamic expansion
of the QGP. First, we use Principal Component Analysis (PCA) to study
event-by-event fluctuations in the spectrum of harmonic flow, $v_n(p_T)$ for
$n=0\text{--}5$, in hydrodynamic simulations of Pb+Pb collisions at the CERN
Large Hadron Collider ($\sqrt{s_{NN}}=2.76\,{\rm TeV}$). The PCA procedure
finds two dominant contributions to the two-particle correlation function: the
leading component is identified with the standard event plane $v_n(p_T)$ and
the subleading component is responsible for factorization breaking and reflects
the radial excitation in the initial state. Second, we use leading order
effective kinetic theory (accurate at weak coupling) to simulate the
pre-equilibrium evolution of transverse energy and flow perturbations. We
construct the linear response Green functions, which map initial perturbations
to the energy momentum tensor at a time when hydrodynamics becomes applicable.
Finally, we develop a set of kinetic equations for thermal noise correlators
which are equivalent to nonlinear hydrodynamics with noise. We use the
hydro-kinetic equations to analyze thermal fluctuations for a Bjorken expansion
and determine the coefficient of the first fractional power ($\propto 1/(\tau
T)^{3/2}$) of the nonlinear noise corrections to energy momentum tensor.


  Traceroute is widely used: from the diagnosis of network problems to the
assemblage of internet maps. Unfortu- nately, there are a number of problems
with traceroute methodology, which lead to the inference of erroneous routes.
This paper studies particular structures arising in nearly all traceroute
measurements. We characterize them as "loops", "cycles", and "diamonds". We
iden- tify load balancing as a possible cause for the appear- ance of false
loops, cycles and diamonds, i.e., artifacts that do not represent the internet
topology. We pro- vide a new publicly-available traceroute, called Paris
traceroute, which, by controlling the packet header con- tents, provides a
truer picture of the actual routes that packets follow. We performed
measurements, from the perspective of a single source tracing towards multiple
destinations, and Paris traceroute allowed us to show that many of the
particular structures we observe are indeed traceroute measurement artifacts.


  Large language models (LLMs) are transforming human-computer interaction and
conceptions of artificial intelligence (AI) with their impressive capacities
for conversing and reasoning in natural language. There is growing interest in
whether LLMs have theory of mind (ToM); the ability to reason about the mental
and emotional states of others that is core to human social intelligence. As
LLMs are integrated into the fabric of our personal, professional and social
lives and given greater agency to make decisions with real-world consequences,
there is a critical need to understand how they can be aligned with human
values. ToM seems to be a promising direction of inquiry in this regard.
Following the literature on the role and impacts of human ToM, this paper
identifies key areas in which LLM ToM will show up in human:LLM interactions at
individual and group levels, and what opportunities and risks for alignment are
raised in each. On the individual level, the paper considers how LLM ToM might
manifest in goal specification, conversational adaptation, empathy and
anthropomorphism. On the group level, it considers how LLM ToM might facilitate
collective alignment, cooperation or competition, and moral judgement-making.
The paper lays out a broad spectrum of potential implications and suggests the
most pressing areas for future research.


  We present the results of the GRANAT/SIGMA hard X-/soft gamma-ray long-term
monitoring of the Galactic Center (GC) region concerning the source GRO
J1744-28, discovered on 1995 Dec. 2 by CGRO/BATSE. SIGMA observed the region
containing the source in 14 opportunities between 1990 and 1997. In two of
these observing sessions, corresponding to March 1996 and March 1997, GRO
J1744-28 was detected with a confidence level greater than 5(sigma) in the
35-75 keV energy band without detection in the 75-150 keV energy band. For the
other sessions, upper limits of the flux are indicated. The particular imaging
capabilities of the SIGMA telescope allow us to identify, specifically, the
source position in the very crowded GC region, giving us a mean flux of (73.1
+/- 5.5)E-11 and (44.7 +/- 6.4)E-11 ergs cm^-2 s^-1 in the 35-75 keV energy
band, for the March 1996 and March 1997 observing sessions, respectively.
Combining the March 1997 SIGMA and BATSE observations, we found evidence
pointing to the type-II nature of the source bursts for this period. For the
same observing campaigns, spectra were obtained in the 35 to 150 keV energy
band. The best fit corresponds to an optically thin thermal Bremsstrahlung with
F(50 keV)=(3.6 +/- 0.6)E-4 phot cm^-2 s^-1 keV^-1 and kT(Bremss)=28 +/- 7 keV,
for the first campaign, and F(50 keV)=(2.3 +/- 0.7)E-4 phot cm^-2 s^-1 keV^-1
and kT(Bremss)=18 (+12/-7) keV, for the second. This kind of soft spectrum is
typical of binary sources containing a neutron star as the compact object, in
contrast to the harder spectra typical of systems containing a black hole
candidate


  It is shown that the averaged null energy condition is fulfilled for a dense,
translationally invariant set of vector states in any local quantum field
theory in two-dimensional Minkowski spacetime whenever the theory has a mass
gap and possesses an energy-momentum tensor. The latter is assumed to be a
Wightman field which is local relative to the observables, generates locally
the translations, is divergence-free, and energetically bounded. Thus the
averaged null energy condition can be deduced from completely generic, standard
assumptions for general quantum field theory in two-dimensional flat spacetime.


  As for the unique continuation property (UCP) of solutions in
$(0,T)\times\Omega$ with a domain $\Omega\subset{\mathbb R}^n,\,n\in{\mathbb
N}$ for a multi-terms time fractional diffusion equation, we have already shown
it by assuming that the solutions are zero for $t\le0$ (see \cite{LN2019}).
Here the strongly elliptic operator for this diffusion equation can depend on
time and the orders of its time fractional derivatives are in $(0,2)$. This
paper is a continuation of the previous study. The aim of this paper is to drop
the assumption that the solutions are zero for $t\le0$. We have achieved this
aim by first using the usual Holmgren transformation together with the argument
in \cite{LN2019} to derive the UCP in $(T_1,T_2)\times B_1$ for some
$0<T_1<T_2<T$ and a ball $B_1\subset\Omega$. Then if $u$ is the solution of the
equation with $u=0$ in $(T_1,T_2)\times B_1$, we show $u=0$ also in
$((0,T_1]\cup[T_2,T))\times B_r$ for some $r<1$ by using the argument in
\cite{LN2019} which uses two Holmgren type transformations different from the
usual one. This together with spatial coordinates transformation, we can obtain
the usual UCP which we call it the classical UCP given in the title of this
paper for our time fractional diffusion equation.


  Quantum Signal Processing (QSP) is a technique that can be used to implement
a polynomial transformation $P(x)$ applied to the eigenvalues of a unitary $U$,
essentially implementing the operation $P(U)$, provided that $P$ satisfies some
conditions that are easy to satisfy. A rich class of previously known quantum
algorithms were shown to be derived or reduced to this technique or one of its
extensions. In this work, we show that QSP can be used to tackle a new problem,
which we call phase extraction, and that this can be used to provide quantum
speed-up for proportional sampling, a problem of interest in machine-learning
applications and quantum state preparation. We show that, for certain sampling
distributions, our algorithm provides an almost-quadratic speed-up over
classical sampling procedures. Then we extend the result by constructing a
sequence of algorithms that increasingly relax the dependence on the space of
elements to sample.


  We study solutions to the integral equation \[ \omega(x) = \Gamma - x^2
\int_{0}^1 K(\theta) \, H(\omega(x\theta)) \, \mathrm d \theta \] where
$\Gamma>0$, $K$ is a weakly degenerate kernel satisfying, among other
properties, $K(\theta) \sim k \, (1-\theta)^\sigma$ as $\theta \to 1$ for
constants $k>0$ and $\sigma \in (0, \log_2 3 -1)$, $H$ denotes the Heaviside
function, and $x \in [0,\infty)$. This equation arises from a
reaction-diffusion equation describing Liesegang precipitation band patterns
under certain simplifying assumptions. We argue that the integral equation is
an analytically tractable paradigm for the clustering of precipitation rings
observed in the full model. This problem is nontrivial as the right hand side
fails a Lipschitz condition so that classical contraction mapping arguments do
not apply.
  Our results are the following. Solutions to the integral equation, which
initially feature a sequence of relatively open intervals on which $\omega$ is
positive ("rings") or negative ("gaps") break down beyond a finite interval
$[0,x^*]$ in one of two possible ways. Either the sequence of rings accumulates
at $x^*$ ("non-degenerate breakdown") or the solution cannot be continued past
one of its zeroes at all ("degenerate breakdown"). Moreover, we show that
degenerate breakdown is possible within the class of kernels considered.
Finally, we prove existence of generalized solutions which extend the integral
equation past the point of breakdown.


  Most widely-used pre-trained language models operate on sequences of tokens
corresponding to word or subword units. By comparison, token-free models that
operate directly on raw text (bytes or characters) have many benefits: they can
process text in any language out of the box, they are more robust to noise, and
they minimize technical debt by removing complex and error-prone text
preprocessing pipelines. Since byte or character sequences are longer than
token sequences, past work on token-free models has often introduced new model
architectures designed to amortize the cost of operating directly on raw text.
In this paper, we show that a standard Transformer architecture can be used
with minimal modifications to process byte sequences. We characterize the
trade-offs in terms of parameter count, training FLOPs, and inference speed,
and show that byte-level models are competitive with their token-level
counterparts. We also demonstrate that byte-level models are significantly more
robust to noise and perform better on tasks that are sensitive to spelling and
pronunciation. As part of our contribution, we release a new set of pre-trained
byte-level Transformer models based on the T5 architecture, as well as all code
and data used in our experiments.


