  There exists a great deal of concrete evidence in favor of the exotic
four-quark nature of light scalars. At the same time, the further expansion of
the area of the $q^2\bar q^2$ model validity for light scalars on ever new
processes seems extremely interesting and important. We analyze the BESIII data
on the decay $J/\psi\to \gamma\pi^0\pi^0$ and show that the results of this
high-statistics experiment can be interpreted in favor of the four-quark nature
of light scalar mesons $f_0(980)$ and $f_0(500)$.


  In this paper, we study the semi-discrete Galerkin finite element method for
parabolic equations with Lipschitz continuous coefficients. We prove the
maximum-norm stability of the semigroup generated by the corresponding elliptic
finite element operator, and prove the space-time stability of the parabolic
projection onto the finite element space in $L^\infty(\Omega_T)$ and
$L^p((0,T);L^q(\Omega))$, $1<p,q<\infty$. The maximal $L^p$ regularity of the
parabolic finite element equation is also established.


  In this work we explore the performance of approximations to electron
correlation in reduced density-matrix functional theory (RDMFT) and of
approximations to the observables calculated within this theory. Our analysis
focuses on the calculation of total energies, occupation numbers,
removal/addition energies, and spectral functions. We use the exactly solvable
Hubbard molecule at 1/4 and 1/2 filling as test systems. This allows us to
analyze the underlying physics and to elucidate the origin of the observed
trends. For comparison we also report the results of the $GW$ approximation,
where the self-energy functional is approximated, but no further hypothesis are
made concerning the approximations of the observables. In particular we focus
on the atomic limit, where the two sites of the molecule are pulled apart and
electrons localize on either site with equal probability, unless a small
perturbation is present: this is the regime of strong electron correlation. In
this limit, using the Hubbard molecule at 1/2 filling with or without a
spin-symmetry-broken ground state, allows us to explore how degeneracies and
spin-symmetry breaking are treated in RDMFT. We find that, within the used
approximations, neither in RDMFT nor in $GW$ the signature of strong
correlation are present in the spin-singlet ground state, whereas both give the
exact result for the spin-symmetry broken case. Moreover we show how the
spectroscopic properties change from one spin structure to the other. Our
findings can be generalized to other situations, which allows us to make
connections to real materials and experiment.


  In real-world scenarios, many factors may harm face recognition performance,
e.g., large pose, bad illumination,low resolution, blur and noise. To address
these challenges, previous efforts usually first restore the low-quality faces
to high-quality ones and then perform face recognition. However, most of these
methods are stage-wise, which is sub-optimal and deviates from the reality. In
this paper, we address all these challenges jointly for unconstrained face
recognition. We propose an Multi-Degradation Face Restoration (MDFR) model to
restore frontalized high-quality faces from the given low-quality ones under
arbitrary facial poses, with three distinct novelties. First, MDFR is a
well-designed encoder-decoder architecture which extracts feature
representation from an input face image with arbitrary low-quality factors and
restores it to a high-quality counterpart. Second, MDFR introduces a pose
residual learning strategy along with a 3D-based Pose Normalization Module
(PNM), which can perceive the pose gap between the input initial pose and its
real-frontal pose to guide the face frontalization. Finally, MDFR can generate
frontalized high-quality face images by a single unified network, showing a
strong capability of preserving face identity. Qualitative and quantitative
experiments on both controlled and in-the-wild benchmarks demonstrate the
superiority of MDFR over state-of-the-art methods on both face frontalization
and face restoration.


  The ultimate information capacity of a three-dimensional hologram for the
case of an optimal use of the dynamic range of a storage medium, number of
pages, the readout conditions is considered. The volume hologram is regarded as
an object of the information theory. For the first time the formalism of the
reciprocal lattice has been introduced in order to estimate the informational
properties of the hologram. The diffraction-limited holographic recording is
analyzed in the framework of the reciprocal lattice formalism. Calculations of
the information capacity of a three-dimensional hologram involve analysis of a
set of multiplexed holograms, each of which has a finite signal-to-noise ratio
determined by the dynamic range of the holographic medium and the geometry of
recording and readout. An optimal number of pages that provides a maxi-mum
information capacity at angular multiplexing is estimated.


  Lately, the problem of code-switching has gained a lot of attention and has
emerged as an active area of research. In bilingual communities, the speakers
commonly embed the words and phrases of a non-native language into the syntax
of a native language in their day-to-day communications. The code-switching is
a global phenomenon among multilingual communities, still very limited acoustic
and linguistic resources are available as yet. For developing effective speech
based applications, the ability of the existing language technologies to deal
with the code-switched data can not be over emphasized. The code-switching is
broadly classified into two modes: inter-sentential and intra-sentential
code-switching. In this work, we have studied the intra-sentential problem in
the context of code-switching language modeling task. The salient contributions
of this paper includes: (i) the creation of Hindi-English code-switching text
corpus by crawling a few blogging sites educating about the usage of the
Internet (ii) the exploration of the parts-of-speech features towards more
effective modeling of Hindi-English code-switched data by the monolingual
language model (LM) trained on native (Hindi) language data, and (iii) the
proposal of a novel textual factor referred to as the code-switch factor
(CS-factor), which allows the LM to predict the code-switching instances. In
the context of recognition of the code-switching data, the substantial
reduction in the PPL is achieved with the use of POS factors and also the
proposed CS-factor provides independent as well as additive gain in the PPL.


  The understanding of the characteristics of a magnetic layer in a different
environment is crucial for any spintronics application. Before practical
applications, thorough scrutiny of such devices is compulsory. Here we study
such a potential Hall device of MgO-capped Hf/GdFeCo bilayer (FeCo-rich) for
magnetization relaxation around nucleation fields at different voltage probe
line widths and dc sensing currents. The device is characterized by anomalous
Hall measurements in transverse and longitudinal Hall geometries for two
different probe widths A (5 micrometer) and B (1 micrometer). The coercivities
of the Hall loops (\r{ho}xy-H and Rxx-H) drop with increasing the sense current
for both the probes. For probe B, the sharp and large drop in coercivity
(\r{ho}xy-H loops) at comparatively lower sensing currents is observed, which
is attributed to the negligible current shunting and presence of pinning site
at B caused by the patterning process. The average domain wall velocities at
various sensing currents for probe B are found to be smaller than probe A, from
the transverse and longitudinal Hall geometry magnetization relaxation
measurements, which agrees with pinning sites and Joule heating effect at probe
B. The notch position in the pattern and the longitudinal Hall resistance curve
peak shape suggest the domain wall propagation direction from probe B to probe
A in the current channel. This study highlights the domain wall propagation at
different nucleation fields, sensing currents, and the Hall probe aspect
ratios.


  We provide easy and readable GNU Octave/MATLAB code for the simulation of
mathematical models described by ordinary differential equations and for the
solution of optimal control problems through Pontryagin's maximum principle.
For that, we consider a normalized HIV/AIDS transmission dynamics model based
on the one proposed in our recent contribution (Silva, C.J.; Torres, D.F.M. A
SICA compartmental model in epidemiology with application to HIV/AIDS in Cape
Verde. Ecol. Complex. 2017, 30, 70--75), given by a system of four ordinary
differential equations. An HIV initial value problem is solved numerically
using the ode45 GNU Octave function and three standard methods implemented by
us in Octave/MATLAB: Euler method and second-order and fourth-order Runge-Kutta
methods. Afterwards, a control function is introduced into the normalized HIV
model and an optimal control problem is formulated, where the goal is to find
the optimal HIV prevention strategy that maximizes the fraction of uninfected
HIV individuals with the least HIV new infections and cost associated with the
control measures. The optimal control problem is characterized analytically
using the Pontryagin Maximum Principle, and the extremals are computed
numerically by implementing a forward-backward fourth-order Runge-Kutta method.
Complete algorithms, for both uncontrolled initial value and optimal control
problems, developed under the free GNU Octave software and compatible with
MATLAB are provided along the article.


  When building classification systems with demographic fairness
considerations, there are two objectives to satisfy: 1) maximizing utility for
the specific task and 2) ensuring fairness w.r.t. a known demographic
attribute. These objectives often compete, so optimizing both can lead to a
trade-off between utility and fairness. While existing works acknowledge the
trade-offs and study their limits, two questions remain unanswered: 1) What are
the optimal trade-offs between utility and fairness? and 2) How can we
numerically quantify these trade-offs from data for a desired prediction task
and demographic attribute of interest? This paper addresses these questions. We
introduce two utility-fairness trade-offs: the Data-Space and Label-Space
Trade-off. The trade-offs reveal three regions within the utility-fairness
plane, delineating what is fully and partially possible and impossible. We
propose U-FaTE, a method to numerically quantify the trade-offs for a given
prediction task and group fairness definition from data samples. Based on the
trade-offs, we introduce a new scheme for evaluating representations. An
extensive evaluation of fair representation learning methods and
representations from over 1000 pre-trained models revealed that most current
approaches are far from the estimated and achievable fairness-utility
trade-offs across multiple datasets and prediction tasks.


  We examine the gender balance of the 18th and 19th meetings of the Cambridge
Workshop on Cool Stellar Systems and the Sun (CS18 and CS19). The percent of
female attendees at both meetings (31% at CS18 and 37% at CS19) was higher than
the percent of women in the American Astronomical Society (25%) and the
International Astronomical Union (18%). The representation of women in Cool
Stars as SOC members, invited speakers, and contributed speakers was similar to
or exceeded the percent of women attending the meetings. We requested that
conference attendees assist in a project to collect data on the gender of
astronomers asking questions after talks. Using this data, we found that men
were over-represented (and women were under-represented) in the question
sessions after each talk. Men asked 79% of the questions at CS18 and 75% of the
questions at CS19, but were 69% and 63% of the attendees respectively. Contrary
to findings from previous conferences, we did not find that the gender balance
of questions was strongly affected by the session chair gender, the speaker
gender, or the length of the question period. We also found that female and
male speakers were asked a comparable number of questions after each talk. The
contrast of these results from previous incarnations of the gender questions
survey indicate that more data would be useful in understanding the factors
that contribute to the gender balance of question askers. We include a
preliminary set of recommendations based on this and other work on related
topics, but also advocate for additional research on the demographics of
conference participants. Additional data on the intersection of gender with
race, seniority, sexual orientation, ability and other marginalized identities
is necessary to fully address the role of gender in asking questions at
conferences.


