  Within the earlier developed high-energy-$\vec k\cdot \vec p$-Hamiltonian
approach to describe graphene-like materials the simulations of non-Abelian Zak
phases and band structure of the quasi-relativistic graphene model with a
flavors number $N=3$ have been performed in approximations with %of zero- and
non-zero values of and without gauge fields (flavors). It has been shown that a
Zak-phases set for non-Abelian Majorana-like excitations (modes) in Dirac
valleys of the quasirelativistic graphene model is the cyclic group
$\mathbf{Z}_{12}$. This group is deformed into $\mathbf{Z}_8$ at sufficiently
high momenta due to deconfinement of the modes. Since the deconfinement removes
the degeneracy of the eightfolding valleys, Weyl nodes and antinodes emerge. We
offer that a Majorana-like mass term of the quasirelativistic model effects on
the graphene band structure in a following way. Firstly the inverse symmetry
emerges at "switching on"\ of the mass term, and secondly the mass term shifts
the location of Weyl nodes and antinodes into the region of higher energies and
accordingly the Majorana-like modes can exist without mixing with the nodes.


  The 2D virtual try-on task has recently attracted a lot of interest from the
research community, for its direct potential applications in online shopping as
well as for its inherent and non-addressed scientific challenges. This task
requires to fit an in-shop cloth image on the image of a person. It is highly
challenging because it requires to warp the cloth on the target person while
preserving its patterns and characteristics, and to compose the item with the
person in a realistic manner. Current state-of-the-art models generate images
with visible artifacts, due either to a pixel-level composition step or to the
geometric transformation. In this paper, we propose WUTON: a Warping U-net for
a Virtual Try-On system. It is a siamese U-net generator whose skip connections
are geometrically transformed by a convolutional geometric matcher. The whole
architecture is trained end-to-end with a multi-task loss including an
adversarial one. This enables our network to generate and use realistic spatial
transformations of the cloth to synthesize images of high visual quality. The
proposed architecture can be trained end-to-end and allows us to advance
towards a detail-preserving and photo-realistic 2D virtual try-on system. Our
method outperforms the current state-of-the-art with visual results as well as
with the Learned Perceptual Image Similarity (LPIPS) metric.


  Phylogenetic networks are rooted directed acyclic graphs that represent
evolutionary relationships between species whose past includes reticulation
events such as hybridisation and horizontal gene transfer. To search the space
of phylogenetic networks, the popular tree rearrangement operation rooted
subtree prune and regraft (rSPR) was recently generalised to phylogenetic
networks. This new operation - called subnet prune and regraft (SNPR) - induces
a metric on the space of all phylogenetic networks as well as on several
widely-used network classes. In this paper, we investigate several problems
that arise in the context of computing the SNPR-distance. For a phylogenetic
tree $T$ and a phylogenetic network $N$, we show how this distance can be
computed by considering the set of trees that are embedded in $N$ and then use
this result to characterise the SNPR-distance between $T$ and $N$ in terms of
agreement forests. Furthermore, we analyse properties of shortest
SNPR-sequences between two phylogenetic networks $N$ and $N'$, and answer the
question whether or not any of the classes of tree-child, reticulation-visible,
or tree-based networks isometrically embeds into the class of all phylogenetic
networks under SNPR.


  We characterize Hopf spaces with finitely generated cohomology as an algebra
over the Steenrod algebra. We "deconstruct" the original space into an H-space
Y with finite mod p cohomology and a finite number of p-torsion Eilenberg-Mac
Lane spaces. We give a precise description of homotopy commutative H-spaces in
this setting.


  The moduli space metric and its Kahler potential for well-separated
non-Abelian vortices are obtained in U(N) gauge theories with N Higgs fields in
the fundamental representation.


  We studied experimentally the role of phonon dimensionality on
electron-phonon (e-p) interaction in thin copper wires evaporated either on
suspended silicon nitride membranes or on bulk substrates, at sub-Kelvin
temperatures. The power emitted from electrons to phonons was measured using
sensitive normal metal-insulator-superconductor (NIS) tunnel junction
thermometers. Membrane thicknesses ranging from 30 nm to 750 nm were used to
clearly see the onset of the effects of two-dimensional (2D) phonon system. We
observed for the first time that a 2D phonon spectrum clearly changes the
temperature dependence and strength of the e-p scattering rate, with the
interaction becoming stronger at the lowest temperatures below $\sim$ 0.5 K for
the 30 nm membranes.


  Context: We describe the new SEPIA (Swedish-ESO PI Instrument for APEX)
receiver, which was designed and built by the Group for Advanced Receiver
Development (GARD), at Onsala Space Observatory (OSO) in collaboration with
ESO. It was installed and commissioned at the APEX telescope during 2015 with
an ALMA Band 5 receiver channel and updated with a new frequency channel (ALMA
Band 9) in February 2016. Aims: This manuscript aims to provide, for observers
who use the SEPIA receiver, a reference in terms of the hardware description,
optics and performance as well as the commissioning results. Methods: Out of
three available receiver cartridge positions in SEPIA, the two current
frequency channels, corresponding to ALMA Band 5, the RF band 158--211 GHz, and
Band 9, the RF band 600--722 GHz, provide state-of-the-art dual polarization
receivers. The Band 5 frequency channel uses 2SB SIS mixers with an average SSB
noise temperature around 45K with IF (intermediate frequency) band 4--8 GHz for
each sideband providing total 4x4 GHz IF band. The Band 9 frequency channel
uses DSB SIS mixers with a noise temperature of 75--125K with IF band 4--12 GHz
for each polarization. Results: Both current SEPIA receiver channels are
available to all APEX observers.


  The first part of this article (Sections I and II) presents oneself an
overview of theory and phenomenology of truly neutral particles based on the
papers of Majorana, Racah, Furry, McLennan and Case. The recent development of
the construct, undertaken by Ahluwalia [{\it Mod. Phys. Lett. A}{\bf 9} (1994)
439; {\it Acta Phys. Polon. B}{\bf 25} (1994) 1267; Preprints LANL
LA-UR-94-1252, LA-UR-94-3118], could be relevant for explanation of the present
experimental situation in neutrino physics and astrophysics.
  In Section III the new fundamental wave equations for self/anti-self
conjugate type-II spinors, proposed by Ahluwalia, are re-casted to covariant
form. The connection with the Foldy-Nigam-Bargmann-Wightman- Wigner (FNBWW)
type quantum field theory is found. The possible applications to the problem of
neutrino oscillations are discussed.


  "Transcription bottlenecks", created by a shortage of effective human
transcribers are one of the main challenges to endangered language (EL)
documentation. Automatic speech recognition (ASR) has been suggested as a tool
to overcome such bottlenecks. Following this suggestion, we investigated the
effectiveness for EL documentation of end-to-end ASR, which unlike Hidden
Markov Model ASR systems, eschews linguistic resources but is instead more
dependent on large-data settings. We open source a Yolox\'ochitl Mixtec EL
corpus. First, we review our method in building an end-to-end ASR system in a
way that would be reproducible by the ASR community. We then propose a novice
transcription correction task and demonstrate how ASR systems and novice
transcribers can work together to improve EL documentation. We believe this
combinatory methodology would mitigate the transcription bottleneck and
transcriber shortage that hinders EL documentation.


  The epsilon-enlargement of a maximal monotone operator is a construct similar
to the Br{\o}ndsted and Rocakfellar epsilon-subdifferential enlargement of the
subdifferential. Like the epsilon-subdifferential, the epsilon-enlargement of a
maximal monotone operator has practical and theoretical applications.
  In a recent paper in Journal of Convex Analysis Burachik and Iusem studied
conditions under which a maximal monotone operator is non-enlargeable, that is,
its epsilon-enlargement coincides with the operator. Burachik and Iusem studied
these non-enlargeable operators in reflexive Banach spaces, assuming the
interior of the domain of the operator to be nonempty. In the present work, we
remove the assumption on the domain of non-enlargeable operators and also
present partial results for the non-reflexive case.


  Sparsity is a ubiquitous feature of many real world signals such as natural
images and neural spiking activities. Conventional compressed sensing utilizes
sparsity to recover low dimensional signal structures in high ambient
dimensions using few measurements, where i.i.d measurements are at disposal.
However real world scenarios typically exhibit non i.i.d and dynamic structures
and are confined by physical constraints, preventing applicability of the
theoretical guarantees of compressed sensing and limiting its applications. In
this thesis we develop new theory, algorithms and applications for non i.i.d
and dynamic compressed sensing by considering such constraints. In the first
part of this thesis we derive new optimal sampling-complexity tradeoffs for two
commonly used processes used to model dependent temporal structures: the
autoregressive processes and self-exciting generalized linear models. Our
theoretical results successfully recovered the temporal dependencies in neural
activities, financial data and traffic data. Next, we develop a new framework
for studying temporal dynamics by introducing compressible state-space models,
which simultaneously utilize spatial and temporal sparsity. We develop a fast
algorithm for optimal inference on such models and prove its optimal recovery
guarantees. Our algorithm shows significant improvement in detecting sparse
events in biological applications such as spindle detection and calcium
deconvolution. Finally, we develop a sparse Poisson image reconstruction
technique and the first compressive two-photon microscope which uses lines of
excitation across the sample at multiple angles. We recovered
diffraction-limited images from relatively few incoherently multiplexed
measurements, at a rate of 1.5 billion voxels per second.


  Flagellated bacteria on nutrient-rich substrates can differentiate into a
swarming state and move in dense swarms across surfaces. A recent experiment
measured the flow in the fluid around an Escherichia coli swarm (Wu, Hosu and
Berg, 2011 Proc. Natl. Acad. Sci. USA 108 4147). A systematic chiral flow was
observed in the clockwise direction (when viewed from above) ahead of the swarm
with flow speeds of about $10~\mu$m/s, about 3 times greater than the radial
velocity at the edge of the swarm. The working hypothesis is that this flow is
due to the action of cells stalled at the edge of a colony that extend their
flagellar filaments outwards, moving fluid over the virgin agar. In this work
we quantitatively test his hypothesis. We first build an analytical model of
the flow induced by a single flagellum in a thin film and then use the model,
and its extension to multiple flagella, to compare with experimental
measurements. The results we obtain are in agreement with the flagellar
hypothesis. The model provides further quantitative insight into the flagella
orientations and their spatial distributions as well as the tangential speed
profile. In particular, the model suggests that flagella are on average
pointing radially out of the swarm and are not wrapped tangentially.