  Radial velocity measurements and simple sine-curve fits to the orbital
velocity variations are presented for the second set of ten contact binary
systems. Eight systems are of the A-type: AH Aur, CK Boo, DK Cyg, UZ Leo, XZ
Leo, V839 Oph, GR Vir and NN Vir. V842 Her is the only W-type, while SV Equ
appears to be a semi-detached system seen at a low orbital inclination rather
than a contact binary. Several of the studied system are prime candidates for
complete light and radial-velocity synthesis solutions.


  We study a family of problems, called \prob{Maximum Solution}, where the
objective is to maximise a linear goal function over the feasible integer
assignments to a set of variables subject to a set of constraints. When the
domain is Boolean (i.e. restricted to $\{0,1\}$), the maximum solution problem
is identical to the well-studied \prob{Max Ones} problem, and the
approximability is completely understood for all restrictions on the underlying
constraints [Khanna et al., SIAM J. Comput., 30 (2001), pp. 1863-1920]. We
continue this line of research by considering domains containing more than two
elements. We present two main results: a complete classification for the
approximability of all maximal constraint languages over domains of cardinality
at most 4, and a complete classification of the approximability of the problem
when the set of allowed constraints contains all permutation constraints. Under
the assumption that a conjecture due to Szczepara holds, we give a complete
classification for all maximal constraint languages. These classes of languages
are well-studied in universal algebra and computer science; they have, for
instance, been considered in connection with machine learning and constraint
satisfaction. Our results are proved by using algebraic results from clone
theory and the results indicates that this approach is very powerful for
classifying the approximability of certain optimisation problems.


  We present new deep $VI$ ground-based photometry of the Local Group dwarf
galaxy Phoenix. Our results confirm that this galaxy is mainly dominated by red
stars, with some blue plume stars indicating recent (100 Myr old) star
formation in the central part of the galaxy. We have performed an analysis of
the structural parameters of Phoenix based on an ESO/SRC scanned plate, in
order to search for differentiated component. The results were then used to
obtain the color-magnitude diagrams for three different regions of Phoenix in
order to study the variation of the properties of its stellar population. The
young population located in the central component of Phoenix shows a clear
asymmetry in its distribution, that could indicate a propagation of star
formation across the central component. The HI cloud found at 6 arcmin
Southwest by Young & Lo (1997) could have been involved in this process.
  We also find the presence of a substantial intermediate-age population in the
central region of Phoenix that would be less abundant or absent in its outer
regions. This result is also consistent with the gradient found in the number
of horizontal branch stars, whose frequency relative to red giant branch stars
increases towards the outer part of the galaxy. These results, together with
those of our morphological study, suggest the existence of an old, metal-poor
population with a spheroidal distribution surrounding the younger inner
component of Phoenix. This two-component structure may resemble the halo-disk
structure observed in spirals, although more data, in particular on kinematics,
are necessary to confirm this.


  Given a fractional Brownian motion \,\,$(B_{t}^{H})_{t\geq 0}$,\, with Hurst
parameter \,$> 1/2$\,\,we study the properties of all solutions of \,\,:
  {equation} X_{t}=B_{t}^{H}+\int_0^t X_{u}d\mu(u), \;\; 0\leq t\leq
1{equation}
  A different stochastic calculus is required for the process because it is not
a semimartingale.


  We present timing and spectral analysis of \emph{Swift}$-$XRT and
\emph{RXTE}$-$PCA observations of the transient Be/X-ray pulsar SWIFT
J0513.4--6547 during its outburst in 2009 and its rebrightening in 2014. From
2009 observations, short term spin-up rate of the source after the peak of the
outburst is found to have about half of the value measured at the peak of the
outburst by Coe et al. When the source is quiescent between 2009 and 2014,
average spin-down rate of the source is measured to be $\sim 1.52 \times
10^{-12}$ Hz s$^{-1}$ indicating a surface dipole magnetic field of $\sim 1.5
\times 10^{13}$ Gauss assuming a propeller state. From 2014 observations, short
term spin-down rate of the source is measured to be about two orders smaller
than this long-term spin-down rate. The orbit of the source is found to be
circular which is atypical for transient Be/X-ray binary systems. Hardness
ratios of the source correlate with the X-ray luminosity up to $8.4\times
10^{36}$ erg s$^{-1}$ in 3-10 keV band, whereas for higher luminosities
hardness ratios remain constant. Pulsed fractions are found to be correlated
with the source flux. Overall \emph{Swift}$-$XRT and \emph{RXTE}$-$PCA energy
spectrum of the source fit equally well to a model consisting of blackbody and
power law, and a model consisting of a power law with high energy cut-off. From
the pulse phase resolved spectra and pulse phase resolved hardness ratios
obtained using \emph{RXTE}$-$PCA, it is shown that spectrum is softer for the
phases between the two peaks of the pulse.


  Several equations used to model and characterize the linear region IV
characteristics of nanoscale field-effect transistors are derived. The meaning
of carrier mobility at the nanoscale is discussed by defining two related
quantities, the apparent mobility and the ballistic mobility. The validity of
Matthiessen's Rule for relating the apparent ability to the ballistic and
diffusive mobilities is examined. Other questions that arise in the analysis
and characterization of nanoscale field-effect transistors are also discussed.
These notes are intended to pull together in one place some key equations
needed to analyze the linear region performance of nanoscale MOSFETs and to
point out some errors in previous publications.


  We consider an ensemble of spherical colloidal particles immersed in a
near-critical solvent such as a binary liquid mixture close to its critical
demixing point. The emerging long-ranged fluctuations of the corresponding
order parameter of the solvent drive the divergence of the correlation length.
Spatial confinements of these critical fluctuations by colloidal solute
particles, acting as cavities in the fluctuating medium, restrict and modify
the fluctuation spectrum in a way which depends on their relative
configuration. This results in effective, so-called critical Casimir forces
(CCFs) acting on the confining surfaces. Using the available knowledge about
CCFs we study the structure and stability of such colloidal suspensions by
employing an approach in terms of effective, one-component colloidal systems.
Applying the approximation of pairwise additive CCFs we calculate the radial
distribution function of the colloids, which is experimentally accessible. We
analyze colloidal aggregation due to CCFs and thus allude to previous
experimental studies which are still under debate


  A novel method for reducing the impact of non-ideal pseudo-random binary
sequence (PRBS) on microwave photonic random demodulators (RDs) in a
photonics-assisted compressed sensing (CS) system is proposed. Different from
the commonly used method that switches the bias point of the optical modulator
in the RD between two quadrature transmission points to mix the signal to be
sampled and the PRBS, this method employs a PRBS with lower amplitude to low
bias the optical modulator so that the impact of non-ideal PRBS on microwave
photonic RDs can be greatly reduced by compressing the amplitude of non-ideal
parts of the PRBS. An experiment is performed to verify the concept. The
optical modulator is properly low-biased via PRBS amplitude compression. The
data rate and occupied bandwidth of the PRBS are 500 Mb/s and 1 GHz, while the
multi-tone signals with a maximum frequency of 100 MHz are sampled at an
equivalent sampling rate of only 50 MSa/s. The results show that the
reconstruction error can be reduced by up to 85%. The proposed method can
significantly reduce the requirements for PRBS in RD-based photonics-assisted
CS systems, providing a feasible solution for reducing the complexity and cost
of system implementation.


  We study probabilistic prediction games when the underlying model is
misspecified, investigating the consequences of predicting using an incorrect
parametric model. We show that for a broad class of loss functions and
parametric families of distributions, the regret of playing a "proper"
predictor -- one from the putative model class -- relative to the best
predictor in the same model class has lower bound scaling at least as
$\sqrt{\gamma n}$, where $\gamma$ is a measure of the model misspecification to
the true distribution in terms of total variation distance. In contrast, using
an aggregation-based (improper) learner, one can obtain regret $d \log n$ for
any underlying generating distribution, where $d$ is the dimension of the
parameter; we exhibit instances in which this is unimprovable even over the
family of all learners that may play distributions in the convex hull of the
parametric family. These results suggest that simple strategies for aggregating
multiple learners together should be more robust, and several experiments
conform to this hypothesis.


  We discuss the reversibility of Brownian heat engine. We perform asymptotic
analysis of Kramers equation on B\"uttiker-Landauer system and show
quantitatively that Carnot efficiency is inattainable even in a fully
overdamping limit. The inattainability is attributed to the inevitable
irreversible heat flow over the temperature boundary.


