  We calculate the thermoelectric power (or thermopower) of many semiconducting
single wall carbon nanotubes (s-SWNTs) within a diameter range 0.5-1.5 nm by
using the Boltzmann transport formalism combined with an extended tight-binding
model. We find that the thermopower of s-SWNTs increases as the tube diameter
decreases. For some s-SWNTs with diameters less than 0.6 nm, the thermopower
can reach a value larger than 2000 {\mu}V/K at room temperature, which is about
6 to 10 times larger than that found in commonly used thermoelectric materials.
The large thermopower values may be attributed to the one-dimensionality of the
nanotubes and to the presence of large band gaps of the small-diameter s-SWNTs.
We derive an analytical formula to reproduce the numerical calculation of the
thermopower and we find that the thermopower of a given s-SWNT is directly
related with its band gap. The formula also explains the shape of the
thermopower as a function of tube diameter, which looks similar to the shape of
the so-called Kataura plot of the band gap dependence on tube diameter.


  {Let ${\Cal X}$ be a self-dual P-polynomial association scheme. Then there
are at most 12 diagonal matrices $T$ such that $(PT)^3=I$. Moreover, all of the
solutions for the classical infinite families of such schemes (including the
Hamming scheme) are classified.


  Fully superconducting machines provide the high power density required for
future electric aircraft propulsion. However, superconducting windings generate
AC losses in AC electrical machine environments. These AC losses are difficult
to remove at low temperatures and they add an extra burden to the aircraft
cooling system. Due to heavy cooling penalty, AC losses in the HTS stator, is
one of the key topics in HTS machine design. In order to evaluate the AC loss
of superconducting stator windings in a rotational machine environment, we
designed and built a novel axial-flux high temperature superconducting (HTS)
machine platform. The AC loss measurement is based on calorimetrically
boiling-off liquid nitrogen. Both total AC loss and magnetisation loss in HTS
stator are measured in a rotational magnetic field condition. This platform is
essential to study ways to minimise AC losses in HTS stator, in order to
maximum the efficiency of fully HTS machines.


  Gamma-ray bursts (GRBs) are short and intense flashes at the cosmological
distances, which are the most luminous explosions in the Universe. The high
luminosities of GRBs make them detectable out to the edge of the visible
universe. So, they are unique tools to probe the properties of high-redshift
universe: including the cosmic expansion and dark energy, star formation rate,
the reionization epoch and the metal evolution of the Universe. First, they can
be used to constrain the history of cosmic acceleration and the evolution of
dark energy in a redshift range hardly achievable by other cosmological probes.
Second, long GRBs are believed to be formed by collapse of massive stars. So
they can be used to derive the high-redshift star formation rate, which can not
be probed by current observations. Moreover, the use of GRBs as cosmological
tools could unveil the reionization history and metal evolution of the
Universe, the intergalactic medium (IGM) properties and the nature of first
stars in the early universe. But beyond that, the GRB high-energy photons can
be applied to constrain Lorentz invariance violation (LIV) and to test
Einstein's Equivalence Principle (EEP). In this paper, we review the progress
on the GRB cosmology and fundamental physics probed by GRBs.


  Since it started observing the sky, the INTEGRAL satellite has revealed new
categories of High Mass X-ray binaries in our Galaxy. These observations raise
important questions on the formation and evolution of such rare and
short-living objects. We present here new observations that enable us to reveal
or constrain the nature of 15 new INTEGRAL sources. After previous photometric
and spectroscopic campaigns of observations in optical and near infrared, new
spectroscopy and photometry were performed at ESO in optical/NIR with EMMI and
SOFI on the NTT in 2008 and 2010 on a sample of INTEGRAL sources. These
observations and precisely the detection of specific features in their spectra
allow the identification of such high-energy objects. Our results indicate
that: 6 of these objects are Be High-Mass X-ray Binaries (BeHMXBs), 6 are
Supergiant High-Mass X-ray Binaries (sgHMXBs) and 3 are still unidentified and
need higher resolution data to be completely unveiled.


  We propose a scheme to investigate whether non-uniform motion degrades
entanglement of a relativistic quantum field that is localised both in space
and in time. For a Dirichlet scalar field in a cavity in Minkowski space, in
small but freely-adjustable acceleration of finite but arbitrarily long
duration, degradation of observable magnitude occurs for massless transverse
quanta of optical wavelength at Earth gravity acceleration and for kaon mass
quanta already at microgravity acceleration. We outline a space-based
experiment for observing the effect and its gravitational counterpart.


