  We study the flow of a pure spin current through zinc oxide by measuring the
spin Hall magnetoresistance (SMR) in thin film trilayer samples consisting of
bismuth-substituted yttrium iron garnet (Bi:YIG), gallium-doped zinc oxide
(Ga:ZnO), and platinum. We investigate the dependence of the SMR magnitude on
the thickness of the Ga:ZnO interlayer and compare to a Bi:YIG/Pt bilayer. We
find that the SMR magnitude is reduced by almost one order of magnitude upon
inserting a Ga:ZnO interlayer, and continuously decreases with increasing
interlayer thickness. Nevertheless, the SMR stays finite even for a
$12\;\mathrm{nm}$ thick Ga:ZnO interlayer. These results show that a pure spin
current indeed can propagate through a several nm-thick degenerately doped zinc
oxide layer. We also observe differences in both the temperature and the field
dependence of the SMR when comparing tri- and bilayers. Finally, we compare our
data to predictions of a model based on spin diffusion. This shows that
interface resistances play a crucial role for the SMR magnitude in these
trilayer structures.


  The wild part of Abhyankar's Inertia Conjecture for a product of certain
Alternating groups is shown for any algebraically closed field of odd
characteristic. For $d$ a multiple of the characteristic of the base field, a
new \'etale $A_d$-cover of the affine line is obtained using an explicit
equation and it is shown that it has the minimal possible upper jump.


  Machine learning has made tremendous progress in recent years and received
large amounts of public attention. Though we are still far from designing a
full artificially intelligent agent, machine learning has brought us many
applications in which computers solve human learning tasks remarkably well.
Much of this progress comes from a recent trend within machine learning, called
deep learning. Deep learning models are responsible for many state-of-the-art
applications of machine learning. Despite their success, deep learning models
are hard to train, very difficult to understand, and often times so complex
that training is only possible on very large GPU clusters. Lots of work has
been done on enabling neural networks to learn efficiently. However, the design
and architecture of such neural networks is often done manually through trial
and error and expert knowledge. This thesis inspects different approaches,
existing and novel, to automate the design of deep feedforward neural networks
in an attempt to create less complex models with good performance that take
away the burden of deciding on an architecture and make it more efficient to
design and train such deep networks.


  This study explores various data-driven methods for performing
background-model selection, and for assigning uncertainty on the
signal-strength estimator that arises due to the choice of background model.
The performance of these methods is evaluated in the context of several
realistic example problems. Furthermore, a novel strategy is proposed that
greatly simplifies the process of performing a bump hunt when little is assumed
to be known about the background. This new approach is shown to greatly reduce
the potential bias in the signal-strength estimator, without degrading the
sensitivity by increasing the variance, and to produce confidence intervals
with valid coverage properties.


  We construct four-dimensional symplectic cobordisms between contact
three-manifolds generalizing an example of Eliashberg. One key feature is that
any handlebody decomposition of one of these cobordisms must involve
three-handles. The other key feature is that these cobordisms contain chains of
symplectically embedded two-spheres of square zero. This, together with
standard gauge theory, is used to show that any contact three-manifold of
non-zero torsion (in the sense of Giroux) cannot be strongly symplectically
fillable. John Etnyre pointed out to the author that the same argument together
with compactness results for pseudo-holomorphic curves implies that any contact
three-manifold of non-zero torsion satisfies the Weinstein conjecture. We also
get examples of weakly symplectically fillable contact three-manifolds which
are (strongly) symplectically cobordant to overtwisted contact three-manifolds,
shedding new light on the structure of the set of contact three-manifolds
equipped with the strong symplectic cobordism partial order.


  Soft growing robots, are a type of robots that are designed to move and adapt
to their environment in a similar way to how plants grow and move with
potential applications where they could be used to navigate through tight
spaces, dangerous terrain, and hard-to-reach areas. This research explores the
application of deep reinforcement Q-learning algorithm for facilitating the
navigation of the soft growing robots in cluttered environments. The proposed
algorithm utilizes the flexibility of the soft robot to adapt and incorporate
the interaction between the robot and the environment into the decision-making
process. Results from simulations show that the proposed algorithm improves the
soft robot's ability to navigate effectively and efficiently in confined
spaces. This study presents a promising approach to addressing the challenges
faced by growing robots in particular and soft robots general in planning
obstacle-aware paths in real-world scenarios.


  In this work, we introduce EyeDentify, a dataset specifically designed for
pupil diameter estimation based on webcam images. EyeDentify addresses the lack
of available datasets for pupil diameter estimation, a crucial domain for
understanding physiological and psychological states traditionally dominated by
highly specialized sensor systems such as Tobii. Unlike these advanced sensor
systems and associated costs, webcam images are more commonly found in
practice. Yet, deep learning models that can estimate pupil diameters using
standard webcam data are scarce. By providing a dataset of cropped eye images
alongside corresponding pupil diameter information, EyeDentify enables the
development and refinement of models designed specifically for less-equipped
environments, democratizing pupil diameter estimation by making it more
accessible and broadly applicable, which in turn contributes to multiple
domains of understanding human activity and supporting healthcare. Our dataset
is available at https://vijulshah.github.io/eyedentify/.


  We consider the problem of finding the best harmonic or analytic approximant
to a given function. We discuss when the best approximant is unique, and what
regularity properties the best approximant inherits from the original function.
All our approximations are done in the mean with respect to Lebesgue measure in
the plane or higher dimensions.


  Electrocardiograms (ECGs) and photoplethysmograms (PPGs) are generally used
to monitor an individual's cardiovascular health. In clinical settings, ECGs
and fingertip PPGs are the main signals used for assessing cardiovascular
health, but the equipment necessary for their collection precludes their use in
daily monitoring. Although PPGs obtained from wrist-worn devices are
susceptible to noise due to motion, they have been widely used to continuously
monitor cardiovascular health because of their convenience. Therefore, we would
like to combine the ease with which PPGs can be collected with the information
that ECGs provide about cardiovascular health by developing models to
synthesize ECG signals from paired PPG signals. We tackled this problem using
generative adversarial networks (GANs) and found that models trained using the
original GAN formulations can be successfully used to synthesize ECG signals
from which heart rate can be extracted using standard signal processing
pipelines. Incorporating a frequency-domain constraint to model training
improved the stability of model performance and also the performance on heart
rate estimation.


  Greedy algorithms for minimizing L0-norm of sparse decomposition have
profound application impact on many signal processing problems. In the sparse
coding setup, given the observations $\mathrm{y}$ and the redundant dictionary
$\mathbf{\Phi}$, one would seek the most sparse coefficient (signal)
$\mathrm{x}$ with a constraint on approximation fidelity. In this work, we
propose a greedy algorithm based on the classic orthogonal matching pursuit
(OMP) with improved sparsity on $\mathrm{x}$ and better recovery rate, which we
name as eOMP. The key ingredient of the eOMP is recursively performing one-step
orthonormalization on the remaining atoms, and evaluating correlations between
residual and orthonormalized atoms. We show a proof that the proposed eOMP
guarantees to maximize the residual reduction at each iteration. Through
extensive simulations, we show the proposed algorithm has better exact recovery
rate on i.i.d. Gaussian ensembles with Gaussian signals, and more importantly
yields smaller L0-norm under the same approximation fidelity compared to the
original OMP, for both synthetic and practical scenarios. The complexity
analysis and real running time result also show a manageable complexity
increase over the original OMP. We claim that the proposed algorithm has better
practical perspective for finding more sparse representations than existing
greedy algorithms.


  The physical picture of interacting magnetic islands provides a useful
paradigm for certain plasma dynamics in a variety of physical environments,
such as the solar corona, the heliosheath, and the Earth's magnetosphere. In
this work, we derive an island kinetic equation to describe the evolution of
the island distribution function (in area and in flux of islands) subject to a
collisional integral designed to account for the role of magnetic reconnection
during island mergers. This equation is used to study the inverse transfer of
magnetic energy through the coalescence of magnetic islands in 2D. We solve our
island kinetic equation numerically for three different types of initial
distribution: delta-distribution, Gaussian and power-law distribution. The time
evolution of several key quantities is found to agree well with our analytical
predictions: magnetic energy decays as $\tilde t^{-1}$, the number of islands
decreases as $\tilde t^{-1}$, and the averaged area of islands grows as $\tilde
t$, where $\tilde t$ is the time normalized to the characteristic reconnection
time scale of islands. General properties of the distribution function and the
magnetic energy spectrum are also studied.


  In this paper we define and study a notion of discrete homology theory for
metric spaces. Instead of working with simplicial homology, our chain complexes
are given by Lipschitz maps from an $n$-dimensional cube to a fixed metric
space. We prove that the resulting homology theory verifies a discrete analogue
of the Eilenberg-Steenrod axioms, and prove a discrete analogue of the
Mayer-Vietoris exact sequence. Moreover, this discrete homology theory is
related to the discrete homotopy theory of a metric space through a discrete
analogue of the Hurewicz theorem. We study the class of groups that can arise
as discrete homology groups and, in this setting, we prove that the fundamental
group of a smooth, connected, metrizable, compact manifold is isomorphic to the
discrete fundamental group of a `fine enough' rectangulation of the manifold.
Finally, we show that this discrete homology theory can be coarsened, leading
to a new non-trivial coarse invariant of a metric space.


  In the future, quantum computers will become widespread and a network of
quantum repeaters will provide them with end-to-end entanglement of remote
quantum bits. As a result, a pervasive quantum computation infrastructure will
emerge, which will unlock several novel applications, including distributed
quantum computing, that is the pooling of resources on multiple computation
nodes to address problem instances that are unattainable by any individual
quantum computer. In this paper, we first investigate the issue of service
differentiation in this new environment. Then, we define the problem of how to
select which computation nodes should participate in each pool, so as to
achieve a fair share of the quantum network resources available. The analysis
is performed via an open source simulator and the results are fully and readily
available.


  We explore the role of the <A^2> gluon condensate in matching Regge models to
the operator product expansion of meson correlators.