  It has been shown that the dissipative gas infall during galaxy formation has
the capability to modify the shape of dark matter halos. In this paper we
perform the first detailed analysis of particle orbits in a cosmological dark
matter halos to understand {\it how} and {\it why} baryons alter its shape. We
perform a series of numerical experiments where we grow a baryonic core inside
a live dark matter halo extracted from a cosmological simulation. We follow the
orbits of more than 300 particles with more that 50000 timesteps. Our results
clearly show that the dissipational component is responsible for repeatedly
deflecting orbits which visit often the center of the system. Moreover the
gravitational potential time dependence associated with the growth of the
baryonic mass, shifts the frequencies of the orbits, making them extremely
chaotic. This randomization makes the orbits explore a large phase space. When
this effect takes place for a significant number of orbits it will be
manifested in the density distribution as an approach to a rounder
configuration. As a consequence, the influence of the central mass on the shape
of the phase space decreases with increasing distance from the center. We
discuss the importance of future analysis of controlled experiments (i.e. using
analytic potentials instead of live DM halos) to better decipher the dynamics
of this phenomenon.


  We propose to implement the softly broken Z_2 symmetry in the usual two Higgs
doublet model (2HDM) to spontaneously broken local U(1)_H gauge symmetry, and
show that the resulting phenomenology can be very rich and is distinctly
different from the usual 2HDMs. Likewise, the exact Z_2 symmetry in ordinary
inert doublet model (IDM) could be a remnant of spontaneously broken local
U(1)_H symmetry stabilizing dark matter (DM). In this case, new channels for DM
pair annihilation into U(1)_H gauge boson(s) open up, allowing the DM mass
below \lesssim 40 GeV, unlike the usual IDM.


  We introduce a notion of Ricci curvature for Cayley graphs that can be
thought of as "medium-scale" because it is neither infinitesimal nor
asymptotic, but based on a chosen finite radius parameter. We argue that it
gives the foundation for a definition of Ricci curvature well adapted to
geometric group theory, beginning by observing that the sign can easily be
characterized in terms of conjugation in the group. With this conjugation
curvature $\kappa$, abelian groups are identically flat, and in the other
direction we show that $\kappa\equiv 0$ implies the group is virtually abelian.
Beyond that, $\kappa$ captures known curvature phenomena in right-angled Artin
groups (including free groups) and nilpotent groups, and has a strong
relationship to other group-theoretic notions like growth rate and dead ends.
We study dependence on generators and behavior under embeddings, and close with
directions for further development and study.


  We present a model of inflation in a supergravity framework in the Einstein
frame where the Higgs field of the next to minimal supersymmetric standard
model (NMSSM) plays the role of the inflaton. Previous attempts which assumed
non-minimal coupling to gravity failed due to a tachyonic instability of the
singlet field during inflation. A canonical K\"{a}hler potential with
\textit{minimal coupling} to gravity can resolve the tachyonic instability but
runs into the $\eta$-problem. We suggest a model which is free of the
$\eta$-problem due to an additional coupling in the K\"{a}hler potential which
is allowed by the Standard Model gauge group. This induces directions in the
potential which we call K-flat. For a certain value of the new coupling in the
(N)MSSM, the K\"{a}hler potential is special, because it can be associated with
a certain shift symmetry for the Higgs doublets, a generalization of the shift
symmetry for singlets in earlier models. We find that K-flat direction has
$H_u^0=-H_d^{0*}.$ This shift symmetry is broken by interactions coming from
the superpotential and gauge fields. This direction fails to produce successful
inflation in the MSSM but produces a viable model in the NMSSM. The model is
specifically interesting in the Peccei-Quinn (PQ) limit of the NMSSM. In this
limit the model can be confirmed or ruled-out not just by cosmic microwave
background observations but also by axion searches.


  Topic modeling and text mining are subsets of Natural Language Processing
(NLP) with relevance for conducting meta-analysis (MA) and systematic review
(SR). For evidence synthesis, the above NLP methods are conventionally used for
topic-specific literature searches or extracting values from reports to
automate essential phases of SR and MA. Instead, this work proposes a
comparative topic modeling approach to analyze reports of contradictory results
on the same general research question. Specifically, the objective is to
identify topics exhibiting distinct associations with significant results for
an outcome of interest by ranking them according to their proportional
occurrence in (and consistency of distribution across) reports of significant
effects. The proposed method was tested on broad-scope studies addressing
whether supplemental nutritional compounds significantly benefit macular
degeneration (MD). Four of these were further supported in terms of
effectiveness upon conducting a follow-up literature search for validation
(omega-3 fatty acids, copper, zeaxanthin, and nitrates). The two not supported
by the follow-up literature search (niacin and molybdenum) also had scores in
the lowest range under the proposed scoring system, suggesting that the
proposed methods score for a given topic may be a viable proxy for its degree
of association with the outcome of interest and can be helpful in the search
for potentially causal relationships. These results underpin the proposed
methods potential to add specificity in understanding effects from broad-scope
reports, elucidate topics of interest for future research, and guide evidence
synthesis in a systematic and scalable way. All of this is accomplished while
yielding valuable insights into the prevention of MD.


  We prove three-dimensional Worsey-Farin refinements inherit their parent
triangulations' shape-regularity.


  We propose an extensible deep learning method that uses reinforcement
learning to train neural networks for offline ranking in information retrieval
(IR). We call our method BanditRank as it treats ranking as a contextual bandit
problem. In the domain of learning to rank for IR, current deep learning models
are trained on objective functions different from the measures they are
evaluated on. Since most evaluation measures are discrete quantities, they
cannot be leveraged by directly using gradient descent algorithms without an
approximation. BanditRank bridges this gap by directly optimizing a
task-specific measure, such as mean average precision (MAP), using gradient
descent. Specifically, a contextual bandit whose action is to rank input
documents is trained using a policy gradient algorithm to directly maximize the
reward. The reward can be a single measure, such as MAP, or a combination of
several measures. The notion of ranking is also inherent in BanditRank, similar
to the current \textit{listwise} approaches. To evaluate the effectiveness of
BanditRank, we conducted a series of experiments on datasets related to three
different tasks, i.e., web search, community, and factoid question answering.
We found that it performs better than state-of-the-art methods when applied on
the question answering datasets. On the web search dataset, we found that
BanditRank performed better than four strong listwise baselines including
LambdaMART, AdaRank, ListNet and Coordinate Ascent.


  This paper motivates the study of decision theory as necessary for aligning
smarter-than-human artificial systems with human interests. We discuss the
shortcomings of two standard formulations of decision theory, and demonstrate
that they cannot be used to describe an idealized decision procedure suitable
for approximation by artificial systems. We then explore the notions of policy
selection and logical counterfactuals, two recent insights into decision theory
that point the way toward promising paths for future research.


  Ground-motion model (GMM) is the basis of many earthquake engineering
studies. In this study, a novel physics-informed symbolic learner (PISL) method
based on the Nest Generation Attenuation-West2 database is proposed to
automatically discover mathematical equation operators as symbols. The
sequential threshold ridge regression algorithm is utilized to distill a
concise and interpretable explicit characterization of complex systems of
ground motions. In addition to the basic variables retrieved from previous
GMMs, the current PISL incorporates two a priori physical conditions, namely,
distance and amplitude saturation. GMMs developed using the PISL, an empirical
regression method (ERM), and an artificial neural network (ANN) are compared in
terms of residuals and extrapolation based on obtained data of peak ground
acceleration and velocity. The results show that the inter- and intra-event
standard deviations of the three methods are similar. The functional form of
the PISL is more concise than that of the ERM and ANN. The extrapolation
capability of the PISL is more accurate than that of the ANN. The PISL-GMM used
in this study provide a new paradigm of regression that considers both physical
and data-driven machine learning and can be used to identify the implied
physical relationships and prediction equations of ground motion variables in
different regions.


  The recently predicted topological magnetoelectric effect and the response to
an electric charge that mimics an induced mirror magnetic monopole are
fundamental attributes of topological states of matter with broken time
reversal symmetry. Using a SQUID-on-tip, acting simultaneously as a tunable
scanning electric charge and as ultrasensitive nanoscale magnetometer, we
induce and directly image the microscopic currents generating the magnetic
monopole response in a graphene quantum Hall electron system. We find a rich
and complex nonlinear behavior governed by coexistence of topological and
nontopological equilibrium currents that is not captured by the monopole
models. Furthermore, by utilizing a tuning fork that induces nanoscale
vibrations of the SQUID-on-tip, we directly image the equilibrium currents of
individual quantum Hall edge states for the first time. We reveal that the edge
states that are commonly assumed to carry only a chiral downstream current, in
fact carry a pair of counterpropagating currents, in which the topological
downstream current in the incompressible region is always counterbalanced by
heretofore unobserved nontopological upstream current flowing in the adjacent
compressible region. The intricate patterns of the counterpropagating
equilibrium-state orbital currents provide new insights into the microscopic
origins of the topological and nontopological charge and energy flow in quantum
Hall systems.


  We have studied the circular photogalvanic effect (CPGE) in Cu/Bi bilayers.
When a circularly polarized light in the visible range is irradiated to the
bilayer from an oblique incidence, we find a photocurrent that depends on the
helicity of light. Such photocurrent appears in a direction perpendicular to
the light plane of incidence but is absent in the parallel configuration. The
helicity dependent photocurrent is significantly reduced for a Bi single layer
film and the effect is nearly absent for a Cu single layer film. Conventional
interpretation of the CPGE suggests the existence of spin-momentum locked
band(s) of a Rashba type in the Cu/Bi bilayer. In contrast to previous reports
on the CPGE studied in other systems, however, the light energy used here to
excite the carriers is much larger than the band gap of Bi. Moreover, the CPGE
of the Cu/Bi bilayer is larger when the energy of the light is larger: the
helicity dependent photocurrent excited with a blue light is nearly two times
larger than that of a red light. We therefore consider the CPGE of the Cu/Bi
bilayer may have a different origin compared to conventional systems.


