  Recently, Gupta et.al. [GKKS2013] proved that over Q any $n^{O(1)}$-variate
and $n$-degree polynomial in VP can also be computed by a depth three
$\Sigma\Pi\Sigma$ circuit of size $2^{O(\sqrt{n}\log^{3/2}n)}$. Over fixed-size
finite fields, Grigoriev and Karpinski proved that any $\Sigma\Pi\Sigma$
circuit that computes $Det_n$ (or $Perm_n$) must be of size $2^{\Omega(n)}$
[GK1998]. In this paper, we prove that over fixed-size finite fields, any
$\Sigma\Pi\Sigma$ circuit for computing the iterated matrix multiplication
polynomial of $n$ generic matrices of size $n\times n$, must be of size
$2^{\Omega(n\log n)}$. The importance of this result is that over fixed-size
fields there is no depth reduction technique that can be used to compute all
the $n^{O(1)}$-variate and $n$-degree polynomials in VP by depth 3 circuits of
size $2^{o(n\log n)}$. The result [GK1998] can only rule out such a possibility
for depth 3 circuits of size $2^{o(n)}$.
  We also give an example of an explicit polynomial ($NW_{n,\epsilon}(X)$) in
VNP (not known to be in VP), for which any $\Sigma\Pi\Sigma$ circuit computing
it (over fixed-size fields) must be of size $2^{\Omega(n\log n)}$. The
polynomial we consider is constructed from the combinatorial design. An
interesting feature of this result is that we get the first examples of two
polynomials (one in VP and one in VNP) such that they have provably stronger
circuit size lower bounds than Permanent in a reasonably strong model of
computation.
  Next, we prove that any depth 4
$\Sigma\Pi^{[O(\sqrt{n})]}\Sigma\Pi^{[\sqrt{n}]}$ circuit computing
$NW_{n,\epsilon}(X)$ (over any field) must be of size $2^{\Omega(\sqrt{n}\log
n)}$. To the best of our knowledge, the polynomial $NW_{n,\epsilon}(X)$ is the
first example of an explicit polynomial in VNP such that it requires
$2^{\Omega(\sqrt{n}\log n)}$ size depth four circuits, but no known matching
upper bound.


  Diffuse interface models are widely used to describe evolution of multi-phase
systems of different nature. Dispersed "inclusions", described by the phase
field distribution, are usually three dimensional objects. When describing
elastic fracture evolution, elements of the dispersed phase are effectively 2d
objects. An example of the model which governs evolution of effectively 1d
dispersed inclusions is phase field model for electric breakdown in solids.
Phase field model is defined by appropriate free energy functional, which
depends on phase field and its derivatives. In this work we show that
codimension of the dispersed "inclusion" significantly restrict the functional
dependency of system energy on the derivatives of the problem state variables.
It is shown that free energy of any phase field model suitable to describe
codimension 2 diffuse objects necessary depends on higher order derivatives of
the phase field or need an additional smoothness of the solution - it should
have first derivatives integrable with a power greater then two. To support
theoretical discussion, some numerical experiments are presented.


  We prove that a family of quasiregular mappings of a domain $\Omega$ which
are uniformly bounded in $L^p$ for some $p>0$ form a normal family. From this
we show how an elliptic estimate on a functional differences implies all
directional derivatives, and thus the complex gradient to be quasiregular.
Consequently the function enjoys much higher regularity than apriori
assumptions suggest.


  Every algorithm which can be executed on a computer can at least in principle
be realized in hardware, i.e. by a discrete physical system. The problem is
that up to now there is no programming language by which physical systems can
constructively be described. Such tool, however, is essential for the compact
description and automatic production of complex systems. This paper introduces
a programming language, called Akton-Algebra, which provides the foundation for
the complete description of discrete physical systems. The approach originates
from the finding that every discrete physical system reduces to a
spatiotemporal topological network of nodes, if the functional and metric
properties are deleted. A next finding is that there exists a homeomorphism
between the topological network and a sequence of symbols representing a
program by which the original nodal network can be reconstructed. Providing
Akton-Algebra with functionality turns it into a flow-controlled general data
processing language, which by introducing clock control and addressing can be
further transformed into a classical programming language. Providing
Akton-Algebra with metrics, i.e. the shape and size of the components, turns it
into a novel hardware system construction language.


  Active learning aims to develop label-efficient algorithms by sampling the
most representative queries to be labeled by an oracle. We describe a
pool-based semi-supervised active learning algorithm that implicitly learns
this sampling mechanism in an adversarial manner. Unlike conventional active
learning algorithms, our approach is task agnostic, i.e., it does not depend on
the performance of the task for which we are trying to acquire labeled data.
Our method learns a latent space using a variational autoencoder (VAE) and an
adversarial network trained to discriminate between unlabeled and labeled data.
The mini-max game between the VAE and the adversarial network is played such
that while the VAE tries to trick the adversarial network into predicting that
all data points are from the labeled pool, the adversarial network learns how
to discriminate between dissimilarities in the latent space. We extensively
evaluate our method on various image classification and semantic segmentation
benchmark datasets and establish a new state of the art on
$\text{CIFAR10/100}$, $\text{Caltech-256}$, $\text{ImageNet}$,
$\text{Cityscapes}$, and $\text{BDD100K}$. Our results demonstrate that our
adversarial approach learns an effective low dimensional latent space in
large-scale settings and provides for a computationally efficient sampling
method. Our code is available at https://github.com/sinhasam/vaal.


  We report our investigation on the doubly virtual TFFs $F_{{\rm
P}\gamma^*}(Q^2_1,Q^2_2)$ for the ${\rm P}\to\gamma^*(q_1)\gamma^*(q_2) \;({\rm
P}=\pi^0,\eta,\eta')$ transitions using the light-front quark model (LFQM).
Performing a LF calculation in the exactly solvable manifestly covariant
Bethe-Salpeter (BS) model as the first illustration, we used $q^+_1=0$ frame
and found that both LF and manifestly covariant calculations produce exactly
the same results for $F_{{\rm P}\gamma^*}(Q^2_1,Q^2_2)$. This confirms the
absence of the LF zero mode in the doubly virtual TFFs. We then mapped this
covariant BS model to the standard LFQM using the more phenomenologically
accessible Gaussian wave function provided by the LFQM analysis of meson mass
spectra. For the numerical analyses of $F_{{\rm P}\gamma^*}(Q^2_1,Q^2_2)$, we
compared our LFQM results with the available experimental data and the
perturbative QCD (pQCD) and the vector meson dominance (VMD) model predictions.
As $(Q^2_1, Q^2_2)\to\infty$, our LFQM result for doubly virtual TFF is
consistent with the pQCD prediction, i.e. $F_{{\rm P}\gamma^*}(Q^2_1,
Q^2_2)\sim 1/(Q^2_1 + Q^2_2)$, while it differs far from the result of VMD
model which behaves $F^{\rm VMD}_{{\rm P}\gamma^*}(Q^2_1, Q^2_2)\sim 1/(Q^2_1
Q^2_2)$. Our LFQM prediction for $F_{\eta'\gamma^*}(Q^2_1,Q^2_2)$ shows an
agreement with the very recent experimental data obtained from the BaBar
collaboration for the ranges of $2< Q^2_1, Q^2_1 <60$ GeV$^2$.


  Unsupervised Graph Domain Adaptation (UGDA) has emerged as a practical
solution to transfer knowledge from a label-rich source graph to a completely
unlabelled target graph. However, most methods require a labelled source graph
to provide supervision signals, which might not be accessible in the real-world
settings due to regulations and privacy concerns. In this paper, we explore the
scenario of source-free unsupervised graph domain adaptation, which tries to
address the domain adaptation problem without accessing the labelled source
graph. Specifically, we present a novel paradigm called GraphCTA, which
performs model adaptation and graph adaptation collaboratively through a series
of procedures: (1) conduct model adaptation based on node's neighborhood
predictions in target graph considering both local and global information; (2)
perform graph adaptation by updating graph structure and node attributes via
neighborhood contrastive learning; and (3) the updated graph serves as an input
to facilitate the subsequent iteration of model adaptation, thereby
establishing a collaborative loop between model adaptation and graph
adaptation. Comprehensive experiments are conducted on various public datasets.
The experimental results demonstrate that our proposed model outperforms recent
source-free baselines by large margins.


  We consider $\text{SU}(2)$ Bogomolny equations on
$\mathbb{R}^2\times\hat{S}^1$ and use the spectral curve defined by the
holonomy in the periodic direction to approximate the fields in the limit of
large size to period ratio. Symmetries of the Nahm transform allow a study of
the effective two dimensional dynamics, which is compared with known results on
the full moduli space. The techniques are applied to systems of higher charge
and higher rank gauge group, allowing a direct comparison to other periodic
Yang-Mills systems.


  We derive a Fast Multipole Method (FMM) where a low-rank approximation of the
kernel is obtained using the Empirical Interpolation Method (EIM). Contrary to
classical interpolation-based FMM, where the interpolation points and basis are
fixed beforehand, the EIM is a nonlinear approximation method which constructs
interpolation points and basis which are adapted to the kernel under
consideration. The basis functions are obtained using evaluations of the kernel
itself. We restrict ourselves to translation-invariant kernels, for which a
modified version of the EIM approximation can be used in a multilevel FMM
context; we call the obtained algorithm Empirical Interpolation Fast Multipole
Method (EIFMM). An important feature of the EIFMM is a built-in error
estimation of the interpolation error made by the low-rank approximation of the
far-field behavior of the kernel: the algorithm selects the optimal number of
interpolation points required to ensure a given accuracy for the result,
leading to important gains for inhomogeneous kernels.


  We describe in terms of spherical twists the Serre functors of many
interesting semiorthogonal components, called residual categories, of the
derived categories of projective varieties. In particular, we show the residual
categories of Fano complete intersections are fractional Calabi--Yau up to a
power of an explicit spherical twist. As applications, we compute the Serre
dimensions of residual categories of Fano complete intersections, thereby
proving a corrected version of a conjecture of Katzarkov and Kontsevich, and
deduce the nonexistence of Serre invariant stability conditions when the
degrees of the complete intersection do not all coincide.