  Recommender System (RS) is an important online application that affects
billions of users every day. The mainstream RS ranking framework is composed of
two parts: a Multi-Task Learning model (MTL) that predicts various user
feedback, i.e., clicks, likes, sharings, and a Multi-Task Fusion model (MTF)
that combines the multi-task outputs into one final ranking score with respect
to user satisfaction. There has not been much research on the fusion model
while it has great impact on the final recommendation as the last crucial
process of the ranking. To optimize long-term user satisfaction rather than
obtain instant returns greedily, we formulate MTF task as Markov Decision
Process (MDP) within a recommendation session and propose a Batch Reinforcement
Learning (RL) based Multi-Task Fusion framework (BatchRL-MTF) that includes a
Batch RL framework and an online exploration. The former exploits Batch RL to
learn an optimal recommendation policy from the fixed batch data offline for
long-term user satisfaction, while the latter explores potential high-value
actions online to break through the local optimal dilemma. With a comprehensive
investigation on user behaviors, we model the user satisfaction reward with
subtle heuristics from two aspects of user stickiness and user activeness.
Finally, we conduct extensive experiments on a billion-sample level real-world
dataset to show the effectiveness of our model. We propose a conservative
offline policy estimator (Conservative-OPEstimator) to test our model offline.
Furthermore, we take online experiments in a real recommendation environment to
compare performance of different models. As one of few Batch RL researches
applied in MTF task successfully, our model has also been deployed on a
large-scale industrial short video platform, serving hundreds of millions of
users.


  We consider the Stokes-Boussinesq (and the stationary
Navier-Stokes-Boussinesq) equations in a slanted, i.e. not aligned with the
gravity's direction, 3d channel and with an arbitrary Rayleigh number. For the
front-like initial data and under the no-slip boundary condition for the flow
and no-flux boundary condition for the reactant temperature, we derive uniform
estimates on the burning rate and the flow velocity, which can be interpreted
as stability results for the laminar front.


  We present a method to infer network connectivity from collective dynamics in
networks of synchronizing phase oscillators. We study the long-term stationary
response to temporally constant driving. For a given driving condition,
measuring the phase differences and the collective frequency reveals
information about how the oscillators are interconnected. Sufficiently many
repetitions for different driving conditions yield the entire network
connectivity from measuring the dynamics only. For sparsely connected networks
we obtain good predictions of the actual connectivity even for formally
under-determined problems.


  Efficient representations of convex sets are of crucial importance for many
algorithms that work with them. It is well-known that sometimes, a complicated
convex set can be expressed as the projection of a much simpler set in higher
dimensions called a lift of the original set. This is a brief survey of recent
developments in the topic of lifts of convex sets. Our focus will be on lifts
that arise from affine slices of real positive semidefinite cones known as psd
or spectrahedral lifts. The main result is that projection representations of a
convex set are controlled by factorizations, through closed convex cones, of an
operator that comes from the convex set. This leads to several research
directions and results that lie at the intersection of convex geometry,
combinatorics, real algebraic geometry, optimization, computer science and
more.


  A brief description is given of BTeV a proposed program at the Fermilab
collider sited at the C0 intersection region. The main goals are measurement of
mixing, CP violation and rare decays in both the $b$ and charm systems. The
detector is a two arm forward-backward spectrometer capable of triggering on
detached vertices and dileptons, and possessing excellent particle
identification, electron, photon and muon detection.


  The recurrent mechanism has recently been introduced into U-Net in various
medical image segmentation tasks. Existing studies have focused on promoting
network recursion via reusing building blocks. Although network parameters
could be greatly saved, computational costs still increase inevitably in
accordance with the pre-set iteration time. In this work, we study a
multi-scale upgrade of a bi-directional skip connected network and then
automatically discover an efficient architecture by a novel two-phase Neural
Architecture Search (NAS) algorithm, namely BiX-NAS. Our proposed method
reduces the network computational cost by sifting out ineffective multi-scale
features at different levels and iterations. We evaluate BiX-NAS on two
segmentation tasks using three different medical image datasets, and the
experimental results show that our BiX-NAS searched architecture achieves the
state-of-the-art performance with significantly lower computational cost.


  I present an open-source, ecologically integrated model for rural
connectivity, merging the location of nodes mesh networks with renewable energy
systems. Employing evolutionary algorithms, this approach optimizes node
placement for internet access and symbiotic energy distribution. This model,
grounded in community collaboration, demonstrates a balance between
technological advancement and environmental stewardship, offering a blueprint
for sustainable infrastructure in similar rural settings.


  A rigorous theory of diffraction scattering from extended objects is
proposed. The present theory is based on a multiple asymptotic expansion of an
integral equation for the exact wave function in terms of the large parameters
of the problem, which are the range of the potential and the momentum
components of the incident particle. For small angles of incidence the density
of positively charged particles on the axis of a chain is always lower than
unit y and the density of negatively charged particles has a maximum for
certain strength of the potential which can exceed considerably unity. The
conditions of validity of the proposed approach are obtained.


  The energy output of photovoltaic (PV) power plants depends on the
environment and thus fluctuates over time. As a result, PV power can cause
instability in the power grid, in particular when increasingly used. Limiting
the rate of change of the power output is a common way to mitigate these
fluctuations, often with the help of large batteries. A reactive controller
that uses these batteries to compensate ramps works in practice, but causes
stress on the battery due to a high energy throughput. In this paper, we
present a deep learning approach that uses images of the sky to compensate
power fluctuations predictively and reduces battery stress. In particular, we
show that the optimal control policy can be computed using information that is
only available in hindsight. Based on this, we use imitation learning to train
a neural network that approximates this hindsight-optimal policy, but uses only
currently available sky images and sensor data. We evaluate our method on a
large dataset of measurements and images from a real power plant and show that
the trained policy reduces stress on the battery.


  We study the representation complexity of model-based and model-free
reinforcement learning (RL) in the context of circuit complexity. We prove
theoretically that there exists a broad class of MDPs such that their
underlying transition and reward functions can be represented by constant depth
circuits with polynomial size, while the optimal $Q$-function suffers an
exponential circuit complexity in constant-depth circuits. By drawing attention
to the approximation errors and building connections to complexity theory, our
theory provides unique insights into why model-based algorithms usually enjoy
better sample complexity than model-free algorithms from a novel representation
complexity perspective: in some cases, the ground-truth rule (model) of the
environment is simple to represent, while other quantities, such as
$Q$-function, appear complex. We empirically corroborate our theory by
comparing the approximation error of the transition kernel, reward function,
and optimal $Q$-function in various Mujoco environments, which demonstrates
that the approximation errors of the transition kernel and reward function are
consistently lower than those of the optimal $Q$-function. To the best of our
knowledge, this work is the first to study the circuit complexity of RL, which
also provides a rigorous framework for future research.


  We introduce a new class of balanced allocation processes which bias towards
underloaded bins (those with load below the mean load) either by skewing the
probability by which a bin is chosen for an allocation (probability bias), or
alternatively, by adding more balls to an underloaded bin (weight bias). A
prototypical process satisfying the probability bias condition is
Mean-Thinning: At each round, we sample one bin and if it is underloaded, we
allocate one ball; otherwise, we allocate one ball to a second bin sample.
Versions of this process have been in use since at least 1986. An example of a
process, introduced by us, which satisfies the weight bias condition is
Twinning: At each round, we only sample one bin. If the bin is underloaded,
then we allocate two balls; otherwise, we allocate only one ball.
  Our main result is that for any process with a probability or weight bias,
with high probability the gap between maximum and minimum load is logarithmic
in the number of bins. This result holds for any number of allocated balls
(heavily loaded case), covers many natural processes that relax the Two-Choice
process, and we also prove it is tight for many such processes, including
Mean-Thinning and Twinning.
  Our analysis employs a delicate interplay between linear, quadratic and
exponential potential functions. It also hinges on a phenomenon we call "mean
quantile stabilization", which holds in greater generality than our framework
and may be of independent interest.


  In this paper, we give a AR$(1)$ type of characterization covering all
multivariate strictly stationary processes indexed by the set of integers.
Consequently, we derive continuous time algebraic Riccati equations for the
parameter matrix of the characterization providing us with a natural way to
define the corresponding estimator under the assumption of square
integrability. In addition, we show that the estimator inherits consistency
from autocovariances of the stationary process and furthermore, the limiting
distribution is given by a linear function of the limiting distribution of the
autocovariances. We also present the corresponding existing results of the
continuous time setting paralleling them to the discrete case.


  Using combinatorial covering properties, we show that there is no
concentrated set of reals of size $\omega_2$ in the Miller model. The main
result refutes a conjecture of Bartoszy\'{n}ski and Halbeisen. We also prove
that there are no $\gamma$-set of reals of size $\omega_2$ in the Miller model.