  Using the VLA, we recently detected a large number of protoplanetary disk
(proplyd) candidates lying within a couple of light years of the massive black
hole Sgr A*. The bow-shock appearance of proplyd candidates point toward the
young massive stars located near Sgr A*. Similar to Orion proplyds, the strong
UV radiation from the cluster of massive stars at the Galactic center is
expected to photoevaporate and photoionize the circumstellar disks around
young, low mass stars, thus allowing detection of the ionized outflows from the
photoionized layer surrounding cool and dense gaseous disks. To confirm this
picture, ALMA observations detect millimeter emission at 226 GHz from five
proplyd candidates that had been detected at 44 and 34 GHz with the VLA. We
present the derived disk masses for four sources as a function of the assumed
dust temperature. The mass of protoplanetary disks from cool dust emission
ranges between 0.03 -- 0.05 solar mass. These estimates are consistent with the
disk masses found in star forming sites in the Galaxy. These measurements show
the presence of on-going star formation with the implication that gas clouds
can survive near Sgr A* and the relative importance of high vs low-mass star
formation in the strong tidal and radiation fields of the Galactic center.


  The ultrafast conversion of coherent excitons into incoherent excitons, as
well as the subsequent exciton diffusion and thermalization, are central topics
in current scientific research due to their relevance in optoelectronics,
photovoltaics and photocatalysis. Current approaches to the exciton dynamics
rely on {\em model} Hamiltonians that depend on already screened
electron-electron and electron-phonon couplings. In this work, we subject the
state-of-the-art methods to scrutiny using the {\em ab initio} Hamiltonian for
electrons and phonons. We offer a rigorous and intuitive proof demonstrating
that the exciton dynamics governed by model Hamiltonians is affected by an
overscreening of the electron-phonon interaction. The introduction of an
auxiliary exciton species, termed the irreducible exciton, enables us to
formulate a theory free from overscreening and derive the excitonic Bloch
equations. These equations describe the time-evolution of coherent,
irreducible, and incoherent excitons during and after the optical excitation.
They are applicable beyond the linear regime, and predict that the total number
of excitons is preserved when the external fields are switched off.


  The calculation of rare loop decays in the Standard Model of Particle Physics
and its extensions is an extremely tedious work. The Mathematica package
MasterTwo facilitates this task. It automatically calculates all loop integrals
reducible to scalar integrals depending on up to two different masses
independent of external momenta. MasterTwo consists of two sub packages,
Fermions and Integrals. Whereas Fermions covers the standard Dirac Algebra,
Integrals performs the Taylor expansion, partial fraction, tensor reduction and
the integration of the thus achieved scalar integrals. The package works
completely inside Mathematica and can be easily customised for both educational
and research purposes.


  This paper explores the consequences of non-Gaussian cosmological
perturbations for the formation of primordial black holes (PBHs). A
non-Gaussian probability distribution function (PDF) of curvature perturbations
is presented with an explicit contribution from the three-point correlation
function to linear order. The consequences of this non-Gaussian PDF for the
large perturbations that form PBHs are then studied. Using the observational
limits for the non-Gaussian parameter $f_{NL}$, new bounds to the mean
amplitude of curvature perturbations are derived in the range of scales
relevant for PBH formation.


  Ab initio calculations of cadmium chalcogenide nanoclusters with terminating
groups (H, O, N, and C atoms bound to the surface sulfur) are considered as a
simulation for the species produced in typical synthesis of bio-conjugates with
luminescent quantum dots. The approaches based on the Hartree-Fock (HF) method
and density functional theory (DFT) are used assuming geometry optimization
keeping the tetrahedral symmetry. The geometry and electronic structure of CdX
(X=S, Se, Te) clusters with size up to Cd17 (HF) and Cd4 (DFT) bound with
hydrogen and -OH, -NH2, -CH3, and -CH2CH3 groups are calculated and the effects
of the terminating groups upon core clusters are discussed.


  In an article, E. Grafarend and B. Schaffrin studied the geometry of
non-linear adjustment of the planar trisection problem using the Gauss Markov
model and the method of the least squares. This paper develops the same method
working on an example of the determination of a point by trilateration in the
three-dimensional geodetic option for determining the coordinates $(x, y, z)$
of an unknown point from measurements known distances to $n$ points.


  We present a unified theoretical framework for computing spin-independent
direct detection rates via various channels relevant for sub-GeV dark matter --
nuclear recoils, electron transitions and single phonon excitations. Despite
the very different physics involved, in each case the rate factorizes into the
particle-level matrix element squared, and an integral over a target material-
and channel-specific dynamic structure factor. We show how the dynamic
structure factor can be derived in all three cases following the same
procedure, and extend previous results in the literature in several aspects.
For electron transitions, we incorporate directional dependence and point out
potential daily modulation signals in anisotropic target materials. For single
phonon excitations, we present a new derivation of the rate formula from first
principles for generic spin-independent couplings, and include the first
calculation of phonon excitation through electron couplings. We also discuss
the interplay between single phonon excitations and nuclear recoils, and
clarify the role of Umklapp processes, which can dominate the single phonon
production rate for dark matter heavier than an MeV. Our results highlight the
complementarity between various search channels in probing different kinematic
regimes of dark matter scattering, and provide a common reference to connect
dark matter theories with ongoing and future direct detection experiments.


  In Deep Learning, Stochastic Gradient Descent (SGD) is usually selected as a
training method because of its efficiency; however, recently, a problem in SGD
gains research interest: sharp minima in Deep Neural Networks (DNNs) have poor
generalization; especially, large-batch SGD tends to converge to sharp minima.
It becomes an open question whether escaping sharp minima can improve the
generalization. To answer this question, we propose SmoothOut framework to
smooth out sharp minima in DNNs and thereby improve generalization. In a
nutshell, SmoothOut perturbs multiple copies of the DNN by noise injection and
averages these copies. Injecting noises to SGD is widely used in the
literature, but SmoothOut differs in lots of ways: (1) a de-noising process is
applied before parameter updating; (2) noise strength is adapted to filter
norm; (3) an alternative interpretation on the advantage of noise injection,
from the perspective of sharpness and generalization; (4) usage of uniform
noise instead of Gaussian noise. We prove that SmoothOut can eliminate sharp
minima. Training multiple DNN copies is inefficient, we further propose an
unbiased stochastic SmoothOut which only introduces the overhead of noise
injecting and de-noising per batch. An adaptive variant of SmoothOut,
AdaSmoothOut, is also proposed to improve generalization. In a variety of
experiments, SmoothOut and AdaSmoothOut consistently improve generalization in
both small-batch and large-batch training on the top of state-of-the-art
solutions.


  Ragas form the foundation for Indian Classical Music. The task of Raga
Recognition has gained traction in the Music Information Retrieval community in
the recent past, which can be attributed to the nuances of Indian Classical
Music that have resulted in a plethora of research problems in Computing. In
this work, we used two different digital audio signal processing techniques to
preprocess audio samples of Carnatic classical ragas that were then processed
by various Deep Learning models. Their results were compared in order to infer
which DASP technique is better suited to the task of raga recognition. We
obtained state of the art results, with our best model reaching a testing
accuracy of 98.1%. We also compared each model ability to distinguish between
similar ragas.


  We consider one source of decoherence for a single trapped ion due to
intensity and phase fluctuations in the exciting laser pulses. For simplicity
we assume that the stochastic processes involved are white noise processes,
which enables us to give a simple master equation description of this source of
decoherence. This master equation is averaged over the noise, and is sufficient
to describe the results of experiments that probe the oscillations in the
electronic populations as energy is exchanged between the internal and
electronic motion. Our results are in good qualitative agreement with recent
experiments and predict that the decoherence rate will depend on vibrational
quantum number in different ways depending on which vibrational excitation
sideband is used.


  We present the results of a commissioning campaign to observe Galactic
globular clusters for the search of microlensing events. The central 10' X 10'
region of the globular cluster NGC 5024 was monitored using the 2-m Himalayan
Chandra Telescope in R-band for a period of about 8 hours on 24 March 2010.
Light curves were obtained for nearly 10,000 stars, using a modified Difference
Image Analysis (DIA) technique. We identified all known variables within our
field of view and revised periods and status of some previously reported
short-period variables. We report about eighty new variable sources and present
their equatorial coordinates, periods, light curves and possible types. Out of
these, 16 are SX Phe stars, 10 are W UMa-type stars, 14 are probable RR Lyrae
stars and 2 are detached eclipsing binaries. Nine of the newly discovered SX
Phe stars and two eclipsing binaries belong to the Blue Straggler Star (BSS)
population.


  We prove an equality, predicted in the physical literature, between the
Jeffrey-Kirwan residues of certain explicit meromorphic forms attached to a
quiver without loops or oriented cycles and its Donaldson-Thomas type
invariants. In the special case of complete bipartite quivers we also show
independently, using scattering diagrams and theta functions, that the same
Jeffrey-Kirwan residues are determined by the the Gross-Hacking-Keel mirror
family to a log Calabi-Yau surface.


  The energy localization hypothesis of the author that energy is localized in
non-vanishing regions of the energy-momentum tensor implies that gravitational
waves do not carry energy in vacuum. If substantiated, this has significant
implications for current research. Support for the hypothesis is provided by a
re-examination of Eddington's classic calculation of energy loss by a spinning
rod. It is emphasized that Eddington did not monitor the entire Tolman energy
integral, concentrating solely upon the change of the 'kinetic' part of the
energy . The 'quadrupole formula' is thus seen to measure the kinetic energy
change. When the derivative of the missing stress-trace integral is computed,
it is seen to cancel the Eddington term and hence the energy of the rod is
conserved, in support of the localization hypothesis. The issue of initial and
final states is addressed.


  A parallel $d$-stable trace is a closed walk which traverses every edge of a
graph exactly twice in the same direction and for every vertex $v$, there is no
subset $X \subseteq N(v)$ with $1 \leq |N| \leq d$ such that every time the
walk enters $v$ from $X$, it also exits to a vertex in $X$. In the past,
$d$-stable traces were investigated as a mathematical model for an innovative
biotechnological procedure -- self-assembling of polypeptide structures. Among
other, it was proven that graphs that admit parallel $d$-stable traces are
precisely Eulerian graphs with minimum degree strictly larger than $d$. In the
present paper we give an alternative, purely combinatorial proof of this
result.


