  We show that the space in which scientific, technological and economic
developments interplay with each other can be mathematically shaped using
pioneering multilayer network and complexity techniques. We build the
tri-layered network of human activities (scientific production, patenting, and
industrial production) and study the interactions among them, also taking into
account the possible time delays. Within this construction we can identify
which capabilities and prerequisites are needed to be competitive in a given
activity, and even measure how much time is needed to transform, for instance,
the technological know-how into economic wealth and scientific innovation,
being able to make predictions with a very long time horizon. Quite
unexpectedly, we find empirical evidence that the naive knowledge flow from
science, to patents, to products is not supported by data, being instead
technology the best predictor for industrial and scientific production for the
next decades.


  The observation of PeV neutrinos is an open window to study New Physics
processes. Among all possible neutrino observables, the neutrino flavor
composition can reveal underlying interactions during the neutrino propagation.
We study the effects on neutrino oscillations of dark matter-neutrino
interactions. We estimate the size of the interaction strength to produce a
sizable deviation with respect to the flavor composition from oscillations in
vacuum. We found that the dark matter distribution produces flavor compositions
non reproducible by other New Physics phenomena. Besides, the dark matter
effect predicts flavor compositions which depend on the neutrino's arrival
direction. This feature might be observed in neutrino telescopes like IceCube
and KM3NET with access to different sky sections. This effect presents a novel
way to test Dark Matter particle models.


  This paper focuses on the problem of scoring and ranking influential users of
Instagram, a visual content sharing online social network (OSN). Instagram is
the second largest OSN in the world with 700 million active Instagram accounts,
32% of all worldwide Internet users. Among the millions of users, photos shared
by more influential users are viewed by more users than posts shared by less
influential counterparts. This raises the question of how to identify those
influential Instagram users.
  In our work, we present and discuss the lack of relevant tools and
insufficient metrics for influence measurement, focusing on a network oblivious
approach and show that the graph-based approach used in other OSNs is a poor
fit for Instagram. In our study, we consider user statistics, some of which are
more intuitive than others, and several regression models to measure users'
influence.


  We discuss the design of state-of-the-art numerical methods for molecular
dynamics, focusing on the demands of soft matter simulation, where the purposes
include sampling and dynamics calculations both in and out of equilibrium. We
discuss the characteristics of different algorithms, including their essential
conservation properties, the convergence of averages, and the accuracy of
numerical discretizations. Formulations of the equations of motion which are
suited to both equilibrium and nonequilibrium simulation include Langevin
dynamics, dissipative particle dynamics (DPD), and the more recently proposed
"pairwise adaptive Langevin" (PAdL) method, which, like DPD but unlike Langevin
dynamics, conserves momentum and better matches the relaxation rate of
orientational degrees of freedom. PAdL is easy to code and suitable for a
variety of problems in nonequilibrium soft matter modeling, our simulations of
polymer melts indicate that this method can also provide dramatic improvements
in computational efficiency. Moreover we show that PAdL gives excellent control
of the relaxation rate to equilibrium. In the nonequilibrium setting, we
further demonstrate that while PAdL allows the recovery of accurate shear
viscosities at higher shear rates than are possible using the DPD method at
identical timestep, it also outperforms Langevin dynamics in terms of stability
and accuracy at higher shear rates.


  We report the dissemination of an ultrastable optical frequency signal to two
distant users simultaneously using a branching network. The ultrastable signal
is extracted along a main fibre link; it is optically tracked with a
narrow-linewidth laser diode, which light is injected in a secondary link. The
propagation noise of both links is actively compensated. We implement this
scheme with two links of 50-km fibre spools, the extraction being setup at the
mid-point of the main link. We show that the extracted signal at the end of the
secondary link exhibits fractional frequency instability of 1.4x10-15 at 1-s
measurement time, almost equal to the 1.3x10-15 instability of the main link
output end. The long-term instabilities are also very similar, at a level of
3-5x10-20 at 3x104-s integration time. We also show that the setting up of this
extraction device, or a simpler one, at the main link input, can test the
proper functioning of the noise rejection on this main link. This work is a
significant step towards a robust and flexible ultra-stable network for
multi-users dissemination.


  We prove an affine analog of Scharlau's reduction theorem for spherical
buildings. To be a bit more precise let $X$ be a euclidean building with
spherical building $\partial X$ at infinity. Then there exists a euclidean
building $\bar X$ such that $X$ splits as a product of $\bar X$ with some
euclidean $k$-space such that $\partial \bar X$ is the thick reduction of
$\partial X$ in the sense of Scharlau. \newline In addition we prove a converse
statement saying that an embedding of a thick spherical building at infinity
extends to an embedding of the euclidean building having the extended spherical
building as its boundary.


  This paper shows how to recover a stochastic volatility model (SVM) from a
market model of the VIX futures term structure. Market models have more
flexibility for fitting of curves than do SVMs, and therefore are better suited
for pricing VIX futures and VIX derivatives. But the VIX itself is a derivative
of the S&P500 (SPX) and it is common practice to price SPX derivatives using an
SVM. Therefore, consistent modeling for both SPX and VIX should involve an SVM
that can be obtained by inverting the market model. This paper's main result is
a method for the recovery of a stochastic volatility function by solving an
inverse problem where the input is the VIX function given by a market model.
Analysis will show conditions necessary for there to be a unique solution to
this inverse problem. The models are consistent if the recovered volatility
function is non-negative. Examples are presented to illustrate the theory, to
highlight the issue of negativity in solutions, and to show the potential for
inconsistency in non-Markov settings.


  Generalizing a geometric idea due to J. Sondow, we give a geometric proof for
the Cantor's Theorem. Moreover, it is given an irrationality measure for some
Cantor series.


  Zheng [Phys. Rev. E {\bf 61}, 153 (2000), cond-mat/9909324] claims that phase
ordering dynamics in the microcanonical $\phi^4$ model displays unusual scaling
laws. We show here, performing more careful numerical investigations, that
Zheng only observed transient dynamics mostly due to the corrections to scaling
introduced by lattice effects, and that Ising-like (model A) phase ordering
actually takes place at late times. Moreover, we argue that energy conservation
manifests itself in different corrections to scaling.


  We apply imitation learning (IL) to tackle the NMT exposure bias problem with
error-correcting oracles, and evaluate an SMT lattice-based oracle which,
despite its excellent performance in an unconstrained oracle translation task,
turned out to be too pruned and idiosyncratic to serve as the oracle for IL.


  In this paper we are interested in the numerical approximation of the
marginal distributions of the Hilbert space valued solution of a stochastic
Volterra equation driven by an additive Gaussian noise. This equation can be
written in the abstract It\^o form as $$ \dd X(t) + \left (\int_0^t b(t-s) A
X(s) \, \dd s \right) \, \dd t = \dd W^{_Q}(t), t\in (0,T]; ~ X(0) =X_0\in H,
$$ \noindent where $W^Q$ is a $Q$-Wiener process on the Hilbert space $H$ and
where the time kernel $b$ is the locally integrable potential $t^{\rho-2}$,
$\rho \in (1,2)$, or slightly more general. The operator $A$ is unbounded,
linear, self-adjoint, and positive on $H$. Our main assumption concerning the
noise term is that $A^{(\nu- 1/\rho)/2} Q^{1/2}$ is a Hilbert-Schmidt operator
on $H$ for some $\nu \in [0,1/\rho]$. The numerical approximation is achieved
via a standard continuous finite element method in space (parameter $h$) and an
implicit Euler scheme and a Laplace convolution quadrature in time (parameter
$\Delta t=T/N$). %Let $X_h^N$ be the discrete solution at time $T$. Eventually
let $\varphi : H\rightarrow \R$ is such that $D^2\varphi$ is bounded on $H$ but
not necessarily bounded and suppose in addition that either its first
derivative is bounded on $H$ and $X_0 \in L^1(\Omega)$ or $\varphi = \| \cdot
\|^2$ and $X_0 \in L^2(\Omega)$. We show that for $\varphi : H\rightarrow \R$
twice continuously differentiable test function with bounded second derivative,
$$ | \E \varphi(X^N_h) - \E \varphi(X(T)) | \leq C \ln
\left(\frac{T}{h^{2/\rho} + \Delta t} \right) (\Delta t^{\rho \nu} + h^{2\nu}),
$$ \noindent for any $0\leq \nu \leq 1/\rho$. This is essentially twice the
rate of strong convergence under the same regularity assumption on the noise.


  The problem of securely outsourcing computation to cloud servers has
attracted a large amount of attention in recent years. The verifiable
computation of Gennaro, Gentry, Parno (Crypto'10) allows a client to verify the
server's computation of a function with substantially less time than performing
the outsourced computation from scratch. In a multi-function model (Parno,
Raykova, Vaikuntanathan; TCC'12) of verifiable computation, the process of
encoding function and the process of preparing input are decoupled such that
any client can freely submit a computation request on its input, without having
to generate an encoding of the function in advance. In this paper, we propose a
multi-matrix verifiable computation scheme that allows the secure outsourcing
of the matrix functions over a finite field. Our scheme is outsourceable. When
it is used to outsource $m$ linear functions, the scheme is roughly $m$ times
faster and has less communication cost than the previously best known scheme by
Fiore and Gennaro (CCS'12), both in the client-side computation and in the
server-side computation. We also show the cost saving with detailed
implementations.


  In this article, we show that for a quasicompact scheme $X$ and $n>0,$ the
$n$-th $K$-group $K_{n}(X)$ is a $\lambda$-module over a $\lambda$-ring
$K_{0}(X)$ in the sense of Hesselholt.


  A framework is presented for modeling and understanding magnetic excitations
in localized, intermediate coupling magnets where the interplay between
spin-orbit coupling, magnetic exchange, and crystal field effects are known to
create a complex landscape of unconventional magnetic behaviors and ground
states. A spin-orbit exciton approach for modeling these excitations is
developed based upon a Hamiltonian which explicitly incorporates single-ion
crystalline electric field and spin exchange terms. This framework is then
leveraged to understand a canonical Van Vleck $j\rm{_{eff}}=0$ singlet ground
state whose excitations are coupled spin and crystalline electric field levels.
Specifically, the anomalous Higgs mode [Jain et al. Nat. Phys. 13, 633 (2017)],
spin-waves [S. Kunkem\"{o}ller et al. Phys. Rev. Lett. 115, 247201 (2015)], and
orbital excitations [L. Das et al. Phys. Rev. X 8, 011048 (2018)] in the
multiorbital Mott insulator Ca$_2$RuO$_4$ are captured and good agreement is
found with previous neutron and inelastic x-ray spectroscopic measurements.
Furthermore, our results illustrate how a crystalline electric field-induced
singlet ground state can support coherent longitudinal, or amplitude
excitations, and transverse wavelike dynamics. We use this description to
discuss mechanisms for accessing a nearby critical point.


