  Studying real-world networks such as social networks or web networks is a
challenge. These networks often combine a complex, highly connected structure
together with a large size. We propose a new approach for large scale networks
that is able to automatically sample user-defined relevant parts of a network.
Starting from a few selected places in the network and a reduced set of
expansion rules, the method adopts a filtered breadth-first search approach,
that expands through edges and nodes matching these properties. Moreover, the
expansion is performed over a random subset of neighbors at each step to
mitigate further the overwhelming number of connections that may exist in large
graphs. This carries the image of a "spiky" expansion. We show that this
approach generalize previous exploration sampling methods, such as Snowball or
Forest Fire and extend them. We demonstrate its ability to capture groups of
nodes with high interactions while discarding weakly connected nodes that are
often numerous in social networks and may hide important structures.


  Developments in precision electroweak physics in the two years since the
symposium are briefly summarized.


  In this article, we investigate a density problem coming from the
linearization of Calder\'on's problem with partial data. More precisely, we
prove that the set of products of harmonic functions on a bounded smooth domain
$\Omega$ vanishing on any fixed closed proper subset of the boundary are dense
in $L^{1}(\Omega)$ in all dimensions $n \geq 2$. This is proved using ideas
coming from the proof of Kashiwara's Watermelon theorem.


  Based on the definition of coherent states for continuous spectra and
analogous to photon added coherent states for discrete spectra, we introduce
the excited coherent states for continuous spectra. It is shown that, the main
axioms of Gazeau-Klauder coherent states will be satisfied, properly.
Nonclassical properties and quantum statistics of coherent states, as well as
the introduced excited coherent states are discussed. In particular, through
the study of quadrature squeezing and amplitude squared squeezing, it will be
observed that both classes of the above states can be classified in the
intelligent states category.


  The DarkSide-20k experiment represents the present goal of the Global Argon
Dark Matter Collaboration program. Bringing together the experience from
previous argon-based detectors, as well as the knowledge gained on large volume
membrane cryostats developed within the DUNE program, the community is now
building a dual-phase LAr-TPC equipped with SiPM arrays for light readout. The
main goal of the experiment is to discover or to extend the current sensitivity
limits on the search for dark matter WIMP-like particles. Currently, the
experiment has entered the construction phase and the external cryostat is
being put in place at Laboratori Nazionali del Gran Sasso (LNGS), Italy.
Detector construction will follow, and data taking is expected to start in late
2026. This contribution will introduce the DarkSide detector and goals, and it
will report on the ongoing construction of the underground infrastructure at
LNGS. Finally, it will concentrate on the current activities on large arrays of
silicon light detectors, that are at the base of the construction of the
detector light readout system.


  We present cosmological parameter constraints from a blind joint analysis of
three two-point correlation functions measured from the Year 3 Hyper
Suprime-Cam (HSC-Y3) imaging data, covering 416 deg$^2$, and the SDSS DR11
spectroscopic galaxies spanning the redshift range $[0.15, 0.70]$. We subdivide
the SDSS galaxies into three volume-limited samples separated in redshift, each
of which acts as a large-scale structure tracer characterized by the
measurement of the projected correlation function, $w_{\rm p}(R)$. We also use
the measurements of the galaxy-galaxy weak lensing signal $\Delta \Sigma(R)$
for each of these SDSS samples which act as lenses for a secure sample of
source galaxies selected from the HSC-Y3 shape catalog based on their
photometric redshifts. We combine these measurements with the cosmic shear
correlation functions, $\xi_{\pm}(\vartheta)$, measured for our HSC source
sample. We model these observables with the minimal bias model of the galaxy
clustering observables in the context of a flat $\Lambda$CDM cosmology. We use
conservative scale cuts, $R>12$ and $8~h^{-1}$Mpc, for $\Delta\Sigma$ and
$w_{\rm p}$, respectively, where the minimal bias model is valid, in addition
to conservative prior on the residual bias in the mean redshift of the HSC
photometric source galaxies. Our baseline analysis yields
$S_8=0.775^{+0.043}_{-0.038}$ (68% C.I.) for the $\Lambda$CDM model, after
marginalizing over uncertainties in other parameters. Our value of $S_8$ is
consistent with that from the Planck 2018 data, but the credible interval of
our result is still relatively large. Our results are statistically consistent
with those of a companion paper, which extends this analysis to smaller scales
with an emulator-based halo model.


  Supermassive black holes (SMBHs) pair and form bound binaries after their
host galaxies merge. In a gas-rich merger, accretion discs are expected to form
around the binary and its components. These discs control the binary orbital
evolution until the system is compact enough for gravitational waves to drive
the SMBHs to coalescence. In this work, we implemented a time-dependent 1D
model to follow the long-term evolution of the coupled binary + discs system,
from a separation of 10^5 down to 20 Schwarzschild radii. We run different
models changing the system parameters, including the binary mass ratio q \leq
0.3 and a factor {\gamma} that controls the inflow across the gap created by
the secondary. We find that our implementation yields higher residual masses
and longer binary residence times than previous studies. Our main conclusion is
the non-steady-state nature of the evolution of the system: the properties the
disc had when the binary was still at large separations influence its whole
evolution. To recover steady state, the binary residence time would have to be
much longer than the inflow time-scale of the disc throughout their entire
history, which in general is not satisfied.


  Emotion recognition (ER) from facial images is one of the landmark tasks in
affective computing with major developments in the last decade. Initial efforts
on ER relied on handcrafted features that were used to characterize facial
images and then feed to standard predictive models. Recent methodologies
comprise end-to-end trainable deep learning methods that simultaneously learn
both, features and predictive model. Perhaps the most successful models are
based on convolutional neural networks (CNNs). While these models have excelled
at this task, they still fail at capturing local patterns that could emerge in
the learning process. We hypothesize these patterns could be captured by
variants based on locally weighted learning. Specifically, in this paper we
propose a CNN based architecture enhanced with multiple branches formed by
radial basis function (RBF) units that aims at exploiting local information at
the final stage of the learning process. Intuitively, these RBF units capture
local patterns shared by similar instances using an intermediate
representation, then the outputs of the RBFs are feed to a softmax layer that
exploits this information to improve the predictive performance of the model.
This feature could be particularly advantageous in ER as cultural / ethnicity
differences may be identified by the local units. We evaluate the proposed
method in several ER datasets and show the proposed methodology achieves
state-of-the-art in some of them, even when we adopt a pre-trained VGG-Face
model as backbone. We show it is the incorporation of local information what
makes the proposed model competitive.


  In the recent years, the interest in electric direct current (DC)
technologies (such as converters, batteries, electric vehicles, etc.) is
increasing due to its potential on energy efficiency and sustainability.
However, the vast majority of electric systems and networks are based on
alternating current (AC), as they also have certain advantages regarding
cost-effective transport and robustness. In this paper, an AC/DC optimal power
flow method for hybrid microgrids and several key performance indicators (KPIs)
for its techno-economic assessment are presented. The combination of both
calculations allows users to clearly determine the viability of their hybrid
microgrids. AC/DC networks have been modelled considering their most common
elements. For the power flow method, a polynomial optimisation is formulated
considering four different objective functions: the minimisation of energy
losses, voltage deviation and operational costs, and also the maximisation of
the microgrid generation. The power flow method and the techno-economic
analysis have been implemented in Python and validated in the Centro de
Desarrollo de Energ\'ias Renovables (CEDER) demonstrator for TIGON. The results
show that the calculated power flow variables and the ones measured at CEDER
are practically the same. In addition, the KPIs have been obtained and compared
for four operating scenarios: baseline, no battery, battery flexibility and
virtual battery (VB) flexibility. The last one result in the most profitable
option.


  Large Language Models (LLMs) have demonstrated exceptional text
understanding. Existing works explore their application in text embedding
tasks. However, there are few works utilizing LLMs to assist multimodal
representation tasks. In this work, we investigate the potential of LLMs to
enhance multimodal representation in multimodal item-to-item (I2I)
recommendations. One feasible method is the transfer of Multimodal Large
Language Models (MLLMs) for representation tasks. However, pre-training MLLMs
usually requires collecting high-quality, web-scale multimodal data, resulting
in complex training procedures and high costs. This leads the community to rely
heavily on open-source MLLMs, hindering customized training for representation
scenarios. Therefore, we aim to design an end-to-end training method that
customizes the integration of any existing LLMs and vision encoders to
construct efficient multimodal representation models. Preliminary experiments
show that fine-tuned LLMs in this end-to-end method tend to overlook image
content. To overcome this challenge, we propose a novel training framework,
NoteLLM-2, specifically designed for multimodal representation. We propose two
ways to enhance the focus on visual information. The first method is based on
the prompt viewpoint, which separates multimodal content into visual content
and textual content. NoteLLM-2 adopts the multimodal In-Content Learning method
to teach LLMs to focus on both modalities and aggregate key information. The
second method is from the model architecture, utilizing a late fusion mechanism
to directly fuse visual information into textual information. Extensive
experiments have been conducted to validate the effectiveness of our method.


  In this paper, we study the sequence of orthogonal polynomials
$\{S_n\}_{n=0}^{\infty}$ with respect to the Sobolev-type inner product
  $$\langle f,g \rangle= \int_{-1}^{1} f(x) g(x) \,d\mu(x) +\sum_{j=1}^{N}
\eta_{j} \,f^{(d_j)}(c_{j}) g^{(d_j)}(c_{j}), $$ where $\mu$ is in the Nevai
class $\mathbf{M}(0,1)$, $\eta_j >0$, $N,d_j \in \mathbb{Z}_{+}$ and
$\{c_1,\dots,c_N\}\subset \mathbb{R} \setminus [-1,1]$. Under some restriction
of order in the discrete part of $\langle\cdot,\cdot \rangle$, we prove that
for sufficiently large $n$ the zeros of $S_n$ are real, simple, $n-N$ of them
lie on $(-1,1)$ and each of the mass points $c_j$ ``attracts'' one of the
remaining $N$ zeros.
  The sequences of associated polynomials $\{S_n^{[k]}\}_{n=0}^{\infty}$ are
defined for each $k\in \mathbb{Z}_{+}$. We prove an analogous of Markov's
Theorem on rational approximation to a function of certain class of holomorphic
functions and we give an estimate of the ``speed'' of convergence.


