  In this paper we present the electromagnetic modeling and beam pattern
measurements of a 16-elements ultra wideband sparse random test array for the
low frequency instrument of the Square Kilometer Array telescope. We discuss
the importance of a small array test platform for the development of
technologies and techniques towards the final telescope, highlighting the most
relevant aspects of its design. We also describe the electromagnetic
simulations and modeling work as well as the embedded-element and array pattern
measurements using an Unmanned Aerial Vehicle system. The latter are helpful
both for the validation of the models and the design as well as for the future
instrumental calibration of the telescope thanks to the stable, accurate and
strong radio frequency signal transmitted by the UAV. At this stage of the
design, these measurements have shown a general agreement between experimental
results and numerical data and have revealed the localized effect of
un-calibrated cable lengths in the inner side-lobes of the array pattern.


  In the present contribution, we derive from kinetic theory a unified fluid
model for multicomponent plasmas by accounting for the electromagnetic field
influence. We deal with a possible thermal nonequilibrium of the translational
energy of the particles, neglecting their internal energy and the reactive
collisions. Given the strong disparity of mass between the electrons and heavy
particles, such as molecules, atoms, and ions, we conduct a dimensional
analysis of the Boltzmann equation. We then generalize the Chapman-Enskog
method, emphasizing the role of a multiscale perturbation parameter on the
collisional operator, the streaming operator, and the collisional invariants of
the Boltzmann equation. The system is examined at successive orders of
approximation, each of which corresponding to a physical time scale. The
multicomponent Navier-Stokes regime is reached for the heavy particles, which
follow a hyperbolic scaling, and is coupled to first order drift-diffusion
equations for the electrons, which follow a parabolic scaling. The transport
coefficients exhibit an anisotropic behavior when the magnetic field is strong
enough. We also give a complete description of the Kolesnikov effect, i.e., the
crossed contributions to the mass and energy transport fluxes coupling the
electrons and heavy particles. Finally, the first and second principles of
thermodynamics are proved to be satisfied by deriving a total energy equation
and an entropy equation. Moreover, the system of equations is shown to be
conservative and the purely convective system hyperbolic, thus leading to a
well-defined structure.


  Tumor segmentation in whole-slide images of histology slides is an important
step towards computer-assisted diagnosis. In this work, we propose a tumor
segmentation framework based on the novel concept of persistent homology
profiles (PHPs). For a given image patch, the homology profiles are derived by
efficient computation of persistent homology, which is an algebraic tool from
homology theory. We propose an efficient way of computing topological
persistence of an image, alternative to simplicial homology. The PHPs are
devised to distinguish tumor regions from their normal counterparts by modeling
the atypical characteristics of tumor nuclei. We propose two variants of our
method for tumor segmentation: one that targets speed without compromising
accuracy and the other that targets higher accuracy. The fast version is based
on the selection of exemplar image patches from a convolution neural network
(CNN) and patch classification by quantifying the divergence between the PHPs
of exemplars and the input image patch. Detailed comparative evaluation shows
that the proposed algorithm is significantly faster than competing algorithms
while achieving comparable results. The accurate version combines the PHPs and
high-level CNN features and employs a multi-stage ensemble strategy for image
patch labeling. Experimental results demonstrate that the combination of PHPs
and CNN features outperforms competing algorithms. This study is performed on
two independently collected colorectal datasets containing adenoma,
adenocarcinoma, signet and healthy cases. Collectively, the accurate tumor
segmentation produces the highest average patch-level F1-score, as compared
with competing algorithms, on malignant and healthy cases from both the
datasets. Overall the proposed framework highlights the utility of persistent
homology for histopathology image analysis.


  Self-organized monocrystalline Au nanoparticles with potential applications
in plasmonics are grown in a SrTiO$_3$ matrix by a novel two-step deposition
process. The crystalline preferred orientation of these Au nanoparticles is
investigated by synchrotron hard x-ray diffraction. Nanoparticles
preferentially align with the (111) direction along the substrate normal (001),
whereas two in-plane orientations are found with $[110]_{SrTiO_3} \: || \:
[110]_{Au}$ and $[100]_{SrTiO_3} \: || \: [110]_{Au}$. Additionally, a smaller
diffraction signal from nanoparticles with the (001) direction parallel to the
substrate normal (001) is observed; once again, two in-plane orientations are
found, with $[100]_{SrTiO_3} \: || \: [100]_{Au}$ and $[100]_{SrTiO_3} \: || \:
[110]_{Au}$. The populations of the two in-plane orientations are found to
depend on the thickness of the gold film deposited in the first step of the
growth.


  Paramagnetic (PM) properties of columnar cobaltosic oxide (Co3O4)
nanoparticles, about 75 nm in diameter, have been investigated by magnetization
measurements at T > TN = 39 K. In zero or low applied field, the effective PM
moment per formula unit (FU), Mu_eff, enhances significantly from the bulk
value of 4.14 Mu_B/FU. It decreases asymptotically from 5.96 Mu_B/FU at Happ =
50 Oe down to 4.21 Mu_B/FU as the applied field increases to Happ = 10 kOe. The
field dependent PM properties are explained by a structural inversion, from the
normal spinel (spin-only moment ~ 3.9 Mu_B/FU) to the inverse spinel structure
(spin-only moment ~ 8.8 Mu_B/FU). The structural inversion is reversible with
the variation of the applied field. The lattice structure becomes almost 100%
normal spinel in the high field exceeding 10 kOe, as revealed by the magnitude
of the effective PM moment. The reversible, field dependent structure inversion
is an important property with promising application potential. It is
interesting for the future investigations.


  Experimental determinations of the pp and pep fluxes have great
potentialities. We briefly review the reasons that make such measurements
privileged tests of neutrino properties. We discuss the predictions for these
fluxes given by four good solutions to the solar neutrino problem: small- and
large-angle MSW and Just-So oscillations into active neutrinos, and small-angle
MSW oscillations into sterile neutrinos. In addition, we examine the impact of
the planned Hellaz detector, which should measure separately the nu_e and nu_mu
fluxes in the pp energy window and the signal from the pep neutrinos, for
distinguishing among the different solutions and for determining the solar
central temperature.


  We present a theoretical study of the electronic transport through
single-molecule junctions incorporating a Pt6 metal cluster bound within an
organic framework. We show that the insertion of this molecule between a pair
of electrodes leads to a fully atomically engineered nano-metallic device with
high conductance at the Fermi level and two sequential high on/off switching
states. The origin of this property can be traced back to the existence of a
HOMO which consists of two degenerate and asymmetric orbitals, lying close in
energy to the Fermi level of the metallic leads. Their degeneracy is broken
when the molecule is contacted to the leads, giving rise to two resonances
which become pinned close to the Fermi level and display destructive
interference.


  To achieve sample efficiency in reinforcement learning (RL), it necessitates
efficiently exploring the underlying environment. Under the offline setting,
addressing the exploration challenge lies in collecting an offline dataset with
sufficient coverage. Motivated by such a challenge, we study the reward-free RL
problem, where an agent aims to thoroughly explore the environment without any
pre-specified reward function. Then, given any extrinsic reward, the agent
computes the policy via a planning algorithm with offline data collected in the
exploration phase. Moreover, we tackle this problem under the context of
function approximation, leveraging powerful function approximators.
  Specifically, we propose to explore via an optimistic variant of the
value-iteration algorithm incorporating kernel and neural function
approximations, where we adopt the associated exploration bonus as the
exploration reward. Moreover, we design exploration and planning algorithms for
both single-agent MDPs and zero-sum Markov games and prove that our methods can
achieve $\widetilde{\mathcal{O}}(1 /\varepsilon^2)$ sample complexity for
generating a $\varepsilon$-suboptimal policy or $\varepsilon$-approximate Nash
equilibrium when given an arbitrary extrinsic reward. To the best of our
knowledge, we establish the first provably efficient reward-free RL algorithm
with kernel and neural function approximators.


  The ultra-luminous X-ray source (ULX) M82 X-1 is one of the most promising
intermediate mass black hole candidates in the local universe based on its high
X-ray luminosities ($10^{40}-10^{41} {\rm erg s^{-1}}$) and quasi-periodic
oscillations, and is possibly associated with a radio flare source. In this
work, applying the sub-pixel technique to the 120 ks Chandra observation (ID:
10543) of M82 X-1, we split M82 X-1 into two sources separated by 1.1$\arcsec$.
The secondary source is not detected in other M82 observations. The radio flare
source is found to associate not with M82 X-1, but with the nearby transient
source S1 with an outburst luminosity of $\sim 10^{39} {\rm erg s^{-1}}$. With
X-ray outburst and radio flare activities analogous to the recently discovered
micro-quasar in M31, S1 is likely to be a micro-quasar hidden in the shadow of
M82 X-1.


  We present design guidelines for incorporating fidgeting into the virtual
world as a tool for students in online lectures. Fidgeting is associated with
increased attention and self-regulation, and has the potential to help students
focus. Currently there are no fidgets, physical or virtual, designed for
preserving attention specifically in online learning environments, and no
heuristics for designing fidgets within this domain. We identify three virtual
fidget proxies to serve as design probes for studying student experiences with
virtual fidgeting. Through a study of eight students using our virtual fidget
proxies in online lectures, we identify eight emergent themes that encompass
student experience with virtual fidgeting in lectures. Based on these themes,
we present four principles for designing domain-specific virtual fidgets for
online lectures. We identify that virtual fidgets for lectures should be
context-aware, visually appealing, easy to adopt, and physically interactive.


  A category used by de Paiva to model linear logic also occurs in Vojtas's
analysis of cardinal characteristics of the continuum. Its morphisms have been
used in describing reductions between search problems in complexity theory. We
describe this category and how it arises in these various contexts. We also
show how these contexts suggest certain new multiplicative connectives for
linear logic. Perhaps the most interesting of these is a sequential composition
suggested by the set-theoretic application.


  Recently, Samorodnitsky proved a strengthened version of Mrs. Gerber's Lemma,
where the output entropy of a binary symmetric channel is bounded in terms of
the average entropy of the input projected on a random subset of coordinates.
Here, this result is applied for deriving novel lower bounds on the entropy
rate of binary hidden Markov processes. For symmetric underlying Markov
processes, our bound improves upon the best known bound in the very noisy
regime. The nonsymmetric case is also considered, and explicit bounds are
derived for Markov processes that satisfy the $(1,\infty)$-RLL constraint.