  In this paper, we investigate the entanglement degrees of pseudoscalar meson
states via quantum algebra Y(su(3)). By making use of transition effect of
generators J of Y(su(3)), we construct various transition operators in terms of
J of Y(su(3)), and act them on eta-pion-eta mixing meson state. The
entanglement degrees of both the initial state and final state are calculated
with the help of entropy theory. The diagrams of entanglement degrees are
presented. Our result shows that a state with desired entanglement degree can
be achieved by acting proper chosen transition operator on an initial state.
This sheds new light on the connect among quantum information, particle physics
and Yangian algebra.


  We derive Fundamental Plane parameters of 15 early-type lens galaxies
identified by the Sloan Lens ACS (SLACS) Survey. The size of the sample allows
us to investigate for the first time the distribution of lens galaxies in the
FP space. After correcting for evolution, we find that lens galaxies occupy a
subset of the local FP. The edge-on projection (approximately M vs M/L) is
indistinguishable from that of normal early-type galaxies. However -- within
the fundamental plane -- the lens galaxies appear to concentrate at the edge of
the region populated by normal early-type galaxies. We show that this is a
result of our selection procedure (approximately velocity dispersion
sigma>240km/s). We conclude that SLACS lenses are a fair sample of high
velocity dispersion early-type galaxies. By comparing the central stellar
velocity dispersion that of the best fit lens model, we find
<f_SIE>=<sigma/sigma_SIE>= =1.01+-0.02 with 0.065 rms scatter. We conclude that
within the Einstein radii the SLACS lenses are very well approximated by
isothermal ellipsoids, requiring a fine tuning of the stellar and dark matter
distribution (bulge-halo ``conspiracy''). Interpreting the offset from the
local FP in terms of evolution of the stellar mass-to-light ratio, we find for
the SLACS lenses d log M/L_B/dz=-0.69+-0.08 (rms 0.11) consistent with the rate
found for field early-type galaxies and with a scenario where most of the stars
were formed at high redshift (>2) with secondary episodes of star formation
providing less than ~10% of the stellar mass below z=1. We discuss star
formation history and structural homogeneity in the context of formation
mechanisms such as collisionless (``dry'') mergers. [Abridged]


  Although the growth of share-based payments with performance conditions
(hereafter, SPPC) is prominent today, the theoretical price of SPPC has not
been sufficiently studied. Reflecting such a situation, the current accounting
standards for share-based payments issued in 2004 have had many problems. This
paper develops a theoretical SPPC price model with a framework for a marginal
utility-based price, which previous studies proposed is the price of contingent
claims in an incomplete market. This paper's contribution is fivefold. First,
we restricted the stochastic process to a certain class to demonstrate how to
consistently change all variables' probability distributions, which affect the
SPPC payoff. Second, we explicitly indicated not only the stochastic processes
of the stock price process and performance variables under the changed
probability, but also how the changes in the performance variables' drift
coefficients related to stock betas. Third, we proposed a convenient model in
application that uses only a few parameters. Fourth, we provided a method to
estimate the parameters and improve the estimation of both the price and
parameters. Fifth, we illustrated the problems in current accounting standards
and indicated how the theoretical price model can significantly improve them.


  The nature of light sources is defined by the statistical fluctuations of the
electromagnetic field. As such, the photon statistics of light sources are
typically associated with distinct emitters. Here, we demonstrate the
possibility of producing light beams with various photon statistics through the
spatial modulation of coherent light. This is achieved by the sequential
encoding of controllable Kolmogorov phase screens in a digital micromirror
device. Interestingly, the flexibility of our scheme allows for the arbitrary
shaping of spatial light modes with engineered photon statistics at different
spatial positions. The performance of our scheme is assessed through the
photon-number-resolving characterization of different families of spatial light
modes with engineered photon statistics. We believe that the possibility of
controlling the photon fluctuations of the light field at arbitrary spatial
locations has important implications for quantum spectroscopy, sensing, and
imaging.


  High-efficient heat dissipation plays critical role for high-power-density
electronics. Experimental synthesis of ultrahigh thermal conductivity boron
arsenide (BAs, 1300 W m-1K-1) cooling substrates into the wide-bandgap
semiconductor of gallium nitride (GaN) devices has been realized. However, the
lack of systematic analysis on the heat transfer across the BAs-GaN interface
hampers the practical applications. In this study, by constructing the accurate
and high-efficient machine learning interatomic potentials, we performed
multiscale simulations of the BAs-GaN heterostructures. Ultrahigh interfacial
thermal conductance (ITC) of 265 MW m-2K-1 is achieved, which lies in the
well-matched lattice vibrations of BAs and GaN. Moreover, the competition
between grain size and boundary resistance was revealed with size increasing
from 1 nm to 100 {\mu}m. Such deep-potential equipped multiscale simulations
not only promote the practical applications of BAs cooling substrates in
electronics, but also offer new approach for designing advanced thermal
management systems.


  We propose Point2Cyl, a supervised network transforming a raw 3D point cloud
to a set of extrusion cylinders. Reverse engineering from a raw geometry to a
CAD model is an essential task to enable manipulation of the 3D data in shape
editing software and thus expand their usages in many downstream applications.
Particularly, the form of CAD models having a sequence of extrusion cylinders
-- a 2D sketch plus an extrusion axis and range -- and their boolean
combinations is not only widely used in the CAD community/software but also has
great expressivity of shapes, compared to having limited types of primitives
(e.g., planes, spheres, and cylinders). In this work, we introduce a neural
network that solves the extrusion cylinder decomposition problem in a
geometry-grounded way by first learning underlying geometric proxies.
Precisely, our approach first predicts per-point segmentation, base/barrel
labels and normals, then estimates for the underlying extrusion parameters in
differentiable and closed-form formulations. Our experiments show that our
approach demonstrates the best performance on two recent CAD datasets, Fusion
Gallery and DeepCAD, and we further showcase our approach on reverse
engineering and editing.


  Mesoscopic systems -- small electric circuits working in quantum regime --
offer us a unique experimental stage to explorer quantum transport in a tunable
and precise way. The purpose of this Review is to show how they can contribute
to statistical physics. We introduce the significance of fluctuation, or
equivalently noise, as noise measurement enables us to address the fundamental
aspects of a physical system. The significance of the fluctuation theorem (FT)
in statistical physics is noted. We explain what information can be deduced
from the current noise measurement in mesoscopic systems. As an important
application of the noise measurement to statistical physics, we describe our
experimental work on the current and current noise in an electron
interferometer, which is the first experimental test of FT in quantum regime.
Our attempt will shed new light in the research field of mesoscopic quantum
statistical physics.


  We examine one-dimensional Bose gas interacting with delta-function potential
using the large-$N$ collective field theory. We show that in the case of
attractive potential the uniform ground state is unstable to small
perturbations and the instability is cured by formation of a collective ground
state, \lq\lq bright soliton'' configuration in corresponding nonlinear
Schr\"odinger field theory.


  In order to evaluate the ground-based infrared telescope sensitivity affected
by the noise from the atmosphere, instruments and detectors, we construct a
sensitivity model that can calculate limiting magnitudes and signal-to-noise
ratio ($S/N$). The model is tested with tentative measurements of $\rm M'$-band
sky brightness and atmospheric extinction obtained at the Ali and Daocheng
sites. We find that the noise caused by an excellent scientific detector and
instruments at $-135^\circ \rm C$ can be ignored compared to the $\rm M'$-band
sky background noise. Thus, when $S/N=3$ and total exposure time is 1 second
for 10 m telescopes, the magnitude limited by the atmosphere is $13.01^{\rm m}$
at Ali and $12.96^{\rm m}$ at Daocheng. Even under less-than-ideal
circumstances, i.e., the readout noise of a deep cryogenic detector is less
than $200e^-$ and the instruments are cooled to below $-87.2^\circ \rm C$, the
above magnitudes decrease by $0.056^{\rm m}$ at most. Therefore, according to
observational requirements with a large telescope in a given infrared band,
astronomers can use this sensitivity model as a tool for guiding site surveys,
detector selection and instrumental thermal-control.


  Chiral symmetry restoration of quarks is investigated at finite density in
quantum chromodynamics. The effective quark mass is calculated with the
Schwinger-Dyson equation in the real-time formalism without the instantaneous
exchange approximation. We present some properties of the quark mass functions
and the quark propagator at zero temperature.


  By application of a straightforward variational procedure we derive a simple,
analytic upper bound on the ground-state energy eigenvalue of a
semirelativistic Hamiltonian for (one or two) spinless particles which
experience some Coulomb-type interaction.


  We derive recently obtained relations, relating the logarithmic gauge
coupling derivative of the hadron mass and the cosmological constant to the
matter and vacuum gluon condensates, within a Hamiltonian framework. The key
idea is a canonical transformation which brings the relevant part of the
Hamiltonian into a suitable form. Furthermore we illustrate the relations
within the Schwinger model and ${\cal N}=2$ super Yang Mills theory
(Seiberg-Witten theory).


  Let $X$ be a finite connected poset, $K$ a field of characteristic zero and
$I(X,K)$ the incidence algebra of $X$ over $K$ seen as a Lie algebra under the
commutator product. In the first part of the paper we show that any
$\frac{1}{2}$-derivation of $I(X,K)$ decomposes into the sum of a
central-valued $\frac 12$-derivation, an inner $\frac{1}{2}$-derivation and a
$\frac{1}{2}$-derivation associated with a map $\sigma:X^2_<\to K$ that is
constant on chains and cycles in $X$. In the second part of the paper we use
this result to prove that any transposed Poisson structure on $I(X,K)$ is the
sum of a structure of Poisson type, a mutational structure and a structure
determined by $\lambda:X^2_e\to K$, where $X^2_e$ is the set of $(x,y)\in X^2$
such that $x<y$ is a maximal chain not contained in a cycle.