  We study the outflow dynamics and clogging phenomena of mixtures of soft,
elastic low-friction spherical grains and hard frictional spheres of similar
size in a quasi-two-dimensional (2D) silo with narrow orifice at the bottom.
Previous work has demonstrated the crucial influence of elasticity and friction
on silo discharge. We show that the addition of small amounts, even as low as
5\%, of hard grains to an ensemble of soft, low-friction grains already has
significant consequences. The mixtures allow a direct comparison of the
probabilities of the different types of particles to clog the orifice. We
analyze these probabilities for the hard, frictional and the soft, slippery
grains on the basis of their participation in the blocking arches, and compare
outflow velocities and durations of non-permanent clogs for different
compositions of the mixtures. Experimental results are compared with numerical
simulations. The latter strongly suggest a significant influence of the
inter-species particle friction.


  A certified strategy for determining sharp intervals of enclosure for the
eigenvalues of matrix differential operators with singular coefficients is
examined. The strategy relies on computing the second order spectrum relative
to subspaces of continuous piecewise linear functions. For smooth perturbations
of the angular Kerr-Newman Dirac operator, explicit rates of convergence due to
regularity of the eigenfunctions are established. Existing benchmarks are
validated and sharpened by several orders of magnitude in the unperturbed
setting.


  We present a novel approach for the task of human pose transfer, which aims
at synthesizing a new image of a person from an input image of that person and
a target pose. We address the issues of limited correspondences identified
between keypoints only and invisible pixels due to self-occlusion. Unlike
existing methods, we propose to estimate dense and intrinsic 3D appearance flow
to better guide the transfer of pixels between poses. In particular, we wish to
generate the 3D flow from just the reference and target poses. Training a
network for this purpose is non-trivial, especially when the annotations for 3D
appearance flow are scarce by nature. We address this problem through a flow
synthesis stage. This is achieved by fitting a 3D model to the given pose pair
and project them back to the 2D plane to compute the dense appearance flow for
training. The synthesized ground-truths are then used to train a feedforward
network for efficient mapping from the input and target skeleton poses to the
3D appearance flow. With the appearance flow, we perform feature warping on the
input image and generate a photorealistic image of the target pose. Extensive
results on DeepFashion and Market-1501 datasets demonstrate the effectiveness
of our approach over existing methods. Our code is available at
http://mmlab.ie.cuhk.edu.hk/projects/pose-transfer


  Recent experiments searching for sub-GeV/$c^2$ dark matter have observed
event excesses close to their respective energy thresholds. Although specific
to the individual technologies, the measured excess event rates have been
consistently reported at or below event energies of a few-hundred eV, or with
charges of a few electron-hole pairs. In the present work, we operated a 1-gram
silicon SuperCDMS-HVeV detector at three voltages across the crystal (0 V, 60 V
and 100 V). The 0 V data show an excess of events in the tens of eV region.
Despite this event excess, we demonstrate the ability to set a competitive
exclusion limit on the spin-independent dark matter--nucleon elastic scattering
cross section for dark matter masses of $\mathcal{O}(100)$ MeV/$c^2$, enabled
by operation of the detector at 0 V potential and achievement of a very low
$\mathcal{O}(10)$ eV threshold for nuclear recoils. Comparing the data acquired
at 0 V, 60 V and 100 V potentials across the crystal, we investigated possible
sources of the unexpected events observed at low energy. The data indicate that
the dominant contribution to the excess is consistent with a hypothesized
luminescence from the printed circuit boards used in the detector holder.


  The sorghum panicle is an important trait related to grain yield and plant
development. Detecting and counting sorghum panicles can provide significant
information for plant phenotyping. Current deep-learning-based object detection
methods for panicles require a large amount of training data. The data labeling
is time-consuming and not feasible for real application. In this paper, we
present an approach to reduce the amount of training data for sorghum panicle
detection via semi-supervised learning. Results show we can achieve similar
performance as supervised methods for sorghum panicle detection by only using
10\% of original training data.


  Thanks to their remarkable generative capabilities, GANs have gained great
popularity, and are used abundantly in state-of-the-art methods and
applications. In a GAN based model, a discriminator is trained to learn the
real data distribution. To date, it has been used only for training purposes,
where it's utilized to train the generator to provide real-looking outputs. In
this paper we propose a novel method that makes an explicit use of the
discriminator in test-time, in a feedback manner in order to improve the
generator results. To the best of our knowledge it is the first time a
discriminator is involved in test-time. We claim that the discriminator holds
significant information on the real data distribution, that could be useful for
test-time as well, a potential that has not been explored before.
  The approach we propose does not alter the conventional training stage. At
test-time, however, it transfers the output from the generator into the
discriminator, and uses feedback modules (convolutional blocks) to translate
the features of the discriminator layers into corrections to the features of
the generator layers, which are used eventually to get a better generator
result. Our method can contribute to both conditional and unconditional GANs.
As demonstrated by our experiments, it can improve the results of
state-of-the-art networks for super-resolution, and image generation.


  We give an algorithm for finding a solution to the Carath\'{e}odory-Fej\'{e}r
interpolation problem on the polydisc $\mathbb D^n,$ whenever it exists. A
necessary condition for the existence of a solution becomes apparent from this
algorithm. A generalization of the well-known theorem due to Nehari has been
obtained. A proof of the Kor\'{a}nyi--Puk\'{a}nszky theorem is given using the
spectral theorem.


  This paper addresses the problem of model compression via knowledge
distillation. To this end, we propose a new knowledge distillation method based
on transferring feature statistics, specifically the channel-wise mean and
variance, from the teacher to the student. Our method goes beyond the standard
way of enforcing the mean and variance of the student to be similar to those of
the teacher through an $L_2$ loss, which we found it to be of limited
effectiveness. Specifically, we propose a new loss based on adaptive instance
normalization to effectively transfer the feature statistics. The main idea is
to transfer the learned statistics back to the teacher via adaptive instance
normalization (conditioned on the student) and let the teacher network
"evaluate" via a loss whether the statistics learned by the student are
reliably transferred. We show that our distillation method outperforms other
state-of-the-art distillation methods over a large set of experimental settings
including different (a) network architectures, (b) teacher-student capacities,
(c) datasets, and (d) domains.


  I present recent results in quantum statistical mechanics, obtained in joint
works with Mathieu Lewin and Phan Th{\`a}nh Nam. We consider a certain
mean-field limit of the grand-canonical ensemble for a Bose gas at positive
temperature. In this limit, the reduced density matrices of the quantum theory
converge to their analogues in classical field theory, given by a non-linear
Gibbs measure. In particular, we deal with cases where the latter must be
defined via a renormalization procedure. The corresponding renormalization at
the level of the original quantum grand-canonical model, with non-commuting
fields, is one of the important difficulties.


  We present The-wiZZ, an open source and user-friendly software for estimating
the redshift distributions of photometric galaxies with unknown redshifts by
spatially cross-correlating them against a reference sample with known
redshifts. The main benefit of The-wiZZ is in separating the angular pair
finding and correlation estimation from the computation of the output
clustering redshifts allowing anyone to create a clustering redshift for their
sample without the intervention of an "expert". It allows the end user of a
given survey to select any sub-sample of photometric galaxies with unknown
redshifts, match this sample's catalog indices into a value-added data file,
and produce a clustering redshift estimation for this sample in a fraction of
the time it would take to run all the angular correlations needed to produce a
clustering redshift. We show results with this software using photometric data
from the Kilo-Degree Survey (KiDS) and spectroscopic redshifts from the Galaxy
and Mass Assembly (GAMA) survey and the Sloan Digital Sky Survey (SDSS). The
results we present for KiDS are consistent with the redshift distributions used
in a recent cosmic shear analysis from the survey. We also present results
using a hybrid machine learning-clustering redshift analysis that enables the
estimation of clustering redshifts for individual galaxies. The-wiZZ can be
downloaded at http://github.com/morriscb/The-wiZZ/.


  The emergence of smart cities demands harnessing advanced technologies like
the Internet of Things (IoT) and Artificial Intelligence (AI) and promises to
unlock cities' potential to become more sustainable, efficient, and ultimately
livable for their inhabitants. This work introduces an intelligent city
management system that provides a data-driven approach to three use cases: (i)
analyze traffic information to reduce the risk of traffic collisions and
improve driver and pedestrian safety, (ii) identify when and where energy
consumption can be reduced to improve cost savings, and (iii) detect
maintenance issues like potholes in the city's roads and sidewalks, as well as
the beginning of hazards like floods and fires. A case study in Aveiro City
demonstrates the system's effectiveness in generating actionable insights that
enhance security, energy efficiency, and sustainability, while highlighting the
potential of AI and IoT-driven solutions for smart city development.


  We prove that a continuous linear operator $T$ on a topological vector space
$X$ with weak topology is mixing if and only if the dual operator $T'$ has no
finite dimensional invariant subspaces. This result implies the
characterization of hypercyclic operators on the space $\omega$ due to Herzog
and Lemmert and implies the result of Bayart and Matheron, who proved that for
any hypercyclic operator $T$ on $\omega$, $T\oplus T$ is also hypercyclic.


  We compute the nucleosynthesis bounds on the masses of stable Dirac and
Majorana neutrinos by solving an evolution equation network comprising of all
neutrino species which in the Dirac case includes different helicity states as
separate species. We will not commit ourselves to any particular value of the
nucleosynthesis bound on effective number of light neutrino degrees of freedom
$N_\nu$, but present all our mass bounds as {\em functions} of $\dNu$. For
example, we find that the excluded region in the mass of a Majorana $\mu$- or
$\tau$- neutrino, $0.31$ MeV $< m_{\nu}^M < 52$ MeV corresponding to a bound
$\dNu < 0.3$ gets relaxed to $0.93$ MeV $< m_{\nu}^M < 31$ MeV if $\dNu < 1.0$
is used instead. For the Dirac neutrinos this latter constraint gives the upper
limits (for $T_{\rm QCD} = 100$ MeV): $\mnm < 0.31$ MeV and $\mnt < 0.37$ MeV.
Also, the constraint $\dNu <1$ {\it allows} a stable Dirac neutrino with
$m^D_{\nu_\tau} > 22$ MeV.


