  The extremely neutron-rich system $^{7}$H was studied in the direct
$^2$H($^8$He,$^3$He)$^7$H transfer reaction with a 26 AMeV secondary $^{8}$He
beam [Bezbakh et al., Phys. Rev. Lett. 124 (2020) 022502]. The missing mass
spectrum and center-of-mass (c.m.) angular distributions of $^{7}$H, as well as
the momentum distribution of the $^{3}$H fragment in the $^{7}$H frame, were
constructed. In addition to the investigation reported in Ref. [Bezbakh et al.,
Phys. Rev. Lett. 124 (2020) 022502], we carried out another experiment with the
same beam but a modified setup, which was cross-checked by the study of the
$^2$H($^{10}$Be,$^3$He$)^{9}$Li reaction. A solid experimental evidence is
provided that two resonant states of $^{7}$H are located in its spectrum at
2.2(5) and 5.5(3) MeV relative to the $^3$H+4$n$ decay threshold. Also, there
are indications that the resonant states at 7.5(3) and 11.0(3) MeV are present
in the measured $^{7}$H spectrum. Based on the energy and angular
distributions, obtained for the studied $^2$H($^8$He,$^3$He)$^7$H reaction, the
weakly populated 2.2(5) MeV peak is ascribed to the $^7$H ground state. It is
highly plausible that the firmly ascertained 5.5(3) MeV state is the $5/2^+$
member of the $^7$H excitation $5/2^+$-$3/2^+$ doublet, built on the $2^+$
configuration of valence neutrons. The supposed 7.5 MeV state can be another
member of this doublet, which could not be resolved in Ref. [Bezbakh et al.,
Phys. Rev. Lett. 124 (2020) 022502]. Consequently, the two doublet members
appeared in the spectrum of $^{7}$H in [Bezbakh et al., Phys. Rev. Lett. 124
(2020) 022502] as a single broad 6.5 MeV peak.


  In recent paper "Quantifying Inequities and Documenting Elitism in
PhD-granting Mathematical Sciences Departments in the United States"
(arXiv:2308.13750) by a group of accomplished and/or aspiring mathematicians,
the authors use data to challenge ``the idea that the mathematical sciences in
the United States is a meritocracy''. We show that, regardless of the validity
of the challenged idea, the arguments in the paper are invalid. Namely, among
other flaws, they rely on a logical trick of asserting the validity of the
conclusion derived from an assumption which the authors neglect to test (and
which actually contradicts their own data).


  We show that Nori's fundamental group scheme \pi(X,x) does not base change
correctly under extension of the base field for certain smooth projective
ordinary curves X of genus 2 defined over a field of characteristic 2.


  This article introduces CluBear, a Python-based open-source package for
interactive massive data analysis. The key feature of CluBear is that it
enables users to conduct convenient and interactive statistical analysis of
massive data with only a traditional single-computer system. Thus, CluBear
provides a cost-effective solution when mining large-scale datasets. In
addition, the CluBear package integrates many commonly used statistical and
graphical tools, which are useful for most commonly encountered data analysis
tasks.


  We propose a novel method for controlling the high-harmonic generation (HHG)
with a high dynamic range in single-layer graphene. We find that, by utilizing
shear strain, a significant enhancement or quenching of HHG is possible over a
range of several orders of magnitude. This feature is made possible by the
resonance mechanism at a van Hove singularity. Therein, the shear strain
controls the configurations of the two Dirac cones, resulting in changes in the
energy and dipole moment at the saddle point of the band dispersion. Our
findings provide a way for modulating or switching light by using a
nano-optomechanical device composed of single-layer graphene.


  This work sets out to compute and discuss effects of spin, velocity and
dimensionality on inter-particle potentials systematically derived from gauge
field-theoretic models. We investigate the interaction of fermionic particles
by the exchange of a vector field in a parity-preserving description in
five-dimensional $(5D)$ space-time. A particular dimensional reduction
prescription is adopted $-$ reduction by dimensional restriction $-$ and
special effects, like a pseudo-spin dependence, show up in four dimensions
$(4D)$. What we refer to as pseudo-spin shall be duly explained. The main idea
we try to convey is that the calculation of the potentials in $5D$ and the
consequent reduction to $4D$ exhibits new effects that are not present if the
potential is calculated in $4D$ after the action has been reduced.


  We present here a result of Monomialization of real analytic two-symmetric
tensor fields over regular real analytic surfaces. We apply it to the
(extension of the pull-back of the) inner metric of a resolved surface of a
real analytic surface singularity. Doing so we recover Hsiang & Pati property
at each point of the resolved surface.


  We consider a semi-supervised classification problem with non-stationary
label-shift in which we observe a labelled data set followed by a sequence of
unlabelled covariate vectors in which the marginal probabilities of the class
labels may change over time. Our objective is to predict the corresponding
class-label for each covariate vector, without ever observing the ground-truth
labels, beyond the initial labelled data set. Previous work has demonstrated
the potential of sophisticated variants of online gradient descent to perform
competitively with the optimal dynamic strategy (Bai et al. 2022). In this work
we explore an alternative approach grounded in statistical methods for adaptive
transfer learning. We demonstrate the merits of this alternative methodology by
establishing a high-probability regret bound on the test error at any given
individual test-time, which adapt automatically to the unknown dynamics of the
marginal label probabilities. Further more, we give bounds on the average
dynamic regret which match the average guarantees of the online learning
perspective for any given time interval.


  We deal with generalizations of the Fundamental Theorem of Projective
Geometry to other related geometries (of dimension $\geq 3$) and non bijective
maps. We consider locally projective geometries and locally affino-projective
geometries (which include the classical M\"obius, Laguerre and Minkowski
geometries).


  This paper introduces a novel approach to solve inverse problems by
leveraging deep learning techniques. The objective is to infer unknown
parameters that govern a physical system based on observed data. We focus on
scenarios where the underlying forward model demonstrates pronounced nonlinear
behaviour, and where the dimensionality of the unknown parameter space is
substantially smaller than that of the observations. Our proposed method builds
upon physics-informed neural networks (PINNs) trained with a hybrid loss
function that combines observed data with simulated data generated by a known
(approximate) physical model. Experimental results on an orbit restitution
problem demonstrate that our approach surpasses the performance of standard
PINNs, providing improved accuracy and robustness.


  Citation networks represent the flow of information between agents. They are
constrained in time and so form directed acyclic graphs which have a causal
structure. Here we provide novel quantitative methods to characterise that
structure by adapting methods used in the causal set approach to quantum
gravity by considering the networks to be embedded in a Minkowski spacetime and
measuring its dimension using Myrheim-Meyer and Midpoint-scaling estimates. We
illustrate these methods on citation networks from the arXiv, supreme court
judgements from the USA, and patents and find that otherwise similar citation
networks have measurably different dimensions. We suggest that these
differences can be interpreted in terms of the level of diversity or narrowness
in citation behaviour.


  Let p be a prime = 3 (mod 4). A number of elegant number-theoretical
properties of the sums T(p) = \sqrt{p}sum_{n=1}^{(p-1)/2} tan(n^2\pi/p) and
C(p) = \sqrt{p}sum_{n=1}^{(p-1)/2} cot(n^2\pi/p) are proved. For example, T(p)
equals p times the excess of the odd quadratic residues over the even ones in
the set {1,2,...,p-1}; this number is positive if p = 3 (mod 8) and negative if
p = 7 (mod 8). In this revised version the connection of these sums with the
class-number h(-p) is also discussed. For example, a very simple formula
expressing h(-p) by means of the aforementioned excess is proved. The
bibliography has been considerably enriched. This article is of an expository
nature.


  One of the main problems of the research area of network coding is to compute
good lower and upper bounds of the achievable cardinality of so-called subspace
codes in $\operatorname{PG}(n,q)$, i.e., the set of subspaces of
$\mathbb{F}_q^n$, for a given minimal distance. Here we generalize a
construction of Etzion and Silberstein to a wide range of parameters. This
construction, named coset construction, improves or attains several of the
previously best-known subspace code sizes and attains the MRD bound for an
infinite family of parameters.


  The Laser Ranging Interferometer onboard the Gravity Recovery and Climate
Experiment Follow-On mission proved the feasibility of an interferometric
sensor for inter-satellite length tracking with sub-nanometer precision,
establishing an important milestone for space laser interferometry and the
general expectation that future gravity missions will employ heterodyne laser
interferometry for satellite-to-satellite ranging. In this paper we present the
design of an on-axis optical bench for next-generation laser ranging which
enhances the received optical power and the transmit beam divergence, enabling
longer interferometer arms and relaxing the optical power requirement of the
laser assembly. All design functionalities and requirements are verified by
means of computer simulations. A thermal analysis is carried out to investigate
the robustness of the proposed optical bench to the temperature fluctuations
found in orbit.


  In this paper we discuss connections between the following properties: (RFM)
residual finiteness of a monoid M; (RFSG) residual finiteness of Schutzenberger
groups of M; and (RFRL) residual finiteness of the natural actions of M on its
Green's R- and L-classes. The general question is whether (RFM) implies (RFSG)
and/or (RFRL), and vice versa. We consider these questions in all the possible
combinations of the following situations: M is an arbitrary monoid; M is an
arbitrary regular monoid; every J-class of M has finitely many R- and
L-classes; M has finitely many left and right ideals. In each case we obtain
complete answers.


  The reliable detection of single quantum particles has revolutionized the
field of quantum optics and quantum information processing. For several years,
researchers have aspired to extend such detection possibilities to larger scale
strongly correlated quantum systems, in order to record in-situ images of a
quantum fluid in which each underlying quantum particle is detected. Here we
report on fluorescence imaging of strongly interacting bosonic Mott insulators
in an optical lattice with single-atom and single-site resolution. From our
images, we fully reconstruct the atom distribution on the lattice and identify
individual excitations with high fidelity. A comparison of the radial density
and variance distributions with theory provides a precise in-situ temperature
and entropy measurement from single images. We observe Mott-insulating plateaus
with near zero entropy and clearly resolve the high entropy rings separating
them although their width is of the order of only a single lattice site.
Furthermore, we show how a Mott insulator melts for increasing temperatures due
to a proliferation of local defects. Our experiments open a new avenue for the
manipulation and analysis of strongly interacting quantum gases on a lattice,
as well as for quantum information processing with ultracold atoms. Using the
high spatial resolution, it is now possible to directly address individual
lattice sites. One could, e.g., introduce local perturbations or access regions
of high entropy, a crucial requirement for the implementation of novel cooling
schemes for atoms on a lattice.


