  Collaborative robots (cobots) are machines designed to work safely alongside
people in human-centric environments. Providing cobots with the ability to
quickly infer the inertial parameters of manipulated objects will improve their
flexibility and enable greater usage in manufacturing and other areas. To
ensure safety, cobots are subject to kinematic limits that result in low
signal-to-noise ratios (SNR) for velocity, acceleration, and force-torque data.
This renders existing inertial parameter identification algorithms
prohibitively slow and inaccurate. Motivated by the desire for faster model
acquisition, we investigate the use of an approximation of rigid body dynamics
to improve the SNR. Additionally, we introduce a mass discretization method
that can make use of shape information to quickly identify plausible inertial
parameters for a manipulated object. We present extensive simulation studies
and real-world experiments demonstrating that our approach complements existing
inertial parameter identification methods by specifically targeting the typical
cobot operating regime.


  In quantum optics, light-matter interaction has conventionally been studied
using small atoms interacting with electromagnetic fields with wavelength
several orders of magnitude larger than the atomic dimensions. In contrast,
here we experimentally demonstrate the vastly different giant atom regime,
where an artificial atom interacts with acoustic fields with wavelength several
orders of magnitude smaller than the atomic dimensions. This is achieved by
coupling a superconducting qubit to surface acoustic waves at two points with
separation on the order of 100 wavelengths. This approach is comparable to
controlling the radiation of an atom by attaching it to an antenna. The slow
velocity of sound leads to a significant internal time-delay for the field to
propagate across the giant atom, giving rise to non-Markovian dynamics. We
demonstrate the non-Markovian character of the giant atom in the frequency
spectrum as well as nonexponential relaxation in the time domain.


  For readability and disambiguation of the written text, appropriate word
segmentation is recommended for documentation, and it also holds for the
digitized texts. If the language is agglutinative while far from scriptio
continua, for instance in the Korean language, the problem becomes more
significant. However, some device users these days find it challenging to
communicate via key stroking, not only for handicap but also for being
unskilled. In this study, we propose a real-time assistive technology that
utilizes an automatic word segmentation, designed for digital minorities who
are not familiar with electronic typing. We propose a data-driven system
trained upon a spoken Korean language corpus with various non-canonical
expressions and dialects, guaranteeing the comprehension of contextual
information. Through quantitative and qualitative comparison with other text
processing toolkits, we show the reliability of the proposed system and its fit
with colloquial and non-normalized texts, which fulfills the aim of supportive
technology.


  cURL (libcurl) is a popular and widely used library distributed with the php
interpreter. It allows php applications to connect to and communicate with
external resources (servers) by using wide variety of communication protocols.
In most cases it is the preferred way of consuming external REST web services.
Programmers usually use it for granted without even thinking of any performance
issues. During an experimental analysis of the Hadoop's WebHDFS API throughput,
it has been noted that read (download) speed from WebHDFS reduces with
increasing the file size. However, this issue does not happen when writing to
WebHDFS. Since the communication between the php application and the WebHDFS
API is handled by the php's cURL library, then the cause of the download speed
decrease could be either the cURL library itself or the API. This paper
presents a series of experimental analyses aiming to determine the cause of the
download speed decrease in previous experiments - whether it is the WebHDFS API
or the php's cURL library. Both parties are tested in multiple ways separately
and independently of each other. Results clearly prove (in two different ways)
that the cause of the download speed decrease is the php's cURL library itself,
not the consumed API.


  We study the consequences of imposing an approximate Galilean symmetry on the
Effective Theory of Inflation, the theory of small perturbations around the
inflationary background. This approach allows us to study the effect of
operators with two derivatives on each field, which can be the leading
interactions due to non-renormalization properties of the Galilean Lagrangian.
In this case cubic non-Gaussianities are given by three independent operators,
containing up to six derivatives, two with a shape close to equilateral and one
peaking on flattened isosceles triangles. The four-point function is larger
than in models with small speed of sound and potentially observable with the
Planck satellite.


  The star BW Vul excites an extremely strong radial pulsation that grows in
its envelope and is responsible for visible shock features in the continuum
flux and spectral line profiles emerging in the atmosphere At two phases
separated by 0.8 cycles. Material propelled upwards in the atmosphere from the
shock returns to the lower photosphere where it creates a second shock just
before the start of the next cycle. We have obtained three nights of echelle
data for this star over about 5 pulsation cycles (P = 0.201 days) in order to
evaluate the effects of on a number of important lines in the spectrum,
including the HeI 5875A and 6678A lines. These data were supplemented by
archival high-dispersion IUE (UV) data from 1994. A comparison of profiles of
the two HeI lines during the peak of the infall activity suggests that
differences in the development of the blue wing at this time are due to heating
and short-lived formations of an optically thin layer above the atmospheric
region compressed by the infall. This discovery and the well-known decreases in
equivalent widths of the CII 6578-83A doublet at the two shock phases, suggests
that shock flattens the temperature gradient and produces heating in heating
the upper atmosphere. Except for absorptions in the blue wings of the UV
resonance lines, we find no evidence for sequential shock delays arriving at
various regions of line formation of the photosphere (a "Van Hoof effect").
Phase lags cited by some former observers may be false indicators arising from
varying degrees of desaturation of multiple lines, such as for the red HeI
lines. In addition, an apparent lag in the equivalent width curve of lines
arising from less excited atomic levels could instead be caused by post-shock
cooling, followed by a rebound shock.


  We study the blowup behavior of a class of strongly perturbed wave equations
with a focusing supercritical power nonlinearity in three spatial dimensions.
We show that the ODE blowup profile of the unperturbed equation still describes
the asymptotics of stable blowup. As a consequence, stable ODE-type blowup is
seen to be a universal phenomenon that exists in a large class of semilinear
wave equations.


  We investigate, from a spacetime perspective, some aspects of Horowitz's
recent conjecture that black strings may catalyze the decay of Kaluza-Klein
spacetimes into a bubble of nothing. We identify classical configurations that
interpolate between flat space and the bubble, and discuss the energetics of
the transition. We investigate the effects of winding tachyons on the size and
shape of the barrier and find no evidence at large compactification radius that
tachyons enhance the tunneling rate. For the interesting radii, of order the
string scale, the question is difficult to answer due to the failure of the
$\alpha^\prime$ expansion.


  This paper is to study some conditions on semigroups, generated by some class
of non-densely defined operators in the closure of its domain, in order that
certain bounded perturbations preserve some regularity properties of the
semigroup such as norm continuity, compactness, differentiability and
analyticity. Furthermore, we study the critical and essential growth bound of
the semigroup under bounded perturbations. The main results generalize the
corresponding results in the case of Hille-Yosida operators. As an
illustration, we apply the main results to study the asymptotic behaviors of a
class of age-structured population models in $ L^p $ spaces ($ 1 \leq p <
\infty $).


  We develop a quantum field theoretical framework to analytically study the
three-body constrained Bose-Hubbard model beyond mean field and non-interacting
spin wave approximations. It is based on an exact mapping of the constrained
model to a theory with two coupled bosonic degrees of freedom with polynomial
interactions, which have a natural interpretation as single particles and
two-particle states. The procedure can be seen as a proper quantization of the
Gutzwiller mean field theory. The theory is conveniently evaluated in the
framework of the quantum effective action, for which the usual symmetry
principles are now supplemented with a ``constraint principle'' operative on
short distances. We test the theory via investigation of scattering properties
of few particles in the limit of vanishing density, and we address the
complementary problem in the limit of maximum filling, where the low lying
excitations are holes and di-holes on top of the constraint induced insulator.
This is the first of a sequence of two papers. The application of the formalism
to the many-body problem, which can be realized with atoms in optical lattices
with strong three-body loss, is performed in a related work [14].


  We present a new approach to path planning, called the "Ariadne's clew
algorithm". It is designed to find paths in high-dimensional continuous spaces
and applies to robots with many degrees of freedom in static, as well as
dynamic environments - ones where obstacles may move. The Ariadne's clew
algorithm comprises two sub-algorithms, called Search and Explore, applied in
an interleaved manner. Explore builds a representation of the accessible space
while Search looks for the target. Both are posed as optimization problems. We
describe a real implementation of the algorithm to plan paths for a six degrees
of freedom arm in a dynamic environment where another six degrees of freedom
arm is used as a moving obstacle. Experimental results show that a path is
found in about one second without any pre-processing.


  The quantum Griffiths singularity (QGS) is a phenomenon driven by quenched
disorders that break conventional scaling invariance and result in a divergent
dynamical critical exponent during quantum phase transitions (QPT). While this
phenomenon has been well-documented in low-dimensional conventional
superconductors and in three-dimensional (3D) magnetic metal systems, its
presence in 3D superconducting systems and in unconventional high-temperature
superconductors (high-Tc SCs) remains unclear. In this study, we report the
observation of robust QGS in the superconductor-metal transition (SMT) of both
quasi-2D and 3D anisotropic unconventional high-Tc superconductor CaFe1-xNixAsF
(x < 5%) bulk single crystals, where the QGS states persist to up to 5.3 K. A
comprehensive quantum phase diagram is established that delineates the 3D
anisotropic QGS of SMT induced by perpendicular and parallel magnetic field.
Our findings reveal the universality of QGS in 3D superconducting systems and
unconventional high-Tc SCs, thereby substantially expanding the range of
applicability of QGS.


  Quenched disorder slows down the scrambling of quantum information. Using a
bottom-up approach, we formulate a kinetic theory of scrambling in a correlated
metal near a superconducting transition, following the scrambling dynamics as
the impurity scattering rate is increased. Within this framework, we rigorously
show that the butterfly velocity $v$ is bounded by the light cone velocity
$v_{\rm lc }$ set by the Fermi velocity. We analytically identify a
disorder-driven dynamical transition occurring at small but finite disorder
strength between a spreading of information characterized at late times by a
discontinuous shock wave propagating at the maximum velocity $v_{\rm lc}$, and
a smooth traveling wave belonging to the Fisher or
Kolmogorov-Petrovsky-Piskunov (FKPP) class and propagating at a slower, if not
considerably slower, velocity $v$. In the diffusive regime, we establish the
relation $v^2/\lambda_{\rm FKPP} \sim D_{\rm el}$ where $\lambda_{\rm FKPP}$ is
the Lyapunov exponent set by the inelastic scattering rate and $D_{\rm el}$ is
the elastic diffusion constant.