  The neutron capture cross section of $^{154}$Gd was measured from 1 eV to 300
keV in the experimental area located 185 m from the CERN n\_TOF neutron
spallation source, using a metallic sample of gadolinium, enriched to 67$\%$ in
$^{154}$Gd. The capture measurement, performed with four C$_{6}$D$_{6}$
scintillation detectors, has been complemented by a transmission measurement
performed at the GELINA time-of-flight facility (JRC-Geel), thus minimising the
uncertainty related to sample composition. An accurate Maxwellian averaged
capture cross section (MACS) was deduced over the temperature range of interest
for s process nucleosynthesis modeling. We report a value of 880(50) mb for the
MACS at $kT=30$ keV, significantly lower compared to values available in
literature. The new adopted $^{154}$Gd(n,$\gamma$) cross section reduces the
discrepancy between observed and calculated solar s-only isotopic abundances
predicted by s-process nucleosynthesis models.


  We obtain an anlogue of Wigner's classical theorem on symmetries for Banach
spaces. The proof is based on a result from the theory of linear preservers.
Moreover, we present two other Wigner-type results for finite dimensional
linear spaces over general fields.


  The third and fourth degree collisional moments for $d$-dimensional inelastic
Maxwell models are exactly evaluated in terms of the velocity moments, with
explicit expressions for the associated eigenvalues and cross coefficients as
functions of the coefficient of normal restitution. The results are applied to
the analysis of the time evolution of the moments (scaled with the thermal
speed) in the free cooling problem. It is observed that the characteristic
relaxation time toward the homogeneous cooling state decreases as the
anisotropy of the corresponding moment increases. In particular, in contrast to
what happens in the one-dimensional case, all the anisotropic moments of degree
equal to or less than four vanish in the homogeneous cooling state for $d\geq
2$.


  In the past few years, there has been a huge growth in Twitter sentiment
analysis having already provided a fair amount of research on sentiment
detection of public opinion among Twitter users. Given the fact that Twitter
messages are generated constantly with dizzying rates, a huge volume of
streaming data is created, thus there is an imperative need for accurate
methods for knowledge discovery and mining of this information. Although there
exists a plethora of twitter sentiment analysis methods in the recent
literature, the researchers have shifted to real-time sentiment identification
on twitter streaming data, as expected. A major challenge is to deal with the
Big Data challenges arising in Twitter streaming applications concerning both
Volume and Velocity. Under this perspective, in this paper, a methodological
approach based on open source tools is provided for real-time detection of
changes in sentiment that is ultra efficient with respect to both memory
consumption and computational cost. This is achieved by iteratively collecting
tweets in real time and discarding them immediately after their process. For
this purpose, we employ the Lexicon approach for sentiment characterizations,
while change detection is achieved through appropriate control charts that do
not require historical information. We believe that the proposed methodology
provides the trigger for a potential large-scale monitoring of threads in an
attempt to discover fake news spread or propaganda efforts in their early
stages. Our experimental real-time analysis based on a recent hashtag provides
evidence that the proposed approach can detect meaningful sentiment changes
across a hashtags lifetime.


  This paper is part of a series on the Azimuthally Symmetric Theory of
Gravitation (ASTG) which is built on Laplace-Poisson's well known equation. We
show herein that the emergent equations from the ASTG, under some critical
conditions determined by the spin, do possess repulsive gravitational fields in
the polar regions of the gravitating body in question. This places the ASTG on
an interesting pedestal to infer the origins of outflows as a repulsive
gravitational phenomenon. Outflows are a ubiquitous phenomenon found in star
forming systems and their true origin is a question yet to be settled. Given
the current thinking on their origin, the direction that the present paper
takes is nothing short of an asymptotic break from conventional wisdom; at the
very least, it is a complete paradigm shift because gravitation is not at all
associated with this process, but rather it is thought to be an all-attractive
force that only tries to squash matter together onto a single point.
Additionally, we show that the emergent Azimuthally Symmetric Gravitational
Field from the ASTG strongly suggests a solution to the supposed Radiation
Problem that is thought to be faced by massive stars in their process of
formation. That is, at about 8-10Msun radiation from the nascent star is
expected to halt the accretion of matter. We show that in-falling material will
fall onto the equatorial disk and from there, this material will be channeled
onto the forming star via the equatorial plane, thus accretion of mass
continues well past the value of about 8-10Msun, albeit via the disk. Along the
equatorial plane, the net force (with the radiation force included) on any
material there-on right up to the surface of the star is directed toward the
forming star, hence accretion of mass by the nascent star is un-hampered.


  We track the evolution of charge correlations in the kagome superconductor
CsV$_3$Sb$_5$ as its parent, long-ranged charge density order is destabilized.
Upon hole-doping doping, interlayer charge correlations rapidly become
short-ranged and their periodicity is reduced by half along the interlayer
direction. Beyond the peak of the first superconducting dome, the parent charge
density wave state vanishes and incommensurate, quasi-1D charge correlations
are stabilized in its place. These competing, unidirectional charge
correlations demonstrate an inherent electronic rotational symmetry breaking in
CsV$_3$Sb$_5$, independent of the parent charge density wave state and reveal a
complex landscape of charge correlations across the electronic phase diagram of
this class of kagome superconductors. Our data suggest an inherent 2$k_f$
charge instability and the phenomenology of competing charge instabilities is
reminiscent of what has been noted across several classes of unconventional
superconductors.


  This paper presents a novel two-step approach for the fundamental problem of
learning an optimal map from one distribution to another. First, we learn an
optimal transport (OT) plan, which can be thought as a one-to-many map between
the two distributions. To that end, we propose a stochastic dual approach of
regularized OT, and show empirically that it scales better than a recent
related approach when the amount of samples is very large. Second, we estimate
a \textit{Monge map} as a deep neural network learned by approximating the
barycentric projection of the previously-obtained OT plan. This
parameterization allows generalization of the mapping outside the support of
the input measure. We prove two theoretical stability results of regularized OT
which show that our estimations converge to the OT plan and Monge map between
the underlying continuous measures. We showcase our proposed approach on two
applications: domain adaptation and generative modeling.


  A d-wave superconducting phase with coexisting intra-unit-cell orbital
current order has the remarkable property that it supports finite size Fermi
pockets of Bougoliubov quasiparticles. Experimentally detectable consequences
of this include a residual $T$-linear term in the specific heat in the absence
of disorder and residual features in the thermal and microwave conductivity in
the low disorder limit.


  In the present paper, we introduce bi-slant submersions from almost Hermitian
manifolds onto Riemannian manifolds as a generalization of invariant,
anti-invariant, semi-invariant, slant, semi-slant and hemi-slant Riemannian
submersions. We mainly focus on bi-slant submersions from Kaehler manifolds. We
provide a proper example of bi-slant submersion, investigate the geometry of
foliations determined by vertical and horizontal distributions, and obtain the
geometry of leaves of these distributions. Moreover, we obtain curvature
relations between the base space, the total space and the fibres, and find
geometric implications of these relations.


  We compare two naturally arising notions of unknotting number for 2-spheres
in the 4-sphere: namely, the minimal number of 1-handle stabilizations needed
to obtain an unknotted surface, and the minimal number of Whitney moves
required in a regular homotopy to the unknotted 2-sphere. We refer to these
invariants as the stabilization number and the Casson-Whitney number of the
sphere, respectively. Using both algebraic and geometric techniques, we show
that the stabilization number is bounded above by one more than the
Casson-Whitney number. We also provide explicit families of spheres for which
these invariants are equal, as well as families for which they are distinct.
Furthermore, we give additional bounds for both invariants, concrete examples
of their non-additivity, and applications to classical unknotting number of
1-knots.


  Due to unbounded input operators in partial differential equations (PDEs)
with boundary inputs, there has been a long-held intuition that input-to-state
stability (ISS) properties and finite gains cannot be established with respect
to disturbances at the boundary. This intuition has been reinforced by many
unsuccessful attempts, as well as by the success in establishing ISS only with
respect to the derivative of the disturbance. Contrary to this intuition, we
establish such a result for parabolic PDEs. Our methodology does not rely on
the transformation of the boundary disturbance to a distributed input and the
stability analysis is performed in time-varying subsets of the state space. The
obtained results are used for the comparison of the gain coefficients of
transport PDEs with respect to inlet disturbances and for the establishment of
the ISS property with respect to control actuator errors for parabolic systems
under boundary feedback control.


  We study the effect of oxygen vacancies on the electronic structure of the
model strongly correlated metal SrVO$_3$. By means of angle-resolved
photoemission (ARPES) synchrotron experiments, we investigate the systematic
effect of the UV dose on the measured spectra. We observe the onset of a
spurious dose-dependent prominent peak at an energy range were the lower
Hubbard band has been previously reported in this compound, raising questions
on its previous interpretation. By a careful analysis of the dose dependent
effects we succeed in disentangling the contributions coming from the oxygen
vacancy states and from the lower Hubbard band. We obtain the intrinsic ARPES
spectrum for the zero-vacancy limit, where a clear signal of a lower Hubbard
band remains. We support our study by means of state-of-the-art ab initio
calculations that include correlation effects and the presence of oxygen
vacancies. Our results underscore the relevance of potential spurious states
affecting ARPES experiments in correlated metals, which are associated to the
ubiquitous oxygen vacancies as extensively reported in the context of a
two-dimensional electron gas (2DEG) at the surface of insulating $d^0$
transition metal oxides.


  We recast the system of Einstein field equations for Locally Rotationally
Symmetric spacetimes into an autonomous system of covariantly defined
geometrical variables. The analysis of this autonomous system gives all the
important global features of the maximal extension of these spacetimes. We
conclude that the dynamical system analysis can be a powerful mathematical tool
for qualitative understanding of the global structure of spacetimes
covariantly, without actually solving the field equations.


  We prove a CR version of the Obata's result for the first eigenvalue of the
sub-Laplacian in the setting of a compact strictly pseudoconvex pseudohermitian
three dimensional manifold with non-negative CR-Panietz operator which
satisfies a Lichnerowicz type condition. We show that if the first positive
eigenvalue of the sub-Laplacian takes the smallest possible value then, up to a
homothety of the pseudohermitian structure, the manifold is the standard
Sasakian three dimensional unit sphere.