  We introduce FindingEmo, a new image dataset containing annotations for 25k
images, specifically tailored to Emotion Recognition. Contrary to existing
datasets, it focuses on complex scenes depicting multiple people in various
naturalistic, social settings, with images being annotated as a whole, thereby
going beyond the traditional focus on faces or single individuals. Annotated
dimensions include Valence, Arousal and Emotion label, with annotations
gathered using Prolific. Together with the annotations, we release the list of
URLs pointing to the original images, as well as all associated source code.


  We study the phase dynamics in coupled Josephson junctions describing by
system of nonlinear differential equations. Results of detailed numerical
simulations of charge creation in the superconducting layers and the
longitudinal plasma wave (LPW) nucleation are presented. We demonstrate the
different time stages in the development of the LPW and present results of FFT
analysis at different values of bias current. The correspondence between the
breakpoint position on the outermost branch of current voltage characteristics
(CVC) and the growing region in time dependence of the electric charge in the
superconducting layer is established. The effects of noise in the bias current
and the external microwave radiation on the charge dynamics of the coupled
Josephson junctions are found. These effects introduce a way to regulate the
process of LPW nucleation in the stack of IJJ.


  Dixit and Jabbari Nooghabi (2010) had derived the MLE and UMVUE of the
probability density function (pdf) and cumulative distributive function (cdf).
Further, it had been shown that MLE is more efficient than UMVUE. He, Zhou and
Zhang (2014) have also derived the same and made a remark that the work of
Dixit and Jabbari Nooghabi (2010) is not correct. We have made a comments with
detail algebra that our results are correct. Further, we have also given the R
code.


  Consider Myerson's optimal auction with respect to an inaccurate prior, e.g.,
estimated from data, which is an underestimation of the true value
distribution. Can the auctioneer expect getting at least the optimal revenue
w.r.t. the inaccurate prior since the true value distribution is larger? This
so-called strong revenue monotonicity is known to be true for single-parameter
auctions when the feasible allocations form a matroid. We find that strong
revenue monotonicity fails to generalize beyond the matroid setting, and
further show that auctions in the matroid setting are the only downward-closed
auctions that satisfy strong revenue monotonicity. On the flip side, we recover
an approximate version of strong revenue monotonicity that holds for all
single-parameter auctions, even without downward-closedness. As applications,
we get sample complexity upper bounds for single-parameter auctions under
matroid constraints, downward-closed constraints, and general constraints. They
improve the state-of-the-art upper bounds and are tight up to logarithmic
factors.


  Code comment generation is a crucial task in the field of automatic software
development. Most previous neural comment generation systems used an
encoder-decoder neural network and encoded only information from source code as
input. Software reuse is common in software development. However, this feature
has not been introduced to existing systems. Inspired by the traditional
IR-based approaches, we propose to use the existing comments of similar source
code as exemplars to guide the comment generation process. Based on an open
source search engine, we first retrieve a similar code and treat its comment as
an exemplar. Then we applied a seq2seq neural network to conduct an
exemplar-based comment generation. We evaluate our approach on a large-scale
Java corpus, and experimental results demonstrate that our model significantly
outperforms the state-of-the-art methods.


  Documenting the construction of an NMT (Neural Machine Translation) system
for En/Ja based on the Transformer architecture leveraging the OpenNMT
framework. A systematic exploration of corpora pre-processing, hyperparameter
tuning and model architecture is carried out to obtain optimal performance. The
system is evaluated using standard auto-evaluation metrics such as BLEU, and my
subjective opinion as a Japanese linguist.


  The ability of generative large language models (LLMs) to perform in-context
learning has given rise to a large body of research into how best to prompt
models for various natural language processing tasks. In this paper, we focus
on machine translation (MT), a task that has been shown to benefit from
in-context translation examples. However no systematic studies have been
published on how best to select examples, and mixed results have been reported
on the usefulness of similarity-based selection over random selection. We
provide a study covering multiple LLMs and multiple in-context example
retrieval strategies, comparing multilingual sentence embeddings. We cover
several language directions, representing different levels of language
resourcedness (English into French, German, Swahili and Wolof). Contrarily to
previously published results, we find that sentence embedding similarity can
improve MT, especially for low-resource language directions, and discuss the
balance between selection pool diversity and quality. We also highlight
potential problems with the evaluation of LLM-based MT and suggest a more
appropriate evaluation protocol, adapting the COMET metric to the evaluation of
LLMs. Code and outputs are freely available at
https://github.com/ArmelRandy/ICL-MT.


  Many unconventional computing models, including some that appear to be quite
different from traditional ones such as Turing machines, happen to characterise
either the complexity class P or PSPACE when working in deterministic
polynomial time (and in the maximally parallel way, where this applies). We
discuss variants of cellular automata and membrane systems that escape this
dichotomy and characterise intermediate complexity classes, usually defined in
terms of Turing machines with oracles, as well as some possible reasons why
this happens.


  Considering the Friedmann--Lema\^{i}tre--Robertson--Walker (FLRW) metric and
the Einstein scalar field system as an underlying gravitational model to
construct fractional cosmological models has interesting implications in both
classical and quantum regimes. Regarding the former, we just review the most
fundamental approach to establishing an extended cosmological model. We
demonstrate that employing new methodologies allows us to obtain exact
solutions. Despite the corresponding standard models, we cannot use any
arbitrary scalar potentials; instead, it is determined from solving three
independent fractional field equations. This article concludes with an overview
of a fractional quantum/semi-classical model that provides an inflationary
scenario.


  Early detection of the coronavirus disease 2019 (COVID-19) helps to treat
patients timely and increase the cure rate, thus further suppressing the spread
of the disease. In this study, we propose a novel deep learning based detection
and similar case recommendation network to help control the epidemic. Our
proposed network contains two stages: the first one is a lung region
segmentation step and is used to exclude irrelevant factors, and the second is
a detection and recommendation stage. Under this framework, in the second
stage, we develop a dual-children network (DuCN) based on a pre-trained
ResNet-18 to simultaneously realize the disease diagnosis and similar case
recommendation. Besides, we employ triplet loss and intrapulmonary distance
maps to assist the detection, which helps incorporate tiny differences between
two images and is conducive to improving the diagnostic accuracy. For each
confirmed COVID-19 case, we give similar cases to provide radiologists with
diagnosis and treatment references. We conduct experiments on a large publicly
available dataset (CC-CCII) and compare the proposed model with
state-of-the-art COVID-19 detection methods. The results show that our proposed
model achieves a promising clinical performance.


  Kernel spectral clustering corresponds to a weighted kernel principal
component analysis problem in a constrained optimization framework. The primal
formulation leads to an eigen-decomposition of a centered Laplacian matrix at
the dual level. The dual formulation allows to build a model on a
representative subgraph of the large scale network in the training phase and
the model parameters are estimated in the validation stage. The KSC model has a
powerful out-of-sample extension property which allows cluster affiliation for
the unseen nodes of the big data network. In this paper we exploit the
structure of the projections in the eigenspace during the validation stage to
automatically determine a set of increasing distance thresholds. We use these
distance thresholds in the test phase to obtain multiple levels of hierarchy
for the large scale network. The hierarchical structure in the network is
determined in a bottom-up fashion. We empirically showcase that real-world
networks have multilevel hierarchical organization which cannot be detected
efficiently by several state-of-the-art large scale hierarchical community
detection techniques like the Louvain, OSLOM and Infomap methods. We show a
major advantage our proposed approach i.e. the ability to locate good quality
clusters at both the coarser and finer levels of hierarchy using internal
cluster quality metrics on 7 real-life networks.


  This work proposes a new approach for mapping GPU threads onto a family of
discrete embedded 2D fractals. A block-space map $\lambda:
\mathbb{Z}_{\mathbb{E}}^{2} \mapsto \mathbb{Z}_{\mathbb{F}}^{2}$ is proposed,
from Euclidean parallel space $\mathbb{E}$ to embedded fractal space
$\mathbb{F}$, that maps in $\mathcal{O}(\log_2 \log_2(n))$ time and uses no
more than $\mathcal{O}(n^\mathbb{H})$ threads with $\mathbb{H}$ being the
Hausdorff dimension of the fractal, making it parallel space efficient. When
compared to a bounding-box (BB) approach, $\lambda(\omega)$ offers a
sub-exponential improvement in parallel space and a monotonically increasing
speedup $n \ge n_0$. The Sierpinski gasket fractal is used as a particular case
study and the experimental performance results show that $\lambda(\omega)$
reaches up to $9\times$ of speedup over the bounding-box approach. A
tensor-core based implementation of $\lambda(\omega)$ is also proposed for
modern GPUs, providing up to $\sim40\%$ of extra performance. The results
obtained in this work show that doing efficient GPU thread mapping on fractal
domains can significantly improve the performance of several applications that
work with this type of geometry.


  The k-body Gaussian Embedded Ensemble of Random Matrices is considered for N
bosons distributed on two single-particle levels. When k = N, the ensemble is
equivalent to the Gaussian Orthogonal Ensemble (GOE), and when k = 2 it
corresponds to the Two-body Random Ensemble (TBRE) for bosons. It is shown that
the energy spectrum leads to a rank function which is of the form of a discrete
generalized beta distribution. The same distribution is obtained assuming N
non-interacting quasiparticles that obey the fractional exclusion statistics
introduced by Haldane two decades ago.


  We review epidemiological models for the propagation of the COVID-19 pandemic
during the early months of the outbreak: from February to May 2020. The aim is
to propose a methodological review that highlights the following
characteristics: (i) the epidemic propagation models, (ii) the modeling of
intervention strategies, (iii) the models and estimation procedures of the
epidemic parameters and (iv) the characteristics of the data used. We finally
selected 80 articles from open access databases based on criteria such as the
theoretical background, the reproducibility, the incorporation of interventions
strategies, etc. It mainly resulted to phenomenological, compartmental and
individual-level models. A digital companion including an online sheet, a
Kibana interface and a markdown document is proposed. Finally, this work
provides an opportunity to witness how the scientific community reacted to this
unique situation.


