  The Dark Matter Time Projection Chamber (DMTPC) collaboration is developing
low-pressure gas TPC detectors for measuring WIMP-nucleon interactions. Optical
readout with CCD cameras allows for the detection for the daily modulation in
the direction of the dark matter wind, while several charge readout channels
allow for the measurement of additional recoil properties. In this article, we
show that the addition of the charge readout analysis to the CCD allows us too
obtain a statistics-limited 90% C.L. upper limit on the $e^-$ rejection factor
of $5.6\times10^{-6}$ for recoils with energies between 40 and 200
keV$_{\mathrm{ee}}$. In addition, requiring coincidence between charge signals
and light in the CCD reduces CCD-specific backgrounds by more than two orders
of magnitude.


  The charge transfer compound (BEDT-TTF)$_2$Ag(CF$_3$)$_4$(TCE) crystallizes
in three polymorphs with different alternating layers: While a phase with a
$\kappa$ packing motif has a low superconducting transition temperature of
$T_c=2.6$ K, two phases with higher $T_c$ of $9.5$ and $11$ K are multi-layered
structures consisting of $\alpha'$ and $\kappa$ layers. We investigate these
three systems within density functional theory and find that the $\alpha'$
layer shows different degrees of charge order for the two $\kappa$-$\alpha'$
systems and directly influences the electronic behavior of the conducting
$\kappa$ layer. We discuss the origin of the distinct behavior of the three
polymorphs and propose a minimal tight-binding Hamiltonian for the description
of these systems based on projective molecular Wannier functions.


  We propose a homology theory for locally compact spaces with ends in which
the ends play a special role. The approach is motivated by results for graphs
with ends, where it has been highly successful. But it was unclear how the
original graph-theoretical definition could be captured in the usual language
for homology theories, so as to make it applicable to more general spaces. In
this paper we provide such a general topological framework: we define a
homology theory which satisfies the usual axioms, but which maintains the
special role for ends that has made this homology work so well for graphs.


  Detecting problematic content, such as hate speech, is a multifaceted and
ever-changing task, influenced by social dynamics, user populations, diversity
of sources, and evolving language. There has been significant efforts, both in
academia and in industry, to develop annotated resources that capture various
aspects of problematic content. Due to researchers' diverse objectives, the
annotations are inconsistent and hence, reports of progress on detection of
problematic content are fragmented. This pattern is expected to persist unless
we consolidate resources considering the dynamic nature of the problem. We
propose integrating the available resources, and leveraging their dynamic
nature to break this pattern. In this paper, we introduce a continual learning
benchmark and framework for problematic content detection comprising over 84
related tasks encompassing 15 annotation schemas from 8 sources. Our benchmark
creates a novel measure of progress: prioritizing the adaptability of
classifiers to evolving tasks over excelling in specific tasks. To ensure the
continuous relevance of our framework, we designed it so that new tasks can
easily be integrated into the benchmark. Our baseline results demonstrate the
potential of continual learning in capturing the evolving content and adapting
to novel manifestations of problematic content.


  Millimeter-scale embedded sensing systems have unique advantages over larger
devices as they are able to capture, analyze, store, and transmit data at the
source while being unobtrusive and covert. However, area-constrained systems
pose several challenges, including a tight energy budget and peak power,
limited data storage, costly wireless communication, and physical integration
at a miniature scale. This paper proposes a novel 6.7$\times$7$\times$5mm
imaging system with deep-learning and image processing capabilities for
intelligent edge applications, and is demonstrated in a home-surveillance
scenario. The system is implemented by vertically stacking custom
ultra-low-power (ULP) ICs and uses techniques such as dynamic behavior-specific
power management, hierarchical event detection, and a combination of data
compression methods. It demonstrates a new image-correcting neural network that
compensates for non-idealities caused by a mm-scale lens and ULP front-end. The
system can store 74 frames or offload data wirelessly, consuming 49.6$\mu$W on
average for an expected battery lifetime of 7 days.


  The Viterbi algorithm, presented in 1967, allows a maximum likelihood
decoding of partial response codes. This study focuses on the duobinary code
which is the first member of this family and has been specified for the digital
part of television systems recommended by International Organizations. Up to
now the error-rate, which is the main criterion of the performance, has been
evaluated by simulation. Although there exist theoretical bounds, these bounds
are not satisfactory for a channel such as broadcasting (by terrestrial
transmitters, cable networks or satellite) which is strongly impaired by noise,
and linear and non-linear distortions. Analytical methods, verified by
simulation, are presented here in order to evaluate the theoretical and exact
values of the error-rate, in the form of series of numerical integrations, for
a transmission in baseband or in radio-frequency with quadriphase modulation
(or AM/VSB for cable networks) and coherent demodulation, in presence of noise
and several distortions. This methodology can be later extended to other
partial response codes, to convolutional codes and their concatenations.


  We study the Drinfeld double of the (equivariant spherical) Cohomological
Hall algebra in the sense of Kontsevich and Soibelman, associated to a smooth
toric Calabi-Yau 3-fold $X$. By general reasons, the COHA acts on the
cohomology of the moduli spaces of certain perverse coherent systems on $X$ via
"raising operators". Conjecturally the COHA action extends to an action of the
Drinfeld double by adding the "lowering operators".
  In this paper, we show that the Drinfeld double is a generalization of the
notion of the Cartan doubled Yangian defined earlier by Finkelberg and others.
We extend this "$3d$ Calabi-Yau perspective" on the Lie theory furthermore by
associating a root system to certain families of $X$. We formulate a conjecture
that the above-mentioned action of the Drinfeld double factors through a
shifted Yangian of the root system. The shift is explicitly determined by the
moduli problem and the choice of stability conditions, and is expressed
explicitly in terms of an intersection number in $X$. We check the conjectures
in several examples, including a special case of an earlier conjecture of
Costello.


  Computer Science education has been evolving over the years to reflect
applied realities. Until about a decade ago, theory of computation, algorithm
design and system software dominated the curricula. Most courses were
considered core and were hence mandatory; the programme structure did not allow
much of a choice or variety. This column analyses why this changed Circa 2010
when elective subjects across scores of topics become part of mainstream
education to reflect the on-going lateral acceleration of Computer Science.
Fundamental discoveries in artificial intelligence, machine learning,
virtualization and cloud computing are several decades old. Many core theories
in data science are centuries old. Yet their leverage exploded only after Circa
2010, when the stage got set for people-centric problem solving in massive
scale. This was due in part to the rush of innovative real-world applications
that reached the common man through the ubiquitous smart phone. AI/ML modules
arrived in popular programming languages; they could be used to build and train
models on powerful - yet affordable - compute on public clouds reachable
through high-speed Internet connectivity. Academia responded by adapting
Computer Science curricula to align it with the changing technology landscape.
The goal of this experiential piece is to trigger a lively discussion on the
past and future of Computer Science education.


  We present Splat-Nav, a real-time navigation pipeline designed to work with
environment representations generated by Gaussian Splatting (GSplat), a popular
emerging 3D scene representation from computer vision. Splat-Nav consists of
two components: 1) Splat-Plan, a safe planning module, and 2) Splat-Loc, a
robust pose estimation module. Splat-Plan builds a safe-by-construction
polytope corridor through the map based on mathematically rigorous collision
constraints and then constructs a B\'ezier curve trajectory through this
corridor. Splat-Loc provides a robust state estimation module, leveraging the
point-cloud representation inherent in GSplat scenes for global pose
initialization, in the absence of prior knowledge, and recursive real-time pose
localization, given only RGB images. The most compute-intensive procedures in
our navigation pipeline, such as the computation of the B\'ezier trajectories
and the pose optimization problem run primarily on the CPU, freeing up GPU
resources for GPU-intensive tasks, such as online training of Gaussian Splats.
We demonstrate the safety and robustness of our pipeline in both simulation and
hardware experiments, where we show online re-planning at 5 Hz and pose
estimation at about 25 Hz, an order of magnitude faster than Neural Radiance
Field (NeRF)-based navigation methods, thereby enabling real-time navigation.


  In this paper, we tackle the task of estimating the 3D orientation of
previously-unseen objects from monocular images. This task contrasts with the
one considered by most existing deep learning methods which typically assume
that the testing objects have been observed during training. To handle the
unseen objects, we follow a retrieval-based strategy and prevent the network
from learning object-specific features by computing multi-scale local
similarities between the query image and synthetically-generated reference
images. We then introduce an adaptive fusion module that robustly aggregates
the local similarities into a global similarity score of pairwise images.
Furthermore, we speed up the retrieval process by developing a fast retrieval
strategy. Our experiments on the LineMOD, LineMOD-Occluded, and T-LESS datasets
show that our method yields a significantly better generalization to unseen
objects than previous works. Our code and pre-trained models are available at
https://sailor-z.github.io/projects/Unseen_Object_Pose.html.


  The determination of the dynamical causes of the morphological Butcher-Oemler
(BO) effect, or the rapid transformation of a large population of late-type
galaxies to earlier Hubble types in the rich cluster environment between
intermediate redshifts and the local universe, has been an important unsolved
problem which is central to our understanding of the general problems of galaxy
formation and evolution. In this article, we survey the existing proposed
mechanisms for cluster galaxy transformation, and discuss their relevance and
limitations to the explanation of the morphological BO effect. A new infrared
diagnostic approach is devised to disentangle the relative importance of
several major physical mechanisms to account for the BO effect, and an example
of the first application of this procedure to a single rich, intermediate
redshift galaxy cluster is given to demonstrate the viability of this approach.
The preliminary result of this analysis favors the interaction-enhanced secular
evolution process as the major cause of the cluster-galaxy morphological
transformation. This conclusion is also supported by a wide range of other
published results which are assembled here to highlight their implications on a
coherent physical origin for the morphological BO effect.