  We introduce a new problem in unsupervised domain adaptation, termed as
Generalized Universal Domain Adaptation (GUDA), which aims to achieve precise
prediction of all target labels including unknown categories. GUDA bridges the
gap between label distribution shift-based and label space mismatch-based
variants, essentially categorizing them as a unified problem, guiding to a
comprehensive framework for thoroughly solving all the variants. The key
challenge of GUDA is developing and identifying novel target categories while
estimating the target label distribution. To address this problem, we take
advantage of the powerful exploration capability of generative flow networks
and propose an active domain adaptation algorithm named GFlowDA, which selects
diverse samples with probabilities proportional to a reward function. To
enhance the exploration capability and effectively perceive the target label
distribution, we tailor the states and rewards, and introduce an efficient
solution for parent exploration and state transition. We also propose a
training paradigm for GUDA called Generalized Universal Adversarial Network
(GUAN), which involves collaborative optimization between GUAN and GFlowNet.
Theoretical analysis highlights the importance of exploration, and extensive
experiments on benchmark datasets demonstrate the superiority of GFlowDA.


  We draw connections between simple neural networks and under-determined
linear systems to comprehensively explore several interesting theoretical
questions in the study of neural networks. First, we emphatically show that it
is unsurprising such networks can achieve zero training loss. More
specifically, we provide lower bounds on the width of a single hidden layer
neural network such that only training the last linear layer suffices to reach
zero training loss. Our lower bounds grow more slowly with data set size than
existing work that trains the hidden layer weights. Second, we show that
kernels typically associated with the ReLU activation function have fundamental
flaws -- there are simple data sets where it is impossible for widely studied
bias-free models to achieve zero training loss irrespective of how the
parameters are chosen or trained. Lastly, our analysis of gradient descent
clearly illustrates how spectral properties of certain matrices impact both the
early iteration and long-term training behavior. We propose new activation
functions that avoid the pitfalls of ReLU in that they admit zero training loss
solutions for any set of distinct data points and experimentally exhibit
favorable spectral properties.


  We prove that if $G$ is finite 2-generated $p$-group of nilpotence class at
most 2 then the group algebra of $G$ with coefficients in the field with $p$
elements determines $G$ up to isomorphisms.


  Artificial intelligence (AI) has emerged as a powerful technology that
improves system performance and enables new features in 5G and beyond.
Standardization, defining functionality and interfaces, is essential for
driving the industry alignment required to deliver the mass adoption of AI in
5G-Advanced and 6G. However, fragmented efforts in different standards bodies,
such as the third generation partnership project (3GPP) and the open radio
access network (O-RAN) Alliance, can lead to confusion and uncertainty about
which standards to follow and which aspects of the standards to embrace. This
article provides a joint 3GPP and O-RAN perspective on the state of the art in
AI adoption in mobile communication systems, including the fundamentals of 5G
architecture and its evolution towards openness and intelligence, AI for
5G-Advanced evolution, and a case study on AI-enabled traffic steering. We also
identify several areas for future exploration to accelerate AI adoption on the
path towards 6G.


  Establishing the CCZ-equivalence of a pair of APN functions is generally
quite difficult. In some cases, when seeking to show that a putative new
infinite family of APN functions is CCZ inequivalent to an already known
family, we rely on computer calculation for small values of n. In this paper we
present a method to prove the inequivalence of quadratic APN functions with the
Gold functions. Our main result is that a quadratic function is CCZ-equivalent
to an APN Gold function if and only if it is EA-equivalent to that Gold
function. As an application of this result, we prove that a trinomial family of
APN functions that exist on finite fields of order 2^n where n = 2 mod 4 are
CCZ inequivalent to the Gold functions. The proof relies on some knowledge of
the automorphism group of a code associated with such a function.


  Mutually unbiased bases (MUBs) are a primitive used in quantum information
processing to capture the principle of complementarity. While constructions of
maximal sets of d+1 such bases are known for systems of prime power dimension
d, it is unknown whether this bound can be achieved for any non-prime power
dimension. In this paper we demonstrate that maximal sets of MUBs come with a
rich combinatorial structure by showing that they actually are the same objects
as the complex projective 2-designs with angle set {0,1/d}. We also give a new
and simple proof that symmetric informationally complete POVMs are complex
projective 2-designs with angle set {1/(d+1)}.


  We present a measurement of two-dimensional (2D) redshift-space power
spectrum for the Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 11
CMASS galaxies in the North Galactic Cap (NGC) based on the method developed by
Jing & Borner (2001). In this method, we first measure the 2D redshift-space
correlation function for the CMASS galaxies, and obtain the 2D power spectrum
based on Fourier Transform of the correlation function. The method is tested
with an N-body mock galaxy catalog, which demonstrates that the method can
yield an accurate and unbiased measurement of the redshift-space power spectrum
given the input 2D correlation function is correct. Compared with previous
measurements in literature that are usually based on direct Fourier Transform
in redshift space, our method has the advantages that the window function and
shot-noise are fully corrected. In fact, our 2D power spectrum, by its
construction, can accurately reproduce the 2D correlation function, and in the
meanwhile can reproduce, for example, the 2D power spectrum of Beutler et al.
(2014) accurately if ours is convolved with the window function they provided.
Thus, our measurement can facilitate a direct comparison with the theoretical
predictions. With this accurate measurement of the 2D power spectrum, we then
develop a method to measure the structure growth rate, by separating the
anisotropic redshift-space power spectrum from the isotropic real-space power
spectrum. We have also carefully corrected for the nonlinearities in the
mapping from real space to redshift space, according to the theoretical model
of Zhang et al. (2013). Finally, we obtain f(zeff)sigma_8(zeff)=0.438\pm0.037
at the effective redshift zeff=0.57, where f(zeff) is the linear growth rate at
redshift zeff. The result is useful for constraining cosmological parameters.
The measurements of 2D power spectrum will be released soon.


  A massive spin-2 field can grow unstably around a black hole, giving rise to
a potential probe of the existence of such fields. In this work, we use
time-domain evolutions to study such instabilities. Considering the linear
regime by solving the equations generically governing a massive tensor field on
the background of a Kerr black hole, we find that black hole spin increases the
growth rate and, most significantly, the mass range of the axisymmetric
(azimuthal number $m=0$) instability, which takes the form of the
Gregory-Laflamme black string instability for zero spin. We also consider the
superradiant unstable modes with $1 \leq m \leq 3$, extending previous results
to higher spin-2 masses, black hole spins, and azimuthal numbers. We find that
the superradiant modes grow slower than the $m=0$ modes, except for a narrow
range of high spins and masses, with $m=1$ and 2 requiring a dimensionless
black hole spin of $a_{\rm BH}\gtrsim 0.95$ to be dominant. Thus, in most of
the parameter space, the backreaction of the $m=0$ instability must be taken
into account when using black holes to constrain massive spin-2 fields. As a
simple model of this, we consider nonlinear evolutions in quadratic gravity, in
particular Einstein-Weyl gravity. We find that, depending on the initial
perturbation, the black hole may approach zero mass with the curvature blowing
up in finite time, or can saturate at a larger mass with a surrounding cloud of
the ghost spin-2 field.


  Transition to hyperchaotic regimes in Rayleigh-Benard convection in a square
periodicity cell is studied by three-dimensional numerical simulations. By
fixing the Prandtl number at P=0.3 and varying the Rayleigh number as a control
parameter, a bifurcation diagram is constructed where a route to hyperchaos
involving quasiperiodic regimes with two and three incommensurate frequencies,
multistability, chaotic intermittent attractors and a sequence of boundary and
interior crises is shown. The three largest Lyapunov exponents exhibit a linear
scaling with the Rayleigh number and are positive in the final hyperchaotic
attractor. Thus, a route to weak turbulence is found.


  In order to constrain the evolutionary history of the Milky Way, we hunt for
faint RR Lyrae stars (RRLs) using Dark Energy Camera data from the High cadence
Transient Survey (HiTS) and the Halo Outskirts With Variable Stars (HOWVAST)
survey. We report the detection of $\sim500$ RRLs, including previously
identified stars and $\sim90$ RRLs not yet reported. We identify 9 new RRLs
beyond $100$ kpc from the Sun, most of which are classified as fundamental-mode
pulsators. The periods and amplitudes of the distant RRLs do not place them in
either one of the two classical Oosterhoff groups, but in the Oosterhoff
intermediate region. We detect two groups of clumped distant RRLs with similar
distances and equatorial coordinates, which we interpret as an indication of
their association with undiscovered bound or unbound satellites. We study the
halo density profile using spheroidal and ellipsoidal ($q=0.7$) models,
following a Markov chain Monte Carlo methodology. For a spheroidal halo, our
derived radial profile is consistent with a broken power-law with a break at
$18.1^{+2.1}_{-1.1}$ kpc separating the inner and the outer halo, and an outer
slope of $-4.47^{+0.11}_{-0.18}$. For an ellipsoidal halo, the break is located
at $24.3^{+2.6}_{-3.2}$ kpc and the outer slope is $-4.57^{+0.17}_{-0.25}$. The
break in the density profile is a feature visible in different directions of
the halo. The similarity of these radial distributions with previous values
reported in the literature seems to depend on the regions of the sky surveyed
(direction and total area) and halo tracer used. Our findings are compatible
with simulations and observations that predict that the outer regions of Milky
Way-like galaxies are mainly composed of accreted material.


  A dichotomy theorem for counting problems due to Creignou and Hermann states
that or any nite set S of logical relations, the counting problem #SAT(S) is
either in FP, or #P-complete. In the present paper we show a dichotomy theorem
for polynomial evaluation. That is, we show that for a given set S, either
there exists a VNP-complete family of polynomials associated to S, or the
associated families of polynomials are all in VP. We give a concise
characterization of the sets S that give rise to "easy" and "hard" polynomials.
We also prove that several problems which were known to be #P-complete under
Turing reductions only are in fact #P-complete under many-one reductions.


  Empathy is an important characteristic to be considered when building a more
intelligent and humanized dialogue agent. However, existing methods did not
fully comprehend empathy as a complex process involving three aspects:
cognition, affection and behavior. In this paper, we propose CAB, a novel
framework that takes a comprehensive perspective of cognition, affection and
behavior to generate empathetic responses. For cognition, we build paths
between critical keywords in the dialogue by leveraging external knowledge.
This is because keywords in a dialogue are the core of sentences. Building the
logic relationship between keywords, which is overlooked by the majority of
existing works, can improve the understanding of keywords and contextual logic,
thus enhance the cognitive ability. For affection, we capture the emotional
dependencies with dual latent variables that contain both interlocutors'
emotions. The reason is that considering both interlocutors' emotions
simultaneously helps to learn the emotional dependencies. For behavior, we use
appropriate dialogue acts to guide the dialogue generation to enhance the
empathy expression. Extensive experiments demonstrate that our
multi-perspective model outperforms the state-of-the-art models in both
automatic and manual evaluation.