  Transfer learning is an emerging and popular paradigm for utilizing existing
knowledge from previous learning tasks to improve the performance of new ones.
In this paper, we propose a novel concept of transfer risk and and analyze its
properties to evaluate transferability of transfer learning. We apply transfer
learning techniques and this concept of transfer risk to stock return
prediction and portfolio optimization problems. Numerical results demonstrate a
strong correlation between transfer risk and overall transfer learning
performance, where transfer risk provides a computationally efficient way to
identify appropriate source tasks in transfer learning, including
cross-continent, cross-sector, and cross-frequency transfer for portfolio
optimization.


  Vertical federated learning (VFL) is a promising area for time series
forecasting in industrial applications, such as predictive maintenance and
machine control. Critical challenges to address in manufacturing include data
privacy and over-fitting on small and noisy datasets during both training and
inference. Additionally, to increase industry adaptability, such forecasting
models must scale well with the number of parties while ensuring strong
convergence and low-tuning complexity. We address those challenges and propose
'Secret-shared Time Series Forecasting with VFL' (STV), a novel framework that
exhibits the following key features: i) a privacy-preserving algorithm for
forecasting with SARIMAX and autoregressive trees on vertically partitioned
data; ii) serverless forecasting using secret sharing and multi-party
computation; iii) novel N-party algorithms for matrix multiplication and
inverse operations for direct parameter optimization, giving strong convergence
with minimal hyperparameter tuning complexity. We conduct evaluations on six
representative datasets from public and industry-specific contexts. Our results
demonstrate that STV's forecasting accuracy is comparable to those of
centralized approaches. They also show that our direct optimization can
outperform centralized methods, which include state-of-the-art diffusion models
and long-short-term memory, by 23.81% on forecasting accuracy. We also conduct
a scalability analysis by examining the communication costs of direct and
iterative optimization to navigate the choice between the two. Code and
appendix are available: https://github.com/adis98/STV


  Discrete-time Quantum Walks (QWs) are transportation models of single quantum
particles over a lattice. Their evolution is driven through causal and local
unitary operators. QWs are a powerful tool for quantum simulation of
fundamental physics as some of them have a continuum limit converging to
well-known physics partial differential equations, such as the Dirac or the
Schr\"odinger equation. In this work, we show how to recover the Dirac equation
in (3+1)-dimensions with a QW evolving in a tetrahedral space. This paves the
way to simulate the Dirac equation on a curved spacetime. This also suggests an
ordered scheme for propagating matter over a spin network, of interest in Loop
Quantum Gravity where matter propagation has remained an open problem.


  We prove that the universal Teichmuller space T(1) carries a new structure of
a complex Hilbert manifold. We show that the connected component of the
identity of T(1), the Hilbert submanifold T_{0}(1), is a topological group. We
define a Weil-Petersson metric on T(1) by Hilbert space inner products on
tangent spaces, compute its Riemann curvature tensor, and show that T(1) is a
Kahler-Einstein manifold with negative Ricci and sectional curvatures. We
introduce and compute Mumford-Miller-Morita characteristic forms for the
vertical tangent bundle of the universal Teichmuller curve fibration over the
universal Teichmuller space. As an application, we derive Wolpert curvature
formulas for the finite-dimensional Teichmuller spaces from the formulas for
the universal Teichmuller space.


  Given the huge impact that Online Social Networks (OSN) had in the way people
get informed and form their opinion, they became an attractive playground for
malicious entities that want to spread misinformation, and leverage their
effect. In fact, misinformation easily spreads on OSN and is a huge threat for
modern society, possibly influencing also the outcome of elections, or even
putting people's life at risk (e.g., spreading "anti-vaccines" misinformation).
Therefore, it is of paramount importance for our society to have some sort of
"validation" on information spreading through OSN. The need for a wide-scale
validation would greatly benefit from automatic tools.
  In this paper, we show that it is difficult to carry out an automatic
classification of misinformation considering only structural properties of
content propagation cascades. We focus on structural properties, because they
would be inherently difficult to be manipulated, with the the aim of
circumventing classification systems. To support our claim, we carry out an
extensive evaluation on Facebook posts belonging to conspiracy theories (as
representative of misinformation), and scientific news (representative of
fact-checked content). Our findings show that conspiracy content actually
reverberates in a way which is hard to distinguish from the one scientific
content does: for the classification mechanisms we investigated, classification
F1-score never exceeds 0.65 during content propagation stages, and is still
less than 0.7 even after propagation is complete.


  A deeper understanding of video activities extends beyond recognition of
underlying concepts such as actions and objects: constructing deep semantic
representations requires reasoning about the semantic relationships among these
concepts, often beyond what is directly observed in the data. To this end, we
propose an energy minimization framework that leverages large-scale commonsense
knowledge bases, such as ConceptNet, to provide contextual cues to establish
semantic relationships among entities directly hypothesized from video signal.
We mathematically express this using the language of Grenander's canonical
pattern generator theory. We show that the use of prior encoded commonsense
knowledge alleviate the need for large annotated training datasets and help
tackle imbalance in training through prior knowledge. Using three different
publicly available datasets - Charades, Microsoft Visual Description Corpus and
Breakfast Actions datasets, we show that the proposed model can generate video
interpretations whose quality is better than those reported by state-of-the-art
approaches, which have substantial training needs. Through extensive
experiments, we show that the use of commonsense knowledge from ConceptNet
allows the proposed approach to handle various challenges such as training data
imbalance, weak features, and complex semantic relationships and visual scenes.


  The decay $\mu^-\to e^- \bar{\nu}_e\nu_\mu$ of a muon that is bound to a
nucleus poses an unavoidable background for experiments such as Mu2e, COMET,
and DeeMe searching for lepton-flavor-violating $\mu^-\to e^-$ conversion and
thus requires a precise understanding. We calculate the electron-energy spectra
of muon decays in orbit near the electron energy endpoint for all stable
isotopes and estimate uncertainties due to the nuclear charge distribution. Our
results enable background studies of mixed or enriched target materials that
are necessary for the next generation of $\mu^-\to e^-$ conversion experiments.


  In the limit of an infinite number of colors, we derive an analytic
expression for the quark condensate in $QCD_{1+1}$ as a function of the quark
mass and the gauge coupling constant. For zero quark mass, a nonvanishing quark
condensate is obtained. Nevertheless, we prove that there is no phase
transition as a function of the quark mass. It is furthermore shown that the
expansion of $\langle 0 | \overline{\psi}\psi |0\rangle$ in the gauge coupling
has zero radius of convergence but that the perturbation series is Borel
summable with finite radius of convergence. The nonanalytic behavior $\langle 0
| \overline{\psi}\psi |0\rangle \stackrel{m_q\rightarrow0}{\sim} - N_C
\sqrt{G^2}$ can only be obtained by summing the perturbation series to infinite
order.


  To better understand discriminations and the effect of affirmative actions in
selection problems (e.g., college admission or hiring), a recent line of
research proposed a model based on differential variance. This model assumes
that the decision-maker has a noisy estimate of each candidate's quality and
puts forward the difference in the noise variances between different
demographic groups as a key factor to explain discrimination. The literature on
differential variance, however, does not consider the strategic behavior of
candidates who can react to the selection procedure to improve their outcome,
which is well-known to happen in many domains.
  In this paper, we study how the strategic aspect affects fairness in
selection problems. We propose to model selection problems with strategic
candidates as a contest game: A population of rational candidates compete by
choosing an effort level to increase their quality. They incur a cost-of-effort
but get a (random) quality whose expectation equals the chosen effort. A
Bayesian decision-maker observes a noisy estimate of the quality of each
candidate (with differential variance) and selects the fraction $\alpha$ of
best candidates based on their posterior expected quality; each selected
candidate receives a reward $S$. We characterize the (unique) equilibrium of
this game in the different parameters' regimes, both when the decision-maker is
unconstrained and when they are constrained to respect the fairness notion of
demographic parity. Our results reveal important impacts of the strategic
behavior on the discrimination observed at equilibrium and allow us to
understand the effect of imposing demographic parity in this context. In
particular, we find that, in many cases, the results contrast with the
non-strategic setting.


  Using non-equilibrium molecular dynamics method(NEMD), we have found that the
thermal conductivity of multilayer graphene nanoribbons monotonously decreases
with the increase of the number of layers, such behavior can be attributed to
the phonon resonance effect of out-of-plane phonon modes. The reduction of
thermal conductivity is found to be proportional to the layer size, which is
caused by the increase of phonon resonance. Our results are in agreement with
recent experiment on dimensional evolution of thermal conductivity in few layer
graphene.


  Open Wilson line operators and generalized star product have been studied
extensively in noncommutative gauge theories. We show that they also show up in
noncommutative scalar field theories as universal structures. We first point
out that dipole picture of noncommutative geometry provides an intuitive
argument for robustness of the open Wilson lines and generalized star products
therein. We calculate one-loop effective action of noncommutative scalar field
theory with cubic self-interaction and show explicitly that the generalized
star products arise in the nonplanar part. It is shown that, at low-energy,
large noncommutativity limit, the nonplanar part is expressible solely in terms
of the {\sl scalar} open Wilson line operator and descendants.


  Text simplification systems generate versions of texts that are easier to
understand for a broader audience. The quality of simplified texts is generally
estimated using metrics that compare to human references, which can be
difficult to obtain. We propose Simple-QE, a BERT-based quality estimation (QE)
model adapted from prior summarization QE work, and show that it correlates
well with human quality judgments. Simple-QE does not require human references,
which makes the model useful in a practical setting where users would need to
be informed about the quality of generated simplifications. We also show that
we can adapt this approach to accurately predict the complexity of
human-written texts.