  NeuraCrypt (Yara et al. arXiv 2021) is an algorithm that converts a sensitive
dataset to an encoded dataset so that (1) it is still possible to train machine
learning models on the encoded data, but (2) an adversary who has access only
to the encoded dataset can not learn much about the original sensitive dataset.
We break NeuraCrypt privacy claims, by perfectly solving the authors' public
challenge, and by showing that NeuraCrypt does not satisfy the formal privacy
definitions posed in the original paper. Our attack consists of a series of
boosting steps that, coupled with various design flaws, turns a 1% attack
advantage into a 100% complete break of the scheme.


  We show that the Hilbert space spanned by a continuously parametrized
wavefunction family---i.e., a quantum state manifold---is dominated by a
subspace, onto which all member states have close to unity projection weight.
Its characteristic dimensionality $D_P$ is much smaller than the full Hilbert
space dimension, and is equivalent to a statistical complexity measure
$e^{S_2}$, where $S_2$ is the $2^{nd}$ Renyi entropy of the manifold. In the
thermodynamic limit, $D_P$ closely approximates the quantum geometric volume of
the manifold under the Fubini-Study metric, revealing an intriguing connection
between information and geometry. This connection persists in compact manifolds
such as a twisted boundary phase, where the corresponding geometric
circumference is lower bounded by a term proportional to its topological index,
reminiscent of entanglement entropy.


  We examine volume computation of general-dimensional polytopes and more
general convex bodies, defined as the intersection of a simplex by a family of
parallel hyperplanes, and another family of parallel hyperplanes or a family of
concentric ellipsoids. Such convex bodies appear in modeling and predicting
financial crises. The impact of crises on the economy (labor, income, etc.)
makes its detection of prime interest. Certain features of dependencies in the
markets clearly identify times of turmoil. We describe the relationship between
asset characteristics by means of a copula; each characteristic is either a
linear or quadratic form of the portfolio components, hence the copula can be
constructed by computing volumes of convex bodies. We design and implement
practical algorithms in the exact and approximate setting, we experimentally
juxtapose them and study the tradeoff of exactness and accuracy for speed. We
analyze the following methods in order of increasing generality: rejection
sampling relying on uniformly sampling the simplex, which is the fastest
approach, but inaccurate for small volumes; exact formulae based on the
computation of integrals of probability distribution functions; an optimized
Lawrence sign decomposition method, since the polytopes at hand are shown to be
simple; Markov chain Monte Carlo algorithms using random walks based on the
hit-and-run paradigm generalized to nonlinear convex bodies and relying on new
methods for computing a ball enclosed; the latter is experimentally extended to
non-convex bodies with very encouraging results. Our C++ software, based on
CGAL and Eigen and available on github, is shown to be very effective in up to
100 dimensions. Our results offer novel, effective means of computing portfolio
dependencies and an indicator of financial crises, which is shown to correctly
identify past crises.


  We investigate in this article regular Heyting algebras by means of Esakia
duality. In particular, we give a characterisation of Esakia spaces dual to
regular Heyting algebras and we show that there are continuum-many varieties of
Heyting algebras generated by regular Heyting algebras. We also study several
logical applications of these classes of objects and we use them to provide
novel topological completeness theorems for inquisitive logic,
$\mathtt{DNA}$-logics and dependence logic.


  Due to the importance of robustness in many real-world optimization problems,
the field of robust optimization has gained a lot of attention over the past
decade. We concentrate on maximum flow problems and introduce a novel robust
optimization model which, compared to known models from the literature,
features several advantageous properties: (i) We consider a general class of
path-based flow problems which can be used to model a large variety of network
routing problems (and other packing problems). (ii) We aim at solutions that
are robust against targeted attacks by a potent adversary who may attack any
flow path of his choice on any edge of the network. (iii) In contrast to
previous robust maximum flow models, for which no efficient algorithms are
known, optimal robust flows for the most important basic variants of our model
can be found in polynomial time.
  We also consider generalizations where the flow player can spend a budget to
protect the network against the interdictor. Here, we show that the problem can
be solved efficiently when the interdiction costs are determined by the flow
player from scratch. However, the problem becomes hard to approximate when the
flow player has to improve an initial protection infrastructure.


  We consider a multiband charge transfer model for a single spin ladder
describing the holes in Sr$_{14-x}$Ca$_x$Cu$_{24}$O$_{41}$. Using Hartree-Fock
approximation we show how the charge density wave, with its periodicity
dependent on doping as recently observed in the experiment, can be stabilized
by purely electronic many-body interactions.


  Affective states regulate our day to day to function and has a tremendous
effect on mental and physical health. Detection of affective states is of
utmost importance for mental health monitoring, smart entertainment selection
and dynamic workload management. In this paper, we discussed relevant
literature on affective state detection using physiology data, the benefits and
limitations of different sensors and methods used for collecting physiology
data, and our rationale for selecting functional near-infrared spectroscopy. We
present the design of an experiment involving nine subjects to evoke the
affective states of meditation, amusement and cognitive load and the results of
the attempt to classify using machine learning. A mean accuracy of 83.04% was
achieved in three class classification with an individual model; 84.39%
accuracy was achieved for a group model and 60.57% accuracy was achieved for
subject independent model using leave one out cross validation. It was found
that prediction accuracy for cognitive load was higher (evoked using a pen and
paper task) than the other two classes (evoked using computer bases tasks). To
verify that this discrepancy was not due to motor skills involved in the pen
and paper task, a second experiment was conducted using four participants and
the results of that experiment has also been presented in the paper.


  The choice of representation plays a key role in self-driving. Bird's eye
view (BEV) representations have shown remarkable performance in recent years.
In this paper, we propose to learn object-centric representations in BEV to
distill a complex scene into more actionable information for self-driving. We
first learn to place objects into slots with a slot attention model on BEV
sequences. Based on these object-centric representations, we then train a
transformer to learn to drive as well as reason about the future of other
vehicles. We found that object-centric slot representations outperform both
scene-level and object-level approaches that use the exact attributes of
objects. Slot representations naturally incorporate information about objects
from their spatial and temporal context such as position, heading, and speed
without explicitly providing it. Our model with slots achieves an increased
completion rate of the provided routes and, consequently, a higher driving
score, with a lower variance across multiple runs, affirming slots as a
reliable alternative in object-centric approaches. Additionally, we validate
our model's performance as a world model through forecasting experiments,
demonstrating its capability to predict future slot representations accurately.
The code and the pre-trained models can be found at
https://kuis-ai.github.io/CarFormer/.


  The effects of locally random magnetic fields are considered in a
nonequilibrium Ising model defined on a square lattice with nearest-neighbors
interactions. In order to generate the random magnetic fields, we have
considered random variables $\{h\}$ that change randomly with time according to
a double-gaussian probability distribution, which consists of two single
gaussian distributions, centered at $+h_{o}$ and $-h_{o}$, with the same width
$\sigma$. This distribution is very general, and can recover in appropriate
limits the bimodal distribution ($\sigma\to 0$) and the single gaussian one
($ho=0$). We performed Monte Carlo simulations in lattices with linear sizes in
the range $L=32 - 512$. The system exhibits ferromagnetic and paramagnetic
steady states. Our results suggest the occurence of first-order phase
transitions between the above-mentioned phases at low temperatures and large
random-field intensities $h_{o}$, for some small values of the width $\sigma$.
By means of finite size scaling, we estimate the critical exponents in the
low-field region, where we have continuous phase transitions. In addition, we
show a sketch of the phase diagram of the model for some values of $\sigma$.


  In economies without monetary transfers, token systems serve as an
alternative to sustain cooperation, alleviate free riding, and increase
efficiency. This paper studies whether a token-based economy can be effective
in marketplaces with thin exogenous supply. We consider a marketplace in which
at each time period one agent requests a service, one agent provides the
service, and one token (artificial currency) is used to pay for service
provision. The number of tokens each agent has represents the difference
between the amount of service provisions and service requests by the agent. We
are interested in the behavior of this economy when very few agents are
available to provide the requested service. Since balancing the number of
tokens across agents is key to sustain cooperation, the agent with the minimum
amount of tokens is selected to provide service among the available agents.
When exactly one random agent is available to provide service, we show that the
token distribution is unstable. However, already when just two random agents
are available to provide service, the token distribution is stable, in the
sense that agents' token balance is unlikely to deviate much from their initial
endowment, and agents return to their initial endowment in finite expected
time. Our results mirror the power of two choices paradigm in load balancing
problems. Supported by numerical simulations using kidney exchange data, our
findings suggest that token systems may generate efficient outcomes in kidney
exchange marketplaces by sustaining cooperation between hospitals.


  In this paper we consider a bosonic Josephson junction described by a
two-mode Bose-Hubbard model, and we thoroughly analyze a quantum phase
transition occurring in the system in the limit of infinite bosonic population.
We discuss the relation between this quantum phase transition and the dynamical
bifurcation occurring in the spectrum of the Discrete Self Trapping equations
describing the system at the semiclassical level. In particular, we identify
five regimes depending on the strength of the effective interaction among
bosons, and study the finite-size effects arising from the finiteness of the
bosonic population. We devote a special attention to the critical regime which
reduces to the dynamical bifurcation point in the thermodynamic limit of
infinite bosonic population. Specifically, we highlight an anomalous scaling in
the population imbalance between the two wells of the trapping potential, as
well as in two quantities borrowed from Quantum Information Theory, i.e. the
entropy of entanglement and the ground-state fidelity. Our analysis is not
limited to the zero temperature case, but considers thermal effects as well.


  Path integral for the $SU(2)$ spin system is reconsidered. We show that the
Nielsen-Rohrlich(NR) formula is equivalent to the spin coherent state
expression so that the phase space in the NR formalism is not topologically
nontrivial. We also perform the WKB approximation in the NR formula and find
that it gives the exact result.


  We investigate metric independent, gauge invariant and closed forms in the
generalized YM theory. These forms are polynomial on the corresponding fields
strength tensors - curvature forms and are analogous to the Pontryagin-Chern
densities in the YM gauge theory. The corresponding secondary characteristic
classes have been expressed in integral form in analogy with the Chern-Simons
form. Because they are not unique, the secondary forms can be dramatically
simplified by the addition of properly chosen differentials of
one-step-lower-order forms. Their gauge variation can also be found yielding
the potential anomalies in the gauge field theory.