  We study for a class of symmetric L\'evy processes with state space $\rn$ the
transition density $p_t(x)$ in terms of two one-parameter families of metrics,
$(d_t)_{t>0}$ and $(\delta_t)_{t>0}$. The first family of metrics describes the
diagonal term $p_t(0)$; it is induced by the characteristic exponent $\psi$ of
the L\'evy process by $d_t(x,y)=\sqrt{t\psi(x-y)}$. The second and new family
of metrics $\delta_t$ relates to $\sqrt{t\psi}$ through the formula
  $$
  \exp(-\delta_t^2(x,y))
  = \Ff[\frac{e^{-t\psi}}{p_t(0)}](x-y)
  $$
  where $\Ff$ denotes the Fourier transform. Thus we obtain the following
"Gaussian" representation of the transition density: $p_t(x)=p_t(0)
e^{-\delta_t^2(x,0)}$ where $p_t(0)$ corresponds to a volume term related to
$\sqrt{t\psi}$ and where an "exponential" decay is governed by $\delta_t^2$.
This gives a complete and new geometric, intrinsic interpretation of $p_t(x)$.


  Visual question answering requires high-order reasoning about an image, which
is a fundamental capability needed by machine systems to follow complex
directives. Recently, modular networks have been shown to be an effective
framework for performing visual reasoning tasks. While modular networks were
initially designed with a degree of model transparency, their performance on
complex visual reasoning benchmarks was lacking. Current state-of-the-art
approaches do not provide an effective mechanism for understanding the
reasoning process. In this paper, we close the performance gap between
interpretable models and state-of-the-art visual reasoning methods. We propose
a set of visual-reasoning primitives which, when composed, manifest as a model
capable of performing complex reasoning tasks in an explicitly-interpretable
manner. The fidelity and interpretability of the primitives' outputs enable an
unparalleled ability to diagnose the strengths and weaknesses of the resulting
model. Critically, we show that these primitives are highly performant,
achieving state-of-the-art accuracy of 99.1% on the CLEVR dataset. We also show
that our model is able to effectively learn generalized representations when
provided a small amount of data containing novel object attributes. Using the
CoGenT generalization task, we show more than a 20 percentage point improvement
over the current state of the art.


  Recent studies on Brownian motors driven by colored non Gaussian noises have
shown that the departure of the noise distribution from Gaussian behavior
induces an enhancement of its current and efficiency. Here we discuss some new
aspects of this phenomenon focusing in some analytical results based in an
adiabatic approximation, and in the analysis of the long probability
distribution tails' role.


  In a sandwich consisting of two superconducting films, one weakly pinning and
one strongly pinning, the vortex positions in both films are determined by the
strongly pinning material and the vortex lattice is disordered in both films.
We used (strongly pinning) NbN and (weakly pinning) MoGe and studied, by
directly imaging the vortex core positions with a scanning tunnelling
microscope, how this disorder is restored with increasing thickness of MoGe
layer or when the interface is made insulating. For clean interfaces we find
that the first reordering of the vortex lattice is found at a layer thickness
wich is compatible with the first bending mode of the vortex lines. Making the
interface insulating we find that order is restored quickly. We argue that this
is can be understood from the competition between the Josephson force working
on the vortex segments on the one hand, and the elastic restoring forces inside
the weakly pinning layer on the other hand.


  Training neural networks involves solving large-scale non-convex optimization
problems. This task has long been believed to be extremely difficult, with fear
of local minima and other obstacles motivating a variety of schemes to improve
optimization, such as unsupervised pretraining. However, modern neural networks
are able to achieve negligible training error on complex tasks, using only
direct training with stochastic gradient descent. We introduce a simple
analysis technique to look for evidence that such networks are overcoming local
optima. We find that, in fact, on a straight path from initialization to
solution, a variety of state of the art neural networks never encounter any
significant obstacles.


  We present new spectroscopic data for 21 barred spiral galaxies, which we use
to explore the effect of bars on disk star formation, and to place constraints
on the characteristic lifetimes of bar episodes. The analysis centres on
regions of heavily suppressed star formation activity, which we term 'star
formation deserts'. Long-slit optical spectroscopy is used to determine H beta
absorption strengths in these desert regions, and comparisons with theoretical
stellar population models are used to determine the time since the last
significant star formation activity, and hence the ages of the bars. We find
typical ages of approx. 1 Gyr, but with a broad range, much larger than would
be expected from measurement errors alone, extending from about 0.25 Gyr to
more than 4 Gyr. Low-level residual star formation, or mixing of stars from
outside the 'desert' regions, could result in a doubling of these age
estimates. The relatively young ages of the underlying populations coupled with
the strong limits on the current star formation rule out a gradual exponential
decline in activity, and hence support our assumption of an abrupt truncation
event.


  Energy landscape approaches have become increasingly popular for analysing a
wide variety of chemical physics phenomena. Basic to many of these applications
has been the inherent structure mapping, which divides up the potential energy
landscape into basins of attraction surrounding the minima. Here, we probe the
nature of this division by introducing a method to compute the basin area
distribution and applying it to some archetypal supercooled liquids. We find
that this probability distribution is a power law over a large number of
decades with the lower-energy minima having larger basins of attraction.
Interestingly, the exponent for this power law is approximately the same as
that for a high-dimensional Apollonian packing, providing further support for
the suggestion that there is a strong analogy between the way the energy
landscape is divided into basins, and the way that space is packed in
self-similar, space-filling hypersphere packings, such as the Apollonian
packing. These results suggest that the basins of attraction provide a
fractal-like tiling of the energy landscape, and that a scale-free pattern of
connections between the minima is a general property of energy landscapes.


  The composition the atmosphere of Venus results from the integration of many
processes entering into play over the entire geological history of the planet.
Determining the elemental abundances and isotopic ratios of noble gases (He,
Ne, Ar, Kr, Xe) and stable isotopes (H, C, N, O, S) in the Venus atmosphere is
a high priority scientific target since it could open a window on the origin
and early evolution of the entire planet. This chapter provides an overview of
the existing dataset on noble gases and stable isotopes in the Venus
atmosphere. The current state of knowledge on the origin and early and
long-term evolution of the Venus atmosphere deduced from this dataset is
summarized. A list of persistent and new unsolved scientific questions stemming
from recent studies of planetary atmospheres (Venus, Earth and Mars) are
described. Important mission requirements pertaining to the measurement of
volatile elements in the atmosphere of Venus as well as potential technical
difficulties are outlined.


  This lecture provides a pedagogical instruction to the basic concepts of the
Skyrme model and its some applications. As the preliminary for understanding
the Skyrme model, we first briefly explain the large $N_c$ expansion, chiral
symmetry and its breaking. Next we give a brief review of nonlinear sigma model
including the power counting scheme of the chiral perturbation theory, starting
from the linear sigma model. We then give an exhaust explanation of the Skyrme
model and its applications. After the presentation of the Skyrme model for
baryons in free space, we introduce how to study the baryonic matter and medium
modified hadron properties by using the Skyrme model. Finally we discuss a way
to incorporate the lowest-lying vector mesons into the Skyrme model based on
the hidden local symmetry. Some possible further developments are also covered.


  The Butterfly lemma we present can be considered a reiteration theorem for
differentials generated from a complex interpolation process for families of
K\"othe spaces. The lemma will be used to clarify the effect of different
configurations in the resulting differential (because although interpolation is
an orientation-free process, the obtention of differentials is not) and to
round off a few aspects of Kalton's interpolation theorem.


  Solving exactly Combinatorial Optimization Problems (COPs) using a
Branch-and-Bound (B&B) algorithm requires a huge amount of computational
resources. Therefore, we recently investigated designing B&B algorithms on top
of graphics processing units (GPUs) using a parallel bounding model. The
proposed model assumes parallelizing the evaluation of the lower bounds on
pools of sub-problems. The results demonstrated that the size of the evaluated
pool has a significant impact on the performance of B&B and that it depends
strongly on the problem instance being solved. In this paper, we design an
adaptative parallel B&B algorithm for solving permutation-based combinatorial
optimization problems such as FSP (Flow-shop Scheduling Problem) on GPU
accelerators. To do so, we propose a dynamic heuristic for parameter
auto-tuning at runtime. Another challenge of this work is to exploit larger
degrees of parallelism by using the combined computational power of multiple
GPU devices. The approach has been applied to the permutation flow-shop
problem. Extensive experiments have been carried out on well-known FSP
benchmarks using an Nvidia Tesla S1070 Computing System equipped with two Tesla
T10 GPUs. Compared to a CPU-based execution, accelerations up to 105 are
achieved for large problem instances.


  We address the problem of distance metric learning (DML), defined as learning
a distance consistent with a notion of semantic similarity. Traditionally, for
this problem supervision is expressed in the form of sets of points that follow
an ordinal relationship -- an anchor point $x$ is similar to a set of positive
points $Y$, and dissimilar to a set of negative points $Z$, and a loss defined
over these distances is minimized. While the specifics of the optimization
differ, in this work we collectively call this type of supervision Triplets and
all methods that follow this pattern Triplet-Based methods. These methods are
challenging to optimize. A main issue is the need for finding informative
triplets, which is usually achieved by a variety of tricks such as increasing
the batch size, hard or semi-hard triplet mining, etc. Even with these tricks,
the convergence rate of such methods is slow. In this paper we propose to
optimize the triplet loss on a different space of triplets, consisting of an
anchor data point and similar and dissimilar proxy points which are learned as
well. These proxies approximate the original data points, so that a triplet
loss over the proxies is a tight upper bound of the original loss. This
proxy-based loss is empirically better behaved. As a result, the proxy-loss
improves on state-of-art results for three standard zero-shot learning
datasets, by up to 15% points, while converging three times as fast as other
triplet-based losses.


  We report a combined theoretical and experimental investigation of magnetic
proximity and Hall transport in Pt/Cr bilayers. Density functional theory
indicates that an interfacial magnetization can be induced in the Pt layer and
a strong magnetocrystalline anisotropy with an easy axis out of plane arises in
the antiferromagnet. A signal ascribed to the anomalous Hall effect is detected
and associated to the interface between Pt and Cr layers. We show that this
effect originates from the combination of proximity-induced magnetization and a
nontrivial topology of the band structure at the interface.


  Shibata et al. reported that humans could learn to repeatedly evoke a
stimulus-associated functional magnetic resonance imaging (fMRI) activity
pattern in visual areas V1/V2 through which visual perceptual learning was
achieved without stimulus presentation. Contrary to their attribution of visual
improvements to neuroplasticity in adult V1/V2, our Hebbian learning
interpretation of these data explains the attainment of better perceptual
decisions without plastic V1/V2.