  Evaluating the in-context learning classification performance of language
models poses challenges due to small dataset sizes, extensive prompt-selection
using the validation set, and intentionally difficult tasks that lead to
near-random performance. The standard random baseline -- the expected accuracy
of guessing labels uniformly at random -- is stable when the evaluation set is
used only once or when the dataset is large. We account for the common practice
of validation set reuse and existing small datasets with a stronger random
baseline: the expected maximum accuracy across multiple random classifiers.
When choosing the best prompt demonstrations across six quantized language
models applied to 16 BIG-bench Lite tasks, more than 20\% of the few-shot
results that exceed the standard baseline do not exceed this stronger random
baseline. When held-out test sets are available, this stronger baseline is also
a better predictor of held-out performance than the standard baseline, avoiding
unnecessary test set evaluations. This maximum random baseline provides an
easily calculated drop-in replacement for the standard baseline.


  We present an improved comparison of the strong coupling of the gluon to
light (q_l=u+d+s), c, and b quarks, determined from multijet rates in
flavor-tagged samples of hadronic Z0 decays recorded with the SLC Large
Detector at the SLAC Linear Collider between 1993 and 1995. Flavor separation
among primary q_l q_l-bar, c c-bar, and b b-bar final states was made on the
basis of the reconstructed mass of long-lived heavy-hadron decay vertices,
yielding tags with high purity and low bias against >= 3-jet final states. We
find alpha_s^c/alpha_s^uds = 1.036+-0.043 (stat) +0.041-0.045 (syst)
+0.020-0.018 (theory) and alpha_s^b/alpha_s^uds = 1.004+-0.018 (stat)
+0.026-0.031 (syst) +0.018-0.029 (theory).


  To learn about basic aspects of nano-scale spherical molecular shells during
their formation, spherically curved two-dimensional N-particle Lennard-Jones
systems are simulated, studying curvature evolution paths at zero-temperature.
For many N-values (N<800) equilibrium configurations are traced as a function
of the curvature radius R. Sharp jumps for tiny changes in R between
trajectories with major differences in topological structure correspond to
avalanche-like transitions. For a typical case, N=25, equilibrium
configurations fall on smooth trajectories in state space which can be traced
in the E-R plane. The trajectories show-up with local energy minima, from which
growth in N at steady curvature can develop.


  Les feuilletages de Hirsch sont des feuilletages par surfaces de vari\'et\'es
compactes ferm\'ees de dimension 3, dont la dynamique transverse est celle d'un
endomorphisme du cercle de degr\'e strictement sup\'erieur \`a 1. Le but de
cette note est de construire, \`a partir d'une g-mesure associ\'ee \`a un tel
endomorphisme, une mesure harmonique sur le feuilletage de Hirsch
correspondant, au sens de Lucy Garnett. Ceci nous permet de donner des exemples
de m\'etriques riemanniennes sur le fibr\'e tangent du feuilletage de Hirsch,
lisses le long des feuilles et continues transversalement, pour lesquelles il
existe plusieurs mesures harmoniques. De tels exemples montrent que le
r\'esultat d'unique ergodicit\'e obtenu par le premier auteur et Victor
Kleptsyn pour les feuilletages transversalement conformes [GAFA, 2007, Vol. 17,
No 4, 1043-1105] n'est valable que lorsque la m\'etrique riemannienne est
H\"old\'erienne transversalement, mais pas juste continue.


  Polyhydroxyalkanoates (PHAs) production with phototrophic mixed cultures
(PMCs) has been recently proposed. These cultures can be selected under the
permanent presence of carbon and the PHA production can be enhanced in
subsequent accumulation steps. To optimize the PHA production in accumulator
reactors, this work evaluated the impact of 1) initial acetate concentration,
2) light intensity, 3) removal of residual nitrogen on the culture performance.
Results indicate that low acetate concentration (<30CmM) and specific light
intensities around 20W/gX are optimal operating conditions that lead to high
polyhydroxybutyrate (PHB) storage yields (0.83+-0.07 Cmol-PHB/Cmol-Acet) and
specific PHB production rates of 2.21+-0.07 Cmol-PHB/Cmol X d. This rate is
three times higher than previously registered in non-optimized accumulation
tests and enabled a PHA content increase from 15 to 30% in less than 4h. Also,
it was shown for the first time, the capability of a PMC to use a real waste,
fermented cheese whey, to produce PHA with a hydroxyvalerate (HV) content of
12%. These results confirm that fermented wastes can be used as substrates for
PHA production with PMCs and that the energy levels in sunlight that lead to
specific light intensities from 10 to 20W/gX are sufficient to drive
phototrophic PHA production processes.


  We study a space-time finite element approach for the nonhomogeneous wave
equation using a continuous time Galerkin method. We present fully implicit
examples in 1+1, 2+1, and 3+1 dimensions using linear quadrilateral,
hexahedral, and tesseractic elements. Krylov solvers with additive Schwarz
preconditioning are used for solving the linear system. We introduce a time
decomposition strategy in preconditioning which significantly improves
performance when compared with unpreconditioned cases.


  We consider a microscopic collision model, i.e., a quantum system interacts
with a hierarchical environment consisting of an auxiliary system and a
reservoir. We show how the non-Markovian character of the system is influenced
by the coupling strength of system-auxiliary system and auxiliary
system-reservoir, initial system-environment correlations and the coherence of
environment. Then we study the relation between non-Markovianity and
thermodynamics properties, by studying the entropy change of system especially
that from heat exchanges with memory effects, and we reveal the essence of
entropy change between positive and negative values during non-Markovian
evolution is due to the contribution of heat flux determined by coherence. And
the information flow between the system and environment is always accompanied
by energy exchange.


  A spectral integral method (IEM) for solving the two-body Schroedinger
equation in configuration space is generalized to the calculation of the
corresponding T-matrix. It is found that the desirable features of the IEM,
such as the economy of mesh-points for a given required accuracy, are carried
over also to the solution of the T-matrix. However the algorithm is
considerably more complex, because the T-matrix is a function of two variables
r and r', rather than only one variable r, and has a slope discontinuity at
r=r'. For a simple exponential potential an accuracy of 7 significant figures
is achieved, with the number N of Chebyshev support points in each partition
equal to 17. For a potential with a large repulsive core, such as the potential
between two He atoms, the accuracy decreases to 4 significant figures, but is
restored to 7 if N is increased to 65.


  The paper is devoted to generalizations of Cencelj-Dranishnikov theorems
relating extension properties of nilpotent CW complexes to its homology groups.
Here are the main results of the paper: \par {\bf Theorem}. Suppose $L$ is a
nilpotent CW complex and $F$ is the homotopy fiber of the inclusion $i$ of $L$
into its infinite symmetric product $SP(L)$. If $X$ is a metrizable space such
that $X\tau K(H_k(L),k)$ for all $k\ge 1$, then $X\tau K(\pi_k(F),k)$ and
$X\tau K(\pi_k(L),k)$ for all $k\ge 2$. \par {\bf Theorem}. Let $X$ be a
metrizable space such that $\dim(X) < \infty$ or $X\in ANR$. Suppose $L$ is a
nilpotent CW complex and $SP(L)$ is its infinite symmetric product. If $X\tau
SP(L)$, then $X\tau L$ in the following cases: \begin{itemize} \item[a.]
$H_1(L)$ is finitely generated. \item[b.] $H_1(L)$ is a torsion group.
\end{itemize}


  In the evolution of 6th Generation (6G) technology, the emergence of
cell-free networking presents a paradigm shift, revolutionizing user
experiences within densely deployed networks where distributed access points
collaborate. However, the integration of intelligent mechanisms is crucial for
optimizing the efficiency, scalability, and adaptability of these 6G cell-free
networks. One application aiming to optimize spectrum usage is Automatic
Modulation Classification (AMC), a vital component for classifying and
dynamically adjusting modulation schemes. This paper explores different
distributed solutions for AMC in cell-free networks, addressing the training,
computational complexity, and accuracy of two practical approaches. The first
approach addresses scenarios where signal sharing is not feasible due to
privacy concerns or fronthaul limitations. Our findings reveal that maintaining
comparable accuracy is remarkably achievable, yet it comes with an increase in
computational demand. The second approach considers a central model and
multiple distributed models collaboratively classifying the modulation. This
hybrid model leverages diversity gain through signal combining and requires
synchronization and signal sharing. The hybrid model demonstrates superior
performance, achieving a 2.5% improvement in accuracy with equivalent total
computational load. Notably, the hybrid model distributes the computational
load across multiple devices, resulting in a lower individual computational
load.


  We first briefly review the state-of-the-art of the large $N_f$ gauge-fermion
theories and then show that the claim made in the paper by Alanne, Blasi and
Dondi that "The singularities in the $\beta$ function and in the fermion mass
anomalous dimension are simultaneously removed providing no hint for a UV fixed
point in the large-N limit" is unwarranted. This is so since the author's
truncation of the beta function violates the large number of matter fields
counting.


  Optical measurements of the real and imaginary frequency dependent
conductivity of uncompensated n-type silicon are reported. The experiments are
done in the quantum limit, $ \hbar\omega > k_{B}T$, across a broad doping range
on the insulating side of the Metal-Insulator transition (MIT). The observed
low energy linear frequency dependence shows characteristics consistent with
theories of a Coulomb glass, but discrepancies exist in the relative magnitudes
of the real and imaginary components. At higher energies we observe a crossover
to a quadratic frequency dependence that is sharper than expected over the
entire dopant range. The concentration dependence gives evidence that the
Coulomb interaction energy is the relevant energy scale that determines this
crossover.


  DenseNet architectures have demonstrated impressive performance in image
classification tasks, but limited research has been conducted on using
character-level DenseNet (char-DenseNet) architectures for text classification
tasks. It is not clear what DenseNet architectures are optimal for text
classification tasks. The iterative task of designing, training and testing of
char-DenseNets is an NP-Hard problem that requires expert domain knowledge.
Evolutionary deep learning (EDL) has been used to automatically design CNN
architectures for the image classification domain, thereby mitigating the need
for expert domain knowledge. This study demonstrates the first work on using
EDL to evolve char-DenseNet architectures for text classification tasks. A
novel genetic programming-based algorithm (GP-Dense) coupled with an
indirect-encoding scheme, facilitates the evolution of performant char DenseNet
architectures. The algorithm is evaluated on two popular text datasets, and the
best-evolved models are benchmarked against four current state-of-the-art
character-level CNN and DenseNet models. Results indicate that the algorithm
evolves performant models for both datasets that outperform two of the
state-of-the-art models in terms of model accuracy and three of the
state-of-the-art models in terms of parameter size.