  CII line intensity mapping (LIM) is a potential technique to probe the early
galaxies from the Epoch of Reionization (EoR). Several experiments e.g.
CONCERTO, TIME, CCAT-p are underway to map the CII LIM signal fluctuations from
the EoR, enabling us to estimate the CII power-spectrum and CII$\times$21-cm
cross-power spectrum. Observed LIM signal will have its time evolution embedded
in it along the Line of Sight (LoS) due to the finite travel time of the signal
from its origin to the observer. We have investigated this so-called light-cone
effect on the observed statistics of our semi-numerically simulated CII signal
from the EoR. Using a suit of simulated CII and neutral hydrogen 21-cm maps and
corresponding light-cone boxes, we have shown that the light-cone effect can
impact the CII power spectrum by more than 15% at large scales ($k\sim 0.1\,
\text{Mpc}^{-1}$, at $z=6.8$). We have also observed that the impact of
light-cone effect on the CII power spectrum drops with decreasing redshift
within the redshift range considered here ($7.2 \lesssim z \lesssim 6$). The
CII$\times$21-cm cross-power spectrum is also affected by light-cone, and in
our models where reionization ends before $z=6$, we find that the maximum
impact on cross-power can reach up to 20%. At $z=6.4$, we find comparatively
pronounced variation in the light-cone effect with reionization history on the
cross power. Faster reionization histories have a more drastic light-cone
effect on cross-power. We conclude that we need to incorporate the light-cone
in order to properly model the signal, constrain the EoR-related astrophysical
parameters and reionization history using the CII$\times$21-cm cross-power
spectrum.


  We report the emergence of coexisting synchronous and asynchronous
subpopulations of oscillators in one dimensional arrays of identical
oscillators by applying a self-feedback control. When a self-feedback is
applied to a subpopulation of the array, similar to chimera states, it splits
into two/more sub-subpopulations coexisting in coherent and incoherent states
for a range of self-feedback strength. By tuning the coupling between the
nearest neighbors and the amount of self-feedback in the perturbed
subpopulation, the size of the coherent and the incoherent sub-subpopulations
in the array can be controlled, although the exact size of them is
unpredictable. We present numerical evidence using the Landau-Stuart (LS)
system and the Kuramoto-Sakaguchi (KS) phase model.


  We investigate the question whether there are cosmological models in 2+1
space-time dimensions which exhibit dynamics similar to BKL oscillations, as
the cosmological singularity is approached. Based on intuition, we conceive a
toy model which displays such oscillatory dynamics. We show that in the phase
space of this model, the cosmological singularity is represented by a
separatrix curve and discuss the model's dynamics within the cosmological
billiards picture. Finally, we offer a physical interpretation for a family of
similar cosmological models in terms of the topological degrees of freedom of
gravity in 2+1 dimensions.


  Bayesian inference provides a systematic framework for integration of data
with mathematical models to quantify the uncertainty in the solution of the
inverse problem. However, the solution of Bayesian inverse problems governed by
complex forward models described by partial differential equations (PDEs)
remains prohibitive with black-box Markov chain Monte Carlo (MCMC) methods. We
present hIPPYlib-MUQ, an extensible and scalable software framework that
contains implementations of state-of-the art algorithms aimed to overcome the
challenges of high-dimensional, PDE-constrained Bayesian inverse problems.
These algorithms accelerate MCMC sampling by exploiting the geometry and
intrinsic low-dimensionality of parameter space via derivative information and
low rank approximation. The software integrates two complementary open-source
software packages, hIPPYlib and MUQ. hIPPYlib solves PDE-constrained inverse
problems using automatically-generated adjoint-based derivatives, but it lacks
full Bayesian capabilities. MUQ provides a spectrum of powerful Bayesian
inversion models and algorithms, but expects forward models to come equipped
with gradients and Hessians to permit large-scale solution. By combining these
two libraries, we created a robust, scalable, and efficient software framework
that realizes the benefits of each and allows us to tackle complex large-scale
Bayesian inverse problems. To illustrate the capabilities of hIPPYlib-MUQ, we
present a comparison of a number of MCMC methods on several inverse problems.
These include problems with linear and nonlinear PDEs, various noise models,
and different parameter dimensions. The results demonstrate that large ($\sim
50\times$) speedups over conventional black box and gradient-based MCMC
algorithms can be obtained by exploiting Hessian information (from the log
posterior), underscoring the power of the integrated hIPPYlib-MUQ framework.


  Let $a, \ b \ (b \geq a)$ and $n \ (n \geq 2)$ be nonnegative integers and
let $\mathcal{T}(a,b,n)$ be the set of such generalised tournaments, in which
every pair of distinct players is connected at most with $b$, and at least with
$a$ arcs. In \cite{Ivanyi2009} we gave a necessary and sufficient condition to
decide whether a given sequence of nonnegative integers $D = (d_1, d_2,...,
d_n)$ can be realized as the out-degree sequence of a $T \in
\mathcal{T}(a,b,n)$. Extending the results of \cite{Ivanyi2009} we show that
for any sequence of nonnegative integers $D$ there exist $f$ and $g$ such that
some element $T \in \mathcal{T}(g,f,n)$ has $D$ as its out-degree sequence, and
for any $(a,b,n)$-tournament $T'$ with the same out-degree sequence $D$ hold
$a\leq g$ and $b\geq f$. We propose a $\Theta(n)$ algorithm to determine $f$
and $g$ and an $O(d_n n^2)$ algorithm to construct a corresponding tournament
$T$.


  From a telecommunication standpoint, the surge in users and services
challenges next-generation networks with escalating traffic demands and limited
resources. Accurate traffic prediction can offer network operators valuable
insights into network conditions and suggest optimal allocation policies.
Recently, spatio-temporal forecasting, employing Graph Neural Networks (GNNs),
has emerged as a promising method for cellular traffic prediction. However,
existing studies, inspired by road traffic forecasting formulations, overlook
the dynamic deployment and removal of base stations, requiring the GNN-based
forecaster to handle an evolving graph. This work introduces a novel inductive
learning scheme and a generalizable GNN-based forecasting model that can
process diverse graphs of cellular traffic with one-time training. We also
demonstrate that this model can be easily leveraged by transfer learning with
minimal effort, making it applicable to different areas. Experimental results
show up to 9.8% performance improvement compared to the state-of-the-art,
especially in rare-data settings with training data reduced to below 20%.


  We use a recent scaling analysis of the quasielastic electron scattering data
from $^{12}$C to predict the quasielastic charge-changing neutrino scattering
cross sections within an uncertainty band. We use a scaling function extracted
from a selection of the $(e,e')$ cross section data, and an effective nucleon
mass inspired by the relativistic mean-field model of nuclear matter. The
corresponding super-scaling analysis with relativistic effective mass (SuSAM*)
describes a large amount of the electron data lying inside a phenomenological
quasielastic band. The effective mass incorporates the enhancement of the
transverse current produced by the relativistic mean field. The scaling
function incorporates nuclear effects beyond the impulse approximation, in
particular meson-exchange currents and short range correlations producing tails
in the scaling function. Besides its simplicity, this model describes the
neutrino data as reasonably well as other more sophisticated nuclear models.


  Recently we have shown that the reduction of the Carruthers-Nieto symmetric
quantum phase fluctuation parameter (U) with respect to its coherent state
value corresponds to an antibunched state, but the converse is not true.
Consequently reduction of U is a stronger criterion of nonclassicality than the
lowest order antibunching. Here we have studied the possibilities of reduction
of $U$ in intermediate states by using the Barnett Pegg formalism. We have
shown that the reduction of phase fluctuation parameter U can be seen in
different intermediate states, such as binomial state, generalized binomial
state, hypergeometric state, negative binomial state, and photon added coherent
state. It is also shown that the depth of nonclassicality can be controlled by
various parameters related to intermediate states. Further, we have provided
specific examples of antibunched states, for which $U$ is greater than its
poissonian state value.


  The challenges of building knowledge-grounded retrieval-based chatbots lie in
how to ground a conversation on its background knowledge and how to match
response candidates with both context and knowledge simultaneously. This paper
proposes a method named Filtering before Iteratively REferring (FIRE) for this
task. In this method, a context filter and a knowledge filter are first built,
which derive knowledge-aware context representations and context-aware
knowledge representations respectively by global and bidirectional attention.
Besides, the entries irrelevant to the conversation are discarded by the
knowledge filter. After that, iteratively referring is performed between
context and response representations as well as between knowledge and response
representations, in order to collect deep matching features for scoring
response candidates. Experimental results show that FIRE outperforms previous
methods by margins larger than 2.8% and 4.1% on the PERSONA-CHAT dataset with
original and revised personas respectively, and margins larger than 3.1% on the
CMU_DoG dataset in terms of top-1 accuracy. We also show that FIRE is more
interpretable by visualizing the knowledge grounding process.


  We propose a simple auto-encoder framework for molecule generation. The
molecular graph is first encoded into a continuous latent representation $z$,
which is then decoded back to a molecule. The encoding process is easy, but the
decoding process remains challenging. In this work, we introduce a simple
two-step decoding process. In a first step, a fully connected neural network
uses the latent vector $z$ to produce a molecular formula, for example CO$_2$
(one carbon and two oxygen atoms). In a second step, a graph convolutional
neural network uses the same latent vector $z$ to place bonds between the atoms
that were produced in the first step (for example a double bond will be placed
between the carbon and each of the oxygens). This two-step process, in which a
bag of atoms is first generated, and then assembled, provides a simple
framework that allows us to develop an efficient molecule auto-encoder.
Numerical experiments on basic tasks such as novelty, uniqueness, validity and
optimized chemical property for the 250k ZINC molecules demonstrate the
performances of the proposed system. Particularly, we achieve the highest
reconstruction rate of 90.5\%, improving the previous rate of 76.7\%. We also
report the best property improvement results when optimization is constrained
by the molecular distance between the original and generated molecules.