  Previous high-resolution angle-resolved photoemission (ARPES) studies of
URu$_2$Si$_2$ have characterized the temperature-dependent behavior of
narrow-band states close to the Fermi level ($E_\mathrm{F}$) at low photon
energies near the zone center, with an emphasis on electronic reconstruction
due to Brillouin zone folding. A substantial challenge to a proper description
is that these states interact with other hole-band states that are generally
absent from bulk-sensitive soft x-ray ARPES measurements. Here we provide a
more global $k$-space context for the presence of such states and their
relation to the bulk Fermi surface topology using synchrotron-based wide-angle
and photon energy-dependent ARPES mapping of the electronic structure using
photon energies intermediate between the low-energy regime and the high-energy
soft x-ray regime. Small-spot spatial dependence, $f$-resonant photoemission,
Si 2$p$ core-levels, x-ray polarization, surface-dosing modification, and
theoretical surface slab calculations are employed to assist identification of
bulk versus surface state character of the $E_\mathrm{F}$-crossing bands and
their relation to specific U- or Si-terminations of the cleaved surface. The
bulk Fermi surface topology is critically compared to density functional theory
and to dynamical mean field theory calculations. In addition to clarifying some
aspects of the previously measured high symmetry $\Gamma$, Z and X points,
incommensurate 0.6a* nested Fermi-edge states located along Z-N-Z are found to
be distinctly different from the density functional theory Fermi surface
prediction. The temperature evolution of these states above $T_{HO}$, combined
with a more detailed theoretical investigation of this region, suggests a key
role of the N-point in the hidden order transition.


  This article introduces a five-tiered route planner for accessing multiple
nodes with multiple autonomous underwater vehicles (AUVs) that enables
efficient task completion in stochastic ocean environments. First, the
pre-planning tier solves the single-AUV routing problem to find the optimal
giant route (GR), estimates the number of required AUVs based on GR
segmentation, and allocates nodes for each AUV to access. Second, the route
planning tier plans individual routes for each AUV. During navigation, the path
planning tier provides each AUV with physical paths between any two points,
while the actuation tier is responsible for path tracking and obstacle
avoidance. Finally, in the stochastic ocean environment, deviations from the
initial plan may occur, thus, an auction-based coordination tier drives online
task coordination among AUVs in a distributed manner. Simulation experiments
are conducted in multiple different scenarios to test the performance of the
proposed planner, and the promising results show that the proposed method
reduces AUV usage by 7.5% compared with the existing methods. When using the
same number of AUVs, the fleet equipped with the proposed planner achieves a
6.2% improvement in average task completion rate.


  We investigate the discrepancy principle for choosing smoothing parameters
for kernel density estimation. The method is based on the distance between the
empirical and estimated distribution functions. We prove some new positive and
negative results on L_1-consistency of kernel estimators with bandwidths chosen
using the discrepancy principle. Consistency crucially depends on a rather weak
H\"older condition on the distribution function. We also unify and extend
previous results on the behaviour of the chosen bandwidth under more strict
smoothness assumptions. Furthermore, we compare the discrepancy principle to
standard methods in a simulation study. Surprisingly, some of the proposals
work reasonably well over a large set of different densities and sample sizes,
and the performance of the methods at least up to n=2500 can be quite different
from their asymptotic behavior.


  Generative adversarial networks (GANs) are innovative techniques for learning
generative models of complex data distributions from samples. Despite
remarkable recent improvements in generating realistic images, one of their
major shortcomings is the fact that in practice, they tend to produce samples
with little diversity, even when trained on diverse datasets. This phenomenon,
known as mode collapse, has been the main focus of several recent advances in
GANs. Yet there is little understanding of why mode collapse happens and why
existing approaches are able to mitigate mode collapse. We propose a principled
approach to handling mode collapse, which we call packing. The main idea is to
modify the discriminator to make decisions based on multiple samples from the
same class, either real or artificially generated. We borrow analysis tools
from binary hypothesis testing---in particular the seminal result of Blackwell
[Bla53]---to prove a fundamental connection between packing and mode collapse.
We show that packing naturally penalizes generators with mode collapse, thereby
favoring generator distributions with less mode collapse during the training
process. Numerical experiments on benchmark datasets suggests that packing
provides significant improvements in practice as well.


  Spatial structure is known to have an impact on the evolution of cooperation,
and so it has been intensively studied during recent years. Previous work has
shown the relevance of some features, such as the synchronicity of the
updating, the clustering of the network or the influence of the update rule.
This has been done, however, for concrete settings with particular games,
networks and update rules, with the consequence that some contradictions have
arisen and a general understanding of these topics is missing in the broader
context of the space of 2x2 games. To address this issue, we have performed a
systematic and exhaustive simulation in the different degrees of freedom of the
problem. In some cases, we generalize previous knowledge to the broader context
of our study and explain the apparent contradictions. In other cases, however,
our conclusions refute what seems to be established opinions in the field, as
for example the robustness of the effect of spatial structure against changes
in the update rule, or offer new insights into the subject, e.g. the relation
between the intensity of selection and the asymmetry between the effects on
games with mixed equilibria.


  There is a growing body of evidence that the running of $\alpha_s$ predicted
by perturbation (PT) theory is not correctly describing the accelerator
experiments at the highest energies. A natural explanation is provided by the
authors' 1992 proposal that in fact the true running predicted by the
nonperturbatively defined lattice QCD is different, leading to an ultraviolet
fixed point near $\alpha_s=.1$. It is explained how this can be understood from
the fact that the conventional perturbative method is ambiguous and does not
provide the correct asymptotic expansion. It is pointed out that there is a
large amount of lattice data that are supporting this scenario rather than the
conventional one.


  Numerically simulating spinful, fermionic systems is of great interest from
the perspective of condensed matter physics. However, the exponential growth of
the Hilbert space dimension with system size renders an exact parameterization
of large quantum systems prohibitively demanding. This is a perfect playground
for neural networks, owing to their immense representative power that often
allows to use only a fraction of the parameters that are needed in exact
methods. Here, we investigate the ability of neural quantum states (NQS) to
represent the bosonic and fermionic $t-J$ model - the high interaction limit of
the Fermi-Hubbard model - on different 1D and 2D lattices. Using autoregressive
recurrent neural networks (RNNs) with 2D tensorized gated recurrent units, we
study the ground state representations upon doping the half-filled system with
holes. Moreover, we present a method to calculate dispersion relations from the
neural network state representation, applicable to any neural network
architecture and any lattice geometry, that allows to infer the low-energy
physics from the NQS. To demonstrate our approach, we calculate the dispersion
of a single hole in the $t-J$ model on different 1D and 2D square and
triangular lattices. Furthermore, we analyze the strengths and weaknesses of
the RNN approach for fermionic systems, pointing the way for an accurate and
efficient parameterization of fermionic quantum systems using neural quantum
states.


  We observed the 2012-2013 superoutburst of the newly identified transient SSS
J122221.7-311523 and found that this object showed successive two
superoutbursts. Superhumps grew in amplitude during the second superoutburst
and showed a characteristic pattern of period change reflecting the growth of
the superhump. Assuming that the periods of superhumps during the growing stage
[0.07721(1) d] and post-superoutburst stage [0.07673(3) d], represent the
dynamical precession rates at the radius of the 3:1 resonance and the radius
immediately after the superoutburst, respectively, we found that this object
has a very small mass ratio q=M2/M1 < 0.05. The possible orbital period from
quiescent data suggests q=0.045, one of the smallest among hydrogen-rich
cataclysmic variables. The long orbital period and low q make this object a
perfect candidate for a period bouncer. We suggest that the peculiar pattern of
double superoutburst is a result of a low q and may be characteristic to period
bouncers.


  We report a series of high-resolution cosmological N-body simulations
designed to explore the formation and properties of dark matter halos with
masses close to the damping scale of the primordial power spectrum of density
fluctuations. We further investigate the effect that the addition of a random
component, v_rms, into the particle velocity field has on the structure of
halos. We adopted as a fiducial model the Lambda Warm Dark Matter cosmology
with a non-thermal sterile neutrino mass of 0.5 keV. The filtering mass
corresponds then to M_f = 2.6x10^12 M_sun/h. Halos of masses close to M_f were
simulated with several million of particles. The results show that, on one
hand, the inner density slope of these halos (at radii <~0.02 the virial radius
Rvir) is systematically steeper than the one corresponding to the NFW fit or to
the CDM counterpart. On the other hand, the overall density profile (radii
larger than 0.02Rvir) is less curved and less concentrated than the NFW fit,
with an outer slope shallower than -3. For simulations with v_rms, the inner
halo density profiles flatten significantly at radii smaller than 2-3 kpc/h
(<~0.010-0.015Rvir). A constant density core is not detected in our
simulations, with the exception of one halo for which the flat core radius is
~1 kpc/h. Nevertheless, if ``cored'' density profiles are used to fit the halo
profiles, the inferred core radii are ~0.1-0.8 kpc/h, in rough agreement with
theoretical predictions based on phase-space constrains, and on dynamical
models of warm gravitational collapse. A reduction of v_rms by a factor of 3
produces a modest decrease in core radii, less than a factor of 1.5. We discuss
the extension of our results into several contexts, for example, to the
structure of the cold DM micro-halos at the damping scale of this model.


  The stock market is volatile and complicated, especially in 2020. Because of
a series of global and regional "black swans," such as the COVID-19 pandemic,
the U.S. stock market triggered the circuit breaker three times within one week
of March 9 to 16, which is unprecedented throughout history. Affected by the
whole circumstance, the stock prices of individual corporations also plummeted
by rates that were never predicted by any pre-developed forecasting models. It
reveals that there was a lack of satisfactory models that could predict the
changes in stocks prices when catastrophic, highly unlikely events occur. To
fill the void of such models and to help prevent investors from heavy losses
during uncertain times, this paper aims to capture the movement pattern of
stock prices under anomalous circumstances. First, we detect outliers in
sequential stock prices by fitting a standard ARIMA model and identifying the
points where predictions deviate significantly from actual values. With the
selected data points, we train ARIMA and LSTM models at the single-stock level,
industry level, and general market level, respectively. Since the public moods
affect the stock market tremendously, a sentiment analysis is also incorporated
into the models in the form of sentiment scores, which are converted from
comments about specific stocks on Reddit. Based on 100 companies' stock prices
in the period of 2016 to 2020, the models achieve an average prediction
accuracy of 98% which can be used to optimize existing prediction
methodologies.