  How can urban movement data be exploited in order to improve the flow of
traffic within a city? Movement data provides valuable information about routes
and specific roads that people are likely to drive on. This allows us to
pinpoint roads that occur in many routes and are thus sensitive to congestion.
Redistributing some of the traffic to avoid unnecessary use of these roads
could be a key factor in improving traffic flow. Many proposed approaches to
combat congestion are either static or do not incorporate any movement data. In
this work, we present a method to redistribute traffic through the introduction
of externally imposed variable costs to each road segment, assuming that all
drivers seek to drive the cheapest route. We use a metaheuristic optimisation
approach to minimise total travel times by optimising a set of road-specific
variable cost parameters, which are used as input for an objective function
based on traffic flow theory. The optimisation scenario for the city centre of
Tokyo considered in this paper was defined using public spatial road network
data, and movement data acquired from Foursquare. Experimental results show
that our proposed scenario has the potential to achieve a 62.6\% improvement of
total travel time in Tokyo compared to that of a currently operational road
network configuration, with no imposed variable costs.


  Introduced over a decade ago, Chrome extensions now exceed 200,000 in number.
In 2020, Google announced a shift in extension development with Manifest
Version 3 (V3), aiming to replace the previous Version 2 (V2) by January 2023.
This deadline was later extended to January 2025. The company's decision is
grounded in enhancing three main pillars: privacy, security, and performance.
This paper presents a comprehensive analysis of the Manifest V3 ecosystem. We
start by investigating the adoption rate of V3, detailing the percentage of
adoption from its announcement up until 2024. Our findings indicate, prior to
the 2023 pause, less than 5% of all extensions had transitioned to V3, despite
the looming deadline for the complete removal of V2, while currently nine out
of ten new extensions are being uploaded in Manifest V3. Furthermore, we
compare the security and privacy enhancements between V2 and V3 and we evaluate
the improved security attributable to V3's safer APIs, examining how certain
APIs, which were vulnerable or facilitated malicious behavior, have been
deprecated or removed in V3. We dynamically execute 517 confirmed malicious
extensions and we see a 87.8% removal of APIs related to malicious behavior due
to the improvements of V3. We discover that only 154 (29.8%) of these
extensions remain functional post-conversion. This analysis leads to the
conclusion that V3 reduces the avenues for abuse of such APIs. However, despite
the reduction in APIs associated with malicious activities, the new Manifest V3
protocol is not immune to such behavior. Our research demonstrates, through a
proof of concept, the adaptability of malicious activities to V3. After the
proof of concept changes are applied, we showcase 290 (56%) of the examined
malicious extensions retain their capability to conduct harmful activities
within the V3 framework.


  We consider exact solutions to the problem of ballistic aggregation in a flow
of adhesive particles, providing a model for large-scale structure formation in
cosmology within the framework of the Zel'dovich approximation. Two different
explicit variational constructions, suggested for this problem by (A.
Shnirelman, 1986) and (E Weinan et al, 1996) in the case of flat symmetry, are
shown to be equivalent. Explicit solutions are constructed for flows of
cylindric and spherical symmetry. Two explicit counterexamples, showing that
natural generalizations of both variational constructions fail to provide
solutions for nonsymmetric multidimensional flows, are presented.


  In this paper, we present a probabilistic algorithm to compute the number of
$\mathbb{F}_p$-points of modular curve $X_1(n)$. Under the Generalized Riemann
Hypothesis(GRH), the algorithm takes
$\textrm{O}(n^{56+\delta+\epsilon}\log^{9+\epsilon} p)$ bit operations, where
$\delta$ is an absolute constant and $\epsilon$ is any positive real number. As
an application, we can compute $#X_1(17)(\mathbb{F}_p)\textrm{mod} 17$ for huge
primes $p$. For example, we have
$#X_1(17)(\mathbb{F}_{10^{1000}+1357})\textrm{mod} 17=3$.


  Despite consistent progress in numerical simulations, the observable
properties of galaxy clusters are difficult to predict ab initio. It is
therefore important to compare both theoretical and observational results to a
direct measure of the cluster mass. This can be done by measuring the
gravitational lensing effects caused by the bending of light by the cluster
mass distribution. In this review we discuss how this phenomenon can be used to
determine cluster masses and study the mass distribution itself. As sample
sizes increase, the accuracy of the weak lensing mass estimates needs to
improve accordingly. We discuss the main practical aspects of these
measurements. We review a number of applications and highlight some recent
results.


  These are lectures notes prepared for a series of seminars I am invited to
give at Princeton Philosophy Department in November 2024. They cover the
conceptual structure of quantum gravity, the relational interpretation of
quantum mechanics, the structure of time, its orientation and the openness of
the future, the physical underpinning of information and meaning, and some
general considerations on the fact that concepts evolve, on perspectivalism and
anti-foundationalism.


  Leukaemia accounts for around 3% of all cancer types diagnosed in adults, and
is the most common type of cancer in children of paediatric age. There is
increasing interest in the use of mathematical models in oncology to draw
inferences and make predictions, providing a complementary picture to
experimental biomedical models. In this paper we recapitulate the state of the
art of mathematical modelling of leukaemia growth dynamics, in time and
response to treatment. We intend to describe the mathematical methodologies,
the biological aspects taken into account in the modelling, and the conclusions
of each study. This review is intended to provide researchers in the field with
solid background material, in order to achieve further breakthroughs in the
promising field of mathematical biology.


  The effect of Pr substitution on the superconductivity and interlayer
coupling of Bi2212 system is investigated. Magnetic and transport measurements
were performed for purposes of characterization. The superconducting transition
temperature Tc first increased and then decreased till it became zero at x=0.6.
The effective superconducting volume also decreases due to Pr substitution.
  From the fluctuation conductivity analyses it is found that the interlayer
coupling constant J decreases monotonically with increase of the Pr content.
This result shows that the Pr doping weakens the CuO2 interlayer coupling of
Bi2212 system due to the loss of local superconductivity in the CuO2 layers.


  Let $n$ be a large number. A subset $A$ of $Z_n$ is complete if $S_A = Z_n$,
where $S_A$ is the collection of the subset sums of $A$. Olson proved that if
$n$ is a prime and $|A|> 2n^{1/2} $, then $S_A$ is complete. We show that a
similar result for the case when $n$ is a composite number, using a different
approach.


  We consider the influence of acceleration on the radiative energy shifts of
atoms in Minkowski space. We study a two-level atom coupled to a scalar quantum
field. Using a Heisenberg picture approach, we are able to separate the
contributions of vacuum fluctuations and radiation reaction to the Lamb shift
of the two-level atom. The resulting energy shifts for the special case of a
uniformly accelerated atom are then compared with those of an atom at rest.


  Does more information elicit users compliance and engagement, or the other
way around? This paper explores the relationship between content strategy and
user experience (UX). Specifically, we examine how the amount of information
provided on marketing web pages, often called landing pages,impact users
willingness to provide their e-mail address (a behavior called conversion in
marketing terms). We describe the results of two large-scale online experiments
(n= 535 and n= 27,900) conducted in real-world commercial settings. The
observed results indicate a negative correlation between the amount of
information on a web page and users decision-making and engagement.


  Superheavy ($M>10^{10}$ GeV) particles produced during inflation may be the
dark matter, independent of their interaction strength. Strongly interacting
superheavy particles will be captured by the sun, and their annihilation in the
center of the sun will produce a flux of energetic neutrinos that should be
detectable by neutrino telescopes. Depending on the particle mass, event rates
in a cubic-kilometer detector range from several per hour to several per year.
The signature of the process is a predominance of tau neutrinos, with a
relatively flat energy spectrum of events ranging from 50 GeV to many TeV, and
with the mean energy of detected tau neutrinos about 3 TeV.


  Bubbles have always captivated our curiosity with their aesthetics and
complexities alike. While the act of blowing bubbles is familiar to everyone,
the underlying physics of these fleeting spheres often eludes reasoning. In
this letter, we discuss the dynamics of inflating a soap bubble using
controlled airflow through a film-coated nozzle. We assess and predict the rate
of inflation by varying the source pressure. Visualising the previously
unexplored internal flow reveals that air enters the bubble as a round jet,
emerging from the nozzle opening and impinges on the expanding concave bubble
interface to form a toroidal vortex. Several scaling laws of the associated
vortical flow spanning the entire bubble and the vortex core are reported. The
observed dynamics of this bubble-confined vortex ring formation indicate
universality in certain aspects when compared to the free laminar vortex rings.


  We investigate the phase diagrams of the effective spin models derived from
Fermi-Hubbard and Bose-Hubbard models with Rashba spin-orbit coupling, using
string bond states, one of the quantum tensor network states methods. We focus
on the role of quantum fluctuation effect in stabilizing the exotic spin phases
in these models. For boson systems, and when the ratio between inter-particle
and intra-particle interaction $\lambda > 1$, the out-of-plane ferromagnetic
(FM) and antiferromagnetic (AFM) phases obtained from quantum simulations are
the same to those obtained from classic model. However, the quantum
order-by-disorder effect reduces the classical in-plane XY-FM and XY-vortex
phases to the quantum X/Y-FM and X/Y-stripe phase when $\lambda < 1$. The
spiral phase and skyrmion phase can be realized in the presence of quantum
fluctuation. For the Fermi-Hubbard model, the quantum fluctuation energies are
always important in the whole parameter regime. %, which are much larger than
those of the Bose model. A general picture to understand the phase diagrams
from symmetry point of view is also presented.


  Convex optimization problems with staged structure appear in several
contexts, including optimal control, verification of deep neural networks, and
isotonic regression. Off-the-shelf solvers can solve these problems but may
scale poorly. We develop a nonconvex reformulation designed to exploit this
staged structure. Our reformulation has only simple bound constraints, enabling
solution via projected gradient methods and their accelerated variants. The
method automatically generates a sequence of primal and dual feasible solutions
to the original convex problem, making optimality certification easy. We
establish theoretical properties of the nonconvex formulation, showing that it
is (almost) free of spurious local minima and has the same global optimum as
the convex problem. We modify PGD to avoid spurious local minimizers so it
always converges to the global minimizer. For neural network verification, our
approach obtains small duality gaps in only a few gradient steps. Consequently,
it can quickly solve large-scale verification problems faster than both
off-the-shelf and specialized solvers.