  We derive the defocusing cubic Gross-Pitaevskii (GP) hierarchy in dimension
$d=3$, from an $N$-body Schr\"{o}dinger equation describing a gas of
interacting bosons in the GP scaling, in the limit $N\rightarrow\infty$. The
main result of this paper is the proof of convergence of the corresponding
BBGKY hierarchy to a GP hierarchy in the spaces introduced in our previous work
on the well-posedness of the Cauchy problem for GP hierarchies,
\cite{chpa2,chpa3,chpa4}, which are inspired by the solutions spaces based on
space-time norms introduced by Klainerman and Machedon in \cite{klma}. We note
that in $d=3$, this has been a well-known open problem in the field. While our
results do not assume factorization of the solutions, consideration of
factorized solutions yields a new derivation of the cubic, defocusing nonlinear
Schr\"odinger equation (NLS) in $d=3$.


  We propose a new model of f(R) gravity containing Arctan function in the
lagrangian. We show here that this model satisfies fifth force constraint
unlike a similar model in Kruglov 2013. In addition to this, we carry out the
fixed point analysis as well as comment on the existence of curvature
singularity in this model. The cosmological evolution for this f(R) gravity
model is also analyzed in the Freidmann Robertson Walker(FRW) background. To
understand observational significance of the model, cosmological parameters are
obtained numerically and compared with those of Lambda cold dark matter
({\Lambda}CDM) model. We also scrutinize the model with supernova data. We
apply Om diagnostic given by Sahni et al. 2008 to the model. Using this
diagnostic, we detect the distinction between cosmic evolution caused by the
f(R) model and {\Lambda}CDM. We find best-fit parameter values of the model
using Baryon Acoustic Oscillations data.


  Elemental rhenium exhibiting type-II superconductivity has been previously
reported to break time-reversal symmetry in the superconducting state. We have
investigated an arc-melted sample of rhenium exhibiting type-I
superconductivity. Low temperature zero-field muon-spin relaxation measurements
indicate that time-reversal symmetry is preserved in the superconducting state.
Muon diffusion is observed, which is due to quantum mechanical tunneling
between interstitial sites. The normal state behavior is characterized by the
conduction electrons screening the muons and thermal broadening, and is typical
for a metal. Energy asymmetries between muon trapping sites and the
superconducting energy gap also characterize the superconducting state
behavior.


  We discuss the cooling dynamics of heavy impurity atoms in a Bose-Einstein
condensate (BEC) by emission of Cherenkov phonons from scattering with the
condensate. In a weakly interacting, low-temperature condensate the
superfluidity of the condensate results in a separation of time-scales of the
thermalization dynamics. Pre-thermalized states are formed with distinct
regions of impurity momenta determined by the mass ratio of impurity and BEC
atoms. This can be employed to detect the mass renormalization of the impurity
upon the formation of a polaron and paves the way to preparing non-equilibrium
impurity-momentum distributions.


  The exact solution for the neutrino mass matrix of the Zee-Babu model is
derived. Tribimaximal mixing imposes conditions on the Yukawa couplings, from
which the normal mass hierarchy is preferred. The derived conditions give a
possibility of Majorana maximal $\mathrm{CP}$ violation in the neutrino sector.
  We have shown that non-zero $\theta_{13}$ is generated if Yukawa couplings
between leptons almost equal to each other. The model gives some regions of the
parameters where neutrino mixing angles and the normal neutrino mass hierarchy
obtained consistent with the recent experimental data.


  We show that one-dimensional (1D) nanostructures and two-dimensional (2D)
supramolecular crystals of organic semiconductors can be grown on substrates
under ambient conditions directly from three-dimensional (3D) organic crystals.
The approach does not require dissolving, melting or evaporating of the source
crystals and is based on the Organic Solid-Solid Wetting Deposition (OSWD). We
exemplify our approach by the pigment quinacridone (QAC). Scanning Tunnelling
Microscopy (STM) investigations show that the structures of the resulting 2D
crystals are similar to the chain arrangement of the alpha and beta QAC
polymorphs and are independent of the 3D source crystal polymorph (gamma).
Furthermore, distinct 1D chains can be produced systematically.


  Intuitively, students who start a homework assignment earlier and spend more
time on it should receive better grades on the assignment. However, existing
literature on the impact of time spent on homework is not clear-cut and comes
mostly from K-12 education. It is not clear that these prior studies can inform
coursework in deep learning due to differences in demographics, as well as the
computational time needed for assignments to be completed. We study this
problem in a post-hoc study of three semesters of a deep learning course at the
University of Maryland, Baltimore County (UMBC), and develop a hierarchical
Bayesian model to help make principled conclusions about the impact on student
success given an approximate measure of the total time spent on the homework,
and how early they submitted the assignment. Our results show that both
submitting early and spending more time positively relate with final grade.
Surprisingly, the value of an additional day of work is apparently equal across
students, even when some require less total time to complete an assignment.


  Number-resolving single photon detectors are essential for the implementation
of numerous innovative quantum information schemes. While several
number-discriminating techniques have been previously presented, the Silicon
Photo-Multiplier (SiPM) detector is a promising candidate due its rather simple
integration in optical setups. On the other hand, the photon statistics
obtained with the SiPM detector suffer from inaccuracies due to inherent
distortions which depend on the geometrical properties of the SiPM. We have
simulated the detection process in a SiPM detector and studied these
distortions. We use results from the simulation in order to interpret
experimental data and study the limits in which available models prevail.


  Privacy and energy are primary concerns for sensor devices that offload
compute to a potentially untrusted edge server or cloud. Homomorphic Encryption
(HE) enables offload processing of encrypted data. HE offload processing
retains data privacy, but is limited by the need for frequent communication
between the client device and the offload server. Existing client-aided
encrypted computing systems are optimized for performance on the offload
server, failing to sufficiently address client costs, and precluding HE offload
for low-resource (e.g., IoT) devices. We introduce Client-aided HE for Opaque
Compute Offloading (CHOCO), a client-optimized system for encrypted offload
processing. CHOCO introduces rotational redundancy, an algorithmic optimization
to minimize computing and communication costs. We design Client-Aided HE for
Opaque Compute Offloading Through Accelerated Cryptographic Operations
(CHOCO-TACO), a comprehensive architectural accelerator for client-side
cryptographic operations that eliminates most of their time and energy costs.
Our evaluation shows that CHOCO makes client-aided HE offloading feasible for
resource-constrained clients. Compared to existing encrypted computing
solutions, CHOCO reduces communication cost by up to 2948x. With hardware
support, client-side encryption/decryption is faster by 1094x and uses 648x
less energy. In our end-to-end implementation of a large-scale DNN (VGG16),
CHOCO uses 37% less energy than local (unencrypted) computation.


  We characterize all fields of definition for a given coherent sheaf over a
projective scheme in terms of projective modules over a finite-dimensional
endomorphism algebra. This yields general results on the essential dimension of
such sheaves. Applying them to vector bundles over a smooth projective curve C,
we obtain an upper bound for the essential dimension of their moduli stack. The
upper bound is sharp if the conjecture of Colliot-Th\'el\`ene, Karpenko and
Merkurjev holds. We find that the genericity property proved for
Deligne-Mumford stacks by Brosnan, Reichstein and Vistoli still holds for this
Artin stack, unless the curve C is elliptic.


  In the era of noisy intermediate-scale quantum (NISQ), variational quantum
circuits (VQCs) have been widely applied in various domains, advancing the
superiority of quantum circuits against classic models. Similar to classic
models, regular VQCs can be optimized by various gradient-based methods.
However, the optimization may be initially trapped in barren plateaus or
eventually entangled in saddle points during training. These gradient issues
can significantly undermine the trainability of VQC. In this work, we propose a
strategy that regularizes model parameters with prior knowledge of the train
data and Gaussian noise diffusion. We conduct ablation studies to verify the
effectiveness of our strategy across four public datasets and demonstrate that
our method can improve the trainability of VQCs against the above-mentioned
gradient issues.


  A new technique was developed for studying the mechanical behavior of
nano-scale thin metal films on substrate is presented. The test structure was
designed on a novel "paddle" cantilever beam specimens with dimensions as few
hundred nanometers to less than 10 nanometers. This beam is in triangle shape
in order to provide uniform plane strain distribution. Standard clean room
processing was used to prepare the paddle sample. The experiment can be
operated by using the electrostatic deflection on the paddle uniform
distributed stress cantilever beam and then measure the deposited thin metal
film materials on top of it. A capacitance technique was used to measurement on
the other side of the deflected plate to measure its deflection with respect to
the force. The measured strain was converted through the capacitance
measurement for the deflection of the cantilever. System performance on the
residual stress measurement of thin films are calculated with three different
forces on the "paddle" cantilever beam, including the force due to the film,
compliance force and electrostatic force.


  A long-standing problem of theoretical physics is the exceptionally small
value of the cosmological constant $\Lambda \sim 10^{-120}$ measured in natural
Planckian units. Here we derive this tiny number from a toroidal string
cosmology based on closed strings. In this picture the dark energy arises from
the correlation between momentum and winding modes that for short distances has
an exponential fall-off with increasing values of the momenta.The freeze-out by
the expansion of the background universe for these transplanckian modes may be
interpreted as a frozen condensate of the closed-string modes in the three
non-compactified spatial dimensions.