  The behavior of quark matter with both external electric field and chiral
chemical potential is theoretically and experimentally interesting to consider.
In this paper, the case of simultaneous presence of imaginary electric field
and chiral chemical potential is investigated using the lattice QCD approach
with $N_f=1+1$ dynamical staggered fermions. We find that overall both the
imaginary electric field and the chiral chemical potential can exacerbate
chiral symmetry breaking, which is consistent with theoretical predictions.
However we also find a non-monotonic behavior of chiral condensation at
specific electric field strengths and chiral chemical potentials. In addition
to this, we find that the behavior of Polyakov loop in the complex plane is not
significantly affected by chiral chemical potential in the region of the
parameters consider in this paper.


  In the example of the Schr\"odinger/KdV equation we treat the theory as
equivalence of two concepts of Liouvillian integrability: quadrature
integrability of linear differential equations with a parameter (spectral
problem) and Liouville's integrability of finite-dimensional Hamiltonian
systems (stationary KdV--equations). Three key objects in this field: new
explicit $\Psi$-function, trace formula and the Jacobi problem provide a
complete solution. The $\Theta$-function language is derivable from these
objects and used for ultimate representation of a solution to the inversion
problem. Relations with non-integrable equations are discussed also.


  In previous papers we developed a deterministic fluid approximation for an
overloaded Markovian queueing system having two customer classes and two
service pools, known in the call-center literature as the X model. The system
uses the fixed-queue-ratio-with-thresholds (FQR-T) control, which we proposed
in a recent paper as a way for one service system to help another in face of an
unexpected overload. Under FQR-T, customers are served by their own service
pool until a threshold is exceeded. Then, one-way sharing is activated with
customers from one class allowed to be served in both pools. The control aims
to keep the two queues at a pre-specified fixed ratio. We supported the fluid
approximation by establishing a many-server heavy-traffic functional weak law
of large numbers (FWLLN) involving an averaging principle. In this paper we
develop a refined diffusion approximation for the same model based on a
many-server heavy-traffic functional central limit theorem (FCLT).


  We develop a theoretical approach to ``spontaneous stochasticity'' in
classical dynamical systems that are nearly singular and weakly perturbed by
noise. This phenomenon is associated to a breakdown in uniqueness of solutions
for fixed initial data and underlies many fundamental effects of turbulence
(unpredictability, anomalous dissipation, enhanced mixing). Based upon analogy
with statistical-mechanical critical points at zero temperature, we elaborate a
renormalization group (RG) theory that determines the universal statistics
obtained for sufficiently long times after the precise initial data are
``forgotten''. We apply our RG method to solve exactly the ``minimal model'' of
spontaneous stochasticity given by a 1D singular ODE. Generalizing prior
results for the infinite-Reynolds limit of our model, we obtain the RG fixed
points that characterize the spontaneous statistics in the near-singular,
weak-noise limit, determine the exact domain of attraction of each fixed point,
and derive the universal approach to the fixed points as a singular
large-deviations scaling, distinct from that obtained by the standard
saddle-point approximation to stochastic path-integrals in the zero-noise
limit. We present also numerical simulation results that verify our analytical
predictions, propose possible experimental realizations of the ``minimal
model'', and discuss more generally current empirical evidence for ubiquitous
spontaneous stochasticity in Nature. Our RG method can be applied to more
complex, realistic systems and some future applications are briefly outlined.


  The optimized random phase approximation (ORPA) for classical liquids is
re-examined in the framework of the generating functional approach to the
integral equations. We show that the two main variants of the approximation
correspond to the addition of the same correction to two different first order
approximations of the homogeneous liquid free energy. Furthermore, we show that
it is possible to consistently use the ORPA with arbitrary reference systems
described by continuous potentials and that the same approximation is
equivalent to a particular extremum condition for the corresponding generating
functional. Finally, it is possible to enforce the thermodynamic consistence
between the thermal and the virial route to the equation of state by requiring
the global extremum condition on the generating functional.


  Connected and Autonomous Vehicles (CAVs) rely on Vehicular Adhoc Networks
with wireless communication between vehicles and roadside infrastructure to
support safe operation. However, cybersecurity attacks pose a threat to VANETs
and the safe operation of CAVs. This study proposes the use of simulation for
modelling typical communication scenarios which may be subject to malicious
attacks. The Eclipse MOSAIC simulation framework is used to model two typical
road scenarios, including messaging between the vehicles and infrastructure -
and both replay and bogus information cybersecurity attacks are introduced. The
model demonstrates the impact of these attacks, and provides an open dataset to
inform the development of machine learning algorithms to provide anomaly
detection and mitigation solutions for enhancing secure communications and safe
deployment of CAVs on the road.


  Spoken language recognition (SLR) is the task of automatically identifying
the language present in a speech signal. Existing SLR models are either too
computationally expensive or too large to run effectively on devices with
limited resources. For real-world deployment, a model should also gracefully
handle unseen languages outside of the target language set, yet prior work has
focused on closed-set classification where all input languages are known
a-priori. In this paper we address these two limitations: we explore efficient
model architectures for SLR based on convolutional networks, and propose a
multilabel training strategy to handle non-target languages at inference time.
Using the VoxLingua107 dataset, we show that our models obtain competitive
results while being orders of magnitude smaller and faster than current
state-of-the-art methods, and that our multilabel strategy is more robust to
unseen non-target languages compared to multiclass classification.


  In this paper we shall consider a few variants of the motivic multiple zeta
values of level two by restricting the summation indices in the definition of
multiple zeta values to some fixed parity patterns. These include Hoffman's
multiple $t$-values, Kaneko and Tsumura's multiple $T$-values, and the multiple
$S$-values studied previously by the authors. By applying Brown and Glanois's
descent theory on the motivic versions of these values we shall derive some
criterion for when these values are ramified and unramified. Assuming
Grothendieck's period conjecture, our results partially confirms a conjecture
of Kaneko and Tsumura about when multiple $T$-values can be expressed as a
rational linear combination of multiple zeta values (i.e., unramified) if their
depth is less than four. Similar results are obtained for motivic multiple
$S$-values. Further, we are able to generalize a result of Charlton to more
families of unramified multiple $t$-values with unit components (i.e.
components equal to 1). We propose some more unsolved problems at the end of
the paper.


  IceCube has observed neutrinos above 100 TeV at a level significantly above
the steeply falling background of atmospheric neutrinos. The astrophysical
signal is seen both in the high-energy starting event analysis from the whole
sky and as a high-energy excess in the signal of neutrino-induced muons from
below. No individual neutrino source, either steady or transient, has yet been
identified. Several follow-up efforts are currently in place in an effort to
find coincidences with sources observed by optical, X-ray and gamma-ray
detectors. This paper, presented at the inauguration of HAWC, reviews the main
results of IceCube and describes the status of plans to move to near-real time
publication of high-energy events by IceCube.


  Nowadays, digital news articles are widely available, published by various
editors and often written in different languages. This large volume of diverse
and unorganized information makes human reading very difficult or almost
impossible. This leads to a need for algorithms able to arrange high amount of
multilingual news into stories. To this purpose, we extend previous works on
Topic Detection and Tracking, and propose a new system inspired from newsLens.
We process articles per batch, looking for monolingual local topics which are
then linked across time and languages. Here, we introduce a novel "replaying"
strategy to link monolingual local topics into stories. Besides, we propose new
fine tuned multilingual embedding using SBERT to create crosslingual stories.
Our system gives monolingual state-of-the-art results on dataset of Spanish and
German news and crosslingual state-of-the-art results on English, Spanish and
German news.


  Thin films of magnesium doped zinc oxide (MZO) were synthesized using the sol
gel method and annealed at different temperatures under ambient condition. The
morphological properties of the post annealed MZO films were investigated using
X ray diffraction (XRD) while atomic force microscopy (AFM) was employed to
probe the topographical alteration of the films. The optical properties of the
post annealed MZO films were examined by UV visible spectroscopy and the Tauc
method was used to estimate the optical band gap. The studies revealed that,
with the rise of annealing temperature the crystallinity and the surface
roughness of the MZO films were increased whereas the optical transmittance and
the energy bandgap were decreased.


  We determine all irreducible representations of primary quasi-cyclotomic
fields in this paper. The methods can be applied to determine the irreducible
representations of any quasi-cyclotomic field. We also compute the Artin
L-functions for a class of quasi-cyclotomic fields.


  The in-plane complex optical properties of the iron-chalcogenide
superconductor FeTe0.55Se0.45 have been determined above and below the critical
temperature Tc = 14 K. At room temperature the conductivity is described by a
weakly-interacting Fermi liquid; however, below 100 K the scattering rate
develops a frequency dependence in the terahertz region, signaling the
increasingly correlated nature of this material. We estimate the dc
conductivity just above Tc to be sigma_dc ~ 3500 Ohm-1cm-1 and the superfluid
density rho_s0 ~ 9 x 10^6 cm-2, which places this material close to the scaling
line rho_s0/8 ~ 8.1 sigma_dc Tc for a BCS dirty-limit superconductor. Below Tc
the optical conductivity reveals two gap features at Delta_1,2 ~ 2.5 and ~ 5.1
meV.


  We design new serial and parallel approximation algorithms for computing a
maximum weight $b$-matching in an edge-weighted graph with a submodular
objective function. This problem is NP-hard; the new algorithms have
approximation ratio $1/3$, and are relaxations of the Greedy algorithm that
rely only on local information in the graph, making them parallelizable. We
have designed and implemented Local Lazy Greedy algorithms for both serial and
parallel computers. We have applied the approximate submodular $b$-matching
algorithm to assign tasks to processors in the computation of Fock matrices in
quantum chemistry on parallel computers. The assignment seeks to reduce the run
time by balancing the computational load on the processors and bounding the
number of messages that each processor sends. We show that the new assignment
of tasks to processors provides a four fold speedup over the currently used
assignment in the NWChemEx software on $8000$ processors on the Summit
supercomputer at Oak Ridge National Lab.


  We review and improve a recently introduced method for the detection of
communities in complex networks. This method combines spectral properties of
some matrices encoding the network topology, with well known hierarchical
clustering techniques, and the use of the modularity parameter to quantify the
goodness of any possible community subdivision. This provides one of the best
available methods for the detection of community structures in complex systems.


  In this paper the theoretical concept of a cold crystal reflector filter will
be presented. The aim of this concept is to balance the shortcoming of the
traditional cold polycrystalline reflector filter, which lies in the
significant reduction of the neutron flux right above (in energy space) or
right below (wavelength space) the first Bragg edge.