  We study the temperature-magnetic field T-B phase diagrams of heavy fermion
(HF) metals, and show that at sufficiently high temperatures outside the
ordered phase the crossover temperature T*(B), regarded as the energy scale,
follows a linear B-dependence, crossing the origin of the T-B phase diagram.
This behavior of T*(B) constitutes the general property, and is formed by the
presence of fermion condensation quantum phase transition hidden within the
ordered phase. Our result is in good agreement with the experimental T-B phase
diagram of the HF metals YbRh2Si2, Yb(Rh{0.93}Co{0.07})2Si2, and
Yb(Rh{0.94}Ir{0.06})2Si2. To support our observations, we analyze the
isothermal magnetization M, and demonstrate that dM/dT exhibits a universal
temperature behavior over magnetic field scaling. The obtained results are in
good agreement with the corresponding data collected on YbRh2Si2 as a function
of magnetic field at different temperatures under hydrostatic pressure.


  We present a versatile open-source framework designed to facilitate
efficient, numerically-tailored Matrix-Matrix Multiplications (MMMs). The
framework offers two primary contributions: first, a fine-tuned, automated
pipeline for arithmetic datapath generation, enabling highly customizable
systolic MMM kernels; second, seamless integration of the generated kernels
into user code, irrespective of the programming language employed, without
necessitating modifications.
  The framework demonstrates a systematic enhancement in accuracy per energy
cost across diverse High Performance Computing (HPC) workloads displaying a
variety of numerical requirements, such as Artificial Intelligence (AI)
inference and Sea Surface Height (SSH) computation. For AI inference, we
consider a set of state-of-the-art neural network models, namely ResNet18,
ResNet34, ResNet50, DenseNet121, DenseNet161, DenseNet169, and VGG11, in
conjunction with two datasets, two computer formats, and 27 distinct
intermediate arithmetic datapaths. Our approach consistently reduces energy
consumption across all cases, with a notable example being the reduction by
factors of $3.3\times$ for IEEE754-32 and $1.4\times$ for Bfloat16 during
ImageNet inference with ResNet50. This is accomplished while maintaining
accuracies of $82.3\%$ and $86\%$, comparable to those achieved with
conventional Floating-Point Units (FPUs). In the context of SSH computation,
our method achieves fully-reproducible results using double-precision words,
surpassing the accuracy of conventional double- and quad-precision arithmetic
in FPUs. Our approach enhances SSH computation accuracy by a minimum of
$5\times$ and $27\times$ compared to IEEE754-64 and IEEE754-128, respectively,
resulting in $5.6\times$ and $15.1\times$ improvements in accuracy per power
cost.


  In this paper we study higher Deligne--Lusztig representations of reductive
groups over finite quotients of discrete valuation rings. At even levels, we
show that these geometrically constructed representations coincide with certain
induced representations in the generic case; this gives a solution to a problem
raised by Lusztig. In particular, we determine the dimensions of these
representations. As an immediate application we verify a conjecture of
Letellier for $\mathrm{GL}_2$ and $\mathrm{GL}_3$.


  We present a brief account of the most salient properties of vortices in
dilute atomic Fermi superfluids near a Feshbach resonance.


  The optical properties of slab waveguides made of indefinite permittivity
($\vep$) materials (IEM) are considered. In this medium the transverse
permittivity is negative while the longitudinal permittivity is positive. At
any given frequency the waveguide supports an infinite number of transverse
magnetic (TM) eigenmodes. For a slab waveguide with a fixed thickness, at most
only one TM mode is forward-wave. The rest of them are backward waves which can
have very large phase index. At a critical thickness, the waveguide supports
degenerate forward- and backward-wave modes with zero group velocity. Above the
critical thickness, the waveguide supports complex-conjugate decay modes
instead of propagating modes. The presence of loss in IEMs will lift the
degeneracy, resulting in modes with finite group velocity. Feasible realization
is proposed. The performance of IEM waveguide is analyzed and possible
applications are discussed which are supported by numerical calculations. These
slab waveguides can be used to make optical delay lines in optical buffers to
slow down and trap light, to form open cavities, to generate strong
longitudinal electric fields, and as phase shifters in optical integrated
circuits.


  We study junctions of Wilson lines in refined SU(N) Chern-Simons theory and
their local relations. We focus on junctions of Wilson lines in antisymmetric
and symmetric powers of the fundamental representation and propose a set of
local relations which realize one-parameter deformations of quantum groups
$\dot{U}_{q}(\mathfrak{sl}_{m})$ and $\dot{U}_{q}(\mathfrak{sl}_{n|m})$.


  We study the case when solution of an ODE at a given initial condition fail
to be unique and investigate what are the possible time-1 sections of the
`solution funnel'. Along the way we give construction of a natural complete
metric on the space of Jordan curves and prove that the generic Jordan curve is
nowhere pierceable by arcs of finite length.


  A measurement of the forward-backward charge asymmetry in top-antitop
(t\bar{t}) pair production in proton-antiproton (p\bar{p}) collisions is
presented. The asymmetry is measured for different jet multiplicities in the
lepton+jets final state on 0.9 fb^{-1} of data collected by the D0 experiment
at the Fermilab Tevatron Collider. The result is sensitive to new physics,
which is demonstrated by setting an upper limit on t\bar{t} production via
heavy neutral gauge bosons (Z^{\prime}).


  We extend the results of hep-th/0310137 to show that a general classical
action for D=2, N=2 sigma models on a non(anti)commutative superspace is not
standard and contains infinite number of terms, which depend on the determinant
of the non(anti)commutativity parameter, C^{\alpha\beta}. We show that using
Kahler normal coordinates the action can be written in a manifestly covariant
manner. We introduce vector multiplets and obtain the N=1/2 supersymmetry
transformations of the theory in the Wess-Zumino gauge. By explicitly deriving
the expressions for vector and twisted superfields on non(anti)commutative
superspace, we study the classical aspects of Gauged linear sigma models.


  The aim of this paper is to establish a pseudo-differential Weyl calculus on
graded nilpotent Lie groups $G$ which extends the celebrated Weyl calculus on
$\mathbb{R}^n$. To reach this goal, we develop a symbolic calculus for a very
general class of quantization schemes, following [Doc. Math., 22, 1539--1592,
2017], using the H\"{o}rmander symbol classes $S^m_{\rho, \delta}(G)$
introduced in [Progress in Mathematics, 314. Birkh\"{a}user/Springer, 2016]. We
particularly focus on the so-called symmetric calculi, for which quantizing and
taking the adjoint commute, among them the Euclidean Weyl calculus, but we also
recover the (non-symmetric) Kohn-Nirenberg calculus, on $\mathbb{R}^n$ and on
general graded groups [Progress in Mathematics, 314. Birkh\"{a}user/Springer,
2016]. Several interesting applications follow directly from our calculus:
expected mapping properties on Sobolev spaces, the existence of one-sided
parametrices and the G\r{a}rding inequality for elliptic operators, and a
generalization of the Poisson bracket for symmetric quantizations on stratified
groups.
  In the particular case of the Heisenberg group $\mathbb{H}_n$, we are able to
answer the fundamental questions of this paper: which, among all the admissible
quantizations, is the natural Weyl quantization on $\mathbb{H}_n$? And which
are the criteria that determine it uniquely? The surprisingly simple but
compelling answers raise the question whether what is true for $\mathbb{R}^n$
and $\mathbb{H}_n$ also extends to general graded groups, which we answer in
the affirmative in this paper.
  Among other things, we discuss and investigate an analogue of the symplectic
invariance property of the Weyl quantization in the setting of graded groups,
as well as the notion of the Poisson bracket for symbols in the setting of
stratified groups, linking it to the symbolic properties of the commutators.


  We study the two-particle wave function of paired atoms in a Fermi gas with
tunable interaction strengths controlled by Feshbach resonance. The Cooper pair
wave function is examined for its bosonic characters, which is quantified by
the correction of Bose enhancement factor associated with the creation and
annihilation composite particle operators. An example is given for a
three-dimensional uniform gas. Two definitions of Cooper pair wave function are
examined. One of which is chosen to reflect the off-diagonal long range order
(ODLRO). Another one corresponds to a pair projection of a BCS state. On the
side with negative scattering length, we found that paired atoms described by
ODLRO are more bosonic than the pair projected definition. It is also found
that at $(k_F a)^{-1} \ge 1$, both definitions give similar results, where more
than 90% of the atoms occupy the corresponding molecular condensates.


  Since the discovery of a broad distribution of very high energy (VHE; >0.1
TeV) gamma-rays in the Central Molecular Zone (CMZ) of the Galaxy in 2006 by
the HESS collaboration, the correlation of this emission with the integrated
intensity of the CS(1-0) molecular line emission has inferred a hadronic origin
for the gamma-rays. Here we describe the beginning of our investigation into
the strength of this correlation utilising new multi-line millimeter data from
the Mopra CMZ and HOP surveys and multi-wavelength GBT radio continuum
observations towards the CMZ and compare these in detail with the diffuse TeV
gamma-ray emission from HESS. The benefit of these new data is that they allow
us to simultaneously observe and analyse correlations using a large number
(>10) of molecular species, some of which contain their isotopologue pairs. The
use of isotopologue pairs is especially powerful, since it allows one to
analyse the optical depth of a number of different molecular species, thus
investigating the nature of the correlation over a range of different physical
conditions. Here we begin by comparing the integrated line emission and
continuum radio emission with the diffuse gamma-ray emission, and, by using
isotopologue pairs such as HCN/H$^{13}$CN, obtain optical depths throughout the
CMZ corresponding to regions of both strong and weak gamma-ray emission. We
find that the radio continuum better matches the peak of the gamma-ray
emission, which corresponds to the more compact -- compared to the relatively
coarse resolution of the gamma-ray images -- sources in the CMZ. Using the
isotopologue pairs, we find that the optical depth at all positions and
velocities within the CMZ are about 2--4. This is similar to that found for the
CS(1--0) line and would underestimate the mass of the CMZ, potentially
explaining why molecular line emission peaks appear offset from the gamma-ray
peaks.