  We present a framework for analyzing non-linear $\mathbb{R}^d$-valued
subdivision schemes which are geometric in the sense that they commute with
similarities in $\mathbb{R}^d$. It admits to establish
$C^{1,\alpha}$-regularity for arbitrary schemes of this type, and
$C^{2,\alpha}$-regularity for an important subset thereof, which includes all
real-valued schemes. Our results are constructive in the sense that they can be
verified explicitly for any scheme and any given set of initial data by a
universal procedure. This procedure can be executed automatically and
rigorously by a computer when using interval arithmetics.


  Smearing of gauge-field configurations in lattice field theory improves the
results of lattice simulations by suppressing high energy modes from
correlation functions. In quantum simulations, high kinetic energy eigenstates
are introduced when the time evolution operator is approximated such as
Trotterization. While improved Trotter product formulae exist to reduce the
errors, they have diminishing accuracy returns with respect to resource costs.
Therefore having an algorithm that has fewer resources than an improved Trotter
formula is desirable. In this work I develop a representation agnostic method
for quantum smearing and show that it reduces the coupling to high energy modes
in the discrete nonabelian gauge theory $D_4$


  A growth mindset has shown promising outcomes for increasing empathy ability.
However, stimulating a growth mindset in VR-based empathy interventions is
under-explored. In the present study, we implemented prosocial VR content, Our
Neighbor Hero, focusing on embodying a virtual character to modulate players'
mindsets. The virtual body served as a stepping stone, enabling players to
identify with the character and cultivate a growth mindset as they followed
mission instructions. We considered several implementation factors to assist
players in positioning within the VR experience, including positive feedback,
content difficulty, background lighting, and multimodal feedback. We conducted
an experiment to investigate the intervention's effectiveness in increasing
empathy. Our findings revealed that the VR content and mindset training
encouraged participants to improve their growth mindsets and empathic motives.
This VR content was developed for college students to enhance their empathy and
teamwork skills. It has the potential to improve collaboration in
organizational and community environments.


  The stochastic description of chemical reaction networks with the kinetic
chemical master equation (CME) is important for studying biological cells, but
it suffers from the curse of dimensionality: The amount of data to be stored
grows exponentially with the number of chemical species and thus exceeds the
capacity of common computational devices for realistic problems. Therefore,
time-dependent model order reduction techniques such as the dynamical low-rank
approximation are desirable. In this paper we propose a dynamical low-rank
algorithm for the kinetic CME using binary tree tensor networks. The
dimensionality of the problem is reduced in this approach by hierarchically
dividing the reaction network into partitions. Only reactions that cross
partitions are subject to an approximation error. We demonstrate by two
numerical examples (a 5-dimensional lambda phage model and a 20-dimensional
reaction cascade) that the proposed method drastically reduces memory
consumption and shows improved computational performance and better accuracy
compared to a Monte Carlo method.


  We derive horizontal fluid motions on the solar surface over large areas
covering the quiet-Sun magnetic network from local correlation tracking of
convective granules imaged in continuum intensity and Doppler velocity by the
Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory
(SDO). From these we calculate horizontal divergence, vertical component of
vorticity, and kinetic helicity of fluid motions. We study the correlations
between fluid divergence and vorticity, and that between vorticity (kinetic
helicity) and magnetic field. We find that the vorticity (kinetic helicity)
around small-scale fields exhibits a hemispherical pattern (in sign) similar to
that followed by the magnetic helicity of large-scale active regions
(containing sunspots). We identify this pattern to be a result of the Coriolis
force acting on supergranular-scale flows (both the outflows and inflows), and
is consistent with earlier studies using local helioseismology. Further, we
show that the magnetic fields cause transfer of vorticity from supergranular
inflow regions to outflow regions, and that they tend to suppress the vortical
motions around them when magnetic flux densities exceed about 300 G (HMI). We
also show that such action of magnetic fields leads to marked changes in the
correlations between fluid divergence and vorticity. These results are
speculated to be of importance to local dynamo action if present, and to the
dynamical evolution of magnetic helicity at the small-scale.


  I make two comments about nuclear matter. First, I consider the effects of a
coupling between the $O(4)$ chiral field, $\vec{\phi}$, and the $\omega_\mu$
meson, $\sim + \, \omega_\mu^2 \, \vec{\phi}^{\, 2}$; for any net baryon
density, a condensate for $\omega_0$ is unavoidably generated. I assume that
with increasing density, a decrease of the chiral condensate and the effective
$\omega_0$ mass gives a stiff equation of state (EoS). In order to match that
onto a soft EoS for quarkyonic matter, I consider an $O(N)$ field at large $N$,
where at nonzero temperature quantum fluctuations disorder any putative pion
"condensate" into a quantum pion liquid (Q$\pi$L) (arXiv:2005.10259). In this
paper I show that the Q$\pi$L persists at zero temperature. If valid
qualitatively at $N=4$, the $\omega_0$ mass goes up sharply and suppresses the
$\omega_0$ condensate. This could generate a spike in the speed of sound at
high density, which is of relevance to neutron stars. Second, I propose a toy
model of a $Z(3)$ gauge theory with three flavors of fermions, where $Z(3)$
vortices confine fermions into baryons. In $1+1$ dimensions this model can be
studied numerically with present techniques, using either classical or quantum
computers.


  We present a new microscopic model of granular medium to study the role of
dynamical correlations and the onset of spatial order induced by the
inelasticity of the interactions. In spite of its simplicity, it features
several different aspects of the rich phenomenology observed in granular
materials and allows to make contact with other topics of statistical mechanics
such as diffusion processes, domain growth, persistence, aging phenomena.
Interestingly, while local observables being controlled by the largest
wavelength fluctuations seem to suggest a purely diffusive behavior, the
formation of spatially extended structures and topological defects, such as
vortices and shocks, reveals a more complex scenario.


  We consider incidences among colored sets of lines in $\mathbb{R}^d$ and
examine whether the existence of certain concurrences between lines of $k$
colors force the existence of at least one concurrence between lines of $k+1$
colors. This question is relevant for problems in 3D reconstruction in computer
vision.


  GRB data accumulated over the years have shown that the distribution of their
time duration is bimodal. While there is some evidence that long bursts are
associated with star-forming regions, nothing is known regarding the class of
short bursts. Their very short timescales are hard to explain with the collapse
of a massive star, but would be naturally produced by the merger of two compact
objects, such as two neutron stars (NS), or a neutron star and a black hole
(BH). As for the case of long bursts, afterglow obervations for short bursts
should help reveal their origin. By using updated population synthesis code
calculations, we simulate a cosmological population of merging NS-NS and NS-BH,
and compute the distribution of their galactic off-sets, the density
distribution of their environment, and, if indeed associated with GRBs, their
expected afterglow characteristics.


  Let $G$ be a connected finite graph. Backman, Baker, and Yuen have
constructed a family of explicit and easy-to-describe bijections
$g_{\sigma,\sigma^*}$ between spanning trees of $G$ and
$(\sigma,\sigma^*)$-compatible orientations, where the
$(\sigma,\sigma^*)$-compatible orientations are the representatives of
equivalence classes of orientations up to cycle-cocycle reversal which are
determined by a cycle signature $\sigma$ and a cocycle signature $\sigma^*$.
Their proof makes use of zonotopal subdivisions and the bijections
$g_{\sigma,\sigma^*}$ are called \emph{geometric bijections}. In this paper, we
extend the geometric bijections to subgraph-orientation correspondences.
Moreover, we extend the geometric constructions accordingly. Our proofs are
purely combinatorial, even for the geometric constructions. We also provide
geometric proofs for partial results, which make use of zonotopal tiling,
relate to Backman, Baker, and Yuen's method, and motivate our combinatorial
constructions. Finally, we explain that the main results hold for \emph{regular
matroids}.


  In a recombining plasma the metastable states are known to accumulate
population thereby slowing down the recombination process. We show that an
account of the doubly-excited autoionizing states, formed due to collisional
recombination of metastable ions, results in a significant acceleration of
recombination. A fully time-dependent collisional-radiative (CR) modeling for
stripped ions of carbon recombining in a cold dense plasma demonstrates an
order of magnitude faster recombination of He-like ions. The CR model used in
calculations is discussed in detail.


  Researchers often use linear regression to analyse randomized experiments to
improve treatment effect estimation by adjusting for imbalances of covariates
in the treatment and control groups. Our work offers a randomization-based
inference framework for regression adjustment in stratified randomized
experiments. Under mild conditions, we re-establish the finite population
central limit theorem for a stratified experiment. We prove that both the
stratified difference-in-means and the regression-adjusted average treatment
effect estimators are consistent and asymptotically normal. The asymptotic
variance of the latter is no greater and is typically lesser than that of the
former. We also provide conservative variance estimators to construct
large-sample confidence intervals for the average treatment effect.


  In this note, we prove that the $\lceil \tfrac{1}{2} \sqrt{n} \log_2^2 n
\rceil^{th}$ power of a connected $n$-vertex Eulerian digraph is Hamiltonian,
and provide an infinite family of digraphs for which the $\lfloor \sqrt{n}/2
\rfloor^{th}$ power is not.


  We investigate the weak cosmic censorship conjecture for Kerr-Sen black
holes, which are solutions to the four-dimensional low-energy effective field
theory for the heterotic string theory, based on the scattering of a charged
scalar field. When the fluxes of the scalar field are assumed to transfer its
conserved quantities to the black hole, extremal and near-extremal black holes
cannot be over-spun and over-charged in their first-order variations, which is
sufficient to conclude that the weak cosmic censorship conjecture is valid for
Kerr-Sen black holes. We confirm our conclusion by relating it to the first,
second, and third laws of thermodynamics.


  In this note we show that the holographic entanglement entropy inequalities
that hold for constant time slices are also valid for large regions and times
in covariant spacetimes containing collapsing black branes, where the leading
part of the entropy is computed by the simplified membrane theory of
arXiv:1803.10244.


  A law of semi-major axes in the Solar system is described. The proposed law
is recommended as an additional criterion for planet definition as it
represents a generating function of planetary distances.


  Any two triangulations of a closed surface with the same number of vertices
can be transformed into each other by a sequence of regular flips, provided the
number of vertices exceeds a number N depending on the surface. Examples show
that in general N is bigger than the minimal number of vertices of a
triangulation. The existence of N was known, but no estimate. This paper
provides an estimate for N that is linear in the Euler characteristic of the
surface.