  Drastic changes in protoplanets' orbits could occur in the early stages of
planetary systems through interactions with other planets and their surrounding
protoplanetary or debris discs. The resulting planetary system could exhibit
orbits with moderate to high eccentricities and/or inclinations, causing
planets to perturb one another as well as the disc significantly. The present
work studies the evolution of systems composed of an initially inclined planet
and a debris disc. We perform N-body simulations of a narrow, self-gravitating
debris disc and a single interior Neptune-like planet. We simulate systems with
various initial planetary inclinations, from coplanar to polar configurations
considering different separations between the planet and the disc. We find that
except when the planet is initially on a polar orbit, the planet-disc system
tends to reach a quasi-coplanar configuration with low vertical dispersion in
the disc. When present, the Zeipel--Kozai--Lidov oscillations induced by the
disc pump the planet's eccentricity and, in turn, affect the disc structure. We
also find that the resulting disc morphology in most of the simulations looks
very similar in both radial and vertical directions once the simulations are
converged. This contrasts strongly with massless disc simulations, where
vertical disc dispersion is set by the initial disc-planet inclination and can
be high for initially highly inclined planets. The results suggest caution in
interpreting an unseen planet's dynamical history based only on the disc's
appearance.


  The VIX call options for the Barndorff-Nielsen and Shephard models will be
discussed. Derivatives written on the VIX, which is the most popular volatility
measurement, have been traded actively very much. In this paper, we give
representations of the VIX call option price for the Barndorff-Nielsen and
Shephard models: non-Gaussian Ornstein--Uhlenbeck type stochastic volatility
models. Moreover, we provide representations of the locally risk-minimizing
strategy constructed by a combination of the underlying riskless and risky
assets. Remark that the representations obtained in this paper are efficient to
develop a numerical method using the fast Fourier transform. Thus, numerical
experiments will be implemented in the last section of this paper.


  We establish a bijection from the set of all permutations (of a given length)
that avoid a pattern $q$ and a context-sensitive language.


  A fully analytical description of the allowed $\beta$ spectrum shape is given
in view of ongoing and planned measurements. Its study forms an invaluable tool
in the search for physics beyond the standard electroweak model and the weak
magnetism recoil term. Contributions stemming from finite size corrections,
mass effects, and radiative corrections are reviewed. A particular focus is
placed on atomic and chemical effects, where the existing description is
extended and analytically provided. The effects of QCD-induced recoil terms are
discussed, and cross-checks were performed for different theoretical
formalisms. Special attention was given to a comparison of the treatment of
nuclear structure effects in different formalisms. Corrections were derived for
both Fermi and Gamow-Teller transitions, and methods of analytical evaluation
thoroughly discussed. In its integrated form, calculated $f$ values were in
agreement with the most precise numerical results within the aimed for
precision. We stress the need for an accurate evaluation of weak magnetism
contributions, and note the possible significance of the oft-neglected induced
pseudoscalar interaction. Together with improved atomic corrections, we then
present an analytical description of the allowed $\beta$ spectrum shape
accurate to a few parts in $10^{-4}$ down to 1\,keV for low to medium $Z$
nuclei, thereby extending the work by previous authors by nearly an order of
magnitude.


  We consider a well-known quasi-static model for the shape of a liquid
droplet. The solution can be described in terms of time-evolving domains in
$\mathbb{R}^n$. We give an example to show that convexity of the domain can be
instantaneously broken.


  In this work we are interested in the characterization of curves that belong
to a given surface. To the best of our knowledge, there is no known general
solution to this problem. Indeed, a solution is only available for a few
examples: planes, spheres, or cylinders. Generally, the characterization of
such curves, both in Euclidean ($E^3$) and in Lorentz-Minkowski ($E_1^3$)
spaces, involves an ODE relating curvature and torsion. However, by equipping a
curve with a relatively parallel moving frame, Bishop was able to characterize
spherical curves in $E^3$ through a linear equation relating the coefficients
which dictate the frame motion. Here we apply these ideas to surfaces that are
implicitly defined by a smooth function, $\Sigma=F^{-1}(c)$, by reinterpreting
the problem in the context of the metric given by the Hessian of $F$, which is
not always positive definite. So, we are naturally led to the study of curves
in $E_1^3$. We develop a systematic approach to the construction of Bishop
frames by exploiting the structure of the normal planes induced by the casual
character of the curve, present a complete characterization of spherical curves
in $E_1^3$, and apply it to characterize curves that belong to a non-degenerate
Euclidean quadric. We also interpret the casual character that a curve may
assume when we pass from $E^3$ to $E_1^3$ and finally establish a criterion for
a curve to lie on a level surface of a smooth function, which reduces to a
linear equation when the Hessian is constant.


  We introduce a new concept in the Absorbing Ideal Theory in commutative
rings, that is, the $\omega$-stable groups. We will provide examples and
non-examples of these groups, and establish their relationship with
H-congruence. Ultimately, we will study the action of these groups considering
the transitivity condition


  The conjecture that among three massive neutrinos $\nu_1, \nu_2, \nu_3$ there
is no direct mixing between $\nu_1$ and $\nu_3$ leads to a two-mixing texture
for three active neutrinos $\nu_e, \nu_\mu, \nu_\tau$. This texture, much
discussed previously, is neatly consistent with the observed deficits of solar
$\nu_e$'s and atmospheric $\nu_\mu$'s, but (without extra mixing with at least
one sterile neutrino $\nu_s$) predicts no LSND effect for accelerator
$\nu_\mu$'s. In this option, the masses $m^2_1\stackrel{<}{\sim} m^2_2 <<
m^2_3$ are readily estimated. The characteristic feature of the two-mixing
neutrino texture that {\it only the close neighbours in the hierarchy of
massive neutrinos $\nu_1, \nu_2, \nu_3$ mix significantly} may be physically
meaningful. Going out from the notion of mixing matrix we construct an
intrinsic occupation-number operator whose {\it eigenvalues} 0, 1, 2 {\it
numerate the three generations of massive neutrinos}. Analogical constructions
work also for charged leptons as well as for up and down quarks.


  Physics education research on explanatory videos has experienced a boost in
recent years. Due to the vast number of explanatory videos available online,
e.g. on YouTube, finding videos of high explaining quality is a challenging
task for learners, teachers, and lecturers alike. Prior research on the
explaining quality of explanatory videos on classical mechanics topics has
uncovered that the surface features provided by YouTube (e.g. number of views
or likes) do not seem to be suitable indicators of the videos' explaining
quality. Instead, the number of content-related comments was found to be
statistically significantly correlated with the explaining quality. To date,
these findings have only been observed in the context of explanatory videos on
classical mechanics topics. The question arises whether similar correlations
between the explaining quality and YouTube surface features can be found for
videos on topics that are difficult to access visually and verbally, for
example from quantum physics. Therefore, we conducted an exploratory study
analyzing the explaining quality of N = 60 YouTube videos on quantum
entanglement and tunnelling. To this end, we made use of a category-based
measure of explanatory videos' explaining quality from the literature. We
report correlations between the videos' explaining quality, and the surface
features provided by YouTube. On the one hand, our results substantiate earlier
findings for mechanics topics. On other hand, partial correlations shed new
light on the relationship between YouTube's surface features and explaining
quality of explanatory videos.


  Extensive histogram Monte-Carlo simulations of the XY antiferromagnet on a
stacked triangular lattice reveal exponent estimates which strongly favor a
scenario of mean-field tricritical behavior for the spin-order transition. The
corresponding chiral-order transition occurs at the same temperature but
appears to be decoupled from the spin-order. These results are relevant to a
wide class of frustrated systems with planar-type order and serve to resolve a
long-standing controversy regarding their criticality.


  Cough is a primary symptom of most respiratory diseases, and changes in cough
characteristics provide valuable information for diagnosing respiratory
diseases. The characterization of cough sounds still lacks concrete evidence,
which makes it difficult to accurately distinguish between different types of
coughs and other sounds. The objective of this research work is to characterize
cough sounds with voiced content and cough sounds without voiced content.
Further, the cough sound characteristics are compared with the characteristics
of speech. The proposed method to achieve this goal utilized spectral roll-off,
spectral entropy, spectral flatness, spectral flux, zero crossing rate,
spectral centroid, and spectral bandwidth attributes which describe the cough
sounds related to the respiratory system, glottal information, and voice model.
These attributes are then subjected to statistical analysis using the measures
of minimum, maximum, mean, median, and standard deviation. The experimental
results show that the mean and frequency distribution of spectral roll-off,
spectral centroid, and spectral bandwidth are found to be higher for cough
sounds than for speech signals. Spectral flatness levels in cough sounds will
rise to 0.22, whereas spectral flux varies between 0.3 and 0.6. The Zero
Crossing Rate (ZCR) of most frames of cough sounds is between 0.05 and 0.4.
These attributes contribute significant information while characterizing cough
sounds.


  In this paper, we introduce a concept of quasihyperbolic John spaces and
provide a necessary and sufficient condition for a space to be quasihyperbolic
John. Using this criteria, we exhibit a simple proof to show that a John space
with a Gromov hyperbolic quasihyperbolization is quasihyperbolic John,
quantitatively. This answers affirmatively to an open question proposed by
Heinonen (Rev.~Math.~Iber, 1989), which was studied by Gehring et al.
(Math.~Scand, 1989). As a tool, we study its connection between several
geometric conditions.


  In this paper, we advocate the use of setwise contests for aggregating a set
of input rankings into an output ranking. We propose a generalization of the
Kemeny rule where one minimizes the number of k-wise disagreements instead of
pairwise disagreements (one counts 1 disagreement each time the top choice in a
subset of alternatives of cardinality at most k differs between an input
ranking and the output ranking). After an algorithmic study of this k-wise
Kemeny aggregation problem, we introduce a k-wise counterpart of the majority
graph. This graph reveals useful to divide the aggregation problem into several
sub-problems, which enables to speed up the exact computation of a consensus
ranking. By introducing a k-wise counterpart of the Spearman distance, we also
provide a 2-approximation algorithm for the k-wise Kemeny aggregation problem.
We conclude with numerical tests.


  We prove that the Garside length a braid is equal to a winding-number type
invariant of the curve diagram of the braid.