  Multi-view clustering methods are essential for the stratification of
patients into sub-groups of similar molecular characteristics. In recent years,
a wide range of methods has been developed for this purpose. However, due to
the high diversity of cancer-related data, a single method may not perform
sufficiently well in all cases. We present Parea, a multi-view hierarchical
ensemble clustering approach for disease subtype discovery. We demonstrate its
performance on several machine learning benchmark datasets. We apply and
validate our methodology on real-world multi-view cancer patient data. Parea
outperforms the current state-of-the-art on six out of seven analysed cancer
types. We have integrated the Parea method into our developed Python package
Pyrea (https://github.com/mdbloice/Pyrea), which enables the effortless and
flexible design of ensemble workflows while incorporating a wide range of
fusion and clustering algorithms.


  This paper introduces adversarial attacks targeting a Graph Neural Network
(GNN) based radio resource management system in point to point (P2P)
communications. Our focus lies on perturbing the trained GNN model during the
test phase, specifically targeting its vertices and edges. To achieve this,
four distinct adversarial attacks are proposed, each accounting for different
constraints, and aiming to manipulate the behavior of the system. The proposed
adversarial attacks are formulated as optimization problems, aiming to minimize
the system's communication quality. The efficacy of these attacks is
investigated against the number of users, signal-to-noise ratio (SNR), and
adversary power budget. Furthermore, we address the detection of such attacks
from the perspective of the Central Processing Unit (CPU) of the system. To
this end, we formulate an optimization problem that involves analyzing the
distribution of channel eigenvalues before and after the attacks are applied.
This formulation results in a Min-Max optimization problem, allowing us to
detect the presence of attacks. Through extensive simulations, we observe that
in the absence of adversarial attacks, the eigenvalues conform to Johnson's SU
distribution. However, the attacks significantly alter the characteristics of
the eigenvalue distribution, and in the most effective attack, they even change
the type of the eigenvalue distribution.


  Radio pulsars are often used as clocks in a wide variety of experiments.
Imperfections in the clock, known as timing noise, have the potential to reduce
the significance of, or even thwart e.g. the attempt to find a stochastic
gravitational wave (GW) background. We measure the timing noise in a group of
129 mostly middle-aged pulsars (i.e. characterstic ages near 1~Myr) observed
with the Parkes radio telescope on a monthly basis since 2014. We examine four
different metrics for timing noise, but it remains unclear which, if any,
provides the best determination. In spite of this, it is evident that these
pulsars have significantly less timing noise than their younger counterparts,
but significantly more than the (much older) millisecond pulsars (MSPs). As
with previous authors, we find a strong correlation between timing noise and
the pulsar spin-down rate, $\dot{\nu}$. However, for a given $\dot{\nu}$ there
is a spread of about a factor 30 in the strength of the timing noise likely
indicating that nuclear conditions in the interior of the stars differs between
objects. We briefly comment on the implications for GW detection through pulsar
timing arrays as the level of timing noise in MSPs may be less than predicted.


  An enhanced approach for network monitoring is to create a network monitoring
tool that has artificial intelligence characteristics. There are a number of
approaches available. One such approach is by the use of a combination of rule
based, fuzzy logic and neural networks to create a hybrid ANFIS system. Such
system will have a dual knowledge database approach. One containing membership
function values to compare to and do deductive reasoning and another database
with rules deductively formulated by an expert (a network administrator). The
knowledge database will be updated continuously with newly acquired patterns.
In short, the system will be composed of 2 parts, learning from data sets and
fine-tuning the knowledge-base using neural network and the use of fuzzy logic
in making decision based on the rules and membership functions inside the
knowledge base. This paper will discuss the idea, steps and issues involved in
creating such a system.


  In this paper we study class $\mathcal{S}^+$ of univalent functions $f$ such
that $\frac{z}{f(z)}$ has real and positive coefficients. For such functions we
give estimates of the Fekete-Szeg\H{o} functional and sharp estimates of their
initial coefficients and logarithmic coefficients. Also, we present necessary
and sufficient conditions for $f\in \mathcal{S}^+$ to be starlike of order
$1/2$.


  A mechanism to find different Weyl invariant p-branes and Dp-branes actions
is explained. Our procedure clarifies the Weyl invariance for such systems.
Besides, by considering gravity-dilaton effective action in higher dimensions
we also derive a Weyl invariant action for p-branes. We argue that this
derivation provides a geometrical scenario for the Weyl invariance of p-branes.
Our considerations can be extended to the case of super-p-branes.


  In his list of open problems, Martin Erickson described a certain game: "Two
players alternately put queens on an n x n chess board so that each new queen
is not in range of any queen already on the board (the color of the queens is
unimportant). The last player who can move wins." Then he asked: "Who should
win?"
  Obviously, for n up to 3, the first player wins, if he does not miss to start
at the central position in the case n=3.
  In this article, we give very simple always winning strategies for the first
player if n is 4 or odd.
  The additionally (in the source package) provided computer program QPGAME3
has been used to check that there are successful strategies for the first
player if n is 6 or 8, and for the second player if n is 10, 12, 14, or 16.
  As discovered during the submission process of the first version of this
article, Hassan A Noon presented consistent results concerning values of n
which are odd or at most 10, in his B.A. thesis and, together with Glen Van
Brummelen, in a journal article.


  Research is taking place to find effective algorithms for content-based image
representation and description. There is a substantial amount of algorithms
available that use visual features (color, shape, texture). Shape feature has
attracted much attention from researchers that there are many shape
representation and description algorithms in literature. These shape image
representation and description algorithms are usually not application
independent or robust, making them undesirable for generic shape description.
This paper presents an object shape representation using Kernel Density Feature
Points Estimator (KDFPE). In this method, the density of feature points within
defined rings around the centroid of the image is obtained. The KDFPE is then
applied to the vector of the image. KDFPE is invariant to translation, scale
and rotation. This method of image representation shows improved retrieval rate
when compared to Density Histogram Feature Points (DHFP) method. Analytic
analysis is done to justify our method, which was compared with the DHFP to
prove its robustness.


  This work presents timing and spectral analysis of 4U 2206+54 using data
obtained from LAXPC instrument onboard India's AstroSat mission. This source
was observed with AstroSat in September 2016 and October 2016. We report
detection of $5648 (4)$ s pulsations at MJD 57669 in the latter observation of
4U 2206+54. The pulse profile is sinusoidal and the inherent shape is
independent of energy up to 30 keV. The pulse fraction increases with energy
from $\sim 0.5$% to $\sim 0.8$%. We report an updated spin down rate of $2.95
(14) \times 10^{-7}$ s s$^{-1}$. This is about 0.40 times smaller than the
previously reported long term value. The energy spectrum is best modelled with
an absorbed power-law with high energy exponential cut-off. We have detected
presence of broad emission line in 4U 2206+54 at an energy of $7$ keV with
equivalent width of $\sim 0.4$ keV.


  We predict the existence of an intriguing "disorder by order" phenomenon in
graphene transport where higher quality (and thus more ordered) samples, while
having higher mobility at high carrier density, will manifest more strongly
insulating (and thus effectively more disordered) behavior as the carrier
density is lowered compared with lower quality samples (with higher disorder)
which exhibit an approximate resistivity saturation phenomenon at low carrier
density near the Dirac point. This predicted behavior simulating a
metal-insulator transition, which we believe to have recently been observed in
an experiment at Manchester University [L. A. Ponomarenko et al., Nat. Phys. 7,
958 (2011)], arises from the suppression of Coulomb disorder induced
inhomogeneous puddles near the charge neutrality point in high quality graphene
samples.


  We formulate an algebraic relativistic method of scattering for systems with
spatially dependent mass based on the J-matrix method. The reference
Hamiltonian is the three-dimensional Dirac Hamiltonian but with a mass that is
position-dependent and having a constant asymptotic limit. Additionally, this
effective mass distribution is locally represented in a finite dimensional
function subspace. The spinor couples to spherically symmetric vector and
pseudo scalar potentials that are short-range such that they are accurately
represented by their matrix elements in the same finite dimensional subspace.
We calculate the relativistic phase shift as a function of energy for a given
configuration and study the effect of spatial variation of the mass on the
energy resonance structure.


  Using chiral supersymmetry, we show that the massless Dirac equation in the
Taub-NUT gravitational instanton field is exactly soluble and explain the
arisal and the use of the dynamical (super) symmetry.


  Deep Learning plays a significant role in assisting humans in many aspects of
their lives. As these networks tend to get deeper over time, they extract more
features to increase accuracy at the cost of additional inference latency. This
accuracy-performance trade-off makes it more challenging for Embedded Systems,
as resource-constrained processors with strict deadlines, to deploy them
efficiently. This can lead to selection of networks that can prematurely meet a
specified deadline with excess slack time that could have potentially
contributed to increased accuracy.
  In this work, we propose: (i) the concept of layer removal as a means of
constructing TRimmed Networks (TRNs) that are based on removing
problem-specific features of a pretrained network used in transfer learning,
and (ii) NetCut, a methodology based on an empirical or an analytical latency
estimator, which only proposes and retrains TRNs that can meet the
application's deadline, hence reducing the exploration time significantly.
  We demonstrate that TRNs can expand the Pareto frontier that trades off
latency and accuracy to provide networks that can meet arbitrary deadlines with
potential accuracy improvement over off-the-shelf networks. Our experimental
results show that such utilization of TRNs, while transferring to a simpler
dataset, in combination with NetCut, can lead to the proposal of networks that
can achieve relative accuracy improvement of up to 10.43% among existing
off-the-shelf neural architectures while meeting a specific deadline, and 27x
speedup in exploration time.