  We consider the following online allocation problem: Given a unit square S,
and a sequence of numbers n_i between 0 and 1, with partial sum bounded by 1;
at each step i, select a region C_i of previously unassigned area n_i in S. The
objective is to make these regions compact in a distance-aware sense: minimize
the maximum (normalized) average Manhattan distance between points from the
same set C_i. Related location problems have received a considerable amount of
attention; in particular, the problem of determining the "optimal shape of a
city", i.e., allocating a single n_i has been studied. We present an online
strategy, based on an analysis of space-filling curves; for continuous shapes,
we prove a factor of 1.8092, and 1.7848 for discrete point sets.


  Depinning of an interface from a random self--affine substrate with roughness
exponent $\zeta_S$ is studied in systems with short--range interactions. In
2$D$ transfer matrix results show that for $\zeta_S<1/2$ depinning falls in the
universality class of the flat case. When $\zeta_S$ exceeds the roughness
($\zeta_0=1/2$) of the interface in the bulk, geometrical disorder becomes
relevant and, moreover, depinning becomes \underline{discontinuous}. The same
unexpected scenario, and a precise location of the associated tricritical
point, are obtained for a simplified hierarchical model. It is inferred that,
in 3$D$, with $\zeta_0=0$, depinning turns first--order already for
$\zeta_S>0$. Thus critical wetting may be impossible to observe on rough
substrates.


  We target the problem of accuracy and robustness in causal inference from
finite data sets. Some state-of-the-art algorithms produce clear output
complete with solid theoretical guarantees but are susceptible to propagating
erroneous decisions, while others are very adept at handling and representing
uncertainty, but need to rely on undesirable assumptions. Our aim is to combine
the inherent robustness of the Bayesian approach with the theoretical strength
and clarity of constraint-based methods. We use a Bayesian score to obtain
probability estimates on the input statements used in a constraint-based
procedure. These are subsequently processed in decreasing order of reliability,
letting more reliable decisions take precedence in case of con icts, until a
single output model is obtained. Tests show that a basic implementation of the
resulting Bayesian Constraint-based Causal Discovery (BCCD) algorithm already
outperforms established procedures such as FCI and Conservative PC. It can also
indicate which causal decisions in the output have high reliability and which
do not.


  Domain adaptation techniques have contributed to the success of deep
learning. Leveraging knowledge from an auxiliary source domain for learning in
labeled data-scarce target domain is fundamental to domain adaptation. While
these techniques result in increasing accuracy, the adaptation process,
particularly the knowledge leveraged from the source domain, remains unclear.
This paper proposes an explainable by design supervised domain adaptation
framework - XSDA-Net. We integrate a case-based reasoning mechanism into the
XSDA-Net to explain the prediction of a test instance in terms of
similar-looking regions in the source and target train images. We empirically
demonstrate the utility of the proposed framework by curating the domain
adaptation settings on datasets popularly known to exhibit part-based
explainability.


  Recent work has shown that the distribution of inter-event times for e-mail
communication exhibits a heavy tail which is statistically consistent with a
cascading Poisson process. In this work we extend the analysis to higher-order
statistics, using the Fano and Allan factors to quantify the extent to which
the empirical data depart from the known correlations of Poissonian statistics.
The analysis shows that the higher-order statistics from the empirical data is
indistinguishable from that of randomly reordered time series, thus
demonstrating that e-mail correspondence is no more bursty or correlated than a
Poisson process. Furthermore synthetic data sets generated by a cascading
Poisson process replicate the burstiness and correlations observed in the
empirical data. Finally, a simple rescaling analysis using the best-estimate
rate of activity, confirms that the empirically observed correlations arise
from a non-homogeneus Poisson process.


  While copper-graphene (Cu/Gr) composites have been promising materials due to
their theoretically high strength and conductivity, their design has been
hampered by the large number of variables affecting their properties. We
applied four different Machine Learning (ML) models to manually collected
datasets compiling the yield strength and ultimate tensile strength of
graphene-reinforced copper composites processed with powder metallurgy
techniques. Our results indicate that ML models can predict the mechanical
properties of Cu/Gr composites with satisfactory accuracy. Feature analysis
provided new insights into the most important factors that affect these
properties.


  The lightcurve of the Large Magellanic Cloud (LMC) variable star MACHO
81.8997.87 shows evidence for photometric variations due to both stellar
pulsation, with a 2.035 day period, and eclipsing behavior, with an 800.4 day
period. The primary star of the system has been identified as a first-overtone
Cepheid but the nature of the secondary star has not been determined. Here we
present multicolor BVI photometry of a primary eclipse of the system and fit a
model to the complete lightcurve to produce an updated set of elements. These
results are combined with 2MASS JHK photometry to give further insight into the
identity of the companion star. We find that the companion is most consistent
with a late-K or an early-M giant but also that there are a number of problems
with this interpretation. The prospects for future observations of this system
are also discussed.


  GitHub's Copilot for Pull Requests (PRs) is a promising service aiming to
automate various developer tasks related to PRs, such as generating summaries
of changes or providing complete walkthroughs with links to the relevant code.
As this innovative technology gains traction in the Open Source Software (OSS)
community, it is crucial to examine its early adoption and its impact on the
development process. Additionally, it offers a unique opportunity to observe
how developers respond when they disagree with the generated content. In our
study, we employ a mixed-methods approach, blending quantitative analysis with
qualitative insights, to examine 18,256 PRs in which parts of the descriptions
were crafted by generative AI. Our findings indicate that: (1) Copilot for PRs,
though in its infancy, is seeing a marked uptick in adoption. (2) PRs enhanced
by Copilot for PRs require less review time and have a higher likelihood of
being merged. (3) Developers using Copilot for PRs often complement the
automated descriptions with their manual input. These results offer valuable
insights into the growing integration of generative AI in software development.


  This letter describes an alternative for the connection of power converters
to a direct current network without the installation of a capacitor in the
DC-Link. The circuit allows the connection of converters through a coil and
avoids short-circuit currents with different instantaneous values of voltage
output. A description of the calculation and the choice of components together
with a real implemented example in a DC network within Smart City project
(Endes Utility) is presented.


  In this work, we use the Shannon informational entropies to study an electron
confined in a double quantum dot; we mean the entropy in the space of
positions, $S_r$, in the space of momentum, $S_p$, and the total entropy, $S_t
= S_r+S_p$. We obtain $S_r$, $S_p$ and $S_t$ as a function of the parameters
$A_2$ and $k$ which rules the height and the width, respectively, of the
internal barrier of the confinement potential. We conjecture that the entropy
$S_r$ maps the degeneracy of states when we vary $A_2$ and also is an indicator
of the level of decoupling/coupling of the double quantum dot. We study the
quantities $S_r$ and $S_p$ as measures of delocalization/localization of the
probability distribution. Furthermore, we analyze the behaviors of the
quantities $S_p$ and $S_t$ as a function of $A_2$ and $k$. Finally, we carried
out an energy analysis and, when possible, compared our results with work
published in the literature.


  CONTEXT: There is growing interest in establishing software engineering as an
evidence-based discipline. To that end, replication is often used to gain
confidence in empirical findings, as opposed to reproduction where the goal is
showing the correctness, or validity of the published results. OBJECTIVE: To
consider what is required for a replication study to confirm the original
experiment and apply this understanding in software engineering. METHOD:
Simulation is used to demonstrate why the prediction interval for confirmation
can be surprisingly wide. This analysis is applied to three recent
replications. RESULTS: It is shown that because the prediction intervals are
wide, almost all replications are confirmatory, so in that sense there is no
'replication crisis', however, the contributions to knowledge are negligible.
CONCLUSIONS: Replicating empirical software engineering experiments,
particularly if they are under-powered or under-reported, is a waste of
scientific resources. By contrast, meta-analysis is strongly advocated so that
all relevant experiments are combined to estimate the population effect.


  A precise dynamical characterization of quantum impurity models with multiple
interacting orbitals is challenging. In quantum Monte Carlo methods, this is
embodied by sign problems. A dynamical sign problem makes it exponentially
difficult to simulate long times. A multi-orbital sign problem generally
results in a prohibitive computational cost for systems with multiple impurity
degrees of freedom even in static equilibrium calculations. Here, we present a
numerically exact inchworm method that simultaneously alleviates both sign
problems, enabling simulation of multi-orbital systems directly in the
equilibrium or nonequilibrium steady-state. The method combines ideas from the
recently developed steady-state inchworm Monte Carlo framework [Phys. Rev.
Lett. 130, 186301 (2023)] with other ideas from the equilibrium multi-orbital
inchworm algorithm [Phys. Rev. Lett. 124, 206405 (2020)]. We verify our method
by comparison with analytical limits and numerical results from previous
methods.


  The paper concerns the understanding of plurals in the framework of
Artificial Intelligence and emphasizes the role of time. The construction of
collection(s) and their evolution across time is often crucial and has to be
accounted for. The paper contrasts a "de dicto" collection where the collection
can be considered as persisting over these situations even if its members
change with a "de re" collection whose composition does not vary through time.
It expresses different criteria of choice between the two interpretations (de
re and de dicto) depending on the context of enunciation.


  We describe MIFS, a second generation integral-field spectrograph for the
VLT, operating in the visible wavelength range. It combines a 1'x1' field of
view with the improved spatial resolution provided by multi-conjugate adaptive
optics and covers a large simultaneous spectral range (0.6-1.0 microns). A
separate mode exploits the highest spatial resolution provided by adaptive
optics. With this unique combination of capabilities, MIFS has a wide domain of
application and a large discovery potential.
  The MIFS low-spatial resolution mode (sampled at 0.2") combined with the
initial MCAO capabilities planned for the VLT will provide ultra-deep fields
with a limiting magnitude for spectroscopy of R~28. MIFS will improve the
present day detection limit of Lyman alpha emitters by a factor of 100, and
will detect low-mass star-forming galaxies to z~7. The MIFS high-spatial
resolution mode (3"x3" field sampled at 0.01") is optimized for the next step
in (MC)AO. It will probe, e.g., the relationship between supermassive central
black holes and their host galaxy and the physics of winds from accretion disks
in young stellar objects at unprecedented spatial resolution.
  MIFS will extend Europe's lead in integral-field spectroscopy. It capitalizes
on new developments in adaptive optics, and is a key step towards
instrumentation for OWL.