  Node representation learning on attributed graphs -- whose nodes are
associated with rich attributes (e.g., texts and protein sequences) -- plays a
crucial role in many important downstream tasks. To encode the attributes and
graph structures simultaneously, recent studies integrate pre-trained models
with graph neural networks (GNNs), where pre-trained models serve as node
encoders (NEs) to encode the attributes. As jointly training large NEs and GNNs
on large-scale graphs suffers from severe scalability issues, many methods
propose to train NEs and GNNs separately. Consequently, they do not take
feature convolutions in GNNs into consideration in the training phase of NEs,
leading to a significant learning bias from that by the joint training. To
address this challenge, we propose an efficient label regularization technique,
namely Label Deconvolution (LD), to alleviate the learning bias by a novel and
highly scalable approximation to the inverse mapping of GNNs. The inverse
mapping leads to an objective function that is equivalent to that by the joint
training, while it can effectively incorporate GNNs in the training phase of
NEs against the learning bias. More importantly, we show that LD converges to
the optimal objective function values by thejoint training under mild
assumptions. Experiments demonstrate LD significantly outperforms
state-of-the-art methods on Open Graph Benchmark datasets.


  We clarify the significance of quasiprobability (QP) in quantum mechanics
that is relevant in describing physical quantities associated with a transition
process. Our basic quantity is Aharonov's weak value, from which the QP can be
defined up to a certain ambiguity parameterized by a complex number. Unlike the
conventional probability, the QP allows us to treat two noncommuting
observables consistently, and this is utilized to embed the QP in Bohmian
mechanics such that its equivalence to quantum mechanics becomes more
transparent. We also show that, with the help of the QP, Bohmian mechanics can
be recognized as an ontological model with a certain type of contextuality.


  We find that the convective motion in the envelopes of red supergiant (RSG)
stars supplies a non-negligible stochastic angular momentum to the mass that a
secondary star accretes in a common envelope evolution (CEE), such that jets
that the secondary star launches wobble. The orbital motion of the secondary
star in a CEE and the density gradient in the envelope impose a non-zero
angular momentum to the accreted mass with a constant direction parallel to the
orbital angular momentum. From one-dimensional stellar evolution simulations
with the numerical code \textsc{mesa} we find that the stochastic convection
motion in the envelope of RSG stars adds a stochastic angular momentum
component with an amplitude that is about 0.1-1 times that of the constant
component due to the orbital motion. We mimic a CEE of the RSG star by removing
envelope mass at a high rate and by depositing energy into its envelope. The
stochastic angular momentum implies that the accretion disk around the
secondary star (which we do not simulate), and therefore the jets that it
launches, wobble with angles of up to tens of degrees with respect to the
orbital angular momentum axis. This wobbling makes it harder for jets to break
out from the envelope and can shape small bubbles in the ejecta that compress
filaments that appear as arcs in the ejected nebula, i.e., in planetary nebulae
when the giant is an asymptotic giant branch star.


  Short-range forecasts of precipitation fields are needed in a wealth of
agricultural, hydrological, ecological and other applications. Forecasts from
numerical weather prediction models are often biased and do not provide
uncertainty information. Here we present a postprocessing technique for such
numerical forecasts that produces correlated probabilistic forecasts of
precipitation accumulation at multiple sites simultaneously. The statistical
model is a spatial version of a two-stage model that represents the
distribution of precipitation by a mixture of a point mass at zero and a Gamma
density for the continuous distribution of precipitation accumulation. Spatial
correlation is captured by assuming that two Gaussian processes drive
precipitation occurrence and precipitation amount, respectively. The first
process is latent and drives precipitation occurrence via a threshold. The
second process explains the spatial correlation in precipitation accumulation.
It is related to precipitation via a site-specific transformation function, so
as to retain the marginal right-skewed distribution of precipitation while
modeling spatial dependence. Both processes take into account the information
contained in the numerical weather forecast and are modeled as stationary
isotropic spatial processes with an exponential correlation function. The
two-stage spatial model was applied to 48-hour-ahead forecasts of daily
precipitation accumulation over the Pacific Northwest in 2004. The predictive
distributions from the two-stage spatial model were calibrated and sharp, and
outperformed reference forecasts for spatially composite and areally averaged
quantities.


  We report magnetic and magnetoelectric studies on BiFeO3 and divalent cation
(A) suvtitute Bi0.7A0.3FeO3 (A = Sr,Ba, and Sr0.5Ba0.5). It is shown that the
rapid increase of magnetization at the Neel temperature (TN = 642 K) is
suppressed in the co-doped compound A = Sr0.5Ba0.5. All the divalent subtituted
compounds show enhanced magnetization and hysteresis loop. Both longitudinal
and transverse magnetoelectric coefficients were measured using the dynamical
lock-in technique. The co-doped compound shows the highest magnetoelectric
coefficient at room temperature although it is not the compound with the
highest saturation magnetization. It is found that as the size of the A-site
cation increses, the transverse magnetoelectric coeffient increases and exceeds
the longitudinal magnetoelectric coefficient. It is suggested that changes in
magnetic domain structure and magnetostriction are possible reasons for the
observed changes in the magnetoelectric coefficients.


  We present a massively parallel algorithm for calculating the self-energy in
self-consistent finite temperature perturbation theory for lattice models. The
algorithm uses analytic functions with appropriate asymptotic high frequency
behavior and fast Fourier transforms to accurately calculate the self-energy at
low-frequency. Traditional methods that truncate the high frequency tails of
the temperature Green's function lead to `contamination' of the low-frequency
behavior of the self-energy. Our algorithm is both accurate and scalable. We
compare results for the Hubbard model using various techniques for handling the
high frequency tails of the temperature Green's function.


  Graph Attention Network (GAT) is a graph neural network which is one of the
strategies for modeling and representing explicit syntactic knowledge and can
work with pre-trained models, such as BERT, in downstream tasks. Currently,
there is still a lack of investigation into how GAT learns syntactic knowledge
from the perspective of model structure. As one of the strategies for modeling
explicit syntactic knowledge, GAT and BERT have never been applied and
discussed in Machine Translation (MT) scenarios. We design a dependency
relation prediction task to study how GAT learns syntactic knowledge of three
languages as a function of the number of attention heads and layers. We also
use a paired t-test and F1-score to clarify the differences in syntactic
dependency prediction between GAT and BERT fine-tuned by the MT task (MT-B).
The experiments show that better performance can be achieved by appropriately
increasing the number of attention heads with two GAT layers. With more than
two layers, learning suffers. Moreover, GAT is more competitive in training
speed and syntactic dependency prediction than MT-B, which may reveal a better
incorporation of modeling explicit syntactic knowledge and the possibility of
combining GAT and BERT in the MT tasks.


  Diffractive X-ray telescopes using zone plates, phase Fresnel lenses, or
related optical elements have the potential to provide astronomers with true
imaging capability with resolution several orders of magnitude better than
available in any other waveband. Lenses that would be relatively easy to
fabricate could have an angular resolution of the order of micro-arc-seconds or
even better, that would allow, for example, imaging of the distorted space-
time in the immediate vicinity of the super-massive black holes in the center
of active galaxies What then is precluding their immediate adoption? Extremely
long focal lengths, very limited bandwidth, and difficulty stabilizing the
image are the main problems. The history, and status of the development of such
lenses is reviewed here and the prospects for managing the challenges that they
present are discussed.


  We use the boundary state formalism to study, from the closed string point of
view, superpositions of branes and anti-branes which are relevant in some
non-perturbative string dualities. Treating the tachyon instability of these
systems as proposed by A. Sen, we show how to incorporate the effects of the
tachyon condensation directly in the boundary state. In this way we manage to
show explicitly that the D1 -- anti-D1 pair of Type I is a stable non-BPS
D-particle, and compute its mass. We also generalize this construction to
describe other non-BPS D-branes of Type I. By requiring the absence of tachyons
in the open string spectrum, we find which configurations are stable and
compute their tensions. Our classification is in complete agreement with the
results recently obtained using the K-theory of space-time.


  We investigate the informational aspect of (1+1)-dimensional directed
percolation, a canonical model of a nonequilibrium continuous transition to a
phase dominated by a single special state called the "absorbing" state. Using a
tensor network scheme, we numerically calculate the time evolution of state
probability distribution of directed percolation. We find a universal
relaxation of Renyi entropy at the absorbing phase transition point as well as
a new singularity in the active phase, slightly but distinctly away from the
absorbing transition point. At the new singular point, the second-order Renyi
entropy has a clear cusp. There we also detect a singular behavior of
"entanglement entropy," defined by regarding the probability distribution as a
wave function. The entanglement entropy vanishes below the singular point and
stays finite above. We confirm that the absorbing state, though its occurrence
is exponentially rare in the active phase, is responsible for these phenomena.
This interpretation provides us with a unified understanding of time evolution
of the Renyi entropy at the critical point as well as in the active phase.


  Breast cancer is the most common cancer in women worldwide, and discovering
the biomarkers of this disease became so vital nowadays and Cell in Cell
structure could be one of them, and it may be used as an available proxy for
tumor malignancy. (CICs) are unusual in that keep morphologically healthy cells
within another cell. They are found in various human cancers and result from
active cell-cell interaction, and it has different kinds. In this study, we
analyzed the microarray data from GEO (GSE103865) to genetically evaluate CICs'
incidence in samples obtained from breast cancer patients to understand the
relationship between the rate of CIC and the prognosis of breast cancer. The
preprocessing was performed using R software. The DAVID website was used to
analyze gene ontology (GO) and Gene and Genome (KEGG) pathways. The
protein-protein interactions (PPIs) of the obtained DEGs were assessed using
the STRING website, and hub modules in Cytoscape and cytoHubba were screened.
According to the results from analyzing the 20 hub genes, we understood that
overexpression of our Top genes is effective in focal adhesion, ECM-receptor
interaction, platelet activation and PI3K-Akt signaling pathway, which shows
that changes in these pathways could be the reason the overexpression of CICs
in breast cancer. These data and research by many others have uncovered various
genes involved in CIC formation and have started to give us an idea of why they
are formed and how they could contribute to breast cancer


  We consider special multiclass spectral, discrepancy, degree, and codegree
properties of expanding graph sequences. As we can prove equivalences and
implications between them and the definition of the generalized quasirandomness
of Lov\'asz--S\'os (2008), they can be regarded as generalized quasirandom
properties akin to the equivalent quasirandom properties of the seminal
Chung--Graham--Wilson paper (1989) in the one-class scenario. Since these
properties are valid for certain deterministic graph sequences, irrespective of
stochastic models, the partial implications also justify for law-dimensional
embedding of large-scale graphs and for discrepancy minimizing spectral
clustering.