  In this paper we investigate the algebraic structure of AdS/CFT in the
strong-coupling limit. We propose an expression for the classical r-matrix with
(deformed) u(2|2) symmetry, which leads to a quasi-triangular Lie bialgebra as
the underlying symmetry algebra. On the fundamental representation our r-matrix
coincides with the classical limit of the quantum R-matrix.


  Machine learning (ML) recourse techniques are increasingly used in
high-stakes domains, providing end users with actions to alter ML predictions,
but they assume ML developers understand what input variables can be changed.
However, a recourse plan's actionability is subjective and unlikely to match
developers' expectations completely. We present GAM Coach, a novel open-source
system that adapts integer linear programming to generate customizable
counterfactual explanations for Generalized Additive Models (GAMs), and
leverages interactive visualizations to enable end users to iteratively
generate recourse plans meeting their needs. A quantitative user study with 41
participants shows our tool is usable and useful, and users prefer personalized
recourse plans over generic plans. Through a log analysis, we explore how users
discover satisfactory recourse plans, and provide empirical evidence that
transparency can lead to more opportunities for everyday users to discover
counterintuitive patterns in ML models. GAM Coach is available at:
https://poloclub.github.io/gam-coach/.


  It has been observed that most manifolds in the Callahan-Hildebrand-Weeks
census of cusped hyperbolic $3$-manifolds are obtained by surgery on the
minimally twisted 5-chain link. A full classification of the exceptional
surgeries on the 5-chain link has recently been completed. In this article, we
provide a complete classification of the sets of exceptional slopes and
fillings for all cusped hyperbolic surgeries on the minimally twisted 5-chain
link, thereby describing the sets of exceptional slopes and fillings for most
hyperbolic manifolds of small complexity. The classification produces the
description of exceptional fillings for many families of one and two cusped
manifolds, and provides supporting evidence for some well-known conjectures.
One such family that appears in the classification is an infinite family of
1-cusped hyperbolic manifolds with four Seifert manifold fillings and a
toroidal filling.


  We discuss the transitions of galaxy morphologies within the CDM paradigm
under the assumption of bulge formation in mergers and disk growth via cooling
of gas and subsequent star formation. Based on the relative importance of these
two competing processes it is possible to make predictions on the expected
morphological mix of galaxies. In particular we here discuss the generation of
massive disk galaxies with low bulge-to-total mass ratios. Our results indicate
that it is difficult to generate enough massive disk galaxies with B/T $< 0.2$
via major mergers and subsequent disk re-growth, if during the major merger
progenitor disks get disrupted completely. On average low B/T galaxies must
have had there last major merger at $z \ge 2$. The main limiting factor is the
ability to re-grow massive disks at late times after the last major merger of a
galaxy. Taking into account the contribution from minor mergers ($4 \ge
M_1/M_2$, $M_1 \ge M_2$) to the formation of bulges, we recover the right
fraction of massive low B/T disk galaxies, indicating that minor mergers play
an important role in the formation of massive low B/T disk galaxies.


  A mixed mimetic spectral element method is applied to solve the rotating
shallow water equations. The mixed method uses the recently developed spectral
element histopolation functions, which exactly satisfy the fundamental theorem
of calculus with respect to the standard Lagrange basis functions in one
dimension. These are used to construct tensor product solution spaces which
satisfy the generalized Stokes theorem, as well as the annihilation of the
gradient operator by the curl and the curl by the divergence. This allows for
the exact conservation of first order moments (mass, vorticity), as well as
quadratic moments (energy, potential enstrophy), subject to the truncation
error of the time stepping scheme. The continuity equation is solved in the
strong form, such that mass conservation holds point wise, while the momentum
equation is solved in the weak form such that vorticity is globally conserved.
While mass, vorticity and energy conservation hold for any quadrature rule,
potential enstrophy conservation is dependent on exact spatial integration. The
method possesses a weak form statement of geostrophic balance due to the
compatible nature of the solution spaces and arbitrarily high order spatial
error convergence.


  We have recently introduced a discrete model of Lorentzian quantum gravity,
given as a regularized non-perturbative state sum over simplicial Lorentzian
space-times, each possessing a unique Wick rotation to Euclidean signature. We
investigate here the phase structure of the Wick-rotated path integral in three
dimensions with the aid of computer simulations. After fine-tuning the
cosmological constant to its critical value, we find a whole range of the
gravitational coupling constant $k_0$ for which the functional integral is
dominated by non-degenerate three-dimensional space-times. We therefore have a
situation in which a well-defined ground state of extended geometry is
generated dynamically from a non-perturbative state sum of fluctuating
geometries. Remarkably, its macroscopic scaling properties resemble those of a
semi-classical spherical universe. Measurements so far indicate that $k_0$
defines an overall scale in this extended phase, without affecting the physics
of the continuum limit. These findings provide further evidence that discrete
{\it Lorentzian} gravity is a promising candidate for a non-trivial theory of
quantum gravity.


  We describe a large class of exact string backgrounds with a null Killing
vector arising, via a limiting \`a la Penrose procedure, from string
backgrounds corresponding to coset conformal field theories for compact groups
G_N/H_N times a free time-like boson U(1)_{-N}. In this way a class of novel
logarithmic conformal field theories (LCFT) emerges, that includes the one
constructed recently as an N\to \infty limit of the SU(2)_N/U(1) X U(1)_{-N}
theory. We explicitly give the exact operator algebra for the basic chiral
fields as well as their representation in terms of free bosons, even though
these are not known in general at finite N. We also compute four-point
functions of various operators in the theory. For the cases of the four- and
five-dimensional models, corresponding to a limit of the theory SO(D+1)_N/SO(D)
X U(1)_{-N} for D=3 and 4, we also present the explicit expressions for the
background fields.


  We show that minimizers of the Heitmann-Radin energy (R. C. Heitmann, C.
Radin, J. Stat. Phys. 22, 281-287, 1980) are unique if and only if the particle
number N belongs to an infinite sequence whose first thirty-five elements are
1, 2, 3, 4, 5, 7, 8, 10, 12, 14, 16, 19, 21, 24, 27, 30, 33, 37, 40, 44, 48,
52, 56, 61, 65, 70, 75, 80, 85, 91, 96, 102, 108, 114, 120 (see the paper for a
closed-form description of this sequence). The proof relies on the discrete
differential geometry techniques introduced in (L. De Luca, G. Friesecke,
preprint).


  This thesis studies some examples of families of K3 surfaces with Picard
lattices of maximal rank. These families occur as invariants of finite
automorphism groups. The Picard-Fuchs differential equations describing the
variation of Hodge structure in these families are considered. Techniques are
developed to find the corresponding monodromy groups as arithmetic Fuchsian
groups acting on the families' period spaces.


  Raman lasers based on bulk diamond are a valuable resource for generating
coherent light in wavelength regimes where no common laser diodes are
available. Nevertheless, the widespread use of such lasers is limited by their
high threshold power requirements on the order of several Watts. Using on-chip
microresonators, a significant reduction of the lasing threshold by more than
two orders of magnitude has been shown. However, these resonators lack a
continuous tuning mechanism and, mainly due to fabrication limitations, their
implementation in the visible remains elusive. Here, we propose a platform for
a diamond Raman laser in the visible. The device is based on a miniaturized,
open-access Fabry-Perot cavity. Our microcavity provides widely-tunable
doubly-resonant enhancement of Raman scattering from high quality
single-crystalline diamond. We demonstrate a $>$THz continuous tuning range of
doubly-resonant Raman scattering, a range limited only by the reflective
stopband of the mirrors. Based on the experimentally determined quality factors
exceeding $300\,000$, our theoretical analysis suggests that, with realistic
improvements, a sub-mW threshold is readily within reach. Our findings pave the
way to the creation of a universal low-power frequency shifter, a potentially
valuable addition to the nonlinear optics toolbox.


  Carbazole-based self-assembled monolayers (PACz-SAMs), anchored via their
phosphonic acid group on a transparent conductive oxide (TCO) have demonstrated
excellent performance as hole-selective layers in inverted perovskite solar
cells. However, the influence of the TCO microstructure on the work function
(WF) shift after SAM anchoring as well as the WF variations at the
micro/nanoscale have not been extensively studied yet. Herein, we investigate
the effect of the Sn-doped In2O3 (ITO) microstructure on the WF distribution
upon 2PACz-SAMs and NiOx/2PACz-SAMs application. For this, ITO substrates with
amorphous and polycrystalline (featuring either nanoscale or microscale-sized
grains) microstructures are studied. A correlation between the ITO grain
orientation and 2PACz-SAMs local potential distribution was found via Kelvin
probe force microscopy and electron backscatter diffraction. These variations
vanish for amorphous ITO or when adding an amorphous NiOx buffer layer, where a
homogeneous surface potential distribution is mapped. Ultraviolet photoelectron
spectroscopy confirmed the ITO WF increase after 2PACz-SAMs deposition.
Considering the importance of polycrystalline TCOs as high mobility and
broadband transparent electrodes, we provide insights to ensure uniform WF
distribution upon application of hole transport SAMs, which is critical towards
enhanced device performance.


  Action localization networks are often structured as a feature encoder
sub-network and a localization sub-network, where the feature encoder learns to
transform an input video to features that are useful for the localization
sub-network to generate reliable action proposals. While some of the encoded
features may be more useful for generating action proposals, prior action
localization approaches do not include any attention mechanism that enables the
localization sub-network to attend more to the more important features. In this
paper, we propose a novel attention mechanism, the Class Semantics-based
Attention (CSA), that learns from the temporal distribution of semantics of
action classes present in an input video to find the importance scores of the
encoded features, which are used to provide attention to the more useful
encoded features. We demonstrate on two popular action detection datasets that
incorporating our novel attention mechanism provides considerable performance
gains on competitive action detection models (e.g., around 6.2% improvement
over BMN action detection baseline to obtain 47.5% mAP on the THUMOS-14
dataset), and a new state-of-the-art of 36.25% mAP on the ActivityNet v1.3
dataset. Further, the CSA localization model family which includes BMN-CSA, was
part of the second-placed submission at the 2021 ActivityNet action
localization challenge. Our attention mechanism outperforms prior
self-attention modules such as the squeeze-and-excitation in action detection
task. We also observe that our attention mechanism is complementary to such
self-attention modules in that performance improvements are seen when both are
used together.