  Convolution is the core component within deep neural networks and it is
computationally intensive and time consuming. Tensor data layouts significantly
impact convolution operations in terms of memory access and computational
efficiency. Yet, there is still a lack of comprehensive performance
characterization on data layouts on SIMD architectures concerning convolution
methods. This paper proposes three novel data layouts for im2win convolution:
NHWC, CHWN, and CHWN8, and introduces a set of general optimization techniques
for both direct and im2win convolutions. We compare the optimized im2win
convolution with the direct convolution and PyTorch's im2col-based convolution
across the aforementioned layouts on SIMD machines. The experiments
demonstrated that the im2win convolution with the new NHWC layout achieved up
to 355% performance speedup over NCHW layout. Our optimizations also
significantly improve the performance of both im2win and direct convolutions.
Our optimized im2win and direct convolutions achieved up to 95% and 94% of
machine's theoretical peak performance, respectively.


  Leggett Garg inequalities (LGIs) provide an elegant way for probing the
incompatibility between the notion of macrorealism and quantum mechanics. For
unitary dynamics, the optimal quantum violation of a LGI is constrained by the
L\"uders bound. In this paper, we have studied two formulations of LGIs in a
three-time LG scenarios, viz., the standard LGIs and third-order LGIs both for
unbiased and biased measurement settings. We show that if the system evolves
under a non-unitary quantum channel between two measurements, the quantum
violations of both forms of LGIs exceed their respective L\"uders bounds and
can even reach their algebraic maximum in sharp measurement settings. We found
that when the measurement is unsharp the quantum violations of both standard
and third-order LGIs for non-unitary quantum channels can be obtained for the
lower value of the unsharpness parameter compared to the unitary dynamics. We
critically examined the violation of L\"uders bound of LGIs and its relation to
the violation of various no-signaling in time conditions, another formalism for
testing macrorealism. It is shown that mere violations of no-signaling
conditions are not enough to warrant the violation of standard LGIs, an
interplay between the violations of various NSIT condition along with a
threshold value play an important role. On the other hand violation of
third-order LGI is obtained when the degree of violation of a specific
no-signaling in time condition reaches a different threshold value.


  Humans can naturally understand an image in depth with the aid of rich
knowledge accumulated from daily lives or professions. For example, to achieve
fine-grained image recognition (e.g., categorizing hundreds of subordinate
categories of birds) usually requires a comprehensive visual concept
organization including category labels and part-level attributes. In this work,
we investigate how to unify rich professional knowledge with deep neural
network architectures and propose a Knowledge-Embedded Representation Learning
(KERL) framework for handling the problem of fine-grained image recognition.
Specifically, we organize the rich visual concepts in the form of knowledge
graph and employ a Gated Graph Neural Network to propagate node message through
the graph for generating the knowledge representation. By introducing a novel
gated mechanism, our KERL framework incorporates this knowledge representation
into the discriminative image feature learning, i.e., implicitly associating
the specific attributes with the feature maps. Compared with existing methods
of fine-grained image classification, our KERL framework has several appealing
properties: i) The embedded high-level knowledge enhances the feature
representation, thus facilitating distinguishing the subtle differences among
subordinate categories. ii) Our framework can learn feature maps with a
meaningful configuration that the highlighted regions finely accord with the
nodes (specific attributes) of the knowledge graph. Extensive experiments on
the widely used Caltech-UCSD bird dataset demonstrate the superiority of our
KERL framework over existing state-of-the-art methods.


  Using fully reconstructed $B_{s}$ mesons, we measure exclusive branching
fractions for the decays $B_s \to D_s^{(*)+}D_s^{(*)-}$. The results are ${\cal
B}(B^0_s\ra D^+_s D^-_s)=(0.58\,^{+0.11}_{-0.09}\,\pm 0.13)%$, ${\cal
B}(B^0_s\ra D^{*\pm}_s D^{\mp}_s)=(1.8\, \pm 0.2\,\pm 0.4)%$, and ${\cal
B}(B^0_s\ra D^{*+}_s D^{*-}_s)=(2.0\,\pm 0.3\,\pm 0.5)%$; the sum is ${\cal
B}(B^0_s\ra D^{(*)+}_s D^{(*)-}_s)=(4.3\,\pm 0.4\,\pm 1.0)%$. Assuming these
decay modes saturate decays to CP-even final states, the branching fraction
determines the relative width difference between the $B_s$ CP-odd and CP-even
eigenstates. Taking \cp\ violation to be negligibly small, we obtain $\dgs/\gs
= 0.090\,\pm 0.009\,{\rm(stat.)}\,\pm 0.022 \,{\rm (syst.)}$, where $\gs$ is
the mean decay width. The results are based on a data sample collected with the
Belle detector at the KEKB $e^+ e^-$ collider running at the $\Upsilon(5S)$
resonance with an integrated luminosity of 121.4 fb$^{-1}$.


  The fundamental properties of neutrinos are reviewed in these lectures. The
first part is focused on the basic characteristics of neutrinos in the Standard
Model and how neutrinos are detected. Neutrino masses and oscillations are
introduced and a summary of the most important experimental results on neutrino
oscillations to date is provided. Then, present and future experimental
proposals are discussed, including new precision reactor and accelerator
experiments. Finally, different approaches for measuring the neutrino mass and
the nature (Majorana or Dirac) of neutrinos are reviewed. The detection of
neutrinos from supernovae explosions and the information that this measurement
can provide are also summarized at the end.


  We consider the linear regression model with observation error in the design.
In this setting, we allow the number of covariates to be much larger than the
sample size. Several new estimation methods have been recently introduced for
this model. Indeed, the standard Lasso estimator or Dantzig selector turn out
to become unreliable when only noisy regressors are available, which is quite
common in practice. We show in this work that under suitable sparsity
assumptions, the procedure introduced in Rosenbaum and Tsybakov (2013) is
almost optimal in a minimax sense and, despite non-convexities, can be
efficiently computed by a single linear programming problem. Furthermore, we
provide an estimator attaining the minimax efficiency bound. This estimator is
written as a second order cone programming minimisation problem which can be
solved numerically in polynomial time.


  In these lectures we start with a pedagogical introduction of the properties
of open and closed superstrings and then, using the open/closed string duality,
we construct the boundary state that provides the description of the maximally
supersymmetric Dp branes in terms of the perturbative string formalism. We then
use it for deriving the corresponding supergravity solution and the Born-Infeld
action and for studying the properties of the maximally supersymmetric gauge
theories living on their worldvolume. In the last section of these lectures we
extend these results to less supersymmetric and non-conformal gauge theories by
considering fractional branes of orbifolds and wrapped branes.
  Lectures given at the School "Frontiers in Number Theory, Physics and
Geometry", Les Houches, March 2003.


  Transverse and longitudinal electroweak gauge boson parton distribution
functions (PDFs) are computed in terms of deep-inelastic scattering structure
functions, following the recently developed method to determine the photon PDF.
The calculation provides initial conditions at the electroweak scale for PDF
evolution to higher energies. Numerical results for the $W^\pm$ and $Z$
transverse, longitudinal and polarized PDFs, as well as the $\gamma Z$
transverse and polarized PDFs are presented.


  The strange contribution to the electric and magnetic form factors of the
nucleon is determined at a range of discrete values of $Q^2$ up to $1.4$
GeV$^2$. This is done by combining recent lattice QCD results for the
electromagnetic form factors of the octet baryons with experimental
determinations of those quantities. The most precise result is a small negative
value for the strange magnetic moment: $G_M^s(Q^2=0) = -0.07\pm0.03\,\mu_N$. At
larger values of $Q^2$ both the electric and magnetic form factors are
consistent with zero to within $2$-sigma.


  The advent of large language models, enabling flexibility through
instruction-driven approaches, has revolutionized many traditional generative
tasks, but large models for 3D data, particularly in comprehensively handling
3D shapes with other modalities, are still under-explored. By achieving
instruction-based shape generations, versatile multimodal generative shape
models can significantly benefit various fields like 3D virtual construction
and network-aided design. In this work, we present ShapeGPT, a shape-included
multi-modal framework to leverage strong pre-trained language models to address
multiple shape-relevant tasks. Specifically, ShapeGPT employs a
word-sentence-paragraph framework to discretize continuous shapes into shape
words, further assembles these words for shape sentences, as well as integrates
shape with instructional text for multi-modal paragraphs. To learn this
shape-language model, we use a three-stage training scheme, including shape
representation, multimodal alignment, and instruction-based generation, to
align shape-language codebooks and learn the intricate correlations among these
modalities. Extensive experiments demonstrate that ShapeGPT achieves comparable
performance across shape-relevant tasks, including text-to-shape,
shape-to-text, shape completion, and shape editing.


  The NEMO (NEutrino Mediterranean Observatory) Collaboration has been carrying
out since 1998 an evaluation programme of deep sea sites suitable for the
construction of the future Mediterranean km3 Cerenkov neutrino telescope. We
investigated the seawater optical and oceanographic properties of several deep
sea marine areas close to the Italian Coast. Inherent optical properties (light
absorption and attenuation coefficients) have been measured as a function of
depth using an experimental apparatus equipped with standard oceanographic
probes and the commercial transmissometer AC9 manufactured by WETLabs. This
paper reports on the visible light absorption and attenuation coefficients
measured in deep seawater of a marine region located in the Southern Ionian
Sea, 60-100 km SE of Capo Passero (Sicily). Data show that blue light
absorption coefficient is about 0.015 1/m (corresponding to an absorption
length of 67 m) close to the one of optically pure water and it doe not show
seasonal variation.


  We propose a method for precision statistical control of quantum processes
based on superconductor phase qubits. Using the universal quantum tomography
method, we provide a detailed analysis of accuracy of tomography for a 2-qubit
gate SQiSW, which arises due to capacitive coupling between qubits. The
developed approach could be successfully applied for quality and efficiency
problems of superconductor quantum information technologies.


  A black hole X-ray binary produces hard X-ray radiation from its corona and
disk when the accreting matter heats up. During an outburst, the disk and
corona co-evolves with each other. However, such an evolution is still unclear
in both its geometry and dynamics. Here we report the unusual decrease of the
reflection fraction in MAXI J1820+070, which is the ratio of the coronal
intensity illuminating the disk to the coronal intensity reaching the observer,
as the corona is observed to contrast during the decay phase. We postulate a
jet-like corona model, in which the corona can be understood as a standing
shock where the material flowing through. In this dynamical scenario, the
decrease of the reflection fraction is a signature of the corona's bulk
velocity. Our findings suggest that as the corona is observed to get closer to
the black hole, the coronal material might be outflowing faster.