  A direct Biot-Savart integration is used to decompose the strain rate into
its local and nonlocal constituents, allowing the vorticity alignment with the
local and nonlocal strain rate eigenvectors to be investigated. These strain
rate tensor constituents are evaluated in a turbulent flow using data from
highly-resolved direct numerical simulations. While the vorticity aligns
preferentially with the intermediate eigenvector of the \textit{combined}
strain rate, as has been observed previously, the present results for the first
time clearly show that the vorticity aligns with the most extensional
eigenvector of the \textit{nonlocal} strain rate. This in turn reveals a
significant linear contribution to the vortex stretching dynamics in turbulent
flows.


  Childhood and graduate school at Ann Arbor Michigan prepared Bill for an
interesting and rewarding career in physics. Along the way came Carol and many
joint discoveries with our many colleagues to whom we both owe this good life.
This summary of Bill's early work prior to their marriage and sabbatical in
Japan is Part I, prepared for Bill's 80th Birthday celebration at the
University of Sheffield in July 2016.


  Composite binary endpoints are increasingly used as primary endpoints in
clinical trials. When designing a trial, it is crucial to determine the
appropriate sample size for testing the statistical differences between
treatment groups for the primary endpoint. As shown in this work, when using a
composite binary endpoint to size a trial, one needs to specify the event rates
and the effect sizes of the composite components as well as the correlation
between them. In practice, the marginal parameters of the components can be
obtained from previous studies or pilot trials, however, the correlation is
often not previously reported and thus usually unknown. We first show that the
sample size for composite binary endpoints is strongly dependent on the
correlation and, second, that slight deviations in the prior information on the
marginal parameters may result in underpowered trials for achieving the study
objectives at a pre-specified significance level. We propose a general strategy
for calculating the required sample size when the correlation is not specified,
and accounting for uncertainty in the marginal parameter values. We present the
web platform CompARE to characterize composite endpoints and to calculate the
sample size just as we propose in this paper. We evaluate the performance of
the proposal with a simulation study, and illustrate it by means of a real case
study using CompARE.


  A simple symmetric random walk is considered on a spider that is a collection
of half lines (we call them legs) joined at the origin. We establish a strong
approximation of this random walk by the so-called Brownian spider. Transition
probabilities are studied, and for a fixed number of legs we investigate how
high the walker can go on the legs in $n$ steps. The heights on the legs are
also investigated when the number of legs goes to infinity.


  In a continuous-variable quantum key distribution (CVQKD) system, the
information is conveyed by coherent state carriers. The quantum continuous
variables are sent through a quantum channel, where the presence of the
eavesdropper adds a white Gaussian noise to the transmission. The amount of
tolerable noise and loss is a crucial point in CVQKD, since it determines the
overall performance of the protocol, including the secure key rates and
transmission distances. In this work, we propose the adaptive multicarrier
quadrature division (AMQD) modulation technique for CVQKD. The method
granulates the Gaussian random input into Gaussian subcarrier continuous
variables in the encoding phase, which are then decoded by a continuous unitary
transformation. The subcarrier coherent variables formulate Gaussian
sub-channels from the physical link with strongly diverse transmission
capabilities, which leads to significantly improved transmission efficiency,
higher tolerable loss, and excess noise. We also investigate a
modulation-variance adaption technique within the AMQD scheme, which provides
optimal capacity-achieving communication over the sub-channels in the presence
of a Gaussian noise.


  We investigate the branching of positive streamers in air and present the
first systematic investigation of splitting into more than two branches. We
study discharges in 100 mbar artificial air that is exposed to voltage pulses
of 10 kV applied to a needle electrode 160 mm above a grounded plate. By
imaging the discharge with two cameras from three angles, we establish that
about every 200th branching event is a branching into three. Branching into
three occurs more frequently for the relatively thicker streamers. In fact, we
find that the surface of the total streamer cross-sections before and after a
branching event is roughly the same.


  Conjectures of Suciu relate the fundamental group of the complement M = C^n\A
of a hyperplane arrangement A to the first resonance variety of H^*(M,Z). We
describe a connection between the first resonance variety and the Orlik-Terao
algebra C(A) of the arrangement. In particular, we show that non-local
components of R^1(A) give rise to determinantal syzygies of C(A). As a result,
Proj(C(A)) lies on a scroll, placing geometric constraints on R^1(A). The key
observation is that C(A) is the homogeneous coordinate ring associated to a nef
but not ample divisor on the blowup of P^2 at the singular points of A.


  Testing network algorithms in physical environments using real hardware is an
important step to reduce the gap between theory and practice in the field, and
an interesting way to explore technologies such as Bluetooth Mesh. We
implemented a Bluetooth Mesh data collection strategy and deployed it in indoor
and outdoor settings, using ESP-32 microcontrollers. This data collection
strategy also covers an alternative packet routing strategy based on Bluetooth
Mesh - MAM - already discussed and simulated in previous work using the OMNET++
simulator. We compared the real-world ESP-32 experiments with the past
simulations, and the results differed significantly: the simulations predicted
a +459\% unique message collection compared to the results we obtained with the
ESP-32. Based on those results, we also identified vast room for improvement in
our ESP-32 implementation for future work, including solving an unexpected
packet duplication in the MAM algorithm implementation. Even so, MAM performed
better than Bluetooth Mesh's default relay strategy, with up to +4.06\% more
(unique) data messages collected. We also discuss some challenges we
experienced when implementing, deploying, and running benchmarks using
Bluetooth Mesh and the ESP-32 platform.


  We present a new data analysis method to study rectangular T^3 ``small
universes" with one or two of its dimensions significantly smaller than the
present horizon (which we refer to as T^1- and T^2-models, respectively). We
find that the 4 year COBE/DMR data set a lower limit on the smallest cell size
for T^1- and T^2-models of 3000h^{-1} Mpc, at 95% confidence, for a scale
invariant power spectrum (n=1).


  Rabi oscillation of a two-level system driven by a pulse train is a basic
process involved in quantum computation. We present a full quantum treatment of
this process and show that the population inversion of this process collapses
exponentially, has no revival phenomenon, and has a dual-pulse structure in
every period. As an application, we investigate the properties of this process
in ion-trap quantum computation. We find that in the Cirac--Zoller computation
scheme, when the wavelength of the driving field is of the order $10^{-6}$ m,
the lower bound of failure probability is of the order $10^{-2}$ after about
$10^2$ controlled-NOT gates. This value is approximately equal to the
generally-accepted threshold in fault-tolerant quantum computation.


  On one-dimensional two-way infinite lattice system, a property of stationary
(space-) translationally invariant states with nonvanishing current
expectations are investigated. We consider GNS representation with respect to
such a state, on which we have a group of space-time translation unitary
operators. We show, by applying Goldstone-theorem-like argument,that spectrum
of the unitary operators, energy-momentum spectrum with respect to the state,
has a singularity at the origin.


  Clause-elimination procedures that simplify formulas in conjunctive normal
form play an important role in modern SAT solving. Before or during the actual
solving process, such procedures identify and remove clauses that are
irrelevant to the solving result. These simplifications usually rely on
so-called redundancy properties that characterize cases in which the removal of
a clause does not affect the satisfiability status of a formula. One
particularly successful redundancy property is that of blocked clauses, because
it generalizes several other redundancy properties. To find out whether a
clause is blocked---and therefore redundant---one only needs to consider its
resolution environment, i.e., the clauses with which it can be resolved. For
this reason, we say that the redundancy property of blocked clauses is local.
In this paper, we show that there exist local redundancy properties that are
even more general than blocked clauses. We present a semantic notion of
blocking and prove that it constitutes the most general local redundancy
property. We furthermore introduce the syntax-based notions of set-blocking and
super-blocking, and show that the latter coincides with our semantic blocking
notion. In addition, we show how semantic blocking can be alternatively
characterized via Davis and Putnam's rule for eliminating atomic formulas.
Finally, we perform a detailed complexity analysis and relate our novel
redundancy properties to prominent redundancy properties from the literature.


  We give a positive answer, in the measurable-group-theory context, to von
Neumann's problem of knowing whether a non-amenable countable discrete group
contains a non-cyclic free subgroup. We also get an embedding result of the
free-group von Neumann factor into restricted wreath product factors.


  Automated planetary transit detection has become vital to prioritize
candidates for expert analysis given the scale of modern telescopic surveys.
While current methods for short-period exoplanet detection work effectively due
to periodicity in the light curves, there lacks a robust approach for detecting
single-transit events. However, volunteer-labelled transits recently collected
by the Planet Hunters TESS (PHT) project now provide an unprecedented
opportunity to investigate a data-driven approach to long-period exoplanet
detection. In this work, we train a 1-D convolutional neural network to
classify planetary transits using PHT volunteer scores as training data. We
find using volunteer scores significantly improves performance over synthetic
data, and enables the recovery of known planets at a precision and rate
matching that of the volunteers. Importantly, the model also recovers transits
found by volunteers but missed by current automated methods.


  Training computer vision models usually requires collecting and labeling vast
amounts of imagery under a diverse set of scene configurations and properties.
This process is incredibly time-consuming, and it is challenging to ensure that
the captured data distribution maps well to the target domain of an application
scenario. Recently, synthetic data has emerged as a way to address both of
these issues. However, existing approaches either require human experts to
manually tune each scene property or use automatic methods that provide little
to no control; this requires rendering large amounts of random data variations,
which is slow and is often suboptimal for the target domain. We present the
first fully differentiable synthetic data pipeline that uses Neural Radiance
Fields (NeRFs) in a closed-loop with a target application's loss function. Our
approach generates data on-demand, with no human labor, to maximize accuracy
for a target task. We illustrate the effectiveness of our method on synthetic
and real-world object detection tasks. We also introduce a new
"YCB-in-the-Wild" dataset and benchmark that provides a test scenario for
object detection with varied poses in real-world environments.


  In addition to fitting the data of 233 extra-solar planets with power laws,
we construct a correlated mass-period distribution function of extrasolar
planets, as the first time in this field. The algorithm to generate a pair of
positively correlated beta-distributed random variables is introduced and used
for the construction of correlated distribution functions. We investigate the
mass-period correlations of extrasolar planets both in the linear and logarithm
spaces, determine the confidence intervals of the correlation coefficients, and
confirm that there is a positive mass-period correlation for the extrasolar
planets. In addition to the paucity of massive close-in planets, which makes
the main contribution on this correlation, there are other fine structures for
the data in the mass-period plane.