  We show how to find higher generating families of subgroups, in the sense of
Abels and Holz, for groups acting on Cohen-Macaulay complexes. We apply this to
groups with a BN-pair to prove higher generation by parabolic and
Levi-subgroups and describe higher generating families of parabolic subgroups
in Aut(F_n).


  Discovering causal relationships from observational data, particularly in the
presence of latent variables, poses a challenging problem. While current local
structure learning methods have proven effective and efficient when the focus
lies solely on the local relationships of a target variable, they operate under
the assumption of causal sufficiency. This assumption implies that all the
common causes of the measured variables are observed, leaving no room for
latent variables. Such a premise can be easily violated in various real-world
applications, resulting in inaccurate structures that may adversely impact
downstream tasks. In light of this, our paper delves into the primary
investigation of locally identifying potential parents and children of a target
from observational data that may include latent variables. Specifically, we
harness the causal information from m-separation and V-structures to derive
theoretical consistency results, effectively bridging the gap between global
and local structure learning. Together with the newly developed stop rules, we
present a principled method for determining whether a variable is a direct
cause or effect of a target. Further, we theoretically demonstrate the
correctness of our approach under the standard causal Markov and faithfulness
conditions, with infinite samples. Experimental results on both synthetic and
real-world data validate the effectiveness and efficiency of our approach.


  A codebook based limited feedback strategy is a practical way to obtain
partial channel state information at the transmitter in a precoded
multiple-input multiple-output (MIMO) wireless system. Conventional codebook
designs use Grassmannian packing, equiangular frames, vector quantization, or
Fourier based constructions. While the capacity and error rate performance of
conventional codebook constructions have been extensively investigated,
constructing these codebooks is notoriously difficult relying on techniques
such as nonlinear search or iterative algorithms. Further, the resulting
codebooks may not have a systematic structure to facilitate storage of the
codebook and low search complexity. In this paper, we propose a new systematic
codebook design based on Kerdock codes and mutually unbiased bases. The
proposed Kerdock codebook consists of multiple mutually unbiased unitary bases
matrices with quaternary entries and the identity matrix. We propose to derive
the beamforming and precoding codebooks from this base codebook, eliminating
the requirement to store multiple codebooks. The propose structure requires
little memory to store and, as we show, the quaternary structure facilitates
codeword search. We derive the chordal distance for two antenna and four
antenna codebooks, showing that the proposed codebooks compare favorably with
prior designs. Monte Carlo simulations are used to compare achievable rates and
error rates for different codebooks sizes.


  We present HST and FUSE observations of the intergalactic absorption toward
PG1116+215 in the 900-3000 A spectral region. We detect 25 Ly-alpha absorbers
at rest-frame equivalent widths W_r > 30 mA, yielding (dN/dz)_Ly-alpha =
154+/-18 over an unblocked redshift path of 0.162. Two additional weak Ly-alpha
absorbers with W_r ~ 15-20 mA are also present. Eight of the Ly-alpha absorbers
have large line widths (b > 40 km/sec). The detection of narrow OVI in the
broad Ly-alpha absorber at z=0.06244 supports the idea that the Ly-alpha
profile is thermally broadened in gas with T > 10^5 K. We find dN/dz ~ 50 for
broad Ly-alpha absorbers with W_r > 30 mA and b > 40 km/sec. If the broad
Ly-alpha lines are dominated by thermal broadening in hot gas, the amount of
baryonic material in these absorbers is enormous, perhaps as much as half the
baryonic mass in the low-redshift universe. We detect OVI absorption in several
of the Ly-alpha clouds along the sight line. Two detections at z=0.13847 and
z=0.16548 are confirmed by the presence of other ions at these redshifts, while
the detections at z=0.04125, 0.05895, 0.05928, and 0.06244 are based upon the
Ly-alpha and OVI detections alone. The information available for 13
low-redshift OVI absorbers with W_r > 50 mA along 5 sight lines yields
(dN/dz)_OVI ~ 14 and Omega_b(OVI) > 0.0027/h_75, assuming a metallicity of 0.1
solar and an OVI ionization fraction < 0.2. The properties and prevalence of
low-redshift OVI absorbers suggest that they too may be a substantial baryon
repository, perhaps containing as much mass as stars and gas inside galaxies.
The redshifts of the OVI absorbers are highly correlated with the redshifts of
galaxies along the sight line, though few of the absorbers lie closer than
600/h_75 kpc to any single galaxy. [abbreviated]