  For a split reductive group $G$ over a number field $k$, let $\rho$ be an
$n$-dimensional complex representation of its complex dual group
$G^\vee(\mathbb{C})$. For any irreducible cuspidal automorphic representation
$\sigma$ of $G(\mathbb{A})$, where $\mathbb{A}$ is the ring of adeles of $k$,
in \cite{JL21}, the authors introduce the $(\sigma,\rho)$-Schwartz space
$\mathcal{S}_{\sigma,\rho}(\mathbb{A}^\times)$ and $(\sigma,\rho)$-Fourier
operator $\mathcal{F}_{\sigma,\rho}$, and study the
$(\sigma,\rho,\psi)$-Poisson summation formula on $\mathrm{GL}_1$, under the
assumption that the local Langlands functoriality holds for the pair $(G,\rho)$
at all local places of $k$, where $\psi$ is a non-trivial additive character of
$k\backslash\mathbb{A}$. Such general formulae on $\mathrm{GL}_1$, as a vast
generalization of the classical Poisson summation formula, are expected to be
responsible for the Langlands conjecture (\cite{L70}) on global functional
equation for the automorphic $L$-functions $L(s,\sigma,\rho)$. In order to
understand such Poisson summation formulae, we continue with \cite{JL21} and
develop a further local theory related to the $(\sigma,\rho)$-Schwartz space
$\mathcal{S}_{\sigma,\rho}(\mathbb{A}^\times)$ and $(\sigma,\rho)$-Fourier
operator $\mathcal{F}_{\sigma,\rho}$. More precisely, over any local field
$k_\nu$ of $k$, we define distribution kernel functions
$k_{\sigma_\nu,\rho,\psi_\nu }(x)$ on $\mathrm{GL}_1$ that represent the
$(\sigma_\nu,\rho)$-Fourier operators $\mathcal{F}_{\sigma_\nu,\rho,\psi_\nu}$
as convolution integral operators, i.e. generalized Hankel transforms, and the
local Langlands $\gamma$-functions $\gamma(s,\sigma_\nu,\rho,\psi_\nu)$ as
Mellin transform of the kernel function. As consequence, we show that any local
Langlands $\gamma$-functions are the gamma functions in the sense of Gelfand,
Graev, and Piatetski-Shapiro in \cite{GGPS}.


  Let $r_5(N)$ be the largest cardinality of a set in $\{1,\ldots,N\}$ which
does not contain $5$ elements in arithmetic progression. Then there exists a
constant $c\in (0,1)$ such that \[r_5(N)\ll \frac{N}{\exp((\log\log N)^{c})}.\]
Our work is a consequence of recent improved bounds on the $U^4$-inverse
theorem of the first author and the fact that $3$-step nilsequences may be
approximated by locally cubic functions on shifted Bohr sets. This combined
with the density increment strategy of Heath-Brown and Szemer{\'e}di, codified
by Green and Tao, gives the desired result.


  Solely relying on test passing to evaluate Large Language Models (LLMs) for
code synthesis may result in unfair assessment or promoting models with data
leakage. As an alternative, we introduce CodeMind, a framework designed to
gauge the code reasoning abilities of LLMs. CodeMind currently supports three
code reasoning tasks: Independent Execution Reasoning (IER), Dependent
Execution Reasoning (DER), and Specification Reasoning (SR). The first two
evaluate models to predict the execution output of an arbitrary code or code
the model could correctly synthesize. The third one evaluates the extent to
which LLMs implement the specified expected behavior.
  Our extensive evaluation of nine LLMs across five benchmarks in two different
programming languages using CodeMind shows that LLMs fairly follow control flow
constructs and, in general, explain how inputs evolve to output, specifically
for simple programs and the ones they can correctly synthesize. However, their
performance drops for code with higher complexity, non-trivial logical and
arithmetic operators, non-primitive types, and API calls. Furthermore, we
observe that, while correlated, specification reasoning (essential for code
synthesis) does not imply execution reasoning (essential for broader
programming tasks such as testing and debugging): ranking LLMs based on test
passing can be different compared to code reasoning.


  An extension of the algebraic-geometric method for nonlinear integrable PDE's
is shown to lead to new piecewise smooth weak solutions of a class of
$N$-component systems of nonlinear evolution equations. This class includes,
among others, equations from the Dym and shallow water equation hierarchies.
The main goal of the paper is to give explicit theta-functional solutions of
these nonlinear PDE's, which are associated to nonlinear subvarieties of
hyperelliptic Jacobians. The main results of the present paper are twofold.
First, we exhibit some of the special features of integrable PDE's that admit
piecewise smooth weak solutions, which make them different from equations whose
solutions are globally meromorphic, such as the KdV equation. Second, we blend
the techniques of algebraic geometry and weak solutions of PDE's to gain
further insight into, and explicit formulas for, piecewise-smooth finite-gap
solutions.


  For a path-connected space X, a well-known theorem of Segal, May and Milgram
asserts that the configuration space of finite points in R^n with labels in X
is weakly homotopy equivalent to the n-th loop-suspension of X. In this paper,
we introduce a space I_n(X) of intervals suitably topologized in R^n with
labels in a space X and show that it is weakly homotopy equivalent to n-th
loop-suspension of X without the assumption on path-connectivity.


  Multiple Input Multiple Output (MIMO) technology is going to be a viable
alternative for future generation wireless broadband services in order to meet
the striving requirements for throughput and system robustness. In Long Term
Evolution (LTE), MIMO technologies have been broadly used to get better
downlink peak rate, cell coverage, as well as average cell throughput. In the
present paper a 2x2 MIMO is taken as baseline configuration for a LTE downlink
under a Microcellular propagation scenario considering a non physical
correlation based channel with Poor and rich scattering environment. The
throughput capacity of the downlink is obtained for poor and rich scattering
environments. Besides, two vital aspects of MIMO technique viz Spatial
Multiplexing (SM) and Transmit Diversity (TD) are investigated in order to see
their effect on throughput of the system. The effect of parameters like Speed
of mobile station, number of Multipath, Rician factor (K) on throughput of such
systems is reported and discussed. The investigations reported in this paper
helps in estimating the throughput capacity of LTE downlink under SM and TD
mode.


  We present the status of our calculation of the first few moments of the
nucleon structure functions. Our calculations are done using domain wall
fermions in the quenched approximation with the DBW2 gauge action at 1.3GeV
inverse lattice spacing.


  The use of Artificial Intelligence (AI) for play-testing is still on the
sidelines of main applications of AI in games compared to performance-oriented
game-playing. One of the main purposes of play-testing a game is gathering data
on the gameplay, highlighting good and bad features of the design of the game,
providing useful insight to the game designers for improving the design. Using
AI agents has the potential of speeding the process dramatically. The purpose
of this research is to map the behavioural space (BSpace) of a game by using a
general method. Using the MAP-Elites algorithm we search the hyperparameter
space Rinascimento AI agents and map it to the BSpace defined by several
behavioural metrics. This methodology was able to highlight both exemplary and
degenerated behaviours in the original game design of Splendor and two
variations. In particular, the use of event-value functions has generally shown
a remarkable improvement in the coverage of the BSpace compared to agents based
on classic score-based reward signals.


  A radioactive waste transmutation system based extensively on existing
nuclear power technology is presented. By replacing the control rods with
neutron sources, we could maintain good power distribution and perform
long-lived waste burning in high flux subcritical reactors. The design is based
on a small pressurized water reactor, fission electric cell (FEC),
target-distributed accelerator (TDA) and power monitoring system with in-core
gamma-ray detectors, now under development in several countries. The TDA, in
which an FEC electric field compensates for lost beam energy in the target,
offers a new approach to obtain large neutron fluxes. The analysis takes into
consideration a wide range of TDA design aspects including the wave model of
observed relativistic phenomena, in-core microwave power source, the FEC with a
multistage collector (anode) and layered cathode.


  We describe a calculation of the O(alpha_s^2) QCD corrections to the fully
differential cross section for W and Z boson production in hadronic collisions.
The result is fully realistic in that it includes spin correlations, finite
width effects, gamma-Z interference and allows for the application of arbitrary
cuts on the leptonic decay products of the W and Z. We have implemented this
calculation into a numerical program. We demonstrate the use of this code by
presenting phenomenological results for several future LHC analyses and recent
Tevatron measurements, including the W cross section in the forward rapidity
region and the central over forward cross section ratio.


  We introduce and analyze a penalty-free formulation of the Shifted Boundary
Method (SBM), inspired by the asymmetric version of the Nitsche method. We
prove its stability and convergence for arbitrary order finite element
interpolation spaces and we test its performance with a number of numerical
experiments. Moreover, while the SBM was previously believed to be only
asymptotically consistent (in the sense of Galerkin orthogonality), we prove
here that it is indeed exactly consistent.


  Given a reference lattice, we define fuzzy intervals to be the fuzzy sets
such that their p-cuts are crisp closed intervals. We show that: given a
complete reference lattice, the collection of its fuzzy intervals is a complete
lattice. Furthermore we show that: if the reference lattice is completely
distributive then the lattice of its fuzzy intervals is distributive.