  The phase diagram of a correlated material is the result of a complex
interplay between several degrees of freedom, providing a map of the material's
behavior. One can understand (and ultimately control) the material's ground
state by associating features and regions of the phase diagram, with specific
physical events or underlying quantum mechanical properties. The phase diagram
of the newly discovered iron arsenic high temperature superconductors is
particularly rich and interesting. In the AE(Fe1-xTx)2As2 class (AE being Ca,
Sr, Ba, T being transition metals), the simultaneous structural/magnetic phase
transition that occurs at elevated temperature in the undoped material, splits
and is suppressed by carrier doping, the suppression being complete around
optimal doping. A dome of superconductivity exists with apparent equal ease in
the orthorhombic / antiferromagnetic (AFM) state as well as in the tetragonal
state with no long range magnetic order. The question then is what determines
the critical doping at which superconductivity emerges, if the AFM order is
fully suppressed only at higher doping values. Here we report evidence from
angle resolved photoemission spectroscopy (ARPES) that critical changes in the
Fermi surface (FS) occur at the doping level that marks the onset of
superconductivity. The presence of the AFM order leads to a reconstruction of
the electronic structure, most significantly the appearance of the small hole
pockets at the Fermi level. These hole pockets vanish, i. e. undergo a Lifshitz
transition, at the onset of superconductivity. Superconductivity and magnetism
are competing states in the iron arsenic superconductors. In the presence of
the hole pockets superconductivity is fully suppressed, while in their absence
the two states can coexist.


  We present an efficient finite difference method for the approximation of
second derivatives, with respect to system parameters, of expectations for a
class of discrete stochastic chemical reaction networks. The method uses a
coupling of the perturbed processes that yields a much lower variance than
existing methods, thereby drastically lowering the computational complexity
required to solve a given problem. Further, the method is simple to implement
and will also prove useful in any setting in which continuous time Markov
chains are used to model dynamics, such as population processes. We expect the
new method to be useful in the context of optimization algorithms that require
knowledge of the Hessian.


  We abstractly formulate an analytic problem that arises naturally in the
study of coordination in multi-agent systems. Let I be a set of arbitrary
cardinality (the set of actions) and assume that for each pair of distinct
actions (i,j), we are given a number \delta(i,j). We say that a function t,
specifying a time for each action, satisfies the timely constraint {\delta} if
for every pair of distinct actions (i,j), we have t(j)-t(i) <= \delta(i,j) (and
thus also t(j)-t(i) >= -\delta(j,i)). While the approach that first comes to
mind for analysing these definitions is an analytic/geometric one, it turns out
that graph-theoretic tools yield powerful results when applied to these
definitions. Using such tools, we characterise the set of satisfiable timely
constraints, and reduce the problem of satisfiability of a timely constraint to
the all-pairs shortest-path problem, and for finite I, furthermore to the
negative-cycle detection problem. Moreover, we constructively show that every
satisfiable timely constraint has a minimal satisfying function - a key
milestone on the way to optimally solving a large class of coordination
problems - and reduce the problem of finding this minimal satisfying function,
as well as the problems of classifying and comparing timely constraints, to the
all-pairs shortest-path problem. At the heart of our analysis lies the
constructive definition of a "nicely-behaved" representative for each class of
timely constraints sharing the same set of satisfying functions. We show that
this canonical representative, as well as the map from such canonical
representatives to the the sets of functions satisfying the classes of timely
constraints they represent, has many desired properties, which provide deep
insights into the structure underlying the above definitions.


  Lepton-number violation (LNV), in general, implies nonzero Majorana masses
for the Standard Model neutrinos. Since neutrino masses are very small, for
generic candidate models of the physics responsible for LNV, the rates for
almost all experimentally accessible LNV observables -- except for neutrinoless
double-beta decay -- are expected to be exceedingly small. Guided by
effective-operator considerations of LNV phenomena, we identify a complete
family of models where lepton number is violated but the generated Majorana
neutrino masses are tiny, even if the new-physics scale is below 1 TeV. We
explore the phenomenology of these models, including charged-lepton
flavor-violating phenomena and baryon-number-violating phenomena, identifying
scenarios where the allowed rates for $\mu^-\to e^+$-conversion in nuclei are
potentially accessible to next-generation experiments.


  Aspects of theoretical electroweak physics are summarized, including the
status of electroweak radiative corrections, the hadronic contribution to the
running of alpha, global fits to precision data and their implication for
testing the standard model and constraining new physics, and electroweak
baryogenesis.


  Nowadays, the videos on the Internet are prevailing. The precise and in-depth
understanding of the videos is a difficult but valuable problem for both
platforms and researchers. The existing video understand models do well in
object recognition tasks but currently still cannot understand the abstract and
contextual features like highlight humor frames in comedy videos. The current
industrial works are also mainly focused on the basic category classification
task based on the appearances of objects. The feature detection methods for the
abstract category remains blank. A data structure that includes the information
of video frames, audio spectrum and texts provide a new direction to explore.
The multimodal models are proposed to make this in-depth video understanding
mission possible. In this paper, we analyze the difficulties in abstract
understanding of videos and propose a multimodal structure to obtain
state-of-the-art performance in this field. Then we select several benchmarks
for multimodal video understanding and apply the most suitable model to find
the best performance. At last, we evaluate the overall spotlights and drawbacks
of the models and methods in this paper and point out the possible directions
for further improvements.


  For a weakly pseudo-Hermitian linear operator, we give a spectral condition
that ensures its pseudo-Hermiticity. This condition is always satisfied
whenever the operator acts in a finite-dimensional Hilbert space. Hence weak
pseudo-Hermiticity and pseudo-Hermiticity are equivalent in finite-dimensions.
This equivalence extends to a much larger class of operators. Quantum systems
whose Hamiltonian is selected from among these operators correspond to
pseudo-Hermitian quantum systems possessing certain symmetries.


  Existing a priori convergence results of the discontinuous Petrov-Galerkin
method to solve the problem of linear elasticity are improved. Using duality
arguments, we show that higher convergence rates for the displacement can be
obtained. Post-processing techniques are introduced in order to prove
superconvergence and numerical experiments {\color{black} confirm} our theory.


  Machine learning and data analytics applications increasingly suffer from the
high latency and energy consumption of conventional von Neumann architectures.
Recently, several in-memory and near-memory systems have been proposed to
remove this von Neumann bottleneck. Platforms based on content-addressable
memories (CAMs) are particularly interesting due to their efficient support for
the search-based operations that form the foundation for many applications,
including K-nearest neighbors (KNN), high-dimensional computing (HDC),
recommender systems, and one-shot learning among others. Today, these platforms
are designed by hand and can only be programmed with low-level code, accessible
only to hardware experts. In this paper, we introduce C4CAM, the first compiler
framework to quickly explore CAM configurations and to seamlessly generate code
from high-level TorchScript code. C4CAM employs a hierarchy of abstractions
that progressively lowers programs, allowing code transformations at the most
suitable abstraction level. Depending on the type and technology, CAM arrays
exhibit varying latencies and power profiles. Our framework allows analyzing
the impact of such differences in terms of system-level performance and energy
consumption, and thus supports designers in selecting appropriate designs for a
given application.


  We construct a quantum frame bundle of the quantum plane $C^2_p$ by requiring
that a $GL_{q,p}(2)$-covariant differential calculus on $C^2_p$ be isomorphic
as a bimodule to the space of sections of the associated quantum cotangent
bundle. We also construct the section space of the associated quantum tangent
bundle, and show that it is naturally dual to the differential calculus.


  We generalise the Kreck-Stolz invariants s_2 and s_3 by defining a new
invariant, the t-invariant, for quaternionic line bundles E over closed
spin-manifolds M of dimension 4k-1 with H^3(M; \Q) = 0 such that c_2(E)\in
H^4(M) is torsion. The t-invariant classifies closed smooth oriented
2-connected rational homology 7-spheres up to almost-diffeomorphism, that is,
diffeomorphism up to connected sum with an exotic sphere. It also detects
exotic homeomorphisms between such manifolds.
  The t-invariant also gives information about quaternionic line bundles over a
fixed manifold and we use it to give a new proof of a theorem of Feder and
Gitler about the values of the second Chern classes of quaternionic line
bundles over HP^k. The t-invariant for S^{4k-1} is closely related to the Adams
e-invariant on the (4k-5)-stem.


  Based on exact limits and quantum Monte Carlo simulations, we obtain, at any
density and spin polarization, an accurate estimate for the energy of a
modified homogeneous electron gas where electrons repel each other only with a
long-range coulombic tail. This allows us to construct an analytic
local-spin-density exchange-correlation functional appropriate to new,
multideterminantal versions of the density functional theory, where quantum
chemistry and approximate exchange-correlation functionals are combined to
optimally describe both long- and short-range electron correlations.


  When equipped with efficient optimization algorithms, the over-parameterized
neural networks have demonstrated high level of performance even though the
loss function is non-convex and non-smooth. While many works have been focusing
on understanding the loss dynamics by training neural networks with the
gradient descent (GD), in this work, we consider a broad class of optimization
algorithms that are commonly used in practice. For example, we show from a
dynamical system perspective that the Heavy Ball (HB) method can converge to
global minimum on mean squared error (MSE) at a linear rate (similar to GD);
however, the Nesterov accelerated gradient descent (NAG) may only converges to
global minimum sublinearly.
  Our results rely on the connection between neural tangent kernel (NTK) and
finite over-parameterized neural networks with ReLU activation, which leads to
analyzing the limiting ordinary differential equations (ODE) for optimization
algorithms. We show that, optimizing the non-convex loss over the weights
corresponds to optimizing some strongly convex loss over the prediction error.
As a consequence, we can leverage the classical convex optimization theory to
understand the convergence behavior of neural networks. We believe our approach
can also be extended to other optimization algorithms and network
architectures.


  We present a purely probabilistic proof of propagation of molecular chaos for
$N$-particle systems in dimension $3$ with interaction forces scaling like
$1/\vert q\vert^{\lambda}$ with $\lambda<2$ and cut-off at $q = N^{-1/3}$. The
proof yields a Gronwall estimate for the maximal distance between exact
microscopic and approximate mean-field dynamics. This can be used to show
propagation of molecular chaos, i.e. weak convergence of the marginals to the
corresponding products of solutions of the respective mean-field equation
without cut-off in a quantitative way. Our results thus lead to a derivation of
the Vlasov equation from the microscopic $N$-particle dynamics with force term
arbitrarily close to the physically relevant Coulomb- and gravitational forces.


  Single electron charging in an individual InAs quantum dot was observed by
electrostatic force measurements with an atomic force microscope (AFM). The
resonant frequency shift and the dissipated energy of an oscillating AFM
cantilever were measured as a function of the tip-back electrode voltage and
the resulting spectra show distinct jumps when the tip was positioned above the
dot. The observed jumps in the frequency shift, with corresponding peaks in
dissipation, are attributed to a single electron tunneling between the dot and
the back electrode governed by Coulomb blockade effect, and are consistent with
a model based on the free energy of the system. The observed phenomenon may be
regarded as the ``force version'' of the Coulomb blockade effect.