  We present a gridded 8 km-resolution data product of the estimated
composition of tree taxa at the time of Euro-American settlement of the
northeastern United States and the statistical methodology used to produce the
product from trees recorded by land surveyors. Composition is defined as the
proportion of stems larger than approximately 20 cm diameter at breast height
for 22 tree taxa, generally at the genus level. The data come from
settlement-era public survey records that are transcribed and then aggregated
spatially, giving count data. The domain is divided into two regions, eastern
(Maine to Ohio) and midwestern (Indiana to Minnesota). Public Land Survey point
data in the midwestern region (ca. 0.8-km resolution) are aggregated to a
regular 8 km grid, while data in the eastern region, from Town Proprietor
Surveys, are aggregated at the township level in irregularly-shaped local
administrative units. The product is based on a Bayesian statistical model fit
to the count data that estimates composition on a regular 8 km grid across the
entire domain. The statistical model is designed to handle data from both the
regular grid and the irregularly-shaped townships and allows us to estimate
composition at locations with no data and to smooth over noise caused by
limited counts in locations with data. The model also allows us to quantify
uncertainty in our composition estimates, making the product suitable for
applications employing data assimilation. We expect this data product to be
useful for understanding the state of vegetation in the northeastern United
States prior to large-scale Euro-American settlement. In addition to specific
regional questions, the data product can also serve as a baseline against which
to investigate how forests and ecosystems change after intensive settlement.
The data product is available at the NIS data portal as version 1.0.


  The latest searches for flavor changing neutral currents (FCNCs) and
measurements of properties in top quark events at the CMS and ATLAS experiments
are presented. The vast amounts of proton-proton collision data recorded during
the CERN LHC Run 2 at $\sqrt{s} = 13$ TeV provide excellent opportunities to
test the standard model (SM) with increasingly high precision and search for
subtle deviations from the SM expectation. Tight constraints on top quark FCNC
decay branching fractions are derived, and asymmetries of observables sensitive
to new physics are measured.


  The aim of this paper is to study the quantum tunneling process for charged
vector particles through the horizons of black holes by using Proca equation.
For this purpose, we have consider a pair of charged accelerating and rotating
black holes with NUT parameter and a black hole in $5D$ gauged supergravity,
respectively. Further, we have studied the tunneling probability and
corresponding Hawking temperature for both black holes by using WKB
approximation.


  We describe parton density function sensitivity studies, using muon final
states produced through the Drell-Yan process via W, Z or $\gamma^*$ down to a
Q^2 of 10 GeV^2. This makes use of LHCb's unique ability to trigger on low
transverse momentum objects. Due to the forward acceptance of LHCb, x values
down to 2 10^{-6} can be probed, where with just 100 pb-1 of data, the gluon
PDF can improved up to 70%.


  The degrees of freedom associated with shape fluctuations and space
orientation of atomic nuclei are analyzed with effective forces and large
configuration spaces. A pedagogical theoretical introduction to the topic of
symmetries restoration broken in the mean field approach as well as to the
concept of generator coordinate is presented. We discuss global properties,
like binding energies, two neutron separation energies and quadrupole moments,
as well as spectroscopic ones like excitation energies or transition
probabilities. The analysis covers selected examples of light and heavy nuclei,
as well as some examples from the stability line to the drip lines.


  We construct a resonant normal form for an area-preserving map near a generic
resonant elliptic fixed point. The normal form is obtained by a simplification
of a formal interpolating Hamiltonian. The resonant normal form is unique and
therefore provides the formal local classification for area-preserving maps
with the elliptic fixed point. The total number of formal invariants is
infinite. We consider the cases of weak (of order $n\ge5$) and strong (of order
$n=3,4$) resonances. We also construct unique normal forms for analytic
families of area-preserving maps.
  We note that our constructions involve non-linear grading functions.


  Disordered systems like liquids, gels, glasses, or granular materials are not
only ubiquitous in daily life and in industrial applications but they are also
crucial for the mechanical stability of cells or the transport of chemical and
biological agents in living organisms. Despite the importance of these systems,
their microscopic structure is understood only on a rudimentary level, thus in
stark contrast to the case of gases and crystals. Since scattering experiments
and analytical calculations usually give only structural information that is
spherically averaged, the three dimensional (3D) structure of disordered
systems is basically unknown. Here we introduce a simple method that allows to
probe the 3D structure of such systems. Using computer simulations we find that
hard-sphere-like liquids have on intermediate and large scales an intricate
structural order given by alternating layers with icosahedral and dodecahedral
symmetries, while open network liquids like silica have a structural order with
tetrahedral symmetry. These results show that liquids have a highly non-trivial
3D structure and that this structural information is encoded in non-standard
correlation functions.


  We propose a conditional stochastic interpolation (CSI) approach to learning
conditional distributions. CSI learns probability flow equations or stochastic
differential equations that transport a reference distribution to the target
conditional distribution. This is achieved by first learning the drift function
and the conditional score function based on conditional stochastic
interpolation, which are then used to construct a deterministic process
governed by an ordinary differential equation or a diffusion process for
conditional sampling. In our proposed CSI model, we incorporate an adaptive
diffusion term to address the instability issues arising during the training
process. We provide explicit forms of the conditional score function and the
drift function in terms of conditional expectations under mild conditions,
which naturally lead to an nonparametric regression approach to estimating
these functions. Furthermore, we establish non-asymptotic error bounds for
learning the target conditional distribution via conditional stochastic
interpolation in terms of KL divergence, taking into account the neural network
approximation error. We illustrate the application of CSI on image generation
using a benchmark image dataset.


  Visual attention is a fundamental mechanism in the human brain, and it
inspires the design of attention mechanisms in deep neural networks. However,
most of the visual attention studies adopted eye-tracking data rather than the
direct measurement of brain activity to characterize human visual attention. In
addition, the adversarial relationship between the attention-related objects
and attention-neglected background in the human visual system was not fully
exploited. To bridge these gaps, we propose a novel brain-inspired adversarial
visual attention network (BI-AVAN) to characterize human visual attention
directly from functional brain activity. Our BI-AVAN model imitates the biased
competition process between attention-related/neglected objects to identify and
locate the visual objects in a movie frame the human brain focuses on in an
unsupervised manner. We use independent eye-tracking data as ground truth for
validation and experimental results show that our model achieves robust and
promising results when inferring meaningful human visual attention and mapping
the relationship between brain activities and visual stimuli. Our BI-AVAN model
contributes to the emerging field of leveraging the brain's functional
architecture to inspire and guide the model design in artificial intelligence
(AI), e.g., deep neural networks.


  We propose the concept of a terahertz (THz) uncooled bolometer based on
n-type and p-type graphene layers (GLs), constituting the absorbing regions,
connected by an array of undoped graphene nanoribbons (GNRs). The GLs absorb
the THz radiation with the GNR array playing the role of the barrier region
(resulting in nGL-GNR-pGL bolometer). The absorption of the incident THz
radiation in the GL n- and p- regions leads to variations of the effective
temperature of electrons and holes and of their Fermi energy resulting in the
variation of the current through the GNRs. Using the proposed device model, we
calculate the dark current and the bolometer responsivity as functions of the
GNR energy gap, applied voltage, and the THz frequency. We demonstrate that the
proposed bolometer can surpass the hot-electron bolometers using traditional
semiconductor heterostructures.


  The chemical evolution history of a galaxy hides clues about how it formed
and has been changing through time. We have studied the chemical evolution
history of the Milky Way (MW) and Andromeda (M31) to find which are common
features in the chemical evolution of disc galaxies as well as which are
galaxy-dependent. We use a semi-analytic multi-zone chemical evolution model.
Such models have succeeded in explaining the mean trends of the observed
chemical properties in these two Local Group spiral galaxies with similar mass
and morphology. Our results suggest that while the evolution of the MW and M31
shares general similarities, differences in the formation history are required
to explain the observations in detail. In particular, we found that the
observed higher metallicity in the M31 halo can be explained by either a) a
higher halo star formation efficiency or b) a larger reservoir of infalling
halo gas with a longer halo formation phase. These two different pictures would
lead to a) a higher [O/Fe] at low metallicities or b) younger stellar
populations in the M31 halo, respectively. Both pictures result in a more
massive stellar halo in M31, which suggests a possible correlation between the
halo metallicity and its stellar mass.


  In this paper we analyze how radiation effects influence the correlation
functions, the excess energy, and in turn the electron correlation energy of
the quantized electron gas at temperature $T=0$. To that aim we resort to a
statistical mechanical description of the quantum problem of electron
correlations, based on the path integral formalism. In previous works we
studied and found accurate results for the usual situation with the
electrostatic Coulomb interaction. Here the additional problem with radiation
is taken into account. This is facilitated by the equivalence to a dielectric
fluid for which correlation functions for dipolar moments are established. From
these functions follows the usual density-density (or charge-charge)
correlation function needed for the longitudinal electrostatic problem, and in
addition the one needed for the transverse radiation problem. While
electrostatic excess energy is negative, the transverse one is positive. This
quantity is small and decreases rapidly for decreasing densities. However, for
high densities it approaches the electrostatic contribution, eventually
becoming even larger. The part of the transverse energy from induced
correlations turns out to be very small. Also, the non-local longitudinal and
transverse dielectric constants of the electron gas are identified from the
induced correlation functions.


  Motivation: Many problems in biomedicine and other areas of the life sciences
can be characterized as control problems, with the goal of finding strategies
to change a disease or otherwise undesirable state of a biological system into
another, more desirable, state through an intervention, such as a drug or other
therapeutic treatment. The identification of such strategies is typically based
on a mathematical model of the process to be altered through targeted control
inputs. This paper focuses on processes at the molecular level that determine
the state of an individual cell, involving signaling or gene regulation. The
mathematical model type considered is that of Boolean networks. The potential
control targets can be represented by a set of nodes and edges that can be
manipulated to produce a desired effect on the system. Experimentally, node
manipulation requires technology to completely repress or fully activate a
particular gene product while edge manipulations only require a drug that
inactivates the interaction between two gene products. Results: This paper
presents a method for the identification of potential intervention targets in
Boolean molecular network models using algebraic techniques. The approach
exploits an algebraic representation of Boolean networks to encode the control
candidates in the network wiring diagram as the solutions of a system of
polynomials equations, and then uses computational algebra techniques to find
such controllers. The control methods in this paper are validated through the
identification of combinatorial interventions in the signaling pathways of
previously reported control targets in two well studied systems, a p53-mdm2
network and a blood T cell lymphocyte granular leukemia survival signaling
network.