  While some galactic bars show recent massive star formation (SF) along them,
some others present a lack of it. Whether bars with low level of SF are a
consequence of low star formation efficiency (SFE), low gas inflow rate, or
dynamical effects, remains a matter of debate. We perform a multi-wavelength
analysis of 12 strongly barred massive galaxies, chosen to host different
degrees of SF along the bar major axis without any prior condition on gas
content. We observe the CO(1-0) and CO(2-1) emission within bars with the
IRAM-30m telescope, which we use to estimate molecular gas masses. SF rates
(SFR) are calculated from GALEX near- and far- ultraviolet (UV) and WISE 12 and
22 micron images within the beam pointings, covering the full bar extent. We
detect molecular gas along the bars of all probed galaxies. The SFE in bars
varies between galaxies by up to an order of magnitude. On average, SFEs are
roughly constant along bars. SFEs are not significantly different from the mean
value in spiral galaxies reported in the literature. Interestingly, the higher
the total stellar mass of the host galaxy, the lower the SFE within their bars.
In particular, the two galaxies in our sample with lowest SFEs and SFR surface
densities (NGC 4548 and NGC 5850) are also the ones hosting massive bulges and
signs of past interactions with nearby companions. The SFE in strong bars is
not systematically inhibited (either in the central, mid- or end-parts of the
bar). Both environmental and internal quenching are likely responsible for the
lowest SFEs reported in this work (Abridged).


  We characterize the unit stable range one 2x2 and 3x3 matrices over
commutative rings. In particular, we characterize the 2x2 matrices which
satisfy the Goodearl-Menal condition. For 2x2 integral matrices we show that
the stable range one and the unit stable range one properties are equivalent,
and, that the only matrix which satisfies the Goodearl-Menal condition is the
zero matrix.


  Optical coherence tomography (OCT) angiography is a well-established in vivo
imaging technique to assess the overall vascular morphology of tissues and is
an emerging field of research for the assessment of blood flow dynamics and
functional parameters such as oxygen saturation. In this study, we present a
modified scanning based correlation mapping OCT (cmOCT) using a 200 kHz high
speed swept source OCT system operating at 1300 nm and demonstrate its wide
field imaging capability in ocular angiographic studies.


  Aramayona and Leininger have provided a "finite rigid subset"
$\mathfrak{X}(\Sigma)$ of the curve complex $\mathscr{C}(\Sigma)$ of a surface
$\Sigma = \Sigma^n_g$, characterized by the fact that any simplicial injection
$\mathfrak{X}(\Sigma) \to \mathscr{C}(\Sigma)$ is induced by a unique element
of the mapping class group $\mathrm{Mod}(\Sigma)$. In this paper we prove that,
in the case of the sphere with $n\geq 5$ marked points, the reduced homology
class of the finite rigid set of Aramayona and Leininger is a
$\mathrm{Mod}(\Sigma)$-module generator for the reduced homology of the curve
complex $\mathscr{C}(\Sigma)$, answering in the affirmative a question posed by
Aramayona and Leininger. For the surface $\Sigma = \Sigma_g^n$ with $g\geq 3$
and $n\in \{0,1\}$ we find that the finite rigid set $\mathfrak{X}(\Sigma)$ of
Aramayona and Leininger contains a proper subcomplex $X(\Sigma)$ whose reduced
homology class is a $\mathrm{Mod}(\Sigma)$-module generator for the reduced
homology of $\mathscr{C}(\Sigma)$ but which is not itself rigid.


  In this paper, we analyze a network of agents that communicate through the
``word of mouth," in which, every agent communicates only with its neighbors.
We introduce the prescription approach, present some of its properties and show
that it leads to a new information state. We also state preliminary structural
results for optimal control strategies in systems that evolve using
word-of-mouth communication. The proposed approach can be generalized to
analyze several decentralized systems.


  In this paper, we introduce Optimal Classification Forests, a new family of
classifiers that takes advantage of an optimal ensemble of decision trees to
derive accurate and interpretable classifiers. We propose a novel mathematical
optimization-based methodology in which a given number of trees are
simultaneously constructed, each of them providing a predicted class for the
observations in the feature space. The classification rule is derived by
assigning to each observation its most frequently predicted class among the
trees in the forest. We provide a mixed integer linear programming formulation
for the problem. We report the results of our computational experiments, from
which we conclude that our proposed method has equal or superior performance
compared with state-of-the-art tree-based classification methods. More
importantly, it achieves high prediction accuracy with, for example, orders of
magnitude fewer trees than random forests. We also present three real-world
case studies showing that our methodology has very interesting implications in
terms of interpretability.


  The W Mass and QCD Working Group discussed a wide variety of topics relating
to present and future measurements of M(W) at LEP2, including QCD backgrounds
to W+W- production. Particular attention was focused on experimental issues
concerning the direct reconstruction and threshold mass measurements, and on
theoretical and experimental issues concerning the four jet final state. This
report summarises the main conclusions.


  We suggest that Davidov's solitons, propagating through the backbone of a
protein, can mediate conformational transition and folding of a protein to its
native state. A simple toy model is presented in which a Non Linear Schrodinger
(NLS) field interacts with another field phi, corresponding to the conformation
angles of the protein. The interaction provides the conformation field with the
energy needed in order to overcome energy barriers for folding, thus avoiding
the need for stochastic thermal activation. Such a transition is therefore a
deterministic controled process.The soliton is compensated for its energy loss
by absorption of the energy gained in the folding process. This scenario does
not change significantly even in the presence of imposed disorder, provided
that the initial momentum of the soliton is large enough.


  We consider the symmetrized moments of three ranks and cranks, similar to the
work of Garvan for the rank and crank of a partition. By using Bailey pairs and
elementary rearrangements, we are able to find useful expressions for these
moments. We then deduce inequalities between the corresponding ordinary
moments. In particular we prove that the crank moment for overpartitions is
always larger than the rank moment for overpartitions; with recent asymptotics
this was known to hold for sufficiently large values of n for each fixed k.
Lastly we provide higher order spt functions for overpartitions, overpartitions
with smallest part even, and partitions with smallest part even and no repeated
odds.


  We argue that, for generic string compactifications, dark energy is likely to
signal the beginning of the end of our universe, perhaps even through
decompactification, with possible implications for the cosmological coincidence
problem. Thanks to the scarcity (absence?) of stable de Sitter vacua, dark
energy in string theory is assumed to take the form of a quintessence field in
slow roll. As it rolls, a tower of heavy states will generically descend,
triggering an apocalyptic phase transition in the low energy cosmological
dynamics after at most a few hundred Hubble times. As a result, dark energy
domination cannot continue indefinitely and there is at least a percentage
chance that we find ourselves in the first Hubble epoch. We use a toy model of
quintessence coupled to a tower of heavy states to explicitly demonstrate the
breakdown in the cosmological dynamics as the tower becomes light. This occurs
through a large number of corresponding particles being produced after a
certain time, overwhelming quintessence. We also discuss some implications for
early universe inflation.


  As a classic statistical model of 3D facial shape and texture, 3D Morphable
Model (3DMM) is widely used in facial analysis, e.g., model fitting, image
synthesis. Conventional 3DMM is learned from a set of well-controlled 2D face
images with associated 3D face scans, and represented by two sets of PCA basis
functions. Due to the type and amount of training data, as well as the linear
bases, the representation power of 3DMM can be limited. To address these
problems, this paper proposes an innovative framework to learn a nonlinear 3DMM
model from a large set of unconstrained face images, without collecting 3D face
scans. Specifically, given a face image as input, a network encoder estimates
the projection, shape and texture parameters. Two decoders serve as the
nonlinear 3DMM to map from the shape and texture parameters to the 3D shape and
texture, respectively. With the projection parameter, 3D shape, and texture, a
novel analytically-differentiable rendering layer is designed to reconstruct
the original input face. The entire network is end-to-end trainable with only
weak supervision. We demonstrate the superior representation power of our
nonlinear 3DMM over its linear counterpart, and its contribution to face
alignment and 3D reconstruction.


  This work generalizes the binary search problem to a $d$-dimensional domain
$S_1\times\cdots\times S_d$, where $S_i=\{0, 1, \ldots,n_i-1\}$ and $d\geq 1$,
in the following way. Given $(t_1,\ldots,t_d)$, the target element to be found,
the result of a comparison of a selected element $(x_1,\ldots,x_d)$ is the
sequence of inequalities each stating that either $t_i < x_i$ or $t_i>x_i$, for
$i\in\{1,\ldots,d\}$, for which at least one is correct, and the algorithm does
not know the coordinate $i$ on which the correct direction to the target is
given. Among other cases, we show asymptotically almost matching lower and
upper bounds of the query complexity to be in $\Omega(n^{d-1}/d)$ and $O(n^d)$
for the case of $n_i=n$. In particular, for fixed $d$ these bounds
asymptotically do match. This problem is equivalent to the classical binary
search in case of one dimension and shows interesting differences for higher
dimensions. For example, if one would impose that each of the $d$ inequalities
is correct, then the search can be completed in $\log_2\max\{n_1,\ldots,n_d\}$
queries. In an intermediate model when the algorithm knows which one of the
inequalities is correct the sufficient number of queries is
$\log_2(n_1\cdot\ldots\cdot n_d)$. The latter follows from a graph search model
proposed by Emamjomeh-Zadeh et al. [STOC 2016].