  While contemporary semantic search systems offer to improve classical
keyword-based search, they are not always adequate for complex domain specific
information needs. The domain of prescription drug abuse, for example, requires
knowledge of both ontological concepts and 'intelligible constructs' not
typically modeled in ontologies. These intelligible constructs convey essential
information that include notions of intensity, frequency, interval, dosage and
sentiments, which could be important to the holistic needs of the information
seeker. We present a hybrid approach to domain specific information retrieval
(or knowledge-aware search) that integrates ontology-driven query
interpretation with synonym-based query expansion and domain specific rules, to
facilitate search in social media. Our framework is based on a context-free
grammar (CFG) that defines the query language of constructs interpretable by
the search system. The grammar provides two levels of semantic interpretation:
1) a top-level CFG that facilitates retrieval of diverse textual patterns,
which belong to broad templates and 2) a low-level CFG that enables
interpretation of certain specific expressions that belong to such patterns.
These low-level expressions occur as concepts from four different categories of
data: 1) ontological concepts, 2) concepts in lexicons (such as emotions and
sentiments), 3) concepts in lexicons with only partial ontology representation,
called lexico-ontology concepts (such as side effects and routes of
administration (ROA)), and 4) domain specific expressions (such as date, time,
interval, frequency and dosage) derived solely through rules. Our approach is
embodied in a novel Semantic Web platform called PREDOSE developed for
prescription drug abuse epidemiology.
  Keywords: Knowledge-Aware Search, Ontology, Semantic Search, Background
Knowledge, Context-Free Grammar


  Approximately forty years ago it was realized that the time development of
decaying systems might not be precisely exponential. Rolf Winter (Phys. Rev.
{\bf 123}, 1503 (1961)) analyzed the simplest nontrivial system - a particle
tunneling out of a well formed by a wall and a delta-function. He calculated
the probability current just outside the well and found irregular oscillations
on a short time scale followed by an exponential decrease followed by more
oscillations and finally by a decrease as a power of the time. We have
reanalyzed this system, concentrating on the survival probability of the
particle in the well rather than the probability current, and find a different
short time behavior.


  The fragmentation function of a scalar leptoquark into possible S-wave bound
states with a heavy anti-quark is calculated to the leading order in
perturbative QCD for the high energy processes at large transverse momenta. The
one-loop equations for the $q^2$-evolution of moments of the fragmentation
function due to the hard gluon emission by the leptoquark are derived. The
integral probabilities of fragmentation are evaluated.


  We investigate the destroying of charged and rotating black holes in
Einstein-Maxwell-(dilaton) theory. We show that a test particle with some
appropriate properties in the black hole background can turn the black hole
into a naked singularity. In this work, we neglect the self-force, self-energy,
and radiative effects as considered by some others in literature. As the
result, we are able to show that the Kerr-Newman and Kaluza-Klein black holes
can be destroyed by the test particle. Furthermore, for Kaluza-Klein black
hole, we consider the overspinning of the black hole by a neutral test scalar
field.


  In traditional studies on language evolution, scholars often emphasize the
importance of sound laws and sound correspondences for phylogenetic inference
of language family trees. However, to date, computational approaches have
typically not taken this potential into account. Most computational studies
still rely on lexical cognates as major data source for phylogenetic
reconstruction in linguistics, although there do exist a few studies in which
authors praise the benefits of comparing words at the level of sound sequences.
Building on (a) ten diverse datasets from different language families, and (b)
state-of-the-art methods for automated cognate and sound correspondence
detection, we test, for the first time, the performance of sound-based versus
cognate-based approaches to phylogenetic reconstruction. Our results show that
phylogenies reconstructed from lexical cognates are topologically closer, by
approximately one third with respect to the generalized quartet distance on
average, to the gold standard phylogenies than phylogenies reconstructed from
sound correspondences.


  The speed at which millimeter-Wave (mmWave) channel estimation can be carried
out is critical for the adoption of mmWave technologies. This is particularly
crucial because mmWave transceivers are equipped with large antenna arrays to
combat severe path losses, which consequently creates large channel matrices,
whose estimation may incur significant overhead. This paper focuses on the
mmWave channel estimation problem. Our objective is to reduce the number of
measurements required to reliably estimate the channel. Specifically, channel
estimation is posed as a "source compression" problem in which measurements
mimic an encoded (compressed) version of the channel. Decoding the observed
measurements, a task which is traditionally computationally intensive, is
performed using a deep-learning-based approach, facilitating a high-performance
channel discovery. Our solution not only outperforms state-of-the-art
compressed sensing methods, but it also determines the lower bound on the
number of measurements required for reliable channel discovery.


  With leaps in machine learning techniques and their applicationon Earth
observation challenges has unlocked unprecedented performance across the
domain. While the further development of these methods was previously limited
by the availability and volume of sensor data and computing resources, the lack
of adequate reference data is now constituting new bottlenecks. Since creating
such ground-truth information is an expensive and error-prone task, new ways
must be devised to source reliable, high-quality reference data on large
scales. As an example, we showcase E URO C ROPS, a reference dataset for crop
type classification that aggregates and harmonizes administrative data surveyed
in different countries with the goal of transnational interoperability.


  Intelligent reflecting surface (IRS)-enabled backscatter communications can
be enabled by an access point (AP) that splits its transmit signal into
modulated and unmodulated parts. This letter integrates non-orthogonal multiple
access (NOMA) with this method to create a two-user primary system and a
secondary system of IRS data. Considering the decoding order, we maximize the
rate of the strongest primary user by jointly optimizing the IRS phase shifts,
power splitting (PS) factor at the AP, and NOMA power coefficients while
guaranteeing the quality of service (QoS) for both weak user and IRS data in
the primary and secondary systems, respectively. The resulting optimization
problem is non-convex. Thus, we split it into three parts and develop an
alternating optimization (AO) algorithm. The advantage is that we derive
closed-form solutions for the PS factor and NOMA power coefficients in the
first two parts. In the third part, we optimize the phase shifts by exploiting
semi-definite relaxation (SDR) and penalty techniques to handle the
unit-modulus constraints. This algorithm achieves substantial gains (e.g.,
40--68%) compared to relevant baseline schemes.


  It is argued that the supersymmetric index of a certain system of branes in
M-theory is equal to the partition function of an integrable three-dimensional
lattice model. The local Boltzmann weights of the lattice model satisfy a
generalization of Zamolodchikov's tetrahedron equation. In a special case the
model is described by a solution of the tetrahedron equation discovered by
Kapranov and Voevodsky and by Bazhanov and Sergeev.


  [Abridged] Filaments and hubs have received special attention recently thanks
to studies showing their role in star formation. While the column density and
velocity structures of both filaments and hubs have been studied, their
magnetic fields (B-field) are not yet characterized. We aim to understand the
role of the B-field in the dynamical evolution of the NGC 6334 hub-filament
network. We present new observations of the dust polarized emission at
850$\mu$m towards NGC 6334 obtained with the JCMT/POL-2. We study the
distribution and dispersion of the polarized intensity ($PI$), the polarization
fraction ($PF$), and the B-field angle ($\theta_{B}$). We derive the power
spectrum of the intensity and $\theta_{B}$ along the ridge crest. Our analyses
show a complex B-field structure when observed over the whole region ($\sim10$
pc), however, at smaller scales ($\sim1$ pc), $\theta_{B}$ varies coherently
along the filaments. The observed power spectrum of $\theta_{B}$ can be well
represented with a power law function with a slope $-1.33\pm0.23$, which is
$\sim20\%$ shallower than that of $I$. This result is compatible with the
properties of simulated filaments and may indicate the processes at play in the
formation of filaments. $\theta_{B}$ rotates from being mostly perpendicular to
the filament crests to mostly parallel as they merge with the hubs. This
variation of $\theta_{B}$ may be tracing local velocity flows of matter
in-falling onto the hubs. Our analysis suggests a variation of the energy
balance along the crests of these filaments, from magnetically
critical/supercritical at their far ends to magnetically subcritical near the
hubs. We detect an increase of $PF$ towards the high-column density star
cluster-forming hubs that may result from the increase of grain alignment
efficiency due to stellar radiation from the newborn stars.


  In hybrid normed ideal perturbations of $n$-tuples of operators, the normed
ideal is allowed to vary with the component operators. We begin extending to
this setting the machinery we developed for normed ideal perturbations based on
the modulus of quasicentral approximation and an adaptation of our
non-commutative generalization of the Weyl--von~Neumann theorem. For commuting
$n$-tuples of hermitian operators, the modulus of quasicentral approximation
remains essentially the same when $\cC_n^-$ is replaced by a hybrid $n$-tuple
$\cC_{p_1,\dots}^-,\dots,\cC^-_{p_n}$, $p_1^{-1} + \dots + p_n^{-1} = 1$. The
proof involves singular integrals of mixed homogeneity.


  We investigate opportunities offered by telematics and analytics to enable
better informed, and more integrated, collaborative management decisions on
construction sites. We focus on efficient refuelling of assets across
construction sites. More specifically, we develop decision support models that,
by leveraging data supplied by different assets, schedule refuelling operations
by minimising the distance travelled by the bowser truck as well as fuel
shortages. Motivated by a practical case study elicited in the context of a
project we recently conducted at Crossrail, we introduce the Dynamic Bowser
Routing Problem. In this problem the decision maker aims to dynamically refuel,
by dispatching a bowser truck, a set of assets which consume fuel and whose
location changes over time; the goal is to ensure that assets do not run out of
fuel and that the bowser covers the minimum possible distance. We investigate
deterministic and stochastic variants of this problem and introduce effective
and scalable mathematical programming models to tackle these cases. We
demonstrate the effectiveness of our approaches in the context of an extensive
computational study designed around data collected on site as well as supplied
by our project partners.
  Keywords: Routing; Dynamic Bowser Routing Problem; Stochastic Bowser Routing
Problem; Mixed-Integer Linear Programming; Construction.


  We study the geometry of the scale invariant Cassinian metric and prove sharp
comparison inequalities between this metric and the hyperbolic metric in the
case when the domain is either the unit ball or the upper half space. We also
prove sharp distortion inequalities for the scale invariant Cassinian metric
under M\"obius transformations.


  his paper describes our techniques to detect hate speech against women and
immigrants on Twitter in multilingual contexts, particularly in English and
Spanish. The challenge was designed by SemEval-2019 Task 5, where the
participants need to design algorithms to detect hate speech in English and
Spanish language with a given target (e.g., women or immigrants). Here, we have
developed two deep neural networks (Bidirectional Gated Recurrent Unit (GRU),
Character-level Convolutional Neural Network (CNN)), and one machine learning
model by exploiting the linguistic features. Our proposed model obtained 57 and
75 F1 scores for Task A in English and Spanish language respectively. For Task
B, the F1 scores are 67 for English and 75.33 for Spanish. In the case of task
A (Spanish) and task B (both English and Spanish), the F1 scores are improved
by 2, 10, and 5 points respectively. Besides, we present visually interpretable
models that can address the generalizability issues of the custom-designed
machine learning architecture by investigating the annotated dataset.