  We review the possible mechanisms for the generation of cosmological magnetic
fields, discuss their evolution in an expanding Universe filled with the cosmic
plasma and provide a critical review of the literature on the subject. We put
special emphasis on the prospects for observational tests of the proposed
cosmological magnetogenesis scenarios using radio and gamma-ray astronomy and
ultra high energy cosmic rays. We argue that primordial magnetic fields are
observationally testable. They lead to magnetic fields in the intergalactic
medium with magnetic field strength and correlation length in a well defined
range. We also state the unsolved questions in this fascinating open problem of
cosmology and propose future observations to address them.


  Traditional learning systems have responded quickly to the COVID pandemic and
moved to online or distance learning. Online learning requires a
personalization method because the interaction between learners and instructors
is minimal, and learners have a specific learning method that works best for
them. One of the personalization methods is detecting the learners' learning
style. To detect learning styles, several works have been proposed using
classification techniques. However, the current detection models become
ineffective when learners have no dominant style or a mix of learning styles.
Thus, the objective of this study is twofold. Firstly, constructing a
prediction model based on regression analysis provides a probabilistic approach
for inferring the preferred learning style. Secondly, comparing regression
models and classification models for detecting learning style. To ground our
conceptual model, a set of machine learning algorithms have been implemented
based on a dataset collected from a sample of 72 students using visual,
auditory, reading/writing, and kinesthetic (VARK's) inventory questionnaire.
Results show that regression techniques are more accurate and representative
for real-world scenarios than classification algorithms, where students might
have multiple learning styles but with different probabilities. We believe that
this research will help educational institutes to engage learning styles in the
teaching process.


  Type Ia Supernovae (SNe Ia) are a critical tool in measuring the accelerating
expansion of the universe. Recent efforts to improve these standard candles
have focused on incorporating the effects of dust on distance measurements with
SNe Ia. In this paper, we use the state-of-the-art Dark Energy Survey 5 year
sample to evaluate two different families of dust models: empirical extinction
models derived from SNe Ia data, and physical attenuation models from the
spectra of galaxies. Among the SNe Ia-derived models, we find that a logistic
function of the total-to-selective extinction RV best recreates the
correlations between supernova distance measurements and host galaxy
properties, though an additional 0.02 magnitudes of grey scatter are needed to
fully explain the scatter in SNIa brightness in all cases. These
empirically-derived extinction distributions are highly incompatible with the
physical attenuation models from galactic spectral measurements. From these
results, we conclude that SNe Ia must either preferentially select extreme ends
of galactic dust distributions, or that the characterisation of dust along the
SNe Ia line-of-sight is incompatible with that of galactic dust distributions.


  Visual cryptography aims to protect images against their possible
illegitimate use. Thus, one can cipher, hash, or add watermarks for protecting
copyright, among others. In this paper we provide a new solution to the problem
of secret sharing for the case when the secret is an image. Our method combines
the Shamir scheme for secret sharing using finite fields of characteristic 2
with the CBC mode of operation of a secure symmetric cryptographic scheme like
AES, so that the security relies on that of the mentioned techniques. The
resulting shares have the same resolution as that of the original image. The
idea of the method could be generalized to other multimedia formats like audio
or video, adapting the method to the corresponding encoded information.


  Approaches based on deep neural networks have achieved striking performance
when testing data and training data share similar distribution, but can
significantly fail otherwise. Therefore, eliminating the impact of distribution
shifts between training and testing data is crucial for building
performance-promising deep models. Conventional methods assume either the known
heterogeneity of training data (e.g. domain labels) or the approximately equal
capacities of different domains. In this paper, we consider a more challenging
case where neither of the above assumptions holds. We propose to address this
problem by removing the dependencies between features via learning weights for
training samples, which helps deep models get rid of spurious correlations and,
in turn, concentrate more on the true connection between discriminative
features and labels. Extensive experiments clearly demonstrate the
effectiveness of our method on multiple distribution generalization benchmarks
compared with state-of-the-art counterparts. Through extensive experiments on
distribution generalization benchmarks including PACS, VLCS, MNIST-M, and NICO,
we show the effectiveness of our method compared with state-of-the-art
counterparts.


  We establish new estimates for the number of $m$-smooth polynomials of degree
$n$ over a finite field $\mathbb{F}_q$, where the main term involves the number
of $m$-smooth permutations on $n$ elements.
  Our estimates imply that the probability that a random polynomial of degree
$n$ is $m$-smooth is asymptotic to the probability that a random permutation on
$n$ elements is $m$-smooth, uniformly for $m\ge (2+\varepsilon)\log_q n$ as
$q^n \to \infty$. This should be viewed as an unconditional analogue of works
of Hildebrand and of Saias in the integer setting, which assume the Riemann
Hypothesis. Moreover, we show that the range $m \ge (2+\varepsilon)\log_q n$ is
sharp; this should be viewed as a resolution of a (polynomial analogue of a)
conjecture of Hildebrand.
  As an application of our estimates, we determine the rate of decay in the
asymptotic formula for the expected degree of the largest prime factor of a
random polynomial.


  We consider the action of the symmetric group $S_n$ on the permutahedron
$\Pi_n$. We prove that if $\sigma$ is a permutation of $S_n$ which has $m$
cycles of lengths $l_1, \ldots, l_m$, then the subpolytope of $\Pi_n$ fixed by
$\sigma$ has normalized volume $n^{m-2} \gcd(l_1, \ldots, l_m)$.


  Electromagnetic surface waves supported by an isotropic chiral material were
investigated via the associated canonical boundary-value problem. Specifically,
two scenarios were considered: surface waves guided by the planar interface of
an isotropic chiral material and (a) an isotropic dielectric material and (b) a
uniaxial dielectric (or plasmonic) material. Both plasmonic and non-plasmonic
achiral partnering materials were investigated. In scenario (a) surface waves
akin to surface-plasmon-polariton (SPP) waves were excited, while in scenario
(b) surface waves akin to Dyakonov waves and akin to SPP waves were excited.
For numerical studies, an isotropic chiral material capable of simultaneously
supporting attenuation and amplification of plane waves, depending upon
circular polarization state, was used. The amplitude of the SPP-like waves
could be either amplified or attenuated, depending upon the relative
permittivity of the isotropic dielectric partnering material for scenario (a),
or depending upon the direction of propagation relative to the optic axis of
the uniaxial dielectric partnering material for scenario (b).


  COVID-19 pandemic tested the capacity of information systems in countries on
the ability to rapidly respond to requirements which were not anticipated. This
article analyzes the socio-technical determinants of agility in building the IS
response to the COVID-19 pandemic in Sri Lanka. We deploy qualitative research
methods to explore the case study of implementation of COVID-19 surveillance
system in Sri Lanka. Three key recommendations are developed for practice
relating to high-level multisectoral governance, use of lightweight digital
platforms and leveraging on existing capacities and infrastructure.


  An (n,d) permutation array (PA) is a set of permutations of length n with the
property that the distance (under some metric) between any two permutations in
the array is at least d. They became popular recently for communication over
power lines. Motivated by an application to flash memories, in this paper the
metric used is the Chebyshev metric. A number of different constructions are
given as well as bounds on the size of such PA.


  THz spectroscopy is an emerging tool for detection of microorganisms and
harmful compounds in the food industry, the study of proteins in biomedicine
and the development of electron-beam X-ray sources for molecular imaging and
lithography. Recording of THz electric field evolution in single-shot is
crucially needed in terahertz spectroscopy of irreversible processes in such
applications as well as for data communication in the THz portion of the
spectrum where there is an abundance of untapped bandwidth. However, achieving
sub-picosecond resolution over a long time window has been an open problem for
electro-optic sampling -- the standard technique for recording terahertz
waveforms. We introduce a new conceptual framework for this open problem that
is inspired by time-stretch theory. The novel framework unveils a solution to
this 20 year-old problem leading to a dramatic enhancement of the achievable
temporal resolution. We validate this new technology in two applications.
First, we present single shot recordings of long free-propagating terahertz
transients with record time resolution. Second, we present recordings of
ultra-short relativistic electron bunches at the European X-ray Free Electron
Laser. These results show that electric signals may be now recorded with
terahertz bandwidth over arbitrarily long windows, thus enabling the
realization of "single-shot terahertz oscilloscopes" and single-shot
time-domain spectroscopy systems with an arbitrary time-bandwidth product.


  In this work, we present the construction of multilingual parallel corpora
with annotation of multiword expressions (MWEs). MWEs include verbal MWEs
(vMWEs) defined in the PARSEME shared task that have a verb as the head of the
studied terms. The annotated vMWEs are also bilingually and multilingually
aligned manually. The languages covered include English, Chinese, Polish, and
German. Our original English corpus is taken from the PARSEME shared task in
2018. We performed machine translation of this source corpus followed by human
post editing and annotation of target MWEs. Strict quality control was applied
for error limitation, i.e., each MT output sentence received first manual post
editing and annotation plus second manual quality rechecking. One of our
findings during corpora preparation is that accurate translation of MWEs
presents challenges to MT systems. To facilitate further MT research, we
present a categorisation of the error types encountered by MT systems in
performing MWE related translation. To acquire a broader view of MT issues, we
selected four popular state-of-the-art MT models for comparisons namely:
Microsoft Bing Translator, GoogleMT, Baidu Fanyi and DeepL MT. Because of the
noise removal, translation post editing and MWE annotation by human
professionals, we believe our AlphaMWE dataset will be an asset for
cross-lingual and multilingual research, such as MT and information extraction.
Our multilingual corpora are available as open access at
github.com/poethan/AlphaMWE.


  We study the mechanism of the $\gamma$ and $\pi^0$ enhancement at large
transverse momenta in proton-nucleus collisions. A proposed mechanism suggests
an increase in the width of the transverse momentum distribution of partons in
a semihard process.


  We represent the first investigation of pole-skipping on both the gravity and
field theory sides. In contrast to the higher dimensional models, there is no
momentum degree of freedom in $(1+1)-$dimensional bulk theory. Thus, we then
consider a scalar field mass as our degree of freedom for the pole-skipping
phenomenon instead of momentum. The pole-skipping frequencies of the scalar
field in 2D gravity are the same as higher dimensional cases: $\omega=-i2\pi
Tn$ for positive integers $n$. At each of these frequencies, there is a
corresponding pole-skipping mass, so the pole-skipping points exist in
$(\omega,m)$ space. We also compute the pole-skipping points of the SYK model
in $(\omega, h)$ space where $h$ is the dimension of the bilinear primary
operator. We find that there is a one-to-one correspondence of the
pole-skipping points between the JT gravity and the SYK model. To obtain the
pole-skipping points, we need to consider the parameter $\epsilon$ related to
the chemical potential on the horizon of charged JT gravity and the
particle-hole asymmetric parameter $\mathcal{E}$ of the complex SYK model as
shift parameters. This highlights the $\epsilon-\mathcal{E}$ correspondence in
relation to pole-skipping phenomenon.