  To sum high energy leading logarithms in a consistent way, one has to impose
the strong ordering in both projectile rapidity and dense target rapidity
simultaneously, which results in a kinematically improved
Balitsky-Kovchegov(BK) equation. We find that beyond this strong ordering
region, the important sub-leading double logarithms arise at high order due to
the incomplete cancellation between real corrections and virtual corrections in
a t-channel calculation. Based on this observation, we further argue that these
double logarithms are the Sudakov type ones, and thus can be resummed into an
exponential leading to a Sudakov suppressed BK equation.


  We discuss collider search strategies of gluinos which are highly degenerate
with the lightest neutralino in mass. This scenario is fairly difficult to
probe with conventional search strategies at colliders, and thus may provide a
hideaway of supersymmetry. Moreover, such a high degeneracy plays an important
role in dark matter physics as the relic abundance of the lightest neutralino
is significantly reduced via coannihilation. In this paper, we discuss ways of
uncovering this scenario with the help of longevity of gluinos; if the mass
difference between the lightest neutralino and gluino is $\lesssim 100$ GeV and
squarks are heavier than gluino, then the decay length of the gluino tends to
be of the order of the detector-size scale. Such gluinos can be explored in the
searches of displaced vertices, disappearing tracks, and anomalously large
energy deposit by (meta)stable massive charged particles. We find that these
searches are complementary to each other, and by combining their results we may
probe a wide range of the compressed gluino region in the LHC experiments.


  We present an analysis of the first far-ultraviolet observations of the SW
Sextantis-type cataclysmic variable DW Ursae Majoris, obtained in November 2001
with the Far Ultraviolet Spectroscopic Explorer. The time-averaged spectrum of
DW UMa shows a rich assortment of emission lines (plus some contamination from
interstellar absorption lines including molecular hydrogen). Accretion disk
model spectra do not provide an adequate fit to the far-ultraviolet spectrum of
DW UMa. We constructed a light curve by summing far-ultraviolet spectra
extracted in 60-sec bins; this shows a modulation on the orbital period, with a
maximum near photometric phase 0.93 and a minimum half an orbit later. No other
periodic variability was found in the light curve data. We also extracted
spectra in bins spanning 0.1 in orbital phase; these show substantial variation
in the profile shapes and velocity shifts of the emission lines during an
orbital cycle of DW UMa. Finally, we discuss possible physical models that can
qualitatively account for the observed far-ultraviolet behavior of DW UMa, in
the context of recent observational evidence for the presence of a
self-occulting disk in DW UMa and the possibility that the SW Sex stars may be
the intermediate polars with the highest mass transfer rates and/or weakest
magnetic fields.


  We establish sufficient conditions for existence of curves minimizing length
as measured with respect to a degenerate metric on the plane while enclosing a
specified amount of Euclidean area. Non-existence of minimizers can occur and
examples are provided. This continues the investigation begun in [ABCDS] where
the metric ds^2 near the singularities equals a quadratic polynomial times the
standard metric. Here we allow the conformal factor to be any smooth
non-negative potential vanishing at isolated points provided the Hessian at
these points is positive definite. These isoperimetric curves, appropriately
parametrized, arise as traveling wave solutions to a bi-stable Hamiltonian
system.


  We explore the question of whether the interior state of massive red
supergiant supernova progenitors can be effectively probed with
asteroseismology. We have computed a suite of ten models with ZAMS masses from
15 to 25 m_sun in intervals of 1 m_sun, including the effects of rotation, with
the stellar evolutionary code MESA. We estimate characteristic frequencies and
convective luminosities of convective zones at two illustrative stages, core
helium burning and off-center convective carbon burning. We also estimate the
power that might be delivered to the surface to modulate the luminous output
considering various efficiencies and dissipation mechanisms. The inner
convective regions should generate waves with characteristic periods of ~20
days in core helium burning, ~10 days in helium shell burning, and 0.1 to 1 day
in shell carbon burning. Acoustic waves may avoid both shock and diffusive
dissipation relatively early in core helium burning throughout most of the
structure. In shell carbon burning, years before explosion, the signal
generated in the helium shell might in some circumstances be weak enough to
avoid shock dissipation, but is subject to strong thermal dissipation in the
hydrogen envelope. Signals from a convective carbon-burning shell are very
likely to be even more severely damped within the envelope. In the most
optimistic case, early in core helium burning, waves arriving close to the
surface could represent luminosity fluctuations of a few millimagnitudes, but
the conditions in the very outer reaches of the envelope suggest severe thermal
damping there.


  We recover a video of the motion taking place in a hidden scene by observing
changes in indirect illumination in a nearby uncalibrated visible region. We
solve this problem by factoring the observed video into a matrix product
between the unknown hidden scene video and an unknown light transport matrix.
This task is extremely ill-posed, as any non-negative factorization will
satisfy the data. Inspired by recent work on the Deep Image Prior, we
parameterize the factor matrices using randomly initialized convolutional
neural networks trained in a one-off manner, and show that this results in
decompositions that reflect the true motion in the hidden scene.


  We prove the compatibility of the local and global Langlands correspondences
at places dividing l for the l-adic Galois representations associated to
regular algebraic conjugate self-dual cuspidal automorphic representations of
GL_n over an imaginary CM field, under the assumption that the automorphic
representations have Iwahori-fixed vectors at places dividing l and have
Shin-regular weight.


  During speech, people spontaneously gesticulate, which plays a key role in
conveying information. Similarly, realistic co-speech gestures are crucial to
enable natural and smooth interactions with social agents. Current end-to-end
co-speech gesture generation systems use a single modality for representing
speech: either audio or text. These systems are therefore confined to producing
either acoustically-linked beat gestures or semantically-linked gesticulation
(e.g., raising a hand when saying "high"): they cannot appropriately learn to
generate both gesture types. We present a model designed to produce arbitrary
beat and semantic gestures together. Our deep-learning based model takes both
acoustic and semantic representations of speech as input, and generates
gestures as a sequence of joint angle rotations as output. The resulting
gestures can be applied to both virtual agents and humanoid robots. Subjective
and objective evaluations confirm the success of our approach. The code and
video are available at the project page
https://svito-zar.github.io/gesticulator .


  After a very brief review of twentieth century elementary particle physics,
prospects for the next century are discussed. First and most important are
technological limits of opportunities; next, the future experimental program,
and finally the status of the theory, in particular its limitations as well as
its opportunities.


  This paper establishes the geometric structure of the lines of principal
curvature of a hypersurface immersed in ${\mathbb R}^4$ in a neighborhood of
the set $\mathcal{S}$ of its principal curvature singularities, consisting of
the points at which atF least two principal curvatures are equal.
  Under generic conditions defined by appropriate transversality hypotheses it
is proved that $\mathcal{S}$ is the union of regular smooth curves
$\mathcal{S}_{12}$ and $\mathcal{S}_{23}$, consisting of partially umbilic
points, where only two principal curvatures coincide. This curve is partitioned
into regular arcs consisting of points of Darbouxian types $D_1,\; D_2,\; D_3$,
with common boundary at isolated semi-Darbouxian transition points of types $
D_{12}$ and $D_{23}$.
  The stratified structure of the partially umbilic separatrix surfaces,
consisting of the boundary of the set of points through which the principal
lines approach $\mathcal S$, established in this work, extends to hypersurfaces
in ${\mathbb R}^4$ the results of Darboux for umbilic points on analytic
surfaces in ${\mathbb R}^3$, reformulated by Gutierrez and Sotomayor, to
describe the umbilic separatrix structures of the umbilic types $D_1,\; D_2,\;
D_3$, and further developed by Garcia, Gutierrez and Sotomayor, for their $
D_{12}$ and $D_{23}$ generic bifurcations. This work complements results of
Garcia on the structure of principal curvature lines around the generic
partially umbilic points of hypersurfaces in ${\mathbb R}^4$.


  We provide partial solutions to two problems posed by Shehtman concerning the
modal logic of the \v{C}ech-Stone compactification of an ordinal space. We use
the Continuum Hypothesis to give a finite axiomatization of the modal logic of
$\beta(\omega^2)$, thus resolving Shehtman's first problem for $n=2$. We also
characterize modal logics arising from the \v{C}ech-Stone compactification of
an ordinal $\gamma$ provided the Cantor normal form of $\gamma$ satisfies an
additional condition. This gives a partial solution of Shehtman's second
problem.


  For Paradigm models, evolution is just-in-time specified coordination
conducted by a special reusable component McPal. Evolution can be treated
consistently and on-the-fly through Paradigm's constraint orchestration, also
for originally unforeseen evolution. UML-like diagrams visually supplement such
migration, as is illustrated for the case of a critical section solution
evolving into a pipeline architecture.


  Immersive informal environments (such as summer camps, residential programs
at museums and science centers, etc.) can provide a venue for young people to
explore their scientific thinking in a less formalized context than is
available in most traditional classrooms. While class instruction is beneficial
for children to develop formal science skills and content knowledge, venues
that offer more opportunities for experimentation and exploration can promote
deeper understandings. In this article I explore the background of science
learning and venues where this learning can take place followed by a review of
the benefits and necessary components of well designed immersive informal
programs.


  The ball-constrained weighted maximin dispersion problem $(\rm P_{ball})$ is
to find a point in an $n$-dimensional Euclidean ball such that the minimum of
the weighted Euclidean distance from given $m$ points is maximized. We propose
a new second-order cone programming relaxation for $(\rm P_{ball})$. Under the
condition $m\le n$, $(\rm P_{ball})$ is polynomial-time solvable since the new
relaxation is shown to be tight. In general, we prove that $({\rm P_{ball}})$
is NP-hard. Then, we propose a new randomized approximation algorithm for
solving $({\rm P_{ball}})$, which provides a new approximation bound of
$\frac{1-O(\sqrt{\ln(m)/n})}{2}$.


  We report a first clear detection of the Zeeman splitting of a CCS emission
line at 45 GHz toward a nearby prestellar dense filament, Taurus Molecular
Cloud-1. We observed HC$_3$N non-Zeeman line simultaneously as the CCS line,
and did not detect any significant splitting of HC$_3$N line. Thus, we conclude
that our detection of the CCS Zeeman splitting is robust. The derived
\textcolor{black}{line-of-sight} magnetic field strength is about 117 $\pm$ 21
$\mu$G, which corresponds to the normalized mass-to-magnetic flux ratio of 2.2
if we adopt the inclination angle of 45$^\circ$. Thus, we conclude that the
TMC-1 filament is magnetically supercritical. Recent radiative transfer
calculations of CCS and HC$_3$N lines along the line of sight suggest that the
filament is collapsing with a speed of $\sim$ 0.6 km s$^{-1}$, which is
comparable to three times the isothermal sound speed. This infall velocity
appears to be consistent with the evolution of a gravitationally-infalling
core.