  We present a comprehensive search and analysis of high-redshift galaxies in a
suite of nine public JWST extragalactic fields taken in Cycle 1, covering a
total effective search area of $\sim358{\rm arcmin^2}$. Through conservative
($8\sigma$) photometric selection, we identify 341 galaxies at $5<z<14$, with
109 having spectroscopic redshift measurements from the literature, including
recent JWST NIRSpec observations. Our regression analysis reveals that the
rest-frame UV size-stellar mass relation follows $R_{\rm eff}\propto
M_*^{0.19\pm0.03}$, similar to that of star-forming galaxies at $z\sim3$, but
scaled down in size by $\sim0.7$dex. We find a much slower rate for the average
size evolution over the redshift range, $R_{\rm eff}\propto(1+z)^{-0.4\pm0.2}$,
than that derived in the literature. A fraction ($\sim13\,\%$) of our sample
are marginally resolved even in the NIRCam imaging ($<100$pc), located at
$>1.5\,\sigma$ below the derived size-mass slope. These compact sources exhibit
a high star formation surface density $\Sigma_{\rm SFR}>10\,M_\odot\,{\rm
yr^{-1}\,kpc^{-2}}$, a range in which only $<0.01\,\%$ of the local
star-forming galaxy sample is found. For those with available NIRSpec data, no
evidence of ongoing supermassive black hole accretion is observed. A potential
explanation for the observed high [OIII]-to-Hbeta ratios could be high shock
velocities, likely originating within intense star-forming regions
characterized by high $\Sigma_{\rm SFR}$. Lastly, we find that the rest-frame
UV and optical sizes of our sample are comparable. Our results are consistent
with these early galaxies building up their structures inside-out and yet to
exhibit the strong color gradient seen at lower redshift.


  The dynamical behavior of switched affine systems is known to be more
intricate than that of the well-studied switched linear systems, essentially
due to the existence of distinct equilibrium points for each subsystem. First,
under arbitrary switching rules, the stability analysis must be generally
carried out with respect to a compact set with non-empty interior rather than
to a singleton. We provide a novel proof technique for existence and outer
approximation of attractive invariant sets of a switched affine system, under
the hypothesis of global uniform stability of its linearization. On the other
hand, considering dwell-time switching signals, forward invariant sets need not
exist for this class of switched systems, even for stable ones. Hence, more
general notions of stability/boundedness are introduced and studied,
highlighting the relations of these concepts to the uniform stability of the
linear part of the system under the same class of dwell-time switching signals.
These results reveal the main differences and specificities of switched affine
systems with respect to linear ones, providing a first step for the analysis of
switched systems composed by subsystems not sharing the same equilibrium.
Numerical methods based on linear matrix inequalities and sum-of-squares
programming are presented and illustrate the developed theory.


  Suppose that $X=\{X_t, t\ge 0; \mathbb{P}_{\mu}\}$ is a supercritical
superprocess in a locally compact separable metric space $E$. Let $\phi_0$ be a
positive eigenfunction corresponding to the first eigenvalue $\lambda_0$ of the
generator of the mean semigroup of $X$. Then
$M_t:=e^{-\lambda_0t}\langle\phi_0, X_t\rangle$ is a positive martingale. Let
$M_\infty$ be the limit of $M_t$. It is known (see, J. Appl. Probab. 46 (2009),
479--496) that $M_\infty$ is non-degenerate iff the $L\log L$ condition is
satisfied. In this paper we are mainly interested in the case when the $L\log
L$ condition is not satisfied. We prove that, under some conditions, there
exist function $\gamma_t$ on $[0, \infty)$ and a non-degenerate random variable
$W$ such that for any finite nonzero Borel measure $\mu$ on $E$, $$
\lim_{t\to\infty}\gamma_t\langle \phi_0,X_t\rangle
=W,\qquad\mbox{a.s.-}\mathbb{P}_{\mu}. $$ We also give the almost sure limit of
$\gamma_t\langle f,X_t\rangle$ for a class of general test functions $f$.


  Wildfire forecasting is notoriously hard due to the complex interplay of
different factors such as weather conditions, vegetation types and human
activities. Deep learning models show promise in dealing with this complexity
by learning directly from data. However, to inform critical decision making, we
argue that we need models that are right for the right reasons; that is, the
implicit rules learned should be grounded by the underlying processes driving
wildfires. In that direction, we propose integrating causality with Graph
Neural Networks (GNNs) that explicitly model the causal mechanism among complex
variables via graph learning. The causal adjacency matrix considers the
synergistic effect among variables and removes the spurious links from highly
correlated impacts. Our methodology's effectiveness is demonstrated through
superior performance forecasting wildfire patterns in the European boreal and
mediterranean biome. The gain is especially prominent in a highly imbalanced
dataset, showcasing an enhanced robustness of the model to adapt to regime
shifts in functional relationships. Furthermore, SHAP values from our trained
model further enhance our understanding of the model's inner workings.


  Contemporary understanding of correlations in quantum many-body systems and
in quantum phase transitions is based to a large extent on the recent intensive
studies of entanglement in many-body systems. In contrast, much less is known
about the role of quantum nonlocality in these systems, mostly because the
available multipartite Bell inequalities involve high-order correlations among
many particles, which are hard to access theoretically, and even harder
experimentally. Standard, "theorist- and experimentalist-friendly" many-body
observables involve correlations among only few (one, two, rarely three...)
particles. Typically, there is no multipartite Bell inequality for this
scenario based on such low-order correlations. Recently, however, we have
succeeded in constructing multipartite Bell inequalities that involve two- and
one-body correlations only, and showed how they revealed the nonlocality in
many-body systems relevant for nuclear and atomic physics [Science 344, 1256
(2014)]. With the present contribution we continue our work on this problem. On
the one hand, we present a detailed derivation of the above Bell inequalities,
pertaining to permutation symmetry among the involved parties. On the other
hand, we present a couple of new results concerning such Bell inequalities.
First, we characterize their tightness. We then discuss maximal quantum
violations of these inequalities in the general case, and their scaling with
the number of parties. Moreover, we provide new classes of two-body Bell
inequalities which reveal nonlocality of the Dicke states---ground states of
physically relevant and experimentally realizable Hamiltonians. Finally, we
shortly discuss various scenarios for nonlocality detection in mesoscopic
systems of trapped ions or atoms, and by atoms trapped in the vicinity of
designed nanostructures.


  Nested datatypes have been widely studied in the past 25 years, both
theoretically using category theory, and practically in programming languages
such as Haskell. They consist in recursive polymorphic datatypes where the type
parameter changes throughout the recursion. They have a variety of applications
such as modelling memory or modelling constraints over regular datatypes
without relying on dependent types. In this work, we focus on a specific subset
of nested datatypes which we call Linked Nested DataTypes (LNDT). We show that
some usual datatypes such has List and Maybe, as well as some well-known nested
datatypes such as Nest and even Bush can be built as various instances of LNDT.
We proceed by presenting LibNDT, a library, developed both in Agda and Coq,
which focuses on the set of constructs that can be spread directly from the
parameter on which a LNDT is built, to the LNDT itself. These spreadable
elements are of two kinds, functions, such as folds and map, and properties,
such as the congruence of map or the satisfaction of a given predicate for at
least one, or all, elements of the structure. We make use of the dependent type
system of both Coq and Agda to model the latter. This paper ends with a
discussion about various interesting topics that were raised throughout our
development such as the issue of termination, the comparison of our tools and
the proof effort required to extend LibNDT with additional elements.


  A new dual plasma coating process to produce platinum-free catalysts for the
oxygen reduction reaction in a fuel cell is introduced. The catalysts thus
produced were analysed with various methods. Electrochemical characterisation
was carried out by cyclic voltammetry, rotating ring- and rotating ring-disk
electrode. The surface porosity of the different catalysts thus obtained was
characterised with the nitrogen gas adsorption technique and scanning electron
microscopy was used to determine the growth mechanisms of the films. It is
shown that catalytically active compounds can be produced with this dual plasma
process. Furthermore, the catalytic activity can be varied significantly by
changing the plasma process parameters. The amount of H$_2$O$_2$ produced was
calculated and shows that a 2 electron mechanism is predominant. The plasma
coating mechanism does not significantly change the surface BET area and pore
size distribution of the carbon support used. Furthermore, scanning electron
microscopy pictures of the produced films are presented and show the preference
of columnar growth mechanisms. By using different carbons as the support it is
shown that there is a strong dependence of the catalytic activity that is
probably related to the chemical properties of the carbon.


  Backpropagation (BP), a foundational algorithm for training artificial neural
networks, predominates in contemporary deep learning. Although highly
successful, it is often considered biologically implausible. A significant
limitation arises from the need for precise symmetry between connections in the
backward and forward pathways to backpropagate gradient signals accurately,
which is not observed in biological brains. Researchers have proposed several
algorithms to alleviate this symmetry constraint, such as feedback alignment
and direct feedback alignment. However, their divergence from backpropagation
dynamics presents challenges, particularly in deeper networks and convolutional
layers. Here we introduce the Product Feedback Alignment (PFA) algorithm. Our
findings demonstrate that PFA closely approximates BP and achieves comparable
performance in deep convolutional networks while avoiding explicit weight
symmetry. Our results offer a novel solution to the longstanding weight
symmetry problem, leading to more biologically plausible learning in deep
convolutional networks compared to earlier methods.


  The expectation of explaining cosmological observations without requiring new
energy sources is forsooth worthy of investigation. In this letter, a new kind
of Cardassian models, called exponential Cardassian models, for the late-time
universe are investigated in the context of the spatially flat FRW universe
scenario. We fit the exponential Cardassian models to current type Ia
supernovae data and find they are consistent with the observations.
Furthermore, we point out that the equation-of-state parameter for the
effective dark fluid component in exponential Cardassian models can naturally
cross the cosmological constant divide $w=-1$ that observations favor mildly
without introducing exotic material that destroy the weak energy condition.


  Methods are described for the nonperturbative calculation of wave functions
and scattering amplitudes in light-cone quantization. Form factors are computed
from the boost-invariant wave functions, which appear as coefficients in a
Fock-state expansion of the field-theoretic eigenstate. A technique is proposed
for calculating scattering amplitudes from matrix elements of a T operator
between such composite-particle eigenstates.


  Dark Energy (DE) acts as a repulsive force that opposes gravitational
attraction. Assuming galaxies maintain a steady state over extended periods,
the estimated upper bound on DE studies its resistance to the attractive
gravitational force from dark matter. Using the SPARC dataset, we fit the
Navarro-Frenk-White (NFW) and Hernquist models to identify the most suitable
galaxies for these models. Introducing the presence of DE in these galaxies
helps establish the upper limit on its repulsive force. This upper bound on DE
sits around $\rho_{\left(<\Lambda\right)} \sim 10^{-25}$~kg/m$^3$, only two
orders of magnitude higher than the one measured by Planck. We discuss the
conditions for detecting DE in different systems and show the consistency of
the upper bound from galaxies to other systems. The upper bound is of the same
order of magnitude as $\rho_{200} = 200 \rho_c$ for both dark matter profiles.
We also address the implications for future measurements on that upper bound
and the condition for detecting the impact of $\Lambda$ on galactic scales.