  Online social networks such as Twitter and Facebook have gained tremendous
popularity for information exchange. The availability of unprecedented amounts
of digital data has accelerated research on information diffusion in online
social networks. However, the mechanism of information spreading in online
social networks remains elusive due to the complexity of social interactions
and rapid change of online social networks. Much of prior work on information
diffusion over online social networks has based on empirical and statistical
approaches. The majority of dynamical models arising from information diffusion
over online social networks involve ordinary differential equations which only
depend on time. In a number of recent papers, the authors propose to use
partial differential equations(PDEs) to characterize temporal and spatial
patterns of information diffusion over online social networks. Built on
intuitive cyber-distances such as friendship hops in online social networks,
the reaction-diffusion equations take into account influences from various
external out-of-network sources, such as the mainstream media, and provide a
new analytic framework to study the interplay of structural and topical
influences on information diffusion over online social networks. In this
survey, we discuss a number of PDE-based models that are validated with real
datasets collected from popular online social networks such as Digg and
Twitter. Some new developments including the conservation law of information
flow in online social networks and information propagation speeds based on
traveling wave solutions are presented to solidify the foundation of the PDE
models and highlight the new opportunities and challenges for mathematicians as
well as computer scientists and researchers in online social networks.


  We discuss in detail a human scale example of the synchronization phenomenon,
namely the dynamics of the rhythmic applause. After a detailed experimental
investigation, we describe the phenomenon with an approach based on the
classical Kuramoto model. Computer simulations based on the theoretical
assumptions, reproduce perfectly the observed dynamics. We argue that a
frustration present in the system is responsible for the interesting interplay
between synchronized and unsynchronized regimes


  The paramagnetic metallic phase of the one-dimensional Kondo lattice model is
studied by the density-matrix renormalization- group method. We observe charge
and spin Friedel oscillations. They reflect the long range charge-charge and
spin-spin correlation functions. The observed oscillations are consistent with
a Tomonaga-Luttinger liquid. From the period of the oscillations it is
concluded that the Fermi surface is large, including both the conduction
electrons and the localized spins, $k_F=\pi (1+n_c)/2$, where $n_c$ is the
density of conduction electrons.


  Let $G$ be a real algebraic group defined over $\mathbb{Q}$, $\Gamma$ be an
arithmetic subgroup of $G$, and $T$ be a maximal $\mathbb{R}$-split torus. A
trajectory in $G/\Gamma$ is divergent if eventually it leaves every compact
subset. In some cases there is a finite collection of explicit algebraic data
which account for the divergence. If this is the case, the divergent trajectory
is called obvious. Given a closed cone in $T$, we study the existence of
non-obvious divergent trajectories under its action in $G/\Gamma$. We get a
sufficient condition for the existence of a non-obvious divergence trajectory
in the general case, and a full classification under the assumption that
$\mbox{rank}_{\mathbb{Q}}G=\mbox{rank}_{\mathbb{R}}G=2$.


  We survey some aspects of the perfect matching problem in hypergraphs, with
particular emphasis on structural characterisation of the existence problem in
dense hypergraphs and the existence of designs.


  The evolution of quantum computers poses a serious threat to contemporary
public-key encryption (PKE) schemes. To address this impending issue, the
National Institute of Standards and Technology (NIST) is currently undertaking
the Post-Quantum Cryptography (PQC) standardization project intending to
evaluate and subsequently standardize the suitable PQC scheme(s). One such
attractive approach, called Bit Flipping Key Encapsulation (BIKE), has made to
the final round of the competition. Despite having some attractive features,
the IND-CCA security of the BIKE depends on the average decoder failure rate
(DFR), a higher value of which can facilitate a particular type of side-channel
attack. Although the BIKE adopts a Black-Grey-Flip (BGF) decoder that offers a
negligible DFR, the effect of weak-keys on the average DFR has not been fully
investigated. Therefore, in this paper, we first perform an implementation of
the BIKE scheme, and then through extensive experiments show that the weak-keys
can be a potential threat to IND-CCA security of the BIKE scheme and thus need
attention from the research community prior to standardization. We also propose
a key-check algorithm that can potentially supplement the BIKE mechanism and
prevent users from generating and adopting weak keys to address this issue.


  Repulsive interactions between particles on a lattice may lead to bound
states, so called doublons. Such states may be created by dynamically tuning
the interaction strength, e.g. using a Feshbach resonance, from attraction to
repulsion. We study the doublon production efficiency as a function of the
tuning rate at which the on-site interaction is varied. An expectation based on
the Landau- Zener law suggests that exponentially few doublons are created in
the adiabatic limit. Contrary to such an expectation, we found that the number
of produced doublons scales as a power law of the tuning rate with the exponent
dependent on the dimensionality of the lattice. The physical reason for this
anomaly is the effective decoupling of doublons from the two-particle continuum
for center of mass momenta close to the corners of the Brillouin zone. The
study of doublon production may be a sensitive tool to extract detailed
information about the band structure.


  The overlapping stage of heavy-ion reactions can be simulated by dynamical
microscopical models, such as those built on the basis of the Molecular
Dynamics (MD) approaches, allowing to study the fragment formation process. The
present performances of the Quantum MD (QMD) code developed at the University
of Milano are discussed, showing results concerning fragment and particle
production at bombarding energies up to $\lsim$ 700 MeV/A, as well as a
preliminary analysis on the isoscaling behaviour of isotopic yield ratios for
reactions with isospin composition N/Z in the (1 - 1.2) range, at a 45 MeV/A
bombarding energy.


  This paper introduces Embarrassingly Easy Text-to-Speech (E2 TTS), a fully
non-autoregressive zero-shot text-to-speech system that offers human-level
naturalness and state-of-the-art speaker similarity and intelligibility. In the
E2 TTS framework, the text input is converted into a character sequence with
filler tokens. The flow-matching-based mel spectrogram generator is then
trained based on the audio infilling task. Unlike many previous works, it does
not require additional components (e.g., duration model, grapheme-to-phoneme)
or complex techniques (e.g., monotonic alignment search). Despite its
simplicity, E2 TTS achieves state-of-the-art zero-shot TTS capabilities that
are comparable to or surpass previous works, including Voicebox and
NaturalSpeech 3. The simplicity of E2 TTS also allows for flexibility in the
input representation. We propose several variants of E2 TTS to improve
usability during inference. See https://aka.ms/e2tts/ for demo samples.


  In this paper, we investigate symmetry breaking effects in strong and
radiative decays of heavy mesons. We study $1/m_Q$ corrections within the heavy
quark effective theory. These effects are studied in a covariant model for
heavy mesons. The numerical results are consistent with the experimental data
and some other theoretical calculations. These provide a vote of confidence for
the validity of this covariant model.


  The accelerated expansion of the Universe is impressively well described by a
cosmological constant. However, the observed value of the cosmological constant
is much smaller than expected based on quantum field theories. Recent efforts
to achieve consistency in these theories have proposed a relationship between
Dark Energy and the most compact objects, such as black holes (BH). However,
experimental tests are very challenging to devise and perform. In this article,
we present a testable model with no cosmological constant, in which the
accelerated expansion can be driven by black holes. The model couples the
expansion of the Universe (the Friedmann equation) with the mass-function of
cosmological haloes (using the Press-Schechter formalism). Through the observed
link between halo-masses and BH-masses one thus gets a coupling between the
expansion rate of the Universe and the BHs. We compare the predictions of this
simple BH model with SN1a data and find a poor agreement with observations. Our
method is sufficiently general that it allows us to also test a fundamentally
different model, also without a cosmological constant, where the accelerated
expansion is driven by a new force proportional to the internal velocity
dispersion of galaxies. Surprisingly enough this model cannot be excluded using
the SN1a data.


  We present an orthogonal expansion for real regular second-order finite
random measures over $\mathbb{R}^{d}$. Such expansion, which may be seen as a
Karhunen-Lo\`eve decomposition, consists in a series expansion of deterministic
real finite measures weighted by uncorrelated real random variables with
summable variances. The convergence of the series is in a
mean-square-$\mathcal{M}_{B}(\mathbb{R}^{d})^{*}$-weak$^{*}$ sense, with
$\mathcal{M}_{B}(\mathbb{R}^{d})$ being the space of bounded measurable
functions over $\mathbb{R}^{d}$. This is proven profiting the extra requirement
for a regular random measure that its covariance structure is identified with a
covariance measure over $\mathbb{R}^{d}\times\mathbb{R}^{d}$. We also obtain a
series decomposition of the covariance measure which converges in a separately
$\mathcal{M}_{B}(\mathbb{R}^{d})^{*}$-weak$^{*}-$total-variation sense. We then
obtain an analogous result for function-regulated regular random measures.


  We develop a deformation method for attaining new magnetic monopole
analytical solutions consistent with generalized Yang-Mills-Higgs model
introduced recently. The new solutions fulfill the usual radially symmetric
ansatz and the boundary conditions suitable to assure finite energy
configurations. We verify our prescription by studying some particular cases
involving both exactly and partially analytical initial configurations whose
deformation leads to new analytic BPS monopoles. The results show consistency
among the models, the deformation procedure and the profile of the new
solutions.


  We have measured fragmentation branching ratios of neutral CnH and CnH+
cations produced in high velocity (4.5 a.u) collisions between incident CnH+
cations and helium atoms. Electron capture gives rise to excited neutral
species CnH and electronic excitation to excited cations CnH+. Thanks to a
dedicated set-up, based on coincident detection of all fragments, the
dissociation of the neutral and cationic parents were recorded separately and
in a complete way. For the fragmentation of CnH, the H-loss channel is found to
be dominant, as already observed by other authors. By contrast, the H-loss and
C-loss channels equally dominate the two-fragment break up of CnH+ species. For
these cations, we provide the first fragmentation data (n > 2). Results are
also discussed in the context of astrochemistry.


  For two decades, NdFeB based magnets have been a critical component in a
range of electrical devices engaged in energy production and conversion. The
magnet shape and the internal microstructure of the selected NdFeB grade govern
their efficiency and size. However, stricter requirements on device efficiency
call for better performing magnets preferably with novel functionality not
achievable today. Here we use 3D metal printing by Selective Laser Melting to
fabricate dense net shape permanent magnets based on NdFeB that exhibit high
magnetic performance. Evidence is provided that the internal microstructure,
not achievable by traditional manufacturing means, is the origin of the solid
magnetic properties. The freedom in magnet body shape and size that ranges from
the millimeter to tens of centimeter scale opens up a design freedom that could
be a catalyzer for the next generation of electrical devices.