  For the finite groups GU(3), SU(3), GL(3), SL(3) over a finite field we solve
the class product problem, i.e., we give a complete list of $m$-tuples of
conjugacy classes whose product does not contain the identity matrix.


  We calculate the inclusive spectra of secondaries produced in soft (minimum
bias) p+Pb collisions in the framework of Quark-Gluon String Model at LHC
energy, and by taking into account the inelastic screening corrections
(percolation effects). The role of these effects is expected to be very large
at very high energies, and they should decrease the spectra about 3 times in
the midrapidity region and increase them about 2 times in the fragmentation
region at the energy of LHC.


  In this paper, we obtain asymptotic formulae on nilmanifolds $\Gamma
\backslash G$, wher $G$ is any stratified (or even graded) nilpotent Lie group
equipped with a co-compact discrete subgroup $\Gamma$. We study especially the
asymptotics related to the sub-Laplacians naturally coming from the stratified
structure of the group $G$ (and more generally any positive Rockland operators
when $G$ is graded). We show that the short-time asymptotic on the diagonal of
the kernels of spectral multipliers contains only a single non-trivial term. We
also study the associated zeta functions.


  We examine, parametrically, the interaction between the magnetosphere of a
rotating, young stellar object (YSO) and a circumstellar accretion disk using
2.5-D (cylindrically symmetric) numerical magnetoydrodynamic simulations. The
interaction drives a collimated outflow, and we find that the jet formation
mechanism is robust. For variations in initial disk density of a factor of 16,
variations of stellar dipole strength of a factor of 4, and for various initial
conditions with respect to the disk truncation radius and the existence of a
disk field, outflows with similar morphologies were consistently produced.
Secondly, the system is self-regulating, where the outflow properties depend
relatively weakly on the parameters above. The large scale magnetic field
structure rapidly evolves to a configuration that removes angular momentum from
the disk at a rate that depends most strongly on the field and weakly on the
rotation rate of the foot-points of the field in the disk and the mass outflow
rate. Third, the simulated jets are episodic, with the timescale of jet
outbursts identical to the timescale of magnetically induced oscillations of
the inner edge of the disk. To better understand the physics controlling these
disk oscillations, we present a semi-analytical model and confirm that the
oscillation period is set by the spin down rate of the disk inner edge.
Finally, our simulations offer strong evidence that it is indeed the
interaction of the stellar magnetosphere with the disk, rather than some
primordial field in the disk itself, that is responsible for the formation of
jets from these systems.


  We study the principal-agent problem with a third party that we call social
planner, whose responsibility is to reconcile the conflicts of interest between
the two players and induce socially optimal outcome in terms of some given
social utility function. The social planner owns no contractual power but
manages to control the information flow between the principal and the agent. We
design a simple workflow with two stages for the social planner. In the first
stage, the problem is reformulated as an optimization problem whose solution is
the optimal utility profile. In the second stage, we investigate information
design and show that binary-signal information structure suffices to induce the
socially optimal outcome determined in the first stage. The result shows that
information plays a key role in social planning in the principal-agent model.


  Analog-to-digital converters (ADCs) stand for a significant part of the total
power consumption in a massive MIMO base station. One-bit ADCs are one way to
reduce power consumption. This paper presents an analysis of the spectral
efficiency of single-carrier and OFDM transmission in massive MIMO systems that
use one-bit ADCs. A closed-form achievable rate, i.e., a lower bound on
capacity, is derived for a wideband system with a large number of channel taps
that employs low-complexity linear channel estimation and symbol detection.
Quantization results in two types of error in the symbol detection. The
circularly symmetric error becomes Gaussian in massive MIMO and vanishes as the
number of antennas grows. The amplitude distortion, which severely degrades the
performance of OFDM, is caused by variations between symbol durations in
received interference energy. As the number of channel taps grows, the
amplitude distortion vanishes and OFDM has the same performance as
single-carrier transmission. A main conclusion of this paper is that wideband
massive MIMO systems work well with one-bit ADCs.


  Coherent coupling between single quantum objects is at the heart of modern
quantum physics. When coupling is strong enough to prevail over decoherence, it
can be used for the engineering of correlated quantum states. Especially for
solid-state systems, control of quantum correlations has attracted widespread
attention because of applications in quantum computing. Such coherent coupling
has been demonstrated in a variety of systems at low temperature1, 2. Of all
quantum systems, spins are potentially the most important, because they offer
very long phase memories, sometimes even at room temperature. Although precise
control of spins is well established in conventional magnetic resonance3, 4,
existing techniques usually do not allow the readout of single spins because of
limited sensitivity. In this paper, we explore dipolar magnetic coupling
between two single defects in diamond (nitrogen-vacancy and nitrogen) using
optical readout of the single nitrogen-vacancy spin states. Long phase memory
combined with a defect separation of a few lattice spacings allow us to explore
the strong magnetic coupling regime. As the two-defect system was well-isolated
from other defects, the long phase memory times of the single spins was not
diminished, despite the fact that dipolar interactions are usually seen as
undesirable sources of decoherence. A coherent superposition of spin pair
quantum states was achieved. The dipolar coupling was used to transfer spin
polarisation from a nitrogen-vacancy centre spin to a nitrogen spin, with
optical pumping of a nitrogen-vacancy centre leading to efficient
initialisation. At the level anticrossing efficient nuclear spin polarisation
was achieved. Our results demonstrate an important step towards controlled spin
coupling and multi-particle entanglement in the solid state.


  The paper studies spin-orbit interaction (i.e. the effect the spin has on the
particle's trajectory in a magnetic field) as a model of quantum computation.
The two-level spin quantum system is examined using the stochastic mechanics
formulation. The control of the entangled spin state is considered as a problem
of control of the mean moment of a particles ensemble along a reference
trajectory. It is shown that such a control can be succeeded by applying an
open-loop control scheme.


  In today's university climate many first and second year classes have over a
hundred students. Large classrooms make some students apprehensive about asking
questions. An anonymous method of submitting questions to an instructor would
allow students to ask their questions without feeling apprehensive. In this
paper we propose a Live Anonymous Question Queue (LAQQ), a system that
facilitates anonymous question submissions in real time to mitigate student
apprehension, increase student participation, and provide real-time feedback to
the instructor. To study the necessary features of an LAQQ, we conducted a
study of a system, namely Google Moderator, which best approached our concept
of an LAQQ. We deployed Google moderator in large lectures and studied its
support of a number of features that we envisioned for an LAQQ. Through our
class observations, interviews with instructors, and surveys with the students,
our results suggest that an LAQQ system must provide support for: notification
of question submission to provide awareness for the instructor, and context for
questions to allow an instructor to easily answer a question. Additionally our
results suggest that an LAQQ system must be accessible and usable on multiple
platforms. Finally our results suggest that in order to be successful in the
classroom an LAQQ system must be fully adopted by the instructor and the
classroom organizational structure must change to accommodate the use of the
LAQQ.


  Future wireless networks are envisioned to simultaneously provide high
data-rate communication and ubiquitous environment-aware services for numerous
users. One promising approach to meet this demand is to employ network-level
integrated sensing and communications (ISAC) by jointly designing the signal
processing and resource allocation over the entire network. However, to unleash
the full potential of network-level ISAC, some critical challenges must be
tackled. Among them, interference management is one of the most significant
ones. In this article, we build up a bridge between interference mitigation
techniques and the corresponding optimization methods, which facilitates
efficient interference mitigation in network-level ISAC systems. In particular,
we first identify several types of interference in network-level ISAC systems,
including self-interference, mutual interference, crosstalk, clutter, and
multiuser interference. Then, we present several promising techniques that can
be utilized to suppress specific types of interference. For each type of
interference, we discuss the corresponding problem formulation and identify the
associated optimization methods. Moreover, to illustrate the effectiveness of
the proposed interference mitigation techniques, two concrete network-level
ISAC systems, namely coordinated cellular network-based and distributed
antenna-based ISAC systems, are investigated from interference management
perspective. Experiment results indicate that it is beneficial to
collaboratively employ different interference mitigation techniques and
leverage the network structure to achieve the full potential of network-level
ISAC. Finally, we highlight several promising future research directions for
the design of ISAC systems.


  We theoretically study a time-reversal-invariant three-dimensional superfluid
model by stacking in $z$ direction identical bilayer models with intralayer
spin-orbit coupling and contrary Zeeman energy splitting for different layer,
which has been suggested recently to realize two-dimensional
time-reversal-invariant topological superfluid. We find that this model shows
two kinds of topologically nontrivial phases: gapless phases with nodal lines
in pairs protected by chiral symmetry and a gapped phase, both of which support
time-reversal-invariant Majorana Fermi arc (MFA) on the $yz$ and $xz$ side
surface. These MFA abide by time-reversal and particle-hole symmetries and are
topologically protected by the winding numbers in mirror subspaces and $Z_2$
numbers of two-dimensional DIII class topological superfluid, different from
MFA in the time-reversal broken Weyl superfluid protected by nonzero Chern
number. This important observation means that MFA in our model represents a new
type of topological state not explored previously. The Zeeman field
configuration in our model is relevant to antiferromagnetic topological
insulator MnBi$_2$Te$_4$, thus our work stimulates the further studies on
superconducting effects in the realistic antiferromagnetic topological
insulator.


