  A long standing conjecture says that the integrality ratio of the subtour LP
for metric TSP is $4/3$. A well known family of graphic TSP instances achieves
this lower bound asymptotically. For Euclidean TSP the best known lower bound
on the integrality ratio was $8/7$. We improve this value by presenting a
family of Euclidean TSP instances for which the integrality ratio of the
subtour LP converges to 4/3.


  We derive a method to study the phase diagram for high temperature
superconductors (HTCS). Our starting point is the Hubbard Hamiltonian with a
weak attractive interaction to obtain the formation of bound pairs. We consider
this attractive potential at different positions for different compounds
accordingly to the experimental results of the coherence length. We then
construct a wave function of the BCS type by a variational method using the
Fourier transform of this extended Hubbard potential and then derive an energy
gap equation. This approach allows us to obtain the critical temperature as
function of the doping concentration which gives very good agreement with the
experimental phase diagrams of YBaCuO and La(Sr,Ba)CuO compounds.


  The network concept is increasingly used for the description of complex
systems. Here we summarize key aspects of the evolvability and robustness of
the hierarchical network-set of macromolecules, cells, organisms, and
ecosystems. Listing the costs and benefits of cooperation as a necessary
behaviour to build this network hierarchy, we outline the major hypothesis of
the paper: the emergence of hierarchical complexity needs cooperation leading
to the ageing (i.e. gradual deterioration) of the constituent networks. A
stable environment develops cooperation leading to over-optimization, and
forming an "always-old" network, which accumulates damage, and dies in an
apoptosis-like process. A rapidly changing environment develops competition
forming a "forever-young" network, which may suffer an occasional
over-perturbation exhausting system-resources, and causing death in a
necrosis-like process. Giving a number of examples we demonstrate how
cooperation evokes the gradual accumulation of damage typical to ageing.
Finally, we show how various forms of cooperation and consequent ageing emerge
as key elements in all major steps of evolution from the formation of
protocells to the establishment of the globalized, modern human society.


  A well-characterised wavefront is important for many X-ray free-electron
laser (XFEL) experiments, especially for single-particle imaging (SPI), where
individual bio-molecules randomly sample a nanometer-region of highly-focused
femtosecond pulses. We demonstrate high-resolution multiple-plane wavefront
imaging of an ensemble of XFEL pulses, focused by Kirkpatrick-Baez (KB)
mirrors, based on mixed-state ptychography, an approach letting us infer and
reduce experimental sources of instability. From the recovered wavefront
profiles, we show that while local photon fluence correction is crucial and
possible for SPI, a small diversity of phase-tilts likely has no impact. Our
detailed characterisation will aid interpretation of data from past and future
SPI experiments, and provides a basis for further improvements to experimental
design and reconstruction algorithms.


  The normal modes of a trapped ion crystal are derived using an approach based
on the Hermitian properties of the systems dynamical matrix. This method is
equivalent to the standard Bogoliubov method, but for classical systems it is
arguably simpler and more general in that canonical coordinates are not
necessary. The theory is developed for stable, unstable, and neutrally-stable
systems. The method is then applied to ion crystals in a Penning trap. Reduced
eigenvalue problems for the case of large applied magnetic field are developed,
for which the spectrum breaks into ExB drift modes, axial modes, and cyclotron
modes. Thermal fluctuation levels in these modes are analyzed and shown to be
consistent with the Bohr-van-Leeuwen theorem, provided that neutrally-stable
modes associated with crystal rotations are included in the analysis. An
expression for the rotational inertia of the crystal is derived, and a magnetic
contribution to this inertia, which dominates in large magnetic fields, is
described. An unusual limit is discovered for the special case of
spherically-symmetric confinement, in which the rotational inertia does not
exist and changes in angular momentum leave the rotation frequency unaffected.


  The understanding of extreme near-field heat transport across vacuum nanogaps
between polar dielectric materials remains an open question. In this work, we
present a molecular dynamic simulation of heat transport across MgO-MgO
nanogaps, together with a consistent comparison with the continuum
fluctuational-electrodynamics theory using local dielectric properties. The
dielectric function is computed by Green-Kubo molecular dynamics with the
anharmonic damping properly included. As a result, the direct atomistic
modeling shows significant deviation from the continuum theory even up to a gap
size of few nanometers due to non-local dielectric response from acoustic and
optical branches as well as phonon tunneling. The lattice anharmonicity is
demonstrated to have a large impact on the energy transmission and thermal
conductance, in contrast to its moderate effect reported for metallic vacuum
nanogaps. The present work thus provides further insight into the physics of
heat transport in the extreme near-field regime between polar materials, and
put forward a methodology to account for anharmonic effects.


  We study the entanglement dynamics of two qubits, each of which is embedded
into its local amplitude-damping reservoir, and the entanglement distribution
among all the bipartite subsystems including qubit-qubit, qubit-reservoir, and
reservoir-reservoir. It is found that the entanglement can be stably
distributed among all components, which is much different to the result
obtained under the Born-Markovian approximation by C. E. L\'{o}pez {\it et al.}
[Phys. Rev. Lett. \textbf{101}, 080503 (2008)], and particularly it also
satisfies an identity. Our unified treatment includes the previous results as
special cases. The result may give help to understand the physical nature of
entanglement under decoherence.


  We are concerned with the decay of long time solutions of the initial value
problem associated with the Schr\"odinger-Korteweg-de Vries system. We use
recent techniques in order to show that solutions of this system decay to zero
in the energy space. The result is independent of the integrability of the
equations involved and it does not require any size assumptions.


  This paper presents a machine-learning-enhanced longitudinal scanline method
to extract vehicle trajectories from high-angle traffic cameras. The Dynamic
Mode Decomposition (DMD) method is applied to extract vehicle strands by
decomposing the Spatial-Temporal Map (STMap) into the sparse foreground and
low-rank background. A deep neural network named Res-UNet+ was designed for the
semantic segmentation task by adapting two prevalent deep learning
architectures. The Res-UNet+ neural networks significantly improve the
performance of the STMap-based vehicle detection, and the DMD model provides
many interesting insights for understanding the evolution of underlying
spatial-temporal structures preserved by STMap. The model outputs were compared
with the previous image processing model and mainstream semantic segmentation
deep neural networks. After a thorough evaluation, the model is proved to be
accurate and robust against many challenging factors. Last but not least, this
paper fundamentally addressed many quality issues found in NGSIM trajectory
data. The cleaned high-quality trajectory data are published to support future
theoretical and modeling research on traffic flow and microscopic vehicle
control. This method is a reliable solution for video-based trajectory
extraction and has wide applicability.


  Vision transformers (ViTs) have achieved impressive results on various
computer vision tasks in the last several years. In this work, we study the
capability of frozen ViTs, pretrained only on visual data, to generalize to
audio-visual data without finetuning any of its original parameters. To do so,
we propose a latent audio-visual hybrid (LAVISH) adapter that adapts pretrained
ViTs to audio-visual tasks by injecting a small number of trainable parameters
into every layer of a frozen ViT. To efficiently fuse visual and audio cues,
our LAVISH adapter uses a small set of latent tokens, which form an attention
bottleneck, thus, eliminating the quadratic cost of standard cross-attention.
Compared to the existing modality-specific audio-visual methods, our approach
achieves competitive or even better performance on various audio-visual tasks
while using fewer tunable parameters and without relying on costly audio
pretraining or external audio encoders. Our code is available at
https://genjib.github.io/project_page/LAVISH/


  Since transmission lines are crucial links in the power system, one line
outage event may bring about interruption or even cascading failure of the
power system. If a quick and accurate line outage detection and localization
can be achieved, the system operator can take necessary actions in time to
mitigate the negative impact. Therefore, the objective of this paper is to
study a method for line outage detection and localization via synchrophasor
measurements. The density of deployed phasor measurement units (PMUs) is
increasing recently, which greatly improves the visibility of the power grid.
Taking advantage of the high-resolution synchrophasor data, the proposed method
utilizes frequency measurement for line outage detection and power change for
localization. The procedure of the proposed method is given. Compared with
conventional methods, it does not require the pre-knowledge on the system.
Simulation study validates the effectiveness of the proposed method.


  We consider the Tur\'an problems of $2$-edge-colored graphs. A
$2$-edge-colored graph $H=(V, E_r, E_b)$ is a triple consisting of the vertex
set $V$, the set of red edges $E_r$ and the set of blue edges $E_b$ with $E_r$
and $E_b$ do not have to be disjoint. The Tur\'an density $\pi(H)$ of $H$ is
defined to be $\lim_{n\to\infty} \max_{G_n}h_n(G_n)$, where $G_n$ is chosen
among all possible $2$-edge-colored graphs on $n$ vertices containing no $H$ as
a sub-graph and $h_n(G_n)=(|E_r(G)|+|E_b(G)|)/{n\choose 2}$ is the formula to
measure the edge density of $G_n$. We will determine the Tur\'an densities of
all $2$-edge-colored bipartite graphs. We also give an important application of
our study on the Tur\'an problems of $\{2, 3\}$-hypergraphs.


  Sound - the oft-neglected sense for Software Engineering - is a crucial
component of our daily lives, playing a vital role in how we interact with the
world around us. In this paper, we challenge the traditional boundaries of
Software Engineering by proposing a new approach based on sound design for
using sound in modelling tools that is on par with visual design. By drawing
upon the seminal work of Moody on the `Physics' of Notations for visual design,
we develop a comprehensive catalogue of principles that can guide the design of
sound notations.
  Using these principles, we develop a catalogue of sounds for UML and report
on an empirical study that supports their usefulness. Our study lays the
foundation for building more sophisticated sound-based notations. The
guidelines for designing symbolic sounds for software models are an essential
starting point for a new research thread that could significantly and
effectively enable the use of sound in modelling tools.


  We establish the interior and boundary H\"older continuity of possibly
sign-changing solutions to a class of doubly nonlinear parabolic equations
whose prototype is \[ \partial_t\big(|u|^{p-2}u\big)-\Delta_p u=0,\quad p>1. \]
The proof relies on the property of expansion of positivity and the method of
intrinsic scaling, all of which are realized by De Giorgi's iteration. Our
approach, while emphasizing the distinct roles of sub(super)-solutions, is
flexible enough to obtain the H\"older regularity of solutions to
initial-boundary value problems of Dirichlet type or Neumann type in a
cylindrical domain, up to the parabolic boundary. In addition, based on the
expansion of positivity, we are able to give an alternative proof of Harnack's
inequality for non-negative solutions. Moreover, as a consequence of the
interior estimates, we also obtain a Liouville-type result.