  From lattice indications we follow a Born-Oppenheimer approximation to build
a quark-antiquark static potential for $J^{PC}=1^{--}$ charmonium states below
their first S- wave meson-meson threshold. We show that a good description of
the mass and decay properties of the experimentally well established
$\psi(4260)$ resonance is feasible.


  Distinguished from conventional band-inversion-induced Weyl semimetals,
chiral topological semimetals host helicoid-arc surface states originating from
time-reversal invariant momenta of the Brillouin zone. Motivated by the
experimental observation of parallel spin-momentum locking in chiral
topological semimetals, we find that parallel spin-momentum locking leads to
helicoid-arc surface states. We also investigate the potential intrinsic
topological phases in a superconducting chiral topological semimetal with
$s_\pm$- wave pairing. We find that a first-order time-reversal invariant
topological superconductor can host one Majorana cone for closed Fermi
surfaces. Interestingly, there are two Majorana cones when the Fermi surfaces
become open. In addition, a second-order topological superconductor with chiral
Majorana states can be realized in the presence of a mixture of $s\pm$- and
$id$-wave pairing. We show that chiral topological semimetals are fascinating
platforms for exploring intrinsic unconventional superconductivity and
topological superconductivity.


  Rapid increase in internet users along with growing power of online review
sites and social media has given birth to sentiment analysis or opinion mining,
which aims at determining what other people think and comment. Sentiments or
Opinions contain public generated content about products, services, policies
and politics. People are usually interested to seek positive and negative
opinions containing likes and dislikes, shared by users for features of
particular product or service. This paper proposed sentence-level lexical based
domain independent sentiment classification method for different types of data
such as reviews and blogs. The proposed method is based on general lexicons
i.e. WordNet, SentiWordNet and user defined lexical dictionaries for semantic
orientation. The relations and glosses of these dictionaries provide solution
to the domain portability problem. The method performs better than word and
text level corpus based machine learning methods for semantic orientation. The
results show the proposed method performs better as it shows precision of 87%
and83% at document and sentence levels respectively for online comments.


  We consider the capacity problem for wireless networks. Networks are modeled
as random unit-disk graphs, and the capacity problem is formulated as one of
finding the maximum value of a multicommodity flow. In this paper, we develop a
proof technique based on which we are able to obtain a tight characterization
of the solution to the linear program associated with the multiflow problem, to
within constants independent of network size. We also use this proof method to
analyze network capacity for a variety of transmitter/receiver architectures,
for which we obtain some conclusive results. These results contain as a special
case (and strengthen) those of Gupta and Kumar for random networks, for which a
new derivation is provided using only elementary counting and discrete
probability tools.


  To characterize natural selection, various analytical methods for detecting
candidate genomic regions have been developed. We propose to perform
genome-wide scans of natural selection using principal component analysis. We
show that the common Fst index of genetic differentiation between populations
can be viewed as a proportion of variance explained by the principal
components. Considering the correlations between genetic variants and each
principal component provides a conceptual framework to detect genetic variants
involved in local adaptation without any prior definition of populations. To
validate the PCA-based approach, we consider the 1000 Genomes data (phase 1)
after removal of recently admixed individuals resulting in 850 individuals
coming from Africa, Asia, and Europe. The number of genetic variants is of the
order of 36 millions obtained with a low-coverage sequencing depth (3X). The
correlations between genetic variation and each principal component provide
well-known targets for positive selection (EDAR, SLC24A5, SLC45A2, DARC), and
also new candidate genes (APPBPP2, TP1A1, RTTN, KCNMA, MYO5C) and non-coding
RNAs. In addition to identifying genes involved in biological adaptation, we
identify two biological pathways involved in polygenic adaptation that are
related to the innate immune system (beta defensins) and to lipid metabolism
(fatty acid omega oxidation). An additional analysis of European data shows
that a genome scan based on PCA retrieves classical examples of local
adaptation even when there are no well-defined populations. PCA-based
statistics, implemented in the PCAdapt R package and the PCAdapt open-source
software, retrieve well-known signals of human adaptation, which is encouraging
for future whole-genome sequencing project, especially when defining
populations is difficult.


  This article comprehensively explores matrices and their prerequisites for
achieving positive semidefiniteness. The study delves into a series of theorems
concerning pure quantum states in the context of weighted graphs. The main
objective of this study is to establish a graph-theoretic framework for the
study of quantum discord and to identify the necessary and sufficient
conditions for zero quantum discord states using unitary operators. This
research aims to advance the understanding of quantum discord and its
implications for quantum information theory with a graph-theoretic framework.


  We show that several popular few-shot learning benchmarks can be solved with
varying degrees of success without using support set Labels at Test-time (LT).
To this end, we introduce a new baseline called Centroid Networks, a
modification of Prototypical Networks in which the support set labels are
hidden from the method at test-time and have to be recovered through
clustering. A benchmark that can be solved perfectly without LT does not
require proper task adaptation and is therefore inadequate for evaluating
few-shot methods. In practice, most benchmarks cannot be solved perfectly
without LT, but running our baseline on any new combinations of architectures
and datasets gives insights on the baseline performance to be expected from
leveraging a good representation, before any adaptation to the test-time
labels.


  We present evidence of metastable rare quantum-fluctuation switching for the
driven dissipative Jaynes-Cummings oscillator coupled to a zero-temperature
bath in the strongly dispersive regime. We show that single-atom complex
amplitude bistability is accompanied by the appearance of a low-amplitude
long-lived transient state, hereinafter called `dark state', having a
distribution with quasi-Poissonian statistics both for the coupled qubit and
cavity mode. We find that the dark state is linked to a spontaneous flipping of
the qubit state, detuning the cavity to a low-photon response. The appearance
of the dark state is correlated with the participation of the two metastable
states in the dispersive bistability, as evidenced by the solution of the
Master Equation and single quantum trajectories.


  During eternal inflation, a landscape of vacua can be populated by the
nucleation of bubbles. These bubbles inevitably collide, and collisions
sometimes displace the field into a new minimum in a process known as a
classical transition. In this paper, we examine some new features of classical
transitions that arise when gravitational effects are included. Using the
junction condition formalism, we study the conditions for energy conservation
in detail, and solve explicitly for the types of allowed classical transition
geometries. We show that the repulsive nature of domain walls, and the de
Sitter expansion associated with a positive energy minimum, can allow for
classical transitions to vacua of higher energy than that of the colliding
bubbles. Transitions can be made out of negative or zero energy (terminal)
vacua to a de Sitter phase, re-starting eternal inflation, and populating new
vacua. However, the classical transition cannot produce vacua with energy
higher than the original parent vacuum, which agrees with previous results on
the construction of pockets of false vacuum. We briefly comment on the possible
implications of these results for various measure proposals in eternal
inflation.


  We consider, in the framework of the central spin $s=1/2$ model, driven
dynamics of two electrons in a double quantum dot subject to hyperfine
interaction with nuclear spins and spin-orbit coupling. The nuclear subsystem
dynamically evolves in response to Landau-Zener singlet-triplet transitions of
the electronic subsystem controlled by external gate voltages. Without noise
and spin-orbit coupling, subsequent Landau-Zener transitions die out after
about $10^4$ sweeps, the system self-quenches, and nuclear spins reach one of
the numerous glassy dark states. We present an analytical model that captures
this phenomenon. We also account for the multi-nuclear-specie content of the
dots and numerically determine the evolution of around $10^7$ nuclear spins in
up to $2\times10^5$ Landau-Zener transitions. Without spin-orbit coupling,
self-quenching is robust and sets in for arbitrary ratios of the nuclear spin
precession times and the waiting time between Landau-Zener sweeps as well as
under moderate noise. In presence of spin-orbit coupling of a moderate
magnitude, and when the waiting time is in resonance with the precession time
of one of the nuclear species, the dynamical evolution of nuclear polarization
results in stroboscopic screening of spin-orbit coupling. However, small
deviations from the resonance or strong spin-orbit coupling destroy this
screening. We suggest that the success of the feedback loop technique for
building nuclear gradients is based on the effect of spin-orbit coupling.


  The angular correlation is a method for measuring the distribution of
structure in the Universe, through the statistical properties of the angular
distribution of galaxies on the sky. We measure the angular correlation of
galaxies from the second data release of the GaLactic and Extragalactic All-sky
Murchison Widefield Array eXtended survey (GLEAM-X) survey, a low-frequency
radio survey covering declinations below +30 degrees. We find an angular
distribution consistent with the LambdaCDM cosmological model assuming the best
fitting cosmological parameters from Planck Collaboration et al. (2020). We fit
a bias function to the discrete tracers of the underlying matter distribution,
finding a bias that evolves with redshift in either a linear or exponential
fashion to be a better fit to the data than a constant bias. We perform a
covariance analysis to obtain an estimation of the properties of the errors, by
analytic, jackknife and sample variance means. Our results are consistent with
previous studies on the topic, and also the predictions of the LambdaCDM
cosmological model.


  We use differential dynamic microscopy and particle tracking to determine the
dynamical characteristics of a coarsening foam in reciprocal and direct space.
At all wavevectors $q$ investigated, the intermediate scattering function
exhibits a compressed exponential decay. However, the access to unprecedentedly
small $q$s highlights the existence of two distinct regimes for the
$q$-dependence of the foam relaxation rate $\Gamma (q)$. At any given foam age,
$\Gamma (q)\sim q$ at high $q$, consistent with directionally-persistent and
intermittent bubble displacements. At low $q$, we find $\Gamma (q) \sim
q^{1.6}$. We show that such change in $q$-dependence of $\Gamma (q)$ relates to
a bubble displacement distribution exhibiting a cut-off length of the order of
the bubble diameter. Investigations of the $q$-dependence of $\Gamma (q)$ at
different foam ages reveal that foam dynamics is not only governed by the
bubble length scale, but also by the strain rate imposed by the bubble growth;
normalizing $\Gamma (q)$ by this strain rate and multiplying $q$ with the
age-dependent bubble radius leads to a collapse of all data sets onto a unique
master-curve.


  We present the evaluation of definite integrals in the classical table by I.
S. Gradshteyn and I. M. Ryzhik that can be reduced to the beta function.