  We propose a combinatorial optimisation model called Limited Query Graph
Connectivity Test. We consider a graph whose edges have two possible states
(On/Off). The edges' states are hidden initially. We could query an edge to
reveal its state. Given a source s and a destination t, we aim to test s-t
connectivity by identifying either a path (consisting of only On edges) or a
cut (consisting of only Off edges). We are limited to B queries, after which we
stop regardless of whether graph connectivity is established. We aim to design
a query policy that minimizes the expected number of queries.
  Our model is mainly motivated by a cyber security use case where we need to
establish whether an attack path exists in a network, between a source and a
destination. Edge query is resolved by manual effort from the IT admin, which
is the motivation behind query minimization.
  Our model is highly related to monotone Stochastic Boolean Function
Evaluation (SBFE). There are two existing exact algorithms for SBFE that are
prohibitively expensive. We propose a significantly more scalable exact
algorithm. While previous exact algorithms only scale for trivial graphs (i.e.,
past works experimented on at most 20 edges), we empirically demonstrate that
our algorithm is scalable for a wide range of much larger practical graphs
(i.e., Windows domain network graphs with tens of thousands of edges).
  We propose three heuristics. Our best-performing heuristic is via reducing
the search horizon of the exact algorithm. The other two are via reinforcement
learning (RL) and Monte Carlo tree search (MCTS). We also derive an anytime
algorithm for computing the performance lower bound. Experimentally, we show
that all our heuristics are near optimal. The exact algorithm based heuristic
outperforms all, surpassing RL, MCTS and 8 existing heuristics ported from SBFE
and related literature.


  Any reliable biomarker has to be specific, generalizable, and reproducible
across individuals and contexts. The exact values of such a biomarker must
represent similar health states in different individuals and at different times
within the same individual to result in the minimum possible false-positive and
false-negative rates. The application of standard cut-off points and risk
scores across populations hinges upon the assumption of such generalizability.
Such generalizability, in turn, hinges upon this condition that the phenomenon
investigated by current statistical methods is ergodic, i.e., its statistical
measures converge over individuals and time within the finite limit of
observations. However, emerging evidence indicates that biological processes
abound with non-ergodicity, threatening this generalizability. Here, we present
a solution for how to make generalizable inferences by deriving ergodic
descriptions of non-ergodic phenomena. For this aim, we proposed capturing the
origin of ergodicity-breaking in many biological processes: cascade dynamics.
To assess our hypotheses, we embraced the challenge of identifying reliable
biomarkers for heart disease and stroke, which, despite being the leading cause
of death worldwide and decades of research, lacks reliable biomarkers and risk
stratification tools. We showed that raw R-R interval data and its common
descriptors based on mean and variance are non-ergodic and non-specific. On the
other hand, the cascade-dynamical descriptors, the Hurst exponent encoding
linear temporal correlations, and multifractal nonlinearity encoding nonlinear
interactions across scales described the non-ergodic heart rate variability
ergodically and were specific. This study inaugurates applying the critical
concept of ergodicity in discovering and applying digital biomarkers of health
and disease.


  This paper presents a predictive model for estimating regularization
parameters of diffeomorphic image registration. We introduce a novel framework
that automatically determines the parameters controlling the smoothness of
diffeomorphic transformations. Our method significantly reduces the effort of
parameter tuning, which is time and labor-consuming. To achieve the goal, we
develop a predictive model based on deep convolutional neural networks (CNN)
that learns the mapping between pairwise images and the regularization
parameter of image registration. In contrast to previous methods that estimate
such parameters in a high-dimensional image space, our model is built in an
efficient bandlimited space with much lower dimensions. We demonstrate the
effectiveness of our model on both 2D synthetic data and 3D real brain images.
Experimental results show that our model not only predicts appropriate
regularization parameters for image registration, but also improving the
network training in terms of time and memory efficiency.