  The paper reviews existing results about the statistical distribution of
zeros for the three main types of zeta functions: number-theoretical,
geometrical, and dynamical. It provides necessary background and some details
about the proofs of main results.


  We reveal a strong influence of a superfluid phase transition on the
character of single-particle excitations of a trapped neutral-atom Fermi gas.
Below the transition temperature the presence of a spatially inhomogeneous
order parameter (gap) shifts up the excitation eigenenergies and leads to the
appearance of in-gap excitations localized in the outer part of the gas sample.
The eigenenergies become sensitive to the gas temperature and are no longer
multiples of the trap frequencies. These features should manifest themselves in
a strong change of the density oscillations induced by modulations of the trap
frequencies and can be used for identifying the superfluid phase transition.


  There have been many claims that quantum mechanics plays a key role in the
origin and/or operation of biological organisms, beyond merely providing the
basis for the shapes and sizes of biological molecules and their chemical
affinities. These range from the suggestion by Schrodinger that quantum
fluctuations produce mutations, to the conjecture by Hameroff and Penrose that
quantum coherence in microtubules is linked to consciousness. I review some of
these claims in this paper, and discuss the serious problem of decoherence. I
advance some further conjectures about quantum information processing in
bio-systems. Some possible experiments are suggested.


  In this paper, we introduce the concept of \emph{Poissonian occupation times}
below level $0$ of spectrally negative L\'evy processes. In this case,
occupation time is accumulated only when the process is observed to be negative
at arrival epochs of an independent Poisson process. Our results extend some
well known continuously observed quantities involving occupation times of
spectrally negative L\'evy processes. As an application, we establish a link
between Poissonian occupation times and insurance risk models with Parisian
implementation delays.


  Let X be a closed manifold with zero Euler characteristic, and let f: X -->
S^1 be a circle-valued Morse function. We define an invariant I which counts
closed orbits of the gradient of f, together with flow lines between the
critical points. We show that our invariant equals a form of topological
Reidemeister torsion defined by Turaev [Math. Res. Lett. 4 (1997) 679-695].
  We proved a similar result in our previous paper [Topology, 38 (1999)
861-888], but the present paper refines this by separating closed orbits and
flow lines according to their homology classes. (Previously we only considered
their intersection numbers with a fixed level set.) The proof here is
independent of the previous proof and also simpler.
  Aside from its Morse-theoretic interest, this work is motivated by the fact
that when X is three-dimensional and b_1(X)>0, the invariant I equals a
counting invariant I_3(X) which was conjectured in our previous paper to equal
the Seiberg-Witten invariant of X. Our result, together with this conjecture,
implies that the Seiberg-Witten invariant equals the Turaev torsion. This was
conjectured by Turaev [Math. Res. Lett. 4 (1997) 679-695] and refines the
theorem of Meng and Taubes [Math. Res. Lett. 3 (1996) 661-674].


  Deep learning has been the answer to many machine learning problems during
the past two decades. However, it comes with two major constraints: dependency
on extensive labeled data and training costs. Transfer learning in deep
learning, known as Deep Transfer Learning (DTL), attempts to reduce such
dependency and costs by reusing an obtained knowledge from a source data/task
in training on a target data/task. Most applied DTL techniques are
network/model-based approaches. These methods reduce the dependency of deep
learning models on extensive training data and drastically decrease training
costs. As a result, researchers detected Covid-19 infection on chest X-Rays
with high accuracy at the beginning of the pandemic with minimal data using DTL
techniques. Also, the training cost reduction makes DTL viable on edge devices
with limited resources. Like any new advancement, DTL methods have their own
limitations, and a successful transfer depends on some adjustments for
different scenarios. In this paper, we review the definition and taxonomy of
deep transfer learning and well-known methods. Then we investigate the DTL
approaches by reviewing recent applied DTL techniques in the past five years.
Further, we review some experimental analyses of DTLs to learn the best
practice for applying DTL in different scenarios. Moreover, the limitations of
DTLs (catastrophic forgetting dilemma and overly biased pre-trained models) are
discussed, along with possible solutions and research trends.


  Ecological momentary assessment (EMA) data have a broad base of application
in the study of time trends and relations. In EMA studies, there are a number
of design considerations which influence the analysis of the data. One general
modeling framework is particularly well-suited for these analyses: state-space
modeling. Here, we present the state-space modeling framework with
recommendations for the considerations that go into modeling EMA data. These
recommendations can account for the issues that come up in EMA data analysis
such as idiographic versus nomothetic modeling, missing data, and stationary
versus non-stationary data. In addition, we suggest R packages in order to
implement these recommendations in practice. Overall, well-designed EMA studies
offer opportunities for researchers to handle the momentary minutiae in their
assessment of psychological phenomena.


  This paper extends an earlier analytical scattering matrix treatment of
conductance and localization in coupled two- and three Anderson chain systems
for weak disorder when evanescent states are present at the Fermi level. Such
states exist typically when the interchain coupling exceeds the width of
propagating energy bands associated with the various transverse eigenvalues of
the coupled tight-binding systems. We calculate reflection- and transmission
coefficients in cases where, besides propagating states, one or two evanescent
states are available at the Fermi level for elastic scattering of electrons by
the disordered systems. We observe important qualitative changes in these
coefficients and in the related localization lengths due to ineffectiveness of
the evanescent modes for transmission and reflection in the various scattering
channels. In particular, the localization lengths are generally significantly
larger than the values obtained when evanescent modes are absent. Effects
associated with disorder mediated coupling between propagating and evanescent
modes are shown to be suppressed by quantum interference effects, in lowest
order for weak disorder.


  Let $G\cong \mathbb Z/m_1\mathbb Z\times\ldots\times \mathbb Z/m_r\mathbb Z$
be a finite abelian group with $m_1\mid\ldots\mid m_r=\exp(G)$. The Kemperman
Structure Theorem characterizes all subsets $A,\,B\subseteq G$ satisfying
$|A+B|<|A|+|B|$ and has been extended to cover the case when $|A+B|\leq
|A|+|B|$. Utilizing these results, we provide a precise structural description
of all finite subsets $A\subseteq G$ with $|nA|\leq (|A|+1)n-3$ when $n\geq 3$
(also when $G$ is infinite), in which case many of the pathological
possibilities from the case $n=2$ vanish, particularly for large $n\geq
\exp(G)-1$. The structural description is combined with other arguments to
generalize a subsequence sum result of Olson asserting that a sequence $S$ of
terms from $G$ having length $|S|\geq 2|G|-1$ must either have every element of
$G$ representable as a sum of $|G|$-terms from $S$ or else have all but
$|G/H|-2$ of its terms lying in a common $H$-coset for some $H\leq G$. We show
that the much weaker hypothesis $|S|\geq |G|+\exp(G)$ suffices to obtain a
nearly identical conclusion, where for the case $H$ is trivial we must allow
all but $|G/H|-1$ terms of $S$ to be from the same $H$-coset. The bound on
$|S|$ is improved for several classes of groups $G$, yielding optimal lower
bounds for $|S|$. We also generalize Olson's result for $|G|$-term subsums to
an analogous one for $n$-term subsums when $n\geq \exp(G)$, with the bound
likewise improved for several special classes of groups. This improves previous
generalizations of Olson's result, with the bounds for $n$ optimal.


  Meridional flow results from slight deviations from the thermal wind balance.
The deviations are relatively large in the boundary layers near the top and
bottom of the convection zone. Accordingly, the meridional flow attains its
largest velocities at the boundaries and decreases inside the convection zone.
The thickness of the boundary layers, where meridional flow is concentrated,
decreases with rotation rate, so that an advection-dominated regime of dynamos
is not probable in rapidly rotating stars. Angular momentum transport by
convection and by the meridional flow produce differential rotation. The
convective fluxes of angular momentum point radially inward in the case of slow
rotation but change their direction to equatorward and parallel to the rotation
axis as the rotation rate increases. The differential rotation of main-sequence
dwarfs is predicted to vary mildly with rotation rate but increase strongly
with stellar surface temperature. The significance of differential rotation for
dynamos has the opposite tendency to increase with spectral type.


  The spectral energy distributions and infrared (IR) spectra of a sample of
obscured AGNs selected in the mid-IR are modeled with recent clumpy torus
models to investigate the nature of the sources, the properties of the
obscuring matter, and dependencies on luminosity. The sample contains 21
obscured AGNs at z=1.3-3 discovered in the largest Spitzer surveys (SWIRE,
NDWFS, & FLS) by means of their extremely red IR to optical colors. All sources
show the 9.7micron silicate feature in absorption and have extreme mid-IR
luminosities (L(6micron)~10^46 erg/s). The IR SEDs and spectra of 12 sources
are well reproduced with a simple torus model, while the remaining 9 sources
require foreground extinction from a cold dust component to reproduce both the
depth of the silicate feature and the near-IR emission from hot dust. The
best-fit torus models show a broad range of inclinations, with no preference
for the edge-on torus expected in obscured AGNs. Based on the unobscured QSO
mid-IR luminosity function, and on a color-selected sample of obscured and
unobscured IR sources, we estimate the surface densities of obscured and
unobscured QSOs at L(6micron)>10^12 Lsun, and z=1.3-3.0 to be about 17-22
deg^-2, and 11.7 deg^-2, respectively. Overall we find that ~35-41% of luminous
QSOs are unobscured, 37-40% are obscured by the torus, and 23-25% are obscured
by a cold absorber detached from the torus. These fractions constrain the torus
half opening angle to be ~67 deg. This value is significantly larger than found
for FIR selected samples of AGN at lower luminosity (~46 deg), supporting the
receding torus scenario. A far-IR component is observed in 8 objects. The
estimated far-IR luminosities associated with this component all exceed
3.3x10^12 Lsun, implying SFRs of 600-3000 Msun/yr. (Abridged)