  Recent values of the proton charge radius derived from the Lamb shift in
electronic hydrogen tend to be larger than those from electron scattering.
Therefore low-momentum-transfer scattering data from different groups and
laboratories have been reanalyzed with Coulomb and recoil corrections included.
This was done by calculating at each scattering angle and scattering energy the
corresponding correction obtained from a partial wave program which includes
recoil approximately. Corrections due to magnetic scattering were also made
before the rms-radius was determined by a fit which allows free normalization
for each experiment. It is shown that an analysis of electron scattering data
including Coulomb corrections lowers the $\chi^2$ of the fit and increases the
proton radius by about (0.008 - 0.013) fm depending on the fit strategy. A
value of r_p = 0.880 (15) fm is obtained which is in good agreement with the
one extracted from Lamb shift measurements.


  Given a hyperplane arrangement of some type in a projective space, the Dunkl
system, developed by Couwenberg, Heckman and Looijenga, is used to study the
geometric structures on its complement, and as a consequence it leads to the
discovery of new ball quotients when the Schwarz conditions are imposed. In
this paper, we study how the space, investigated in this system, looks like
when there is no Schwarz conditions imposed. As a result, we show that the
space in question is still of a particular type of structure, namely, the
structure of a cone-manifold. With this point of view, Thurston's work on
moduli of cone metrics on the sphere appears as a special case in this set-up.


  Characterising the long-term evolution of magnetic activity on Sun-like stars
is important not only for stellar physics but also for understanding the
environment in which planets evolve. In the past decades, many photometric
surveys of open clusters have produced extensive rotation period measurements
on Sun-like stars of different ages. I use these data to infer the long-term
evolution of Ca II chromospheric and X-ray coronal emission on solar mass stars
combining a best fit parametric model of their rotation evolution with
empirical rotation-activity relationships.


  Standard stellar evolution model predicts a severe depletion of lithium (Li)
abundance during the first dredge-up process (FDU). Yet a small fraction of
giant stars are still found to preserve a considerable amount of Li in their
atmospheres after FDU. Those giants are usually identified as Li-rich by a
widely used criterion, A(Li) $ > 1.5$\,{\it dex}. A large number of works
dedicated to search for and investigate this minority of the giant family, and
the amount of Li-rich giants has been largely expanded, especially in the era
of big data. In this paper, we present a catalog of Li-rich giants found from
the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) survey
with Li abundances derived from a template matching method developed for LAMOST
low-resolution spectra. The catalog contains $10,535$ Li-rich giants with Li
abundances from $\sim 1.5$\,{\it dex} to $\sim 4.9$\,{\it dex}. We also confirm
that the ratio of Li-rich phenomenon among giant stars is about one percent, or
for a more expression, $1.29\%$ from our statistically important sample. This
is the largest Li-rich giant sample ever reported to date, which significantly
exceeds amount of all the reported Li-rich giants combined. The catalog will
help the community to better understand the Li-rich phenomenon in giant stars.


  GRAVITY is the four-beam, near- infrared, AO-assisted, fringe tracking,
astrometric and imaging instrument for the Very Large Telescope Interferometer
(VLTI). It is requiring the development of one of the most complex instrument
software systems ever built for an ESO instrument. Apart from its many
interfaces and interdependencies, one of the most challenging aspects is the
overall performance and stability of this complex system. The three infrared
detectors and the fast reflective memory network (RMN) recorder contribute a
total data rate of up to 20 MiB/s accumulating to a maximum of 250 GiB of data
per night. The detectors, the two instrument Local Control Units (LCUs) as well
as the five LCUs running applications under TAC (Tools for Advanced Control)
architecture, are interconnected with fast Ethernet, RMN fibers and dedicated
fiber connections as well as signals for the time synchronization. Here we give
a simplified overview of all subsystems of GRAVITY and their interfaces and
discuss two examples of high-level applications during observations: the
acquisition procedure and the gathering and merging of data to the final FITS
file.


  We demonstrate that the large-spin expansion of the energy of
Gubser-Klebanov-Polyakov (GKP) strings that rotate in RxS2 and AdS3 can be
expressed in terms of Lambert's W-function. We compute the leading, subleading
and next-to-subleading series of exponential corrections to the infinite-volume
dispersion relation of GKP strings that rotate in RxS2. These strings are dual
to certain long operators of N=4 SYM theory and provide their scaling
dimensions at strong coupling. We also show that the strings obey a short-long
(strings) duality. For the folded GKP strings that spin inside AdS3 and are
dual to twist-2 operators, we confirm the known formulas for the leading and
next-to-leading coefficients of their anomalous dimensions and derive the
corresponding expressions for the next-to-next-to-leading coefficients.


  We study the sizes of the Voronoi cells of $k$ uniformly chosen vertices in a
random split tree of size $n$. We prove that, for $n$ large, the largest of
these $k$ Voronoi cells contains most of the vertices, while the sizes of the
remaining ones are essentially all of order $n\exp(-\mathrm{const}\sqrt{\log
n})$. This discrepancy persists if we modify the definition of the Voronoi
cells by (a) introducing random edge lengths (with suitable moment
assumptions), and (b) assigning different "influence" parameters (called
"speeds" in the paper) to each of the $k$ vertices. Our findings are in
contrast to corresponding results on random uniform trees and on the continuum
random tree, where it is known that the vector of the relative sizes of the $k$
Voronoi cells is asymptotically uniformly distributed on the
$(k-1)$-dimensional simplex.


  We propose a new measurement of the ratio of positron-proton to
electron-proton, elastic scattering at DESY to determine the contributions
beyond single-photon exchange, which are essential to the QED description of
the most fundamental process in hadronic physics. A 20~cm long liquid hydrogen
target together with the extracted beam from the DESY synchrotron would yield
an average luminosity of
$2.12\times10^{35}$~cm$^{-2}\cdot$s$^{-1}\cdot$sr$^{-1}$ ($\sim200$ times the
luminosity achieved by OLYMPUS). A commissioning run at 2 GeV followed by
measurements at 3 GeV would provide new data up to $Q^2=4.6$~(GeV/$c$)$^2$
(twice the range of current measurements). Lead tungstate calorimeters would be
used to detect the scattered leptons at polar angles of $30^\circ$, $50^\circ$,
$70^\circ$, $90^\circ$, and $110^\circ$. The measurements could be scheduled to
not interfere with the operation of PETRA. We present rate estimates and
simulations for the planned measurements including background considerations.
Initial measurements at the DESY test beam facility using prototype lead
tungstate calorimeters in 2019, 2021, and 2022 were made to check the Monte
Carlo simulations and the performance of the calorimeters. These tests also
investigated different readout schemes (triggered and streaming). Various
upgrades are possible to shorten the running time and to make higher beam
energies and thus greater $Q^2$ ranges accessible.


  Data-to-text (D2T) generation is a crucial task in many natural language
understanding (NLU) applications and forms the foundation of task-oriented
dialog systems. In the context of conversational AI solutions that can work
directly with local data on the user's device, architectures utilizing large
pre-trained language models (PLMs) are impractical for on-device deployment due
to a high memory footprint. To this end, we propose TrICy, a novel lightweight
framework for an enhanced D2T task that generates text sequences based on the
intent in context and may further be guided by user-provided triggers. We
leverage an attention-copy mechanism to predict out-of-vocabulary (OOV) words
accurately. Performance analyses on E2E NLG dataset (BLEU: 66.43%, ROUGE-L:
70.14%), WebNLG dataset (BLEU: Seen 64.08%, Unseen 52.35%), and our Custom
dataset related to text messaging applications, showcase our architecture's
effectiveness. Moreover, we show that by leveraging an optional trigger input,
data-to-text generation quality increases significantly and achieves the new
SOTA score of 69.29% BLEU for E2E NLG. Furthermore, our analyses show that
TrICy achieves at least 24% and 3% improvement in BLEU and METEOR respectively
over LLMs like GPT-3, ChatGPT, and Llama 2. We also demonstrate that in some
scenarios, performance improvement due to triggers is observed even when they
are absent in training.


  By finding a p-adic obstruction, we construct many examples of positive
characteristic complete noetherian local rings which do not admit any
module-finite Cohen-Macaulay extension. These examples should be contrasted
with a result of Hochster-Huneke: the union of all finite extensions is always
Cohen-Macaulay.


  Deep active learning (AL) seeks to minimize the annotation costs for training
deep neural networks. BAIT, a recently proposed AL strategy based on the Fisher
Information, has demonstrated impressive performance across various datasets.
However, BAIT's high computational and memory requirements hinder its
applicability on large-scale classification tasks, resulting in current
research neglecting BAIT in their evaluation. This paper introduces two methods
to enhance BAIT's computational efficiency and scalability. Notably, we
significantly reduce its time complexity by approximating the Fisher
Information. In particular, we adapt the original formulation by i) taking the
expectation over the most probable classes, and ii) constructing a binary
classification task, leading to an alternative likelihood for gradient
computations. Consequently, this allows the efficient use of BAIT on
large-scale datasets, including ImageNet. Our unified and comprehensive
evaluation across a variety of datasets demonstrates that our approximations
achieve strong performance with considerably reduced time complexity.
Furthermore, we provide an extensive open-source toolbox that implements recent
state-of-the-art AL strategies, available at
https://github.com/dhuseljic/dal-toolbox.


  We investigate corner states in a photonic two-dimensional (2D)
Su-Schrieffer-Heeger (SSH) model on a square lattice with zero gauge flux. By
considering intracelluar next-nearest-neighbor (NNN) hoppings, we discover a
broad class of corner states in the 2D SSH model, and show that they are robust
against certain fabrication disorders. Moreover, these corner states are
located around the corners, but not at the corner points, so we refer to them
as general corner states. We analytically identify that the general corner
states are induced by the intracelluar NNN hoppings (long-range interactions)
and split off from the edge-state bands. Our work show a simple way to induce
unique corner states by the long-range interactions, and offers opportunities
for designing novel photonic devices.