  Motivated by the observations of intracellular phase separations and the
wetting of cell membranes by protein droplets, we study the nonequilibrium
surface wetting by Monte Carlo simulations of a lattice gas model involving
particle creation. We find that, even when complete wetting should occur in
equilibrium, the fast creation of particles can hinder the surface wetting for
a long time due to the bulk droplet formation. Performing molecular dynamics
simulations, we show that this situation also holds in colloidal particle
systems when the disorder density is sufficiently high. The results suggest an
intracellular control mechanism of surface wetting by changing the speed of
component synthesis.


  A broad effort is underway to improve the sensitivity of nuclear magnetic
resonance through the use of dynamic nuclear polarization. Nitrogen-vacancy
(NV) centers in diamond offer an appealing platform because these paramagnetic
defects can be optically polarized efficiently at room temperature. However,
work thus far has been mainly limited to single crystals because most
polarization transfer protocols are sensitive to misalignment between the NV
and magnetic field axes. Here we study the spin dynamics of NV-13C pairs in the
simultaneous presence of optical excitation and microwave frequency sweeps at
low magnetic fields. We show that a subtle interplay between illumination
intensity, frequency sweep rate, and hyperfine coupling strength leads to
efficient, sweep-direction-dependent 13C spin polarization over a broad range
of orientations of the magnetic field. In particular, our results strongly
suggest that finely-tuned, moderately coupled nuclear spins are key to the
hyperpolarization process, which makes this mechanism distinct from other known
dynamic polarization channels. These findings pave the route to applications
where powders are intrinsically advantageous, including the hyper-polarization
of target fluids in contact with the diamond surface or the use of
hyperpolarized particles as contrast agents for in-vivo imaging.


  As a foreseeable future mode of transport with lower emissions and higher
efficiencies, electric vehicles have received worldwide attention. For
convenient centralized management, taxis are considered as the fleet with
electrification priority. In this work, we focus on the study on electric taxis
dispatching, with consideration of picking up customers and recharging, based
on real world traffic data of a large number of taxis in Beijing. First, the
assumed electric taxi charging stations are located using the K mean method.
Second, based on the station locations and the order demands, which are in form
of origin-destination pairs and extracted from the trajectory data, a
dispatching strategy as well as the simulation framework is developed with
consideration of reducing customer waiting time, mitigating electric taxi
charging congestion, and balancing order number distribution among electric
taxis. The proposed method models the electric taxi charging behaviors
temporally discretely from the aspects of charging demands and availability of
chargers, and further incorporates a centralized and intelligent fleet
dispatching platform, which is capable of handling taxi service requests and
arranging electric taxis' recharging in real time. The methodology in this
paper is readily applicable to dispatching of different types of electric
vehicle fleet with similar dataset available. Among the method, we use queueing
theory to model the electric vehicle charging station waiting phenomena and
include this factor into dispatching platform. Carbon emission is also surveyed
and analyzed.


  We consider a setting where one has to organize one or several group
activities for a set of agents. Each agent will participate in at most one
activity, and her preferences over activities depend on the number of
participants in the activity. The goal is to assign agents to activities based
on their preferences. We put forward a general model for this setting, which is
a natural generalization of anonymous hedonic games. We then focus on a special
case of our model, where agents' preferences are binary, i.e., each agent
classifies all pairs of the form "(activity, group size)" into ones that are
acceptable and ones that are not. We formulate several solution concepts for
this scenario, and study them from the computational point of view, providing
hardness results for the general case as well as efficient algorithms for
settings where agents' preferences satisfy certain natural constraints.


  We discuss the effect of the recent change of $V_{\rm us}$ by three standard
deviations on the standard model predictions for neutron beta decay
observables. We also discuss the effect the experimental error bars of $V_{\rm
us}$ have on such predictions. Refined precision tests of the standard model
will be made by a combined effort to improve measurements in neutron beta decay
and in strangeness-changing decays. By itself the former will yield very
precise measurements of $V_{\rm ud}$ and make also very precise predictions for
$V_{\rm us}$.