  In this paper, we study risk-sensitive Reinforcement Learning (RL), focusing
on the objective of Conditional Value at Risk (CVaR) with risk tolerance
$\tau$. Starting with multi-arm bandits (MABs), we show the minimax CVaR regret
rate is $\Omega(\sqrt{\tau^{-1}AK})$, where $A$ is the number of actions and
$K$ is the number of episodes, and that it is achieved by an Upper Confidence
Bound algorithm with a novel Bernstein bonus. For online RL in tabular Markov
Decision Processes (MDPs), we show a minimax regret lower bound of
$\Omega(\sqrt{\tau^{-1}SAK})$ (with normalized cumulative rewards), where $S$
is the number of states, and we propose a novel bonus-driven Value Iteration
procedure. We show that our algorithm achieves the optimal regret of
$\widetilde O(\sqrt{\tau^{-1}SAK})$ under a continuity assumption and in
general attains a near-optimal regret of $\widetilde O(\tau^{-1}\sqrt{SAK})$,
which is minimax-optimal for constant $\tau$. This improves on the best
available bounds. By discretizing rewards appropriately, our algorithms are
computationally efficient.


  In the context of the genome rearrangement problem, we analyze two well known
models, namely the reversal and the prefix reversal models, by exploiting the
connection with the notion of permutation pattern. More specifically, for any
$k$, we provide a characterization of the set of permutations having distance
$\leq k$ from the identity (which is known to be a permutation class) in terms
of what we call generating peg permutations and we describe some properties of
its basis, which allow to compute such a basis for small values of $k$.


  A Finsler metric, whose holomorphic curvature is bounded from above by a
negative constant, is constructed on the moduli stack of canonically polarized
manifolds including singularities. Demailly's version of Ahlfors' lemma yields
hyperbolicity of the moduli stack.


  Despite the impressive results achieved by deep learning based 3D
reconstruction, the techniques of directly learning to model 4D human captures
with detailed geometry have been less studied. This work presents a novel
framework that can effectively learn a compact and compositional representation
for dynamic human by exploiting the human body prior from the widely used SMPL
parametric model. Particularly, our representation, named H4D, represents a
dynamic 3D human over a temporal span with the SMPL parameters of shape and
initial pose, and latent codes encoding motion and auxiliary information. A
simple yet effective linear motion model is proposed to provide a rough and
regularized motion estimation, followed by per-frame compensation for pose and
geometry details with the residual encoded in the auxiliary code. Technically,
we introduce novel GRU-based architectures to facilitate learning and improve
the representation capability. Extensive experiments demonstrate our method is
not only efficacy in recovering dynamic human with accurate motion and detailed
geometry, but also amenable to various 4D human related tasks, including motion
retargeting, motion completion and future prediction. Please check out the
project page for video and code: https://boyanjiang.github.io/H4D/.


  We analyze and completely describe the four cases when the Hitchin fibration
on a $2$-dimensional moduli space of irregular Higgs bundles over
$\mathbb{C}P^{1}$ has a single singular fiber. The case when the fiber at
infinity is of type $I_0^*$ is further analyzed, and we give constructions of
all the possible configurations of singular curves inelliptic fibrations having
this type of singular fiber at infinity.


  On the base of advantages in gene geography and anthropophenetics the
phenogeographical method for anthropological research is initiated and
experienced using dental data. Statistical and cartographical analyses are
provided for 498 living Eurasian populations. Mapping principal components
supplied evidence for the phene pool structure in Eurasian populations and for
reconstructions of our species history on the continent. The longitudinal
variability seems to be the most important regularity revealed by principal
components analysis (PCA) and mapping proving the division of the whole area
into western and eastern main provinces. So, the most ancient scenario in the
history of Eurasian populations was developing from two perspective different
groups: western group related to ancient populations of West Asia and the
eastern one rooted by ancestry in South and/or East Asia. In spite of the
enormous territory and the revealed divergence the populations of the continent
have undergone wide scale and intensive time-space interaction. Many details in
the revealed landscapes could be backgrounded to different historical events.
The most amazing results are obtained for proving migrations and assimilation
as two essential phenomena in Eurasian history: the wide spread of the western
combination through the whole continent till the Pacific coastline and the
envision of the movement of the paradox combinations of eastern and western
markers from South or Central Asia to the east and to the west. Taking into
account that no additional eastern combinations in the total variation in Asian
groups have been found but mixed or western markers' sets and that eastern
dental characteristics are traced in Asia since Homo erectus, the assumption is
made in favour of the hetero-level assimilation in the Eastern province and of
net-like evolution of our species.


  We present our results of searching for differences in light curves of
modulated and non-modulated RRab stars in the Galactic bulge. We examined a
sample of more than 8000 stars. The most important results are that Blazhko
stars have shorter pulsation periods, less skewed mean light curves, lower mean
amplitudes, larger rise-time, no difference in spatial distribution and
metallicity.


  This article is devoted to the theoretical and numerical analysis of a
network of excitatory and inhibitory neurons of Hodgkin-Huxley (HH) type, for
which the topology is inspired by that of a single local layer of visual cortex
V1. Our model is related to the recent CSY model and therefore differs from
other classical models in the field. It combines a driven stochastic drive --
which may be interpreted as an ambient drive for each neuron -- with recurrent
inputs resulting from the network activity. After a review of the dynamics of a
single HH equation for both the deterministic and the stochastically driven
case, we proceed to an analysis of the network. This analysis reveals emergent
properties of the system such as partial synchronization and synchronization
(defined here as a state of the network for which all the neurons spike within
a short interval of time), correlation between excitatory and inhibitory
conductances, and oscillations in the gamma-band frequency. The collective
behavior enumerated herein is observed when the input-amplitude parameter
$S^{EE}$ measuring excitatory-to-excitatory coupling (recurrent excitation)
increases to within a certain range. Of note, our work indicates a distinct
mechanism for obtaining the emergent properties, some of which have been
classically observed. As a consequence our article contributes to the
understanding of how assemblies of inhibitory and excitatory cells interact
together to produce rhythms in the network. It also aims to bring problems from
neuroscience to the realm of mathematics, where they can be analyzed
rigorously.


  We present a new fermionic solution of the supersymmetric matrix model. The
solution satisfies the commutation and anticommutation relations for
noncommutative superspace. Therefore the solution can be considered as an
implementation of noncommutative superspace on the matrix model.


  Adjoint-based sensitivity analysis methods are powerful tools for engineers
who use flow simulations for design. However, the conventional adjoint method
breaks down for scale-resolving simulations like large-eddy simulation (LES) or
direct numerical simulation (DNS), which exhibit the chaotic dynamics inherent
in turbulent flows. Sensitivity analysis based on least-squares shadowing (LSS)
avoids the issues encountered by conventional methods, but has a high
computational cost. The following report outlines a new, more computationally
efficient formulation of LSS, non-intrusive LSS, and estimates its cost for
several canonical flows using Lyapunov analysis.


  In this article, a new nonparametric and robust method of forecasting
hierarchical functional time series is presented. The method is compared with
Hyndman and Shang's method with respect to their unbiasedness, effectiveness,
robustness, and computational complexity. Taking into account results of the
analytical, simulation and empirical studies, we come to the conclusion that
our proposal is superior over the proposal of Hyndman and Shang with respect to
some statistical criteria and especially with respect to robustness and
computational complexity. An empirical usefulness of our method is presented on
example of management of a certain web portal divided into four subservices. An
extensive simulation study involving hierarchical systems consisted of FAR(1)
processes and Wiener processes has been conducted as well.


  We consider the Bak-Tang-Wiesenfeld sandpile model on square lattices in
different dimensions (D>=6). A finite size scaling analysis of the avalanche
probability distributions yields the values of the distribution exponents, the
dynamical exponent, and the dimension of the avalanches. Above the upper
critical dimension D_u=4 the exponents equal the known mean field values. An
analysis of the area probability distributions indicates that the avalanches
are fractal above the critical dimension.


  In this article we discuss the problem of the extreme fine-tuning necessary
to achieve the top quark mass scale $m_t$ within the dynamical symmetry
breaking of the phenomenological $t \bar t$ condensate model. Inspired by the
vector-like phenomenon of chiral gauge theories at short distances, we
postulate that the $W^\pm$-gauge bosons possess a vector-like gauge coupling
and contribute to the gap-equations of quark self-energy functions in the
high-energy region. We find that the gap-equations of different charge sectors
are coupled, and as a result the unnatural fine-tuning is overcome and the
ratio of the top and bottom masses is related to the electric charge:
$({m_b\over m_t})^2\simeq {\alpha\over3\pi}$, provided the quadratic divergence
is removed by setting the four-fermion coupling $G=4+o({m_b^2\over m_t^2})$.


  There is growing evidence that electronic and molecular networks present some
common universal properties, among which the existence of a self-organized
intermediate phase. In glasses, the latter is revealed by the reversibility
window obtained from complex calorimetric measurements at the glass transition.
Here we focus on amorphous networks and we show how this intermediate phase can
be understood from a rigidity percolation analysis on size increasing clusters.
This provides benchmarks and guidance for an electromechanical analogy with
high temperature super conductors


  In this study, it is demonstrated that Recurrent Neural Networks (RNNs),
specifically those utilizing Gated Recurrent Unit (GRU) architecture, possess
the capability to learn the complex dynamics of rate-and-state friction laws
from synthetic data. The data employed for training the network is generated
through the application of traditional rate-and-state friction equations
coupled with the aging law for state evolution. A novel aspect of our approach
is the formulation of a loss function that explicitly accounts for the direct
effect by means of automatic differentiation. It is found that the RNN, with
its GRU architecture, effectively learns to predict changes in the friction
coefficient resulting from velocity jumps (with and without noise in the target
data), thereby showcasing the potential of machine learning models in
understanding and simulating the physics of frictional processes.