  We examine the reach of a Beta-beam experiment with two detectors at
carefully chosen baselines for exploring neutrino mass parameters. Locating the
source at CERN, the two detectors and baselines are: (a) a 50 kton iron
calorimeter (ICAL) at a baseline of around 7150 km which is roughly the magic
baseline, e.g., ICAL@INO, and (b) a 50 kton Totally Active Scintillator
Detector at a distance of 730 km, e.g., at Gran Sasso. We choose 8B/8Li source
ions with a boost factor \gamma of 650 for the magic baseline while for the
closer detector we consider 18Ne/6He ions with a range of Lorentz boosts. We
find that the locations of the two detectors complement each other leading to
an exceptional high sensitivity. With \gamma=650 for 8B/8Li and \gamma=575 for
18Ne/6He and total luminosity corresponding to 5\times (1.1 \times 10^{18}) and
5\times (2.9\times 10^{18}) useful ion decays in neutrino and antineutrino
modes respectively, we find that our two detector set-up can probe maximal CP
violation and establish the neutrino mass ordering if \sin^22\theta_{13} is
1.4\times 10^{-4} and 2.7\times 10^{-4}, respectively, or more. The sensitivity
reach for \sin^22\theta_{13} itself is 5.5 \times 10^{-4}. With a factor of 10
higher luminosity, the corresponding \sin^22\theta_{13} reach of this set-up
would be 1.8\times 10^{-5}, 4.6\times 10^{-5} and 5.3\times 10^{-5}
respectively for the above three performance indicators. CP violation can be
discovered for 64% of the possible \delta_{CP} values for \sin^22\theta_{13}
\geq 10^{-3} (\geq 8\times 10^{-5}), for the standard luminosity (10 times
enhanced luminosity). Comparable physics performance can be achieved in a
set-up where data from CERN to INO@ICAL is combined with that from CERN to the
Boulby mine in United Kingdom, a baseline of 1050 km.


  The present paper focuses on the construction of a set of submatrices of a
circulant matrix such that it is a smaller set to verify that the circulant
matrix is an MDS (maximum distance separable) one, comparing to the complete
set of square submatrices needed in general case. The general MDS verification
method requires to test for singular submatrices: if at least one square
submatrix is singular the matrix is not MDS. However, the complexity of the
general method dramatically increases for matrices of a greater dimension. We
develop an algorithm that constructs a smaller subset of submatrices thanks to
a simple structure of circulant matrices. The algorithm proposed in the paper
reduces the size of the testing set by approximately two matrix orders.


  Power corrections to hadronic event shapes are estimated using a recently
suggested relationship between perturbative and non-perturbative effects in
QCD. The infrared cutoff dependence of perturbative calculations is related to
non-perturbative contributions with the same dependence on the energy scale
$Q$. Corrections proportional to $1/Q$ are predicted, in agreement with
experiment. An empirical proportionality between the magnitudes of perturbative
and non-perturbative coefficients is noted.


  Constructing reduced representations of high-dimensional systems is a
fundamental problem in physical chemistry. Many unsupervised machine learning
methods can automatically find such low-dimensional representations. However,
an often overlooked problem is what high-dimensional representation should be
used to describe systems before dimensionality reduction. Here, we address this
issue using a recently developed method called reweighted diffusion map [J.
Chem. Theory Comput. 2022, 18, 7179-7192]. We show how high-dimensional
representations can be quantitatively selected by exploring the spectral
decomposition of Markov transition matrices built from data obtained from
standard or enhanced sampling atomistic simulations. We demonstrate the
performance of the method in several high-dimensional examples.


  The study of rotation and activity in low-mass stars or brown dwarfs of
spectral classes M and L has seen enormous progress during the last years. I
summarize the results from different works that measured activity, rotation,
and sometimes magnetic fields. The generation of magnetic activity seems to be
unchanged at the threshold to completely convective stars, i.e. no change in
the efficiency of the magnetic dynamos is observed. On the other hand, a sudden
change in the strength of rotational braking appears at the threshold mass to
full convection, and strong evidence exists for rotational braking weakening
with lower mass. A probable explanation is that the field topology changes from
dipolar to small scale structure as the objects become fully convective.


  Machine learning algorithms such as random forests or xgboost are gaining
more importance and are increasingly incorporated into production processes in
order to enable comprehensive digitization and, if possible, automation of
processes. Hyperparameters of these algorithms used have to be set
appropriately, which can be referred to as hyperparameter tuning or
optimization. Based on the concept of tunability, this article presents an
overview of theoretical and practical results for popular machine learning
algorithms. This overview is accompanied by an experimental analysis of 30
hyperparameters from six relevant machine learning algorithms. In particular,
it provides (i) a survey of important hyperparameters, (ii) two parameter
tuning studies, and (iii) one extensive global parameter tuning study, as well
as (iv) a new way, based on consensus ranking, to analyze results from multiple
algorithms. The R package mlr is used as a uniform interface to the machine
learning models. The R package SPOT is used to perform the actual tuning
(optimization). All additional code is provided together with this paper.


  We consider Maxwell fields associated with any shear-free null geodesic
congruence on Minkowski or Riemannian background space-time. Bounded singular
loci of these fields are treated as particle-like formations, possess
"self-quantized" electric charge and undergo self-consistent time dynamics.
Complicated singular solutions of Maxwell (as well as related complex
Yang-Mills) equations can be obtained in a purely algebraic way using the Kerr
theorem.


  Let $M_X(r,\xi)$ be the moduli space of stable vector bundles, on a smooth
complex projective curve $X$, of rank $r$ and fixed determinant $\xi$ such that
$\deg(\xi)$ is coprime to $r$. If $E$ is a vector bundle $M_X(r,\xi)$ whose
restriction to every Hecke curve in $M_X(r,\xi)$ is trivial, we prove that $E$
is trivial.


  Grain boundary engineering via dopant segregation can dramatically change the
properties of a material. For metallic systems, most current studies concerning
interfacial segregation and subsequent transitions of grain boundary structure
are limited to binary alloys, yet many important alloy systems contain more
than one type of dopant. In this work, hybrid Monte Carlo/molecular dynamics
simulations are performed to investigate the behavior of dopants at interfaces
in two model ternary alloy systems: Cu-Zr-Ag and Al-Zr-Cu. Trends in boundary
segregation are studied, as well as the propensity for the grain boundary
structure to become disordered at high temperature and doping concentration.
For Al-Zr-Cu, we find that the two solutes prefer to occupy different sites at
the grain boundary, leading to a synergistic doping effect. Alternatively, for
Cu-Zr-Ag, there is site competition because the preferred segregation sites are
the same. Finally, we find that thicker amorphous intergranular films can be
formed in ternary systems by controlling the concentration ratio of different
solute elements.


  We show that the requirement of Poincare invariance (more specifically
invariance under boosts/rotations that mix brane directions with transverse
directions) places severe constraints on the form of actions describing
multiple D-branes, determining an infinite series of correction terms to the
currently known actions. For the case of D0-branes, we argue that up to field
redefinitions, there is a unique Lorentz transformation rule for the coordinate
matrices consistent with the Poincare algebra. We characterize all independent
Poincare invariant structures by describing the leading term of each and
providing an implicit construction of a Poincare invariant completion. Our
construction employs new matrix-valued Lorentz covariant objects built from the
coordinate matrices, which transform simply under the (extremely complicated)
Lorentz transformation rule for the matrix coordinates.


  A theory of complexity for pairs (M,G) with M an arbitrary closed 3-manifold
and G a 3-valent graph in M was introduced by the first two named authors,
extending the original notion due to Matveev. The complexity c is known to be
always additive under connected sum away from the graphs, but not always under
connected sum along (unknotted) arcs of the graphs. In this article we prove
the slightly surprising fact that if in M there is a sphere intersecting G
transversely at one point, and this point belongs to an edge e of G, then e can
be canceled from G without affecting the complexity. Using this fact we
completely characterize the circumstances under which complexity is additive
under connected sum along graphs. For the set of pairs (M,K) with K a knot in
M, we also prove that any function that is fully additive under connected sum
along knots is actually a function of the ambient manifold only.


  We obtain a refinement of the degrees of freedom estimate of Landau and
Pollak. More precisely, we estimate, in terms of $\epsilon$, the increase in
the degrees of freedom resulting upon allowing the functions to contain a
certain prescribed amount of energy $\epsilon $ outside a region delimited by a
set $T$ in time and a set $\Omega $ in frequency. In this situation, the lower
asymptotic Nyquist density $\vert T\vert \vert \Omega \vert /2\pi$ is increased
to $(1+\epsilon)\vert T\vert \vert \Omega \vert /2\pi$. At the technical level,
we prove a pseudospectra version of the classical spectral dimension result of
Landau and Pollak, in the multivariate setting of Landau. Analogous results are
obtained for Gabor localization operators in a compact region of the
time-frequency plane.


  In 1950 the first successful numerical weather forecast was obtained by
solving the barotropic vorticity equation using the Electronic Numerical
Integrator and Computer (ENIAC), which marked the beginning of the age of
numerical weather prediction. Here, we ask the question of how these numerical
forecasts would have turned out, if machine learning based solvers had been
used instead of standard numerical discretizations. Specifically, we recreate
these numerical forecasts using physics-informed neural networks. We show that
physics-informed neural networks provide an easier and more accurate
methodology for solving meteorological equations on the sphere, as compared to
the ENIAC solver.


  Recently, we have reported on the existence of some monopoles, multimonopole,
and antimonopoles configurations. In this paper we would like to present more
monopoles, multimonopole, and antimonopoles configurations of the magnetic
ansatz of Ref.\cite{kn:9} when the parameters $p$ and $b$ of the solutions
takes different serial values. These exact solutions are a different kind of
BPS solution. They satisfy the first order Bogomol'nyi equation but possess
infinite energy. They can have radial, axial, or rotational symmetry about the
z-axis. We classified these serial solutions as (i) the multimonopole at the
origin; (ii) the finitely separated 1-monopoles; (iii) the screening solutions
of multimonopole and (iv) the axially symmetric monopole solutions. We also
give a construction of their anti-configurations with all the magnetic charges
of poles in the configurations reversed. Half-integer topological magnetic
charge multimonopole also exist in some of these series of solutions.