  Blockchain has attracted much attention from both academia and industry since
emerging in 2008. Due to the inconvenience of the deployment of large-scale
blockchains, blockchain simulators are used to facilitate blockchain design and
implementation. We evaluate state-of-the-art simulators applied to both Bitcoin
and Ethereum and find that they suffer from low performance and scalability
which are significant limitations. To build a more general and faster
blockchain simulator, we extend an existing blockchain simulator, i.e.
BlockSim. We add a network module integrated with a network topology generation
algorithm and a block propagation algorithm to generate a realistic blockchain
network and simulate the block propagation efficiently. We design a binary
transaction pool structure and migrate BlockSim from Python to C++ so that
bitwise operations can be used to accelerate the simulation and reduce memory
usage. Moreover, we modularize the simulator based on five primary blockchain
processes. Significant blockchain elements including consensus protocols (PoW
and PoS), information propagation algorithms (Gossip) and finalization rules
(Longest rule and GHOST rule) are implemented in individual modules and can be
combined flexibly to simulate different types of blockchains. Experiments
demonstrate that the new simulator reduces the simulation time by an order of
magnitude and improves scalability, enabling us to simulate more than ten
thousand nodes, roughly the size of the Bitcoin and Ethereum networks. Two
typical use cases are proposed to investigate network-related issues which are
not covered by most other simulators.


  The goal of object detection is to determine the class and location of
objects in an image. This paper proposes a novel anchor-free, two-stage
framework which first extracts a number of object proposals by finding
potential corner keypoint combinations and then assigns a class label to each
proposal by a standalone classification stage. We demonstrate that these two
stages are effective solutions for improving recall and precision,
respectively, and they can be integrated into an end-to-end network. Our
approach, dubbed Corner Proposal Network (CPN), enjoys the ability to detect
objects of various scales and also avoids being confused by a large number of
false-positive proposals. On the MS-COCO dataset, CPN achieves an AP of 49.2%
which is competitive among state-of-the-art object detection methods. CPN also
fits the scenario of computational efficiency, which achieves an AP of
41.6%/39.7% at 26.2/43.3 FPS, surpassing most competitors with the same
inference speed. Code is available at https://github.com/Duankaiwen/CPNDet


  We modify the new method for the statistical estimation of the tail
distribution of earthquake seismic moments introduced by Pisarenko et al.
[2009] and apply it to the earthquake catalog of Japan (1923-2007). The method
is based on the two main limit theorems of the theory of extreme values and on
the derived duality between the Generalized Pareto Distribution (GPD) and
Generalized Extreme Value distribution (GEV). We obtain the distribution of
maximum earthquake magnitudes in future time intervals of arbitrary duration
tau. This distribution can be characterized by its quantile Qq(tau) at any
desirable statistical level q. The quantile Qq(tau) provides a much more stable
and robust characteristic than the traditional absolute maximum magnitude Mmax
(Mmax can be obtained as the limit of Qq(tau) as q tends to 1, and tau tends to
infinity). The best estimates of the parameters governing the distribution of
Qq(tay) for Japan (1923-2007) are the following: Form parameter for GEV =
-0.1901 +- 0.0717; position parameter GEV(tau=200)= 6.3387 +- 0.0380; spread
parameter for GEV(tau=200)= 0.5995 +- 0.0223; Q_0.90,GEV(tau=10)= 8.34 +- 0.32.
We also estimate Qq(tau) for a set of q-values and future time periods in the
range for tau between 1 and 50 years from 2007. For comparison, the absolute
maximum estimate Mmax from GEV, which is equal to 9.57 +- 0.86, has a scatter
more than twice that of the 90 percent quantile Q_{0.90,GEV}(tau=10) of the
maximum magnitude over the next 10 years counted from 2007.


  Most of the recent literature on image Super-Resolution (SR) can be
classified into two main approaches. The first one involves learning a
corruption model tailored to a specific dataset, aiming to mimic the noise and
corruption in low-resolution images, such as sensor noise. However, this
approach is data-specific, tends to lack adaptability, and its accuracy
diminishes when faced with unseen types of image corruptions. A second and more
recent approach, referred to as Robust Super-Resolution (RSR), proposes to
improve real-world SR by harnessing the generalization capabilities of a model
by making it robust to adversarial attacks. To delve further into this second
approach, our paper explores the universality of various methods for enhancing
the robustness of deep learning SR models. In other words, we inquire: "Which
robustness method exhibits the highest degree of adaptability when dealing with
a wide range of adversarial attacks ?". Our extensive experimentation on both
synthetic and real-world images empirically demonstrates that median randomized
smoothing (MRS) is more general in terms of robustness compared to adversarial
learning techniques, which tend to focus on specific types of attacks.
Furthermore, as expected, we also illustrate that the proposed universal robust
method enables the SR model to handle standard corruptions more effectively,
such as blur and Gaussian noise, and notably, corruptions naturally present in
real-world images. These results support the significance of shifting the
paradigm in the development of real-world SR methods towards RSR, especially
via MRS.