  Very high energy gamma-ray flares from the Crab nebular detected by AGILE and
Fermi satellites challenge our understanding of the pulsar wind nebulae. The
short duration of the flares, only few days, is particularly puzzling since it
is much shorter than the dynamical times scale of the nebular.
  In this work we investigate analytically and via numerical simulations the
electromagnetic signatures expected from the large amplitude low frequency
magnetosonic waves generated within the Crab nebular which induce the
corrugation perturbations of the termination shock. As a result, the oblique
termination shock produces time-dependent, mildly relativistic post-shock flow.
Using the relativistic MHD version of the RIEMANN code, we simulate the
interaction of the termination shock with downstream perturbations.
  We demonstrate that mild Doppler boosting of the synchrotron emission in the
post-shock flow can produce bright, short time scale flares.


  Machine learning (ML) and artificial intelligence (AI) have become hot topics
in many information processing areas, from chatbots to scientific data
analysis. At the same time, there is uncertainty about the possibility of
extending predominant ML technologies to become general solutions with
continuous learning capabilities. Here, a simple, yet comprehensive,
theoretical framework for intelligent systems is presented. A combination of
Mirror Compositional Representations (MCR) and a Solution-Critic Loop (SCL) is
proposed as a generic approach for different types of problems. A prototype
implementation is presented for document comparison using English Wikipedia
corpus.


  Automated multi-label chest X-rays (CXR) image classification has achieved
substantial progress in clinical diagnosis via utilizing sophisticated deep
learning approaches. However, most deep models have high computational demands,
which makes them less feasible for compact devices with low computational
requirements. To overcome this problem, we propose a knowledge distillation
(KD) strategy to create the compact deep learning model for the real-time
multi-label CXR image classification. We study different alternatives of CNNs
and Transforms as the teacher to distill the knowledge to a smaller student.
Then, we employed explainable artificial intelligence (XAI) to provide the
visual explanation for the model decision improved by the KD. Our results on
three benchmark CXR datasets show that our KD strategy provides the improved
performance on the compact student model, thus being the feasible choice for
many limited hardware platforms. For instance, when using DenseNet161 as the
teacher network, EEEA-Net-C2 achieved an AUC of 83.7%, 87.1%, and 88.7% on the
ChestX-ray14, CheXpert, and PadChest datasets, respectively, with fewer
parameters of 4.7 million and computational cost of 0.3 billion FLOPS.


  We formulate a theory combining the principles of a scalar-tensor gravity and
Rastall's proposal of a violation of the usual conservation laws. We obtain a
scalar-tensor theory with two parameters $\omega$ and $\lambda$, the latter
quantifying the violation of the usual conservation laws. The only exact
spherically symmetric solution is that of Robinson-Bertotti besides
Schwarzschild solution. A PPN analysis reveals that General Relativity results
are reproduced when $\lambda = 0$. The cosmological case displays a possibility
of deceleration/acceleration or acceleration/deceleration transitions during
the matter dominated phase depending on the values of the free parameters.


  This is a short review of two-dimensional Dirac fermions in graphene and
similar systems such as boron nitride, quasi-2D organic salts
$\alpha$-(BEDT-TTF)$_2$I$_3$, artificial graphene with cold atoms in optical
lattices, etc. The emphasis is on magnetic field properties (semi-classical
quantization of cyclotron orbits, Landau levels, quantum Hall effect,
magneto-plasmons, magneto-phonon resonance, magneto-transport, etc.) and on
topological properties (Berry phase, winding number, inter-band effects,
merging transition of Dirac points, etc.).


  The numerical range in the quaternionic setting is, in general, a non convex
subset of the quaternions. The essential numerical range is a refinement of the
numerical range that only keeps the elements that have, in a certain sense,
infinite multiplicity. We prove that the essential numerical range of a bounded
linear operator on a quaternionic Hilbert space is convex. A quaternionic
analogue of Lancaster theorem, relating the closure of the numerical range and
its essential numerical range, is also provided.


  Topological defects are distinctive signatures of liquid crystals. They
profoundly affect the viscoelastic behavior of the fluid by constraining the
orientational structure in a way that inevitably requires global changes not
achievable with any set of local deformations. In active nematic liquid
crystals topological defects not only dictate the global structure of the
director, but also act as local sources of motion, behaving as self-propelled
particles. In this article we present a detailed analytical and numerical study
of the mechanics of topological defects in active nematic liquid crystals.


  We establish some existence results for a class of critical elliptic problems
with singular exponential nonlinearities. We do not assume any global sign
conditions on the nonlinearity, which makes our results new even in the
nonsingular case.


  In this work, we prove that if a triangular algebra $A$ admits a strongly
simply connected universal Galois covering for a given presentation then the
fundamental group associated to this presentation is free.


  We study the effective quark mass induced by the finite separation of the
domain walls in the domain-wall formulation of chiral fermion as the function
of the size of the fifth dimension ($L_s$), the gauge coupling ($\beta$) and
the physical volume ($V$). We measure the mass by calculating the small
eigenvalues of the hermitian domain-wall Dirac operator ($H_{\rm
DWF}(m_0=1.8))$ in the topologically-nontrivial quenched SU(3) gauge
configurations. We find that the induced quark mass is nearly independent of
the physical volume, decays exponentially as a function of $L_s$, and has a
strong dependence on the size of quantum fluctuations controlled by $\beta$.
The effect of the choice of the lattice gluon action is also studied.


  We prove super-quadratic lower bounds for the growth of the filling area
function of a certain class of Carnot groups. This class contains groups for
which it is known that their Dehn function grows no faster than $n^2\log n$. We
therefore obtain the existence of (finitely generated) nilpotent groups whose
Dehn functions do not have exactly polynomial growth and we thus answer a
well-known question about the possible growth rate of Dehn functions of
nilpotent groups.


  In this paper we investigate some stochastic models for tumor-immune systems.
To describe these models we used a Wiener process, as the noise has a
stabilization effect. Their dynamics are studied in terms of stochastic
stability in the equilibrium points, by constructing the Lyapunov exponent,
depending on the parameters that describe the model. We have studied and
analyzed a Kuznetsov-Taylor like stochastic model and a Bell stochastic model
for tumor-immune systems. These stochastic models are studied from stability
point of view and they were represented using the Euler second order scheme.


  We describe a new method for measuring the extragalactic background light
(EBL) through the detection of $\gamma$-ray inverse Compton (IC) emission due
to scattering of the EBL photons off relativistic electrons in the lobes of
radio galaxies. Our method has no free physical parameters and is a powerful
tool when the lobes are characterized by a high energy sharp break or cutoff in
their electron energy distribution (EED). We show that such a feature will
produce a high energy IC `imprint' of the EBL spectrum in which the radio lobes
are embedded, and show how this imprint can be used to derive the EBL. We apply
our method to the bright nearby radio galaxy Fornax A, for which we
demonstrate, using WMAP and EGRET observations, that the EED of its lobes is
characterized by a conveniently located cutoff, bringing the IC EBL emission
into the {\sl Fermi} energy range. We show that {\sl Fermi} will set upper
limits to the optical EBL and measure the more elusive infrared EBL.


  Sleep staging is a clinically important task for diagnosing various sleep
disorders, but remains challenging to deploy at scale because it because it is
both labor-intensive and time-consuming. Supervised deep learning-based
approaches can automate sleep staging but at the expense of large labeled
datasets, which can be unfeasible to procure for various settings, e.g.,
uncommon sleep disorders. While self-supervised learning (SSL) can mitigate
this need, recent studies on SSL for sleep staging have shown performance gains
saturate after training with labeled data from only tens of subjects, hence are
unable to match peak performance attained with larger datasets. We hypothesize
that the rapid saturation stems from applying a sub-optimal pretraining scheme
that pretrains only a portion of the architecture, i.e., the feature encoder,
but not the temporal encoder; therefore, we propose adopting an architecture
that seamlessly couples the feature and temporal encoding and a suitable
pretraining scheme that pretrains the entire model. On a sample sleep staging
dataset, we find that the proposed scheme offers performance gains that do not
saturate with amount of labeled training data (e.g., 3-5\% improvement in
balanced sleep staging accuracy across low- to high-labeled data settings),
reducing the amount of labeled training data needed for high performance (e.g.,
by 800 subjects). Based on our findings, we recommend adopting this SSL
paradigm for subsequent work on SSL for sleep staging.


  Many of the recent remarkable advances in computer vision and language models
can be attributed to the success of transfer learning via the pre-training of
large foundation models. However, a theoretical framework which explains this
empirical success is incomplete and remains an active area of research.
Flatness of the loss surface and neural collapse have recently emerged as
useful pre-training metrics which shed light on the implicit biases underlying
pre-training. In this paper, we explore the geometric complexity of a model's
learned representations as a fundamental mechanism that relates these two
concepts. We show through experiments and theory that mechanisms which affect
the geometric complexity of the pre-trained network also influence the neural
collapse. Furthermore, we show how this effect of the geometric complexity
generalizes to the neural collapse of new classes as well, thus encouraging
better performance on downstream tasks, particularly in the few-shot setting.


  We prove a Hasse principle for solving equations of the form ax+by+cz=0 where
x, y, z belong to a given finite index subgroup of the multiplicative group of
rational numbers. From this we deduce a Hasse principle for diagonal curves
over fields of algebraic numbers with finitely generated Galois group.


  The direction of parsec-scale jets in active galactic nuclei (AGNs) is
essential information for many astrophysical and astrometric studies, including
linear polarization and magnetic field structure, frequency-dependent
synchrotron opacity, proper motion, and reference frame alignment. We developed
a rigorous, simple, and completely automated method to measure the directions
from calibrated interferometric visibility data at frequencies ranging from 1.4
GHz to 86 GHz. We publish the results for 9220 AGNs with the typical accuracy
below 10 degrees. An internal check of the method comparing the directions
between different observing frequencies as well as with previous publications
verifies the robustness of the measured values.