  We analytically calculate the electronic friction tensor for a molecule near
a metal surface in the case that the electronic Hamiltonian is complex-valued,
e.g. the case that there is spin-orbit coupling and/or an external magnetic
field. In such a case, even at equilibrium, we show that the friction tensor is
not symmetric. Instead, the tensor is the real-valued sum of one positive
definite tensor (corresponding to dissipation) plus one antisymmetric tensor
(corresponding to a Berry pseudomagnetic force). Moreover, we find that this
Berry force can be much larger than the dissipational force, suggesting the
possibility of strongly spin-polarized chemicurrents or strongly spin-dependent
rate constants for systems with spin-orbit coupling.


  We present results by the ALPHA collaboration for the $\Lambda$-parameter in
3-flavour QCD and the strong coupling constant at the electroweak scale,
$\alpha_s(m_Z)$, in terms of hadronic quantities computed on the CLS gauge
configurations. The first part of this proceedings contribution contains a
review of published material \cite{Brida:2016flw,DallaBrida:2016kgh} and yields
the $\Lambda$-parameter in units of a low energy scale, $1/L_{\rm had}$. We
then discuss how to determine this scale in physical units from experimental
data for the pion and kaon decay constants. We obtain $\Lambda_{\overline{\rm
MS}}^{(3)} = 332(14)$ MeV which translates to $\alpha_s(M_Z)=0.1179(10)(2)$
using perturbation theory to match between 3-, 4- and 5-flavour QCD.


  The most general exclusion single species reaction-diffusion models with
nearest-neighbor interactions one a one dimensional lattice are investigated,
for which the evolution of full intervals are closed. Using a generating
function method, the probability that n consecutive sites be full is
investigated. The stationary values of these probabilities, as well as the
spectrum of the time translation generator (Hamiltonian) of these are obtained.
It is shown that depending on the reaction rates the model could exhibit a
dynamical phase transition.


  We propose a model-free reinforcement learning architecture, called
distributed attentional actor architecture after conditional attention (DA6-X),
to provide better interpretability of conditional coordinated behaviors. The
underlying principle involves reusing the saliency vector, which represents the
conditional states of the environment, such as the global position of agents.
Hence, agents with DA6-X flexibility built into their policy exhibit superior
performance by considering the additional information in the conditional states
during the decision-making process. The effectiveness of the proposed method
was experimentally evaluated by comparing it with conventional methods in an
objects collection game. By visualizing the attention weights from DA6-X, we
confirmed that agents successfully learn situation-dependent coordinated
behaviors by correctly identifying various conditional states, leading to
improved interpretability of agents along with superior performance.


  In an onion routing protocol, messages travel through several intermediaries
before arriving at their destinations, they are wrapped in layers of encryption
(hence they are called "onions"). The goal is to make it hard to establish who
sent the message. It is a practical and widespread tool for creating anonymous
channels.
  For the standard adversary models -- network, passive, and active -- we
present practical and provably secure onion routing protocols. Akin to Tor, in
our protocols each party independently chooses the routing paths for his
onions. For security parameter $\lambda$, our differentially private solution
for the active adversary takes $O(\log^2\lambda)$ rounds and requires every
participant to transmit $O(\log^{4} \lambda)$ onions in every round.


  A filament eruption on 2010 October 21 observed from three different
viewpoints by the Solar Terrestrial Relations Observatory (STEREO) and the
Solar Dynamic Observatory (SDO) is analyzed with invoking also data from the
Solar and Heliospheric Observatory (SOHO) and the Kanzelhoehe Solar
Observatory. The position of the filament just before the eruption at the
central meridian not far from the center of the solar disk was favorable for
photospheric magnetic field measurements in the area below the filament.
Because of this, we were able to calculate with high precision the distribution
of the coronal potential magnetic field near the filament. We found that the
filament began to erupt when it approached the height in the corona where the
magnetic field decay index was greater than one. We determined also that during
the initial stage of the eruption the filament moved along the magnetic neutral
surface.


  We find non-rational conformal field theories in two dimensions, which are
solvable due to their correlators being related to correlators of Liouville
theory. Their symmetry algebra consists of the dimension-two stress-energy
tensor, and two dimension-one fields. The theories come in a family with two
parameters: the central charge c and a complex number m. The special case m=0
corresponds to Liouville theory (plus two free bosons), and m=1 corresponds to
the H3+ model. In the case m=2 we show that the correlators obey third-order
differential equations, which are associated to a subsingular vector of the
symmetry algebra.


  We use Fermi liquid theory to study the mechanical impedance of 3He-4He
mixtures at low temperatures. The theory is applied to the case of vibrating
wires, immersed in the liquid. We present numerical results based on a direct
solution of Landau-Boltzmann equation for the 3He quasiparticle distribution
for the full scale of the quasiparticle mean-free-path. The two-fluid nature of
mixtures is taken into account in the theory, and the effect of Fermi liquid
interactions and boundary conditions are studied in detail. The results are in
fair quantitative agreement with experimental data. In particular, we can
reproduce the anomalous decrease in inertia, observed in vibrating wire
experiments reaching the ballistic limit. The essential effect of the
experimental container and second-sound resonances is demonstrated.


  As a key driver of the secular evolution of disc galaxies, bar formation is
potentially linked to the surrounding tidal field. We systematically
investigate the dependence of bars on both the small (${<}2\,\mathrm{Mpc}/h$)
and large-scale (${>}5\,\mathrm{Mpc}/h$) tidal fields using galaxies observed
between $0.01{<}z{<}0.11$ by the Sloan Digital Sky Survey (SDSS). We
characterise bar strength using the ellipticity of the isophote that
corresponds to the bar, $e_{\mathrm{bar}}$, derived from the galaxy image after
subtracting the 2D disc component. We demonstrate the efficacy of our bar
detection method by performing an extensive comparison with the visual
identifications from SDSS and the DESI Legacy Surveys. Using the Yang et al.
SDSS group catalogue, we confirm the results from a recent study that the
average $e_{\mathrm{bar}}$ of galaxies within interacting clusters is higher
than that within isolated ones at $0.01{<}z{<}0.06$, but this small-scale tidal
enhancement of bars disappears after we increase the cluster sample by a factor
of five to $z{=}0.11$. On large scales, we explore the dependence of
$e_{\mathrm{bar}}$ on $\alpha_{5}$, the tidal anisotropy of the density field
defined over $5\,\mathrm{Mpc}/h$. We do not detect any such dependence for
$98\%$ of the galaxies with $\alpha_{5}{<}10$. Intriguingly, among the $2\%$
with $\alpha_{5}{\ge}10$, we detect some hint of a boost in bar strength in the
underdense regions and a suppression in the overdense regions. Combining our
results on both scales, we conclude that there is little evidence for the tidal
dependence of bar formation in the local Universe, except for the extremely
anisotropic environments.


  In this work, we present a new computer vision task named video object of
interest segmentation (VOIS). Given a video and a target image of interest, our
objective is to simultaneously segment and track all objects in the video that
are relevant to the target image. This problem combines the traditional video
object segmentation task with an additional image indicating the content that
users are concerned with. Since no existing dataset is perfectly suitable for
this new task, we specifically construct a large-scale dataset called
LiveVideos, which contains 2418 pairs of target images and live videos with
instance-level annotations. In addition, we propose a transformer-based method
for this task. We revisit Swin Transformer and design a dual-path structure to
fuse video and image features. Then, a transformer decoder is employed to
generate object proposals for segmentation and tracking from the fused
features. Extensive experiments on LiveVideos dataset show the superiority of
our proposed method.


  We report on cw measurements of probe beam absorption and four-wave-mixing
spectra in a $^{85}$Rb magneto-optical trap taken while the trap is in
operation. The trapping beams are used as pump light. We concentrate on the
central feature of the spectra at small pump-probe detuning and attribute its
narrow resonant structures to the superposition of Raman transitions between
light-shifted sublevels of the ground atomic state and to atomic recoil
processes. These two contributions have different dependencies on trap
parameters and we show that the former is inhomogeneously broadened. The strong
dependence of the spectra on the probe-beam polarization indicates the
existence of large optical anisotropy of the cold-atom sample, which is
attributed to the recoil effects. We point out that the recoil-induced
resonances can be isolated from other contributions, making pump-probe
spectroscopy a highly sensitive diagnostic tool for atoms in a working MOT.


  We discussed twisted quantum deformations of D=4 Lorentz and Poincare
algebras. In the case of Poincare algebra it is shown that almost all classical
r-matrices of S.Zakrzewski classification can be presented as a sum of
subordinated r-matrices of Abelian and Jordanian types. Corresponding twists
describing quantum deformations are obtained in explicit form. This work is an
extended version of the paper \url{arXiv:0704.0081v1 [math.QA]}.


  In this paper we study super-isolated abelian varieties, that is, abelian
varieties over finite fields whose isogeny class contains a single isomorphism
class. The goal of this paper is to (1) characterize whether a product of
super-isolated varieties is super-isolated, and (2) characterize which
super-isolated abelian varieties admit principal polarizations, and how many up
to polarized isomorphisms.


  A common approach to modeling dispersion interactions and overcoming the
inaccurate description of long-range correlation effects in electronic
structure calculations is the use of pairwise-additive potentials, as in the
Tkatchenko-Scheffler [Phys. Rev. Lett. 102, 073005 (2009)] method. In previous
work [Phys. Rev. B 104, 054106 (2021)], we have shown how these are amenable to
highly efficient atomistic simulation by machine learning their local
parametrization. However, the atomic polarizability and the electron
correlation energy have a complex and non-local many-body character and some of
the dispersion effects in complex systems are not sufficiently described by
these types of pairwise-additive potentials. Currently, one of the most widely
used rigorous descriptions of the many-body effects is based on the many-body
dispersion (MBD) model [Phys. Rev. Lett. 108, 236402 (2012)]. In this work, we
show that the MBD model can also be locally parametrized to derive a local
approximation for the highly non-local many-body effects. With this local
parametrization, we develop an atom-wise formulation of MBD that we refer to as
linear MBD (lMBD), as this decomposition enables linear scaling with system
size. This model provides a transparent and controllable approximation to the
full MBD model with tunable convergence parameters for a fraction of the
computational cost observed in electronic structure calculations with popular
density-functional theory codes. We show that our model scales linearly with
the number of atoms in the system and is easily parallelizable. Furthermore, we
show how using the same machinery already established in previous work for
predicting Hirshfeld volumes with machine learning enables access to
large-scale simulations with MBD-level corrections.