  Recent research has shown that mixed-initiative conversational search, based
on the interaction between users and computers to clarify and improve a query,
provides enormous advantages. Nonetheless, incorporating additional information
provided by the user from the conversation poses some challenges. In fact,
further interactions could confuse the system as a user might use words
irrelevant to the information need but crucial for correct sentence
construction in the context of multi-turn conversations. To this aim, in this
paper, we have collected two conversational keyword extraction datasets and
propose an end-to-end document retrieval pipeline incorporating them.
Furthermore, we study the performance of two neural keyword extraction models,
namely, BERT and sequence to sequence, in terms of extraction accuracy and
human annotation. Finally, we study the effect of keyword extraction on the
end-to-end neural IR performance and show that our approach beats
state-of-the-art IR models. We make the two datasets publicly available to
foster research in this area.


  We investigate the effect of unparticles in the pure $ b \to d $ penguin
processes $ B^0 \to K^0 \bar K^0$ and $B^{+,0} \to \phi \pi^{+,0} $. Since
these processes receive dominant contributions due to the {\it top} quark in
the loop, direct and mixing-induced CP asymmetry parameters in these processes
are expected to be vanishingly small in the standard model. We find that due to
the unparticle effect sizable nonzero CP violation could be possible in these
channels.


  We recompute the quark-monopole potential from supersymmetric SL(3,R)
deformation of IIB supergravity background dual to deformed Coulomb branch flow
of the N=4 super Yang-Mills theory. The marginal deformations strengthen the
Coulombic attraction between quarks and monopoles.


  The hybrid CONe white dwarfs (WDs) have been suggested to be possible
progenitors of type Ia supernovae (SNe Ia). In this article, we systematically
studied the hybrid CONe WD + He star scenario for the progenitors of SNe Ia, in
which a hybrid CONe WD increases its mass to the Chandrasekhar mass limit by
accreting He-rich material from a non-degenerate He star. According to a series
of detailed binary population synthesis simulations, we obtained the SN Ia
birthrates and delay times for this scenario. The SN Ia birthrates for this
scenario are ~0.033-0.539*10^(-3)yr^(-1), which roughly accounts for 1-18% of
all SNe Ia. The estimated delay times are ~28Myr-178Myr, which are the youngest
SNe Ia predicted by any progenitor model so far. We suggest that SNe Ia from
this scenario may provide an alternative explanation of type Iax SNe. We also
presented some properties of the donors at the point when the WDs reach the
Chandrasekhar mass. These properties may be a good starting point for
investigating the surviving companions of SNe Ia, and for constraining the
progenitor scenario studied in this work.


  Binary neural networks (BNNs) have received increasing attention due to their
superior reductions of computation and memory. Most existing works focus on
either lessening the quantization error by minimizing the gap between the
full-precision weights and their binarization or designing a gradient
approximation to mitigate the gradient mismatch, while leaving the "dead
weights" untouched. This leads to slow convergence when training BNNs. In this
paper, for the first time, we explore the influence of "dead weights" which
refer to a group of weights that are barely updated during the training of
BNNs, and then introduce rectified clamp unit (ReCU) to revive the "dead
weights" for updating. We prove that reviving the "dead weights" by ReCU can
result in a smaller quantization error. Besides, we also take into account the
information entropy of the weights, and then mathematically analyze why the
weight standardization can benefit BNNs. We demonstrate the inherent
contradiction between minimizing the quantization error and maximizing the
information entropy, and then propose an adaptive exponential scheduler to
identify the range of the "dead weights". By considering the "dead weights",
our method offers not only faster BNN training, but also state-of-the-art
performance on CIFAR-10 and ImageNet, compared with recent methods. Code can be
available at https://github.com/z-hXu/ReCU.


  We use a statistical approach to determine the relationship between the
stellar masses of galaxies and the masses of the dark matter halos in which
they reside. We obtain a parameterized stellar-to-halo mass (SHM) relation by
populating halos and subhalos in an N-body simulation with galaxies and
requiring that the observed stellar mass function be reproduced. We find good
agreement with constraints from galaxy-galaxy lensing and predictions of
semi-analytic models. Using this mapping, and the positions of the halos and
subhalos obtained from the simulation, we find that our model predictions for
the galaxy two-point correlation function (CF) as a function of stellar mass
are in excellent agreement with the observed clustering properties in the SDSS
at z=0. We show that the clustering data do not provide additional strong
constraints on the SHM function and conclude that our model can therefore
predict clustering as a function of stellar mass. We compute the conditional
mass function, which yields the average number of galaxies with stellar masses
in the range [m, m+dm] that reside in a halo of mass M. We study the redshift
dependence of the SHM relation and show that, for low mass halos, the SHM ratio
is lower at higher redshift. The derived SHM relation is used to predict the
stellar mass dependent galaxy CF and bias at high redshift. Our model predicts
that not only are massive galaxies more biased than low mass ones at all
redshifts, but the bias increases more rapidly with increasing redshift for
massive galaxies than for low mass ones. We present convenient fitting
functions for the SHM relation as a function of redshift, the conditional mass
function, and the bias as a function of stellar mass and redshift.


  We formulate the QCD critical point focusing effect on transverse velocity
($\beta_{t}$) dependence of antiproton to proton ($\bar{p}/p$) ratio, which was
recently proposed by Asakawa {\it et al.} as an experimental signature of QCD
critical point in high energy heavy ion collisions (HICs). For quantitative
analysis, Ultra-relativistic Quantum Molecular Dynamics (UrQMD) transport model
and THERMal heavy-IoN generATOR (THERMINATOR) are applied to calculate the
corresponding $\beta_{t}$ dependence of $\bar{p}/p$ ratio for three gedanken
focused isentropic trajectories with different focusing degree on QCD phase
diagram. Finally, we obtained an observable anomaly in $\beta_{t}$ dependence
of $\bar{p}/p$ ratio, which can be employed as a signature of QCD critical
point.


  We present a calculation of asymmetric nuclear matter properties in a
relativistic Brueckner Hartree Fock framework. Following other calculations the
components of the self-energies are extracted by projecting on Lorentz
invariant amplitudes. It is shown that for asymmetric nuclear matter one needs
a sixth invariant. We present a set of invariants which in the limit of
symmetric nuclear matter reduces to the conventional set. We argue that the
existence of such a properly behaving set is also crucial for the application
of the projection method in symmetric nuclear matter. Results for the equation
of state and other observables are presented. Special attention is payed to an
analysis in terms of mean-field effective coupling constants. Apart from the
usual ones we also find significant strength in the isovector scalar channel,
which can be interpreted as an effective $\delta$-meson.


  We study the analytic structure of semiclassical conformal blocks, namely of
the 1-point conformal block on the torus and of the 4-point conformal block on
the sphere, as functions of the intermediate dimension. We interpret their
discontinuities, which can be revealed with the use of a particular resummation
procedure, holographically in terms of configurations of geodesics in AdS3. In
other words, we study the behavior of the $\Omega$-deformed $\mathcal N=2$ SYM
theory in the Nekrasov-Shatashvili limit near those singular points, where
naively the W-boson with non-vanishing angular momentum becomes massless. Upon
a proper resummation of instanton contributions, these singularities disappear,
which is similar to the Seiberg-Witten solution in the undeformed case where
there is no massless W-boson. It is shown that the order parameter undergoes a
non-analytic behavior near positions of the poles. The jump of the order
parameter is interpreted holographically in terms of geodesic networks


  This paper employs a policy iteration reinforcement learning (RL) method to
study continuous-time linear quadratic mean-field control problems in the
infinite horizon. The drift and diffusion terms in the dynamics involve the
state as well as the control. We investigate the stability and convergence of
the RL algorithm using a Lyapunov Recursion. Instead of solving a pair of
coupled Riccati equations, the RL technique focuses on strengthening an
auxiliary function and the cost functional as the objective functions and
updating the new policy to compute the optimal control via state trajectories.
A numerical example sheds light on the established theoretical results.


  We model the transport of cosmic ray nuclei in the Galaxy by means of a new
numerical code. Differently from previous numerical models we account for a
generic spatial distribution of the diffusion coefficient. We found that in the
case of radially uniform diffusion, the main secondary/primary ratios (B/C, N/O
and sub-Fe/Fe) and the modulated antiproton spectrum match consistently the
available observations. Convection and re-acceleration do not seem to be
required in the energy range we consider: $1 < E < 10^3$ GeV/nucleon. We
generalize these results accounting for radial dependence of the diffusion
coefficient, which is assumed to trace that of the cosmic ray sources. While
this does not affect the prediction of secondary/primary ratios, the simulated
longitude profile of the diffuse $\gamma$-ray emission is significantly
different from the uniform case and may agree with EGRET measurements without
invoking ad hoc assumptions on the galactic gas density distribution.


  We perform extensive simulations of the sandpile model on a Sierpinski
gasket. Critical exponents for waves and avalanches are determined. We extend
the existing theory of waves to the present case. This leads to an exact value
for the exponent $\tau_w$ which describes the distribution of wave sizes:
$\tau_w = \ln{(9/5)}/\ln{3}$. Numerically, it is found that the number of waves
in an avalanche is proportional to the number of distinct sites toppled in the
avalanche. This leads to a conjecture for the exponent $\tau$ that determines
the distribution of avalanche sizes: $\tau=1+\tau_w = \ln{(27/5)}/\ln{3}$. Our
predictions are in good agreement with the numerical results.


  The 3x+1 problem concerns iteration of the map T(n) =(3n+1)/2 if n odd; n/2
if n even. The 3x +1 Conjecture asserts that for every positive integer n>1 the
forward orbit of n includes the integer 1. This paper is an annotated
bibliography of work done on the 3x+1 problem published from 2000 through 2009,
plus some later papers that were preprints by 2009. This is a sequel to an
annotated bibliography on the 3x+1 problem covering 1963-1999.
  At present the 3x+1 Conjecture remains unsolved.