  We consider the problem of reducing the (semi)total domination number of
graph by one by contracting edges. It is known that this can always be done
with at most three edge contractions and that deciding whether one edge
contraction suffices is an $\mathsf{NP}$-hard problem. We show that for every
fixed $k \in \{2,3\}$, deciding whether exactly $k$ edge contractions are
necessary is $\mathsf{NP}$-hard and further provide for $k=2$ complete
complexity dichotomies on monogenic graph classes.


  Background: Drug-drug interactions (DDIs) refer to processes triggered by the
administration of two or more drugs leading to side effects beyond those
observed when drugs are administered by themselves. Due to the massive number
of possible drug pairs, it is nearly impossible to experimentally test all
combinations and discover previously unobserved side effects. Therefore,
machine learning based methods are being used to address this issue.
  Methods: We propose a Siamese self-attention multi-modal neural network for
DDI prediction that integrates multiple drug similarity measures that have been
derived from a comparison of drug characteristics including drug targets,
pathways and gene expression profiles.
  Results: Our proposed DDI prediction model provides multiple advantages: 1)
It is trained end-to-end, overcoming limitations of models composed of multiple
separate steps, 2) it offers model explainability via an Attention mechanism
for identifying salient input features and 3) it achieves similar or better
prediction performance (AUPR scores ranging from 0.77 to 0.92) compared to
state-of-the-art DDI models when tested on various benchmark datasets. Novel
DDI predictions are further validated using independent data resources.
  Conclusions: We find that a Siamese multi-modal neural network is able to
accurately predict DDIs and that an Attention mechanism, typically used in the
Natural Language Processing domain, can be beneficially applied to aid in DDI
model explainability.


  Dead Reckoning mechanisms are usually used to estimate the position of
simulated entity in virtual environment. However, this technique often ignores
available contextual information that may be influential to the state of an
entity, sacrificing remote predictive accuracy in favor of low computational
complexity. A novel extension of Dead Reckoning is suggested in this paper to
increase the network availability and fulfill the required Quality of Service
in large scale distributed simulation application. The proposed algorithm is
referred to as ANFIS Dead Reckoning, which stands for Adaptive Neuro-based
Fuzzy Inference System Dead Reckoning is based on a fuzzy inference system
which is trained by the learning algorithm derived from the neuronal networks
and fuzzy inference theory. The proposed mechanism takes its based on the
optimization approach to calculate the error threshold violation in networking
games. Our model shows it primary benefits especially in the decision making of
the behavior of simulated entities and preserving the consistence of the
simulation.


  This work focuses on the newly discovered bifurcation phase transition of CDT
quantum gravity. We define various order parameters and investigate which is
most suitable to study this transition in numerical simulations. By analyzing
the behaviour of the order parameters we present evidence that the transition
separating the bifurcation phase and the physical phase of CDT is likely a
second or higher-order transition, a result that may have important
implications for the continuum limit of CDT.


  We give a determination of the equivalence group of Euler-Bernoulli equation
and of one of its generalizations, and thus derive some symmetry properties of
this equation.


  Virtual Machines (VMs) with Just-In-Time (JIT) compilers are traditionally
thought to execute programs in two phases: the initial warmup phase determines
which parts of a program would most benefit from dynamic compilation, before
JIT compiling those parts into machine code; subsequently the program is said
to be at a steady state of peak performance. Measurement methodologies almost
always discard data collected during the warmup phase such that reported
measurements focus entirely on peak performance. We introduce a fully automated
statistical approach, based on changepoint analysis, which allows us to
determine if a program has reached a steady state and, if so, whether that
represents peak performance or not. Using this, we show that even when run in
the most controlled of circumstances, small, deterministic, widely studied
microbenchmarks often fail to reach a steady state of peak performance on a
variety of common VMs. Repeating our experiment on 3 different machines, we
found that at most 43.5% of <VM, benchmark> pairs consistently reach a steady
state of peak performance.


  Variational autoencoders learn distributions of high-dimensional data. They
model data with a deep latent-variable model and then fit the model by
maximizing a lower bound of the log marginal likelihood. VAEs can capture
complex distributions, but they can also suffer from an issue known as "latent
variable collapse," especially if the likelihood model is powerful.
Specifically, the lower bound involves an approximate posterior of the latent
variables; this posterior "collapses" when it is set equal to the prior, i.e.,
when the approximate posterior is independent of the data. While VAEs learn
good generative models, latent variable collapse prevents them from learning
useful representations. In this paper, we propose a simple new way to avoid
latent variable collapse by including skip connections in our generative model;
these connections enforce strong links between the latent variables and the
likelihood function. We study generative skip models both theoretically and
empirically. Theoretically, we prove that skip models increase the mutual
information between the observations and the inferred latent variables.
Empirically, we study images (MNIST and Omniglot) and text (Yahoo). Compared to
existing VAE architectures, we show that generative skip models maintain
similar predictive performance but lead to less collapse and provide more
meaningful representations of the data.


  The Bumblebee vector model of spontaneous Lorentz symmetry breaking (LSB) in
Bianchi type I (BI) Universe to observe its effect on cosmological evolution is
an interesting aspect of study in anisotropic cosmology. In this study, we have
considered a Bumblebee field under vacuum expectation value condition (VEV)
with BI metric and studied the cosmological parameters along with observational
data. Further, we have studied the effect of anisotropy and the Bumblebee field
in cosmic evolution. We have also studied the effect of both anisotropy and
Bumblebee field while considering the Universe as a dynamical system. We have
found that there are some prominent roles of both anisotropy and the Bumblebee
field in cosmic evolutions. We have also observed an elongated matter-dominated
phase as compared to standard cosmology. Moreover, while studying the dynamical
system analysis, we have also observed the shift of critical points from
standard $\Lambda$CDM results showing the anisotropy and the Bumblebee field
effect.


  Girstmair in [1, Theorem 1] gave a generalization of Murty's irreducibility
criterion (see [2, Theorem 1]). In this article, we further generalize these
criteria.


  We discuss a subtlety involved in the calculation of multifractal spectra
when these are expressed as Legendre-Fenchel transforms of functions analogous
to free energy functions. We show that the Legendre-Fenchel transform of a free
energy function yields the correct multifractal spectrum only when the latter
is wholly concave. If the spectrum has no definite concavity, then the
transform yields the concave envelope of the spectrum rather than the spectrum
itself. Some mathematical and physical examples are given to illustrate this
result, which lies at the root of the nonequivalence of the microcanonical and
canonical ensembles. On a more positive note, we also show that the
impossibility of expressing nonconcave multifractal spectra through
Legendre-Fenchel transforms of free energies can be circumvented with the help
of a generalized free energy function, which relates to a recently introduced
generalized canonical ensemble. Analogies with the calculation of rate
functions in large deviation theory are finally discussed.


  We propose a novel method for sorting events of multiparticle production
according to the azimuthal anisotropy of their momentum distribution. Although
the method is quite general, we advocate its use in analysis of
ultra-relativistic heavy-ion collisions where large number of hadrons is
produced. The advantage of our method is that it can automatically sort out
samples of events with histograms that indicate similar distributions of
hadrons. It takes into account the whole measured histograms with all orders of
anisotropy instead of a specific observable (e.g. $v_2$, $v_3$, $q_2$). It can
be used for more exclusive experimental studies of flow anisotropies which are
then more easily compared to theoretical calculations. It may also be useful in
the construction of mixed-events background for correlation studies as it
allows to select events with similar momentum distribution.


  We investigate the Brazilian personal income distribution using data from
National Household Sample Survey (PNAD), an annual research available by the
Brazilian Institute of Geography and Statistics (IBGE). It provides general
characteristics of the country's population. Using PNAD data background we also
confirm the effectiveness of a semi-empirical model that reconciles Pareto
power-law for high-income people and Boltzmann- Gibbs distribution for the rest
of population. We use three measures of income inequality: the Pareto index,
the average income and the crossover income. In order to cope with many
dimensions of the income inequality, we calculate these three indices and also
the Gini coefficient for the general population as well as for two kinds of
population dichotomies: black / indigenous / mixed race versus white / yellow;
and men versus women. We also followed the time series of these indices for the
period 2001-2014. The results suggest a decreasing of Brazilian income
inequality over the selected period. Another important result is that
historically-disadvantaged subgroups (Women and black / indigenous / mixed
race),that are the majority of the population, have a more equalitarian income
distribution. These groups have also a smaller monthly income than the others
and this social structure remained virtually unchanged in the period of time.


  We obtain large deviations estimates for both sequential and random
compositions of intermittent maps. We also address the question of whether or
not centering is necessary for the quenched central limit theorems (CLT)
obtained by Nicol, T\"or\"ok and Vaienti for random dynamical systems comprised
of intermittent maps. Using recent work of Abdelkader and Aimino, Hella and
Stenlund we extend the results of Nicol, T\"or\"ok and Vaienti on quenched
central limit theorems (CLT) for centered observables over random compositions
of intermittent maps: first by enlarging the parameter range over which the
quenched CLT holds; and second by showing that the variance in the quenched CLT
is almost surely constant (and the same as the variance of the annealed CLT)
and that centering is needed to obtain this quenched CLT.


  Concerns about the reproducibility of deep learning research are more
prominent than ever, with no clear solution in sight. The relevance of machine
learning research can only be improved if we also employ empirical rigor that
incorporates reproducibility guidelines, especially so in the medical imaging
field. The Medical Imaging with Deep Learning (MIDL) conference has made
advancements in this direction by advocating open access, and recently also
recommending authors to make their code public - both aspects being adopted by
the majority of the conference submissions. This helps the reproducibility of
the methods, however, there is currently little or no support for further
evaluation of these supplementary material, making them vulnerable to poor
quality, which affects the impact of the entire submission. We have evaluated
all accepted full paper submissions to MIDL between 2018 and 2022 using
established, but slightly adjusted guidelines on reproducibility and the
quality of the public repositories. The evaluations show that publishing
repositories and using public datasets are becoming more popular, which helps
traceability, but the quality of the repositories has not improved over the
years, leaving room for improvement in every aspect of designing repositories.
Merely 22% of all submissions contain a repository that were deemed repeatable
using our evaluations. From the commonly encountered issues during the
evaluations, we propose a set of guidelines for machine learning-related
research for medical imaging applications, adjusted specifically for future
submissions to MIDL.