  The magnetic chemically peculiar (mCP) stars of the upper main sequence
exhibit strong, globally-organised magnetic fields which are inclined to the
rotational axis and facilitate the development of surface abundance
inhomogeneities resulting in photometric and spectroscopic variability.
Therefore, mCP stars are perfectly suited for a direct measurement of the
rotational period without the need for any additional calibrations. We have
investigated the rotational properties of mCP stars based on an unprecedentedly
large sample consisting of more than 500 objects with known rotational periods.
Using precise parallaxes from the Hipparcos and Gaia satellite missions,
well-established photometric calibrations and state-of-the-art evolutionary
models, we have determined the location of our sample stars in the
Hertzsprung-Russell diagram and derived astrophysical parameters such as
stellar masses, effective temperature, radii, inclinations and critical
rotational velocities. We have confirmed the conservation of angular momentum
during the main sequence evolution; no signs of additional magnetic braking
were found. The inclination angles of the rotational axes are randomly
distributed, although an apparent excess of fast rotators with comparable
inclination angles has been observed. We have found a rotation rate of
$\upsilon/\upsilon_{\rm crit} \geq 0.5$ for several stars, whose
characteristics cannot be explained by current models. For the first time, we
have derived the relationship between mass and rotation rate of mCP stars, and
provide an analysis that links mass and rotation with magnetic field strength.
Our sample is unique and offers crucial input for forthcoming evolutionary
models that include the effects of magnetic fields for upper main sequence
stars.


  We study the late-time relaxation of a perturbed Schwarzschild black hole,
driven by a source term representing an infalling particle in generic orbits.
We consider quasi-circular and eccentric binaries, dynamical captures and
radial infalls, with orbital dynamics driven by an highly accurate analytical
radiation reaction. After reviewing the description of the late-time behaviour
as an integral over the whole inspiral history, we derive an analytical
expression exactly reproducing the slow relaxation observed in our numerical
evolutions, obtained with a hyperboloidal compactified grid, for a given
particle trajectory. We find this signal to be a superposition of an infinite
number of power-laws, the slowest decaying term being Price's law. Next, we use
our model to explain the several orders-of-magnitude enhancement of tail terms
for binaries in non-circular orbits, shedding light on recent unexpected
results obtained in numerical evolutions. In particular, we show the dominant
terms controlling the enhancement to be activated when the particle is far from
the BH, with small tangential and radial velocities soon before the plunge. As
we corroborate with semi-analytical calculations, this implies that for large
eccentricities the tail amplitude can be correctly extracted even when starting
to evolve only from the last apastron before merger. We discuss the
implications of these findings on the extraction of late-time tail terms in
non-linear evolutions, and possible observational consequences. We also briefly
comment on the scattering scenario, and on the connection with the soft
graviton theorem.


  The sparse identification of nonlinear dynamical systems (SINDy) is a
data-driven technique employed for uncovering and representing the fundamental
dynamics of intricate systems based on observational data. However, a primary
obstacle in the discovery of models for nonlinear partial differential
equations (PDEs) lies in addressing the challenges posed by the curse of
dimensionality and large datasets. Consequently, the strategic selection of the
most informative samples within a given dataset plays a crucial role in
reducing computational costs and enhancing the effectiveness of SINDy-based
algorithms. To this aim, we employ a greedy sampling approach to the snapshot
matrix of a PDE to obtain its valuable samples, which are suitable to train a
deep neural network (DNN) in a SINDy framework. SINDy based algorithms often
consist of a data collection unit, constructing a dictionary of basis
functions, computing the time derivative, and solving a sparse identification
problem which ends to regularised least squares minimization. In this paper, we
extend the results of a SINDy based deep learning model discovery (DeePyMoD)
approach by integrating greedy sampling technique in its data collection unit
and new sparsity promoting algorithms in the least squares minimization unit.
In this regard we introduce the greedy sampling neural network in sparse
identification of nonlinear partial differential equations (GN-SINDy) which
blends a greedy sampling method, the DNN, and the SINDy algorithm. In the
implementation phase, to show the effectiveness of GN-SINDy, we compare its
results with DeePyMoD by using a Python package that is prepared for this
purpose on numerous PDE discovery


  LiDAR odometry estimation and 3D semantic segmentation are crucial for
autonomous driving, which has achieved remarkable advances recently. However,
these tasks are challenging due to the imbalance of points in different
semantic categories for 3D semantic segmentation and the influence of dynamic
objects for LiDAR odometry estimation, which increases the importance of using
representative/salient landmarks as reference points for robust feature
learning. To address these challenges, we propose a saliency-guided approach
that leverages attention information to improve the performance of LiDAR
odometry estimation and semantic segmentation models. Unlike in the image
domain, only a few studies have addressed point cloud saliency information due
to the lack of annotated training data. To alleviate this, we first present a
universal framework to transfer saliency distribution knowledge from color
images to point clouds, and use this to construct a pseudo-saliency dataset
(i.e. FordSaliency) for point clouds. Then, we adopt point cloud-based
backbones to learn saliency distribution from pseudo-saliency labels, which is
followed by our proposed SalLiDAR module. SalLiDAR is a saliency-guided 3D
semantic segmentation model that integrates saliency information to improve
segmentation performance. Finally, we introduce SalLONet, a self-supervised
saliency-guided LiDAR odometry network that uses the semantic and saliency
predictions of SalLiDAR to achieve better odometry estimation. Our extensive
experiments on benchmark datasets demonstrate that the proposed SalLiDAR and
SalLONet models achieve state-of-the-art performance against existing methods,
highlighting the effectiveness of image-to-LiDAR saliency knowledge transfer.
Source code will be available at https://github.com/nevrez/SalLONet.


  Rotational superradiance is one of the most fascinating phenomena in
black-hole physics. Here, with the aim of probing quantum properties of
superradiance in the lab, we investigate the interaction of the acoustic waves
with quantum vortices in Bose-Einstein condensates (BEC) in the framework of
dissipative mean-field model. We find the conditions of the acoustic-induced
quantum phase slips in condensates with ring topology and discuss the
possibility of observing an acoustic analogue of quantum superradiance in
ultracold atomic gases.


  We report on a non-perturbative determination of the parameters of the
lattice Heavy Quark Effective Theory (HQET) Lagrangian and of the time
component of the heavy-light axial-vector current with Nf=2 flavors of massless
dynamical quarks. The effective theory is considered at the 1/mh order, and the
heavy mass mh covers a range from slightly above the charm to beyond the beauty
region. These HQET parameters are needed to compute, for example, the b-quark
mass, the heavy-light spectrum and decay constants in the static approximation
and to order 1/mh in HQET. The determination of the parameters is done
non-perturbatively. The computation reported in this paper uses the plaquette
gauge action and two different static actions for the heavy quark described by
HQET. For the light-quark action we choose non-perturbatively O(a)-improved
Wilson fermions.


  In this work we obtain results related to the approximation of
$h$-dimensional dominant subspaces and low rank approximations of matrices $
A\in\mathbb K^{m\times n}$ (where $\mathbb K=\mathbb R$ or $\mathbb C)$ in case
there is no singular gap at the index $h$, i.e. if $\sigma_h=\sigma_{h+1}$
(where $\sigma_1\geq \ldots\geq \sigma_p\geq 0$ denote the singular values of $
A$, and $p=\min\{m,n\}$). In order to do this, we develop a novel perspective
for the convergence analysis of the classical deterministic block Krylov
methods in this context. Indeed, starting with a matrix $ X\in\mathbb
K^{n\times r}$ with $r\geq h$ satisfying a compatibility assumption with some
$h$-dimensional right dominant subspace, we show that block Krylov methods
produce arbitrarily good approximations for both problems mentioned above. Our
approach is based on recent work by Drineas, Ipsen, Kontopoulou and
Magdon-Ismail on approximation of structural left dominant subspaces. The main
difference between our work and previous work on this topic is that instead of
exploiting a singular gap at $h$ (which is zero in this case) we exploit the
nearest existing singular gaps.


  Electronic states of the hybrid perovskite enable their promising
applications as distinctive optoelectronic materials. The understanding of
their electronic structures and charge characters remains highly controversial.
The electronic mechanism such as reabsorption, Urbach tail and indirect band
for interpreting dual-peak emissions is one of the controversial focuses.
Herein, we report that through heterojunction enhanced exciton dissociation and
global tracing of multiple radiative electronic states across wide temperature
regions, we have succeeded in directly observing free carrier emissions from
the hybrid lead bromide perovskite and clearly discriminating the direct
continuum band and exciton states. The widely-concerned dual-peak emissions are
clarified to be excitonic, arising from two types of exciton states of the
perovskite. These excitons possess giant binding energies and superior phase
stability compared to conventional inorganic semiconductors, providing
important implications for exploiting the excitonic mechanism for realizing
novel optoelectronic applications.


  During the evolution of a pulsar, various phase transitions may occur in its
dense interior, such as superfluid transition, as well as transition to various
exotic phases of quantum chromodynamics (QCD). We propose a technique which
allows to probe these phases and associated transitions by detecting changes in
rotation of the star arising from density changes and fluctuations during the
transition affecting star's moment of inertia. Our results suggest that these
changes may be observable, and may possibly account for glitches and (recently
observed) anti-glitches. Accurate measurements of pulsar timing and intensity
modulations (arising from wobbling of star due to development of the
off-diagonal components of moment of inertia) may be used to pin down the
particular phase transition occurring inside the pulsar core. We also discuss
the possibility of observing gravitational waves from the changes in the
quadrupole moment arising from these rapidly evolving density fluctuations.


  Semantic communication is an increasingly popular framework for wireless
image transmission due to its high communication efficiency. With the aid of
the joint-source-and-channel (JSC) encoder implemented by neural network,
semantic communication directly maps original images into symbol sequences
containing semantic information. Compared with the traditional separate source
and channel coding design used in bitlevel communication systems, semantic
communication systems are known to be more efficient and accurate especially in
the low signal-to-the-noise ratio (SNR) regime. This thus prompts an critical
while yet to be tackled issue of security in semantic communication: it makes
the eavesdropper more easier to crack the semantic information as it can be
decoded even in a quite noisy channel. In this letter, we develop a semantic
communication framework that accounts for both semantic meaning decoding
efficiency and its risk of privacy leakage. To achieve this, targeting wireless
image transmission, we on the one hand propose an JSC autoencoder featuring
residual for efficient semantic meaning extraction and transmission, and on the
other hand, propose a data-driven scheme that balances the efficiency-privacy
tradeoff. Extensive experimental results are provided to show the effectiveness
and robustness of the proposed scheme.