  We propose a dynamical theory of how the chemical energy stored in a battery
generates the electromotive force (emf). In this picture, the battery's
half-cell acts as an engine, cyclically extracting work from its underlying
chemical disequilibrium. We show that the double layer at the
electrode-electrolyte interface can exhibit a rapid self-oscillation that pumps
an electric current, thus accounting for the persistent conversion of chemical
energy into electrical work equal to the emf times the separated charge. We
suggest a connection between this mechanism and the slow self-oscillations
observed in various electrochemical cells, including batteries, as well as the
enhancement of the current observed when ultrasound is applied to the
half-cell. Finally, we propose more direct experimental tests of the
predictions of this dynamical theory.


  In this paper we study the action of non abelian subgroup G generated by
affine homotheties on C^n. We prove that there exist a subgroup H of C\{0}, a
G-invariant affine subspace E of C^n and b in E such that the closure of any
orbit G(z) is equal to H(z-a)+E, z in C^n. In particular, every orbit in E is
dense in it. Moreover, if the complementary U=C^n \E is non empty, every orbit
of U is minimal in it.


  Quantum anomalous Hall effect (QAHE) has been experimentally observed in
magnetically doped topological insulators. However, ultra-low temperature
(usually below 300 mK), which is mainly attributed to inhomogeneous magnetic
doping, becomes a daunting challenge for potential applications. Here, a
\textit{nonmagnetic}-doping strategy is proposed to produce ferromagnetism and
realize QAHE in topological insulators. We numerically demonstrated that
magnetic moments can be induced by nitrogen or carbon substitution in
Bi$_2$Se$_3$, Bi$_2$Te$_3$, and Sb$_2$Te$_3$, but only nitrogen-doped
Sb$_2$Te$_3$ exhibits long-range ferromagnetism and preserve large bulk band
gap. We further show that its corresponding thin-film can harbor QAHE at
temperatures of 17-29 Kelvin, which is two orders of magnitude higher than the
typical temperatures in similar systems. Our proposed \textit{nonmagnetic}
doping scheme may shed new light in experimental realization of
high-temperature QAHE in topological insulators.


  We study the numerical strong stability of explicit schemes for the numerical
approximation of the solution to a BSDE where the driver has polynomial growth
in the primary variable and satisfies a monotone decreasing condition, and we
introduce an explicit scheme with adapted time-steps that guarantee numerical
strong stability. We then prove the convergence of this scheme and illustrate
it with numerical simulations.


  This paper reviews recent work on a new geometric object called a bundle
gerbe and discusses some new examples arising in quantum field theory. One
application is to an Atiyah-Patodi-Singer index theory construction of the
bundle of fermionic Fock spaces parametrized by vector potentials in odd space
dimensions and a proof that this leads in a simple manner to the known
Schwinger terms (Mickelsson-Faddeev cocycle) for the gauge group action. This
gives an explicit computation of the Dixmier-Douady class of the associated
bundle gerbe. The method works also in other cases of fermions in external
fields (external gravitational field, for example) provided that the APS
theorem can be applied; however, we have worked out the details only in the
case of vector potentials. Another example, in which the bundle gerbe curvature
plays a role, arises from the WZW model on Riemann surfaces. A further example
is the `existence of string structures' question. We conclude by showing how
global Hamiltonian anomalies fit within this framework.


  A lattice model of critical dense polymers is solved exactly for finite
strips. The model is the first member of the principal series of the recently
introduced logarithmic minimal models. The key to the solution is a functional
equation in the form of an inversion identity satisfied by the commuting
double-row transfer matrices. This is established directly in the planar
Temperley-Lieb algebra and holds independently of the space of link states on
which the transfer matrices act. Different sectors are obtained by acting on
link states with s-1 defects where s=1,2,3,... is an extended Kac label. The
bulk and boundary free energies and finite-size corrections are obtained from
the Euler-Maclaurin formula. The eigenvalues of the transfer matrix are
classified by the physical combinatorics of the patterns of zeros in the
complex spectral-parameter plane. This yields a selection rule for the
physically relevant solutions to the inversion identity and explicit finitized
characters for the associated quasi-rational representations. In particular, in
the scaling limit, we confirm the central charge c=-2 and conformal weights
Delta_s=((2-s)^2-1)/8 for s=1,2,3,.... We also discuss a diagrammatic
implementation of fusion and show with examples how indecomposable
representations arise. We examine the structure of these representations and
present a conjecture for the general fusion rules within our framework.


  Artificial intelligence (AI)-driven zero-touch massive network slicing is
envisioned to be a disruptive technology in beyond 5G (B5G)/6G, where tenancy
would be extended to the final consumer in the form of advanced digital
use-cases. In this paper, we propose a novel model-free deep reinforcement
learning (DRL) framework, called collaborative statistical Actor-Critic (CS-AC)
that enables a scalable and farsighted slice performance management in a
6G-like RAN scenario that is built upon mobile edge computing (MEC) and massive
multiple-input multiple-output (mMIMO). In this intent, the proposed CS-AC
targets the optimization of the latency cost under a long-term statistical
service-level agreement (SLA). In particular, we consider the Q-th delay
percentile SLA metric and enforce some slice-specific preset constraints on it.
Moreover, to implement distributed learners, we propose a developed variant of
soft Actor-Critic (SAC) with less hyperparameter sensitivity. Finally, we
present numerical results to showcase the gain of the adopted approach on our
built OpenAI-based network slicing environment and verify the performance in
terms of latency, SLA Q-th percentile, and time efficiency. To the best of our
knowledge, this is the first work that studies the feasibility of an AI-driven
approach for massive network slicing under statistical SLA.


  When training deep neural networks, the phenomenon of $\textit{dying
neurons}$ $\unicode{x2013}$units that become inactive or saturated, output zero
during training$\unicode{x2013}$ has traditionally been viewed as undesirable,
linked with optimization challenges, and contributing to plasticity loss in
continual learning scenarios. In this paper, we reassess this phenomenon,
focusing on sparsity and pruning. By systematically exploring the impact of
various hyperparameter configurations on dying neurons, we unveil their
potential to facilitate simple yet effective structured pruning algorithms. We
introduce $\textit{Demon Pruning}$ (DemP), a method that controls the
proliferation of dead neurons, dynamically leading to network sparsity.
Achieved through a combination of noise injection on active units and a
one-cycled schedule regularization strategy, DemP stands out for its simplicity
and broad applicability. Experiments on CIFAR10 and ImageNet datasets
demonstrate that DemP surpasses existing structured pruning techniques,
showcasing superior accuracy-sparsity tradeoffs and training speedups. These
findings suggest a novel perspective on dying neurons as a valuable resource
for efficient model compression and optimization.


  The paper considers grad-div stabilized equal-order finite elements (FE)
methods for the linearized Navier-Stokes equations. A block triangular
preconditioner for the resulting system of algebraic equations is proposed
which is closely related to the Augmented Lagrangian (AL) preconditioner. A
field-of-values analysis of a preconditioned Krylov subspace method shows
convergence bounds that are independent of the mesh parameter variation.
Numerical studies support the theory and demonstrate the robustness of the
approach also with respect to the viscosity parameter variation, as is typical
for AL preconditioners when applied to inf-sup stable FE pairs. The numerical
experiments also address the accuracy of grad-div stabilized equal-order FE
method for the steady state Navier-Stokes equations.


  Neutrino physics is currently suffering from lack of knowledge from at least
four major ingredients. One of them is the presence or not of new sterile
neutrino states at the mass scale of around 1 eV. Settling this point should be
the highest priority for the neutrino community. We will discuss the
state-of-the art of experimental searches for sterile neutrinos with
accelerators, both at long and short baselines.


  Intermediate mass-ratio coalescences are potential signals of ground-based
and space-based gravitational observatories. Accurate modeling of their
waveforms within general relativity can be achieved within black hole
perturbation theory including self-force and finite size effects. In this
paper, we present analytic results to the Teukolsky perturbation of equatorial
orbits in the near-horizon region of an extremely high spin black hole
including spin coupling and finite size effects at leading order in the high
spin limit while neglecting the self-force. We detail the critical behavior
occuring close to the smallest specific angular momentum, and we discuss
features of spin and quadrupole couplings.


  Distributionally robust optimization (DRO) is a widely used framework for
optimizing objective functionals in the presence of both randomness and
model-form uncertainty. A key step in the practical solution of many DRO
problems is a tractable reformulation of the optimization over the chosen model
ambiguity set, which is generally infinite dimensional. Previous works have
solved this problem in the case where the objective functional is an expected
value. In this paper we study objective functionals that are the sum of an
expected value and a variance penalty term. We prove that the corresponding
variance-penalized DRO problem over an $f$-divergence neighborhood can be
reformulated as a finite-dimensional convex optimization problem. This result
also provides tight uncertainty quantification bounds on the variance.


  A Multiregression Dynamic Model (MDM) is a class of multivariate time series
that represents various dynamic causal processes in a graphical way. One of the
advantages of this class is that, in contrast to many other Dynamic Bayesian
Networks, the hypothesised relationships accommodate conditional conjugate
inference. We demonstrate for the first time how straightforward it is to
search over all possible connectivity networks with dynamically changing
intensity of transmission to find the Maximum a Posteriori Probability (MAP)
model within this class. This search method is made feasible by using a novel
application of an Integer Programming algorithm. The efficacy of applying this
particular class of dynamic models to this domain is shown and more
specifically the computational efficiency of a corresponding search of 11-node
Directed Acyclic Graph (DAG) model space. We proceed to show how diagnostic
methods, analogous to those defined for static Bayesian Networks, can be used
to suggest embellishment of the model class to extend the process of model
selection. All methods are illustrated using simulated and real resting-state
functional Magnetic Resonance Imaging (fMRI) data.


  We numerically study a particle in a box with moving walls. In the case where
the walls are oscillating sinusoidally with small amplitude, we show that
states up to the fourth state can be populated with more than 80 percent
population, while higher-lying states can also be selectively excited. This
work introduces a way of controlling quantum systems which does not rely on
(dipole) selection rules.


  Since the initial proposal in 1990s, the method of electron lenses has been
successfully developed and employed at the high energy particle colliders, like
Tevatron and RHIC. Here we propose a new set of electron multi beam elements
(electron grids, electron lattices, electron grates and other) which employ
many beams in various spatial configurations, with possible time varying
currents. We present major principles of operation and generation of such
systems and briefly discuss a set of possible applications in accelerators.