  The role of three-nucleon forces in ab initio calculations of nuclear systems
is investigated. The difference between genuine and induced many-nucleon forces
is emphasized. Induced forces arise in the process of solving the nuclear
many-body problem as technical intermediaries towards calculationally converged
results. Genuine forces make up the Hamiltonian; they represent the chosen
underlying dynamics. The hierarchy of contributions arising from two-, three-
and many-nucleon forces is discussed. Signals for the need of the inclusion of
genuine three-nucleon forces are studied in nuclear systems, technically best
under control, especially in three-nucleon and four-nucleon systems. Genuine
three-nucleon forces are important for details in the decription of some
observables. Their contributions to observables are small on the scale set by
two-nucleon forces.


  We introduce $\textit{InExtremIS}$, a weakly supervised 3D approach to train
a deep image segmentation network using particularly weak train-time
annotations: only 6 extreme clicks at the boundary of the objects of interest.
Our fully-automatic method is trained end-to-end and does not require any
test-time annotations. From the extreme points, 3D bounding boxes are extracted
around objects of interest. Then, deep geodesics connecting extreme points are
generated to increase the amount of "annotated" voxels within the bounding
boxes. Finally, a weakly supervised regularised loss derived from a Conditional
Random Field formulation is used to encourage prediction consistency over
homogeneous regions. Extensive experiments are performed on a large open
dataset for Vestibular Schwannoma segmentation. $\textit{InExtremIS}$ obtained
competitive performance, approaching full supervision and outperforming
significantly other weakly supervised techniques based on bounding boxes.
Moreover, given a fixed annotation time budget, $\textit{InExtremIS}$
outperforms full supervision. Our code and data are available online.


  We improve the understanding of the $\textit{golden ratio algorithm}$, which
solves monotone variational inequalities (VI) and convex-concave min-max
problems via the distinctive feature of adapting the step sizes to the local
Lipschitz constants. Adaptive step sizes not only eliminate the need to pick
hyperparameters, but they also remove the necessity of global Lipschitz
continuity and can increase from one iteration to the next.
  We first establish the equivalence of this algorithm with popular VI methods
such as reflected gradient, Popov or optimistic gradient descent-ascent in the
unconstrained case with constant step sizes. We then move on to the constrained
setting and introduce a new analysis that allows to use larger step sizes, to
complete the bridge between the golden ratio algorithm and the existing
algorithms in the literature. Doing so, we actually eliminate the link between
the golden ratio $\frac{1+\sqrt{5}}{2}$ and the algorithm. Moreover, we improve
the adaptive version of the algorithm, first by removing the maximum step size
hyperparameter (an artifact from the analysis) to improve the complexity bound,
and second by adjusting it to nonmonotone problems with weak Minty solutions,
with superior empirical performance.


  We study the universal far from equilibrium dynamics of magnons in Heisenberg
ferromagnets. We show that such systems exhibit universal scaling in momentum
and time of the quasiparticle distribution function, with the universal
exponents distinct from those recently observed in Bose-Einstein condensates.
This new universality class originates from the SU(2) symmetry of the
Hamiltonian, which leads to a strong momentum-dependent magnon-magnon
scattering amplitude. We compute the universal exponents using the Boltzmann
kinetic equation and incoherent initial conditions that can be realized with
microwave pumping of magnons. We compare our numerical results with analytic
estimates of the scaling exponents and demonstrate the robustness of the
scaling to variations in the initial conditions. Our predictions can be tested
in quench experiments of spin systems in optical lattices and pump-probe
experiments in ferromagnetic insulators such as yttrium iron garnet.


  This note describes a new approach to classifying graphs that leverages graph
generative models (GGM). Assuming a GGM that defines a joint probability
distribution over graphs and their class labels, I derive classification
formulas for the probability of a class label given a graph. A new conditional
ELBO can be used to train a generative graph auto-encoder model for
discrimination. While leveraging generative models for classification has been
well explored for non-relational i.i.d. data, to our knowledge it is a novel
approach to graph classification.


  The sequential allocation protocol is a simple and popular mechanism to
allocate indivisible goods, in which the agents take turns to pick the items
according to a predefined sequence. While this protocol is not strategy-proof,
it has been shown recently that finding a successful manipulation for an agent
is an NP-hard problem (Aziz et al., 2017). Conversely, it is also known that
finding an optimal manipulation can be solved in polynomial time in a few
cases: if there are only two agents or if the manipulator has a binary or a
lexicographic utility function. In this work, we take a parameterized approach
to provide several new complexity results on this manipulation problem. More
precisely, we give a complete picture of its parameterized complexity w.r.t.
the following three parameters: the number $n$ of agents, the number $\mu(a_1)$
of times the manipulator $a_1$ picks in the picking sequence, and the maximum
range $\mathtt{rg}^{\max}$ of an item. This third parameter is a correlation
measure on the preference rankings of the agents. In particular, we show that
the problem of finding an optimal manipulation can be solved in polynomial time
if $n$ or $\mu(a_1)$ is a constant, and that it is fixed-parameter tractable
w.r.t. $\mathtt{rg}^{\max}$ and $n+\mu(a_1)$. Interestingly enough, we show
that w.r.t. the single parameters $n$ and $\mu(a_1)$ it is W[1]-hard. Moreover,
we provide an integer program and a dynamic programming scheme to solve the
manipulation problem and we show that a single manipulator can increase the
utility of her bundle by a multiplicative factor which is at most 2.


  Recent research on discourse relations has found that they are cued not only
by discourse markers (DMs) but also by other textual signals and that signaling
information is indicative of genres. While several corpora exist with discourse
relation signaling information such as the Penn Discourse Treebank (PDTB,
Prasad et al. 2008) and the Rhetorical Structure Theory Signalling Corpus
(RST-SC, Das and Taboada 2018), they both annotate the Wall Street Journal
(WSJ) section of the Penn Treebank (PTB, Marcus et al. 1993), which is limited
to the news domain. Thus, this paper adapts the signal identification and
anchoring scheme (Liu and Zeldes, 2019) to three more genres, examines the
distribution of signaling devices across relations and genres, and provides a
taxonomy of indicative signals found in this dataset.


  We study the structure and properties of hot MHD accretion onto a Kerr black
hole. In such a system, the hole is magnetically coupled to the inflowing gas
and exerts a torque onto the accretion flow. A hot settling flow can form
around the hole and transport the angular momentum outward, to the outer edge
of the flow. Unlike other hot flows, such as advection- and
convection-dominated flows and inflow-outflow solutions (ADAFs, CDAFs, and
ADIOS), the properties of the hot settling flow are determined by the spin of
the central black hole, but are insensitive to the mass accretion rate.
Therefore, it may be possible to identify rapidly spinning BHs simply from
their broad-band spectra.
  Observationally, the hot settling flow around a Kerr hole is somewhat similar
to other hot flows in that they all have hard, power-law spectra and relatively
low luminosities. Thus, most black hole candidates in the low/hard and,
perhaps, intermediate X-ray state may potentially accrete via the hot settling
flow. However, a settling flow will be somewhat more luminous than
ADAFs/CDAFs/ADIOS, will exhibit high variability in X-rays, and may have
relativistic jets. This suggests that galactic microquasars and active galactic
nuclei may be powered by hot settling flows. We identify several galactic X-ray
sources as the best candidates.


  A distorted-wave version of the renormalisation group is applied to
scattering by an inverse-square potential and to three-body systems. In
attractive three-body systems, the short-distance wave function satisfies a
Schroedinger equation with an attractive inverse-square potential, as shown by
Efimov. The resulting oscillatory behaviour controls the renormalisation of the
three-body interactions, with the renormalisation-group flow tending to a limit
cycle as the cut-off is lowered. The approach used here leads to single-valued
potentials with discontinuities as the bound states are cut off. The
perturbations around the cycle start with a marginal term whose effect is
simply to change the phase of the short-distance oscillations, or the
self-adjoint extension of the singular Hamiltonian. The full power counting in
terms of the energy and two-body scattering length is constructed for
short-range three-body forces.


  We construct and study the Yang-Mills measure in two dimensions. According to
the informal description given by the physicists, it is a probability measure
on the space of connections modulo gauge transformations on a principal bundle
with compact structure group. We are interested in the case where the base
space of this bundle is a compact orientable surface.
  The construction of the measure in a discrete setting, where the base space
of the fiber bundle is replaced by a graph traced on a surface, is quite well
understood thanks to the work of E. Witten. In contrast, the continuum limit of
this construction, which should allow to put a genuine manifold as base space,
still remains problematic.
  This work presents a complete and unified approach of the discrete theory and
of its continuum limit. We give a geometrically consistent definition of the
Yang-Mills measure, under the form of a random holonomy along a wide, intrinsic
and natural class of loops. This definition allows us to study combinatorial
properties of the measure, like its Markovian behaviour under the surgery of
surfaces, as well as properties specific to the continuous setting, for
example, some of its microscopic properties. In particular, we clarify the
links between the Yang-Mills measure and the white noise and show that there is
a major difference between the Abelian and semi-simple theories. We prove that
it is possible to construct a white noise using the measure as a starting point
and vice versa in the Abelian case but we show a result of asymptotic
independence in the semi-simple case which suggests that it is impossible to
extract a white noise from the measure.


  We examine in detail nonperturbative corrections for low lying energies of a
symmetric triple-well potential with non-equivalent vacua, for which there have
been disagreement about asymptotic formulas and controversy over the validity
of the dilute gas approximation. We carry out investigations from various
points of view, including not only a numerical comparison of the
nonperturbative corrections with the exact values but also the prediction of
the large order behavior of the perturbation series, consistency with the
perturbative corrections, and comparison with the WKB approximation. We show
that all the results support our formula previously obtained from the valley
method calculation beyond the dilute gas approximation.


  We prove completeness, interpolation, decidability and an omitting types
theorem for certain multi dimensional modal logics where the states are not
abstract entities but have an inner structure. The states will be sequences.
Our approach is algebraic addressing (varieties generated by) complex algebras
of Kripke semantics for such logic. Those algebras, whose elements are sets of
states are common reducts of cylindric and polyadic algebras


  Berry connection is conventionally defined as a static gauge field in the
Brillouin zone. Here we show that for three-dimensional (3d) time-reversal
invariant superconductors, a generalized Berry gauge field behaves as a
fluctuating field of a Chern-Simons gauge theory. The gapless nodal lines in
the momentum space play the role of Wilson loop observables, while their
linking and knot invariants modify the gravitational theta angle. This angle
induces a topological gravitomagnetoelectric effect where a temperature
gradient induces a rotational energy flow. We also show how topological strings
may be realized in the 6 dimensional phase space, where the physical space
defects play the role of topological D-branes.