  Automatically verifying the identity of a person by means of biometrics is an
important application in day-to-day activities such as accessing banking
services and security control in airports. To increase the system reliability,
several biometric devices are often used. Such a combined system is known as a
multimodal biometric system. This paper reports a benchmarking study carried
out within the framework of the BioSecure DS2 (Access Control) evaluation
campaign organized by the University of Surrey, involving face, fingerprint,
and iris biometrics for person authentication, targeting the application of
physical access control in a medium-size establishment with some 500 persons.
While multimodal biometrics is a well-investigated subject, there exists no
benchmark for a fusion algorithm comparison. Working towards this goal, we
designed two sets of experiments: quality-dependent and cost-sensitive
evaluation. The quality-dependent evaluation aims at assessing how well fusion
algorithms can perform under changing quality of raw images principally due to
change of devices. The cost-sensitive evaluation, on the other hand,
investigates how well a fusion algorithm can perform given restricted
computation and in the presence of software and hardware failures, resulting in
errors such as failure-to-acquire and failure-to-match. Since multiple
capturing devices are available, a fusion algorithm should be able to handle
this nonideal but nevertheless realistic scenario. In both evaluations, each
fusion algorithm is provided with scores from each biometric comparison
subsystem as well as the quality measures of both template and query data. The
response to the call of the campaign proved very encouraging, with the
submission of 22 fusion systems. To the best of our knowledge, this is the
first attempt to benchmark quality-based multimodal fusion algorithms.


  For long time non-pure beta emitters are avoided from PET imaging due to
extra dose and increase in background from Compton scattering. But advent of
high-resolution Compton camera system opens up new domain of imaging. Various
non-pure beta emitters are formed as beam irradiation byproduct in therapy
which can be used in online beam range verification. In this case, the number
of usable counts for imaging is generally 1-3 order lesser than normal PET
scan. On the other hand, we know that in human PET scanner, 30-60\% can be
tissue scattered coincidences in 3D case containing 80\% single scattered
events. In this work, we have investigated feasibility of imaging using only
single scattered coincidences for non-pure beta emitters in a Compton-PET
system. The locus of tissue scatter point can be reduced to in generally two
points after using Compton cone from both ends of 511 keV detections. Finally,
annihilation point is estimated using Compton cone of 1157 keV gamma and
time-of-flight information for the 511 keV. We believe independent assessment
of underlying activity from single scattered data sets will increase confidence
in image interpretation.


  Given a $g$-dimensional abelian variety $A$ over a finite field
$\mathbf{F}_q$, the Weil conjectures imply that the normalized Frobenius
eigenvalues generate a multiplicative group of rank at most $g$. The Pontryagin
dual of this group is a compact abelian Lie group that controls the
distribution of high powers of the Frobenius endomorphism. This group, which we
call the Serre--Frobenius group, encodes the possible multiplicative relations
between the Frobenius eigenvalues. In this article, we classify all possible
Serre--Frobenius groups that occur for $g \le 3$. We also give a partial
classification for simple ordinary abelian varieties of prime dimension $g>3$.


  For challenging machine learning problems such as zero-shot learning and
fine-grained categorization, embedding learning is the machinery of choice
because of its ability to learn generic notions of similarity, as opposed to
class-specific concepts in standard classification models. Embedding learning
aims at learning discriminative representations of data such that similar
examples are pulled closer, while pushing away dissimilar ones. Despite their
exemplary performances, supervised embedding learning approaches require huge
number of annotations for training. This restricts their applicability for
large datasets in new applications where obtaining labels require extensive
manual efforts and domain knowledge. In this paper, we propose to learn an
embedding in a completely unsupervised manner without using any class labels.
Using a graph-based clustering approach to obtain pseudo-labels, we form
triplet-based constraints following a metric learning paradigm. Our novel
embedding learning approach uses a probabilistic notion, that intuitively
minimizes the chances of each triplet violating a geometric constraint. Due to
nature of the search space, we learn the parameters of our approach using
Riemannian geometry. Our proposed approach performs competitive to
state-of-the-art approaches.


  We investigate the turbulence-induced dissipation of the large scales in a
statistically homogeneous flow using an "optimal closure," which one of us (BT)
has recently exposed in the context of Hamiltonian dynamics. This statistical
closure employs a Gaussian model for the turbulent scales, with corresponding
vanishing third cumulant, and yet it captures an intrinsic damping. The key to
this apparent paradox lies in a clear distinction between true ensemble
averages and their proxies, most easily grasped when one works directly with
the Liouville equation rather than the cumulant hierarchy. We focus on a simple
problem for which the optimal closure can be fully and exactly worked out: the
relaxation arbitrarily far-from-equilibrium of a single energy shell towards
Gibbs equilibrium in an inviscid shell model of 3D turbulence. The predictions
of the optimal closure are validated against DNS and contrasted with those
derived from EDQNM closure.


  Necessary and sufficient conditions for existence of boundary value problem
of Schrodinger equation are obtained in linear and nonlinear cases. Periodic
analytical solutions are represented using generalized Green's operator


  In this paper we prove the central limit theorem for Hotelling's $T^2$
statistic when the dimension of the random vectors is proportional to the
sample size.


  Isolated and full monolayer adsorption of various carboranethiol
(C$_2$B$_{10}$H$_{12}$S) isomers on gold (111) surface have been investigated
using both the standard and van der Waals density functional theoretical
calculations. The effect of differing molecular dipole moment orientations on
the low energy adlayer geometries, the binding characteristics and the
electronic properties of the self-assembled monolayers of these isomers have
been studied. Specifically, the binding energy and work function changes
associated with different molecules show a correlation with their dipole
moments. The adsorption is favored for the isomers with dipole moments parallel
to the surface. Of the two possible unit cell structures, the (5$\times$5) was
found to be more stable than the ($\sqrt{19}\times\sqrt{19}$)R23.4$^o$ one.


  Quantum neural networks (QNNs), represented by parameterized quantum
circuits, can be trained in the paradigm of supervised learning to map input
data to predictions. Much work has focused on theoretically analyzing the
expressive power of QNNs. However, in almost all literature, QNNs' expressive
power is numerically validated using only simple univariate functions. We
surprisingly discover that state-of-the-art QNNs with strong expressive power
can have poor performance in approximating even just a simple sinusoidal
function. To fill the gap, we propose four expressibility-enhancing strategies
for QNNs: Sinusoidal-friendly embedding, redundant measurement,
post-measurement function, and random training data. We analyze the
effectiveness of these strategies via mathematical analysis and/or numerical
studies including learning complex sinusoidal-based functions. Our results from
comparative experiments validate that the four strategies can significantly
increase the QNNs' performance in approximating complex multivariable functions
and reduce the quantum circuit depth and qubits required.


  The associated production of jets and vector bosons is an important process
at hadron colliders. An overview over recent Tevatron vector boson+jets
measurements is given with an emphasis on comparisons between data and the
predictions of various theory models.


  For a compact Lie group G, we use G-equivariant Poincar\'e duality for
ordinary RO(G)-graded homology to define an equivariant intersection product,
the dual of the equivariant cup product. Using this, we give a homological
construction of the equivariant Lefschetz number and a simple proof of the
equivariant Lefschetz fixed point theorem. With similar techniques, an
equivariant Fuller index with values in the rationalized Burnside ring is
constructed.


  We present 1.3 and 3.6cm radio continuum images and a HCO+ spectrum of the
massive star forming region G75.78+0.34 obtained with the Very Large Array
(VLA) and with the Berkley Illinois Maryland Association (BIMA) interferometer.
Three structures were detected in the continuum emission: one associated to the
well known cometary HII region, plus two more compact structures located at 6"
east and at 2" south of cometary HII region. Using the total flux and intensity
peak we estimated an electron density of ~1.5x10^4 cm^{-3}, an emission measure
of ~6x10^{7} cm^{-6} pc, a mass of ionized gas of ~3 M_Sun and a diameter of
0.05 pc for the cometary HII region, being typical values for an ultracompact
HII region. The HCO^+ emission is probably originated from the molecular
outflows previously observed in HCN and CO.


  Unstable spinor Bose-Einstein condensates are ideal candidates to create
nonlinear three-mode interferometers. Our analysis goes beyond the standard
SU(1,1) parametric approach and therefore provides the regime of parameters
where sub shot-noise sensitivities can be reached with respect to the input
total average number of particles. Decoherence due to particle losses and
finite detection efficiency are also considered.


  It is not unusual for a data analyst to encounter data sets distributed
across several computers. This can happen for reasons such as privacy concerns,
efficiency of likelihood evaluations, or just the sheer size of the whole data
set. This presents new challenges to statisticians as even computing simple
summary statistics such as the median becomes computationally challenging.
Furthermore, if other advanced statistical methods are desired, novel
computational strategies are needed. In this paper we propose a new approach
for distributed analysis of massive data that is suitable for generalized
fiducial inference and is based on a careful implementation of a "divide and
conquer" strategy combined with importance sampling. The proposed approach
requires only small amount of communication between nodes, and is shown to be
asymptotically equivalent to using the whole data set. Unlike most existing
methods, the proposed approach produces uncertainty measures (such as
confidence intervals) in addition to point estimates for parameters of
interest. The proposed approach is also applied to the analysis of a large set
of solar images.


  In France, income tax is computed from taxpayers' individual returns, using
an algorithm that is authored, designed and maintained by the French Public
Finances Directorate (DGFiP). This algorithm relies on a legacy custom language
and compiler originally designed in 1990, which unlike French wine, did not age
well with time. Owing to the shortcomings of the input language and the
technical limitations of the compiler, the algorithm is proving harder and
harder to maintain, relying on ad-hoc behaviors and workarounds to implement
the most recent changes in tax law. Competence loss and aging code also mean
that the system does not benefit from any modern compiler techniques that would
increase confidence in the implementation. We overhaul this infrastructure and
present Mlang, an open-source compiler toolchain whose goal is to replace the
existing infrastructure. Mlang is based on a reverse-engineered formalization
of the DGFiP's system, and has been thoroughly validated against the private
DGFiP test suite. As such, Mlang has a formal semantics; eliminates previous
handwritten workarounds in C; compiles to modern languages (Python); and
enables a variety of instrumentations, providing deep insights about the
essence of French income tax computation. The DGFiP is now officially
transitioning to Mlang for their production system.