  Population boundary is a classic indicator of climatic response in ecology.
In addition to known challenges, the spatial and dynamical characteristics of
the boundary are not only affected by the spatial gradient in the environmental
factors, but also by local heterogeneities in the regional characteristics.
Here, we capture the effects of quenched heterogeneities on the ecological
boundary with the disordered contact process in one and two dimensions with a
linear spatial trend in the local control parameter. We apply the
strong-disorder renormalization group method to calculate the sites occupied
with an $O(1)$ probability in the stationary state, readily yielding the
population front's position as the outermost site locally as well as globally
for the entire boundary. We show that under a quasistatic change of the global
environment, mimicking climate change, the front advances intermittently: long
quiescent periods are interrupted by rare but long jumps. The characteristics
of this intermittent dynamics are found to obey universal scaling laws in terms
of the gradient, conjectured to be related to the correlation-length exponent
of the model. Our results suggest that current observations might misleadingly
show little to no climate response for an extended period of time, concealing
the long-term effects of climate change.


  Event-triggered control has shown the potential for providing improved
control performance at the same average sampling rate when compared to
time-triggered control. While this observation motivates numerous
event-triggered control schemes, proving it from a theoretical perspective has
only been achieved for a limited number of settings. Inspired by existing
performance analyses for the single-loop case, we provide a first fundamental
performance comparison of time- and event-triggered control in a multi-agent
consensus setting. For this purpose, we consider undirected connected network
topologies without communication delays, a level-triggering rule for
event-triggered control, and the long-term average of the quadratic deviation
from consensus as a performance measure. The main finding of our analysis is
that time-triggered control provably outperforms event-triggered control beyond
a certain number of agents in our particular setting. We thereby provide an
illustrative distributed problem setup in which event-triggered control results
in a performance disadvantage when compared to time-triggered control in the
case of large networks. Moreover, we derive the asymptotic order of the
performance measure under both triggering schemes which gives more insights
into the cost relationship for large numbers of agents. Thus, by presenting an
analysis for a particular setup, this work points out that transferring an
event-triggering scheme from the single-loop to the multi-agent setting can
lead to a loss of the often presumed superiority of event-triggered control
over time-triggered control. In particular, the design of performant
decentralized event-triggering schemes can therefore pose additional challenges
when compared to the analogue single-loop case.


  We compute $K$-theory invariants of algebras of pseudodifferential operators
on manifolds with corners and prove an equivariant index theorem for operators
invariant with respect to an action of $\R^k.$ We discuss the relation between
our results and the $\eta$-invariant.


  Simulating quantum circuits with classical computers requires resources
growing exponentially in terms of system size. Real quantum computer with
noise, however, may be simulated polynomially with various methods considering
different noise models. In this work, we simulate random quantum circuits in 1D
with Matrix Product Density Operators (MPDO), for different noise models such
as dephasing, depolarizing, and amplitude damping. We show that the method
based on Matrix Product States (MPS) fails to approximate the noisy output
quantum states for any of the noise models considered, while the MPDO method
approximates them well. Compared with the method of Matrix Product Operators
(MPO), the MPDO method reflects a clear physical picture of noise (with inner
indices taking care of the noise simulation) and quantum entanglement (with
bond indices taking care of two-qubit gate simulation). Consequently, in case
of weak system noise, the resource cost of MPDO will be significantly less than
that of the MPO due to a relatively small inner dimension needed for the
simulation. In case of strong system noise, a relatively small bond dimension
may be sufficient to simulate the noisy circuits, indicating a regime that the
noise is large enough for an `easy' classical simulation. Moreover, we propose
a more effective tensor updates scheme with optimal truncations for both the
inner and the bond dimensions, performed after each layer of the circuit, which
enjoys a canonical form of the MPDO for improving simulation accuracy. With
truncated inner dimension to a maximum value $\kappa$ and bond dimension to a
maximum value $\chi$, the cost of our simulation scales as $\sim
ND\kappa^3\chi^3$, for an $N$-qubit circuit with depth $D$.


  We show how the unformizable representation for Abelian integrals of
nontrivial genera arise. The technique makes use of the famous Chudnovsky's
Fuchsian linear differential equations and their relation to the sixth Painleve
transcendent.


  We test the theoretical free energy surface (FES) for two-step nucleation
(TSN) proposed by Iwamatsu [J. Chem. Phys. 134, 164508 (2011)] by comparing the
predictions of the theory to numerical results for the FES recently reported
from Monte Carlo simulations of TSN in a simple lattice system [James, et al.,
J. Chem. Phys. 150, 074501 (2019)]. No adjustable parameters are used to make
this comparison. That is, all the parameters of the theory are evaluated
directly for the model system, yielding a predicted FES which we then compare
to the FES obtained from simulations. We find that the theoretical FES
successfully predicts the numerically-evaluated FES over a range of
thermodynamic conditions that spans distinct regimes of behavior associated
with TSN. All the qualitative features of the FES are captured by the theory
and the quantitative comparison is also very good. Our results demonstrate that
Iwamatsu's extension of classical nucleation theory provides an excellent
framework for understanding the thermodynamics of TSN.


  Taking up four model universes we study the behaviour and contribution of
dark energy to the accelerated expansion of the universe, in the modified scale
covariant theory of gravitation. Here, it is seen that though this modified
theory may be a cause of the accelerated expansion it cannot totally outcast
the contribution of dark energy in causing the accelerated expansion. In one
case the dark energy is found to be the sole cause of the accelerated
expansion. The dark energy contained in these models come out to be of the
$\Lambda$CDM type and quintessence type comparable to the modern observations.
Some of the models originated with a big bang, the dark energy being prevalent
inside the universe before the evolution of this era. One of the models
predicts big rip singularity, though at a very distant future. It is
interestingly found that the interaction between the dark energy and the other
part of the universe containing different matters is enticed and enhanced by
the gauge function $\phi(t)$ here.


  Recent advances in Fluid Dynamical modeling of heavy ion collisions are
presented, with particular attention to mesoscopic systems, QGP formation in
the pre FD regime and QGP hadronization coinciding with the final freeze-out.


  From cement cohesion to DNA condensation, a proper statistical physics
treatment of systems with long range forces is important for a number of
applications in physics, chemistry, and biology. We compute here the effective
force between fixed charged macromolecules, screened by oppositely charged
mobile ions (counterions). We treat the problem in a one dimensional
configuration, that allows for interesting discussion and derivation of exact
results, remaining at a level of mathematical difficulty compatible with an
undergraduate course. Emphasis is put on the counter-intuitive but fundamental
phenomenon of like-charge attraction, that our treatment brings for the first
time to the level of undergraduate teaching. The parity of the number of
counterions is shown to play a prominent role, which sheds light on the binding
mechanism at work when like-charge macromolecules do attract.


  The uncertainty in the nuclear matrix elements (NMEs) of $0\nu\beta\beta$
decay for $^{76}$Ge, $^{82}$Se, $^{128}$Te, $^{130}$Te, and $^{136}$Xe in the
self-consistent quasiparticle random phase approximation (QRPA) method is
investigated by using eighteen Skyrme interactions supplemented with either a
volume- or surface-type of pairing interactions. The NMEs for the isotopes
concerned (except $^{136}$Xe) are less sensitive to the particle-hole ($ph$)
interactions, while strongly dependent on the employed isovector
particle-particle ($pp$) pairing interactions even though the pairing strengths
are optimized to the same pairing gap. The results indicate that a precise
determination of the isovector $pp$ pairing interaction in the Skyrme energy
density functional is of importance to reduce the uncertainty in the NMEs
within the QRPA framework.


  A novel inversion technique is proposed to compute parametric maps showing
the temperature, density and chemical composition of cosmic hot gas from X-ray
hyper-spectral images. The parameters are recovered by constructing a unique
non-linear mapping derived by combining a physics-based modelling of the X-ray
spectrum with the selection of optimal bandpass filters. Preliminary results
and analysis are presented.


  We systematically analyze quantum corrections in see-saw scenarios, including
effects from above the see-saw scales. We derive approximate renormalization
group equations for neutrino masses, lepton mixings and CP phases, yielding an
analytic understanding and a simple estimate of the size of the effects. Even
for hierarchical masses, they often exceed the precision of future experiments.
Furthermore, we provide a software package allowing for a convenient numerical
renormalization group analysis, with heavy singlets being integrated out
successively at their mass thresholds. We also discuss applications to model
building and related topics.


  Goal-oriented Reinforcement Learning, where the agent needs to reach the goal
state while simultaneously minimizing the cost, has received significant
attention in real-world applications. Its theoretical formulation, stochastic
shortest path (SSP), has been intensively researched in the online setting.
Nevertheless, it remains understudied when such an online interaction is
prohibited and only historical data is provided. In this paper, we consider the
offline stochastic shortest path problem when the state space and the action
space are finite. We design the simple value iteration-based algorithms for
tackling both offline policy evaluation (OPE) and offline policy learning
tasks. Notably, our analysis of these simple algorithms yields strong
instance-dependent bounds which can imply worst-case bounds that are
near-minimax optimal. We hope our study could help illuminate the fundamental
statistical limits of the offline SSP problem and motivate further studies
beyond the scope of current consideration.


  Galaxy filaments are the dominant feature in the overall structure of the
cosmic web. The study of the filamentary web is an important aspect in
understanding galaxy evolution and the evolution of matter in the Universe. A
map of the filamentary structure is an adequate probe of the web. We propose
that photometric redshift galaxies are significantly positively associated with
the filamentary structure detected from the spatial distribution of
spectroscopic redshift galaxies. The catalogues of spectroscopic and
photometric galaxies are seen as point-process realisations in a sphere, and
the catalogue of filamentary spines is proposed to be a realisation of a random
set in a sphere. The positive association between these sets was studied using
a bivariate $J-$function, which is a summary statistics studying clustering. A
quotient $D$ was built to estimate the distance distribution of the filamentary
spine to galaxies in comparison to the distance distribution of the filamentary
spine to random points in $3-$dimensional Euclidean space. This measure gives a
physical distance scale to the distances between filamentary spines and the
studied sets of galaxies. The bivariate $J-$function shows a statistically
significant clustering effect in between filamentary spines and photometric
redshift galaxies. The quotient $D$ confirms the previous result that smaller
distances exist with higher probability between the photometric galaxies and
filaments. The trend of smaller distances between the objects grows stronger at
higher redshift. Additionally, the quotient $D$ for photometric galaxies gives
a rough estimate for the filamentary spine width of about $1$~Mpc. Photometric
redshift galaxies are positively associated with filamentary spines detected
from the spatial distribution of spectroscopic galaxies.