  This is the first of several short notes in which I will describe phenomena
that illustrate GR=QM. In it I explain that the gravitational attraction that a
black hole exerts on a nearby test object is a consequence of a fundamental law
of quantum mechanics---the tendency for complexity to grow. It will also be
shown that the Einstein bound on velocities is closely related to the
quantum-chaos bound of Maldacena, Shenker, and Stanford.


  We study the parameterized complexity of a variant of the classic video game
Snake that models real-world problems of motion planning. Given a snake-like
robot with an initial position and a final position in an environment (modeled
by a graph), our objective is to determine whether the robot can reach the
final position from the initial position without intersecting itself.
Naturally, this problem models a wide-variety of scenarios, ranging from the
transportation of linked wagons towed by a locomotor at an airport or a
supermarket to the movement of a group of agents that travel in an `ant-like'
fashion and the construction of trains in amusement parks. Unfortunately,
already on grid graphs, this problem is PSPACE-complete [Biasi and Ophelders,
2016]. Nevertheless, we prove that even on general graphs, the problem is
solvable in time $k^{\mathcal{O}(k)}|I|^{\mathcal{O}(1)}$ where $k$ is the size
of the snake, and $|I|$ is the input size. In particular, this shows that the
problem is fixed-parameter tractable (FPT). Towards this, we show how to employ
color-coding to sparsify the configuration graph of the problem to have size
$k^{\mathcal{O}(k)}|I|^{\mathcal{O}(1)}$ rather than $|I|^{\mathcal{O}(k)}$. We
believe that our approach will find other applications in motion planning.
Additionally, we show that the problem is unlikely to admit a polynomial kernel
even on grid graphs, but it admits a treewidth-reduction procedure. To the best
of our knowledge, the study of the parameterized complexity of motion planning
problems (where the intermediate configurations of the motion are of
importance) has so far been largely overlooked. Thus, our work is pioneering in
this regard.


  Word embeddings have found their way into a wide range of natural language
processing tasks including those in the biomedical domain. While these vector
representations successfully capture semantic and syntactic word relations,
hidden patterns and trends in the data, they fail to offer interpretability.
Interpretability is a key means to justification which is an integral part when
it comes to biomedical applications. We present an inclusive study on
interpretability of word embeddings in the medical domain, focusing on the role
of sparse methods. Qualitative and quantitative measurements and metrics for
interpretability of word vector representations are provided. For the
quantitative evaluation, we introduce an extensive categorized dataset that can
be used to quantify interpretability based on category theory. Intrinsic and
extrinsic evaluation of the studied methods are also presented. As for the
latter, we propose datasets which can be utilized for effective extrinsic
evaluation of word vectors in the biomedical domain. Based on our experiments,
it is seen that sparse word vectors show far more interpretability while
preserving the performance of their original vectors in downstream tasks.


  The inverse and linear seesaw scenarios are two typical extensions of the
canonical seesaw mechanism, which contain much more sterile degrees of freedom
but can naturally explain the smallness of three active neutrino masses at a
sufficiently low energy scale (e.g., the TeV scale). To fully describe the
mixing among three active neutrinos, three sterile neutrinos and three extra
gauge-singlet neutral fermions in either of these two seesaw paradigms, we
present the {\it first} full parametrization of the $9\times 9$ flavor mixing
matrix in terms of 36 rotation angles and 36 CP-violating phases. The exact
inverse and linear seesaw formulas are derived, respectively; and possible
deviations of the $3\times 3$ active neutrino mixing matrix from its unitary
limit are discussed by calculating the effective Jarlskog invariants and
unitarity nonagons.


  The inertia subgroup $I_n(\pi)$ of a surgery obstruction group $L_n(\pi)$ is
generated by elements which act trivially on the set of homotopy triangulations
$\Cal S(X)$ for some closed topological manifold $X^{n-1}$ with $\pi_1(X)=\pi$.
This group is a subgroup of the group $C_n(\pi)$ which consists of the elements
which can be realized by normal maps of closed manifolds. In all known cases
these groups coincide and the computation of them is one of the basic problems
of surgery theory. The computation of the group $C_n(\pi)$ is equivalent to the
computation the image of the assembly map $A:H_{n}(B\pi, \bold L_{\bullet})\to
L_{n}(\pi)$. Every Browder-Livesay filtration of the manifold $X$ provides a
collection of Browder-Livesay invariants which are the forbidden invariants in
the closed manifold surgery problem. In the present paper we describe all
possible forbidden invariants which can give a Browder-Livesay filtration for
computing the inertia subgroup. Our approach is a natural generalization of the
approach of Hambleton and Kharshiladze. More precisely, we prove that a
Browder-Livesay filtration of a given manifold can give the following forbidden
invariants for an element $x\in L_n(\pi_1(X))$ to belong to the subgroup
$I_n(\pi)$: the nontrivial Browder-Livesay invariants in codimensions 0, 1, 2
and a nontrivial class of obstructions of a restriction of a normal map to a
submanifold in codimension 3.


  The aim of this paper is to expose some geometrical properties of the locally
Minkowski-Cartan space with the Berwald-Moor metric of momenta. This space is
regarded as a particular case of the $m$-th root Cartan space. Thus, Section 2
studies the $v$-covariant derivation components of the $m$-th root Cartan
space. Section 3 computes the $v$-curvature d-tensor $S^{hijk}$ of the m-th
root Cartan space and studies conditions for $S3$-likeness. Section 4 computes
the $T$-tensor $T^{hijk}$ of the m-th root Cartan space. Section 5
particularizes the preceding geometrical results for the Berwald-Moor metric of
momenta.


  Logarithmic conformal field theory is a rich and vibrant area of modern
mathematical physics with well-known applications to both condensed matter
theory and string theory. Our limited understanding of these theories is based
upon detailed studies of various examples that one may regard as archetypal.
These include the c=-2 triplet model, the Wess-Zumino-Witten model on SL(2;R)
at level k=-1/2, and its supergroup analogue on GL(1|1). Here, the latter model
is studied algebraically through representation theory, fusion and modular
invariance, facilitating a subsequent investigation of its cosets and extended
algebras. The results show that the archetypes of logarithmic conformal field
theory are in fact all very closely related, as are many other examples
including, in particular, the SL(2|1) models at levels 1 and -1/2. The
conclusion is then that the archetypal examples of logarithmic conformal field
theory are practically all the same, so we should not expect that their
features are in any way generic. Further archetypal examples must be sought.


  We show that the quasifission paths predicted by the one-body dissipation
dynamics, in the slowest phase of a binary reaction, follow a quasistatic path,
which represents a sequence of states of thermal equilibrium at a fixed value
of the deformation coordinate. This establishes the use of the statistical
particle-evaporation model in the case of dynamical time-evolving systems. Pre-
and post-scission multiplicities of neutrons and total multiplicities of
protons and alpha particles in fission reactions of 63Cu+92Mo, 60Ni+100Mo,
63Cu+100Mo at 10 MeV/u and 20Ne+144,148,154Sm at 20 MeV/u are reproduced
reasonably well with statistical model calculations performed along dynamic
trajectories whose slow stage (from the most compact configuration up to the
point where the neck starts to develop) lasts some 35x10^{-21} s.


  Python is known to be a versatile language, well suited both for beginners
and advanced users. Some elements of the language are easier to understand than
others: some are found in any kind of code, while some others are used only by
experienced programmers. The use of these elements lead to different ways to
code, depending on the experience with the language and the knowledge of its
elements, the general programming competence and programming skills, etc. In
this paper, we present pycefr, a tool that detects the use of the different
elements of the Python language, effectively measuring the level of Python
proficiency required to comprehend and deal with a fragment of Python code.
Following the well-known Common European Framework of Reference for Languages
(CEFR), widely used for natural languages, pycefr categorizes Python code in
six levels, depending on the proficiency required to create and understand it.
We also discuss different use cases for pycefr: identifying code snippets that
can be understood by developers with a certain proficiency, labeling code
examples in online resources such as Stackoverflow and GitHub to suit them to a
certain level of competency, helping in the onboarding process of new
developers in Open Source Software projects, etc. A video shows availability
and usage of the tool: https://tinyurl.com/ypdt3fwe.


  In this contribution we report on the study of the optical emission lines and
X-ray spectra of a sample of Type 1 AGNs, collected at the Sloan Digital Sky
Survey database and observed by the XMM Newton satellite. Exploiting the
different instruments carried onboard XMM, we identify the spectral components
of the soft and hard energy bands (in the range from 0.3 keV up to 10 keV). The
properties of the X-ray continuum and of the Fe Kalpha line feature are
investigated in relation to the optical broad emission line profiles and
intensity ratios. The resulting picture of emission, absorption and reflection
processes is interpreted by means of a BLR structural model that was developed
on the basis of independent optical and radio observations.


  In this paper we consider Hibi rings and Rees rings attached to a poset. We
classify the ideal lattices of posets whose Hibi relations are indispensable
and the ideal lattices of posets whose Hibi relations form a quadratic Groebner
basis with respect to the rank lexicographic order. Similar classifications are
obtained for Rees rings of Hibi ideals.


  The median absolute deviation (MAD) is a robust measure of scale that is
simple to implement and easy to interpret. Motivated by this, we introduce
interval estimators of the MAD to make reliable inferences for dispersion for a
single population and ratios and differences of MADs for comparing two
populations. Our simulation results show that the coverage probabilities of the
intervals are very close to the nominal coverage for a variety of
distributions. We have used partial influence functions to investigate the
robustness properties of the difference and ratios of independent MADs.


  Considering the growing necessity for precise modeling of power electronics
amidst operational and environmental uncertainties, this paper introduces an
innovative methodology that ingeniously combines model-driven and data-driven
approaches to enhance the stability of power electronics interacting with
grid-forming microgrids. By employing the physics-informed neural network
(PINN) as a foundation, this strategy merges robust data-fitting capabilities
with fundamental physical principles, thereby constructing an accurate system
model. By this means, it significantly enhances the ability to understand and
replicate the dynamics of power electronics systems under complex working
conditions. Moreover, by incorporating advanced learning-based control methods,
the proposed method is enabled to make precise predictions and implement the
satisfactory control laws even under serious uncertain conditions. Experimental
validation demonstrates the effectiveness and robustness of the proposed
approach, highlighting its substantial potential in addressing prevalent
uncertainties in controlling modern power electronics systems.