  We present a phase-resolved spectroscopic study of the secondary star in the
cataclysmic variable U Gem. We use our data to measure the radial velocity
semi-amplitude, systemic velocity and rotational velocity of the secondary
star. Combining this with literature data allows us to determine masses and
radii for both the secondary star and white dwarf which are independent of any
assumptions about their structure. We use these to compare their properties
with those of field stars and find that both components follow field
mass-radius relationships. The secondary star has the mass, radius, luminosity
and photometric temperature of an M2 star, but a spectroscopic temperature of
M4. The latter may well be due to a high metallicity. There is a troubling
inconsistency between the radius of the white dwarf inferred from its
gravitational redshift and inclination and that inferred from its temperature,
flux, and astrometric distance.
  We find that there are two fundamental limits to the accuracy of the
parameters we can derive. First the radial velocity curve of the secondary star
deviates from a sinusoid, in part because of its asphericity (which can be
modelled) and in part because the line flux is not evenly distributed over its
surface. Second we cannot be certain which spectral type is the best match for
the lines of the secondary star, and the derived rotational velocity is a
function of the spectral type of the template star used.


  We present two proofs that all closed, orientable 3-manifolds are
parallelisable. Both are based on the Lickorish-Wallace surgery presentation;
one proof uses a refinement due to Kaplan and some basic contact geometry. This
complements a recent paper by Benedetti-Lisca.


  We give a selective review of some recent developments in causal inference,
intended for researchers who are not familiar with graphical models and
causality, and with a focus on methods that are applicable to large data sets.
We mainly address the problem of estimating causal effects from observational
data. For example, one can think of estimating the effect of single or multiple
gene knockouts from wild-type gene expression data, that is, from gene
expression measurements that were obtained without doing any gene knockout
experiments.
  We assume that the observational data are generated from a causal structure
that can be represented by a directed acyclic graph (DAG). First, we discuss
estimation of causal effects when the underlying causal DAG is known. In
large-scale networks, however, the causal DAG is often unknown. Next, we
therefore discuss causal structure learning, that is, learning information
about the causal structure from observational data. We then combine these two
parts and discuss methods to estimate (bounds on) causal effects from
observational data when the causal structure is unknown. We also illustrate
this method on a yeast gene expression data set. We close by mentioning several
extensions of the discussed work.


  An attempt is made to summarize the discussion at the Workshop, except for
the panel discussion on the ability of the LHC detectors to accommodate forward
reactions. The Workshop focused on two main topics. The first topic was forward
physics at the LHC. Predictions were made for forward reactions, including
elastic scattering and soft diffractive processes, intopic was forward physics
at the LHC. Predictions were made for forward reactions, including elastic
scattering and soft diffractive processes, in terms of (multi) Pomeron
exchange, using knowledge gained at lower energies. The survival probability of
rapidity gaps accompanying hard subprocesses was studied. The nature of the
Pomeron, before and after QCD, was exposed, and some aspects of small x physics
at the LHC were considered. The second topic of the Workshop concerned the
accuracy of the luminosity measuring processes at the LHC. Attention
concentrated on three methods. The classic approach based on the optical
theorem, secondly, the observation of the pure QED process of lepton-pair
(l^+l^-) production by photon-photon fusion and, finally, the measurement of
inclusive W and Z production.


  Modeling strategic conflict from a game theoretical perspective involves
dealing with epistemic uncertainty. Payoff uncertainty models are typically
restricted to simple probability models due to computational restrictions.
Recent breakthroughs Artificial Intelligence (AI) research applied to Poker
have resulted in novel approximation approaches such as counterfactual regret
minimization, that can successfully deal with large-scale imperfect games. By
drawing from these ideas, this work addresses the problem of arbitrary
continuous payoff distributions. We propose a method, Harsanyi-Counterfactual
Regret Minimization, to solve two-player zero-sum extensive-form games with
arbitrary payoff distribution models. Given a game $\Gamma$, using a Harsanyi
transformation we generate a new game $\Gamma^\#$ to which we later apply
Counterfactual Regret Minimization to obtain $\varepsilon$-Nash equilibria. We
include numerical experiments showing how the method can be applied to a
previously published problem.


  Parametric imaging of the cerebral metabolic rate for glucose (CMRGlc) using
[18F]-fluorodeoxyglucose positron emission tomography is considered.
Traditional imaging is hindered due to low signal to noise ratios at individual
voxels. We propose to minimize the total variation of the tracer uptake rates
while requiring good fit of traditional Patlak equations. This minimization
guarantees spatial homogeneity within brain regions and good distinction
between brain regions. Brain phantom simulations demonstrate significant
improvement in quality of images by the proposed method as compared to Patlak
images with post-filtering using Gaussian or median filters.


  We study the dynamic network of relationships among avatars in the massively
multiplayer online game Planetside 2. In the spring of 2014, two separate
servers of this game were merged, and as a result, two previously distinct
networks were combined into one. We observed the evolution of this network in
the seven month period following the merger and report our observations. We
found that some structures of original networks persist in the combined network
for a long time after the merger. As the original avatars are gradually
removed, these structures slowly dissolve, but they remain observable for a
surprisingly long time. We present a number of visualizations illustrating the
post-merger dynamics and discuss time evolution of selected quantities
characterizing the topology of the network.


  Adversarial Robustness Toolbox (ART) is a Python library supporting
developers and researchers in defending Machine Learning models (Deep Neural
Networks, Gradient Boosted Decision Trees, Support Vector Machines, Random
Forests, Logistic Regression, Gaussian Processes, Decision Trees, Scikit-learn
Pipelines, etc.) against adversarial threats and helps making AI systems more
secure and trustworthy. Machine Learning models are vulnerable to adversarial
examples, which are inputs (images, texts, tabular data, etc.) deliberately
modified to produce a desired response by the Machine Learning model. ART
provides the tools to build and deploy defences and test them with adversarial
attacks. Defending Machine Learning models involves certifying and verifying
model robustness and model hardening with approaches such as pre-processing
inputs, augmenting training data with adversarial samples, and leveraging
runtime detection methods to flag any inputs that might have been modified by
an adversary. The attacks implemented in ART allow creating adversarial attacks
against Machine Learning models which is required to test defenses with
state-of-the-art threat models. Supported Machine Learning Libraries include
TensorFlow (v1 and v2), Keras, PyTorch, MXNet, Scikit-learn, XGBoost, LightGBM,
CatBoost, and GPy. The source code of ART is released with MIT license at
https://github.com/IBM/adversarial-robustness-toolbox. The release includes
code examples, notebooks with tutorials and documentation
(http://adversarial-robustness-toolbox.readthedocs.io).


  We consider the problem of analytically continuing energies computed with the
Bethe ansatz, as posed by the study of non-compact integrable spin chains. By
introducing an imaginary extensive twist in the Bethe equations, we show that
one can expand the analytic continuation of energies in the scaling limit
around another 'pseudo-vacuum' sitting at a negative number of Bethe roots, in
the same way as around the usual pseudo-vacuum. We show that this method can be
used to compute the energy levels of some states of the $SL(2,\mathbb{C})$
integrable spin chain in the infinite-volume limit, and as a proof of principle
recover the ground-state value previously obtained in [1] (for the case of
spins $s=0, \bar{s}=-1$) by extrapolating results in small sizes. These results
represent, as far as we know, the first (partial) description of the spectrum
of $SL(2,\mathbb{C})$ non-compact spin chains in the thermodynamic limit.


  In this paper, we study degree conditions for three types of disjoint
directed path cover problems: many-to-many $k$-DDPC, one-to-many $k$-DDPC and
one-to-one $k$-DDPC, which are intimately connected to other famous topics in
graph theory, such as Hamiltonicity and $k$-linkage, and have a strong
background of applications.
  Firstly, we get two sharp minimum semi-degree sufficient conditions for the
unpaired many-to-many $k$-DDPC problem and a sharp Ore-type degree condition
for the paired many-to-many $2$-DDPC problem. Secondly, we obtain a minimum
semi-degree sufficient condition for the one-to-many $k$-DDPC problem on a
digraph with order $n$, and show that the bound for the minimum semi-degree is
sharp when $n+k$ is even and is sharp up to an additive constant 1 otherwise.
Finally, we give a minimum semi-degree sufficient condition for the one-to-one
$k$-DDPC problem on a digraph with order $n$, and show that the bound for the
minimum semi-degree is sharp when $n+k$ is odd and is sharp up to an additive
constant 1 otherwise.


  Next-generation sequencing technology enables routine detection of bacterial
pathogens for clinical diagnostics and genetic research. Whole genome
sequencing has been of importance in the epidemiologic analysis of bacterial
pathogens. However, few whole genome sequencing-based genotyping pipelines are
available for practical applications. Here, we present the whole genome
sequencing-based single nucleotide polymorphism (SNP) genotyping method and
apply to the evolutionary analysis of methicillin-resistant Staphylococcus
aureus. The SNP genotyping method calls genome variants using next-generation
sequencing reads of whole genomes and calculates the pair-wise Jaccard
distances of the genome variants. The method may reveal the high-resolution
whole genome SNP profiles and the structural variants of different isolates of
methicillin-resistant S. aureus (MRSA) and methicillin-susceptible S. aureus
(MSSA) strains. The phylogenetic analysis of whole genomes and particular
regions may monitor and track the evolution and the transmission dynamic of
bacterial pathogens. The computer programs of the whole genome sequencing-based
SNP genotyping method are available to the public at
https://github.com/cyinbox/NGS.


  We investigate the flavour and quark-antiquark structure of the polarized
nucleon by calculating the parton distribution functions of the nucleon sea
using the meson cloud model. We find that the SU(2) flavor symmetry in the
light antiquark sea and quark-antiquark symmetry in the strange quark sea are
broken, {\it i.e.} $\Delta\ubar < \Delta \dbar$ and $\Delta s < \Delta \sbar$.
The polarization of the strange sea is found to be positive, which is in
contradiction to previous analyses. We predict a much larger quark-antiquark
asymmetry in the polarized strange quark sea than that in the unpolarized
strange quark sea. Our results for both polarized light quark sea and polarized
strange quark sea are consistent with the recent HERMES data.