  We have performed an exhaustive search for Gribov copies on the lattice and
their possible dependence from finite temperature effects. We show that, for
each value of lattice size, Gribov copies are dense in configuration space at
low temperature but their density tend to lower when the temperature increases.
We have investigated lattice sizes running from $16^3\times 8$ to $16^4$.


  In this paper, we present a thorough study of maximizing a regularized
non-monotone submodular function subject to various constraints, i.e., $\max \{
g(A) - \ell(A) : A \in \mathcal{F} \}$, where $g \colon 2^\Omega \to
\mathbb{R}_+$ is a non-monotone submodular function, $\ell \colon 2^\Omega \to
\mathbb{R}_+$ is a normalized modular function and $\mathcal{F}$ is the
constraint set. Though the objective function $f := g - \ell$ is still
submodular, the fact that $f$ could potentially take on negative values
prevents the existing methods for submodular maximization from providing a
constant approximation ratio for the regularized submodular maximization
problem. To overcome the obstacle, we propose several algorithms which can
provide a relatively weak approximation guarantee for maximizing regularized
non-monotone submodular functions. More specifically, we propose a continuous
greedy algorithm for the relaxation of maximizing $g - \ell$ subject to a
matroid constraint. Then, the pipage rounding procedure can produce an integral
solution $S$ such that $\mathbb{E} [g(S) - \ell(S)] \geq e^{-1}g(OPT) -
\ell(OPT) - O(\epsilon)$. Moreover, we present a much faster algorithm for
maximizing $g - \ell$ subject to a cardinality constraint, which can output a
solution $S$ with $\mathbb{E} [g(S) - \ell(S)] \geq (e^{-1} - \epsilon) g(OPT)
- \ell(OPT)$ using $O(\frac{n}{\epsilon^2} \ln \frac 1\epsilon)$ value oracle
queries. We also consider the unconstrained maximization problem and give an
algorithm which can return a solution $S$ with $\mathbb{E} [g(S) - \ell(S)]
\geq e^{-1} g(OPT) - \ell(OPT)$ using $O(n)$ value oracle queries.


  A right-handed stop not much heavier or even lighter than the Z boson has
today desirable phenomenological consequences. We study how it can result
within the usual radiative scenario of electroweak symmetry breaking. A
restriction on the gaugino mass parameters, M_2 < 0.3 m_10, arises if soft
terms satisfy relations suggested by unification theories. Moreover, requiring
to get a light stop without unnatural fine-tunings below the per-cent level, we
obtain another more interesting upper bound on the chargino mass, M_chargino <
M_Z, and we derive interesting conclusions about the masses of the gluino, M_3
\sim (150-300) GeV, of the heavy stop, M_stop \sim (250-500) GeV, and of the
left/right mixing angle in the stop sector, |\theta_stop| < 0.3.


  We investigate the selective forces that promote the emergence of modularity
in nature. We demonstrate the spontaneous emergence of modularity in a
population of individuals that evolve in a changing environment. We show that
the level of modularity correlates with the rapidity and severity of
environmental change. The modularity arises as a synergistic response to the
noise in the environment in the presence of horizontal gene transfer. We
suggest that the hierarchical structure observed in the natural world may be a
broken symmetry state, which generically results from evolution in a changing
environment.


  We derive an analytic expression for the mean magnification due to strong
gravitational lensing, using a simple lens model, a singular isothermal sphere
embedded in an external shear field. We compute separate expressions for
2-image and 4-image lensing. For 4-image lensing, the mean magnification takes
a particularly simple form: <mu>_4 = 3.6/(gamma*(1-gamma^2)), where gamma is
the external shear. We compare our analytic results to a numerical evaluation
of the full magnification distribution. The results can be used to understand
the magnification bias that favors the discovery of four-image systems over
two-image systems in flux-limited lens surveys.


  In this work, we report thermal conductivity(k) of magnesium telluride (MgTe)
with various crystallographic phases such as rocksalt, zincblende, wurtzite and
nickel arsenic (NiAs) using density functional theory and Boltzmann transport
equation. Our first principles calculations results show the low thermal
conductivity of MgTe with kNiAs < krocksalt < kwurtzite < kzincblende. We
systematically investigated the phonon group velocity, phonon scattering rate
and mode contributed thermal conductivity of transverse acoustic (TA),
longitudinal acoustic (LA) and optical phonons. Our first principles
calculations shows that ultra-low thermal conductivity of 2.645 Wm-1K-1 for
NiAs phase is due to the dominant scattering of TA and LA phonons by low
frequency optical phonons. We also analyzed the length dependence thermal
conductivity of MgTe at nanometer length-scales. At nanometer length scales
such as 50 nm for NiAs phase, room temperature thermal conductivity of less
than 1.4 Wm-1K-1 shows a promising nature of MgTe for thermoelectric
applications.


  We have modelled X-ray burst oscillations observed with the Rossi X-ray
Timing Explorer (RXTE) from two low mass X-ray binaries (LMXB): 4U 1636-53 with
a frequency of 580 Hz, and 4U 1728-34 at a frequency of 363 Hz. We have
computed least squares fits to the oscillations observed during the rising
phase of bursts using a model which includes emission from either a single
circular hot spot or a pair of circular antipodal hot spots on the surface of a
neutron star. We model the spreading of the thermonuclear hot spots by assuming
that the hot spot angular size grows linearly with time. We calculate the flux
as a function of rotational phase from the hot spots and take into account
photon deflection in the relativistic gravitational field of the neutron star
assuming the exterior spacetime is the Schwarzschild metric. We find acceptable
fits with our model and we use these to place constraints on the compactness of
the neutron stars in these sources. For 4U 1636-53, in which detection of a 290
Hz sub-harmonic supports the two spot model, we find that the compactness
(i.e., mass/radius ratio) is constrained to be M/R < 0.163 at 90% confidence (G
= c = 1). This requires a relatively stiff equation of state (EOS) for the
stellar interior. For example, if the neutron star has a mass of 1.4 Msun then
its radius must be > 12.8 km. Fits using a single hot spot model are not as
highly constraining. We discuss the implications of our findings for recent
efforts to calculate the EOS of dense nucleon matter and the structure of
neutron stars.


  Software debloating is an emerging field of study aimed at improving the
security and performance of software by removing excess library code and
features that are not needed by the end user (called bloat). Software bloat is
pervasive, and several debloating techniques have been proposed to address this
problem. While these techniques are effective at reducing bloat, they are not
practical for the average user, risk creating unsound programs and introducing
vulnerabilities, and are not well suited for debloating complex software such
as network protocol implementations. In this paper, we propose CARVE, a simple
yet effective security-focused debloating technique that overcomes these
limitations. CARVE employs static source code annotation to map software
features source code, eliminating the need for advanced software analysis
during debloating and reducing the overall level of technical sophistication
required by the user. CARVE surpasses existing techniques by introducing
debloating with replacement, a technique capable of preserving software
interoperability and mitigating the risk of creating an unsound program or
introducing a vulnerability. We evaluate CARVE in 12 debloating scenarios and
demonstrate security and performance improvements that meet or exceed those of
existing techniques.


  We relativize the notion of a compact object in an abelian category with
respect to a fixed subclass of objects. We show that the standard closure
properties persist to hold in this case. Furthermore, we describe categorical
and set-theoretical conditions under which all products of compact objects
remain compact.


  Location based services (LBS) have become very popular in recent years. They
range from map services (e.g., Google Maps) that store geographic locations of
points of interests, to online social networks (e.g., WeChat, Sina Weibo,
FourSquare) that leverage user geographic locations to enable various
recommendation functions. The public query interfaces of these services may be
abstractly modeled as a kNN interface over a database of two dimensional points
on a plane: given an arbitrary query point, the system returns the k points in
the database that are nearest to the query point. In this paper we consider the
problem of obtaining approximate estimates of SUM and COUNT aggregates by only
querying such databases via their restrictive public interfaces. We distinguish
between interfaces that return location information of the returned tuples
(e.g., Google Maps), and interfaces that do not return location information
(e.g., Sina Weibo). For both types of interfaces, we develop aggregate
estimation algorithms that are based on novel techniques for precisely
computing or approximately estimating the Voronoi cell of tuples. We discuss a
comprehensive set of real-world experiments for testing our algorithms,
including experiments on Google Maps, WeChat, and Sina Weibo.


  State-of-the-art methods in generative representation learning yield semantic
disentanglement, but typically do not consider physical scene parameters, such
as geometry, albedo, lighting, or camera. We posit that inverse rendering, a
way to reverse the rendering process to recover scene parameters from an image,
can also be used to learn physically disentangled representations of scenes
without supervision. In this paper, we show the utility of inverse rendering in
learning representations that yield improved accuracy on downstream clustering,
linear classification, and segmentation tasks with the help of our novel
Leave-One-Out, Cycle Contrastive loss (LOOCC), which improves disentanglement
of scene parameters and robustness to out-of-distribution lighting and
viewpoints. We perform a comparison of our method with other generative
representation learning methods across a variety of downstream tasks, including
face attribute classification, emotion recognition, identification, face
segmentation, and car classification. Our physically disentangled
representations yield higher accuracy than semantically disentangled
alternatives across all tasks and by as much as 18%. We hope that this work
will motivate future research in applying advances in inverse rendering and 3D
understanding to representation learning.


  Neural Architecture Search has proven an effective method of automating
architecture engineering. Recent work in the field has been to look for
architectures subject to multiple objectives such as accuracy and latency to
efficiently deploy them on different target hardware. Once-for-All (OFA) is one
such method that decouples training and search and is able to find
high-performance networks for different latency constraints. However, the
search phase is inefficient at incorporating multiple latency targets. In this
paper, we introduce two strategies (Top-down and Bottom-up) that use warm
starting and randomized network pruning for the efficient incorporation of
multiple latency targets in the OFA network. We evaluate these strategies
against the current OFA implementation and demonstrate that our strategies
offer significant running time performance gains while not sacrificing the
accuracy of the subnetworks that were found for each latency target. We further
demonstrate that these performance gains are generalized to every design space
used by the OFA network.