  The general expression for the local matrix of a quantum chain $L(\theta)$
with the site space in any representation of $su(3)$ is obtained. This is made
by generalizing $L(\theta)$ from the fundamental representation and imposing
the fulfilment of the Yang-Baxter equation. With these operators and using a
generalization of the nested Bethe ansatz, the Bethe equations for a multistate
quantum chain combining two arbitrary representations of $su(3)$ are obtained .


  Federated learning (FL) is a popular solution for distributed machine
learning (ML). While FL has traditionally been studied for supervised ML tasks,
in many applications, it is impractical to assume availability of labeled data
across devices. To this end, we develop Cooperative Federated unsupervised
Contrastive Learning ({\tt CF-CL)} to facilitate FL across edge devices with
unlabeled datasets. {\tt CF-CL} employs local device cooperation where either
explicit (i.e., raw data) or implicit (i.e., embeddings) information is
exchanged through device-to-device (D2D) communications to improve local
diversity. Specifically, we introduce a \textit{smart information push-pull}
methodology for data/embedding exchange tailored to FL settings with either
soft or strict data privacy restrictions. Information sharing is conducted
through a probabilistic importance sampling technique at receivers leveraging a
carefully crafted reserve dataset provided by transmitters. In the implicit
case, embedding exchange is further integrated into the local ML training at
the devices via a regularization term incorporated into the contrastive loss,
augmented with a dynamic contrastive margin to adjust the volume of latent
space explored. Numerical evaluations demonstrate that {\tt CF-CL} leads to
alignment of latent spaces learned across devices, results in faster and more
efficient global model training, and is effective in extreme non-i.i.d. data
distribution settings across devices.


  There is an increasing interest in the study of metamaterials and periodic
materials across disciplines. These are anisotropic and their properties
present directionality. For example, the wave speed depends on the propagation
direction. Furthermore, they are heterogeneous, and their directionality
depends on their spectra. Common approaches to describe anisotropy have been
used in the large-wavelength approximation corresponding to static properties.
Here we present an anisotropy measure based on the dynamic behavior. It
receives dispersion surfaces from Bloch analyses and outputs a curve/surface
with bulk directionality encoded on it. We present results for elastodynamics,
but it is applicable to other phenomena.


  The tropical variety of a $d$-dimensional prime ideal in a polynomial ring
with complex coefficients is a pure $d$-dimensional polyhedral fan. This fan is
shown to be connected in codimension one. We present algorithmic tools for
computing the tropical variety, and we discuss our implementation of these
tools in the Gr\"obner fan software \texttt{Gfan}. Every ideal is shown to have
a finite tropical basis, and a sharp lower bound is given for the size of a
tropical basis for an ideal of linear forms.


  GERDA is an experiment designed to look for the neutrinoless double beta
decay of Ge-76. The experiment uses an array of high-purity germanium
detectors, enriched in Ge-76, directly immersed in liquid argon. GERDA is
presently operating eight enriched coaxial detectors (approximately 15 kg of
Ge-76) and about 30 new custom-made enriched BEGe detectors will be deployed in
the next phase (additional 20 kg of Ge-76). The paper describes the GERDA
off-line analysis of the high-purity germanium detector data. Firstly we
present the signal processing flow, focusing on the digital filters and on the
algorithms used. Secondly we discuss the rejection of non-physical events and
the data quality monitoring. The analysis is performed completely with the
GERDA software framework (GELATIO), designed to support a multi-channel
processing and to perform a modular analysis of digital signals.


  This paper presents a modeling framework for tracking the spawning migration
of the capelin, which is a fish species in the Barents Sea. The framework
combines an individual-based model (IBM) with artificial neural networks
(ANNs). The ANNs determine the direction of the fish's movement based on local
environmental information, while a genetic algorithm and fitness function
assess the suitability of the proposed directions. The framework's efficacy is
demonstrated by comparing the spatial distributions of modeled and empirical
potential spawners.
  The proposed model successfully replicates the southeastward movement of
capelin during their spawning migration, accurately capturing the distribution
of spawning fish over historical spawning sites along the eastern coast of
northern Norway.
  Furthermore, the paper compares three migration models: passive swimmers,
taxis movement based on temperature gradients, and restricted-area search,
along with our proposed approach. The results reveal that our approach
outperforms the other models in mimicking the migration pattern. Most spawning
stocks managed to reach the spawning sites, unlike the other models where water
currents played a significant role in pushing the fish away from the coast. The
temperature gradient detection model and restricted-area search model are found
to be inadequate for accurately simulating capelin spawning migration in the
Barents Sea due to complex oceanographic conditions.


  Brillouin light scattering measurements were performed in the backscattering
geometry on a Bi-substituted rare earth iron garnet. We observed two different
peaks, one attributed to a surface spin wave in the dipole-exchange regime. The
other is referred to as a backscattering magnon mode, because the incident
light in this case is scattered backward by exchange-dominated spin wave inside
the material. We propose a method to estimate the exchange stiffness constant
from the frequency of the backscattering magnon mode. The obtained value is
comparable with the previously reported values for Y$ _3 $Fe$ _5 $O$ _{12} $.


  We fitted the neutrino oscillations, baryogenesis and neutrinoless double
beta decay using Anti-GUT model.


  The extraction of a signal from some observational data sets that contain
different contaminant emissions, often at a greater level than the signal
itself, is a common problem in Astrophysics and Cosmology. The signal can be
recovered, for instance, using a simple Wiener filter. However, in certain
cases, additional information may also be available, such as a second
observation which correlates to a certain level with the sought signal. In
order to improve the quality of the reconstruction, it would be useful to
include as well this additional information. Under these circumstances, we have
constructed a linear filter, the linear covariance-based filter, that extracts
the signal from the data but takes also into account the correlation with the
second observation. To illustrate the performance of the method, we present a
simple application to reconstruct the so-called Integrated Sachs-Wolfe effect
from simulated observations of the Cosmic Microwave Background and of
catalogues of galaxies.


  Layered two-dimensional materials have shown novel optoelectronic properties
and are well suited to be integrated in planar photonic circuits. For example,
graphene has been utilized for wideband photodetection. Because graphene lacks
a band gap, however, graphene photodetectors suffer from very high dark
current. In contrast, layered black phosphorous, the latest addition to the
family of 2D materials, is well-suited for photodetector applications due to
its narrow but finite band gap. Here, we demonstrate a gated multilayer black
phosphorus photodetector integrated on a silicon photonic waveguide operating
in the near-infrared telecom band. In a significant advantage over graphene
devices, black phosphorus photodetectors can operate under a bias with very low
dark current and attain intrinsic responsivity up to 135 mA/W and 657 mA/W in
11.5nm and 100 nm thick devices, respectively, at room temperature. The
photocurrent is dominated by the photovoltaic effect with a high response
bandwidth exceeding 3 GHz.


  Coordinate descent algorithms are popular for huge-scale optimization
problems due to their low cost per-iteration. Coordinate descent methods apply
to problems where the constraint set is separable across coordinates. In this
paper, we propose a new variant of the cyclic coordinate descent method that
can handle polyhedral constraints provided that the polyhedral set does not
have too many extreme points such as L1-ball and the standard simplex. Loosely
speaking, our proposed algorithm PolyCD, can be viewed as a hybrid of cyclic
coordinate descent and the Frank-Wolfe algorithms. We prove that PolyCD has a
O(1/k) convergence rate for smooth convex objectives. Inspired by the away-step
variant of Frank-Wolfe, we propose PolyCDwA, a variant of PolyCD with away
steps which has a linear convergence rate when the loss function is smooth and
strongly convex. Empirical studies demonstrate that PolyCDwA achieves strong
computational performance for large-scale benchmark problems including
L1-constrained linear regression, L1-constrained logistic regression and kernel
density estimation.


  Motion capture from sparse inertial sensors has shown great potential
compared to image-based approaches since occlusions do not lead to a reduced
tracking quality and the recording space is not restricted to be within the
viewing frustum of the camera. However, capturing the motion and global
position only from a sparse set of inertial sensors is inherently ambiguous and
challenging. In consequence, recent state-of-the-art methods can barely handle
very long period motions, and unrealistic artifacts are common due to the
unawareness of physical constraints. To this end, we present the first method
which combines a neural kinematics estimator and a physics-aware motion
optimizer to track body motions with only 6 inertial sensors. The kinematics
module first regresses the motion status as a reference, and then the physics
module refines the motion to satisfy the physical constraints. Experiments
demonstrate a clear improvement over the state of the art in terms of capture
accuracy, temporal stability, and physical correctness.


  We describe the polynomial time complexity algorithm for computing first
coefficients of the skein (Homflypt) and Kauffman polynomial invariants of
links, discovered by D.Vertigan in 1992 but never published.


  QCD and related gauge theories have a sign problem when a $\theta$-term is
included; this complicates the extraction of physical information from
Euclidean space calculations as one would do in lattice studies. The sign
problem arises in this system because the partition function for configurations
with fixed topological charge $Q$, $\mathcal{Z}_Q$, are summed weighted by
$\exp(i Q \theta)$ to obtain the partition function for fixed $\theta$, $
\mathcal{Z}(\theta)$. The sign problem gets exponentially worse numerically as
the space-time volume is increased. Here it is shown that apart from the
practical numerical issues associated with large volumes, there are some
interesting issues of principle. A key quantity is the energy density as a
function of $\theta$, $\varepsilon(\theta) = -\log \left( \mathcal{Z}(\theta)
\right )/V$. This is expected to be well defined in the large 4-volume limit.
Similarly, one expects the energy density for a fixed topological density
$\tilde{\varepsilon}(Q/V) = -\log \left(\mathcal{Z}_Q \right )/V$ to be well
defined in the limit of large 4-volumes. Intuitively, one might expect that if
one had the infinite volume expression for $\tilde{\varepsilon}(Q/V)$ to
arbitrary accuracy, that one could reconstruct $\varepsilon(\theta)$ by
directly summing over the topological sectors of the partition function. We
show here that there are circumstances where this is not the case. In
particular, this occurs in regions where the curvature of $\varepsilon(\theta)$
is negative.