  In this paper we investigate the role of causal heat transport in a spatially
homogeneous, locally-rotationally symmetric Bianchi type-V cosmological model.
In particular, the causal temperature profile of the cosmological fluid is
obtained within the framework of extended irreversible thermodynamics. We
demonstrate that relaxational effects can alter the temperature profile when
the cosmological fluid is out of hydrostatic equilibrium.


  The task of face attribute manipulation has found increasing applications,
but still remains challenging with the requirement of editing the attributes of
a face image while preserving its unique details. In this paper, we choose to
combine the Variational AutoEncoder (VAE) and Generative Adversarial Network
(GAN) for photorealistic image generation. We propose an effective method to
modify a modest amount of pixels in the feature maps of an encoder, changing
the attribute strength continuously without hindering global information. Our
training objectives of VAE and GAN are reinforced by the supervision of face
recognition loss and cycle consistency loss for faithful preservation of face
details. Moreover, we generate facial masks to enforce background consistency,
which allows our training to focus on manipulating the foreground face rather
than background. Experimental results demonstrate our method, called
Mask-Adversarial AutoEncoder (M-AAE), can generate high-quality images with
changing attributes and outperforms prior methods in detail preservation.


  We consider dynamic stair climbing with the HRP-4 humanoid robot as part of
an Airbus manufacturing use-case demonstrator. We share experimental knowledge
gathered so as to achieve this task, which HRP-4 had never been challenged to
before. In particular, we extend walkingstabilization based on linear inverted
pendulum tracking by quadratic programming-based wrench distribution and a
whole-body admittance controller that applies both end-effector and CoM
strategies. While existing stabilizers tend to use either one or the other, our
experience suggests that the combination of these two approaches improves
tracking performance. We demonstrate this solution in an on-site experiment
where HRP-4 climbs an industrial staircase with 18.5 cm high steps, and release
our walking controller as open source software.


  Over a hundred planetary nebulae (PNe) are known to have H-deficient central
stars that mimic the spectroscopic appearance of massive Wolf-Rayet stars. The
formation of these low-mass Wolf-Rayet stars, denoted [WR] stars, remains
poorly understood. While several binary formation scenarios have been proposed,
there are too few [WR] binaries known to determine their feasibility. Out of
nearly 50 post-common-envelope (post-CE) binary central stars known, only PN
G222.8$-$04.2 ([WC7], $P=1.26$ d) and NGC 5189 ([WO1], $P=4.05$ d) have a [WR]
component. The available data suggests that post-CE central stars with [WR]
components lack main sequence companions and have a wider orbital separation
than typical post-CE binaries. There is also some indirect evidence for wide
binaries that could potentially lead to the discovery of more [WR] binaries.


  Regression with $\chi^2$ constructed from the covariance matrix should not be
used for some combinations of covariance matrices and fitting functions. Using
the technique for unsuitable combinations can amplify systematic errors. This
amplification is uncontrolled, and can produce arbitrarily inaccurate results
that might not be ruled out by a $\chi^2$ test. In addition, this technique can
give incorrect (artificially small) errors for fit parameters. I give a test
for this instability and a more robust (but computationally more intensive)
method for fitting correlated data.


  A popular curve shown in introductory maths textbooks, seems like a circle.
But it is actually a different curve. This paper discusses some elementary
approaches to identify the geometric object, including novel technological
means by using GeoGebra. We demonstrate two ways to refute the false
impression, two suggestions to find a correct conjecture, and four ways to
confirm the result by proving it rigorously.
  All of the discussed approaches can be introduced in classrooms at various
levels from middle school to high school.


  This paper deals with the theory of rectifiability in arbitrary Carnot
groups, and in particular with the study of the notion of
$\mathscr{P}$-rectifiable measure.
  First, we show that in arbitrary Carnot groups the natural
\textit{infinitesimal} definition of rectifiabile measure, i.e., the definition
given in terms of the existence of \textit{flat} tangent measures, is
equivalent to the global definition given in terms of coverings with
intrinsically differentiable graphs, i.e., graphs with \textit{flat} Hausdorff
tangents. In general we do not have the latter equivalence if we ask the
covering to be made of intrinsically Lipschitz graphs.
  Second, we show a geometric area formula for the centered Hausdorff measure
restricted to intrinsically differentiable graphs in arbitrary Carnot groups.
The latter formula extends and strengthens other area formulae obtained in the
literature in the context of Carnot groups.
  As an application, our analysis allows us to prove the intrinsic
$C^1$-rectifiability of almost all the preimages of a large class of Lipschitz
functions between Carnot groups. In particular, from the latter result, we
obtain that any geodesic sphere in a Carnot group equipped with an arbitrary
left-invariant homogeneous distance is intrinsic $C^1$-rectifiable.


  We consider a surface $M$ immersed in $\mathbb{R}^3$ with induced metric
$g=\psi\delta_2$ where $\delta_2$ is the two dimensional Euclidean metric. We
then construct a system of partial differential equations that constrain $M$ to
lift to a minimal surface via the Weierstrauss-Enneper representation demanding
the metric is of the above form. It is concluded that the associated surfaces
connecting the prescribed minimal surface and its conjugate surface satisfy the
system. Moreover, we find a non-trivial symmetry of the PDE which generates a
one parameter family of surfaces isometric to a specified minimal surface. We
demonstrate an instance of the analysis for the helicoid and catenoid.


  Let $G$ be a simple algebraic group over an algebraically closed field and
let $X$ be an irreducible subvariety of $G^r$ with $r \geqslant 2$. In this
paper, we consider the general problem of determining if there exists a tuple
$(x_1, \ldots, x_r) \in X$ such that $\langle x_1, \ldots, x_r \rangle$ is
Zariski dense in $G$. We are primarily interested in the case where $X = C_1
\times \cdots \times C_r$ and each $C_i$ is a conjugacy class of $G$ comprising
elements of prime order modulo the center of $G$. In this setting, our main
theorem gives a complete solution to the problem when $G$ is a symplectic or
orthogonal group. By combining our results with earlier work on linear and
exceptional groups, this gives a complete solution for all simple algebraic
groups. We also present several applications. For example, we use our main
theorem to show that many faithful representations of symplectic and orthogonal
groups are generically free. We also establish new asymptotic results on the
probabilistic generation of finite simple groups by pairs of prime order
elements, completing a line of research initiated by Liebeck and Shalev over 25
years ago.


  A set of one dimensional interfaces involving attachment and detachment of
$k$-particle neighbors is studied numerically using both large scale
simulations and finite size scaling analysis. A labeling algorithm introduced
by Barma and Dhar in related spin Hamiltonians enables to characterize the
asymptotic behavior of the interface width according to the initial state of
the substrate. For equal deposition--evaporation probability rates it is found
that in most cases the initial conditions induce regimes of saturated width. In
turn, scaling exponents obtained for initially flat interfaces indicate power
law growths which depend on $k$. In contrast, for unequal probability rates the
interface width exhibits a logarithmic growth for all $k > 1$ regardless of the
initial state of the substrate.


  The Fermi-Hubbard model is of fundamental importance in condensed-matter
physics, yet is extremely challenging to solve numerically. Finding the ground
state of the Hubbard model using variational methods has been predicted to be
one of the first applications of near-term quantum computers. Here we carry out
a detailed analysis and optimisation of the complexity of variational quantum
algorithms for finding the ground state of the Hubbard model, including costs
associated with mapping to a real-world hardware platform. The depth
complexities we find are substantially lower than previous work. We performed
extensive numerical experiments for systems with up to 12 sites. The results
suggest that the variational ans\"atze we used -- an efficient variant of the
Hamiltonian Variational ansatz and a novel generalisation thereof -- will be
able to find the ground state of the Hubbard model with high fidelity in
relatively low quantum circuit depth. Our experiments include the effect of
realistic measurements and depolarising noise. If our numerical results on
small lattice sizes are representative of the somewhat larger lattices
accessible to near-term quantum hardware, they suggest that optimising over
quantum circuits with a gate depth less than a thousand could be sufficient to
solve instances of the Hubbard model beyond the capacity of classical exact
diagonalisation.


  Conventional kernel adaptive filtering (KAF) uses a prescribed, positive
definite, nonlinear function to define the Reproducing Kernel Hilbert Space
(RKHS), where the optimal solution for mean square error estimation is
approximated using search techniques. Instead, this paper proposes to embed the
full statistics of the input data in the kernel definition, obtaining the first
analytical solution for nonlinear regression and nonlinear adaptive filtering
applications. We call this solution the Functional Wiener Filter (FWF).
Conceptually, the methodology is an extension of Parzen's work on the
autocorrelation RKHS to nonlinear functional spaces. We provide an extended
functional Wiener equation, and present a solution to this equation in an
explicit, finite dimensional, data-dependent RKHS. We further explain the
necessary requirements to compute the analytical solution in RKHS, which is
beyond traditional methodologies based on the kernel trick. The FWF analytic
solution to the nonlinear minimum mean square error problem has better accuracy
than other kernel-based algorithms in synthetic, stationary data. In real world
time series, it has comparable accuracy to KAF but displays constant complexity
with respect to number of training samples. For evaluation, it is as
computationally efficient as the Wiener solution (with a larger number of
dimensions than the linear case). We also show how the difference equation
learned by the FWF from data can be extracted leading to system identification
applications, which extend the possible applications of the FWF beyond optimal
nonlinear filtering.


  Interacting one-dimensional electron systems are generally referred to as
"Luttinger liquids", after the effective low-energy theory in which spin and
charge behave as separate degrees of freedom with independent energy scales.
The "spin-incoherent Luttinger liquid" describes a finite-temperature regime
that is realized when the temperature is very small relative to the Fermi
energy, but larger than the characteristic spin energy scale. Similar physics
can take place in the ground-state, when a Luttinger Liquid is coupled to a
spin bath, which effectively introduces a "spin temperature" through its
entanglement with the spin degree of freedom. We show that the spin-incoherent
state can be written as a factorized wave-function, with a spin wave-function
that can be described within a valence bond formalism. This enables us to
calculate exact expressions for the momentum distribution function and the
entanglement entropy. This picture holds not only for two antiferromagnetically
coupled t-J chains, but also for the t-J-Kondo chain with strongly interacting
conduction electrons. We argue that this theory is quite universal and may
describe a family of problems that could be dubbed "spin-incoherent".