  A major amendment in recent models of hierarchical galaxy formation is the
inclusion of so-called AGN feedback. The energy input from an active central
massive black hole is invoked to suppress star formation in early-type galaxies
at later epochs. A major problem is that this process is poorly understood, and
compelling observational evidence for its mere existence is still missing. In
search for signatures of AGN feedback, we have compiled a sample of 16,000
early-type galaxies in the redshift range 0.05<z<0.1 from the SDSS database.
Key in our approach is the use of a purely morphological selection criterion
through visual inspection which produces a sample that is not biased against
recent star formation and nuclear activity. The objects with emission (~20 per
cent) are offset from the red sequence and form a well-defined pattern in the
colour-mass diagram. Star forming early-types inhabit the blue cloud, while
early-types with AGN are located considerably closer to and almost on the red
sequence. Star formation-AGN composites are found right between these two
extremes. We further derive galaxy star formation histories using a novel
method that combines multiwavelength photometry from near-UV to near-IR and
stellar absorption indices. We find that in those objects deviating from the
red sequence star formation occurred several 100 Myr in the past involving 1-10
per cent of the total stellar mass. We identify an evolutionary sequence from
star formation via nuclear activity to quiescence. This transition process
lasts about 1 Gyr, and the peak AGN phase occurs roughly half a Gyr after the
starburst. The most likely interpretation is that star formation is suppressed
by nuclear activity in these objects before they settle on the red sequence.


  We study the non-Markovian effects on the dynamics of entanglement in an
exactly-solvable model that involves two independent oscillators each coupled
to its own stochastic noise source. First, we develop Lie algebraic and
functional integral methods to find an exact solution to the single-oscillator
problem which includes an analytic expression for the density matrix and the
complete statistics, i.e., the probability distribution functions for
observables. For long bath time-correlations, we see non-monotonic evolution of
the uncertainties in observables. Further, we extend this exact solution to the
two-particle problem and find the dynamics of entanglement in a subspace. We
find the phenomena of `sudden death' and `rebirth' of entanglement.
Interestingly, all memory effects enter via the functional form of the energy
and hence the time of death and rebirth is controlled by the amount of noisy
energy added into each oscillator. If this energy increases above (decreases
below) a threshold, we obtain sudden death (rebirth) of entanglement.


  Presence of dust in galaxies removes half or more of the stellar energy from
the UV-optical budget of the Universe and has profound impact on our
understanding of how galaxies evolve. Measures of opacity in local galaxies are
reviewed together with widely used theoretical and empirical methods for
quantifying its effects. Existing evidence shows that the dust content of
nearby galaxies depends not only on their morphology, but also on their
luminosity and activity level. A digression is devoted to starbursts in view of
their potential relevance for measures of opacity in distant galaxies. Scarcity
of coherent multiwavelength datasets hampers our ability to derive reliable
obscuration estimates in intermediate and high redshift galaxies. This, in
turn, limits the reliability of inferred physical quantities, such as star
formation rates, stellar population ages, galaxy luminosity functions, and
others.


  The LTP (LISA Testflight Package), to be flown aboard the ESA / NASA LISA
Pathfinder mission, aims to demonstrate drag-free control for LISA test masses
with acceleration noise below 30 fm/s^2/Hz^1/2 from 1-30 mHz. This paper
describes the LTP measurement of random, position independent forces acting on
the test masses. In addition to putting an overall upper limit for all source
of random force noise, LTP will measure the conversion of several key
disturbances into acceleration noise and thus allow a more detailed
characterization of the drag-free performance to be expected for LISA.


  We show that extremal correlators of chiral primary operators in N=4
supersymmetric Yang-Mills theory with SU(N) gauge group are neither
renormalised at first order in perturbation theory nor receive contribution
from any instanton sector at leading order in the semiclassical expansion. This
lends support to the strongest version of a new prediction recently put forward
on the basis of the AdS/SCFT correspondence.


  We briefly summarise the current status of neutrino masses and mixing, paying
special attention to the prospects for observing new leptonic interactions.


  The aim of this paper is to demonstrate that in models with large extra
dimensions under special conditions one can extract information from the
interior of 4D black holes. For this purpose we study an induced geometry on a
test brane in the background of a higher dimensional static black string or a
black brane. We show that at the intersection surface of the test brane and the
bulk black string/brane the induced metric has an event horizon, so that the
test brane contains a black hole. We call it a brane hole. When the test brane
moves with a constant velocity V with respect to the bulk black object it also
has a brane hole, but its gravitational radius r_e is greater than the size of
the bulk black string/brane r_0 by the factor (1-V^2)^{-1}. We show that bulk
`photon' emitted in the region between r_0 and r_e can meet the test brane
again at a point outside r_e. From the point of view of observers on the test
brane the events of emission and capture of the bulk `photon' are connected by
a spacelike curve in the induced geometry. This shows an example in which extra
dimensions can be used to extract information from the interior of a lower
dimensional black object. Instead of the bulk black string/brane, one can also
consider a bulk geometry without horizon. We show that nevertheless the induced
geometry on the moving test brane can include a brane hole. In such a case the
extra dimensions can be used to extract information from the complete region of
the brane hole interior. We discuss thermodynamic properties of brane holes and
interesting questions which arise when such an extra dimensional channel for
the information mining exists.


  Electric field noise from fluctuating patch potentials is a significant
problem for a broad range of precision experiments, including trapped ion
quantum computation and single spin detection. Recent results demonstrated
strong suppression of this noise by cryogenic cooling, suggesting an underlying
thermal process. We present measurements characterizing the temperature and
frequency dependence of the noise from 7 to 100 K, using a single Sr+ ion
trapped 75 um above the surface of a gold plated surface electrode ion trap.
The noise amplitude is observed to have an approximate 1/f spectrum around 1
MHz, and grows rapidly with temperature as T^beta for beta from 2 to 4. The
data are consistent with microfabricated cantilever measurements of non-contact
friction but do not extrapolate to the DC measurements with neutral atoms or
contact potential probes.


  The main result of this paper is a decomposition theorem for a measure on the
one-dimensional torus. Given a sufficiently large subset $S$ of the positive
integers, an arbitrary measure on the torus is decomposed as the sum of two
measures. The first one $\mu_1$ has the property that the random walk with
initial distribution $\mu_1$ evolved by the action of $S$ equidistributes very
fast. The second measure $\mu_2$ in the decomposition is concentrated on very
small neighborhoods of a small number of points.


  A model is presented to illustrate that vacuum neutrino oscillations can be
essentially T and CP invariant up to a certain energy but strongly T and CP
noninvariant at much higher energies. Detailed model results for the vacuum
probabilities P(nu_mu to nu_e) and P(nu_e to nu_mu) are given, which may be
relevant to proposed long-baseline neutrino-oscillation experiments.


  We propose a scheme for the determination of the coupling parameters in a
chain of interacting spins. This requires only time-resolved measurements over
a single particle, simple data post-processing and no state initialization or
prior knowledge of the state of the chain. The protocol fits well into the
context of quantum-dynamics characterization and is efficient even when the
spin-chain is affected by general dissipative and dephasing channels. We
illustrate the performance of the scheme by analyzing explicit examples and
discuss possible extensions.


  We show that the cohomology for some 2-generated selfinjective algebras is
finitely generated. This applies in particular to the algebras $A5(\beta)$ for
$\beta\neq 0$ in the classification of connected Hopf algebras of dimension
$p^3$ over characteristic $p$ by Nguyen-Wang-Wang.


  Scene text detection, an essential step of scene text recognition system, is
to locate text instances in natural scene images automatically. Some recent
attempts benefiting from Mask R-CNN formulate scene text detection task as an
instance segmentation problem and achieve remarkable performance. In this
paper, we present a new Mask R-CNN based framework named Pyramid Mask Text
Detector (PMTD) to handle the scene text detection. Instead of binary text mask
generated by the existing Mask R-CNN based methods, our PMTD performs
pixel-level regression under the guidance of location-aware supervision,
yielding a more informative soft text mask for each text instance. As for the
generation of text boxes, PMTD reinterprets the obtained 2D soft mask into 3D
space and introduces a novel plane clustering algorithm to derive the optimal
text box on the basis of 3D shape. Experiments on standard datasets demonstrate
that the proposed PMTD brings consistent and noticeable gain and clearly
outperforms state-of-the-art methods. Specifically, it achieves an F-measure of
80.13% on ICDAR 2017 MLT dataset.


  We consider the problem of finding a solution to the incompressible Euler
equations $$ \omega_t + v\cdot \nabla \omega = 0 \quad \hbox{ in } \mathbb{R}^2
\times (0,\infty), \quad v(x,t) = \frac 1{2\pi} \int_{{\mathbb R}^2} \frac
{(y-x)^\perp}{|y-x|^2} \omega (y,t)\, dy $$ that is close to a superposition of
traveling vortices as $t\to \infty$. We employ a constructive approach by
gluing classical traveling waves: two vortex-antivortex pairs traveling at main
order with constant speed in opposite directions. More precisely, we find an
initial condition that leads to a 4-vortex solution of the form $$ \omega (x,t)
= \omega_0(x-ct\, e ) - \omega_0 ( x+ ct \, e) + o(1) \ \hbox{ as } t\to\infty
$$ where $$ \omega_0( x ) = \frac 1{\varepsilon^{2}} W \left ( \frac {x-q}
\varepsilon \right ) - \frac 1{\varepsilon^{2}}W \left ( \frac {x+q}
\varepsilon \right ) + o(1) \ \hbox{ as } \varepsilon \to 0 $$ and $W(y)$ is a
certain fixed smooth profile, radially symmetric, positive in the unit disc
zero outside.


  Complex systems of intracellular biochemical reactions have a central role in
regulating cell identities and functions. Biochemical reaction systems are
typically studied using the language and tools of graph theory. However, graph
representations only describe pairwise interactions between molecular species,
and so are not well suited to modelling complex sets of reactions that may
involve numerous reactants and/or products. Here we make use of a
recently-developed hypergraph theory of chemical reactions that naturally
allows for higher-order interactions to explore the geometry and quantify
functional redundancy in biochemical reactions systems. Our results constitute
a general theory of automorphisms for oriented hypergraphs and describe the
effect of automorphism group structure on hypergraph Laplacian spectra.