  The exploration of cold polar molecules in different geometries is a rapidly
developing experimental and theoretical pursuit. Recently, the implementation
of optical lattices has enabled confinement in stacks of planes, the number of
which is also controllable. Here we consider the bound state structure of two
polar molecules confined in two adjacent planes as function of the polarization
angle of the dipole moment of the molecules. We prove analytically and present
numerical evidence for the existence of bound states for arbitrary dipole
moments and polarization directions in this two-dimensional geometry. The
spatial structure of the bound states is dominated by two-dimensional s- and
p-waves, where the latter exceeds 40 percent over a large range of polarization
angles for intermediate or strong dipole strength. Finally, we consider the
influence of the dimer bound states on the potential many-body ground-state of
the system.


  We consider a sheaf of exterior algebras on a simplicial poset $S$ and
introduce a notion of homological characteristic function. Two natural objects
are associated with these data: a graded sheaf $\mathcal{I}$ and a graded
cosheaf $\widehat{\Pi}$. When $S$ is a homology manifold, we prove the
isomorphism $H^{n-1-p}(S;\mathcal{I})\cong H_{p}(S;\widehat{\Pi})$ which can be
considered as an extension of the Poincare duality. In general, there is a
spectral sequence $E^2_{p,q}\cong H^{n-1-p}(S;\mathcal{U}_{n-1+q}\otimes
\mathcal{I})\Rightarrow H_{p+q}(S;\widehat{\Pi})$, where $\mathcal{U}_*$ is the
local homology stack on $S$. This spectral sequence, in turn, extends
Zeeman--McCrory spectral sequence. This sheaf-theoretical result is applied to
toric topology. We consider a manifold $X$ with a locally standard action of a
compact torus and acyclic proper faces of the orbit space. A principal torus
bundle $Y$ is associated with $X$, so that $X\cong Y/\sim$. The orbit type
filtration on $X$ is covered by the topological filtration on $Y$. We prove
that homological spectral sequences associated with these two filtrations are
isomorphic in many nontrivial positions.


  Variational Autoencoders (VAEs) are a popular generative model, but one in
which conditional inference can be challenging. If the decomposition into query
and evidence variables is fixed, conditional VAEs provide an attractive
solution. To support arbitrary queries, one is generally reduced to Markov
Chain Monte Carlo sampling methods that can suffer from long mixing times. In
this paper, we propose an idea we term cross-coding to approximate the
distribution over the latent variables after conditioning on an evidence
assignment to some subset of the variables. This allows generating query
samples without retraining the full VAE. We experimentally evaluate three
variations of cross-coding showing that (i) they can be quickly optimized for
different decompositions of evidence and query and (ii) they quantitatively and
qualitatively outperform Hamiltonian Monte Carlo.


  In this paper, by a concise and elementary approach, we sharpen and
generalize Shafer's inequality for the arc sine function, and some known
results are extended and generalized.


  Analytical periodic solutions for weakly Coupled Map Lattices are shown in an
explicit form as well as in a recurrence relation. The results establish a link
between a matricial representation and recurrence relations of the solutions.


  We analyze union-find using potential functions motivated by continuous
algorithms, and give alternate proofs of the $O(\log\log{n})$, $O(\log^{*}n)$,
$O(\log^{**}n)$, and $O(\alpha(n))$ amortized cost upper bounds. The proof of
the $O(\log\log{n})$ amortized bound goes as follows. Let each node's potential
be the square root of its size, i.e., the size of the subtree rooted from it.
The overall potential increase is $O(n)$ because the node sizes increase
geometrically along any tree path. When compressing a path, each node on the
path satisfies that either its potential decreases by $\Omega(1)$, or its
child's size along the path is less than the square root of its size: this can
happen at most $O(\log\log{n})$ times along any tree path.


  This paper considers systems of balance law with a dissipative non local
source. A global in time well posedness result is obtained. Estimates on the
dependence of solutions from the flow and from the source term are also
provided. The technique relies on a recent result on quasidifferential
equations in metric spaces.


  Radiatively inefficient (or advection dominated) disks are discussed at an
introductory level. Ion supported and radiation supported flows are discussed,
the different consequences of advection dominated flows onto black holes vs.
solid surfaces (neutron stars, white dwarfs), hydrodynamics, the role of the
ratio of specific heats, and the possible connection between ADAFs and
outflows.


  We propose that the spectrum of light mesons (the $\pi$ and $\rho$, together
with their radial excitations) can be calculated in the limit of vanishing
light quark masses by studying gauge theory (open string theory) on suitable
higher dimensional background geometries. Using the metric proposed by Witten
for glueball calculations as a paradigmatic example, we find a spectrum which
is in startlingly good agreement with the masses tabulated by the Particle Data
Group. These calculations have only one free parameter, corresponding to the
overall mass scale. We make predictions for the next several particles in the
spectrum.


  Let (M,I,J,K) be a compact hypercomplex manifold admitting an HKT-metric.
Assume that the canonical bundle of (M,I) is trivial as a holomorphic line
bundle. We show that the holonomy of Obata connection on M is contained in
SL(n,H). In Appendix we apply these arguments to compact nilmanifolds equipped
with abelian hypercomplex structures, showing that such manifolds have holonomy
in SL(n,H).


  We present preliminary BaBar measurements of hadronic cross sections in
$e^+e^-$ annihilation using the radiative return technique. The cross sections
for $e^+e^- \to p\bar{p} $, $ 3(\pi^+\pi^-)$, $ 2(\pi^+\pi^-)2\pi^0$, and $
K^+K^-2(\pi^+\pi^-)$ are measured. Measurements of the proton form factor and
of the ratio $G_E/G_M$ are also shown.


  We consider a queue where the server is the Euclidean space, and the
customers are random closed sets (RACS) of the Euclidean space. These RACS
arrive according to a Poisson rain and each of them has a random service time
(in the case of hail falling on the Euclidean plane, this is the height of the
hailstone, whereas the RACS is its footprint). The Euclidean space serves
customers at speed 1. The service discipline is a hard exclusion rule: no two
intersecting RACS can be served simultaneously and service is in the First In
First Out order: only the hailstones in contact with the ground melt at speed
1, whereas the other ones are queued; a tagged RACS waits until all RACS
arrived before it and intersecting it have fully melted before starting its own
melting. We give the evolution equations for this queue. We prove that it is
stable for a sufficiently small arrival intensity, provided the typical
diameter of the RACS and the typical service time have finite exponential
moments. We also discuss the percolation properties of the stationary regime of
the RACS in the queue.


  We argue that models coming from a variety of fields, such as matching models
and discrete choice models among others, share a common structure that we call
matching function equilibria with partial assignment. This structure includes
an aggregate matching function and a system of nonlinear equations. We provide
a proof of existence and uniqueness of an equilibrium and propose an efficient
algorithm to compute it. For a subclass of matching models, we also develop a
new parameter-free approach for constructing the counterfactual matching
equilibrium. It has the advantage of not requiring parametric estimation when
computing counterfactuals. We use our procedure to analyze the impact of the
elimination of the Social Security Student Benefit Program in 1982 on the
marriage market in the United States. We estimate several candidate models from
our general class of matching functions and select the best fitting model using
information based criterion.


  This is a set of lecture notes that developed out of courses on the lambda
calculus that I taught at the University of Ottawa in 2001 and at Dalhousie
University in 2007 and 2013. Topics covered in these notes include the untyped
lambda calculus, the Church-Rosser theorem, combinatory algebras, the
simply-typed lambda calculus, the Curry-Howard isomorphism, weak and strong
normalization, polymorphism, type inference, denotational semantics, complete
partial orders, and the language PCF.


  Producing a large amount of annotated speech data for training ASR systems
remains difficult for more than 95% of languages all over the world which are
low-resourced. However, we note human babies start to learn the language by the
sounds of a small number of exemplar words without hearing a large amount of
data. We initiate some preliminary work in this direction in this paper. Audio
Word2Vec is used to obtain embeddings of spoken words which carry phonetic
information extracted from the signals. An autoencoder is used to generate
embeddings of text words based on the articulatory features for the phoneme
sequences. Both sets of embeddings for spoken and text words describe similar
phonetic structures among words in their respective latent spaces. A mapping
relation from the audio embeddings to text embeddings actually gives the
word-level ASR. This can be learned by aligning a small number of spoken words
and the corresponding text words in the embedding spaces. In the initial
experiments only 200 annotated spoken words and one hour of speech data without
annotation gave a word accuracy of 27.5%, which is low but a good starting
point.


  We consider an evolving plane curve with two endpoints that can move freely
on the $x$-axis with generating constant contact angles. We discuss the
asymptotic behavior of global-in-time solutions when the evolution of this
plane curve is governed by area-preserving curvature flow equation. The main
result shows that any moving curve converges to a traveling wave if the moving
curve starts from an embedded convex curve and remains bounded in global time.


  This review provides a comprehensive study of the nonlinear transport
properties of magnons, which are electrically emitted or absorbed inside
extended YIG films by spin transfer effects via a YIG$\vert$Pt interface. Our
purpose is to experimentally elucidate the pertinent picture behind the
asymmetric electrical variation of the magnon transconductance analogous to an
electric diode. The feature is rooted in the variation of the density of
low-lying spin excitations via an electrical shift of the magnon chemical
potential. As the intensity of the spin transfer increases in the forward
direction (regime of magnon emission), the transport properties of low-energy
magnon go through 3 distinct regimes: \textit{i)} at low currents, where the
spin current is a linear function of the electrical current, the spin transport
is ballistic and set by the film thickness; \textit{ii)} for amplitudes of the
order of the damping compensation threshold, it switches to a highly correlated
regime limited by magnon-magnon relaxation process and marked by a saturation
of the magnon transconductance. Here the main bias, that controls the magnon
density, are thermal fluctuations beneath the emitter. \textit{iii)} As the
temperature under the emitter approaches the Curie temperature, scattering with
high-energy magnons dominates, leading to diffusive transport. We note that
such sequence of transport regimes bears analogy with electron hydrodynamic
transport in ultra-pure media predicted by Radii Gurzhi. This study restricted
to low energy part of the magnon manifold complements part II of this
review\cite{kohno_2F}, which concentrates instead on the whole spectrum of
propagating magnons.