  Chinese text recognition is more challenging than Latin text due to the large
amount of fine-grained Chinese characters and the great imbalance over classes,
which causes a serious overfitting problem. We propose to apply Maximum Entropy
Regularization to regularize the training process, which is to simply add a
negative entropy term to the canonical cross-entropy loss without any
additional parameters and modification of a model. We theoretically give the
convergence probability distribution and analyze how the regularization
influence the learning process. Experiments on Chinese character recognition,
Chinese text line recognition and fine-grained image classification achieve
consistent improvement, proving that the regularization is beneficial to
generalization and robustness of a recognition model.


  This article is about the architecture of a lossless wavelet filter bank with
reprogrammable logic. It is based on second generation of wavelets with a
reduced of number of operations. A new basic structure for parallel
architecture and modules to forward and backward integer discrete wavelet
transform is proposed.


  The Earth appears non-chondritic in its abundances of refractory lithophile
elements, posing a significant problem for our understanding of its formation
and evolution. It has been suggested that this non-chondritic composition may
be explained by collisional erosion of differentiated planetesimals of
originally chondritic composition. In this work, we present N-body simulations
of terrestrial planet formation that track the growth of planetary embryos from
planetesimals. We simulate evolution through the runaway and oligarchic growth
phases under the Grand Tack model and in the absence of giant planets. These
simulations include a state-of-the-art collision model which allows multiple
collision outcomes, such as accretion, erosion, and bouncing events, that
enables tracking of the evolving core mass fraction of accreting planetesimals.
We show that the embryos grown during this intermediate stage of planet
formation exhibit a range of core mass fractions, and that with significant
dynamical excitation, enough mantle can be stripped from growing embryos to
account for the Earth's non-chondritic Fe/Mg ratio. We also find that there is
a large diversity in the composition of remnant planetesimals, with both
iron-rich and silicate-rich fragments produced via collisions.


  We present a generalized model of a diffusion-reaction system where the
reaction occurs only on the boundary. This model reduces to that of Barato and
Hinrichsen when the occupancy of the boundary site is restricted to zero or
one. In the limit when there is no restriction on the occupancy of the boundary
site, the model reduces to an age dependent Galton-Watson branching process and
admits an analytic solution. The model displays a boundary-induced phase
transition into an absorbing state with rational critical exponents and
exhibits aging at criticality below a certain fractal dimension of the
diffusion process. Surprisingly the behavior in the critical regime for
intermediate occupancy restriction $N$ varies with $N$. In fact, by varying the
lifetime of the active boundary particle or the diffusion coefficient in the
bulk, the critical exponents can be continuously modified.


  Due to availability of large spectrum chunks, the sub-TeraHertz (subTHz)
frequency band can support Ultra-WideBand (UWB) wireless communications, paving
the way for unprecedented increase in the wireless network capacity. This fact
is expected to be the next breakthrough for the upcoming sixth Generation (6G)
standards. However, the technology of subTHz transceivers is not yet mature
enough to apply the advanced signal processing currently being implemented for
millimeter wave wireless communications. In this paper, we consider the Time
Reversal (TR) precoding technique, which provides simple and robust processing
capable to offer highly focalized in time and space UWB waveforms, exploiting
the spatial diversity of wireless channels. We first investigate experimentally
the performance of subTHz TR focusing in complex media inside a leaking
reverberation cavity. We then combine TR with received spatial modulation to
realize data communication using a simple non-coherent receiver with two
antennas. Our results showcase the capability of TR to offer focusing in time
in the order of few nanoseconds and in space in the order of less than 1 mm.


  Probabilistic graphical models (PGMs) are tools for solving complex
probabilistic relationships. However, suboptimal PGM structures are primarily
used in practice. This dissertation presents three contributions to the PGM
literature. The first is a comparison between factor graphs and cluster graphs
on graph colouring problems such as Sudokus - indicating a significant
advantage for preferring cluster graphs. The second is an application of
cluster graphs to a practical problem in cartography: land cover classification
boosting. The third is a PGMs formulation for constraint satisfaction problems
and an algorithm called purge-and-merge to solve such problems too complex for
traditional PGMs.


  (Abridged) X-ray clusters are conventionally divided into two classes: "cool
core" (CC) clusters and "non-cool core" (NCC) clusters. Yet relatively little
attention has been given to the origins of this dichotomy and, in particular,
to the energetics and thermal histories of the two classes. We develop a model
for the entropy profiles of clusters starting from the configuration
established by gravitational shock heating and radiative cooling. At large
radii, gravitational heating accounts for the observed profiles and their
scalings well. However, at small and intermediate radii, radiative cooling and
gravitational heating cannot be combined to explain the observed profiles of
either type of cluster. The inferred entropy profiles of NCC clusters require
that material is preheated prior to cluster collapse in order to explain the
absence of low entropy (cool) material in these systems. We show that a similar
modification is also required in CC clusters in order to match their properties
at intermediate radii. In CC clusters, this modification is unstable, and an
additional process is required to prevent cooling below a temperature of a few
keV. We show that this can be achieved by adding a self-consistent AGN feedback
loop in which the lowest-entropy, most rapidly cooling material is heated so
that it rises buoyantly to mix with material at larger radii. The resulting
model does not require fine tuning and is in excellent agreement with a wide
variety of observational data. Some of the other implications of this model are
briefly discussed.


  Coupled parametric oscillators were recently employed as simulators of
artificial Ising networks, with the potential to solve computationally hard
minimization problems. We demonstrate a new dynamical regime within the
simplest network - two coupled parametric oscillators, where the oscillators
never reach a steady state, but show persistent, full-scale, coherent beats,
whose frequency reflects the coupling properties and strength. We present a
detailed theoretical and experimental study and show that this new dynamical
regime appears over a wide range of parameters near the oscillation threshold
and depends on the nature of the coupling (dissipative or energy preserving).
Thus, a system of coupled parametric oscillators transcends the Ising
description and manifests unique coherent dynamics, which may have important
implications for coherent computation machines.


  In this article, we establish the adjoint equation for
Nikiforov-Uvarov-Suslov difference equation of hypergeometric type on
non-uniform lattices, and prove it to be a difference equation of
hypergeometric type on non-uniform lattices as well. The particular solutions
of the adjoint equation are then obtained. As an appliction of these particular
solutions, we use them to obtain the particular solutions for the original
difference equation of hypergeometric type on non-uniform lattices. Finally, we
prove another kind of fundamental theorems for Nikiforov-Uvarov-Suslov
difference equation of hypergeometric type, which are essentially new results,
its expression is different from Suslov's Theorem.


  Let $F$ be an infinite field, and let $M_{n}(F)$ be the algebra of $n\times
n$ matrices over $F$. Suppose that this algebra is equipped with an elementary
grading whose neutral component coincides with the main diagonal. In this
paper, we find a basis for the graded polynomial identities of $M_{n}(F)$ with
the transpose involution. Our results generalize for infinite fields of
arbitrary characteristic previous results in the literature which were obtained
for the field of complex numbers and for a particular class of elementary
G-gradings.


  Systematic characterization of biological effects to genetic perturbation is
essential to the application of molecular biology and biomedicine. However, the
experimental exhaustion of genetic perturbations on the genome-wide scale is
challenging. Here, we show that TranscriptionNet, a deep learning model that
integrates multiple biological networks to systematically predict
transcriptional profiles to three types of genetic perturbations based on
transcriptional profiles induced by genetic perturbations in the L1000 project:
RNA interference (RNAi), clustered regularly interspaced short palindromic
repeat (CRISPR) and overexpression (OE). TranscriptionNet performs better than
existing approaches in predicting inducible gene expression changes for all
three types of genetic perturbations. TranscriptionNet can predict
transcriptional profiles for all genes in existing biological networks and
increases perturbational gene expression changes for each type of genetic
perturbation from a few thousand to 26,945 genes. TranscriptionNet demonstrates
strong generalization ability when comparing predicted and true gene expression
changes on different external tasks. Overall, TranscriptionNet can systemically
predict transcriptional consequences induced by perturbing genes on a
genome-wide scale and thus holds promise to systemically detect gene function
and enhance drug development and target discovery.


  In this paper, we obtain an unconditional density theorem concerning the
low-lying zeros of Hasse-Weil L-functions for a family of elliptic curves. From
this together with the Riemann hypothesis for these L-functions, we infer the
majorant of 27/14 (which is strictly less than 2) for the average rank of the
elliptic curves in the family under consideration. This upper bound for the
average rank enables us to deduce that, under the same assumption, a positive
proportion of elliptic curves have algebraic ranks equaling their analytic
ranks and finite Tate-Shafarevic group. Statements of this flavor were known
previously under the additional assumptions of GRH for Dirichlet L-functions
and symmetric square L-functions which are removed in the present paper.


  High-performance computing of array signal processing problems is a critical
task as real-time system performance is required for many applications. Noise
subspace-based Direction-of-Arrival (DOA) estimation algorithms are popular in
the literature since they provide higher angular resolution and higher
robustness. In this study, we investigate various optimization strategies for
high-performance DOA estimation on GPU and comparatively analyze alternative
implementations (MATLAB, C/C++ and CUDA). Experiments show that up to 3.1x
speedup can be achieved on GPU compared to the baseline multi-threaded CPU
implementation. The source code is publicly available at the following link:
https://github.com/erayhamza/NssDOACuda


  The dynamical Jahn--Teller effect on fullerene sites in insulating
Cs$_3$C$_{60}$ is investigated fully ab initio. The vibronic excitations of
rotational type are at $\ge$65 cm$^{-1}$ while the net kinetic contribution to
the Jahn--Teller stabilization energy constitutes ca 90 meV. This means that no
localization of distortions by intermolecular interactions is possible in these
fullerides, therefore, free rotations of deformations take place independently
on each C$_{60}$. The latter destroy the orbital ordering and establish a
conventional exchange interaction between $S=1/2$ on fullerene sites. The
corresponding exchange model is derived and predicts the N\'{e}el temperature
for A15 Cs$_3$C$_{60}$ close to experiment.