  Increasing the number of electrons in electrostatically confined quantum dots
can enable faster qubit gates. Although this has been experimentally
demonstrated, a detailed quantitative understanding has been missing. Here we
study one- and three-electron quantum dots in silicon/silicon-germanium
heterostructures within the context of electrically-driven spin resonance
(EDSR) using full configuration interaction and tight binding approaches. Our
calculations show that anharmonicity of the confinement potential plays an
important role: while the EDSR Rabi frequency of electrons in a harmonic
potential is indifferent to the electron number, soft anharmonic confinements
lead to larger and hard anharmonic confinements lead to smaller Rabi
frequencies. We also confirm that double dots allow fast Rabi oscillations, and
further suggest that purposefully engineered confinements can also yield
similarly fast Rabi oscillations in a single dot. Finally, we discuss the role
of interface steps. These findings have important implications for the design
of multielectron Si/SiGe quantum dot qubits.


  We theoretically investigate collective phase synchronization between
interacting groups of globally coupled noisy identical phase oscillators
exhibiting macroscopic rhythms. Using the phase reduction method, we derive
coupled collective phase equations describing the macroscopic rhythms of the
groups from microscopic Langevin phase equations of the individual oscillators
via nonlinear Fokker-Planck equations. For sinusoidal microscopic coupling, we
determine the type of the collective phase coupling function, i.e., whether the
groups exhibit in-phase or anti-phase synchronization. We show that the
macroscopic rhythms can exhibit effective anti-phase synchronization even if
the microscopic phase coupling between the groups is in-phase, and vice versa.
Moreover, near the onset of collective oscillations, we analytically obtain the
collective phase coupling function using center-manifold and phase reductions
of the nonlinear Fokker-Planck equations.


  This paper proposes a novel electric vehicle (EV) classification scheme for a
photovoltaic (PV) powered EV charging station (CS) that reduces the effect of
intermittency of electricity supply as well as reducing the cost of energy
trading of the CS. Since not all EV drivers would like to be environmentally
friendly, all vehicles in the CS are divided into three categories: 1) premium,
2) conservative, and 3) green, according to their charging behavior. Premium
and conservative EVs are considered to be interested only in charging their
batteries, with noticeably higher rate of charging for premium EVs. Green
vehicles are more environmentally friendly, and thus assist the CS to reduce
its cost of energy trading by allowing the CS to use their batteries as
distributed storage. A different charging scheme is proposed for each type of
EV, which is adopted by the CS to encourage more EVs to be green. A basic mixed
integer programming (MIP) technique is used to facilitate the proposed
classification scheme. It is shown that the uncertainty in PV generation can be
effectively compensated, along with minimization of total cost of energy
trading to the CS, by consolidating more green EVs. Real solar and pricing data
are used for performance analysis of the system. It is demonstrated that the
total cost to the CS reduces considerably as the percentage of green vehicles
increases, and also that the contributions of green EVs in winter are greater
than those in summer.


  Flowchart-grounded troubleshooting dialogue (FTD) systems, which follow the
instructions of a flowchart to diagnose users' problems in specific domains
(e.g., vehicle, laptop), have been gaining research interest in recent years.
However, collecting sufficient dialogues that are naturally grounded on
flowcharts is costly, thus FTD systems are impeded by scarce training data. To
mitigate the data sparsity issue, we propose a plan-based synthetic data
generation (PlanSDG) approach that generates diverse synthetic dialog data at
scale by transforming concise flowchart into dialogues. Specifically, its
generative model employs a variational-base framework with a hierarchical
planning strategy that includes global and local latent planning variables.
Experiments on the FloDial dataset show that synthetic dialogue produced by
PlanSDG improves the performance of downstream tasks, including flowchart path
retrieval and response generation, in particular on the Out-of-Flowchart
settings. In addition, further analysis demonstrate the quality of synthetic
data generated by PlanSDG in paths that are covered by current sample dialogues
and paths that are not covered.


  For convex hypersurfaces in the affine space $\mathbb{A}^{n+1}$ ($n\geq2$),
A.-M.\ Li introduced the notion of $\alpha$-normal field as a generalization of
the affine normal field. By studying a Monge-Amp\`ere equation with gradient
blowup boundary condition, we show that regular domains in $\mathbb{A}^{n+1}$,
defined with respect to a proper convex cone and satisfying some regularity
assumption if $n\geq3$, are foliated by complete convex hypersurfaces with
constant Gauss-Kronecker curvature relative to the Li-normalization. When
$n=2$, a key feature is that no regularity assumption is required, and the
result extends our recent work about the $\alpha=1$ case.


  The entropic force attracts a lot of interest for its multifunctional
properties. For instance, Einstein's field equation, Newton's law of
gravitation and the Friedmann equation can be derived from the entropic force.
In this paper, utilizing a new kind of rainbow gravity model that was proposed
by Magueijo and Smolin, we explore the quantum gravity corrections to the
entropic force. First, we derive the modified thermodynamics of a rainbow black
hole via its surface gravity. Then, according to Verlinde's theory, the quantum
corrections to the entropic force are obtained. The result shows that the
modified entropic force is related not only to the properties of the black hole
but also the Planck length $\ell_p$, and the rainbow parameter $\gamma$.
Furthermore, based on the rainbow gravity corrected entropic force, the
modified Einstein's field equation and the modified Friedmann equation are also
derived.


  Recently some 4d asymptotically $AdS_2 \times S^2$ regular bubbling solutions
written in terms of an arbitrary profile function has appeared in literature.
We discuss the possibility of extending these solutions to asymptotically flat
spaces, therefore building a model for an horizonless and singularity-free
fuzzball with the same asymptotic charges of the associated black hole,
directly in general relativity. A negative conclusion is reached within an
axial-symmetric ansatz inside the IWP family.


  In this paper, first we introduce the notion of a Reynolds operator on an
$n$-Lie algebra and illustrate the relationship between Reynolds operators and
derivations on an $n$-Lie algebra. We give the cohomology theory of Reynolds
operators on an $n$-Lie algebra and study infinitesimal deformations of
Reynolds operators using the second cohomology group. Then we introduce the
notion of NS-$n$-Lie algebras, which are generalizations of both $n$-Lie
algebras and $n$-pre-Lie algebras. We show that an NS-$n$-Lie algebra gives
rise to an $n$-Lie algebra together with a representation on itself. Reynolds
operators and Nijenhuis operators on an $n$-Lie algebra naturally induce
NS-$n$-Lie algebra structures. Finally, we construct Reynolds $(n+1)$-Lie
algebras and Reynolds $3$-Lie algebras from Reynolds $n$-Lie algebras and
Reynolds commutative associative algebras respectively.


  We present an analysis of the carbon dwarf G77-61, a known binary, based on
high resolution Keck spectra. G77-61 has a very-low metallicity, although not
as extreme as what was previously conjectured. This star is very
carbon-enhanced, the spectra showing very strong CH, CN, and C$_2$ bands of
both $^{13}$C and $^{12}$C isotopes. Atomic lines are sparse, and often
blended, but we were able to derive abundances for Na, Mg, Ca, Cr, Mn, and Fe.
Its [Fe/H] of -4.03$\pm$0.1 places it among the lowest metallicity stars known,
and its very high [C/Fe]=+2.6, and [N/Fe]=+2.6 among the most C and N-rich
metal-poor stars. The carbon isotopic ratio is $^{12}$C/$^{13}C=5\pm$1. No
overabundance of s- or r-process elements is detectable, but the upper limits
that can be set on these overabundances are not very constraining.


  The vacuum-driven nonperturbative factors $L_i$ for quark and gluon Green's
functions are shown to define the nonperturbative dynamics of QGP in the
leading approximation. EoS obtained recently in the framework of this approach
is compared in detail with known lattice data for $\mu=0$ including $P/T^4$,
$\epsilon/T^4$, $\frac{\epsilon-3P}{T^4}$. The basic role in the dynamics at
$T\la 3T_c$ is played by the factors $L_i$ which are approximately equal to the
modulus of Polyakov line for quark $L_{fund}$ and gluon $L_{adj}$. The
properties of $L_i$ are derived from field correlators and compared to lattice
data, in particular the Casimir scaling property $L_{adj}
=(L_{fund})^{\frac{C_2(adj)}{C_2(fund)}}$ follows in the Gaussian approximation
valid for small vacuum correlation lengths. Resulting curves for $P/T^4$,
$\epsilon/T^4$, $\frac{\epsilon-3P}{T^4}$ are in a reasonable agreement with
lattice data, the remaining difference points out to an effective attraction
among QGP constituents.


  We study pencils of hypersurfaces over finite fields $\mathbb{F}_q$ such that
each of the $q+1$ members defined over $\mathbb{F}_q$ is smooth.


  We present the calibration and reduction of Event Horizon Telescope (EHT)
1.3mm radio wavelength observations of the supermassive black hole candidate at
the center of the radio galaxy M87 and the quasar 3C 279, taken during the 2017
April 5-11 observing campaign. These global very long baseline interferometric
observations include for the first time the highly sensitive Atacama Large
Millimeter/submillimeter Array (ALMA); reaching an angular resolution of 25
micro-as, with characteristic sensitivity limits of ~1 mJy on baselines to ALMA
and ~10 mJy on other baselines. The observations present challenges for
existing data processing tools, arising from the rapid atmospheric phase
fluctuations, wide recording bandwidth, and highly heterogeneous array. In
response, we developed three independent pipelines for phase calibration and
fringe detection, each tailored to the specific needs of the EHT. The final
data products include calibrated total intensity amplitude and phase
information. They are validated through a series of quality assurance tests
that show consistency across pipelines and set limits on baseline systematic
errors of 2% in amplitude and 1 degree in phase. The M87 data reveal the
presence of two nulls in correlated flux density at ~3.4 and ~8.3 giga-lambda
and temporal evolution in closure quantities, indicating intrinsic variability
of compact structure on a timescale of days, or several light-crossing times
for a few billion solar-mass black hole. These measurements provide the first
opportunity to image horizon-scale structure in M87.