  By a bifurcation argument we prove that the capillary-gravity Whitham
equation features asymmetrical periodic travelling wave solution of arbitrarily
small amplitude. Such waves exist only in the weak surface tension regime
$0<T<\frac{1}{3}$ and are necessarily bimodal; they are located at double
bifurcation points satisfying a certain symmetry breaking condition. Our
bifurcation argument is an extension of the one applied by Ehrnstr\"om et al to
find symmetric waves: Here, two additional scalar equations arise. Combining
the variational structure of our problem with its translation symmetry, we show
that these two additional equations are in fact linearly dependent, and can (at
'symmetry breaking' bifurcation points) be solved by incorporating the surface
tension as a bifurcation parameter. Contrary to the symmetric case, only very
specific modal pairs $(k_1,k_2)$ give rise to (small) asymmetrical periodic
waves and we here provide a partial characterization of such pairs.


  Purpose: The aim of this work is to introduce a single model-based deep
network that can provide high-quality reconstructions from undersampled
parallel MRI data acquired with multiple sequences, acquisition settings and
field strengths.
  Methods: A single unrolled architecture, which offers good reconstructions
for multiple acquisition settings, is introduced. The proposed scheme adapts
the model to each setting by scaling the CNN features and the regularization
parameter with appropriate weights. The scaling weights and regularization
parameter are derived using a multi-layer perceptron model from conditional
vectors, which represents the specific acquisition setting. The perceptron
parameters and the CNN weights are jointly trained using data from multiple
acquisition settings, including differences in field strengths, acceleration,
and contrasts. The conditional network is validated using datasets acquired
with different acquisition settings.
  Results: The comparison of the adaptive framework, which trains a single
model using the data from all the settings, shows that it can offer
consistently improved performance for each acquisition condition. The
comparison of the proposed scheme with networks that are trained independently
for each acquisition setting shows that it requires less training data per
acquisition setting to offer good performance.
  Conclusion: The Ada-MoDL framework enables the use of a single model-based
unrolled network for multiple acquisition settings. In addition to eliminating
the need to train and store multiple networks for different acquisition
settings, this approach reduces the training data needed for each acquisition
setting.


  Across a growing number of domains, human experts are expected to learn from
and adapt to AI with superior decision making abilities. But how can we
quantify such human adaptation to AI? We develop a simple measure of human
adaptation to AI and test its usefulness in two case studies. In Study 1, we
analyze 1.3 million move decisions made by professional Go players and find
that a positive form of adaptation to AI (learning) occurred after the players
could observe the reasoning processes of AI, rather than mere actions of AI.
These findings based on our measure highlight the importance of explainability
for human learning from AI. In Study 2, we test whether our measure is
sufficiently sensitive to capture a negative form of adaptation to AI (cheating
aided by AI), which occurred in a match between professional Go players. We
discuss our measure's applications in domains other than Go, especially in
domains in which AI's decision making ability will likely surpass that of human
experts.


  We consider the manipulation of Bose-Einstein condensate vortices by optical
potentials generated by focused laser beams. It is shown that for appropriate
choices of the laser strength and width it is possible to successfully
transport vortices to various positions inside the trap confining the
condensate atoms. Furthermore, the full bifurcation structure of possible
stationary single-charge vortex solutions in a harmonic potential with this
type of impurity is elucidated. The case when a moving vortex is captured by a
stationary laser beam is also studied, as well as the possibility of dragging
the vortex by means of periodic optical lattices.


  In the semi-supervised setting where labeled data are largely limited, it
remains to be a big challenge for message passing based graph neural networks
(GNNs) to learn feature representations for the nodes with the same class label
that is distributed discontinuously over the graph. To resolve the
discontinuous information transmission problem, we propose a control principle
to supervise representation learning by leveraging the prototypes (i.e., class
centers) of labeled data. Treating graph learning as a discrete dynamic process
and the prototypes of labeled data as "desired" class representations, we
borrow the pinning control idea from automatic control theory to design
learning feedback controllers for the feature learning process, attempting to
minimize the differences between message passing derived features and the class
prototypes in every round so as to generate class-relevant features.
Specifically, we equip every node with an optimal controller in each round
through learning the matching relationships between nodes and the class
prototypes, enabling nodes to rectify the aggregated information from
incompatible neighbors in a graph with strong heterophily. Our experiments
demonstrate that the proposed PCGCN model achieves better performances than
deep GNNs and other competitive heterophily-oriented methods, especially when
the graph has very few labels and strong heterophily.


  We study the kinetics of chiral transitions in quark matter using a
phenomenological framework (Ginzburg-Landau model). We focus on the effect of
inertial terms on the coarsening dynamics subsequent to a quench from the
massless quark phase to the massive quark phase. The domain growth process
shows a crossover from a fast inertial regime [with $L(t) \sim t (\ln
t)^{1/2}$] to a diffusive Cahn-Allen regime [with $L(t)\sim t^{1/2}$].


  The present knowledge of Lamb shift, fine- and hyperfine structure of the
$\mathrm{2S}$ and $\mathrm{2P}$ states in muonic deuterium is reviewed in
anticipation of the results of a first measurement of several $\mathrm{2S-2P}$
transition frequencies in muonic deuterium ($\mu\mathrm{d}$). A term-by-term
comparison of all available sources reveals reliable values and uncertainties
of the QED and nuclear structure-dependent contributions to the Lamb shift,
which are essential for a determination of the deuteron rms charge radius from
$\mu\mathrm{d}$. Apparent discrepancies between different sources are resolved,
in particular for the difficult two-photon exchange contributions. Problematic
single-sourced terms are identified which require independent recalculation.


  Nanostructured materials have recently emerged as a promising approach for
material appearance design. Research has mainly focused on creating structural
colours by wave interference, leaving aside other important aspects that
constitute the visual appearance of an object, such as the respective weight of
specular and diffuse reflectances, object macroscopic shape, illumination and
viewing conditions. Here, we report the potential of disordered optical
metasurfaces for harnessing visual appearance. We develop a multiscale
modelling platform for the predictive rendering of macroscopic objects covered
by metasurfaces in realistic settings, showing how nanoscale resonances and
mesoscale interferences can be used to spectrally and angularly shape reflected
light and thus create unusual visual effects at the macroscale. We validate
this property with realistic synthetic images of macroscopic objects and
centimetre-scale samples observable with the naked eye. This framework opens
new perspectives in many branches of fine and applied visual arts.


  We present the physics program of the Belle II experiment, located on the
intensity frontier SuperKEKB $e^+e^-$ collider. Belle II collected its first
collisions in 2018, and is expected to operate for the next decade. It is
anticipated to collect 50/ab of collision data over its lifetime. This book is
the outcome of a joint effort of Belle II collaborators and theorists through
the Belle II theory interface platform (B2TiP), an effort that commenced in
2014. The aim of B2TiP was to elucidate the potential impacts of the Belle II
program, which includes a wide scope of physics topics: B physics, charm, tau,
quarkonium, electroweak precision measurements and dark sector searches. It is
composed of nine working groups (WGs), which are coordinated by teams of
theorist and experimentalists conveners: Semileptonic and leptonic B decays,
Radiative and Electroweak penguins, phi_1 and phi_2 (time-dependent CP
violation) measurements, phi_3 measurements, Charmless hadronic B decay, Charm,
Quarkonium(like), tau and low-multiplicity processes, new physics and global
fit analyses. This book highlights "golden- and silver-channels", i.e. those
that would have the highest potential impact in the field. Theorists
scrutinised the role of those measurements and estimated the respective
theoretical uncertainties, achievable now as well as prospects for the future.
Experimentalists investigated the expected improvements with the large dataset
expected from Belle II, taking into account improved performance from the
upgraded detector.


  The discovery of new mechanisms of controlling magnetic properties by
electric fields or currents furthers the fundamental understanding of magnetism
and has important implications for practical use. Here, we present a novel
approach of utilizing resistive switching to control magnetic anisotropy. We
study a ferromagnetic oxide that exhibits an electrically triggered
metal-to-insulator phase transition producing a volatile resistive switching.
This switching occurs in a characteristic spatial pattern: the formation of a
transverse insulating barrier inside a metallic matrix resulting in an unusual
ferromagnetic/paramagnetic/ferromagnetic configuration. We found that the
formation of this voltage-driven paramagnetic insulating barrier is accompanied
by the emergence of a strong uniaxial magnetic anisotropy that overpowers the
intrinsic material anisotropy. Our results demonstrate that resistive switching
is an effective tool for manipulating magnetic properties. Because resistive
switching can be induced in a very broad range of materials, our findings could
enable a new class of voltage-controlled magnetism systems.


  Angiogenesis - the growth of new blood vessels from a pre-existing
vasculature - is key in both physiological processes and on several
pathological scenarios such as cancer progression or diabetic retinopathy. For
the new vascular networks to be functional, it is required that the growing
sprouts merge either with an existing functional mature vessel or with another
growing sprout. This process is called anastomosis. We present a systematic 2D
and 3D computational study of vessel growth in a tissue to address the
capability of angiogenic factor gradients to drive anastomosis formation. We
consider that these growth factors are produced only by tissue cells in
hypoxia, i.e. until nearby vessels merge and become capable of carrying blood
and irrigating their vicinity. We demonstrate that this increased production of
angiogenic factors by hypoxic cells is able to promote vessel anastomoses
events in both 2D and 3D. The simulations also verify that the morphology of
these networks has an increased resilience toward variations in the endothelial
cell's proliferation and chemotactic response. The distribution of tissue
cell`s and the concentration of the growth factors they produce are the major
factors in determining the final morphology of the network.


  Quantum Darwinism is a paradigm to understand how classically objective
reality emerges from within a fundamentally quantum universe. Despite the
growing attention that this field of research as been enjoying, it is currently
not known what specific properties a given Hamiltonian describing a generic
quantum system must have to allow the emergence of classicality. Therefore, in
the present work, we consider a broadly applicable generic model of an
arbitrary finite-dimensional system interacting with an environment formed from
an arbitrary collection of finite-dimensional degrees of freedom via an
unspecified, potentially time-dependent Hamiltonian containing at most two-body
interaction terms. We show that such models support quantum Darwinism if the
set of operators acting on the system which enter the Hamiltonian satisfy a set
of commutation relations with a pointer observable and with one other. We
demonstrate our results by analyzing a wide range of example systems: a qutrit
interacting with a qubit environment, a qubit-qubit model with interactions
alternating in time, and a series of collision models including a minimal model
of a quantum Maxwell demon.