  We prove the existence of a one-parameter family of self-similar solutions
with time dependent tails for Smoluchowski's coagulation equation, for a class
of kernels $K(x,y)$ which are homogeneous of degree one and satisfy $K(x,1)\to
k_0>0$ as $x\to 0$. In particular, we establish the existence of a critical
$\rho_*>0$ with the property that for all $\rho\in(0,\rho_*)$ there is a
positive and differentiable self-similar solution with finite mass $M$ and
decay $A(t)x^{-(2+\rho)}$ as $x\to\infty$, with $A(t)=e^{M(1+\rho)t}$.
Furthermore, we show that (weak) self-similar solutions in the class of
positive measures cannot exist for large values of the parameter $\rho$.


  Multi-organ segmentation in medical images is a widely researched task and
can save much manual efforts of clinicians in daily routines. Automating the
organ segmentation process using deep learning (DL) is a promising solution and
state-of-the-art segmentation models are achieving promising accuracy. In this
work, We proposed a novel data augmentation strategy for increasing the
generalizibility of multi-organ segmentation datasets, namely AnatoMix. By
object-level matching and manipulation, our method is able to generate new
images with correct anatomy, i.e. organ segmentation mask, exponentially
increasing the size of the segmentation dataset. Initial experiments have been
done to investigate the segmentation performance influenced by our method on a
public CT dataset. Our augmentation method can lead to mean dice of 76.1,
compared with 74.8 of the baseline method.


  Ultrafast electronic dynamics in solids lies at the core of modern condensed
matter and materials physics. To build up a practical ab initio method for
studying solids under photoexcitation, we develop a momentum-resolved real-time
time dependent density functional theory (rt-TDDFT)algorithm using numerical
atomic basis, together with the implementation of both the length and vector
gauge of the electromagnetic field. When applied to simulate elementary
excitations in two-dimensional materials such as graphene, different excitation
modes, only distinguishable in momentum space, are observed. The
momentum-resolved rt-TDDFT is important and computationally efficient for the
study of ultrafast dynamics in extended systems.


  In this paper, we generalize Walcher's computation of the open Gromov-Witten
invariants of the quintic hypersurface to Fano and Calabi-Yau projective
hypersurfaces. Our main tool is the open virtual structure constants. We also
propose the generalized mirror transformation for the open Gromov-Witten
invariants, some parts of which are proven explicitly. We also discuss possible
modification of the multiple covering formula for the case of higher
dimensional Calabi-Yau manifolds. The generalized disk invariants for some
Calabi-Yau and Fano manifolds are shown and they are certainly integers after
re-summation by the modified multiple covering formula. This paper also
contains the direct integration method of the period integrals for higher
dimensional Calabi-Yau hypersurfaces in the appendix.


  I introduce a model-free methodology to assess the impact of disaster risk on
the market return. Using S&P500 returns and the risk-neutral quantile function
derived from option prices, I employ quantile regression to estimate local
differences between the conditional physical and risk-neutral distributions.
The results indicate substantial disparities primarily in the left-tail,
reflecting the influence of disaster risk on the equity premium. These
differences vary over time and persist beyond crisis periods. On average, the
bottom 5% of returns contribute to 17% of the equity premium, shedding light on
the Peso problem. I also find that disaster risk increases the stochastic
discount factor's volatility. Using a lower bound observed from option prices
on the left-tail difference between the physical and risk-neutral quantile
functions, I obtain similar results, reinforcing the robustness of my findings.


  We use the Hopf algebra structure of the time-ordered algebra of field
operators to generate all connected weighted Feynman graphs in a recursive and
efficient manner. The algebraic representation of the graphs is such that they
can be evaluated directly as contributions to the connected n-point functions.
The recursion proceeds by loop order and vertex number.


  Stereo-matching is a fundamental problem in computer vision. Despite recent
progress by deep learning, improving the robustness is ineluctable when
deploying stereo-matching models to real-world applications. Different from the
common practices, i.e., developing an elaborate model to achieve robustness, we
argue that collecting multiple available datasets for training is a cheaper way
to increase generalization ability. Specifically, this report presents an
improved RaftStereo trained with a mixed dataset of seven public datasets for
the robust vision challenge (denoted as iRaftStereo_RVC). When evaluated on the
training sets of Middlebury, KITTI-2015, and ETH3D, the model outperforms its
counterparts trained with only one dataset, such as the popular Sceneflow.
After fine-tuning the pre-trained model on the three datasets of the challenge,
it ranks at 2nd place on the stereo leaderboard, demonstrating the benefits of
mixed dataset pre-training.


  We have investigated the motion characteristics of a movable piston immersed
in a one dimensional gas of hard rods by event-oriented molecular dynamics in
the absence of thermal noise. Periodic and reflecting boundary conditions are
explored. It is shown that the piston undergoes systematic oscillations with
decaying amplitudes in short times before it comes to global thermodynamic
equilibrium. Moreover, the diffusion of the piston is explored and analytical
expressions for its equilibrium mean-squared displacement is obtained. It is
shown that MSD of the piston does not differ much from the normal rods despite
its mass and length are significantly larger.


  The isotropy in the ultra high energy cosmic ray (UHECR) flux observed by
Yakutsk and AGASA experiments, is a very strong constraint to production and
propagation models alike. Most of the scenarios proposed in the literature
should produce a sizable anisotropy as either extragalactic luminous or dark
matter is normally associated with the invoked particle sources. We explore the
possibility that the magnetic fields in fossil cocoons of former radio galaxies
-- so called {\it radio ghosts} -- are able to scatter UHECR in the
intergalactic medium giving rise to the observed isotropy. We show, through
numerical simulations, under which conditions this process can be operative and
the magnitude of the effect. We further demonstrate, that if radio ghosts mix
with the ambient medium, they might be able to produce the observed magnetic
fields in clusters of galaxies. In the case of mixing, the UHECR isotropization
would be even stronger than in our conservative estimates.


  For a smooth cubic fourfold Y, we study the moduli space M of semistable
objects of Mukai vector $2\lambda_1+2\lambda_2$ in the Kuznetsov component of
Y. We show that with a certain choice of stability conditions, M admits a
symplectic resolution $\tilde M$, which is a smooth projective hyperk\"ahler
manifold, deformation equivalent to the 10-dimensional examples constructed by
O'Grady. As applications, we show that a birational model of $\tilde M$
provides a hyperk\"ahler compactification of the twisted family of intermediate
Jacobians associated to Y. This generalizes the previous result of Voisin
arXiv:1611.06679 in the very general case. We also prove that $\tilde M$ is the
MRC quotient of the main component of the Hilbert scheme of elliptic quintic
curves in Y, confirming a conjecture of Castravet.


  In complex lane change (LC) scenarios, semantic interpretation and safety
analysis of dynamic interactive pattern are necessary for autonomous vehicles
to make appropriate decisions. This study proposes an unsupervised learning
framework that combines primitive-based interactive pattern recognition methods
and risk analysis methods. The Hidden Markov Model with the Gaussian mixture
model (GMM-HMM) approach is developed to decompose the LC scenarios into
primitives. Then the Dynamic Time Warping (DTW) distance based K-means
clustering is applied to gather the primitives to 13 types of interactive
patterns. Finally, this study considers two types of time-to-collision (TTC)
involved in the LC process as indicators to analyze the risk of the interactive
patterns and extract high-risk LC interactive patterns. The results obtained
from The Highway Drone Dataset (highD) demonstrate that the identified LC
interactive patterns contain interpretable semantic information. This study
explores the spatiotemporal evolution law and risk formation mechanism of the
LC interactive patterns and the findings are useful for comprehensively
understanding the latent interactive patterns, improving the rationality and
safety of autonomous vehicle's decision-making.


  We study the hot medium effects in high-multiplicity proton-proton (pp)
collisions at $\sqrt{s_{NN}}=13$ TeV via the charmonium probes. The hot medium
is described with the hydrodynamic model, while charmonium evolutions in the
medium are studied with a time-dependent Schr\"odinger equation. The hot medium
dissociation on charmonium is considered with the temperature-dependent complex
potential parametrized with the results from lattice QCD calculations. The
ratio $\sigma_{\psi(2S)}/\sigma_{J/\psi}$ of $J/\psi$ and $\psi(2S)$ production
cross sections are calculated and compared with the LHCb experimental data in
pp collisions. Our calculations explain the charmonium relative suppression in
different transverse momentum and multiplicity bins. The suppression of this
ratio is mainly affected by the effects of the deconfined medium. It is less
affected by the initial effects before the generation of the heavy quark pair.
We suggest this to be a clear signal of the small QGP droplets generated in
high multiplicity pp collisions.


  The performance of DNNs trained on clean images has been shown to decrease
when the test images have common corruptions. In this work, we interpret
corruption robustness as a domain shift and propose to rectify batch
normalization (BN) statistics for improving model robustness. This is motivated
by perceiving the shift from the clean domain to the corruption domain as a
style shift that is represented by the BN statistics. We find that simply
estimating and adapting the BN statistics on a few (32 for instance)
representation samples, without retraining the model, improves the corruption
robustness by a large margin on several benchmark datasets with a wide range of
model architectures. For example, on ImageNet-C, statistics adaptation improves
the top1 accuracy of ResNet50 from 39.2% to 48.7%. Moreover, we find that this
technique can further improve state-of-the-art robust models from 58.1% to
63.3%.


  We report on a novel mechanism of BCS-like superconductivity, mediated by a
pair of Bogoliubov quasiparticles (bogolons). It takes place in hybrid systems
consisting of a two-dimensional electron gas in a transition metal
dichalcogenide monolayer in the vicinity of a Bose-Einstein condensate. Taking
a system of two-dimensional indirect excitons as a testing ground we show, that
the bogolon-pair-mediated electron pairing mechanism is stronger than
phonon-mediated and single bogolon-mediated ones. We develop a microscopic
theory of bogolon-pair-mediated superconductivity, based on the
Schrieffer-Wolff transformation and the Gor'kov's equations, study the
temperature dependence of the superconducting gap and estimate the critical
temperature of superconducting transition for various concentrations of the
electron gas and the condensate densities.