  The dynamics of a hot electron cloud in the solar corona-like plasma based on
the numerical solution of kinetic equations of weak turbulence theory is
considered. Different finite difference schemes are examined to fit the exact
analytical solutions of quasilinear equations in hydrodynamic limit
(gas-dynamic solution). It is shown that the scheme suggested demonstrates
correct asymptotic behavior and can be employed to solve initial value problems
for an arbitrary initial electron distribution function.


  Several recent theories suggest that light moduli or particles in "large"
extra dimensions could mediate macroscopic forces exceeding gravitational
strength at length scales below a millimeter. Such new forces can be
parameterized as a Yukawa-type correction to the Newtonian potential of
strength $\alpha$ relative to gravity and range $\lambda$. To extend the search
for such new physics we have improved our apparatus utilizing cryogenic
micro-cantilevers capable of measuring attonewton forces, which now includes a
switchable magnetic force for calibration. Our most recent experimental
constraints on Yukawa-type deviations from Newtonian gravity are more than
three times as stringent as our previously published results, and represent the
best bound in the range of 5 - 15 microns, with a 95 percent confidence
exclusion of forces with $|\alpha| > 14,000$ at $\lambda$ = 10 microns.


  We propose a human pose estimation framework that solves the task in the
regression-based fashion. Unlike previous regression-based methods, which often
fall behind those state-of-the-art methods, we formulate the pose estimation
task into a sequence prediction problem that can effectively be solved by
transformers. Our framework is simple and direct, bypassing the drawbacks of
the heatmap-based pose estimation. Moreover, with the attention mechanism in
transformers, our proposed framework is able to adaptively attend to the
features most relevant to the target keypoints, which largely overcomes the
feature misalignment issue of previous regression-based methods and
considerably improves the performance. Importantly, our framework can
inherently take advantages of the structured relationship between keypoints.
Experiments on the MS-COCO and MPII datasets demonstrate that our method can
significantly improve the state-of-the-art of regression-based pose estimation
and perform comparably with the best heatmap-based pose estimation methods.


  Machine learning offers great potential for automated prediction of
post-stroke symptoms and their response to rehabilitation. Major challenges for
this endeavour include the very high dimensionality of neuroimaging data, the
relatively small size of the datasets available for learning, and how to
effectively combine neuroimaging and tabular data (e.g. demographic information
and clinical characteristics). This paper evaluates several solutions based on
two strategies. The first is to use 2D images that summarise MRI scans. The
second is to select key features that improve classification accuracy.
Additionally, we introduce the novel approach of training a convolutional
neural network (CNN) on images that combine regions-of-interest extracted from
MRIs, with symbolic representations of tabular data. We evaluate a series of
CNN architectures (both 2D and a 3D) that are trained on different
representations of MRI and tabular data, to predict whether a composite measure
of post-stroke spoken picture description ability is in the aphasic or
non-aphasic range. MRI and tabular data were acquired from 758 English speaking
stroke survivors who participated in the PLORAS study. The classification
accuracy for a baseline logistic regression was 0.678 for lesion size alone,
rising to 0.757 and 0.813 when initial symptom severity and recovery time were
successively added. The highest classification accuracy 0.854 was observed when
8 regions-of-interest was extracted from each MRI scan and combined with lesion
size, initial severity and recovery time in a 2D Residual Neural Network.Our
findings demonstrate how imaging and tabular data can be combined for high
post-stroke classification accuracy, even when the dataset is small in machine
learning terms. We conclude by proposing how the current models could be
improved to achieve even higher levels of accuracy using images from hospital
scanners.


  Error-tolerant recognition enables the recognition of strings that deviate
mildly from any string in the regular set recognized by the underlying finite
state recognizer. Such recognition has applications in error-tolerant
morphological processing, spelling correction, and approximate string matching
in information retrieval. After a description of the concepts and algorithms
involved, we give examples from two applications: In the context of
morphological analysis, error-tolerant recognition allows misspelled input word
forms to be corrected, and morphologically analyzed concurrently. We present an
application of this to error-tolerant analysis of agglutinative morphology of
Turkish words. The algorithm can be applied to morphological analysis of any
language whose morphology is fully captured by a single (and possibly very
large) finite state transducer, regardless of the word formation processes and
morphographemic phenomena involved. In the context of spelling correction,
error-tolerant recognition can be used to enumerate correct candidate forms
from a given misspelled string within a certain edit distance. Again, it can be
applied to any language with a word list comprising all inflected forms, or
whose morphology is fully described by a finite state transducer. We present
experimental results for spelling correction for a number of languages. These
results indicate that such recognition works very efficiently for candidate
generation in spelling correction for many European languages such as English,
Dutch, French, German, Italian (and others) with very large word lists of root
and inflected forms (some containing well over 200,000 forms), generating all
candidate solutions within 10 to 45 milliseconds (with edit distance 1) on a
SparcStation 10/41. For spelling correction in Turkish, error-tolerant


  We present mean reddenings toward 3525 RR0 Lyrae stars from the Galactic
bulge fields of the MACHO Survey. These reddenings are determined using the
color at minimum $V$-band light of the RR0 Lyrae stars themselves and are found
to be in general agreement with extinction estimates at the same location
obtained from other methods. Using 3256 stars located in the Galactic Bulge, we
derive the selective extinction coefficient $R_{V,VR}=A_V/E(V-R) = 4.3 \pm
0.2$. This value is what is expected for a standard extinction law with
$R_{V,BV} = 3.1 \pm 0.3$.


  Dynamics in biological networks are in general robust against several
perturbations. We investigate a coupled map network as a model motivated by
gene regulatory networks and design systems which are robust against phenotypic
perturbations (perturbations in dynamics), as well as systems which are robust
against mutation (perturbations in network structure). To achieve such a
design, we apply a multicanonical Monte Carlo method. Analysis based on the
maximum Lyapunov exponent and parameter sensitivity shows that systems with
marginal stability, which are regarded as systems at the edge of chaos, emerge
when robustness against network perturbations is required. This emergence of
the edge of chaos is a self-organization phenomenon and does not need a fine
tuning of parameters.


  We study the storage of multiple phase-coded patterns as stable dynamical
attractors in recurrent neural networks with sparse connectivity. To determine
the synaptic strength of existent connections and store the phase-coded
patterns, we introduce a learning rule inspired to the spike-timing dependent
plasticity (STDP). We find that, after learning, the spontaneous dynamics of
the network replay one of the stored dynamical patterns, depending on the
network initialization. We study the network capacity as a function of
topology, and find that a small- world-like topology may be optimal, as a
compromise between the high wiring cost of long range connections and the
capacity increase.


  Recurrent neural networks are increasing popular models for sequential
learning. Unfortunately, although the most effective RNN architectures are
perhaps excessively complicated, extensive searches have not found simpler
alternatives. This paper imports ideas from physics and functional programming
into RNN design to provide guiding principles. From physics, we introduce type
constraints, analogous to the constraints that forbids adding meters to
seconds. From functional programming, we require that strongly-typed
architectures factorize into stateless learnware and state-dependent firmware,
reducing the impact of side-effects. The features learned by strongly-typed
nets have a simple semantic interpretation via dynamic average-pooling on
one-dimensional convolutions. We also show that strongly-typed gradients are
better behaved than in classical architectures, and characterize the
representational power of strongly-typed nets. Finally, experiments show that,
despite being more constrained, strongly-typed architectures achieve lower
training and comparable generalization error to classical architectures.


  We introduce romanization encoding for script-heavy languages to optimize
multilingual and code-switching Automatic Speech Recognition (ASR) systems. By
adopting romanization encoding alongside a balanced concatenated tokenizer
within a FastConformer-RNNT framework equipped with a Roman2Char module, we
significantly reduce vocabulary and output dimensions, enabling larger training
batches and reduced memory consumption. Our method decouples acoustic modeling
and language modeling, enhancing the flexibility and adaptability of the
system. In our study, applying this method to Mandarin-English ASR resulted in
a remarkable 63.51% vocabulary reduction and notable performance gains of
13.72% and 15.03% on SEAME code-switching benchmarks. Ablation studies on
Mandarin-Korean and Mandarin-Japanese highlight our method's strong capability
to address the complexities of other script-heavy languages, paving the way for
more versatile and effective multilingual ASR systems.


  In a two-dimensional two-component plasma, the second moment of the number
density correlation function has the simple value $\{12 \pi
[1-(\Gamma/4)]^2\}^{-1}$, where $\Gamma$ is the dimensionless coupling
constant. This result is derived directly by using diagrammatic methods.


  We present nonperturbative light-front energy eigenstates in the broken phase
of a two dimensional $\frac{\lambda}{4!}\phi^4$ quantum field theory using
Discrete Light Cone Quantization and extrapolate the results to the continuum
limit. We establish degeneracy in the even and odd particle sectors and extract
the masses of the lowest two states and the vacuum energy density for
$\lambda=0.5$ and 1.0. We present two novel results: the Fourier transform of
the form factor of the lowest excitation as well as the number density of
elementary constituents of that state. A coherent state with kink - antikink
structure is revealed.


  We show that planar cal N=4 Yang-Mills theory at zero 't Hooft coupling can
be efficiently described in terms of 8 bosonic and 8 fermionic oscillators. We
show that these oscillators can serve as world-sheet variables, the string
bits, of a discretized string. There is a one to one correspondence between the
on shell gauge invariant words of the free Y-M theory and the states in the
oscillators' Hilbert space, obeying a local gauge and cyclicity constraints.
The planar two-point functions and the three-point functions of all gauge
invariant words are obtained by the simple delta-function overlap of the
corresponding discrete string world sheet. At first order in the 't Hooft
coupling, i.e. at one-loop in the Y-M theory, the logarithmic corrections of
the planar two-point and the three-point functions can be incorporated by
nearest neighbour interactions among the discretized string bits. In the SU(2)
sub-sector we show that the one-loop corrections to the structure constants can
be uniquely determined by the symmetries of the bit picture. For the SU(2)
sub-sector we construct a gauged, linear, discrete world-sheet model for the
oscillators, with only nearest neighbour couplings, which reproduces the
anomalous dimension Hamiltonian up to two loops. This model also obeys BMN
scaling to all loops.