  Some developments of nuclear-structure physics uniquely related to Copenhagen
School are sketched based on theoretical considerations versus experimental
findings and one-particle versus collective aspects. Based on my personal
overview I pick up the following topics; (1) Study of vibration in terms of
particle-vibration coupling; (2) One-particle motion in deformed and rotating
potentials, and yrast spectroscopy in high-spin physics; (3) Triaxial shape in
nuclei: wobbling motion and chiral bands; (4) Nuclear structure of drip line
nuclei: in particular, shell-structure (or magic numbers) change and spherical
or deformed halo phenomena; (5) shell structure in oblate deformation.


  Given a network property or a data structure, a local certification is a
labeling that allows to efficiently check that the property is satisfied, or
that the structure is correct. The quality of a certification is measured by
the size of its labels: the smaller, the better.This notion plays a central
role in self-stabilization, because the size of the certification is a lower
bound (and often an upper bound) on the memory needed for silent
self-stabilizing construction of distributed data structures. From the point of
view of distributed computing in general, it is also a measure of the locality
of a property (e.g. properties of the network itself, such as planarity).
  When it comes to the size of the certification labels, one can identify three
important regimes: the properties for which the optimal size is polynomial in
the number of vertices of the graph, the ones that require only polylogarithmic
size, and the ones that can be certified with a constant number of bits. The
first two regimes are well studied, with several upper and lower bounds,
specific techniques, and active research questions. On the other hand, the
constant regime has never been really explored, at least on the lower bound
side. The main contribution of this paper is the first non-trivial lower bound
for this low regime. More precisely, we show that by using certification on
just one bit (a binary certification), one cannot certify $k$-colorability for
$k\geq 3$. To do so, we develop a new technique, based on the notion of score,
and both local symmetry arguments and a global parity argument. We hope that
this technique will be useful for establishing stronger results. We complement
this result by a discussion of the implication of lower bounds for this
constant-size regime, and with an upper bound for a related problem,
illustrating that in some cases one can do better than the natural upper bound.


  Optical diffraction tomography (ODT) is a tomographic technique that can be
used to measure the three-dimensional (3D) refractive index distribution within
living cells without the requirement of any marker. In principle, ODT can be
regarded as a generalization of optical projection tomography which is
equivalent to computerized tomography (CT). Both optical tomographic techniques
require projection-phase images of cells measured at multiple angles. However,
the reconstruction of the 3D refractive index distribution post-measurement
differs for the two techniques. It is known that ODT yields better results than
projection tomography, because it takes into account diffraction of the imaging
light due to the refractive index structure of the sample. Here, we apply ODT
to biological cells in a microfluidic chip which combines optical trapping and
microfluidic flow to achieve an optofluidic single-cell rotation. In
particular, we address the problem that arises when the trapped cell is not
rotating about an axis perpendicular to the imaging plane, but instead about an
arbitrarily tilted axis. In this paper we show that the 3D reconstruction can
be improved by taking into account such a tilted rotational axis in the
reconstruction process.


  This thesis report studies methods to solve Visual Question-Answering (VQA)
tasks with a Deep Learning framework. As a preliminary step, we explore Long
Short-Term Memory (LSTM) networks used in Natural Language Processing (NLP) to
tackle Question-Answering (text based). We then modify the previous model to
accept an image as an input in addition to the question. For this purpose, we
explore the VGG-16 and K-CNN convolutional neural networks to extract visual
features from the image. These are merged with the word embedding or with a
sentence embedding of the question to predict the answer. This work was
successfully submitted to the Visual Question Answering Challenge 2016, where
it achieved a 53,62% of accuracy in the test dataset. The developed software
has followed the best programming practices and Python code style, providing a
consistent baseline in Keras for different configurations.


  In this paper, we build the global determinant method of Salberger by
Arakelov geometry explicitly. As an application, we study the dependence on the
degree of the number of rational points of bounded height in plane curves. We
will also explain why some constants will be more explicit if we admit the
Generalized Riemann Hypothesis.


  We introduce and investigate binary $(k,k)$-designs -- combinatorial
structures which are related to binary orthogonal arrays. We derive general
linear programming bound and propose as a consequence a universal bound on the
minimum possible cardinality of $(k,k)$-designs for fixed $k$ and length $n$.
Designs which attain our bound are investigated.


  The quasiclassically exact solution for the second inertial parameter $\cal
B$ is found in self-consistent way. It is shown that superdeformation and
nonuniform pairing arising from the rotation induced pair density significantly
reduce this inertial parameter. The different limiting cases for $\cal B$,
which allow to study an interplay between rapid rotation, pairing correlations,
and mean field deformation, are considered. The new signature for the
transition from pairing to normal phase is suggested in terms of the variation
of ${\cal B}/{\cal A}$ versus spin. Experimental data indicate the existence of
such transition in the three superdeformed mass regions.


  We consider a dissipative tight-binding chain. The dissipation manifests as
tunneling into/out of the chain from/to a memoryless environment. The evolution
of the system is described by the Lindblad equation. Already infinitesimally
small dissipation along the chain induces a quantum phase transition (QPT).
This is a decoherence QPT: the reduced density matrix of a subsystem (far from
the ends of the chain) can be represented as the tensor product of single-site
density matrices. We analyze the QPT in the thermodynamic limit by looking at
the entropy and the response function in the bulk. We also explore the
properties of the boundaries of the chain close to the transition point and
observe that the boundaries behave as if they undergo a second-order phase
transition with power-law divergence of the correlation functions and response
function. Disorder is known to localize one-dimensional systems, but the
coupling to the memoryless environment pushes the system back into the
delocalized state even in the presence of disorder.


  Information transfer rates in optical communications may be dramatically
increased by making use of spatially non-Gaussian states of light. Here we
demonstrate the ability of deep neural networks to classify
numerically-generated, noisy Laguerre-Gauss modes of up to 100 quanta of
orbital angular momentum with near-unity fidelity. The scheme relies only on
the intensity profile of the detected modes, allowing for considerable
simplification of current measurement schemes required to sort the states
containing increasing degrees of orbital angular momentum. We also present
results that show the strength of deep neural networks in the classification of
experimental superpositions of Laguerre-Gauss modes when the networks are
trained solely using simulated images. It is anticipated that these results
will allow for an enhancement of current optical communications technologies.


  We investigate the properties of a frustrated spin-5/2 chain with
next-nearest neighbor two and three-site interactions, with two questions in
mind: the nature of the transition into the dimerized phase induced by the
three-site interaction, and the possible presence of a critical floating phase
at intermediate values of the next-nearest neighbor interaction. We provide
strong evidence that the continuous transition into the dimerized phase, which
has been found to be generically in the Wess-Zumino-Witten SU(2)$_{2S}$
universality class up to spin $S=2$, is SU(2)$_5$ only at two isolated points
of the phase diagram, and that it is SU(2)$_3$ in between, in agreement with
the presence of two relevant operators allowed by symmetry for SU(2)$_5$, and
with the conservation of the parity of the level index along the
renormalization flow between SU(2)$_k$ theories with different values of $k$.
We also find that the dimerization induced by the next-nearest neighbor
interaction is a three step process, with first a small partially dimerized
phase followed by a broad critical floating phase with incommensurate
correlations before the fully dimerized phase is reached. Implications for the
iron oxide ${\mathrm{Bi}}_{3}{\mathrm{FeMo}}_{2}{\mathrm{O}}_{12}$ are briefly
discussed.


  The effect of weak localization on spin relaxation in a two-dimensional
system with a spin-split spectrum is considered. It is shown that the spin
relaxation slows down due to the interference of electron waves moving along
closed paths in opposite directions. As a result, the averaged electron spin
decays at large times as $1/t$. It is found that the spin dynamics can be
described by a Boltzmann-type equation, in which the weak localization effects
are taken into account as nonlocal-in-time corrections to the collision
integral. The corrections are expressed via a spin-dependent return
probability. The physical nature of the phenomenon is discussed and it is shown
that the "nonbackscattering" contribution to the weak localization plays an
essential role. It is also demonstrated that the magnetic field, both
transversal and longitudinal, suppresses the power tail in the spin
polarization.


  We describe a system that helps identify manuscripts submitted to multiple
journals at the same time. Also, we discuss potential applications of the
near-duplicate detection technology when run with manuscript text content,
including identification of simultaneous submissions, prevention of duplicate
published articles, and improving article transfer service.


  Using ultrafast optical pump-probe spectroscopy, we have measured carrier
relaxation times in epitaxial graphene layers grown on SiC wafers. We find two
distinct time scales associated with the relaxation of nonequilibrium
photogenerated carriers. An initial fast relaxation transient in the 70-120 fs
range is followed by a slower relaxation process in the 0.4-1.7 ps range. The
slower relaxation time is found to be inversely proportional to the degree of
crystalline disorder in the graphene layers as measured by Raman spectroscopy.
We relate the measured fast and slow time constants to carrier-carrier and
carrier-phonon intraband and interband scattering processes in graphene.


  The purpose of this paper is to study categorifications of tensor products of
finite dimensional modules for the quantum group for sl(2). The main
categorification is obtained using certain Harish-Chandra bimodules for the
complex Lie algebra gl(n). For the special case of simple modules we naturally
deduce a categorification via modules over the cohomology ring of certain flag
varieties. Further geometric categorifications and the relation to Steinberg
varieties are discussed. We also give a categorical version of the quantised
Schur-Weyl duality and an interpretation of the (dual) canonical bases and the
(dual) standard bases in terms of projective, tilting, standard and simple
Harish-Chandra bimodules.


  We propose a new approach to the quantization of the damped harmonic
oscillator in the framework of deformation quantization. The quantization is
performed in the Schr\"{o}dinger picture by a star-product induced by a
modified "Poisson bracket". We determine the eigenstates in the damped regime
and compute the transition probability between states of the undamped harmonic
oscillator after the system was submitted to dissipation.


  We present a theorical investigation of the optical transmission of a
dielectric grating carved in a tungsten layer. For appropriate wavelengths
tungsten shows indeed a dielectric behaviour. Our numerical simulations leads
to theoretical results similar to those found with metallic systems studied in
earlier works. The interpretation of our results rests on the idea that the
transmission is correlated with the resonant response of eigenmodes coupled to
evanescent diffraction orders.