  Across black hole (BH) and neutron star (NS) low-mass X-ray binaries (LMXBs),
there appears to be some correlation between certain high- and low-frequency
quasi-periodic oscillations (QPOs). In a previous paper, we showed that for BH
LMXBs, this could be explained by the simultaneous oscillation and precession
of a hot, thick, torus-like corona. In the current work, we extend this idea to
NS LMXBs by associating the horizontal branch oscillations (HBO) with
precession and the upper-kiloHertz (ukHz) QPO with vertical epicyclic motion.
For the Atoll source 4U 1608-52, the model can match many distinct,
simultaneous observations of the HBO and ukHz QPO by varying the inner and
outer radius of the torus, while maintaining fixed values for the mass (M_{NS})
and spin (a_*) of the neutron star. The best fit values are M_{NS} = 1.38 \pm
0.03 M_\odot and a_* = 0.325 \pm 0.005. By combining these constraints with the
measured spin frequency, we are able to obtain an estimate for the moment of
inertia of I_{NS} = 1.40 \pm 0.02 \times 10^{45} g cm^2, which places
constraints on the equation of state. The model is unable to fit the lower-kHz
QPO, but evidence suggests that QPO may be associated with the boundary layer
between the accretion flow and the neutron star surface, which is not treated
in this work.


  Biological membranes are fundamental components of living organisms that play
an undeniable role in their survival. Molecular dynamics (MD) serves as an
essential computational tool for studying biomembranes on molecular and
atomistic scales. The status quo of MD simulations of biomembranes studies a
nanometer-sized membrane patch periodically extended under periodic boundary
conditions (PBC). In nature, membranes are usually composed of different lipids
in their two layers (referred to as leaflets). This compositional asymmetry
imposes a fixed ratio of lipid numbers between the two leaflets in a
periodically constrained membrane, which needs to be set appropriately. The
widely adopted methods of defining leaflet lipid ratio suffer from the lack of
control over the mechanical tension of each leaflet, which could significantly
influence research findings. In this study, we investigate the role of
membrane-building protocol and the resulting initial stress state on the
interaction between small molecules and asymmetric membranes. We model the
outer membrane of Pseudomonas aeruginosa bacteria using two different building
protocols and probe their interactions with the Pseudomonas Quinolone Signal
(PQS). Our results show that differential stress could shift the position of
free energy minimum for the PQS molecule between the two leaflets of the
asymmetric membrane. This work provides critical insights into the relationship
between the initial per-leaflet tension and the spontaneous intercalation of
PQS.


  We study the (1+1)-dimensional quasiperiodic multicolor solitons due to
cascading quadratic nonlinear response in generalized one-dimensional
quasiperiodic optical superlattice waveguides and show that the dynamic
equations describing the quasi-phase-matched multicolor solitons include
quasiperiodicity-induced Kerr effects, such as self- and cross-phase
modulation, third harmonic generation and four-wave mixing. We demonstrate the
stability of this multicolor solitons by means of a Lyapunov analysis based on
the energy integral of the wave coupling equations and investigate the dynamics
of the multicolor solitons using a virial identity, which predicts a stable
propagation of the mutually trapped solitons. We finally establish the analytic
stability criterion for the multicolor solitons by applying a multiscale
asymptotic method.


  Air-Cherenkov telescopes have detected pulsations at energies above 50 GeV
from a growing number of Fermi pulsars. These include the Crab, Vela, PSR
B1706-44 and Geminga, with the first two having pulsed detections above 1 TeV.
In some cases, there appears to be very-high-energy (VHE) emission that is an
extension of the Fermi spectra to high energies, while in other cases,
additional higher-energy spectral components that require a separate emission
mechanism may be present. We present results of broad-band spectral modeling
using global magnetosphere fields and multiple emission mechanisms that include
synchro-curvature (SC) and inverse Compton scattered (ICS) radiation from
accelerated particles (primaries) and synchrotron-self Compton (SSC) emission
from lower-energy pairs. Our models predict three distinct VHE components: SC
from primaries whose high-energy tail can extend to 100 GeV, SSC from pairs
that can extend to several TeV and ICS from primary particles accelerated in
the current sheet, scattering pair synchrotron radiation, that appears beyond
10 TeV. Our models suggest that H.E.S.S.-II and MAGIC have detected the
high-energy tail of the primary SC component that produces the Fermi spectrum
in Vela, Geminga and PSR B1706-44. We argue that the ICS component peaking
above 10 TeV from Vela has been seen by H.E.S.S. Detection of this emission
component from the Crab and other pulsars is possible with HAWC and CTA, and
directly measures the maximum particle energy in pulsars.


  In this article, we study the effect of vector-valued interventions in votes
under a binary voter model, where each voter expresses their vote as a $0-1$
valued random variable to choose between two candidates. We assume that the
outcome is determined by the majority function, which is true for a democratic
system. The term intervention includes cases of counting errors, reporting
irregularities, electoral malpractice etc. Our focus is to analyze the effect
of the intervention on the final outcome. We construct statistical tests to
detect significant irregularities in elections under two scenarios, one where
exit poll data is available and more broadly under the assumption of a cost
function associated with causing the interventions. Relevant theoretical
results on the consistency of the test procedures are also derived. Through a
detailed simulation study, we show that the test procedure has good power and
is robust across various settings. We also implement our method on three
real-life data sets. The applications provide results consistent with existing
knowledge and establish that the method can be adopted for crucial problems
related to political elections.


  We prove that n independent abelian functions admit an algebraic addition
theorem, with no appeal to theta functions.


  Multisite local orbitals, which are formed from linear combinations of
pseudo-atomic orbitals from a target atom and its neighbor atoms, have been
introduced in the large-scale density functional theory calculation code
CONQUEST. Multisite local orbitals correspond to local molecular orbitals so
that the number of required local orbitals can be minimal. The multisite
support functions are determined by using the localized filter diagonalization
(LFD) method [Phys. Rev. B 2009, 80, 205104]. Two new methods, the double
cutoff method and the smoothing method, are introduced to the LFD method to
improve efficiency and stability. The Hamiltonian and overlap matrices with
multisite local orbitals are constructed by efficient sparse-matrix
multiplications in CONQUEST. The investigation of the calculated energetic and
geometrical properties and band structures of bulk Si, Al and DNA systems
demonstrate the accuracy and the computational efficiency of the present
method. The representability of both occupied and unoccupied band structures
with the present method has been also confirmed.


  We prove that the problem of determining whether a finite logical matrix
determines an algebraizable logic is complete for EXPTIME. The same result
holds for the classes of order algebraizable, weakly algebraizable,
equivalential and protoalgebraic logics. Finally, the same problem for the
class of truth-equational logic is shown to be hard for EXPTIME.


  Based on an analytic scheme and neglecting the back reaction effect several
crucial properties of holographic $ s $- wave superconductors have been
investigated in the presence of an external magnetic field in the background of
a $ D $ dimensional Schwarzschild AdS space time. Inspired by low energy limit
of heterotic string theory, in the present paper we replace the conventional
Maxwell action by a Power Maxwell action. Immersing the holographic
superconductors in an external static magnetic field the spatially dependent
condensate solutions have been obtained analytically. Interestingly enough it
is observed that condensation can form only below a certain critical field
strength ($ B_c $). Finally, and most importantly it is observed that the value
of this critical field strength increases as the mass of the scalar particles
gets higher, which indicates the onset of a harder condensation.


  I present a method using only a few ground-based observations of magnified
microlensing events to routinely measure the parallaxes of WFIRST events if
WFIRST is in an L2 orbit. This could be achieved for all events with Amax > 30
using target-ofopportunity observations of select WFIRST events, or with a
complementary, ground-based survey of the WFIRST field, which can push beyond
this magnification limit. When combined with a measurement of the angular size
of the Einstein ring, which is almost always measured in planetary events,
these parallax measurements will routinely give measurements of the lens masses
and hence, the absolute masses of the planets. They can also lead to mass
measurements for dark, isolated objects such as brown dwarfs, free-floating
planets, and stellar remnants if the size of the Einstein ring is measured.


  Graphene, made of sp2 hybridized carbon, is characterized with a Dirac band,
representative of its underlying 2D hexagonal lattice. Fundamental
understanding of graphene has recently spurred a surge of searching for 2D
topological quantum phases in solid-state materials. Here, we propose a new
form of 2D material, consisting of sd2 hybridized transition metal atoms in
hexagonal lattice, called sd2 graphene. The sd2 graphene is characterized with
bond-centered electronic hopping, which transforms the apparent atomic
hexagonal lattice into the physics of kagome lattice that may exhibit a wide
range of topological quantum phases. Based on first-principles calculations,
room temperature quantum anomalous Hall states with an energy gap of 0.1 eV are
demonstrated for one such lattice made of W, which can be epitaxially grown on
a semiconductor surface of 1/3 monolayer Cl-covered Si(111), with high
thermodynamic and kinetic stability.


  We study Picard groups and $K_0$ groups for the cluster algebras that come
from the cluster categories of \cite{HernandezLeclerc}. This is inspired by a
study of boundaries and defects in the associated spin chains. We also do some
discussion for their formal deformation quantizations.


  In this work we study the Nekrasov--Shatashvili limit of the Nekrasov
instanton partition function of Yang--Mills field theories with ${\cal N}=2$
supersymmetry and gauge group SU(N). The theories are coupled with fundamental
matter. A path integral expression of the full instanton partition function is
derived. It is checked that in the Nekrasov--Shatashvili (thermodynamic) limit
the action of the field theory obtained in this way reproduces exactly the
equation of motion used in the saddle-point calculations.