  The growing demand for short-term property renting has led to the boom of a
new category called PropTech (Property + Technology). Such a rise in demand
attracted many entrepreneurs and investors leading to the inception of powerful
and centralised players in the category (like Airbnb). Unfortunately, some big
players are controlling the entire industry in a centralised way in turn,
performing anti-competitive practices which leave no room for the small
players. Such an ecosystem can be made more transparent and decentralised by
executing transactions and order-fulfilment on a blockchain. To extend the
functionality of the system, we can also perform the renting of small services
and appliances via the integration of IoT. Implementing such a system will make
the process more transparent, robust and decentralised.


  Let $B(\Lambda_0)$ be the level 1 highest weight crystal of the quantum
affine algebra $U_q(A_n^{(1)})$. We construct an explicit crystal isomorphism
between the geometric realization $\mathbb{B}(\Lambda_0)$ of $B(\Lambda_0)$ via
quiver varieties and the path realization ${\mathcal P}^{\rm ad}(\Lambda_0)$ of
$B(\Lambda_0)$ arising from the adjoint crystal $\adjoint$.


  We show that any one-counter automaton with $n$ states, if its language is
non-empty, accepts some word of length at most $O(n^2)$. This closes the gap
between the previously known upper bound of $O(n^3)$ and lower bound of
$\Omega(n^2)$. More generally, we prove a tight upper bound on the length of
shortest paths between arbitrary configurations in one-counter transition
systems (weaker bounds have previously appeared in the literature).


  GCRT J1745-3009 is a transient bursting radio source located in the direction
of the Galactic center. It was discovered in a 330 MHz VLA observation from
2002 September 30--October 1 and subsequently rediscovered in a 330 MHz GMRT
observation from 2003 September 28 by Hyman et al. Here we report a new radio
detection of the source in 330 MHz GMRT data taken on 2004 March 20. The
observed properties of the single burst detected differ significantly from
those measured previously, suggesting that GCRT J1745-3009 was detected in a
new physical state. The 2004 flux density was ~0.05 Jy, ~10x weaker than the
single 2003 burst and ~30x weaker than the five bursts detected in 2002. We
derive a very steep spectral index, alpha = -13.5 +/- 3.0, across the bandpass,
a new result previously not detectable due to limitations in the analysis of
the 2002 and 2003 observations. Also, the burst was detected for only ~2 min.,
in contrast to the 10 min. duration observed in the earlier bursts. Due to
sparse sampling, only the single burst was detected in 2004, as in the 2003
epoch, and we cannot rule out additional undetected bursts that may have
occurred with the same ~77 min. periodicity observed in 2002 or with a
different periodicity. Considering our total time on source throughout both our
archival and active monitoring campaigns, we estimate the source exhibits
detectable bursting activity ~7% of the time.


  This study provides the results of simultaneous multicolor observations for
the first Visorsat (STARLINK-1436) and the ordinary Starlink satellite,
STARLINK-1113 in the $U$, $B$, $V$, $g'$, $r$, $i$, $R_{\rm C}$, $I_{\rm C}$,
$z$, $J$, $H$, and $K_s$ bands to quantitatively investigate the extent to
which Visorsat reduces its reflected light. Our results are as follows: (1) in
most cases, Virorsat is fainter than STARLINK-1113, and the sunshade on
Visorsat, therefore, contributes to the reduction of the reflected sunlight;
(2) the magnitude at 550 km altitude (normalized magnitude) of both satellites
often reaches the naked-eye limiting magnitude ($<$ 6.0); (3) from a blackbody
radiation model of the reflected flux, the peak of the reflected components of
both satellites is around the $z$ band; and (4) the albedo of the near infrared
range is larger than that of the optical range. Under the assumption that
Visorsat and STARLINK-1113 have the same reflectivity, we estimate the covering
factor, $C_{\rm f}$, of the sunshade on Visorsat, using the blackbody radiation
model: the covering factor ranges from $0.18 \leq C_{\rm f} \leq 0.92$. From
the multivariable analysis of the solar phase angle (Sun-target-observer), the
normalized magnitude, and the covering factor, the phase angle versus covering
factor distribution presents a moderate anti-correlation between them,
suggesting that the magnitudes of Visorsat depend not only on the phase angle
but also on the orientation of the sunshade along our line of sight. However,
the impact on astronomical observations from Visorsat-designed satellites
remains serious. Thus, new countermeasures are necessary for the Starlink
satellites to further reduce reflected sunlight.


  We survey the model theoretic approach to a variety of ultrapower embedding
problems in operator algebras.


  Let $(X, H)$ be a normal complex projective polarized variety and $\mathscr
E$ an $H$-semistable sheaf on $X$. We prove that the restriction $\mathscr
E\big|_C$ to a sufficiently positive general complete intersection curve $C
\subset X$ passing through a prescribed finite set of points $S \subset X$
remains semistable, provided that at each $p \in S$, the variety $X$ is smooth
and the factors of a Jordan-H\"older filtration of $\mathscr E$ are locally
free. As an application, we obtain a generalization of Miyaoka's generic
semipositivity theorem.


  Imitation learning targets deriving a mapping from states to actions, a.k.a.
policy, from expert demonstrations. Existing methods for imitation learning
typically require any actions in the demonstrations to be fully available,
which is hard to ensure in real applications. Though algorithms for learning
with unobservable actions have been proposed, they focus solely on state
information and overlook the fact that the action sequence could still be
partially available and provide useful information for policy deriving. In this
paper, we propose a novel algorithm called Action-Guided Adversarial Imitation
Learning (AGAIL) that learns a policy from demonstrations with incomplete
action sequences, i.e., incomplete demonstrations. The core idea of AGAIL is to
separate demonstrations into state and action trajectories, and train a policy
with state trajectories while using actions as auxiliary information to guide
the training whenever applicable. Built upon the Generative Adversarial
Imitation Learning, AGAIL has three components: a generator, a discriminator,
and a guide. The generator learns a policy with rewards provided by the
discriminator, which tries to distinguish state distributions between
demonstrations and samples generated by the policy. The guide provides
additional rewards to the generator when demonstrated actions for specific
states are available. We compare AGAIL to other methods on benchmark tasks and
show that AGAIL consistently delivers comparable performance to the
state-of-the-art methods even when the action sequence in demonstrations is
only partially available.


  Atherosclerosis is a chronic, progressive disease that primarily affects the
arterial walls. It is one of the major causes of cardiovascular disease.
Magnetic Resonance (MR) black-blood vessel wall imaging (BB-VWI) offers crucial
insights into vascular disease diagnosis by clearly visualizing vascular
structures. However, the complex anatomy of the neck poses challenges in
distinguishing the carotid artery (CA) from surrounding structures, especially
with changes like atherosclerosis. In order to address these issues, we propose
GAPNet, which is a consisting of a novel geometric prior deduced from.


  Faraday waves are capillary ripples that form on the surface of a fluid being
subject to vertical shaking. Although it is well known that the form and shape
of the waves pattern depend on driving amplitude and frequency, only recent
studies discovered the existence of a horizontal velocity field at the surface,
called Faraday flow, which exhibits attributes of two-dimensional turbulence.
However, despite the increasing attention towards the inverse energy flux in
the Faraday flow and other not strictly two-dimensional systems, very little is
known about the velocity fields developing beneath the fluid surface. In this
study planar velocity fields are measured by means of particle image
velocimetry (PIV) with high spatial and temporal resolution on the water
surface and below it. A sudden drop in velocity is observed immediately below
the water surface, such that at 5 mm below the water surface the mean absolute
velocities are already about 6.5 times smaller than the surface velocity.
Additionally, the flow structures below the surface are found to comprise much
larger spatial scales than those on the surface. These large structures are
also found to be slow and temporarily persistent, as proven by analysing the
autocorrelation of the velocity fields in time.


  Hard exudates (HE) is the most specific biomarker for retina edema. Precise
HE segmentation is vital for disease diagnosis and treatment, but automatic
segmentation is challenged by its large variation of characteristics including
size, shape and position, which makes it difficult to detect tiny lesions and
lesion boundaries. Considering the complementary features between segmentation
and super-resolution tasks, this paper proposes a novel hard exudates
segmentation method named SS-MAF with an auxiliary super-resolution task, which
brings in helpful detailed features for tiny lesion and boundaries detection.
Specifically, we propose a fusion module named Multi-scale Attention Fusion
(MAF) module for our dual-stream framework to effectively integrate features of
the two tasks. MAF first adopts split spatial convolutional (SSC) layer for
multi-scale features extraction and then utilize attention mechanism for
features fusion of the two tasks. Considering pixel dependency, we introduce
region mutual information (RMI) loss to optimize MAF module for tiny lesions
and boundary detection. We evaluate our method on two public lesion datasets,
IDRiD and E-Ophtha. Our method shows competitive performance with
low-resolution inputs, both quantitatively and qualitatively. On E-Ophtha
dataset, the method can achieve $\geq3\%$ higher dice and recall compared with
the state-of-the-art methods.


  A numerical method is developed leading to algebraic systems based on
generalized Lyapunov-Sylvester operators to approximate the solution of
two-dimensional Kuramoto-Sivashinsky equation. It consists of an order
reduction method and a finite difference discretization which is proved to be
uniquely solvable, stable and convergent by using Lyapunov criterion and
manipulating generalized Lyapunov-Sylvester operators. Some numerical
implementations are provided at the end to validate the theoretical results.


  Light flavour baryons are studied in a non-relativistic potential model with
colour Coulomb plus linear confinement potential using a simple variational
method. The ground-state masses and magnetic moments of the light baryons are
computed using a spin- and isospin-dependent potential. We extend our scheme to
predict the transition magnetic moments of $B_{(J^P={3/2}^+)} \rightarrow
B_{(J^P={1/2}^+)}\gamma$ processes. We also compute the radiative decay widths
and branching ratios of light baryons. A comparison of our results with those
of other works and experimental data is also presented.