  In these lecture notes I give an elementary introduction to elliptic
hypergeometric functions. I focus on motivating the main ideas and
constructions, rather than giving a comprehensive survey. The lectures include
a brief explanation of the historical origin of elliptic hypergeometric
functions in the context of solvable lattice models. In particular, I give a
new proof of the fact that fused Boltzmann weights for the elliptic
solid-on-solid model can be expressed as elliptic hypergeometric sums.


  Recently, motivated by certain loop quantum gravity inspired corrections, it
was shown that for spherically symmetric midisuperspace models infinitely many
second derivative theories of gravity exist (as revealed by the presence of
three arbitrary functions in the corresponding Lagrangian/Hamiltonian) and not
just those allowed by spherically symmetric general relativity. This freedom
can be interpreted as the freedom to accommodate certain quantum gravity
corrections in these models even in the absence of higher curvature terms (at a
semi-classical level, at least). For a particular choice of the arbitrary
functions it is shown that the new theories map to spherically symmetric
general relativity in arbitrary number of (integer) dimensions thus explicitly
demonstrating that when working with midisuperspace models, one loses the
information about the dimensionality of the full spacetime. In addition, it is
shown that these new theories can accommodate scenarios of fractional spacetime
dimensions as well as those of emergent spacetime dimensions -- a possibility
suggested by various approaches to quantum gravity.


  We evaluate two-meson couplings of Theta+, using experimental information of
nucleon resonances decaying into pi pi N channels, in which the two pions are
in scalar- and vector-type correlations. We examine two assignments of spin and
parity of J^P=1/2^+ and 3/2^-, for which the experimental spectra of known
resonances with exotic baryons are properly reproduced by an octet-antidecuplet
representation mixing scheme. With the obtained coupling constants, total cross
sections of the reactions pi- p -> K- Theta+ and K+ p -> pi+ Theta+ are
calculated. Substantial interference of two terms may occur in the reaction
processes for the J^P=1/2^+ case, whereas the interference effect is rather
small for the 3/2^- case.


  Rejoinder on Causal Inference Through Potential Outcomes and Principal
Stratification: Application to Studies with ``Censoring'' Due to Death by D. B.
Rubin [math.ST/0612783]


  Leveraging large language models for machine translation has demonstrated
promising results. However, it does require the large language models to
possess the capability of handling both the source and target languages in
machine translation. When it is challenging to find large models that support
the desired languages, resorting to continuous learning methods becomes a
costly endeavor. To mitigate these expenses, we propose an innovative approach
called RD (Relay Decoding), which entails concatenating two distinct large
models that individually support the source and target languages. By
incorporating a simple mapping layer to facilitate the connection between these
two models and utilizing a limited amount of parallel data for training, we
successfully achieve superior results in the machine translation task.
Experimental results conducted on the Multi30k and WikiMatrix datasets validate
the effectiveness of our proposed method.


  We investigate the scaling properties of the Barkhausen effect, recording the
noise in several soft ferromagnetic materials: polycrystals with different
grain sizes and amorphous alloys. We measure the Barkhausen avalanche
distributions and determine the scaling exponents. In the limit of vanishing
external field rate, we can group the samples in two distinct classes,
characterized by exponents \tau = 1.50 \pm 0.05 or \tau = 1.27 \pm 0.03, for
the avalanche size distributions. We interpret these results in terms of the
depinning transition of domain walls and obtain an expression relating the
cutoff of the distributions to the demagnetizing factor which is in
quantitative agreement with experiments.


  We prove logarithmic stability in the determination of the time-dependent
scalar potential in a 1-periodic quantum cylindrical waveguide, from the
boundary measurements of the solution to the dynamic Schr\"odinger equation.


  Owing to the widespread adoption of the Internet of Things, a vast amount of
sensor information is being acquired in real time. Accordingly, the
communication cost of data from edge devices is increasing. Compressed sensing
(CS), a data compression method that can be used on edge devices, has been
attracting attention as a method to reduce communication costs. In CS,
estimating the appropriate compression ratio is important. There is a method to
adaptively estimate the compression ratio for the acquired data using
reinforcement learning (RL). However, the computational costs associated with
existing RL methods that can be utilized on edges are often high. In this
study, we developed an efficient RL method for edge devices, referred to as the
actor--critic online sequential extreme learning machine (AC-OSELM), and a
system to compress data by estimating an appropriate compression ratio on the
edge using AC-OSELM. The performance of the proposed method in estimating the
compression ratio is evaluated by comparing it with other RL methods for edge
devices. The experimental results indicate that AC-OSELM demonstrated the same
or better compression performance and faster compression ratio estimation than
the existing methods.


  We consider the energy of the Universe, from the pseudo-tensor point of
view(Berman,1981). We find zero values, when the calculations are well-done.The
doubts concerning this subject are clarified, with the novel idea that the
justification for the calculation lies in the association of the equivalence
principle, with the nature of co-motional observers, as demanded in Cosmology.
In Section 4, we give a novel calculation for the zero-total energy result.


  We study hadronic transitions between bottomonium states using 496 fb$^{-1}$
data collected at the $\Upsilon(4S)$ resonance with the Belle detector at the
KEKB asymmetric energy $e^{+}e^{-}$ collider. We measure: ${\cal
B}(\Upsilon(4S)\to\pi^+\pi^-\Upsilon(1S))=(8.2\pm 0.5 {\rm(stat.)} \pm 0.4
{\rm(syst.)})\times10^{-5}$, ${\cal
B}(\Upsilon(4S)\to\pi^+\pi^-\Upsilon(2S))=(7.9\pm 1.0 {\rm(stat.)} \pm 0.4
{\rm(syst.)})\times10^{-5}$, and ${\cal
B}(\Upsilon(4S)\to\eta\Upsilon(1S))=(1.70\pm 0.23 {\rm(stat.)} \pm 0.08
{\rm(syst.)})\times10^{-4}$. We measure the ratio of branching fractions ${\cal
R} = {\cal B}(\Upsilon(4S)\to\eta\Upsilon(1S))/{\cal
B}(\Upsilon(4S)\to\pi^+\pi^-\Upsilon(1S)) = 2.07\pm 0.30 {\rm(stat.)} \pm 0.11
{\rm(syst.)}$. We search for the decay
$\Upsilon(1^3D_{1,2})\to\eta\Upsilon(1S)$, but do not find significant evidence
for such a transition. We also measure the initial state radiation production
cross sections of the $\Upsilon(2S,3S)$ resonances and we find values
compatible with the expected ones. Finally, the analysis of the
$\Upsilon(4S)\to\pi^+\pi^-\Upsilon(1S)$ events shows indications for a resonant
contribution due to the $f_0(980)$ meson.


  The Congruence Lattice Problem asks whether every algebraic distributive
lattice is isomorphic to the congruence lattice of a lattice. It was hoped that
a positive solution would follow from E. T. Schmidt's construction or from the
approach of P. Pudlak, M. Tischendorf, and J. Tuma. In a previous paper, we
constructed a distributive algebraic lattice $A$ with $\aleph\_2$ compact
elements that cannot be obtained by Schmidt's construction. In this paper, we
show that the same lattice $A$ cannot be obtained using the Pudlak,
Tischendorf, Tuma approach. The basic idea is that every congruence lattice
arising from either method satisfies the Uniform Refinement Property, which is
not satisfied by our example. This yields, in turn, corresponding negative
results about congruence lattices of sectionally complemented lattices and
two-sided ideals of von Neumann regular rings.


  The Zero divisor Graph of a commutative ring $R$, denoted by $\Gamma[R]$, is
a graph whose vertices are non-zero zero divisors of $R$ and two vertices are
adjacent if their product is zero. Chemical graph theory is a branch of
mathematical chemistry which deals with the non-trivial applications of graph
theory to solve molecular problems. Graphs containing finite commutative rings
also have wide applications in robotics, information and communication theory,
elliptical curve and cryptography, physics and statistics. In this paper, we
consider the zero divisor graph $\Gamma[\mathbb{Z}_{p^n}]$ where $p$ is a
prime. We derive the standard form of the Eccentric Connectivity Index,
Augmented Eccentric Connectivity Index, and Ediz Eccentric Connectivity Index
of the zero divisor graph $\Gamma[\mathbb{Z}_{p^n}]$.


  Layered transition metal chalcogenides have stimulated a wide research
interest due to their many exotic physical properties. In this paper, we
studied the magnetotransport properties of the exfoliated TaNiTe5, a recently
discovered Dirac nodal-line semimetal. A giant positive magnetoresistance (MR)
is observed when the current is parallel to the crystallographic c axis, while
it is strongly diminished when the current flows along the a axis. The observed
giant MR is gradually suppressed either on reducing the thickness of nanoflake
or on increasing temperature. By performing MR measurement in tilted magnetic
fields, the interlayer coupling is found to be weakened both by reducing the
thickness and by increasing temperature. We propose a mechanism of
electron-electron interaction-assisted interlayer transport as a origin of the
giant MR. The mechanism is likely to provide a explanation for the giant MR in
other layered materials.