  In this article we study the morphology, kinematics and ionization properties
of the giant ionized gas nebulae surrounding two high redshift radio galaxies,
4C40.36 (z=2.27) and 4C48.48 (z=2.34).}{Integral Field Spectroscopy
observations were taken using the PPAK bundle of the PMAS spectrograph, mounted
on the 3.5m on the Calar Alto Observatory, in order to cover a field-of-view of
64" X 72" centered in each radio galaxy. The observations spanned over 5
nights, using two different spectral resolutions (with FWHM~4 AA and ~8 AA
respectively), covering the optical wavelength range from ~3700 AA to ~7100 AA,
which corresponds to the rest-frame ultraviolet range from ~1100 AA to ~2000 AA
>. Various emission lines are detected within this wavelength range, including
Lyalpha (1216 AA), NV (1240 AA), CIV (1549 AA), HeII (1640 AA), OIII] (1663 AA)
and CIII] (1909\AA). The dataset was used to derive the spatial distribution of
the flux intensity of each of these lines and the gas kinematics. The
properties of the emission lines in the nuclear regions were studied in
detail.In agreement with previous studies, we find that both objects are
embedded in a large ionized gas nebula, where Ly alpha emission is extended
across ~100 kpc or more. The CIV and HeII emission lines are also spatially
extended. The nebulae are generally aligned with the radio axis, although we
detect emission far from it. In 4C+48.48, there is a band of low Ly-alpha/CIV
running perpendicular to the radio axis, at the location of the active nucleus.
This feature might be the observational signature of an edge-on disk of neutral
gas. The kinematics of both nebulae are inconsistent with stable rotation,
although they are not inconsistent with infall or outflow.


  Phishing is a form of cybercrime and a threat that allows criminals,
phishers, to deceive end users in order to steal their confidential and
sensitive information. Attackers usually attempt to manipulate the psychology
and emotions of victims. The increasing threat of phishing has made its study
worthwhile and much research has been conducted into the issue. This paper
explores the emotional factors that have been reported in previous studies to
be significant in phishing victimization. In addition, we compare what security
organizations and researchers have highlighted in terms of phishing types and
categories as well as training in tackling the problem, in a literature review
which takes into account all major credible and published sources.


  This paper proposes the concept, analysis and design of a
sinusoidally-modulated graphene leaky-wave antenna with beam scanning
capabilities at a fixed frequency. The antenna operates at terahertz
frequencies and is composed of a graphene sheet transferred onto a
back-metallized substrate and a set of polysilicon DC gating pads located
beneath it. In order to create a leaky-mode, the graphene surface reactance is
sinusoidally-modulated via graphene's field effect by applying adequate DC bias
voltages to the different gating pads. The pointing angle and leakage rate can
be dynamically controlled by adjusting the applied voltages, providing
versatile beamscanning capabilities. The proposed concept and achieved
performance, computed using realistic material parameters, are extremely
promising for beamscanning at THz frequencies, and could pave the way to
graphene-based reconfigurable transceivers and sensors.


  The bound state problem for a fermion-antifermion system is considered taking
into account a finite decay width of the constituents. We propose an exactly
solvable relativistic zero order equation similar to that of Barbieri and
Remiddi, but including a constant width. We focus especially on the $t\bar{t}$
system for which we reconsider our recent calculation of the bound state
corrections to the toponium width, which was performed in the narrow width
approximation and needed the use of second order Bethe-Salpeter perturbation
theory. We show that one obtains the same result already in first order BS
perturbation theory if one uses our present approach. Furthermore the large
cancellations of gauge dependent terms is demonstrated to be a consequence of a
Ward identity. This cancellation mechanism is shown to be valid for general
fermion-antifermion systems.


  The coalescence of compact binary stars is expected to produce a stochastic
background of gravitational waves (GW) observable with future GW detectors.
Such backgrounds are usually characterized by their power spectrum as a
function of frequency. Here, we present a method to calculate the full 1-point
distribution of strain fluctuations. We focus on time series data, but our
approach generalizes to the frequency domain. We illustrate how this
probability distribution can be evaluated numerically. In addition, we derive
accurate analytical asymptotic expressions for the large strain tail, which
demonstrate that it is dominated by the nearest source. As an application, we
calculate the distribution of strain fluctuations for the astrophysical GW
background produced by binary mergers of compact stars in the Universe, and the
distribution of the observed confusion background obtained upon subtracting
bright, resolved sources from the signal. We quantify the extent to which they
deviate from a Gaussian distribution. Our approach could be useful for the
spectral shape reconstruction of stochastic GW backgrounds.


  A new method for separating intensity variations of a source's radio emission
having various physical natures is proposed. The method is based on a joint
analysis of the structure function of the intensity variations and the
asymmetry function, which is a generalization of the asymmetry coefficient and
characterizes the asymmetry of the distribution function of the intensity
fluctuations on various scales for the inhomogeneities in the diffractive
scintillation pattern. Relationships for the asymmetry function in the cases of
a logarithmic normal distribution of the intensity fluctuations and a normal
distribution of the field fluctuations are derived. Theoretical relationships
and observational data on interstellar scintillations of pulsars (refractive,
diffractive, and weak scintillations) are compared. Pulsar scintillations match
the behavior expected for a normal distribution of the field fluctuations
(diffractive scintillation) or logarithmic normal distribution of the intensity
fluctuations (refractive and weak scintillation). Analysis of the asymmetry
function is a good test for distinguishing scintillations against the
background of variations that have different origins.


  We propose a novel approach of wet electrospinning to yield fiber-reinforced
polymer ceramic composites, where a reactive ceramic precursor gel is used as a
collector. We illustrate our approach by generating polyethylene oxide (PEO)
fibers in a potassium silicate gel; the gel is later activated using metakaolin
to yield a ceramic-0.5 wt% PEO fiber composite. An increase of 29% and 22% is
recorded for the fabricated polymer ceramic composites in terms of indentation
modulus and indentation hardness respectively. Our initial findings demonstrate
the process viability and might lead to a potentially scalable manufacturing
approach for fiber-reinforced polymer ceramic composites.


  Let $O_\tau(\Gamma)$ be a family of algebras \textit{quantizing} the
coordinate ring of $\mathbb{C}^2 / \Gamma$, where $\Gamma$ is a finite subgroup
of $\mathrm{SL}_2(\mathbb{C})$, and let $G_{\Gamma}$ be the automorphism group
of $O_\tau$. We study the natural action of $G_\Gamma$ on the space of right
ideals of $O_\tau$ (equivalently, finitely generated rank $1$ projective
$O_\tau$-modules). It is known that the later can be identified with disjoint
union of algebraic (quiver) varieties, and this identification is
$G_\Gamma$-equivariant. In the present paper, when $\Gamma \cong \mathbb{Z}_2$,
we show that the $G_{\Gamma}$-action on each quiver variety is transitive. We
also show that the natural embedding of $G_\Gamma$ into $\mathrm{Pic}(O_\tau)$,
the Picard group of $O_\tau$, is an isomorphism. These results are used to
prove that there are countably many non-isomorphic algebras Morita equivalent
to $O_\tau$, and explicit presentation of these algebras are given. Since
algebras $O_\tau(\mathbb{Z}_2)$ are isomorphic to primitive factors of
$U(sl_2)$, we obtain a complete description of algebras Morita equivalent to
primitive factors. A structure of the group $G_{\Gamma}$, where $\Gamma$ is an
arbitrary cyclic group, is also investigated. Our results generalize earlier
results obtained for the (first) Weyl algebra $A_1$.


  Quantum simulation is a promising application of future quantum computers.
Product formulas, or Trotterization, are the oldest and still remain an
appealing method to simulate quantum systems. For an accurate product formula
approximation, the state-of-the-art gate complexity depends on the number of
terms in the Hamiltonian and a local energy estimate. In this work, we give
evidence that product formulas, in practice, may work much better than
expected. We prove that the Trotter error exhibits a qualitatively better
scaling for the vast majority of input states, while the existing estimate is
for the worst states. For general $k$-local Hamiltonians and higher-order
product formulas, we obtain gate count estimates for input states drawn from
any orthogonal basis. The gate complexity significantly improves over the worst
case for systems with large connectivity. Our typical-case results generalize
to Hamiltonians with Fermionic terms, with input states drawn from a
fixed-particle number subspace, and with Gaussian coefficients (e.g., the SYK
models). Technically, we employ a family of simple but versatile inequalities
from non-commutative martingales called $\textit{uniform smoothness}$, which
leads to $\textit{Hypercontractivity}$, namely $p$-norm estimates for $k$-local
operators. This delivers concentration bounds via Markov's inequality. For
optimality, we give analytic and numerical examples that simultaneously match
our typical-case estimates and the existing worst-case estimates. Therefore,
our improvement is due to asking a qualitatively different question, and our
results open doors to the study of quantum algorithms in the average case.


  We study spinor field theories as an origin to induce space-time evolution.
Self-interacting spinor fields with canonical and non-canonical kinetic terms
are considered in a Friedman-Robertson-Walker universe. The deceleration
parameter is calculated by solving the equation of motion and the Friedman
equation, simultaneously. It is shown that the spinor fields can accelerate and
decelerate the universe expansion. To construct realistic models we discuss the
contributions from the dynamical symmetry breaking.


  Deep learning applications are drastically progressing in seismic processing
and interpretation tasks. However, the majority of approaches subsample data
volumes and restrict model sizes to minimise computational requirements.
Subsampling the data risks losing vital spatio-temporal information which could
aid training whilst restricting model sizes can impact model performance, or in
some extreme cases, renders more complicated tasks such as segmentation
impossible. This paper illustrates how to tackle the two main issues of
training of large neural networks: memory limitations and impracticably large
training times. Typically, training data is preloaded into memory prior to
training, a particular challenge for seismic applications where data is
typically four times larger than that used for standard image processing tasks
(float32 vs. uint8). Using a microseismic use case, we illustrate how over
750GB of data can be used to train a model by using a data generator approach
which only stores in memory the data required for that training batch.
Furthermore, efficient training over large models is illustrated through the
training of a 7-layer UNet with input data dimensions of 4096X4096. Through a
batch-splitting distributed training approach, training times are reduced by a
factor of four. The combination of data generators and distributed training
removes any necessity of data 1 subsampling or restriction of neural network
sizes, offering the opportunity of utilisation of larger networks,
higher-resolution input data or moving from 2D to 3D problem spaces.


  Prunning front is calculated for a bouncing ball system and the results are
compared to a braid analysis. To Appear in Physical Review E.