  Recent observations of the debris disk of AU Mic have revealed asymmetric,
fast outward-moving arch-like structures above the disk midplane. No model can
readily explain the characteristics of these features. We present a model
aiming to reproduce the dynamics of these structures, more specifically their
high projected speeds and their apparent position. We test the hypothesis of
dust emitted by a point source and then expelled from the system by the strong
stellar wind of this young, M-type star. In this model, we make the assumption
that the dust grains follow the same dynamics as the structures. We perform
numerical simulations of test particle trajectories to explore the available
parameter space, in particular the radial location $R_{0}$ of the dust
producing parent body and the size of the dust grains as parameterized by
$\beta$ (ratio of stellar wind and radiation pressure forces over gravitation).
We consider both the case of a static and an orbiting parent body. We find
that, for all considered scenarii, there is always a set of ($R_0, \beta$)
parameters able to fit the observed features. The common characteristics of
these solutions is that they all require a high value of $\beta$, of around 6.
This means that the star is probably very active and the grains composing the
structures are sub-micronic, in order to reach such high $\beta$ values. As for
the location of the hypothetical parent body, we constrain it to lie around 8
au (orbiting case) or 28 au (static case). We show that the scenario of
sequential dust releases by an unseen, punctual parent body is able to explain
the radial behaviour of the observed structures. We predict the evolution of
the structures to help future observations to discriminate between the
different parent body configurations that have been considered. We expect new
structures to appear on the northwest side of the disk in the coming years.


  In this proceeding we propose a new procedure to impose the Schroedinger
functional Dirichlet boundary condition on the overlap Dirac operator and the
domain-wall fermion using an orbifolding projection.
  With this procedure the zero mode problem with Dirichlet boundary condition
can easily be avoided.


  String theory has been claimed to give rise to natural fuzzy dark matter
candidates in the form of ultralight axions. In this paper we revisit this
claim by a detailed study of how moduli stabilisation affects the masses and
decay constants of different axion fields which arise in type IIB flux
compactifications. We find that obtaining a considerable contribution to the
observed dark matter abundance without tuning the axion initial misalignment
angle is not a generic feature of 4D string models since it requires a mild
violation of the $S f\lesssim M_P$ bound, where $S$ is the instanton action and
$f$ the axion decay constant. Our analysis singles out $C_4$-axions,
$C_2$-axions and thraxions as the best candidates to realise fuzzy dark matter
in string theory. For all these ultralight axions we provide predictions which
can be confronted with present and forthcoming observations.


  Starting from the representation of a function $f(x,y)$ as a formal power
series with Taylor coefficients $f_{m,n}$, we establish a formal series for the
implicit function $y=y(x)$ such that $f(x,y)=0$ and the coefficients of the
series for $y$ depend exclusively on the $f_{m,n}$. The solution to this
problem provided here relies on using partial Bell polynomials and their
orthogonal companions.


  We obtain a Lagrangian for a lightlike dimensional reduction of the
nonabelian M5 brane theory in six dimension. We assume that the six-manifold
has at least one conformal Killing spinor and two commuting lightlike Killing
vector fields, and we perform the dimensional reduction along one of these
lightlike directions.


  For two-dimensional holographic CFTs, we demonstrate the role of Berry phases
for relating the non-factorization of the Hilbert space to the presence of
wormholes. The wormholes are characterized by a non-exact symplectic form that
gives rise to the Berry phase. For wormholes connecting two spacelike regions
in gravitational spacetimes, we find that the non-exactness is linked to a
variable appearing in the phase space of the boundary CFT. This variable
corresponds to a loop integral in the bulk. Through this loop integral,
non-factorization becomes apparent in the dual entangled CFTs. Furthermore, we
classify Berry phases in holographic CFTs based on the type of dual bulk
diffeomorphism involved. We distinguish between Virasoro, gauge and modular
Berry phases, each corresponding to a spacetime wormhole geometry in the bulk.
Using kinematic space, we extend a relation between the modular Hamiltonian and
the Berry curvature to the finite temperature case. We find that the Berry
curvature, given by the Crofton form, characterizes the topological transition
of the entanglement entropy in presence of a black hole.


  The transients following GRB970228 and GRB970508 showed that these (and
probably all) GRBs are cosmological. However, the host galaxies expected to be
associated with these and other bursts are largely absent, indicating that
either bursts are further than expected or the host galaxies are underluminous.
This apparent discrepancy does not invalidate the cosmological hypothesis, but
instead host galaxy observations can test more sophisticated models.


  We study the dynamics of one and two dimensional disordered lattice
bosons/fermions initialized to a Fock state with a pattern of $1$ and $0$
particles on $A$ and ${\bar A}$ sites. For non-interacting systems we establish
a universal relation between the long time density imbalance between $A$ and
${\bar A}$ site, $I(\infty)$, the localization length $\xi_l$, and the geometry
of the initial pattern. For alternating initial pattern of $1$ and $0$
particles in 1 dimension, $I(\infty)=\tanh[a/\xi_l]$, where $a$ is the lattice
spacing. For systems with mobility edge, we find analytic relations between
$I(\infty)$, the effective localization length $\tilde{\xi}_l$ and the fraction
of localized states $f_l$. The imbalance as a function of disorder shows
non-analytic behaviour when the mobility edge passes through a band edge. For
interacting bosonic systems, we show that dissipative processes lead to a decay
of the memory of initial conditions. However, the excitations created in the
process act as a bath, whose noise correlators retain information of the
initial pattern. This sustains a finite imbalance at long times in strongly
disordered interacting systems.


  A remote data acquisition chassis, called a HOTLink Rack Monitor, HRM, has
been developed for use in the Fermilab control system. This chassis provides
for 64 analog input channels, 8 analog output channels, and 8 bytes of digital
I/O. The interface to the host VMEbus crate is by way of a 320 MHz HOTLink
serial connection to a PMC mezzanine module. With no processor intervention,
all data sources in the remote chassis are read at 100 microsec intervals,
timestamped, and stored in a 2 MB circular buffer on the PMC module. In
operation, the memory always contains the most recent 16k samples of 10 kHz
data from all 64 analog input channels. An expansion module that resides in the
HRM chassis records snapshot data for 8 analog channels, each channel
consisting of up to 16k readings, digitized at rates up to 10 MHz. Snapshot
data is also returned and stored in buffers on the PMC module. Because the HRM
presents a memory-mapped interface to the host, it is independent of the
operating system and may be used in any system that supports PMC mezzanine
modules.


  Diagrammatic expansions are a central tool for treating correlated electron
systems. At thermal equilibrium, they are most naturally defined within the
Matsubara formalism. However, extracting any dynamic response function from a
Matsubara calculation ultimately requires the ill-defined analytical
continuation from the imaginary- to the real-frequency domain. It was recently
proposed [Phys. Rev. B 99, 035120 (2019)] that the internal Matsubara
summations of any interaction-expansion diagram can be performed analytically
by using symbolic algebra algorithms. The result of the summations is then an
analytical function of the complex frequency rather than Matsubara frequency.
Here we apply this principle and develop a diagrammatic Monte Carlo technique
which yields results directly on the real-frequency axis. We present results
for the self-energy $\Sigma(\omega)$ of the doped 32x32 cyclic square-lattice
Hubbard model in a non-trivial parameter regime, where signatures of the
pseudogap appear close to the antinode. We discuss the behavior of the
perturbation series on the real-frequency axis and in particular show that one
must be very careful when using the maximum entropy method on truncated
perturbation series. Our approach holds great promise for future application in
cases when analytical continuation is difficult and moderate-order perturbation
theory may be sufficient to converge the result.


  Electron transport in suspended and non-suspended GaAs point contacts (PCs)
of different widths is experimentally studied. The superballistic contribution
to the conductance, that demonstrates a distinctive quadratic dependence on the
PC width and temperature growth, is extracted from the experiment. The studied
PCs are shown to be described in the framework of hydrodynamic electron flow a
in wide temperature range. At low temperatures, $T$, the viscosity is found out
to obey the law $1/T^2$ expected for 2D systems, while at higher temperatures
it has the dependence $1/T$. Similar measurements performed after the
suspension of PCs, i.e. their separation from substrate, show that the electron
viscosity reduces in the whole temperature range, that indicates an enhanced
electron-electron interaction in suspended structures.


  In this paper, we describe the growth and characterization of 530-nm-thick
superlattices (100 periods) of AlxGa1-xN/AlN (x = 0, 0.1) Stranski-Krastanov
quantum dots for application as the active region of electron-beam pumped
ultraviolet lamps. Highly dense (>10e11 cm-2) quantum dot layers are deposited
by molecular beam epitaxy, and we explore the effect of the III/V ratio during
the growth process on their optical performance. The study considers structures
emitting in the 244-335 nm range at room temperature, with a relative linewidth
in the 6-11% range, mainly due to the QD diameter dispersion inherent in
self-assembled growth. Under electron pumping, the emission efficiency remains
constant for acceleration voltages below 9 kV. The correlation of this
threshold with the total thickness of the superlattice and the penetration
depth of the electron beam confirms the homogeneity of the nanostructures along
the growth axis. Below the threshold, the emission intensity scales linearly
with the injected current. The internal quantum efficiency is characterized at
low injection, which reveals the material properties in terms of non-radiative
processes, and high injection, which emulates carrier injection in operation
conditions. In quantum dots synthesized with III/V ratio < 0.75, the internal
quantum efficiency remains around 50% from low injection to pumping power
densities as high as 200 kW/cm2, being the first kind of nanostructures that
present such stable behaviour.


  Finding microscopic models for metallic states that exhibit quantum critical
properties such as $\omega/T$ scaling is a major theoretical challenge. We
calculate the local dynamical spin susceptibility $\chi(T,\omega)$ for a
Hubbard model at half filling using Dynamical Mean-Field Theory, which is exact
in infinite dimensions. Qualitatively distinct behavior is found in the
different regions of the phase diagram: Mott insulator, Fermi liquid metal, bad
metal, and a quantum critical region above the finite temperature critical
point. The signature of the latter is $\omega/T$ scaling where $T$ is the
temperature. Our results are consistent with previous results showing scaling
of the dc electrical conductivity and are relevant to experiments on organic
charge transfer salts.