  In classical thermodynamics the work cost of control can typically be
neglected. On the contrary, in quantum thermodynamics the cost of control
constitutes a fundamental contribution to the total work cost. Here, focusing
on quantum refrigeration, we investigate how the level of control determines
the fundamental limits to cooling and how much work is expended in the
corresponding process. \jona{We compare two extremal levels of control. First
coherent operations, where the entropy of the resource is left unchanged, and
second incoherent operations, where only energy at maximum entropy (i.e. heat)
is extracted from the resource. For minimal machines, we find that the lowest
achievable temperature and associated work cost depend strongly on the type of
control, in both single-cycle and asymptotic regimes. We also extend our
analysis to general machines.} Our work provides a unified picture of the
different approaches to quantum refrigeration developed in the literature,
including algorithmic cooling, autonomous quantum refrigerators, and the
resource theory of quantum thermodynamics.


  Local memory - the ability to extract information from a subsystem about its
initial state - is a central feature of many-body localization. We introduce,
investigate, and compare several information-theoretic quantifications of
memory and discover a hierarchical relationship among them. We also find that
while the Holevo quantity is the most complete quantifier of memory, vastly
outperforming the imbalance, its decohered counterpart is significantly better
at capturing the critical properties of the many-body localization transition
at small system sizes. This motivates our suggestion that one can emulate the
thermodynamic limit by artifically decohering otherwise quantum quantities.
Applying this method to the von Neumann entropy results in critical exponents
consistent with analytic predictions, a feature missing from similar small
finite-size system treatments. In addition, the decohering process makes
experiments significantly simpler by avoiding quantum state tomography.


  We consider a uniformly rectifiable set $\Gamma \subset \mathbb R^n$ of
dimension $d<n-1$. By using degenerate elliptic operators on the complement
$\Omega = \mathbb R^n \setminus \Gamma$, Guy David, Svitlana Mayboroda, and the
author introduced a notion of harmonic measure on $\Gamma$.
  We prove in the present article that this harmonic measure on $\Gamma$
satisfies the $A^\infty$-property, that is the harmonic measure and the
$d$-dimension Hausdorff measure on $\Gamma$ are mutually absolutely continuous
in a quantitative and scale invariant way. Thus, we give an alternate proof of
a recent theorem of David and Mayboroda, which itself extends a result of
Hofmann and Martell to the case where the uniformly rectifiable set $\Gamma$ is
not of codimension 1.
  The proof is surprisingly simple - in particular does not follow the route
used by David and Mayboroda, or by Hofmann and Martell - but is specific to the
case when $d<n-1$.


  The dominant method of energy loss by a loop of cosmic D-strings in models of
warped brane inflation is studied. It is shown that the energy loss via
Ramond-Ramond field radiation can dominate by many orders of magnitude over the
energy radiation via gravitational wave emission. The ratio of these two energy
loss mechanisms depends on the energy scale of inflation, the mass scale of
string theory and whether it is a single-throat or a multi-throat inflationary
scenario. This can have important consequences for the detection of cosmic
superstrings in the near future. It is argued that the bounds from cosmic
microwave background anisotropies and big bang nucleosynthesis are the dominant
cosmological sources to constrain the physical parameters of the network of
cosmic superstrings, whereas the role of the gravitational wave-based
experiments may be secondary.


  In this note I elaborate on some features of a recent proposal of Papadodimas
and Raju for a CFT description of the interior of a one-sided AdS black hole in
a pure state. I clarify the treatment of 1/N corrections, and explain how the
proposal is able to avoid some of the pitfalls that have disrupted other recent
ideas. I argue however that the proposal has the uncomfortable property that
states in the CFT Hilbert space do not have definite physical interpretations,
unlike in ordinary quantum mechanics. I also contrast the "state-dependence" of
the proposal with more familiar phenomena, arguing that, unlike in quantum
mechanics, the measurement process (including the apparatus) in something like
the PR proposal or its earlier manifestations cannot be described by unitary
evolution. These issues render the proposal somewhat ambiguous, and it seems
new ideas would be needed to make some version of it work. I close with some
brief speculation on to what extent quantum mechanics should hold for the
experience of an infalling observer.


  In this paper we propose an algorithm for the numerical solution of arbitrary
differential equations of fractional order. The algorithm is obtained by using
the following decomposition of the differential equation into a system of
differential equation of integer order connected with inverse forms of
Abel-integral equations. The algorithm is used for solution of the linear and
non-linear equations.


  De novo design of molecules has recently enjoyed the power of generative deep
neural networks. Current approaches aim to generate molecules either resembling
the properties of the molecules of the training set or molecules that are
optimized with respect to specific physicochemical properties. None of the
methods generates molecules specific to a target protein. In the approach
presented here, we introduce a method which is conditioned on the protein
target sequence to generate de novo molecules that are relevant to the target.
We use an implementation adapted from Google's "Show and Tell" image caption
generation method, to generate SMILES strings of molecules from protein
sequence embeddings generated by a deep bi-directional language model ELMo.
ELMo is used to generate contextualized embedding vectors of the protein
sequence. Using reinforcement learning, the trained model is further optimized
through augmented episodic likelihood to increase the diversity of the
generated compounds compared to the training set. We used the model to generate
compounds for two major drug target families, i.e. for GPCRs and Tyrosine
Kinase targets. The model generated compounds which are structurally different
form the training set, while also being more similar to compounds known to bind
to the two families of drug targets compared to a random set of molecules. The
compounds further display reasonable synthesizability and drug-likeness scores.


  Brain age is a critical measure that reflects the biological ageing process
of the brain. The gap between brain age and chronological age, referred to as
brain PAD (Predicted Age Difference), has been utilized to investigate
neurodegenerative conditions. Brain age can be predicted using MRIs and machine
learning techniques. However, existing methods are often sensitive to
acquisition-related variabilities, such as differences in acquisition
protocols, scanners, MRI sequences, and resolutions, significantly limiting
their application in highly heterogeneous clinical settings. In this study, we
introduce Synthetic Brain Age (SynthBA), a robust deep-learning model designed
for predicting brain age. SynthBA utilizes an advanced domain randomization
technique, ensuring effective operation across a wide array of
acquisition-related variabilities. To assess the effectiveness and robustness
of SynthBA, we evaluate its predictive capabilities on internal and external
datasets, encompassing various MRI sequences and resolutions, and compare it
with state-of-the-art techniques. Additionally, we calculate the brain PAD in a
large cohort of subjects with Alzheimer's Disease (AD), demonstrating a
significant correlation with AD-related measures of cognitive dysfunction.
SynthBA holds the potential to facilitate the broader adoption of brain age
prediction in clinical settings, where re-training or fine-tuning is often
unfeasible. The SynthBA source code and pre-trained models are publicly
available at https://github.com/LemuelPuglisi/SynthBA.


  The alpha element to iron peak element ratio, for example [Mg/Fe], is a
commonly applied indicator of the galaxy star formation timescale (SFT) since
the two groups of elements are mainly produced by different types of supernovae
that explode over different timescales. However, it is insufficient to consider
only [Mg/Fe] when estimating the SFT. The [Mg/Fe] yield of a stellar population
depends on its metallicity. Therefore, it is possible for galaxies with
different SFTs and at the same time different total metallicity to have the
same [Mg/Fe]. This effect has not been properly taken into consideration in
previous studies. In this study, we assume the galaxy-wide stellar initial mass
function (gwIMF) to be canonical and invariant. We demonstrate that our
computation code reproduces the SFT estimations of previous studies where only
the [Mg/Fe] observational constraint is applied. We then demonstrate that once
both metallicity and [Mg/Fe] observations are considered, a more severe
"downsizing relation" is required. This means that either low-mass ellipticals
have longer SFTs (> 4 Gyr for galaxies with mass below $10^{10}$ M$_\odot$) or
massive ellipticals have shorter SFTs ($\approx 200$ Myr for galaxies more
massive than $10^{11}$ M$_\odot$) than previously thought. This modification
increases the difficulty in reconciling such SFTs with other observational
constraints. We show that applying different stellar yield modifications does
not relieve this formation timescale problem. The quite unrealistically short
SFT required by [Mg/Fe] and total metallicity would be prolonged if a variable
stellar gwIMF were assumed. Since a systematically varying gwIMF has been
suggested by various observations this could present a natural solution to this
problem.


  Recently, rutile RuO$_2$ has raised interest for its itinerant
antiferromagnetism, crystal Hall effect, and strain-induced superconductivity.
Understanding and manipulating these properties demands resolving the
electronic structure and the relative roles of the rutile crystal field and
$4d$ spin-orbit coupling (SOC). Here, we use O-K and Ru $M_3$ x-ray absorption
(XAS) and Ru $M_3$ resonant inelastic x-ray scattering (RIXS) to disentangle
the contributions of crystal field, SOC, and electronic correlations in
RuO$_2$. The locally orthorhombic site symmetry of the Ru ions introduces
significant crystal field contributions beyond the approximate octahedral
coordination yielding a crystal field energy scale of $\Delta(t_{2g})\approx 1$
eV breaking the degeneracy of the $t_{2g}$ orbitals. This splitting exceeds the
Ru SOC ($\approx160$ meV) suggesting a more subtle role of SOC, primarily
through the modification of itinerant (rather than local) $4d$ electronic
states, ultimately highlighting the importance of the local symmetry in
RuO$_2$. Remarkably, our analysis can be extended to other members of the
rutile family, thus advancing the comprehension of the interplay among crystal
field symmetry, electron correlations, and SOC in transition metal compounds
with the rutile structure.


  The variational lower bound (a.k.a. ELBO or free energy) is the central
objective for many established as well as many novel algorithms for
unsupervised learning. During learning such algorithms change model parameters
to increase the variational lower bound. Learning usually proceeds until
parameters have converged to values close to a stationary point of the learning
dynamics. In this purely theoretical contribution, we show that (for a very
large class of generative models) the variational lower bound is at all
stationary points of learning equal to a sum of entropies. For standard machine
learning models with one set of latents and one set of observed variables, the
sum consists of three entropies: (A) the (average) entropy of the variational
distributions, (B) the negative entropy of the model's prior distribution, and
(C) the (expected) negative entropy of the observable distribution. The
obtained result applies under realistic conditions including: finite numbers of
data points, at any stationary point (including saddle points) and for any
family of (well behaved) variational distributions. The class of generative
models for which we show the equality to entropy sums contains many well-known
generative models. As concrete examples we discuss Sigmoid Belief Networks,
probabilistic PCA and (Gaussian and non-Gaussian) mixture models. The result
also applies for standard (Gaussian) variational autoencoders, a special case
that has been shown previously (Damm et al., 2023). The prerequisites we use to
show equality to entropy sums are relatively mild. Concretely, the
distributions of a given generative model have to be of the exponential family,
and the model has to satisfy a parameterization criterion (which is usually
fulfilled). Proving the equality of the ELBO to entropy sums at stationary
points (under the stated conditions) is the main contribution of this work.