  In the process of understanding nuclear collisions, reliable extrapolations
from pp collisions, based on Glauber models, are highly desirable, though
seldomly accurate. We review the inclusion of diffractive excitations and argue
that they provide an important contribution to centrality observables in pA
collisions. We present a method for distinguishing between diffractively and
non-diffractively wounded nucleons, and a proof-of-principle for an
extrapolation of multiparton interaction models built on this.


  Preprocessing of information is an essential step for the effective design of
machine learning applications. Feature construction and selection are powerful
techniques used for this aim. In this paper, a feature selection and
construction approach is presented for the detection of wind turbine generator
heating faults. Data were collected from Supervisory Control and Data
Acquisition (SCADA) system of a wind turbine. The original features directly
collected from the data collection system consist of wind characteristics,
operational data, temperature measurements and status information. In addition
to these original features, new features were created in the feature
construction step to obtain information that can be more powerful indications
of the faults. After the construction of new features, a hybrid feature
selection technique was implemented to find out the most relevant features in
the overall set to increase the classification accuracy and decrease the
computational burden. Feature selection step consists of filter and
wrapper-based parts. Filter based feature selection was applied to exclude the
features which are non-discriminative and wrapper-based method was used to
determine the final features considering the redundancies and mutual relations
amongst them. Artificial Neural Networks were used both in the detection phase
and as the induction algorithm of the wrapper-based feature selection part. The
results show that, the proposed approach contributes to the fault detection
system to be more reliable especially in terms of reducing the number of false
fault alarms.


  While providing low latency is a fundamental requirement in deploying
recommendation services, achieving high resource utility is also crucial in
cost-effectively maintaining the datacenter. Co-locating multiple workers of a
model is an effective way to maximize query-level parallelism and server
throughput, but the interference caused by concurrent workers at shared
resources can prevent server queries from meeting its SLA. Hera utilizes the
heterogeneous memory requirement of multi-tenant recommendation models to
intelligently determine a productive set of co-located models and its resource
allocation, providing fast response time while achieving high throughput. We
show that Hera achieves an average 37.3% improvement in effective machine
utilization, enabling 26% reduction in required servers, significantly
improving upon the baseline recommedation inference server.


  Goal-conditioned reinforcement learning (RL) is a promising direction for
training agents that are capable of solving multiple tasks and reach a diverse
set of objectives. How to \textit{specify} and \textit{ground} these goals in
such a way that we can both reliably reach goals during training as well as
generalize to new goals during evaluation remains an open area of research.
Defining goals in the space of noisy and high-dimensional sensory inputs poses
a challenge for training goal-conditioned agents, or even for generalization to
novel goals. We propose to address this by learning factorial representations
of goals and processing the resulting representation via a discretization
bottleneck, for coarser goal specification, through an approach we call DGRL.
We show that applying a discretizing bottleneck can improve performance in
goal-conditioned RL setups, by experimentally evaluating this method on tasks
ranging from maze environments to complex robotic navigation and manipulation.
Additionally, we prove a theorem lower-bounding the expected return on
out-of-distribution goals, while still allowing for specifying goals with
expressive combinatorial structure.


  We consider defining time as a function of a cyclical field, an abstraction
of a clock. The definition of time corresponds to a novel interpretation of the
relationship between space-time coordinates of observers at different locations
in space. As a first test of the utility of this definition, we show that it
leads to a Lorentz covariant description of space-time. This derivation of
Lorenz covariance provides a starting point for considering more general
constructions that relate to physical laws. The definition of time couples time
to space, making time not orthogonal to space, and making dynamics a result of
geometry, providing a vehicle for curved space-time theories that generalize
general relativity.


  We prove a sharp Sobolev inequality on manifolds with nonnegative Ricci
curvature. Moreover, we prove a Michael-Simon inequality for submanifolds in
manifolds with nonnegative sectional curvature. Both inequalities depend on the
asymptotic volume ratio of the ambient manifold.


  Based on a covariant coalescence model with a blast-wave-like parametrization
for the phase-space configuration of constituent particles at freeze-out, we
derive an approximate analytical formula for the yields of clusters produced in
relativistic heavy-ion collisions. Compared to previous existing formulae, the
present work additionally considers the contributions from the longitudinal
dimension in momentum space, the relativistic corrections and the finite size
effects of the produced clusters relative to the spatial distribution of
constituent particles at freeze-out. The new analytical coalescence formula
provides a useful tool to evaluate the yield of produced clusters, such as
light nuclei from nucleon coalescence and hadrons from quark coalescence, in
heavy-ion collisions. As a first application of the new analytical formula, we
explore the strangeness population factor $S_3 = ^3_{\Lambda}$H/($^3$He$\times
\Lambda/$p) based on nucleon/$\Lambda$ coalescence as well as the production of
exotic hadrons based on quark coalescence, in central Pb+Pb collisions at
$\sqrt{s_{NN}}=2.76$ TeV. The results are compared with the predictions from
other models.


  We show that most of the applications of SU_q(2) fermions to statistical
mechanics and quantum field theory, previously discussed in literature, are
based on a wrong statement about the connection between deformed and undeformed
fermion operators. Then we exclude various classes of ansatz and we put some
constraints about the form of such relation.


  Data can be collected in scientific studies via a controlled experiment or
passive observation. Big data is often collected in a passive way, e.g. from
social media. In studies of causation great efforts are made to guard against
bias and hidden confounders or feedback which can destroy the identification of
causation by corrupting or omitting counterfactuals (controls). Various
solutions of these problems are discussed, including randomization.


  Despite the significant success achieved by deep learning methods in medical
image segmentation, researchers still struggle in the computer-aided diagnosis
of abdominal lymph nodes due to the complex abdominal environment, small and
indistinguishable lesions, and limited annotated data. To address these
problems, we present a pipeline that integrates the conditional diffusion model
for lymph node generation and the nnU-Net model for lymph node segmentation to
improve the segmentation performance of abdominal lymph nodes through
synthesizing a diversity of realistic abdominal lymph node data. We propose
LN-DDPM, a conditional denoising diffusion probabilistic model (DDPM) for lymph
node (LN) generation. LN-DDPM utilizes lymph node masks and anatomical
structure masks as model conditions. These conditions work in two conditioning
mechanisms: global structure conditioning and local detail conditioning, to
distinguish between lymph nodes and their surroundings and better capture lymph
node characteristics. The obtained paired abdominal lymph node images and masks
are used for the downstream segmentation task. Experimental results on the
abdominal lymph node datasets demonstrate that LN-DDPM outperforms other
generative methods in the abdominal lymph node image synthesis and better
assists the downstream abdominal lymph node segmentation task.


  In this note we reply to the criticisms by Felici concerning some aspects of
the recent frame-dragging test performed by Iorio with the Mars Global Surveyor
(MGS) spacecraft in the gravitational field of Mars.


  Optimal Transport (OT) has recently emerged as a powerful framework for
learning minimal-displacement maps between distributions. The predominant
approach involves a neural parametrization of the Monge formulation of OT,
typically assuming the same space for both distributions. However, the setting
across ``incomparable spaces'' (e.g., of different dimensionality),
corresponding to the Gromov- Wasserstein distance, remains underexplored, with
existing methods often imposing restrictive assumptions on the cost function.
In this paper, we present a novel neural formulation of the Gromov-Monge (GM)
problem rooted in one of its fundamental properties: invariance to strong
isomorphisms. We operationalize this property by decomposing the learnable OT
map into two components: (i) an approximate strong isomorphism between the
source distribution and an intermediate reference distribution, and (ii) a
GM-optimal map between this reference and the target distribution. Our
formulation leverages and extends the Monge gap regularizer of Uscidda & Cuturi
(2023) to eliminate the need for complex architectural requirements of other
neural OT methods, yielding a simple but practical method that enjoys favorable
theoretical guarantees. Our preliminary empirical results show that our
framework provides a promising approach to learn OT maps across diverse spaces.


  The system-environment interaction is simulated by light propagating in
coupled photonic waveguides. The profile of the electromagnetic field provides
intuitive physical insight to study the Markovian and non-Markovian dynamics of
open quantum systems. The transition from non-Markovian to Markovian process is
demonstrated by increasing the size of environment, as the energy evolution
changes from oscillating to an exponential decay, and the revival period
increases. Moreover, the dynamical decoupling with a sequence of phase
modulations is introduced to such a photonic open system to form a band
structure in time dimension, where the energy dissipation can be significantly
accelerated or inhibited. It opens the possibility to tune the dissipation in
photonic system, similar to the dynamic decoupling of spins.


  In this paper, we extend the class of strongly $E$-preinvex and strongly
$E$-invex functions to quasi strongly $E$-preinvex, quasi strongly $E$-invex
and pseudo strongly $E$-invex functions. Some nontrivial suitable examples have
been constructed in support of our definitions. Several interesting properties
and relationships of these functions are discussed. Furthermore, to show the
application of our results, we consider a nonlinear programming problem and
show that the local minimum point is also a strictly global minimum.


  There are various applications, where companies need to decide to which
individuals they should best allocate treatment. To support such decisions,
uplift models are applied to predict treatment effects on an individual level.
Based on the predicted treatment effects, individuals can be ranked and
treatment allocation can be prioritized according to this ranking. An implicit
assumption, which has not been doubted in the previous uplift modeling
literature, is that this treatment prioritization approach tends to bring
individuals with high treatment effects to the top and individuals with low
treatment effects to the bottom of the ranking. In our research, we show that
heteroskedastictity in the training data can cause a bias of the uplift model
ranking: individuals with the highest treatment effects can get accumulated in
large numbers at the bottom of the ranking. We explain theoretically how
heteroskedasticity can bias the ranking of uplift models and show this process
in a simulation and on real-world data. We argue that this problem of ranking
bias due to heteroskedasticity might occur in many real-world applications and
requires modification of the treatment prioritization to achieve an efficient
treatment allocation.