  We present a multifrequency dataset for an optically-selected,
volume-limited, complete sample of 118 late-type galaxies (>= S0a) in the Virgo
cluster. The database includes UV, visible, near-IR, mid-IR, far-IR, radio
continuum photometric data as well as spectroscopic data of Halpha, CO and HI
lines, homogeneously reduced, obtained from our own observations or compiled
from the literature. Assuming the energy balance between the absorbed stellar
light and that radiated in the IR by dust, we calibarte an empirical
attenuation law suitable for correcting photometric and spectroscopic data of
normal galaxies. The data, corrected for internal extinction, are used to
construct the spectral energy distribution (SED) of each individual galaxy, and
combined to trace the median SED of galaxies in various classes of
morphological type and luminosity. Low-luminosity, dwarf galaxies have on
average bluer stellar continua and higher far-IR luminosities per unit galaxy
mass than giant, early-type spirals. If compared to nearby starburst galaxies
such as M82 and Arp 220, normal spirals have relatively similar observed
stellar spectra but 10-100 times lower IR luminosities. The temperature of the
cold dust component increases with the far-IR luminosity, from giant spirals to
dwarf irregulars. The SED are used to separate the stellar emission from the
dust emission in the mid-IR regime. We show that the contribution of the
stellar emission at 6.75 micron to the total emission of galaxies is generally
important, from ~ 80% in Sa to ~ 20% in Sc.


  Let $E/\mathbb{Q}$ be an elliptic curve, $p$ a prime where $E$ has ordinary
reduction and $K_{\infty}/K$ the anticyclotomic $\mathbb{Z}_p$-extension of a
quadratic imaginary field $K$ satisfying the Heegner hypothesis. We give
sufficient conditions on $E$ and $p$ in order to ensure that
$\text{Sel}_{p^{\infty}}(E/K_{\infty})$ is a cofree $\Lambda$-module of rank
one. We also show that these conditions imply that $\text{rank}(E(K_n))=p^n$
for all $n \geq 0$ and that the $p$-primary subgroup of the Tate-Shafarevich
group of $E/K_n$ is trivial for all $n \geq 0$.


  The filling volume functions of the n-th quaternionic Heisenberg group grow,
up to dimension n, as fast as the ones of the Euclidean space. We identify the
growth rate of the filling volume function in dimension n+1, which is strictly
faster than the growth rate of the (n+1)-dimensional filling volume function of
the Euclidean space.


  Requiring less data for accurate models, few-shot learning has shown
robustness and generality in many application domains. However, deploying
few-shot models in untrusted environments may inflict privacy concerns, e.g.,
attacks or adversaries that may breach the privacy of user-supplied data. This
paper studies the privacy enhancement for the few-shot learning in an untrusted
environment, e.g., the cloud, by establishing a novel privacy-preserved
embedding space that preserves the privacy of data and maintains the accuracy
of the model. We examine the impact of various image privacy methods such as
blurring, pixelization, Gaussian noise, and differentially private pixelization
(DP-Pix) on few-shot image classification and propose a method that learns
privacy-preserved representation through the joint loss. The empirical results
show how privacy-performance trade-off can be negotiated for privacy-enhanced
few-shot learning.


  We exploit Krattenthaler's bijection between the set
$S_n(3\textrm{-}1\textrm{-}2)$ of permutations in $S_n$ avoiding the classical
pattern $3\textrm{-}1\textrm{-}2$ and Dyck $n$-paths to study the distribution
of every consecutive pattern of length 3 on the set
$S_n(3\textrm{-}1\textrm{-}2)$. We show that these consecutive patterns split
into 3 equidistribution classes, by means of an involution on Dyck paths due to
E.Deutsch. In addition, we state equidistribution theorems concerning triplets
of statistics relative to the occurrences of the consecutive patterns of length
3 in a permutation.


  We consider a nonlinear model that is a combination of the anisotropic
two-dimensional classical Heisenberg and Toda-like lattices. In the framework
of the Hirota direct approach, we present the field equations of this model as
a bilinear system, which is closely related to the Ablowitz-Ladik hierarchy,
and derive its N-soliton solutions.


  A search for the production of heavy resonances decaying into top
quark-antiquark pairs is presented. The analysis is performed in the
lepton+jets and fully hadronic channels using data collected in proton-proton
collisions at sqrt(s) = 13 TeV using the CMS detector at the LHC, corresponding
to an integrated luminosity of 2.6 inverse femtobarns. The selection is
optimized for massive resonances, where the top quarks have large Lorentz
boosts. No evidence for resonant t t-bar production is found in the data, and
upper limits on the production cross section of heavy resonances are set. The
exclusion limits for resonances with masses above 2 TeV are significantly
improved compared to those of previous analyses at sqrt(s) = 8 TeV.


  We renormalize QCD at three loops in the modified regularization invariant,
RI', scheme in arbitrary covariant gauge and deduce that the four loop
beta-function is equivalent to the MSbar result. The anomalous dimensions of
the scalar, vector and tensor currents are then determined in the RI' scheme at
three loops by considering the insertion of the operator in a quark two-point
function. The expression for the scalar current agrees with the quark mass
anomalous dimension and we deduce an expression for the four loop RI' mass
anomalous dimension in arbitrary covariant gauge and for any Lie group.


  I review our recent Chandra surveys of the center region of the Milky Way and
other nearby edge-on galaxies. Our Galactic center survey provides an
unprecedented high-resolution, broad-band X-ray panorama of a 0.8x2 square
degree swath along the Galactic plane. Our preliminary analysis has led a
detection of about 1000 discrete sources. We find that the diffuse X-ray
emission dominates over the contribution from faint discrete sources and is
globally associated with distinct interstellar structures observed at radio and
mid-infrared wavelengths. We study how high-energy activities in the center
region affect the immediate vicinity and may influence other aspects of the
Galaxy. We have further observed nearby edge-on late-type disk galaxies in
fields of low foreground Galactic extinction to gain external perspectives of
the global disk/halo interaction. We have detected a giant diffuse
X-ray-emitting corona around the galactic disk of NGC 4631. Extraplanar diffuse
X-ray emission is also detected around NGC 3556. These X-ray-emitting coronae
morphologically resemble the radio halos of these galaxies, indicating a close
connection between outflows of hot gas, cosmic rays, and magnetic field from
the galactic disks. There is only marginal evidence for extraplanar diffuse
X-ray emission in NGC 4244 -- a galaxy with an extremely low star formation
rate. In general, the extraplanar diffuse X-ray emission is evidently related
to recent massive star forming activities in the galactic disks, especially in
their central regions.


  The extropy is a measure of information introduced by Lad et al. (2015) as
dual to entropy. As the entropy, it is a shift-independent information measure.
We introduce here the notion of weighted extropy, a shift-dependent information
measure which gives higher weights to large values of observed random
variables. We also study the weighted residual and past extropies as weighted
versions of extropy for residual and past lifetimes. Bivariate versions extropy
and weighted extropy are also provided. Several examples are presented through
out to illustrate the various concepts introduced here.


  This paper provides an introduction to a number of astrophysics problems
related to strong magnetic fields. The first part deals with issues related to
atoms, condensed matter and high-energy processes in very strong magnetic
fields, and how these issues influence various aspects of neutron star
astrophysics. The second part deals with classical astrophysical effects of
magnetic fields: Even relatively "weak" fields can play a strong role in
various astrophysical problems, ranging from stars, accretion disks and
outflows, to the formation and merger of compact objects.


  In N.V. Krylov, Approximating value functions for controlled degenerate
diffusion processes by using piece-wise constant policies, Electron. J.
Probab., 4(2), 1999, it is proved under standard assumptions that the value
functions of controlled diffusion processes can be approximated with order 1/6
error by those with controls which are constant on uniform time intervals. In
this note we refine the proof and show that the provable rate can be improved
to 1/4, which is optimal in our setting. Moreover, we demonstrate the
improvements this implies for error estimates derived by similar techniques for
approximation schemes, bringing these in line with the best available results
from the PDE literature.


  Ultralight bosons near rotating black holes can grow significantly via
superradiant energy extraction, potentially reaching field values close to the
Planck scale, thereby turning black holes into effective transducers for these
fields. The interaction between a boson field and fermions can initiate a
parametric production of fermions, potentially halting the exponential growth
and leading to a saturated state of the boson cloud. This dynamic offers a
framework for establishing limits on boson-neutrino interactions, which have
traditionally been restricted by neutrino self-interaction considerations. At
the saturation phase, boson clouds have the capacity to propel neutrinos to
TeV-scale energies, generating fluxes that surpass atmospheric neutrino fluxes
from nearby black holes. This mechanism extends to dark sector fermions,
leading to the generation of boosted dark matter. These fluxes open novel
avenues for observation through high-energy neutrino detectors like IceCube, as
well as through dark matter direct detection, by directing observational
efforts towards targeted black holes.


  The ancillary file Corrigendum.pdf contains an explanation why our proof does
not work.