  We present simple classical dynamical models to address the question of
introducing a stochastic nature in a time variable. These models include noise
in the time variable but not in the "space" variable, which is opposite to the
normal description of stochastic dynamics. The notable feature is that these
models can induce a "resonance" with varying noise strengths in the time
variable. Thus, they provide a different mechanism for ``stochastic
resonance'', which has been discussed within the normal context of stochastic
dynamics. In a broader context, we expect these simple models and associated
behaviours may serve as one of the starting points to construct and investigate
an extended classical dynamical theory where both the space and time variables
are taken as stochastic. We need to explore if such approaches can be further
developed for both classical and quantum applications.


  In this paper existence and nonexistence results of positive radial solutions
of a Dirichlet $m$-Laplacian problem with different weights and a diffusion
term inside the divergence of the form $\big(a(|x|)+g(u)\big)^{-\gamma}$, with
$\gamma>0$ and $a$, $g$ positive functions satisfying natural growth
conditions, are proved. Precisely, we obtain a new critical exponent
$m^*_{\alpha,\beta,\gamma}$, which extends the one relative to case with no
diffusion and it divides existence from nonexistence of positive radial
solutions. The results are obtained via several tools such as a suitable
modification of the celebrated blow up technique, Liouville type theorems, a
fixed point theorem and a Poho\v zaev-Pucci-Serrin type identity.


  We study the field theoretical description of a generic theory of gravity
flowing to Einstein General Relativity in IR. We prove that, if ghost-free, in
the weakly coupled regime such a theory can never become weaker than General
Relativity. Using this fact, as a byproduct, we suggest that in a ghost-free
theory of gravity trans-Planckian propagating quantum degrees of freedom cannot
exist. The only physical meaning of a trans-Planckian pole is the one of a
classical state (Black Hole) which is described by the light IR quantum degrees
of freedom and gives exponentially-suppressed contributions to virtual
processes. In this picture Einstein gravity is UV self-complete, although not
Wilsonian, and sub-Planckian distances are unobservable in any healthy theory
of gravity. We then finally show that this UV/IR correspondence puts a severe
constraint on any attempt of conventional Wilsonian UV-completion of
trans-Planckian gravity. Specifically, there is no well-defined energy domain
in which gravity could become asymptotically weak or safe.


  The influence of magnetostriction on static fluctuations of the magnetic
moment in ferromagnetic polycrystals has been theoretically studied. Conditions
have been found under which magnetoelastic interaction leads to stabilization
of long-range magnetic order in these systems.


  Large-scale machine learning training suffers from two prior challenges,
specifically for nuclear-norm constrained problems with distributed systems:
the synchronization slowdown due to the straggling workers, and high
communication costs. In this work, we propose an asynchronous Stochastic Frank
Wolfe (SFW-asyn) method, which, for the first time, solves the two problems
simultaneously, while successfully maintaining the same convergence rate as the
vanilla SFW. We implement our algorithm in python (with MPI) to run on Amazon
EC2, and demonstrate that SFW-asyn yields speed-ups almost linear to the number
of machines compared to the vanilla SFW.


  There is a pressing need to develop new rechargeable battery technologies
that can offer higher energy storage, faster charging, and lower costs. Despite
the success of existing methods for the simulation of battery materials, they
can sometimes fall short of delivering accurate and reliable results. Quantum
computing has been discussed as an avenue to overcome these issues, but only
limited work has been done to outline how they may impact battery simulations.
In this work, we provide a detailed answer to the following question: how can a
quantum computer be used to simulate key properties of a lithium-ion battery?
Based on recently-introduced first-quantization techniques, we lay out an
end-to-end quantum algorithm for calculating equilibrium cell voltages, ionic
mobility, and thermal stability. These can be obtained from ground-state
energies of materials, which is the core calculation executed by the quantum
computer using qubitization-based quantum phase estimation. The algorithm
includes explicit methods for preparing approximate ground states of periodic
materials in first quantization. We bring these insights together to perform
the first estimation of the resources required to implement a quantum algorithm
for simulating a realistic cathode material, dilithium iron silicate.


  In this work we construct an optimal shrinkage estimator for the precision
matrix in high dimensions. We consider the general asymptotics when the number
of variables $p\rightarrow\infty$ and the sample size $n\rightarrow\infty$ so
that $p/n\rightarrow c\in (0, +\infty)$. The precision matrix is estimated
directly, without inverting the corresponding estimator for the covariance
matrix. The recent results from the random matrix theory allow us to find the
asymptotic deterministic equivalents of the optimal shrinkage intensities and
estimate them consistently. The resulting distribution-free estimator has
almost surely the minimum Frobenius loss. Additionally, we prove that the
Frobenius norms of the inverse and of the pseudo-inverse sample covariance
matrices tend almost surely to deterministic quantities and estimate them
consistently. At the end, a simulation is provided where the suggested
estimator is compared with the estimators for the precision matrix proposed in
the literature. The optimal shrinkage estimator shows significant improvement
and robustness even for non-normally distributed data.


  In this paper a new high order semi-implicit discontinuous Galerkin method
(SI-DG) is presented for the solution of the incompressible Navier-Stokes
equations on staggered space-time adaptive Cartesian grids (AMR) in two and
three space-dimensions. The pressure is written in the form of piecewise
polynomials on the main grid, which is dynamically adapted within a
cell-by-cell AMR framework. According to the time dependent main grid,
different face-based spatially staggered dual grids are defined for the
piece-wise polynomials of the respective velocity components. Arbitrary high
order of accuracy is achieved in space, while a very simple semi-implicit time
discretization is obtained via an explicit discretization of the nonlinear
convective terms, and an implicit discretization of the pressure gradient in
the momentum equation and of the divergence of the velocity field in the
continuity equation. The real advantages of the staggered grid arise in the
solution of the Schur complement associated with the saddle point problem of
the discretized incompressible Navier-Stokes equations, i.e. after substituting
the discrete momentum equations into the discrete continuity equation. This
leads to a linear system for only one unknown, the scalar pressure. Indeed, the
resulting linear pressure system is shown to be symmetric and
positive-definite. The new space-time adaptive staggered DG scheme has been
thoroughly verified for a large set of non-trivial test problems in two and
three space dimensions, for which analytical, numerical or experimental
reference solutions exist. To the knowledge of the authors, this is the first
staggered semi-implicit DG scheme for the incompressible Navier-Stokes
equations on space-time adaptive meshes in two and three space dimensions.


  The Kodaira dimension of Shimura varieties has been studied by many people.
Kondo and Gritsenko-Hulek-Sankaran studied the singularities of orthogonal
Shimura varieties related to the moduli spaces of polarized K3 surfaces. They
proved that they have canonical singularities and are of general type if the
polarization degree is sufficiently large. In this paper, we work on similar
problems for unitary Shimura varieties. We show that they have canonical
singularities if $n > 4$. As an application, we show that certain unitary
Shimura varieties associated with Hermitian forms over the rings of integers of
$\mathbb{Q}(\sqrt{-1})$, $\mathbb{Q}(\sqrt{-3})$ are of general type. We use
modular forms of low weight vanishing on ramification divisors, which are the
restrictions of the quasi-pullbacks of the Borcherds form $\Phi_{12}$.


  We calculate contributions to the photon and gluon magnetic dipole operators
that mediate b -> s gamma and b -> d gamma transitions in the Randall-Sundrum
model of a warped extra dimension with anarchic bulk fermions and a brane
localized Higgs. Unlike the Standard Model, there are large contributions to
the left-handed b quark decays, parameterized by the Wilson coefficient C'_7,
due to the pattern of bulk fermion localization, and sizable contributions from
the gluonic penguins, C(')_8, through renormalization group mixing. Further,
unlike the Randall-Sundrum result for mu -> e gamma, the unprimed Wilson
coefficients receive non-negligible contributions from the misalignment of the
bulk fermion spectrum with the Standard Model flavor sector. We compare the
size of effects and the constraints imposed by the branching ratios Br(B -> X_s
gamma) and <Br(B -> X_d gamma)> within the minimal and the custodial model.
Within the custodial framework, we study the effect on a number of benchmark
observables and find that Br(B -> X_s mu^+ mu^-) and the forward-backward
asymmetry in B -> K^* mu^+ mu^- remain close to their Standard Model
predictions. On the other hand, there can be large enhancements of the
time-dependent CP asymmetry in B -> K^* gamma and the transverse asymmetry
A_T^(2).


  Author suggests and analyzes new universal trapping method of comparatively
slow-speed particles of a rarefied gas medium in the potential well induced by
the nonhomogeneous electromagnetic field increasing with time (up to some
moment). Given method is especially effective at inelastic collisions of
particles with walls of the gas cell when necessary preliminary slowdown of
particles is possible for their following capture even to a highly shallow
potential depth. Corresponding sufficiently compact and simple electromagnetic
traps may be used for capture and accumulation not only slow-speed micro- and
nano-particles in the high vacuum but also atoms and molecules of a rarefied
gas in a cell.


  Optimal engine operation during a transient driving cycle is the key to
achieving greater fuel economy, engine efficiency, and reduced emissions. In
order to achieve continuously optimal engine operation, engine calibration
methods use a combination of static correlations obtained from dynamometer
tests for steady-state operating points and road and/or track performance data.
As the parameter space of control variables, design variable constraints, and
objective functions increases, the cost and duration for optimal calibration
become prohibitively large. In order to reduce the number of dynamometer tests
required for calibrating modern engines, a large-scale simulation-driven
machine learning approach is presented in this work. A parallel, fast, robust,
physics-based reduced-order engine simulator is used to obtain performance and
emission characteristics of engines over a wide range of control parameters
under various transient driving conditions (drive cycles). We scale the
simulation up to 3,906 nodes of the Theta supercomputer at the Argonne
Leadership Computing Facility to generate data required to train a machine
learning model. The trained model is then used to predict various engine
parameters of interest. Our results show that a deep-neural-network-based
surrogate model achieves high accuracy for various engine parameters such as
exhaust temperature, exhaust pressure, nitric oxide, and engine torque. Once
trained, the deep-neural-network-based surrogate model is fast for inference:
it requires about 16 micro sec for predicting the engine performance and
emissions for a single design configuration compared with about 0.5 s per
configuration with the engine simulator. Moreover, we demonstrate that transfer
learning and retraining can be leveraged to incrementally retrain the surrogate
model to cope with new configurations that fall outside the training data
space.


  In this paper, the exponential B-spline functions are used for the numerical
solution of the RLW equation. Three numerical examples related to propagation
of single solitary wave, interaction of two solitary waves and wave generation
are employed to illustrate the accuracy and the efficiency of the method.
Obtained results are compared with some early studies.