  To support complex data-intensive applications such as personalized
recommendations, targeted advertising, and intelligent services, the data
management community has focused heavily on the design of systems to support
training complex models on large datasets. Unfortunately, the design of these
systems largely ignores a critical component of the overall analytics process:
the deployment and serving of models at scale. In this work, we present Velox,
a new component of the Berkeley Data Analytics Stack. Velox is a data
management system for facilitating the next steps in real-world, large-scale
analytics pipelines: online model management, maintenance, and serving. Velox
provides end-user applications and services with a low-latency, intuitive
interface to models, transforming the raw statistical models currently trained
using existing offline large-scale compute frameworks into full-blown,
end-to-end data products capable of recommending products, targeting
advertisements, and personalizing web content. To provide up-to-date results
for these complex models, Velox also facilitates lightweight online model
maintenance and selection (i.e., dynamic weighting). In this paper, we describe
the challenges and architectural considerations required to achieve this
functionality, including the abilities to span online and offline systems, to
adaptively adjust model materialization strategies, and to exploit inherent
statistical properties such as model error tolerance, all while operating at
"Big Data" scale.


  In this paper we present and analyse a construction of irreducible
polynomials over odd prime fields via the transforms which take any polynomial
$f \in \mathbf{F}_p[x]$ of positive degree $n$ to $\left(\frac{x}{k} \right)^n
\cdot f(k(x+x^{-1}))$, for some specific values of the odd prime $p$ and $k \in
\mathbf{F}_p$.


  Convolutional neural networks (CNNs) have become the most successful approach
in many vision-related domains. However, they are limited to domains where data
is abundant. Recent works have looked at multi-task learning (MTL) to mitigate
data scarcity by leveraging domain-specific information from related tasks. In
this paper, we present a novel soft-parameter sharing mechanism for CNNs in a
MTL setting, which we refer to as Deep Collaboration. We propose taking into
account the notion that task relevance depends on depth by using lateral
transformation blocs with skip connections. This allows extracting
task-specific features at various depth without sacrificing features relevant
to all tasks. We show that CNNs connected with our Deep Collaboration obtain
better accuracy on facial landmark detection with related tasks. We finally
verify that our approach effectively allows knowledge sharing by showing
depth-specific influence of tasks that we know are related.


  A particular challenge for both autonomous and human driving is dealing with
risk associated with dynamic occlusion, i.e., occlusion caused by other
vehicles in traffic. Based on the theory of hypergames, we develop a novel
multi-agent dynamic occlusion risk (DOR) measure for assessing situational risk
in dynamic occlusion scenarios. Furthermore, we present a white-box,
scenario-based, accelerated safety validation framework for assessing safety of
strategic planners in AV. Based on evaluation over a large naturalistic
database, our proposed validation method achieves a 4000% speedup compared to
direct validation on naturalistic data, a more diverse coverage, and ability to
generalize beyond the dataset and generate commonly observed dynamic occlusion
crashes in traffic in an automated manner.


  For a Hausdorff topologized semilattice $X$ its $Lawson\;\; number$
$\bar\Lambda(X)$ is the smallest cardinal $\kappa$ such that for any distinct
points $x,y\in X$ there exists a family $\mathcal U$ of closed neighborhoods of
$x$ in $X$ such that $|\mathcal U|\le\kappa$ and $\bigcap\mathcal U$ is a
subsemilattice of $X$ that does not contain $y$. It follows that
$\bar\Lambda(X)\le\bar\psi(X)$, where $\bar\psi(X)$ is the smallest cardinal
$\kappa$ such that for any point $x\in X$ there exists a family $\mathcal U$ of
closed neighborhoods of $x$ in $X$ such that $|\mathcal U|\le\kappa$ and
$\bigcap\mathcal U=\{x\}$.
  We prove that a compact Hausdorff semitopological semilattice $X$ is Lawson
(i.e., has a base of the topology consisting of subsemilattices) if and only if
$\bar\Lambda(X)=1$. Each Hausdorff topological semilattice $X$ has Lawson
number $\bar\Lambda(X)\le\omega$. On the other hand, for any infinite cardinal
$\lambda$ we construct a Hausdorff zero-dimensional semitopological semilattice
$X$ such that $|X|=\lambda$ and $\bar\Lambda(X)=\bar\psi(X)=cf(\lambda)$.
  A topologized semilattice $X$ is called (i) $\omega$-$Lawson$ if
$\bar\Lambda(X)\le\omega$; (ii) $complete$ if each non-empty chain $C\subset X$
has $\inf C\in\overline{C}$ and $\sup C\in\overline{C}$. We prove that for any
complete subsemilattice $X$ of an $\omega$-Lawson semitopological semilattice
$Y$, the partial order $\le_X=\{(x,y)\in X\times X:xy=x\}$ of $X$ is closed in
$Y\times Y$ and hence $X$ is closed in $Y$. This implies that for any
continuous homomorphism $h:X\to Y$ from a compete topologized semilattice $X$
to an $\omega$-Lawson semitopological semilattice $Y$ the image $h(X)$ is
closed in $Y$.


  We present a variational method which uses a quartic exponential function as
a trial wave-function to describe time-dependent quantum mechanical systems. We
introduce a new physical variable $y$ which is appropriate to describe the
shape of wave-packet, and calculate the effective action as a function of both
the dispersion $\sqrt{< \hat{q}^2>}$ and $y$. The effective potential
successfully describes the transition of the system from the false vacuum to
the true vacuum. The present method well describes the long time evolution of
the wave-function of the system after the symmetry breaking, which is shown in
comparison with the direct numerical computations of wave-function.


  We discuss the use of Low Gain Avalanche (LGAD) silicon detectors for two
specific applications, namely measuring cosmic rays in space in collaboration
with NASA] and beam properties and received doses for patients undergoing
cancer treatment in flash beam therapy.


  Recent trends in high-performance computing and deep learning have led to the
proliferation of studies on large-scale deep neural network training. However,
the frequent communication requirements among computation nodes drastically
slows the overall training speeds, which causes bottlenecks in distributed
training, particularly in clusters with limited network bandwidths. To mitigate
the drawbacks of distributed communications, researchers have proposed various
optimization strategies. In this paper, we provide a comprehensive survey of
communication strategies from both an algorithm viewpoint and a computer
network perspective. Algorithm optimizations focus on reducing the
communication volumes used in distributed training, while network optimizations
focus on accelerating the communications between distributed devices. At the
algorithm level, we describe how to reduce the number of communication rounds
and transmitted bits per round. In addition, we elucidate how to overlap
computation and communication. At the network level, we discuss the effects
caused by network infrastructures, including logical communication schemes and
network protocols. Finally, we extrapolate the potential future challenges and
new research directions to accelerate communications for distributed deep
neural network training.


This paper has been withdrawn by the author


  We analyze the optical and UV spectra of an additional sample of 20
Magellanic Cloud O stars, and draw conclusions from the complete sample of 40
stars. We find (1) The SMC O3-7 dwarfs are about 4000 K cooler than their
Galactic counterparts; this is in the sense expected from the decreased
signficiance of line-blanketing and wind-blanketing at lower metallicities. The
difference decreases with later types, becoming negligible by B0 V. A similar
difference is found for the supergiants. (2) The wind momentum of these stars
scales with luminosity and metallicty in the ways predicted by
radiatively-driven wind theory. (3) A comparison of the masses derived from
spectroscopy with those derived from stellar evolutionary theory shows a
significant discrepancy for stars hotter than 45000, although good agreement is
found for cooler stars. (4) For the hottest O stars (O2-3.5) neither the
NIII/NIV ratio, nor even the HeI/HeII ratio, does a good job of predicting the
effective temperature by itself. Instead, a full analysis is needed to derive
physical parameters. Thus there are O3.5V stars which are as hot or hotter than
stars classified as O2V. (5) The two stars with the most discordant radial
velocities in our sample happen to be O3 "field stars". This provides the first
compelling observational evidence that the "field" O stars in the Magellanic
Clouds may be runaway OB stars, ejected from their birth place.


  Optical microcavities trap light in compact volumes by the mechanisms of
almost total internal reflection or distributed Bragg reflection, enable light
amplification, and select out specific (resonant) frequencies of light that can
be emitted or coupled into optical guides, and lower the thresholds of lasing.
Such resonators have radii from 1 to 100 mkm and can be fabricated in a wide
range of materials. Devices based on optical resonators are essential for
cavity quantum-electro-dynamic experiments, frequency stabilization, optical
filtering and switching, light generation, biosensing, and nonlinear optics.


  We consider three notions of divisibility in the Cuntz semigroup of a
C*-algebra, and show how they reflect properties of the C*-algebra. We develop
methods to construct (simple and non-simple) C*-algebras with specific
divisibility behaviour. As a byproduct of our investigations, we show that
there exists a sequence $(A_n)$ of simple unital infinite dimensional
C*-algebras such that the product $\prod_{n=1}^\infty A_n$ has a character.


  Cumulative entropy regularization introduces a regulatory signal to the
reinforcement learning (RL) problem that encourages policies with high-entropy
actions, which is equivalent to enforcing small deviations from a uniform
reference marginal policy. This has been shown to improve exploration and
robustness, and it tackles the value overestimation problem. It also leads to a
significant performance increase in tabular and high-dimensional settings, as
demonstrated via algorithms such as soft Q-learning (SQL) and soft actor-critic
(SAC). Cumulative entropy regularization has been extended to optimize over the
reference marginal policy instead of keeping it fixed, yielding a
regularization that minimizes the mutual information between states and
actions. While this has been initially proposed for Markov Decision Processes
(MDPs) in tabular settings, it was recently shown that a similar principle
leads to significant improvements over vanilla SQL in RL for high-dimensional
domains with discrete actions and function approximators.
  Here, we follow the motivation of mutual-information regularization from an
inference perspective and theoretically analyze the corresponding Bellman
operator. Inspired by this Bellman operator, we devise a novel
mutual-information regularized actor-critic learning (MIRACLE) algorithm for
continuous action spaces that optimizes over the reference marginal policy. We
empirically validate MIRACLE in the Mujoco robotics simulator, where we
demonstrate that it can compete with contemporary RL methods. Most notably, it
can improve over the model-free state-of-the-art SAC algorithm which implicitly
assumes a fixed reference policy.