  The scaling behavior of the thermoelectric transport properties in disordered
systems is studied in the energy region near the metal-insulator transition.
Using an energy-dependent conductivity $\sigma$ obtained experimentally, we
extend our linear-response-based transport calculations in the
three-dimensional Anderson model of localization. Taking a dynamical scaling
exponent $z$ in agreement with predictions from scaling theories, we show that
the temperature-dependent $\sigma$, the thermoelectric power $S$, the thermal
conductivity $K$ and the Lorenz number $L_0$ obey scaling.


  At low temperatures, dynamics in amorphous silicon occurs through a sequence
of discrete activated events that locally reorganize the topological network.
Using the activation-relaxation technique, a data base containing over 8000
such events is generated, and the events are analyzed with respect to their
energy barrier and asymmetry, displacement and volume expansion/contraction.
Special attention is paid to those events corresponding to diffusing
coordination defects. The energetics is not clearly correlated with the
displacement, nor with the defect density in well relaxed configurations. We
find however some correlation with the local volume expansion: it tends to
increase by about 4 eV/\AA$^3$. The topological properties of these events are
also studied; they show an unexpectedly rich diversity.


  On this article there are presented the main results of the theory of
electron phenomena in an unordered condensed matter that can be described as an
inhomogeneously deformed crystalline lattice with dislocations. The
one-electron effective Hamiltonian is derived by introducing a new basis for
the expansion of the sought functions. It allows to overcome the difficulty of
the discontinuity of the displacement field. The same method is used for the
derivation of the equations for eigentones of a deformed crystal, and the
equations of the theory of dynamical scattering of electrons by it. The general
problem of the description of the totality of electron states in a deformed
crystal is discussed, and some new solutions describing localized electron or
vibration states are obtained. There is derived and researched the equation of
an electrical field generated by the deformation in a crystal with
consideration for the interaction of electrons and ions by self-consisted field
theory. The tensor kinetic coefficients are obtained, which are proportional to
the deformation tensor. Such kind of these coefficients can explain some
experimental effects. The appearing of the local superconducting regions and
the subsidiary solenoidal current that is generated by a transport one in a
deformed superconductor is considered. The behavior and properties of the
Abrikosov's vortex in anisotropic and/or deformed superconductors are
researched. In more detail this theory has been published in the monograph by
the same author of the same title in Russian.


  Food classification from images is a fine-grained classification problem.
Manual curation of food images is cost, time and scalability prohibitive. On
the other hand, web data is available freely but contains noise. In this paper,
we address the problem of classifying food images with minimal data curation.
We also tackle a key problems with food images from the web where they often
have multiple cooccuring food types but are weakly labeled with a single label.
We first demonstrate that by sequentially adding a few manually curated samples
to a larger uncurated dataset from two web sources, the top-1 classification
accuracy increases from 50.3% to 72.8%. To tackle the issue of weak labels, we
augment the deep model with Weakly Supervised learning (WSL) that results in an
increase in performance to 76.2%. Finally, we show some qualitative results to
provide insights into the performance improvements using the proposed ideas.


  We have obtained accurate CCD narrow band H_beta and H_alpha photometry of
Giant HII Regions in M 33, NGC 6822 and M 101. Comparison with previous
determinations of emission line fluxes show large discrepancies; their probable
origins are discussed. Combining our new photometric data with global velocity
dispersion (sigma) derived from emission line widths we have reviewed the
L(H_beta)-sigma relation. A reanalysis of the properties of the GEHRs included
in our sample shows that age spread and the superposition of components in
multiple regions introduce a considerable spread in the regression. Combining
the information available in the literature regarding ages of the associated
clusters, evolutionary footprints on the interstellar medium, and kinematical
properties of the knots that build up the multiple GEHRs, we have found that a
subsample - which we refer to as young and single GEHRs - do follow a tight
relation in the L-sigma plane.


  We investigate several aspects of exact black hole solutions in
asymptotically Lifshitz spacetime, which were proposed in 0812.0530. Firstly,
we calculate the tidal forces and find that in the near horizon region of such
black hole backgrounds, the tidal forces diverge in the near extremal limit.
Secondly, we evaluate the Wilson loops in both extremal and finite temperature
cases. Finally, we obtain the corresponding shear viscosity and square of the
sound speed and find that the ratio of shear viscosity to entropy density takes
the universal value $1/4\pi$ in arbitrary dimensions while the square of the
speed of sound saturates the conjectured bound 1/3 in five dimensions.


  We apply the renormalization group theory to the dynamical systems with the
simplest example of basic biological motifs. This includes the interpretation
of complex networks as the perturbation to simple network. This is the first
step to build our original framework to infer the properties of biological
networks, and the basis work to see its effectiveness to actual complex
systems.


  We present nbodykit, an open-source, massively parallel Python toolkit for
analyzing large-scale structure (LSS) data. Using Python bindings of the
Message Passing Interface (MPI), we provide parallel implementations of many
commonly used algorithms in LSS. nbodykit is both an interactive and scalable
piece of scientific software, performing well in a supercomputing environment
while still taking advantage of the interactive tools provided by the Python
ecosystem. Existing functionality includes estimators of the power spectrum, 2
and 3-point correlation functions, a Friends-of-Friends grouping algorithm,
mock catalog creation via the halo occupation distribution technique, and
approximate N-body simulations via the FastPM scheme. The package also provides
a set of distributed data containers, insulated from the algorithms themselves,
that enable nbodykit to provide a unified treatment of both simulation and
observational data sets. nbodykit can be easily deployed in a high performance
computing environment, overcoming some of the traditional difficulties of using
Python on supercomputers. We provide performance benchmarks illustrating the
scalability of the software. The modular, component-based approach of nbodykit
allows researchers to easily build complex applications using its tools. The
package is extensively documented at http://nbodykit.readthedocs.io, which also
includes an interactive set of example recipes for new users to explore. As
open-source software, we hope nbodykit provides a common framework for the
community to use and develop in confronting the analysis challenges of future
LSS surveys.


  Let $\Sigma (X,\mathbb{C})$ denote the collection of all the rings between
$C^*(X,\mathbb{C})$ and $C(X,\mathbb{C})$. We show that there is a natural
correlation between the absolutely convex ideals/ prime ideals/maximal
ideals/$z$-ideals/$z^\circ$-ideals in the rings $P(X,\mathbb{C})$ in
$\Sigma(X,\mathbb{C})$ and in their real-valued counterparts
$P(X,\mathbb{C})\cap C(X)$. It is shown that the structure space of any such
$P(X,\mathbb{C})$ is $\beta X$. We show that for any maximal ideal $M$ in
$C(X,\mathbb{C}), C(X,\mathbb{C})/M$ is an algebraically closed field. We give
a necessary and sufficient condition for the ideal
$C_{\mathcal{P}}(X,\mathbb{C})$ of $C(X,\mathbb{C})$ to be a prime ideal, and
we examine a few special cases thereafter.


  Energy storage is needed to enable dispatchable renewable energy supply and
thereby full decarbonization of the grid. However, this can only occur with
drastic cost reductions compared to current battery technology, with predicted
targets for the cost per unit energy (CPE) below $20/kWh. Notably, for full
decarbonization, long duration storage up to 100 hrs will be needed at such low
costs, and prior analyses have shown that in such high renewable penetration
scenarios, CPE is more critical than other parameters such as round trip
efficiency or cost per unit power when comparing the costs of different
technologies. Here, we introduce an electricity storage concept that stores
electricity as sensible heat in graphite storage blocks and uses multi-junction
thermophotovoltaics (TPV) as a heat engine to convert it back to electricity on
demand. This design is an outgrowth of the system proposed by Amy et al. in
2019, which has been modified here to use a solid graphite medium and molten
tin as a heat transfer fluid rather than silicon as both. The reason for this
is two-fold: (1) the CPE of graphite is almost 10X lower than that of silicon,
which derives from the lower cost per unit mass (i.e., $0.5/kg vs. $1.5/kg) and
the higher heat capacity per unit mass (2000 J/kg-K vs. 950 J/kg-K); and (2)
the melting point tin and solubility of tin in graphite are much lower than
that of silicon, which lessens the number of issues that have to overcome along
the research and development pathway. The usage of graphite also eliminates the
need for a second tank, but the main disadvantage of using a solid medium is
that one cannot easily provide a steady discharge rate, as the power output
from the storage will change with time, as the graphite cools during discharge.
Thus, the objective of this work is to examine how these changes in the system
design effect the overall technoeconomics.


  We calculate the kaon HBT radius parameters for high energy heavy ion
collisions, assuming a first order phase transition from a thermalized
Quark-Gluon-Plasma to a gas of hadrons. At high transverse momenta K_T ~ 1
GeV/c direct emission from the phase boundary becomes important, the emission
duration signal, i.e., the R_out/R_side ratio, and its sensitivity to T_c (and
thus to the latent heat of the phase transition) are enlarged. Moreover, the
QGP+hadronic rescattering transport model calculations do not yield unusual
large radii (R_i<9fm). Finite momentum resolution effects have a strong impact
on the extracted HBT parameters (R_i and lambda) as well as on the ratio
R_out/R_side.


  The quantum nature of light-matter interactions in a circularly polarized
vacuum field was probed by spontaneous emission from quantum dots in
three-dimensional chiral photonic crystals. Due to the circularly polarized
eigenmodes along the helical axis in the GaAs-based mirror-asymmetric
structures we studied, we observed highly circularly polarized emission from
the quantum dots. Both spectroscopic and time-resolved measurements confirmed
that the obtained circularly polarized light was influenced by a large
difference in the photonic density of states between the orthogonal components
of the circular polarization in the vacuum field.


  We determine the space-dependent source term for a two-parameter fractional
diffusion problem subject to nonlocal non-self-adjoint boundary conditions and
two local time-distinct datum. A bi-orthogonal pair of bases is used to
construct a series representation of the solution and the source term. The two
local time conditions spare us from measuring the fractional integral initial
conditions commonly associated with fractional derivatives. On the other hand,
they lead to delicate $2\times 2$ linear systems for the Fourier coefficients
of the source term and of the fractional integral of the solution at $t=0$. The
asymptotic behavior and estimates of the generalized Mittag-Leffler function
are used to establish the solvability of these linear systems, and to obtain
sufficient conditions for the existence of our construction. Analytical and
numerical examples are provided.