  This research is our forth article related to the topic of cyclic O-C
changes, third components as the physical process that cause these changes and
elliptical orbits of the third components. Here five more eclipsing binary
stars were investigated: EQ Tau, IR Cas, IV Cas, RY Aqr and RZ Com. All of them
have cyclic O-C curve with superposition of parabolic trend. We computed the
mass transfer rate, minima possible mass of the third component and their
errors for each of the researched stars.


  We present a novel privacy-preserving scheme for deep neural networks (DNNs)
that enables us not to only apply images without visual information to DNNs for
both training and testing but to also consider data augmentation in the
encrypted domain for the first time. In this paper, a novel pixel-based image
encryption method is first proposed for privacy-preserving DNNs. In addition, a
novel adaptation network is considered that reduces the influence of image
encryption. In an experiment, the proposed method is applied to a well-known
network, ResNet-18, for image classification. The experimental results
demonstrate that conventional privacy-preserving machine learning methods
including the state-of-the-arts cannot be applied to data augmentation in the
encrypted domain and that the proposed method outperforms them in terms of
classification accuracy.


  We study some algebraic properties of the 'vector supersymmetry' (VSUSY)
algebra, a graded extension of the four-dimensional Poincare' algebra with two
odd generators, a vector and a scalar, and two central charges. The
anticommutator between the two odd generators gives the four-momentum operator,
from which the name vector supersymmetry. We construct the Casimir operators
for this algebra and we show how both algebra and Casimirs can be derived by
contraction from the simple orthosymplectic algebra OSp(3,2|2). In particular,
we construct the analogue of superspin for vector supersymmetry and we show
that, due to the algebraic structure of the Casimirs, the multiplets are either
doublets of spin (s,s+1) or two spin 1/2 states. Finally, we identify an odd
operator, which is an invariant in a subclass of representations where a
BPS-like algebraic relation between the mass and the values of the central
charges is satisfied.


  Principal components (PCA) and hierarchical clustering are two of the most
heavily used techniques for analyzing the differences between nucleic acid
sequence samples sampled from a given environment. However, a classical
application of these techniques to distances computed between samples can lack
transparency because there is no ready interpretation of the axes of classical
PCA plots, and it is difficult to assign any clear intuitive meaning to either
the internal nodes or the edge lengths of trees produced by distance-based
hierarchical clustering methods such as UPGMA. We show that more interesting
and interpretable results are produced by two new methods that leverage the
special structure of phylogenetic placement data. Edge principal components
analysis enables the detection of important differences between samples that
contain closely related taxa. Each principal component axis is simply a
collection of signed weights on the edges of the phylogenetic tree, and these
weights are easily visualized by a suitable thickening and coloring of the
edges. Squash clustering outputs a (rooted) clustering tree in which each
internal node corresponds to an appropriate "average" of the original samples
at the leaves below the node. Moreover, the length of an edge is a suitably
defined distance between the averaged samples associated with the two incident
nodes, rather than the less interpretable average of distances produced by
UPGMA. We present these methods and illustrate their use with data from the
microbiome of the human vagina.


  The effect of pressure on structural and thermodynamic properties of ice Ih
has been studied by means of path-integral molecular dynamics simulations at
temperatures between 50 and 300 K. Interatomic interactions were modeled by
using the effective q-TIP4P/F potential for flexible water. Positive
(compression) and negative (tension) pressures have been considered, which
allowed us to approach the limits for the mechanical stability of this solid
water phase. We have studied the pressure dependence of the crystal volume,
bulk modulus, interatomic distances, atomic delocalization, and kinetic energy.
The spinodal point at both negative and positive pressures is derived from the
vanishing of the bulk modulus. For P < 0, the spinodal pressure changes from
-1.38 to -0.73 GPa in the range from 50 to 300 K. At positive pressure the
spinodal is associated to ice amorphization, and at low temperatures is found
between 1.1 and 1.3 GPa. Quantum nuclear effects cause a reduction of the
metastability region of ice Ih.


  Within the framework of self-adjoint operator of time in non-relativistic
quantum mechanics the equation describing change of the state of quantum system
with respect to energy is introduced. The operator of time appears to be the
generator of the change of energy while the operator of energy, that is
conjugate to the operator of time, generates time evolution in this proposal.
Two examples, one with discrete time and the other with continuous one, are
given and the generalization of Schroedinger equation is proposed.


  Active galactic nuclei (AGNs) can be divided into two major classes, namely
radio-loud and radio-quiet AGNs. A small subset of the radio-loud AGNs is
called blazars, which are believed to be unified with Fanaroff-Riley type I and
type II (FRI&II) radio galaxies. Following our previous work, we present a
latest sample of 966 sources with measured radio flux densities of the core and
extended components. The sample includes 83 BL Lacs, 473 FSRQs, 101 Seyferts,
245 galaxies, 52 FRIs&IIs and 12 unidentified sources. We then calculate the
radio core-dominance parameters and spectral indices and study their
relationship. Our analysis shows that the core-dominance parameters and
spectral indices are quite different for different types of sources. We also
corroborate that the correlation between core-dominance parameter and radio
spectral index extends over all the sources in a large sample presented.


  Paper has been withdrawn due to an error in the basic argument that the
states corresponding to the zeros of Riemann Zeta with Re[s]<1/2 allow a
Fourier expansion in the basis provided by the states having Re[s]>= 1/2.


  Recent advancements in quantum computing, alongside successful deployments of
quantum communication, hold promises for revolutionizing mobile networks. While
Quantum Machine Learning (QML) presents opportunities, it contends with
challenges like noise in quantum devices and scalability. Furthermore, the high
cost of quantum communication constrains the practical application of QML in
real-world scenarios. This paper introduces a noise-aware clustered quantum
federated learning system that addresses noise mitigation, limited quantum
device capacity, and high quantum communication costs in distributed QML. It
employs noise modelling and clustering to select devices with minimal noise and
distribute QML tasks efficiently. Using circuit partitioning to deploy smaller
models on low-noise devices and aggregating similar devices, the system
enhances distributed QML performance and reduces communication costs.
Leveraging circuit cutting, QML techniques are more effective for smaller
circuit sizes and fidelity. We conduct experimental evaluations to assess the
performance of the proposed system. Additionally, we introduce a noisy dataset
for QML to demonstrate the impact of noise on proposed accuracy.


  Knowledge-based Visual Question Answering (KVQA) tasks require answering
questions about images using extensive background knowledge. Despite
significant advancements, generative models often struggle with these tasks due
to the limited integration of external knowledge. In this paper, we introduce
EchoSight, a novel multimodal Retrieval-Augmented Generation (RAG) framework
that enables large language models (LLMs) to answer visual questions requiring
fine-grained encyclopedic knowledge. To strive for high-performing retrieval,
EchoSight first searches wiki articles by using visual-only information,
subsequently, these candidate articles are further reranked according to their
relevance to the combined text-image query. This approach significantly
improves the integration of multimodal knowledge, leading to enhanced retrieval
outcomes and more accurate VQA responses. Our experimental results on the
Encyclopedic VQA and InfoSeek datasets demonstrate that EchoSight establishes
new state-of-the-art results in knowledge-based VQA, achieving an accuracy of
41.8% on Encyclopedic VQA and 31.3% on InfoSeek.


  We construct an $\mathfrak{sl}_2$-action on equivariant
$\mathfrak{gl}_N$-link homologies. As a consequence we obtain an action of
$\mathfrak{sl}_2$ on these homologies as well as a $p$-DG structures for $p$ a
prime number. We explore topological applications of these structures.


  Twitter is a social media platform where users express opinions over a
variety of issues. Posts offering grievances or complaints can be utilized by
private/ public organizations to improve their service and promptly gauge a
low-cost assessment. In this paper, we propose an iterative methodology which
aims to identify complaint based posts pertaining to the transport domain. We
perform comprehensive evaluations along with releasing a novel dataset for the
research purposes.


  Contextual word embeddings obtained from pre-trained language model (PLM)
have proven effective for various natural language processing tasks at the word
level. However, interpreting the hidden aspects within embeddings, such as
syntax and semantics, remains challenging. Disentangled representation learning
has emerged as a promising approach, which separates specific aspects into
distinct embeddings. Furthermore, different linguistic knowledge is believed to
be stored in different layers of PLM. This paper aims to disentangle semantic
sense from BERT by applying a binary mask to middle outputs across the layers,
without updating pre-trained parameters. The disentangled embeddings are
evaluated through binary classification to determine if the target word in two
different sentences has the same meaning. Experiments with cased
BERT$_{\texttt{base}}$ show that leveraging layer-wise information is effective
and disentangling semantic sense further improve performance.


  We develop a theory to measure the variance and covariance of probability
distributions defined on the nodes of a graph, which takes into account the
distance between nodes. Our approach generalizes the usual (co)variance to the
setting of weighted graphs and retains many of its intuitive and desired
properties. Interestingly, we find that a number of famous concepts in graph
theory and network science can be reinterpreted in this setting as variances
and covariances of particular distributions. As a particular application, we
define the maximum variance problem on graphs with respect to the effective
resistance distance, and characterize the solutions to this problem both
numerically and theoretically. We show how the maximum variance distribution is
concentrated on the boundary of the graph, and illustrate this in the case of
random geometric graphs. Our theoretical results are supported by a number of
experiments on a network of mathematical concepts, where we use the variance
and covariance as analytical tools to study the (co-)occurrence of concepts in
scientific papers with respect to the (network) relations between these
concepts.


  Millimeter-wave communication is a challenge in the highly mobile vehicular
context. Traditional beam training is inadequate in satisfying low overheads
and latency. In this paper, we propose to combine machine learning tools and
situational awareness to learn the beam information (power, optimal beam index,
etc) from past observations. We consider forms of situational awareness that
are specific to the vehicular setting including the locations of the receiver
and the surrounding vehicles. We leverage regression models to predict the
received power with different beam power quantizations. The result shows that
situational awareness can largely improve the prediction accuracy and the model
can achieve throughput with little performance loss with almost zero overhead.


  We introduce the concept of topological finite-determinacy for germs of
analytic functions within a fixed ideal $I$, which provides a notion of
topological finite-determinacy of functions with non-isolated singularities. We
prove the following statement which generalizes classical results of Thom and
Varchenko: let $A$ be the complement in the ideal $I$ of the space of germs
whose topological type remains unchanged under a deformation within the ideal
that only modifies sufficiently large order terms of the Taylor expansion; then
$A$ has infinite codimension in $I$ in a suitable sense. We also prove the
existence of generic topological types of families of germs of $I$ parametrized
by an irreducible analytic set.