  In this paper, we discuss the development and deployment of a robust
autonomous system capable of performing various tasks in the maritime domain
under unknown dynamic conditions. We investigate a data-driven approach based
on modular design for ease of transfer of autonomy across different maritime
surface vessel platforms. The data-driven approach alleviates issues related to
a priori identification of system models that may become deficient under
evolving system behaviors or shifting, unanticipated, environmental influences.
Our proposed learning-based platform comprises a deep Koopman system model and
a change point detector that provides guidance on domain shifts prompting
relearning under severe exogenous and endogenous perturbations. Motion control
of the autonomous system is achieved via an optimal controller design. The
Koopman linearized model naturally lends itself to a linear-quadratic regulator
(LQR) control design. We propose the C3D control architecture Cascade Control
with Change Point Detection and Deep Koopman Learning. The framework is
verified in station keeping task on an ASV in both simulation and real
experiments. The approach achieved at least 13.9 percent improvement in mean
distance error in all test cases compared to the methods that do not consider
system changes.


  This paper details the techniques and algorithms implemented in Kahler, a
Python library that implements discrete exterior calculus on arbitrary
Hermitian manifolds. Borrowing techniques and ideas first implemented in PyDEC,
Kahler provides a uniquely general framework for computation using discrete
exterior calculus. Manifolds can have arbitrary dimension, topology, bilinear
Hermitian metrics, and embedding dimension. Kahler comes equipped with tools
for generating triangular meshes in arbitrary dimensions with arbitrary
topology. Kahler can also generate discrete sharp operators and implement de
Rham maps. Computationally intensive tasks are automatically parallelized over
the number of cores detected. The program itself is written in Cython--a
superset of the Python language that is translated to C and compiled for extra
speed. Kahler is applied to several example problems: normal modes of a
vibrating membrane, electromagnetic resonance in a cavity, the quantum harmonic
oscillator, and the Dirac-Kahler equation. Convergence is demonstrated on
random meshes.


  Jupiter's zonal jets and Great Red Spot are well known from still images. Yet
the planet's atmosphere is highly unsteady, which suggests that the actual
material transport barriers delineating its main features should be
time-dependent. Rare video footages of Jupiter's clouds provide an opportunity
to verify this expectation from optically reconstructed velocity fields.
Available videos, however, provide short-time and temporally aperiodic velocity
fields that defy classical dynamical systems analyses focused on asymptotic
features. To this end, we use here the recent theory of geodesic transport
barriers to uncover finite-time mixing barriers in the wind field extracted
from a video captured by NASA's Cassini space mission. More broadly, the
approach described here provides a systematic and frame-invariant way to
extract dynamic coherent structures from time-resolved remote observations of
unsteady continua.


  Results of dedicated Monte Carlo simulations of beam-induced background (BIB)
in the ATLAS experiment at the Large Hadron Collider (LHC) are presented and
compared with data recorded in 2012. During normal physics operation this
background arises mainly from scattering of the 4 TeV protons on residual gas
in the beam pipe. Methods of reconstructing the BIB signals in the ATLAS
detector, developed and implemented in the simulation chain based on the FLUKA
Monte Carlo simulation package, are described. The interaction rates are
determined from the residual gas pressure distribution in the LHC ring in order
to set an absolute scale on the predicted rates of BIB so that they can be
compared quantitatively with data. Through these comparisons the origins of the
BIB leading to different observables in the ATLAS detectors are analysed. The
level of agreement between simulation results and BIB measurements by ATLAS in
2012 demonstrates that a good understanding of the origin of BIB has been
reached.


  We present a method based on matched multifrequency filters for extracting
cluster catalogs from Sunyaev-Zel'dovich (SZ) surveys. We evaluate its
performance in terms of completeness, contamination rate and photometric
recovery for three representative types of SZ survey: a high resolution single
frequency radio survey (AMI), a high resolution ground-based multiband survey
(SPT), and the Planck all-sky survey. These surveys are not purely flux
limited, and they loose completeness significantly before their point-source
detection thresholds. Contamination remains relatively low at <5% (less than
30%) for a detection threshold set at S/N=5 (S/N=3). We identify photometric
recovery as an important source of catalog uncertainty: dispersion in recovered
flux from multiband surveys is larger than the intrinsic scatter in the Y-M
relation predicted from hydrodynamical simulations, while photometry in the
single frequency survey is seriously compromised by confusion with primary
cosmic microwave background anisotropy. The latter effect implies that
follow-up observations in other wavebands (e.g., 90 GHz, X-ray) of single
frequency surveys will be required. Cluster morphology can cause a bias in the
recovered Y-M relation, but has little effect on the scatter; the bias would be
removed during calibration of the relation. Point source confusion only
slightly decreases multiband survey completeness; single frequency survey
completeness could be significantly reduced by radio point source confusion,
but this remains highly uncertain because we do not know the radio counts at
the relevant flux levels.


  We use the time-dependent invariant method in a geometric approach (Jacobi
fields) to quantize the motion of a free falling point particle in the
Schwarzschild black hole. Assuming that the particle comes from infinity, we
obtain the relativistic Schr\"{o}dinger wave function for this system.


  This paper introduces a novel formulation of the clustering problem, namely
the Minimum Sum-of-Squares Clustering of Infinitely Tall Data (MSSC-ITD), and
presents HPClust, an innovative set of hybrid parallel approaches for its
effective solution. By utilizing modern high-performance computing techniques,
HPClust enhances key clustering metrics: effectiveness, computational
efficiency, and scalability. In contrast to vanilla data parallelism, which
only accelerates processing time through the MapReduce framework, our approach
unlocks superior performance by leveraging the multi-strategy
competitive-cooperative parallelism and intricate properties of the objective
function landscape. Unlike other available algorithms that struggle to scale,
our algorithm is inherently parallel in nature, improving solution quality
through increased scalability and parallelism, and outperforming even advanced
algorithms designed for small and medium-sized datasets. Our evaluation of
HPClust, featuring four parallel strategies, demonstrates its superiority over
traditional and cutting-edge methods by offering better performance in the key
metrics. These results also show that parallel processing not only enhances the
clustering efficiency, but the accuracy as well. Additionally, we explore the
balance between computational efficiency and clustering quality, providing
insights into optimal parallel strategies based on dataset specifics and
resource availability. This research advances our understanding of parallelism
in clustering algorithms, demonstrating that a judicious hybridization of
advanced parallel approaches yields optimal results for MSSC-ITD. Experiments
on synthetic data further confirm HPClust's exceptional scalability and
robustness to noise.


  This paper presents the potential of applying physics-informed neural
networks for solving nonlinear multiphysics problems, which are essential to
many fields such as biomedical engineering, earthquake prediction, and
underground energy harvesting. Specifically, we investigate how to extend the
methodology of physics-informed neural networks to solve both the forward and
inverse problems in relation to the nonlinear diffusivity and Biot's equations.
We explore the accuracy of the physics-informed neural networks with different
training example sizes and choices of hyperparameters. The impacts of the
stochastic variations between various training realizations are also
investigated. In the inverse case, we also study the effects of noisy
measurements. Furthermore, we address the challenge of selecting the
hyperparameters of the inverse model and illustrate how this challenge is
linked to the hyperparameters selection performed for the forward one.


  This paper takes a fresh look at the economic theory that is motivation for
pricing models, such as critical peak pricing (CPP), or surge pricing, and the
demand response models advocated by policy makers and in the power systems
literature.
  The economic analysis in this paper begins with two premises: 1) a meaningful
analysis requires a realistic model of stakeholder/consumer rationality, and 2)
the relationship between electric power and the ultimate use of electricity are
only loosely related in many cases. The most obvious examples are refrigerators
and hot water heaters that consume power intermittently to maintain constant
temperature. Based on a realistic model of user preferences, it is shown that
the use of CPP and related pricing schemes will eventually destabilize the grid
with increased participation. Analysis of this model also leads to a
competitive equilibrium, along with a characterization of the dynamic prices in
this equilibrium. However, we argue that these prices will not lead to a robust
control solution that is acceptable to either grid operators or consumers.
These findings are presented in this paper to alert policy makers of the risk
of implementing real time prices to control our energy grid.
  Competitive equilibrium theory can only provide a caricature of a real-world
market, since complexities such as sunk cost and risk are not included. The
paper explains why these approximations are especially significant in the power
industry. It concludes with policy recommendations to bolster the reliability
of the power grid, with a focus on planning across different timescales and
alternate approaches to leveraging demand-side flexibility in the grid.


  Canonical quantization covers a broad class of classical systems, but that
does not include all the problems of interest. Affine quantization has the
benefit of providing a successful quantization of many important problems
including the quantization of half-harmonic oscillators [1] nonrenormalizable
scalar fields, such as $(\varphi^{12})_3$ [2], and $(\varphi^4)_4$ [3], as well
as the quantum theory of Einstein's general relativity [4]. The features that
distinguish affine quantization are emphasized, especially, that affine
quantization differs from canonical quantization only by the choice of
classical variables promoted to quantum operators. Coherent states are used to
ensure proper quantizations are physically correct. While quantization of
nonrenormalizable covariant scalars and gravity are difficult, we focus on
appropriate ultralocal scalars and gravity which are fully soluble while, in
that case, implying that affine quantization is the proper procedure to ensure
the validity of affine quantizations for nonrenormalizable covariant scalar
fields and Einstein's gravity.