  We present an extension of the Keldysh-Green's function method, which allows
to calculate the full distribution of transmitted particles through a
mesoscopic superconductor. The method is applied to the statistics of
supercurrent in short contacts. If the current is carried by Andreev bound
states the distribution corresponds to switching between long trains of
electrons going in opposite directions. For weak (gapless) superconductors or
tunnel junctions we find that at low temperatures the distribution has negative
`probabilities'. Accounting for the quantum mechanical nature of the measuring
device shows that these negative values can indeed be measured.


  Spanning two decades, the Encyclopaedia of DNA Elements (ENCODE) is a
collaborative research project that aims to identify all the functional
elements in the human and mouse genomes. To best serve the scientific
community, all data generated by the consortium is shared through a web-portal
(https://www.encodeproject.org/) with no access restrictions. The fourth and
final phase of the project added a diverse set of new samples (including those
associated with human disease), and a wide range of new assays aimed at
detection, characterization and validation of functional genomic elements. The
ENCODE data portal hosts results from over 23,000 functional genomics
experiments, over 800 functional elements characterization experiments
(including in vivo transgenic enhancer assays, reporter assays and CRISPR
screens) along with over 60,000 results of computational and integrative
analyses (including imputations, predictions and genome annotations). The
ENCODE Data Coordination Center (DCC) is responsible for development and
maintenance of the data portal, along with the implementation and utilisation
of the ENCODE uniform processing pipelines to generate uniformly processed
data. Here we report recent updates to the data portal. Specifically, we have
completely redesigned the home page, improved search interface, added several
new pages to highlight collections of biologically related data (deeply
profiled cell lines, immune cells, Alzheimer's Disease, RNA-Protein
interactions, degron matrix and a matrix of experiments organised by human
donors), added single-cell experiments, and enhanced the cart interface for
visualisation and download of user-selected datasets.


  It is necessary to make assumptions in order to derive models to be used for
cosmological predictions and comparison with observational data. In particular,
in standard cosmology the spatial curvature is assumed to be constant and zero
(or at least very small). But there is, as yet, no fully independent constraint
with an appropriate accuracy that gaurentees a value for the magnitude of the
effective normalized spatial curvature $\Omega_{k}$ of less than approximately
$0.01$. Moreover, a small non-zero measurement of $\Omega_{k}$ at such a level
perhaps indicates that the assumptions in the standard model are not satisfied.
It has also been increasingly emphasised that spatial curvature is, in general,
evolving in relativistic cosmological models. We review the current situation,
and conclude that the possibility of such a non-zero value of $\Omega_k$ should
be taken seriously.


  In this paper, two robust model predictive control (MPC) schemes are proposed
for tracking control of nonholonomic systems with bounded disturbances:
tube-MPC and nominal robust MPC (NRMPC). In tube-MPC, the control signal
consists of a control action and a nonlinear feedback law based on the
deviation of the actual states from the states of a nominal system. It renders
the actual trajectory within a tube centered along the optimal trajectory of
the nominal system. Recursive feasibility and input-to-state stability are
established and the constraints are ensured by tightening the input domain and
the terminal region. While in NRMPC, an optimal control sequence is obtained by
solving an optimization problem based on the current state, and the first
portion of this sequence is applied to the real system in an open-loop manner
during each sampling period. The state of nominal system model is updated by
the actual state at each step, which provides additional a feedback. By
introducing a robust state constraint and tightening the terminal region,
recursive feasibility and input-to-state stability are guaranteed. Simulation
results demonstrate the effectiveness of both strategies proposed.


  The exploration of social conversations for addressing patient's needs is an
important analytical task in which many scholarly publications are contributing
to fill the knowledge gap in this area. The main difficulty remains the
inability to turn such contributions into pragmatic processes the
pharmaceutical industry can leverage in order to generate insight from social
media data, which can be considered as one of the most challenging source of
information available today due to its sheer volume and noise. This study is
based on the work by Scott Spangler and Jeffrey Kreulen and applies it to
identify structure in social media through the extraction of a topical taxonomy
able to capture the latent knowledge in social conversations in health-related
sites. The mechanism for automatically identifying and generating a taxonomy
from social conversations is developed and pressured tested using public data
from media sites focused on the needs of cancer patients and their families.
Moreover, a novel method for generating the category's label and the
determination of an optimal number of categories is presented which extends
Scott and Jeffrey's research in a meaningful way. We assume the reader is
familiar with taxonomies, what they are and how they are used.


  When computing the average speed of a car over different time periods from
given GPS data, it is conventional wisdom that the maximal average speed over
all time intervals of fixed length decreases if the interval length increases.
However, this intuition is wrong. We investigate this phenomenon and make
rigorous in which sense this intuition is still true.


  The new formal analogy between superfluid systems and cosmology, which
emerges by taking into account the back-reaction of the vacuum to the quanta of
sound waves \cite{noi}, enables us to put forward some common features between
these two different areas of physics. We find the condition that allows us to
justify a General Relativity (GR) derivation of the hydrodynamical equation for
the superfluid in a four-dimensional space whose metric is the Unruh one
\cite{Unruh}. Furthermore we show how, in the particular case taken into
account, our hydrodynamical equation can be deduced within a four-dimensional
space from the wave equation of a massless scalar field.


  We consider the discretization effects of a string-bit model simulating the
near-BMN operators in the super--Yang--Mills model. The fermionic sector of
this model is altered by the so called species doubling. We analyze the
possibilities to cure this disease and propose an alternative formulation of
the fermionic sector free from the above drawbacks. Also we propose a
formulation of string bits with exact supersymmetry, which produces however an
even number of continuous strings in the limit $J\to\infty$.


  The thermodynamic limit of photovoltaic efficiency for a single-junction
solar cell can be readily predicted using the bandgap of the active light
absorbing material. Such an approach overlooks the energy loss due to
non-radiative electron-hole processes. We propose a practical ab initio
procedure to determine the maximum efficiency of a thin-film solar cell that
takes into account both radiative and non-radiative recombination. The required
input includes the frequency-dependent optical absorption coefficient, as well
as the capture cross-sections and equilibrium populations of point defects. For
kesterite-structured Cu$_2$ZnSnS$_4$, the radiative limit is reached for a film
thickness of around 2.6 micrometer, where the efficiency gain due to light
absorption is counterbalanced by losses due to the increase in recombination
current.


  The CRYRING accelerator, previously located at the Manne Siegbahn Laboratory
of Stockholm University, has been chosen by the FLAIR collaboration as the
central accelerator for the planned facility. It has been modified to allow for
high-energy injection and extraction and is capable of providing fast and slow
extracted beams of antiprotons and highly charged ions. It is currently being
installed at the ESR of GSI Darmstadt where it can be used with highly charged
ions. The future possibilities for its use with slow antiprotons will be
discussed.


  We introduce an efficient lattice regularization scheme for quantum Monte
Carlo calculations of realistic electronic systems. The kinetic term is
discretized by a finite difference Laplacian with two mesh sizes, a and a',
where a'/a is an irrational number so that the electronic coordinates are not
defined on a particular lattice but on the continuous configuration space. The
regularized Hamiltonian goes to the continuous limit for a -> 0 and provides
several advantages. In particular, it allows the inclusion of non-local
potentials in a consistent variational scheme, substantially improving the
accuracy upon previous non-variational approaches.


  We provide a general method for computing rational Chow rings of moduli of
smooth complete intersections. We specialize this result in different ways: to
compute the integral Picard group of the associated stack ; to obtain an
explicit presentation of rational Chow rings of moduli of smooth complete
intersections of codimension two; to prove old and new results on moduli of
smooth curves of genus $\leq 5$ and polarized K3 surfaces of degree $\leq 8$.


  We present a new phase field crystal model for structural transformations in
multi-component alloys. The formalism builds upon the two-point correlation
kernel developed in Greenwood et al. for describing structural transformations
in pure materials [Phys. Rev. Lett. 105, 045702 (2010)]. We introduce an
effective twopoint correlation function for multi-component alloys that uses
the local species concentrations to interpolate between different crystal
structures. A simplified version of the model is derived for the particular
case of threecomponent (ternary) alloys, and its equilibrium properties are
demonstrated. Dynamical equations of motion for the density and multiple
species concentration fields are derived, and the robustness of the model is
illustrated with examples of complex microstructure evolution in dendritic
solidification and solid-state precipitation.


  Non-equilibrium two-parameter pumping transport through graphene ribbons,
attached to reservoirs is described. A tight-binding model is solved using
Keldysh formalism, and the crossover between adiabatic and non-adiabatic
regimes is studied. Pumped dc currents through armchair ribbons show properties
common in two-dimensional systems. The width-dependent dc current in zigzag
ribbons, reveals that edge states akin to those in two-dimensional topological
insulators, do not contribute to pumped transport in the adiabatic regime. The
interplay between propagating and evanescent modes is discussed.


  We derive necessary and sufficient conditions for an Ore extension of a Hopf
algebra to have a Hopf algebra structure of a certain type. This construction
generalizes the notion of Hopf-Ore extension, called a generalized Hopf-Ore
extension. We describe the generalized Hopf-Ore extensions of the enveloping
algebras of Lie algebras. For some Lie algebras g, the generalized Hopf-Ore
extensions of U(g) are classified.


  Quantum circuit Born machines (QCBMs) and training via variational quantum
algorithms (VQAs) are key applications for near-term quantum hardware. QCBM
ans\"atze designs are unique in that they do not require prior knowledge of a
physical Hamiltonian. Many ans\"atze are built from fixed designs. In this
work, we train and compare the performance of QCBM models built using two
commonly employed parameterizations and two commonly employed entangling layer
designs. In addition to comparing the overall performance of these models, we
look at features and characteristics of the loss landscape -- connectivity of
minima in particular -- to help understand the advantages and disadvantages of
each design choice. We show that the rotational gate choices can improve loss
landscape connectivity.


  We have performed terahertz time-domain spectroscopy of carrier-doped
nanoporous crystal 12CaO7Al2O3 showing the Mott variable range hopping at room
temperature. The real part of the dielectric constant clearly demonstrates the
nature of localized carriers. The frequency dependence of both the real and
imaginary parts of the dielectric constant can be simply explained by assuming
two contributions: a dielectric response by the parent compound with no
carriers and an AC hopping conduction with the Jonscher law generally reported
up to GHz range. The possible obedience to the Jonscher law in the THz range
suggests a relaxation time of the hopping carriers much faster than 1ps in the
carrier-doped 12CaO7Al2O3.