  Accurate lesion detection in computer tomography (CT) slices benefits
pathologic organ analysis in the medical diagnosis process. More recently, it
has been tackled as an object detection problem using the Convolutional Neural
Networks (CNNs). Despite the achievements from off-the-shelf CNN models, the
current detection accuracy is limited by the inability of CNNs on lesions at
vastly different scales. In this paper, we propose a Multi-Scale Booster (MSB)
with channel and spatial attention integrated into the backbone Feature Pyramid
Network (FPN). In each pyramid level, the proposed MSB captures fine-grained
scale variations by using Hierarchically Dilated Convolutions (HDC). Meanwhile,
the proposed channel and spatial attention modules increase the network's
capability of selecting relevant features response for lesion detection.
Extensive experiments on the DeepLesion benchmark dataset demonstrate that the
proposed method performs superiorly against state-of-the-art approaches.


  It is known that in f(R) theories of gravity with an independent connection
which can be both non-metric and non symmetric, this connection can always be
algebraically eliminated in favour of the metric and the matter fields, so long
as it is not coupled to the matter explicitly. We show here that this is a
special characteristic of f(R) actions, and it is not true for actions that
include other curvature invariants. This contradicts some recent claims in the
literature. We clarify the reasons of this contradiction.


  We investigate the transverse profile of the chromoelectric field generated
by a quark-antiquark pair in the vacuum of (2+1) flavor QCD. Monte Carlo
simulations are performed adopting the HISQ/tree action discretization, as
implemented in the publicly available MILC code, suitably modified to measure
the chromoelectric field. We work on the line of constant physics, with
physical strange quark mass $m_s$ and light to strange mass ratio $m_l/m_s =
1/20$.


  In this paper we prove global existence for solutions of the Vlasov-Poisson
system in convex bounded domains with specular boundary conditions and with a
prescribed outward electrical field at the boundary.


  In this paper, we reconstruct the $f(R)$ theory using the viscous $\omega$CDM
scenario (V-$\omega$CDM) constrained by several differently astrophysical
observations, including Type Ia supernovae (SNe Ia), observational Hubble
parameter data (OHD), Wilkinson Microwave Anisotropy Probe 9-year data
(WMAP-9), Planck observations and the single data point from the newest event
GW150914 (GW). We find that the joint constraints from SNe Ia+OHD+Planck+GW
data-sets give out a tighter restriction for the parameters of the
V-$\omega$CDM scenario than other constraints. Subsequently, we find that there
exists a substantially high degeneracy among these reconstructed $f(R)$
theories in the past, and that they start exhibiting differently evolutional
behaviors around the present epoch as well as much different in the distant
future. Furthermore, we make a comparison between the reconstructed $f(R)$
theory with the V-$\omega$CDM scenario and that with the $\omega$CDM scenario
constrained by using the SNe Ia+OHD+Planck+GW data-sets in the $f(R)-R$ plane,
and find that the two scenarios share an extremely high degeneracy, which
implies that the effect of bulk viscosity plays a negligible role in the
V-$\omega$CDM model during the whole evolutionary processes of the universe. In
addition, one can also find that the reconstructed $f(R)$ theories using the
two models just deviate from the standard Einstein gravity a little.


  Etruscan mirrors constitute a significant category in Etruscan art,
characterized by elaborate figurative illustrations featured on their backside.
A laborious and costly aspect of their analysis and documentation is the task
of manually tracing these illustrations. In previous work, a methodology has
been proposed to automate this process, involving photometric-stereo scanning
in combination with deep neural networks. While achieving quantitative
performance akin to an expert annotator, some results still lack qualitative
precision and, thus, require annotators for inspection and potential
correction, maintaining resource intensity. In response, we propose a deep
neural network trained to interactively refine existing annotations based on
human guidance. Our human-in-the-loop approach streamlines annotation,
achieving equal quality with up to 75% less manual input required. Moreover,
during the refinement process, the relative improvement of our methodology over
pure manual labeling reaches peak values of up to 26%, attaining drastically
better quality quicker. By being tailored to the complex task of segmenting
intricate lines, specifically distinguishing it from previous methods, our
approach offers drastic improvements in efficacy, transferable to a broad
spectrum of applications beyond Etruscan mirrors.


  We have studied electronic conductivity and shot noise of bilayer graphene
(BLG) sheets at high bias voltages and low bath temperature $T_0=4.2$ K. As a
function of bias, we find initially an increase of the differential
conductivity, which we attribute to self-heating. At higher bias, the
conductivity saturates and even decreases due to backscattering from optical
phonons. The electron-phonon interactions are also responsible for the decay of
the Fano factor at bias voltages $V>0.1$ V. The high bias electronic
temperature has been calculated from shot noise measurements, and it goes up to
$\sim1200$ K at $V=0.75$ V. Using the theoretical temperature dependence of BLG
conductivity, we extract an effective electron-optical phonon scattering time
$\tau_{e-op}$. In a 230 nm long BLG sample of mobility $\mu=3600$
cm$^2$V$^{-1}$s$^{-1}$, we find that $\tau_{e-op}$ decreases with increasing
voltage and is close to the charged impurity scattering time $\tau_{imp}=60$ fs
at $V=0.6$ V.


  In radio galaxies such as M84 dust features tend to be nearly perpendicular
to radio jets yet are not aligned with the galaxy isophotes. The timescale for
precession in the galaxy is short suggesting that an alternative mechanism
causes the gas disk to be misaligned with the galaxy. In M84 we estimate the
pressure on the disk required to overcome the torque from the galaxy and find
that it is small compared to the thermal pressure in the hot ambient ISM
estimated from the X-ray emission. We therefore propose that pressure gradients
in a jet associated hot interstellar medium exert a torque on the gas disk in
M84 causing it to be misaligned with the galaxy isophotal major axis. This
mechanism could explain the connection between gas disk angular momentum and
jet axes in nearby radio galaxies.
  By integrating the light of the galaxy through a warped gas and dust disk we
find that the geometry of gas disk in M84 is likely to differ that predicted
from a simple precession model. We find that the morphology of the gas disk in
M84 is consistent with a warped geometry where precession is caused by a
combination of a galactic torque and a larger torque due to pressure gradients
in the ambient X-ray emitting gas. Precession occurs at an axis between the jet
and galaxy major axis, but nearer to the jet axis implying that the pressure
torque is 2-4 times larger than the galactic torque. A better model to the
morphology of the disk is achieved when precession takes place about an
elliptical rather than circular path. This suggests that the isobars in the hot
medium are strongly dependent on angle from the jet axis.


  While the ultrastructure of the myelin has been considered to be a
quasi-crystalline stable system, nowadays its multiscale complex dynamics
appears to play a key role for its functionality, degeneration and repair
processes following neurological diseases and trauma. In this work, we have
investigated the axons interactions associated to the nerve functionality,
measuring the spatial distribution of the orientational fluctuations of axons
in a Xenopus Laevis sciatic nerve. At this aim, we have used Scanning micro
X-ray Diffraction (SmXRD), a non-invasive already applied to other
heterogeneous systems presenting complex geometries from microscale to
nanoscale. We have found that the orientational spatial fluctuations of fresh
axons show a correlated disorder described by Levy flight distribution. Thus,
we have studied how this correlated disorder evolves during the degeneration of
the nerve. Our results show that the spatial distribution of axons
orientational fluctuations in unfresh, aged nerve loose the correlated disorder
assuming a randomly disordered behaviour. This work allows a deeper
understanding of nerve states and paves the way to study other materials and
biomaterials with the same technique to detect and to characterize their states
and supramolecular structure, associated with dynamic structural changes at the
nanoscale and mesoscale.


  The analysis of pseudo-colour diagrams, the so-called chromosome maps, of
Galactic globular clusters (GCs) permits to classify them into type I and type
II clusters. Type II GCs are characterized by an above-the-average complexity
of their chromosome maps and some of them are known to display star-to-star
variations of slow neutron-capture reaction elements including iron. This is at
the basis of the hypothesis that type II GCs may have an extragalactic origin
and were subsequently accreted by the Milky Way. We performed a Principal
Component Analysis to explore possible correlations among various GCs
parameters in the light of this new classification. The analysis revealed that
cluster type correlates mainly with relative age. The cause of this relation
was further investigated finding that more metal-rich type II clusters, also
appear to be younger and more distant from the Galactic centre. A depletion of
type II clusters for positive values of Galactic coordinate Z was also
observed, with no type II clusters detected above Z$\sim2$ kpc. Type II cluster
orbits also have larger eccentricities than type I ones.


  We propose a modular anomaly equation for the prepotential of the N=2* super
Yang-Mills theory on R^4 with gauge group U(N) in the presence of an
Omega-background. We then study the behaviour of the prepotential in a large-N
limit, in which N goes to infinity with the gauge coupling constant kept fixed.
In this regime instantons are not suppressed. We focus on two representative
choices of gauge theory vacua, where the vacuum expectation values of the
scalar fields are distributed either homogeneously or according to the Wigner
semi-circle law. In both cases we derive an all-instanton exact formula for the
prepotential. As an application, we show that the gauge theory partition
function on S^4 at large N localises around a Wigner distribution for the
vacuum expectation values leading to a very simple expression in which the
instanton contribution becomes independent of the coupling constant.


  Let G be a linear algebraic group over an algebraically closed field of
characteristic p whose corresponding root system is irreducible. In this paper
we calculate the Weyl filtration dimension of the induced G-modules,
\nabla(\lambda) and the simple G-modules L(\lambda), for \lambda a regular
weight. We use this to calculate some Ext groups of the form
Ext^*(\nabla(\lambda),\Delta(\mu)), Ext^*(L(\lambda),L(\mu)), and
Ext^*(\nabla(\lambda), \nabla(\mu)), where \lambda, \mu are regular and
\Delta(\mu) is the Weyl module of highest weight \mu. We then deduce the
projective dimensions and injective dimensions for L(\lambda), \nabla(\lambda)
and \Delta(\lambda) for \lambda a regular weight in associated generalised
Schur algebras. We also deduce the global dimension of the Schur algebras for
GL_n, S(n,r), when p>n and for S(mp,p) with m an integer.