  The main aim of this work is to present the interpretation of the Ising type
models as a kind of field theory in the framework of noncommutative geometry.
We present the method and construct sample models of field theory on discrete
spaces using the introduced tools of discrete geometry. We write the action for
few models, then we compare them with various models of statistical physics. We
construct also the gauge theory with a discrete gauge group.


  We calculate the joint min--max distribution and the Edwards-Anderson's order
parameter for the circular model of $1 / f$-noise. Both quantities, as well as
generalisations, are obtained exactly by combining the freezing-duality
conjecture and Jack-polynomial techniques. Numerical checks come with
significantly improved control of finite-size effects in the glassy phase, and
the results convincingly validate the freezing-duality conjecture. Application
to diffusive dynamics is discussed. We also provide a formula for the
pre-factor ratio of the joint/marginal Carpentier-Le Doussal tail for
minimum/maximum which applies to any logarithmic random energy model.


  We present a novel acceleration method for the solution of parametric ODEs by
single-step implicit solvers by means of greedy kernel-based surrogate models.
In an offline phase, a set of trajectories is precomputed with a high-accuracy
ODE solver for a selected set of parameter samples, and used to train a kernel
model which predicts the next point in the trajectory as a function of the last
one. This model is cheap to evaluate, and it is used in an online phase for new
parameter samples to provide a good initialization point for the nonlinear
solver of the implicit integrator. The accuracy of the surrogate reflects into
a reduction of the number of iterations until convergence of the solver, thus
providing an overall speedup of the full simulation. Interestingly, in addition
to providing an acceleration, the accuracy of the solution is maintained, since
the ODE solver is still used to guarantee the required precision. Although the
method can be applied to a large variety of solvers and different ODEs, we will
present in details its use with the Implicit Euler method for the solution of
the Burgers equation, which results to be a meaningful test case to demonstrate
the method's features.


  Recent research has described an improved method of Fowler-Nordheim plot
analysis, based on the definition and evaluation of a slope correction factor
and a new form of intercept correction factor. In this improved approach there
exists a basic approximation that neglects certain terms in the general theory,
and focuses on the influence of the form of the tunneling barrier on the values
of basic slope ({\sigma}B) and intercept ({\rho}B) correction factors. Simple
formulae exist that allow these to be evaluated numerically for a barrier of
arbitrary well-behaved form. This paper makes an initial exploration of the
effects of barrier form on FN plot analysis. For a planar emitter, two models
for the correlation-and-exchange (C and E) potential energy (PE) are used. For
the Schottky-Nordheim barrier, it is shown that numerical and analytical
approaches generate equivalent results. This agreement supports the validity of
the numerical methods used. Comparisons with results for the Cutler-Gibbons
barrier show that small differences in the assumed C and E PE make little
difference to values of {\sigma}B and {\rho}B. Schottky's planar image PE has
then been used, in conjunction with the electrostatic PE variation associated
with a spherical emitter model, to explore the influence of apex radius r_a on
correction-factor values, for values of r_a greater than 20 nm. Both {\sigma}B
and {\rho}B increase significantly as r_a decreases, especially {\rho}B. At low
values of barrier field F, {\sigma}B depends approximately linearly on 1/F,
with a slope that depends on r_a. Suggestions are made for how the exploratory
work described in this paper might be extended.


  Robust and effective scaling of models from small to large width typically
requires the precise adjustment of many algorithmic and architectural details,
such as parameterization and optimizer choices. In this work, we propose a new
perspective on parameterization by investigating a key assumption in prior work
about the alignment between parameters and data and derive new theoretical
results under weaker assumptions and a broader set of optimizers. Our extensive
empirical investigation includes tens of thousands of models trained with all
combinations of three optimizers, four parameterizations, several alignment
assumptions, more than a dozen learning rates, and fourteen model sizes up to
26.8B parameters. We find that the best learning rate scaling prescription
would often have been excluded by the assumptions in prior work. Our results
show that all parameterizations, not just maximal update parameterization
(muP), can achieve hyperparameter transfer; moreover, our novel per-layer
learning rate prescription for standard parameterization outperforms muP.
Finally, we demonstrate that an overlooked aspect of parameterization, the
epsilon parameter in Adam, must be scaled correctly to avoid gradient underflow
and propose Adam-atan2, a new numerically stable, scale-invariant version of
Adam that eliminates the epsilon hyperparameter entirely.


  A new tool to extract cross-identifications and radio continuum spectra from
radio catalogues contained in the VIZIER database of the CDS is presented. The
code can handle radio surveys at different frequencies with different
resolutions. It has been applied to 22 survey catalogues at 11 different
frequencies containing a total of 3.5 million sources, which resulted in over
700000 independent radio cross-identifications and ~67000 independent radio
spectra with more than two frequency points. A validation of the code has been
performed using independent radio cross-correlations from the literature. The
mean error of the determined spectral index is +-0.3. The code produces an
output of variable format that can easily be adapted to the purpose of the
user.


  Results on matrix canonical forms are used to give a complete description of
the higher rank numerical range of matrices arising from the study of quantum
error correction. It is shown that the set can be obtained as the intersection
of closed half planes (of complex numbers). As a result, it is always a convex
set in $\mathcal C$. Moreover, the higher rank numerical range of a normal
matrix is a convex polygon determined by the eigenvalues. These two
consequences confirm the conjectures of Choi et al. on the subject. In
addition, the results are used to derive a formula for the optimal upper bound
for the dimension of a totally isotropic subspace of a square matrix, and
verify the solvability of certain matrix equations.


  The present text is a collection of notes about differential geometry
prepared to some extent as part of tutorials about topics and applications
related to tensor calculus. They can be regarded as continuation to the
previous notes on tensor calculus as they are based on the materials and
conventions given in those documents. They can be used as a reference for a
first course on the subject or as part of a course on tensor calculus.


  A careful analysis of the Fayet-Iliopoulos (FI) model shows that its energy
momentum tensor and supersymmetry current are not gauge invariant. Since the
corresponding charges are gauge invariant, the model is consistent. However,
our observation about the currents gives a new perspective on its restrictive
renormalization group flow and explains why FI-terms never appear in dynamical
supersymmetry breaking. This lack of gauge invariance is at the root of the
complications of coupling the model to supergravity. We show that this is
possible only if the full supergravity theory (including all higher derivative
corrections) has an additional exact continuous global symmetry. A consistent
quantum gravity theory cannot have such symmetries and hence FI-terms cannot
appear. Our results have consequences for various models of particle physics
and cosmology.


  The 19 towers constituting the CUORE detector are an assembly of
ultra-radio-pure components made of copper and PTFE plus tellurium dioxide
crystals for a total of more than 10,000 pieces. A dedicated assembly line is
mandatory to handle and assembly those parts minimizing the risk of
recontaminations induced by external agents both during their construction and
their storage prior to installation inside the cryostat. The assembly strategy
and the tools design, proposed in this paper and developed for CUORE, offer
solutions that can be extended to the construction of similar-size detector
with strict requirements on contamination.


  A system of bosons studied within the mean field framework has two
fascinating phenomena: a liquid-gas first order phase transition and
Bose-Einstein condensation. Interplay between these two phenomena is being
investigated. Depending on the mean-field potential parameters one can observe
two types of critical points, called "Boltzmann" and "Bose", that belong to
different universality classes with distinct sets of critical exponents. As
examples of Bose and Boltzmann CPs pion and $\alpha$ matter are considered,
respectively. In general, the phase diagram can have one of the CPs or both of
them.


  In this paper, we construct three binary linear codes $C(SO^+(2,q))$,
$C(O^+(2,q))$, $C(SO^+(4,q))$, respectively associated with the orthogonal
groups $SO^+(2,q)$, $O^+(2,q)$, $SO^+(4,q)$, with $q$ powers of two. Then we
obtain recursive formulas for the power moments of Kloosterman and
2-dimensional Kloosterman sums in terms of the frequencies of weights in the
codes. This is done via Pless power moment identity and by utilizing the
explicit expressions of Gauss sums for the orthogonal groups. We emphasize
that, when the recursive formulas for the power moments of Kloosterman sums are
compared, the present one is computationally more effective than the previous
one constructed from the special linear group $SL(2,q)$. We illustrate our
results with some examples.


  Considering corrections from two-loop Feynman diagrams which involve gluino
at large $\tan\beta$, we analyze the effects of possible CP phases on the rare
B decays: $\bar{B}_{_{s}} \to l^+l^-$ and $\bar{B}\to Kl^+l^-$ in the CP
violating minimal supersymmetric extension of the standard model. It is shown
that the results of exact two loop calculations obviously differ from that
including one-loop contributions plus threshold radiative corrections. The
numerical analysis indicates that the possibly large CP phases strongly affect
the theoretical estimation of the branching ratios, and this results coincide
with the conclusion of some other works appearing in recent literature.


  In transportation planning and development, transport network design problem
seeks to optimize specific objectives (e.g. total travel time) through choosing
among a given set of projects while keeping consumption of resources (e.g.
budget) within their limits. Due to the numerous cases of choosing projects,
solving such a problem is very difficult and time-consuming. Based on particle
swarm optimization (PSO) technique, a heuristic solution algorithm for the
bi-level problem is designed. This paper evaluates the algorithm performance in
the response of changing certain basic PSO parameters.


  Experimental measurements of the surface tension of colloidal interfaces have
long been in conflict with computer simulations. In this work we show that the
surface tension of colloids as measured by surface fluctuations picks up a
gravity dependent contribution which removes the discrepancy. The presence of
this term puts a strong constraint on the structure of the interface which
allows one to identify corrections to the fundamental equation of equilibrium
capillarity and deduce bottom-up the microscopic origin of a growth model with
close relation to the Kardar-Parisi-Zhang equation.