  The 2$\alpha+t$ cluster structure in $^{11}$B is investigated by the
microscopic generator coordinate method (GCM) with the Brink cluster wave
functions. With a proper choice of the parameters of the effective interaction,
the calculated energy spectrum shows reasonable agreement with the observed
low-lying spectra of both parities. On the basis of the calculated radii,
monopole and $B(E2)$ transition strengths, several developed cluster states of
$^{11}$B are suggested. For the negative-parity states, in addition to the
well-known $3/2_3^-$ cluster state, the $1/2_2^-$ and $5/2_3^-$ states are also
proposed as the well-developed cluster states. For the positive-parity states,
it is found that many states around the 2$\alpha+t$ threshold show the feature
of developed clusters. In particular, the $1/2_2^+$ state is found to have a
linear-chain-like structure, which is consistent with the previous
antisymmetrized molecular dynamics calculation, but contradicts to the
orthogonality condition model calculation. It is also found that many of these
positive-parity cluster candidates have the non-negligible isoscalar dipole
transition strengths, which require the experimental confirmation.


  Current climate shift has significant impacts on both quality and quantity of
snow precipitation. This directly influences spatial variability of the
snowpack as well as cumulative snow height. Contemporary glacier retreat
reorganizes periglacial morphology: while the glacier area decreases, the
moraine area increases. The latter is becoming a new water storage potential
almost as important as the glacier itself, but with a much more complex
topography. This work hence fills one of the missing variables of the
hydrological budget equation of an arctic glacier basin by providing an
estimate of the snow water equivalent (SWE) of the moraine contribution. Such a
result is achieved by investigating Structure from Motion (SfM) image
processing applied to pictures collected from an Unmanned Aerial Vehicle (UAV)
as a method to produce snow depth maps over the proglacial moraine area.
Several UAV campaigns were carried out on a small glacial basin in Spitsbergen
(Arctic): measurements were made at the maximum snow accumulation season (late
April) while reference topography maps were acquired at the end of hydrological
year (late September) when the moraine is mostly free of snow. Snow depth is
determined from Digital Surface Model (DSM) subtraction. Using dedicated and
natural ground control points for relative positioning of the DSMs, the
relative DSM georeferencing with sub-meter accuracy removes the main source of
uncertainty when assessing snow depth. For areas where snow is deposited on
bare rock surfaces, the correlation between avalanche probe in-situ snow depth
measurements and DSM differences is excellent. Differences in ice covered areas
between the two measurement techniques are attributed to the different
quantities measured.


  Solving partial differential equations with deep learning makes it possible
to reduce simulation times by multiple orders of magnitude and unlock
scientific methods that typically rely on large numbers of sequential
simulations, such as optimization and uncertainty quantification. Two of the
largest challenges of adopting scientific AI for industrial problem settings is
that training datasets must be simulated in advance and that neural networks
for solving large-scale PDEs exceed the memory capabilities of current GPUs. We
introduce a distributed programming API in the Julia language for simulating
training data in parallel on the cloud and without requiring users to manage
the underlying HPC infrastructure. In addition, we show that model-parallel
deep learning based on domain decomposition allows us to scale neural networks
for solving PDEs to commercial-scale problem settings and achieve above 90%
parallel efficiency. Combining our cloud API for training data generation and
model-parallel deep learning, we train large-scale neural networks for solving
the 3D Navier-Stokes equation and simulating 3D CO2 flow in porous media. For
the CO2 example, we simulate a training dataset based on a commercial carbon
capture and storage (CCS) project and train a neural network for CO2 flow
simulation on a 3D grid with over 2 million cells that is 5 orders of
magnitudes faster than a conventional numerical simulator and 3,200 times
cheaper.


  We consider the issue of biases in scholarly research, specifically, in peer
review. There is a long standing debate on whether exposing author identities
to reviewers induces biases against certain groups, and our focus is on
designing tests to detect the presence of such biases. Our starting point is a
remarkable recent work by Tomkins, Zhang and Heavlin which conducted a
controlled, large-scale experiment to investigate existence of biases in the
peer reviewing of the WSDM conference. We present two sets of results in this
paper. The first set of results is negative, and pertains to the statistical
tests and the experimental setup used in the work of Tomkins et al. We show
that the test employed therein does not guarantee control over false alarm
probability and under correlations between relevant variables coupled with any
of the following conditions, with high probability, can declare a presence of
bias when it is in fact absent: (a) measurement error, (b) model mismatch, (c)
reviewer calibration. Moreover, we show that the setup of their experiment may
itself inflate false alarm probability if (d) bidding is performed in non-blind
manner or (e) popular reviewer assignment procedure is employed. Our second set
of results is positive and is built around a novel approach to testing for
biases that we propose. We present a general framework for testing for biases
in (single vs. double blind) peer review. We then design hypothesis tests that
under minimal assumptions guarantee control over false alarm probability and
non-trivial power even under conditions (a)--(c) as well as propose an
alternative experimental setup which mitigates issues (d) and (e). Finally, we
show that no statistical test can improve over the non-parametric tests we
consider in terms of the assumptions required to control for the false alarm
probability.


  In this paper, we prove asymptotic formulas of mixed moments of $\rm GL(2)$
and its symmetric square $L$-functions for both Hecke--Maass cusp forms and
holomorphic Hecke eigenforms in short intervals. As an application, we prove
quantitative simultaneous non-vanishing of central values of these
$L$-functions.


  High pressure xenon Time Projection Chambers (TPC) based on secondary
scintillation (electroluminescence) signal amplification are being proposed for
rare event detection such as directional dark matter, double electron capture
and double beta decay detection. The discrimination of the rare event through
the topological signature of primary ionisation trails is a major asset for
this type of TPC when compared to single liquid or double-phase TPCs, limited
mainly by the high electron diffusion in pure xenon. Helium admixtures with
xenon can be an attractive solution to reduce the electron diffusion
significantly, improving the discrimination efficiency of these optical TPCs.
We have measured the electroluminescence (EL) yield of Xe-He mixtures, in the
range of 0 to 30% He and demonstrated the small impact on the EL yield of the
addition of helium to pure xenon. For a typical reduced electric field of 2.5
kV/cm/bar in the scintillation region, the EL yield is lowered by ~ 2%, 3%, 6%
and 10% for 10%, 15%, 20% and 30% of helium concentration, respectively. This
decrease is less than what has been obtained from the most recent simulation
framework in the literature. The impact of the addition of helium on EL
statistical fluctuations is negligible, within the experimental uncertainties.
The present results are an important benchmark for the simulation tools to be
applied to future optical TPCs based on Xe-He mixtures.


  Non-intrusive load monitoring (NILM) or energy disaggregation aims to break
down total household energy consumption into constituent appliances. Prior work
has shown that providing an energy breakdown can help people save up to 15\% of
energy. In recent years, deep neural networks (deep NNs) have made remarkable
progress in the domain of NILM. In this paper, we demonstrate the performance
of Gaussian Processes (GPs) for NILM. We choose GPs due to three main reasons:
i) GPs inherently model uncertainty; ii) equivalence between infinite NNs and
GPs; iii) by appropriately designing the kernel we can incorporate domain
expertise. We explore and present the challenges of applying our GP approaches
to NILM.


  Embedding tables are usually huge in click-through rate (CTR) prediction
models. To train and deploy the CTR models efficiently and economically, it is
necessary to compress their embedding tables at the training stage. To this
end, we formulate a novel quantization training paradigm to compress the
embeddings from the training stage, termed low-precision training (LPT). Also,
we provide theoretical analysis on its convergence. The results show that
stochastic weight quantization has a faster convergence rate and a smaller
convergence error than deterministic weight quantization in LPT. Further, to
reduce the accuracy degradation, we propose adaptive low-precision training
(ALPT) that learns the step size (i.e., the quantization resolution) through
gradient descent. Experiments on two real-world datasets confirm our analysis
and show that ALPT can significantly improve the prediction accuracy,
especially at extremely low bit widths. For the first time in CTR models, we
successfully train 8-bit embeddings without sacrificing prediction accuracy.
The code of ALPT is publicly available.


  We study the free energy landscape of a minimal model for relaxor
ferroelectrics. Using a variational method which includes leading correlations
beyond the mean-field approximation as well as disorder averaging at the level
of a simple replica theory, we find metastable paraelectric states with a
stability region that extends to zero temperature. The free energy of such
states exhibits an essential singularity for weak compositional disorder
pointing to their necessary occurrence. Ferroelectric states appear as local
minima in the free energy at high temperatures and become stable below a
coexistence temperature $T_c$. We calculate the phase diagram in the electric
field-temperature plane and find a coexistence line of the polar and non-polar
phases which ends at a critical point. First-order phase transitions are
induced for fields sufficiently large to cross the region of stability of the
metastable paraelectric phase. These polar and non-polar states have distinct
structure factors from those of conventional ferroelectrics. We use this
theoretical framework to compare and to gain physical understanding of various
experimental results in typical relaxors.


  We describe calibration data, and discuss performance of the photon-counting
flight detectors for the Ultraviolet Imaging Telescopes on the Astrosat
observatory. The paper describes dark current, flat field and light-spot images
for FUV, NUV, and Visible band detectors at more than one wavelength setting
for each. We also report on nominal gain and low-gain operations, full- and
sub-window read rates, and non-photon-counting modes of operation, all expected
to be used in flight. We derive corrections to the event centroids from the
CMOS readout arrays, for different centroid algorithms. We derive spatial
resolution values for each detector and plots of point-source signal saturation
for different flux levels. We also discuss ways to correct for saturation in
extended object images.


  Aspect-based sentiment analysis (ABSA) aims to predict fine-grained
sentiments of comments with respect to given aspect terms or categories. In
previous ABSA methods, the importance of aspect has been realized and verified.
Most existing LSTM-based models take aspect into account via the attention
mechanism, where the attention weights are calculated after the context is
modeled in the form of contextual vectors. However, aspect-related information
may be already discarded and aspect-irrelevant information may be retained in
classic LSTM cells in the context modeling process, which can be improved to
generate more effective context representations. This paper proposes a novel
variant of LSTM, termed as aspect-aware LSTM (AA-LSTM), which incorporates
aspect information into LSTM cells in the context modeling stage before the
attention mechanism. Therefore, our AA-LSTM can dynamically produce
aspect-aware contextual representations. We experiment with several
representative LSTM-based models by replacing the classic LSTM cells with the
AA-LSTM cells. Experimental results on SemEval-2014 Datasets demonstrate the
effectiveness of AA-LSTM.