  Observations of far-infrared (FIR) and submillimeter (SMM) polarized emission
are used to study magnetic fields and dust grains in dense regions of the
interstellar medium (ISM). These observations place constraints on models of
molecular clouds, star-formation, grain alignment mechanisms, and grain size,
shape, and composition. The FIR/SMM polarization is strongly dependent on
wavelength. We have attributed this wavelength dependence to sampling different
grain populations at different temperatures. To date, most observations of
polarized emission have been in the densest regions of the ISM. Extending these
observations to regions of the diffuse ISM, and to microwave frequencies, will
provide additional tests of grain and alignment models.
  An understanding of polarized microwave emission from dust is key to an
accurate measurement of the polarization of the cosmic microwave background.
The microwave polarization spectrum will put limits on the contributions to
polarized emission from spinning dust and vibrating magnetic dust.


  Many complex systems are modular. Such systems can be represented as
"component systems", i.e., sets of elementary components, such as LEGO bricks
in LEGO sets. The bricks found in a LEGO set reflect a target architecture,
which can be built following a set-specific list of instructions. In other
component systems, instead, the underlying functional design and constraints
are not obvious a priori, and their detection is often a challenge of both
scientific and practical importance, requiring a clear understanding of
component statistics. Importantly, some quantitative invariants appear to be
common to many component systems, most notably a common broad distribution of
component abundances, which often resembles the well-known Zipf's law. Such
"laws" affect in a general and non-trivial way the component statistics,
potentially hindering the identification of system-specific functional
constraints or generative processes. Here, we specifically focus on the
statistics of shared components, i.e., the distribution of the number of
components shared by different system-realizations, such as the common bricks
found in different LEGO sets. To account for the effects of component
heterogeneity, we consider a simple null model, which builds
system-realizations by random draws from a universe of possible components.
Under general assumptions on abundance heterogeneity, we provide analytical
estimates of component occurrence, which quantify exhaustively the statistics
of shared components. Surprisingly, this simple null model can positively
explain important features of empirical component-occurrence distributions
obtained from data on bacterial genomes, LEGO sets, and book chapters. Specific
architectural features and functional constraints can be detected from
occurrence patterns as deviations from these null predictions, as we show for
the illustrative case of the "core" genome in bacteria.


  The characterization of Earth-like exoplanets and precision tests of
cosmological models using next-generation telescopes such as the ELT will
demand precise calibration of astrophysical spectrographs in the visible
region, where stellar absorption lines are most abundant. Astrocombs--lasers
providing a broadband sequence of ultra-narrow, drift-free, regularly spaced
optical frequencies on a multi-GHz grid--promise an atomically-traceable,
versatile calibration scale, but their realization is challenging because of
the need for ultra-broadband frequency conversion of mode-locked infrared
lasers into the blue-green region. Here, we introduce a new concept achieving a
broad, continuous spectrum by combining second-harmonic generation and
sum-frequency-mixing in an aperiodically-poled MgO:PPLN waveguide to generate
gap-free 390-520 nm light from a 1 GHz Ti:sapphire laser frequency comb. We
lock a low-dispersion Fabry-Perot etalon to extract a sub-comb of bandwidth
from 392-472 nm with a spacing of 30 GHz, visualizing the thousands of
resulting comb modes on a high resolution cross-dispersion spectrograph.
Complementary experimental data and simulations demonstrate the effectiveness
of the approach for eliminating the spectral gaps present in
second-harmonic-only conversion, in which weaker fundamental frequencies are
suppressed by the quadratic \{chi}^((2)) nonlinearity. Requiring only ~100 pJ
pulse energies, our concept establishes a practical new route to broadband
UV-visible generation at GHz repetition rates.


  Social platforms have emerged as crucial platforms for disseminating
information and discussing real-life social events, offering researchers an
excellent opportunity to design and implement novel event detection frameworks.
However, most existing approaches only exploit keyword burstiness or network
structures to detect unspecified events. Thus, they often need help identifying
unknown events regarding the challenging nature of events and social data.
Social data, e.g., tweets, is characterized by misspellings, incompleteness,
word sense ambiguation, irregular language, and variation in aspects of
opinions. Moreover, extracting discriminative features and patterns for
evolving events by exploiting the limited structural knowledge is almost
infeasible. To address these challenges, in this paper, we propose a novel
framework, namely EnrichEvent, that leverages the linguistic and contextual
representations of streaming social data. In particular, we leverage contextual
and linguistic knowledge to detect semantically related tweets and enhance the
effectiveness of the event detection approaches. Eventually, our proposed
framework produces cluster chains for each event to show the evolving variation
of the event through time. We conducted extensive experiments to evaluate our
framework, validating its high performance and effectiveness in detecting and
distinguishing unspecified social events.


  High-accuracy determination of isotope shift factors, which plays a crucial
role in accurate extraction of nuclear charge radius, is well-known to be
challenging experimentally and theoretically. Nonetheless, based on an accurate
measurement of the isotope shifts for the Cd$^+$ $5s~^2S_{1/2}-5p~^2P_{3/2}$
and the Cd $5s^2~^1S_0-5s5p~^1P_1$ transition, a multidimensional King-plot
analysis is performed to extract the atomic field shift and mass shift factors
accurately. The results are further cross-checked against results from atomic
structure calculations using a high-accuracy configuration interaction and
many-body perturbation theory. Combined with previous isotope shifts, nuclear
charge radii for $^{100-130}$Cd of the highest accuracy are reported. For the
neutron-rich region, accuracies for the charge radii are improved by nearly one
order of magnitude. This work provides a coherent and systematic extraction of
Cd nuclear charge radii from isotope shifts. The results offer stringent
testing and possible challenges to the latest advances in nuclear theory and
excellent benchmarking to the atomic structure calculations.


  We study the interplay of spin- and charge degrees of freedom in a doped
Ising antiferromagnet, where the motion of charges is restricted to one
dimension. The phase diagram of this mixed-dimensional $t - J_z$ model can be
understood in terms of spin-less chargons coupled to a Z2 lattice gauge field.
The antiferromagnetic couplings give rise to interactions between Z2 electric
field lines which, in turn, lead to a robust stripe phase at low temperatures.
At higher temperatures, a confined meson-gas phase is found for low doping
whereas at higher doping values, a robust deconfined chargon-gas phase is seen
which features hidden antiferromagnetic order. We confirm these phases in
quantum Monte Carlo simulations. Our model can be implemented and its phases
detected with existing technology in ultracold atom experiments. The critical
temperature for stripe formation with a sufficiently high hole concentration is
around the spin-exchange energy $J_z$, i.e., well within reach of current
experiments.


  We show that the modified Vakhnenko-Parkes equation, introduced recently by
Wazwaz, is an avatar of the sine-Gordon equation.


  Chandra obtained two High Energy Transmission Grating (HETG) spectra of the
stellar-mass black hole GRO J1655-40 during its 2005 outburst, revealing a rich
and complex disk wind. Soon after its launch, the Neil Gehrels Swift
Observatory began monitoring the same outburst. Some X-ray Telescope (XRT)
observations were obtained in a mode that makes it impossible to remove strong
Mn calibration lines, so the Fe K-alpha line region in the spectra was
previously neglected. However, these lines enable a precise calibration of the
energy scale, facilitating studies of the absorption-dominated disk wind and
its velocity shifts. Here, we present fits to 15 Swift/XRT spectra, revealing
variability and evolution in the outflow. The data strongly point to a
magnetically driven disk wind: both the higher velocity (e.g., v ~ 10^4 km/s)
and lower velocity (e.g., v ~ 10^3 km/s) wind components are typically much
faster than is possible for thermally driven outflows (v < 200 km/s), and
photoionization modeling yields absorption radii that are two orders of
magnitude below the Compton radius that defines the typical inner extent of
thermal winds. Moreover, correlations between key wind parameters yield an
average absorption measure distribution (AMD) that is consistent with
magnetohydrodynamic wind models. We discuss our results in terms of recent
observational and theoretical studies of black hole accretion disks and
outflows, and future prospects.


  In this paper we shall show the equivalence of algebraic and analytic
localisation for algebras of smooth deformation quantization for several
situations. The proofs are based on old work by Whitney, Malgrange and Tougeron
on the commutative algebra of smooth function rings from the 60's and 70's.


  Robust visual recognition under adverse weather conditions is of great
importance in real-world applications. In this context, we propose a new method
for learning semantic segmentation models robust against fog. Its key idea is
to consider the fog condition of an image as its style and close the gap
between images with different fog conditions in neural style spaces of a
segmentation model. In particular, since the neural style of an image is in
general affected by other factors as well as fog, we introduce a fog-pass
filter module that learns to extract a fog-relevant factor from the style.
Optimizing the fog-pass filter and the segmentation model alternately gradually
closes the style gap between different fog conditions and allows to learn
fog-invariant features in consequence. Our method substantially outperforms
previous work on three real foggy image datasets. Moreover, it improves
performance on both foggy and clear weather images, while existing methods
often degrade performance on clear scenes.


  Few-shot learning aims to adapt knowledge learned from previous tasks to
novel tasks with only a limited amount of labeled data. Research literature on
few-shot learning exhibits great diversity, while different algorithms often
excel at different few-shot learning scenarios. It is therefore tricky to
decide which learning strategies to use under different task conditions.
Inspired by the recent success in Automated Machine Learning literature
(AutoML), in this paper, we present Meta Navigator, a framework that attempts
to solve the aforementioned limitation in few-shot learning by seeking a
higher-level strategy and proffer to automate the selection from various
few-shot learning designs. The goal of our work is to search for good parameter
adaptation policies that are applied to different stages in the network for
few-shot classification. We present a search space that covers many popular
few-shot learning algorithms in the literature and develop a differentiable
searching and decoding algorithm based on meta-learning that supports
gradient-based optimization. We demonstrate the effectiveness of our
searching-based method on multiple benchmark datasets. Extensive experiments
show that our approach significantly outperforms baselines and demonstrates
performance advantages over many state-of-the-art methods. Code and models will
be made publicly available.


  We study the observability of the $\tautau$ decay mode of a Higgs boson
produced in the $s$-channel at a muon collider. We find that the spin
correlations of the $\tautau$ in $\tau\to \pi\nu_{\tau}, \rho\nu_{\tau}$ decays
are discriminative between the Higgs boson signal and the Standard Model
background. Observation of the predicted distinctive distribution can confirm
the spin-0 nature of the Higgs resonance. The relative coupling strength of the
Higgs boson to $b$ and $\tau$ can also be experimentally determined.