  We use methods from birational geometry to study the Hodge and weight
filtrations on the localization along a hypersurface. We focus on the lowest
piece of the Hodge filtration of the submodules arising from the weight
filtration. This leads to a sequence of ideal sheaves called weighted
multiplier ideals. The last ideal of this sequence is a multiplier ideal (and a
Hodge ideal), and we prove that the first is the adjoint ideal. We also study
the local and global properties of weighted multiplier ideals and their
applications to singularities of hypersurfaces of smooth varieties.


  Using variational cluster perturbation theory we study the competition
between d-wave superconductivity (dSC) and antiferromagnetism (AF) in the the
t-t'-t''-U Hubbard model. Large scale computer calculations reproduce the
overall ground state phase diagram of the high-temperature superconductors as
well as the one-particle excitation spectra for both hole- and electron-doping.
We identify clear signatures of the Mott gap as well as of AF and of dSC that
should be observable in photoemission experiments.


  In existing systems, to perform any bulk data movement operation (copy or
initialization), the data has to first be read into the on-chip processor, all
the way into the L1 cache, and the result of the operation must be written back
to main memory. This is despite the fact that these operations do not involve
any actual computation. RowClone exploits the organization and operation of
commodity DRAM to perform these operations completely inside DRAM using two
mechanisms. The first mechanism, Fast Parallel Mode, copies data between two
rows inside the same DRAM subarray by issuing back-to-back activate commands to
the source and the destination row. The second mechanism, Pipelined Serial
Mode, transfers cache lines between two banks using the shared internal bus.
RowClone significantly reduces the raw latency and energy consumption of bulk
data copy and initialization. This reduction directly translates to improvement
in performance and energy efficiency of systems running copy or
initialization-intensive workloads


  Much hope for finding new physics phenomena at microscopic scale relies on
the observations obtained from High Energy Physics experiments, like the ones
performed at the Large Hadron Collider (LHC). However, current experiments do
not indicate clear signs of new physics that could guide the development of
additional Beyond Standard Model (BSM) theories. Identifying signatures of new
physics out of the enormous amount of data produced at the LHC falls into the
class of anomaly detection and constitutes one of the greatest computational
challenges. In this article, we propose a novel strategy to perform anomaly
detection in a supervised learning setting, based on the artificial creation of
anomalies through a random process. For the resulting supervised learning
problem, we successfully apply classical and quantum Support Vector Classifiers
(CSVC and QSVC respectively) to identify the artificial anomalies among the SM
events. Even more promising, we find that employing an SVC trained to identify
the artificial anomalies, it is possible to identify realistic BSM events with
high accuracy. In parallel, we also explore the potential of quantum algorithms
for improving the classification accuracy and provide plausible conditions for
the best exploitation of this novel computational paradigm.


  Turbulence and angular momentum transport in accretion disks remains a topic
of debate. With the realization that dead zones are robust features of
protoplanetary disks, the search for hydrodynamical sources of turbulence
continues. A possible source is the baroclinic instability (BI), which has been
shown to exist in unmagnetized non-barotropic disks. We present shearing box
simulations of baroclinicly unstable, magnetized, 3D disks, in order to assess
the interplay between the BI and other instabilities, namely the
magneto-rotational instability (MRI) and the magneto-elliptical instability. We
find that the vortices generated and sustained by the baroclinic instability in
the purely hydrodynamical regime do not survive when magnetic fields are
included. The MRI by far supersedes the BI in growth rate and strength at
saturation. The resulting turbulence is virtually identical to an MRI-only
scenario. We measured the intrinsic vorticity profile of the vortex, finding
little radial variation in the vortex core. Nevertheless, the core is disrupted
by an MHD instability, which we identify with the magneto-elliptic instability.
This instability has nearly the same range of unstable wavelengths as the MRI,
but has higher growth rates. In fact, we identify the MRI as a limiting case of
the magneto-elliptic instability, when the vortex aspect ratio tends to
infinity (pure shear flow). We conclude that vortex excitation and
self-sustenance by the baroclinic instability in protoplanetary disks is viable
only in low ionization, i.e., the dead zone. Our results are thus in accordance
with the layered accretion paradigm. A baroclinicly unstable dead zone should
be characterized by the presence of large-scale vortices whose cores are
elliptically unstable, yet sustained by the baroclinic feedback. As magnetic
fields destroy the vortices and the MRI outweighs the BI, the active layers are
unmodified.


  Lately, there has been a global effort by multiple research groups to detect
COVID-19 from voice. Different researchers use different kinds of information
from the voice signal to achieve this. Various types of phonated sounds and the
sound of cough and breath have all been used with varying degree of success in
automated voice-based COVID-19 detection apps. In this paper, we show that
detecting COVID-19 from voice does not require custom-made non-standard
features or complicated neural network classifiers rather it can be
successfully done with just standard features and simple binary classifiers. In
fact, we show that the latter is not only more accurate and interpretable but
also more computationally efficient in that they can be run locally on small
devices. We demonstrate this on a human-curated dataset of over 1000 subjects,
collected and calibrated in clinical settings.


  For a Bose-Einstein condensate placed in a rotating trap, we give a
simplified expression of the Gross-Pitaevskii energy in the Thomas Fermi
regime, which only depends on the number and shape of the vortex lines.
  Then we check numerically that when there is one vortex line, our simplified
expression leads to solutions with a bent vortex for a range of rotationnal
velocities and trap parameters which are consistent with the experiments.


  We demonstrate the newly developed resource for exoplanet researchers - The
Exoplanet Transit Database. This database is designed to be a web application
and it is open for any exoplanet observer. It came on-line in September 2008.
The ETD consists of three individual sections. One serves for predictions of
the transits, the second one for processing and uploading new data from the
observers. We use a simple analytical model of the transit to calculate the
central time of transit, its duration and the depth of the transit. These
values are then plotted into the observed - computed diagrams (O-C), that
represent the last part of the application.


  Let $X$ be a smooth complete intersection over $\mathbb{C}$ of dimension
$n-k$ in the projective space $\mathbf{P}^n_{\mathbb{C}}$, for given positive
integers $n$ and $k$. For a given integral homology cycle $[\gamma] \in
H_{n-k}(X(\mathbb{C}),\mathbb{Z})$, the period integral is defined to be a
linear map from the de Rham cohomology group to $\mathbb{C}$ given by $[\omega]
\mapsto \int_\gamma \omega$. The goal of this article is to interpret this
period integral as a linear map from the polynomial ring with $n+k+1$ variables
to $\mathbb{C}$ and use this interpretation to develop a deformation theory of
period integrals of $X$. The period matrix is an invariant defined by the
period integrals of the \textit{rational} de Rham cohomology, which compares
the \textit{rational} structures ($\mathbb{Q}$-subspace structures) of the de
Rham cohomology over $\mathbb{C}$ and the singular homology with coefficient
$\mathbb{C}$. As a main result, when $X'$ is another projective smooth complete
intersection variety deformed from $X$, we provide an explicit formula for the
period matrix of $X'$ in terms of the period matrix of $X$ and the Bell
polynomials evaluated at the deformation data. Our result can be thought of as
a modern deformation theoretic treatment of the period integrals based on the
Maurer-Cartan equation of a dgla (differential graded Lie algebra).


  We study the magnetization of a cylindrical type-II superconductor filament
covered by a coaxial soft-magnet sheath and exposed to an applied transverse
magnetic field. Examining penetration of magnetic flux into the superconductor
core of the filament on the basis of the Bean model of the critical state, we
find that the presence of a non-hysteretic magnetic sheath can strongly enhance
the field of full penetration of magnetic flux. The average magnetization of
the superconductor/magnet heterostructure under consideration and hysteresis AC
losses in the core of the filament are calculated as well.


  A new interactive quantum zero-knowledge protocol for identity authentication
implementable in currently available quantum cryptographic devices is proposed
and demonstrated. The protocol design involves a verifier and a prover knowing
a pre-shared secret, and the acceptance or rejection of the proof is determined
by the quantum bit error rate. It has been implemented in modified Quantum Key
Distribution devices executing two fundamental cases. In the first case, all
players are honest, while in the second case, one of the users is a malicious
player. We demonstrate an increase of the quantum bit error rate around 25% in
the latter case compared to the case of honesty. The protocol has also been
validated for distances from a back-to-back setup to more than 60 km between
verifier and prover. The security and robustness of the protocol has been
analysed, demonstrating its completeness, soundness and zero-knowledge
properties.


  Online output prediction is an indispensable part of any model predictive
control implementation, especially when simplifications of the underlying
physical model have been considered and/or the operating conditions change
quite often. Furthermore, the selection of an output prediction model is
strongly related to the data available, while designing/altering the data
collection process may not be an option. Thus, in several scenarios, selecting
the most appropriate prediction model needs to be performed during runtime. To
this end, this paper introduces a supervisory output prediction scheme,
tailored specifically for input-output stable bilinear systems, that intends on
automating the process of selecting the most appropriate prediction model
during runtime. The selection process is based upon a reinforcement-learning
scheme, where prediction models are selected according to their prior
prediction performance. An additional selection process is concerned with
appropriately partitioning the control-inputs' domain in order to also allow
for switched-system approximations of the original bilinear dynamics. We show
analytically that the proposed scheme converges (in probability) to the best
model and partition. We finally demonstrate these properties through
simulations of temperature prediction in residential buildings.


  We propose an empirical estimator of the preferential attachment function $f$
in the setting of general preferential attachment trees. Using a supercritical
continuous-time branching process framework, we prove the almost sure
consistency of the proposed estimator. We perform simulations to study the
empirical properties of our estimators.


  The 2-D phase unwrapping problem aims at retrieving a "phase" image from its
modulo $2\pi$ observations. Many applications, such as interferometry or
synthetic aperture radar imaging, are concerned by this problem since they
proceed by recording complex or modulated data from which a "wrapped" phase is
extracted. Although 1-D phase unwrapping is trivial, a challenge remains in
higher dimensions to overcome two common problems: noise and discontinuities in
the true phase image. In contrast to state-of-the-art techniques, this work
aims at simultaneously unwrap and denoise the phase image. We propose a robust
convex optimization approach that enforces data fidelity constraints expressed
in the corrupted phase derivative domain while promoting a sparse phase prior.
The resulting optimization problem is solved by the Chambolle-Pock primal-dual
scheme. We show that under different observation noise levels, our approach
compares favorably to those that perform the unwrapping and denoising in two
separate steps.


  We consider the problem of dynamically maintaining (approximate) all-pairs
effective resistances in separable graphs, which are those that admit an
$n^{c}$-separator theorem for some $c<1$. We give a fully dynamic algorithm
that maintains $(1+\varepsilon)$-approximations of the all-pairs effective
resistances of an $n$-vertex graph $G$ undergoing edge insertions and deletions
with $\tilde{O}(\sqrt{n}/\varepsilon^2)$ worst-case update time and
$\tilde{O}(\sqrt{n}/\varepsilon^2)$ worst-case query time, if $G$ is guaranteed
to be $\sqrt{n}$-separable (i.e., it is taken from a class satisfying a
$\sqrt{n}$-separator theorem) and its separator can be computed in
$\tilde{O}(n)$ time. Our algorithm is built upon a dynamic algorithm for
maintaining \emph{approximate Schur complement} that approximately preserves
pairwise effective resistances among a set of terminals for separable graphs,
which might be of independent interest.
  We complement our result by proving that for any two fixed vertices $s$ and
$t$, no incremental or decremental algorithm can maintain the $s-t$ effective
resistance for $\sqrt{n}$-separable graphs with worst-case update time
$O(n^{1/2-\delta})$ and query time $O(n^{1-\delta})$ for any $\delta>0$, unless
the Online Matrix Vector Multiplication (OMv) conjecture is false.
  We further show that for \emph{general} graphs, no incremental or decremental
algorithm can maintain the $s-t$ effective resistance problem with worst-case
update time $O(n^{1-\delta})$ and query-time $O(n^{2-\delta})$ for any $\delta
>0$, unless the OMv conjecture is false.


  In this paper we study the non-holomorphic strict endoscopic parts of inner
cohomology spaces of a modular Siegel threefold respect to local systems. First
we show that there is a non-zero subspace of the strict endoscopic part such
that it is constructed by global theta lift of automorphic froms of
$(GL(2)\times GL(2))/G_{m}$. Secondly, we present an explicit analytic
calculation of levels of lifted forms into GSp(4), based on the paramodular
representations theory for $GSp(4; F)$. Finally, we prove the conjecture, by C.
Faber and G. van der Geer, that gives a description of the strict endoscopic
part for Betti cohomology and (real) Hodge structures in the category of mixed
Hodge structures, in which the modular Siegel threefold has level structure
one.


  This study introduces LLM4DESIGN, a highly automated system for generating
architectural and environmental design proposals. LLM4DESIGN, relying solely on
site conditions and design requirements, employs Multi-Agent systems to foster
creativity, Retrieval Augmented Generation (RAG) to ground designs in realism,
and Visual Language Models (VLM) to synchronize all information. This system
resulting in coherent, multi-illustrated, and multi-textual design schemes. The
system meets the dual needs of narrative storytelling and objective drawing
presentation in generating architectural and environmental design proposals.
Extensive comparative and ablation experiments confirm the innovativeness of
LLM4DESIGN's narrative and the grounded applicability of its plans,
demonstrating its superior performance in the field of urban renewal design.
Lastly, we have created the first cross-modal design scheme dataset covering
architecture, landscape, interior, and urban design, providing rich resources
for future research.


  This is a programmatic paper, marking out two directions in which the study
of social media can contribute to broader problems of social science:
understanding cultural evolution and understanding collective cognition. Under
the first heading, I discuss some difficulties with the usual, adaptationist
explanations of cultural phenomena, alternative explanations involving network
diffusion effects, and some ways these could be tested using social-media data.
Under the second I describe some of the ways in which social media could be
used to study how the social organization of an epistemic community supports
its collective cognitive performance.


  A novel theory of $F(R)$ gravity with the Lagrangian density ${\cal
L}=[R-(b/\beta)\arctan\left(\beta R\right)]/(2\kappa^2)$ is analyzed. Constant
curvature solutions of the model are found, and the potential of the scalar
field and the mass of a scalar degree of freedom in Einstein's frame are
derived. The cosmological parameters of the model are calculated, which are in
agreement with the PLANCK data. Critical points for the de Sitter phase and the
matter dominated epoch of autonomous equations are obtained and studied.


  Complex oxide functionality, such as ferroelectricity, magnetism or
superconductivity is often achieved in epitaxial thin-film geometries. Oxygen
vacancies tend to be the dominant type of defect in these materials but a
fundamental understanding of their stability and electronic structure has so
far mostly been established in the bulk or strained bulk, neglecting interfaces
and surfaces present in a thin-film geometry. We investigate here, via density
functional theory calculations, oxygen vacancies in the model system of a
SrMnO$_3$ (SMO) thin film grown on a SrTiO$_3$ (STO) (001) substrate.
Structural and electronic differences compared to bulk SMO result mainly from
undercoordination at the film surface. The changed crystal field leads to a
depletion of subsurface valence-band states and transfer of this charge to
surface Mn atoms, both of which strongly affect the defect chemistry in the
film. The result is a strong preference of oxygen vacancies in the surface
region compared to deeper layers. Finally, for metastable oxygen vacancies in
the substrate, we predict a spatial separation of the defect from its excess
charge, the latter being accommodated in the film but close to the substrate
boundary. These results show that surface and interface effects lead to
significant differences in stability and electronic structure of oxygen
vacancies in thin-film geometries compared to the (strained) bulk.


  Network Intrusion Detection System (NIDS) is an essential tool in securing
cyberspace from a variety of security risks and unknown cyberattacks. A number
of solutions have been implemented for Machine Learning (ML), and Deep Learning
(DL) based NIDS. However, all these solutions are vulnerable to adversarial
attacks, in which the malicious actor tries to evade or fool the model by
injecting adversarial perturbed examples into the system. The main aim of this
research work is to study powerful adversarial attack algorithms and their
defence method on DL-based NIDS. Fast Gradient Sign Method (FGSM), Jacobian
Saliency Map Attack (JSMA), Projected Gradient Descent (PGD) and Carlini &
Wagner (C&W) are four powerful adversarial attack methods implemented against
the NIDS. As a defence method, Adversarial Training is used to increase the
robustness of the NIDS model. The results are summarized in three phases, i.e.,
1) before the adversarial attack, 2) after the adversarial attack, and 3) after
the adversarial defence. The Canadian Institute for Cybersecurity Intrusion
Detection System 2017 (CICIDS-2017) dataset is used for evaluation purposes
with various performance measurements like f1-score, accuracy etc.


  We show that semi-infinite cohomology of a finite dimensional graded algebra
(satisfying some additional requirements) are a particular case of a general
categorical construction. The motivating example is provided by small quantum
groups at a root of unity.


  The well-known Funk metric F(x,y) is projectively flat with constant flag
curvature K=-1/4 and the Hilbert metric H(x,y):=(F(x,y)+F(x,-y))/2 is
projectively flat with constant curvature K=-1. These metrics are the special
solutions to Hilbert's Fourth Problem. In this paper, we construct a
non-trivial R-flat spray using the Funk metric. It is then an inverse problem
in the calculus of variation to find a Finsler metric that induces the R-flat
spray. We find an explicit solution to this inverse problem and obtain a
non-trivial projectively flat Finsler metric with K=0.


  We derive quantitative criteria for the existence of density for stochastic
line integrals and iterated line integrals along solutions of hypoelliptic
differential equations driven by fractional Brownian motion. As an application,
we also study the signature uniqueness problem for these rough differential
equations.


  Let $(G_n)_{n \in \mathbb{N}}$ be a sequence of groups equipped with a
$d$-ary cloning system and denote by $\mathscr{T}_d(G_*)$ the resulting
Thompson-like group. In previous work joint with Zaremsky, we obtained
structural results concerning the group von Neumann algebra of
$\mathscr{T}_d(G_*)$, denoted by $L(\mathscr{T}_d(G_*))$. Under some natural
assumptions on the $d$-ary cloning system, we proved that
$L(\mathscr{T}_d(G_*))$ is a type $\text{II}_1$ factor. With a few additional
natural assumptions, we proved that $L(\mathscr{T}_d(G_*))$ is, moreover, a
McDuff factor. In this paper, we further analyze the structure of
$L(\mathscr{T}_d(G_*))$, in particular the inclusion $L(F_d) \subseteq
L(\mathscr{T}_d(G_*))$, where $F_d$ is the smallest of the Higman--Thompson
groups. First, we prove that if the $d$-ary cloning system is "diverse", then
the inclusion $L(F_d) \subseteq L(\mathscr{T}_d(G_*))$ is irreducible, a
considerable improvement of the result that $L(\mathscr{T}_d(G_*))$ is a type
$\text{II}_1$ factor. This allows us to compute the normalizer of $L(F_d)$ in
$L(\mathscr{T}_d(G_*))$, which turns out to be "trivial" in the diverse case,
implying that the inclusion is also singular. Then we prove that the inclusion
$L(F_d) \subseteq L(\mathscr{T}_d(G_*))$ satisfies the weak asymptotic
homomorphism property, which yields another proof that the inclusion is
singular. Finally, we finish the paper with an application: Using
irreducibility, our conditions for when $L(\mathscr{T}_d(G_*))$ is a type
$\text{II}_1$ McDuff factor, and the fact that $F_d$ is character rigid (in the
sense of Peterson), we prove that the groups $F_d$ are McDuff (in the sense of
Deprez-Vaes).


  In the first paper of this series, we showed that the ratio c of individual
dose to ambient dose did not change with time in Date City, Fukushima
Prefecture, after the Fukushima Daiichi Nuclear Power Plant accident. The
purpose of the present paper, the second in a series, is to estimate the
lifetime doses of the Date City residents, based on continuous glass badge
monitoring data, extrapolated by means of the ambient-dose-rate reduction
function obtained from the airborne monitoring data. As a result, we found that
the mean additional lifetime dose of residents living in Date City is not
expected to exceed 18 mSv. In addition, effects of decontamination on the
reduction of individual doses were not evident. This method of combining
individual doses and the ambient doses, as developed in this study, has made it
possible to predict with reasonable certainty the lifetime doses of residents
who continue to live in this radiologically contaminated area.