  We study the dynamics of a carrier, which performs a biased motion under the
influence of an external field E, in an environment which is modeled by dynamic
percolation and created by hard-core particles. The particles move randomly on
a simple cubic lattice, constrained by hard-core exclusion, and they
spontaneously annihilate and re-appear at some prescribed rates. Using
decoupling of the third-order correlation functions into the product of the
pairwise carrier-particle correlations we determine the density profiles of the
"environment" particles, as seen from the stationary moving carrier, and
calculate its terminal velocity, V_c, as the function of the applied field and
other system parameters. We find that for sufficiently small driving forces the
force exerted on the carrier by the "environment" particles shows a
viscous-like behavior. An analog Stokes formula for such dynamic percolative
environments and the corresponding friction coefficient are derived. We show
that the density profile of the environment particles is strongly
inhomogeneous: In front of the stationary moving carrier the density is higher
than the average density, $\rho_s$, and approaches the average value as an
exponential function of the distance from the carrier. Past the carrier the
local density is lower than $\rho_s$ and the relaxation towards $\rho_s$ may
proceed differently depending on whether the particles number is or is not
explicitly conserved.


  Learning policies for bipedal locomotion can be difficult, as experiments are
expensive and simulation does not usually transfer well to hardware. To counter
this, we need al- gorithms that are sample efficient and inherently safe.
Bayesian Optimization is a powerful sample-efficient tool for optimizing
non-convex black-box functions. However, its performance can degrade in higher
dimensions. We develop a distance metric for bipedal locomotion that enhances
the sample-efficiency of Bayesian Optimization and use it to train a 16
dimensional neuromuscular model for planar walking. This distance metric
reflects some basic gait features of healthy walking and helps us quickly
eliminate a majority of unstable controllers. With our approach we can learn
policies for walking in less than 100 trials for a range of challenging
settings. In simulation, we show results on two different costs and on various
terrains including rough ground and ramps, sloping upwards and downwards. We
also perturb our models with unknown inertial disturbances analogous with
differences between simulation and hardware. These results are promising, as
they indicate that this method can potentially be used to learn control
policies on hardware.


  The cosmic microwave background (CMB) serves as a backlight to large-scale
structure during the epoch of reionization, where Thomson scattering gives rise
to temperature anisotropies on small angular scales from the kinetic Sunyaev
Zel'dovich (kSZ) effect. In this paper, we demonstrate that the technique of
kSZ tomography (velocity reconstruction), based on cross correlations between
CMB temperature and 21cm surveys, can significantly improve constraints on
models of inhomogeneous reionization and provide information about large-scale
modes that are poorly characterized by 21cm measurements themselves due to
foreground contamination.


  Mobile health (mHealth) applications have become increasingly valuable in
preventive healthcare and in reducing the burden on healthcare organizations.
The aim of this paper is to investigate the factors that influence user
acceptance of mHealth apps and identify the underlying structure that shapes
users' behavioral intention. An online study that employed factorial survey
design with vignettes was conducted, and a total of 1,669 participants from
eight countries across four continents were included in the study. Structural
equation modeling was employed to quantitatively assess how various factors
collectively contribute to users' willingness to use mHealth apps. The results
indicate that users' digital literacy has the strongest impact on their
willingness to use them, followed by their online habit of sharing personal
information. Users' concerns about personal privacy only had a weak impact.
Furthermore, users' demographic background, such as their country of residence,
age, ethnicity, and education, has a significant moderating effect. Our
findings have implications for app designers, healthcare practitioners, and
policymakers. Efforts are needed to regulate data collection and sharing and
promote digital literacy among the general population to facilitate the
widespread adoption of mHealth apps.


  Luminescence thermometry has been extensively exploited in the last decades
both from the fundamental and applied point of views. The application of
photoluminescent nanoparticles on the microscopic level based on rare-earth
doped (RED) nanostructures is yet a challenge. Distinct underlying physical
mechanisms in the RED nanomaterials have been exploited, such as intensity
ratio between radiative transitions associated with thermally coupled energy
levels, energy peak and lifetime of an excited state variations with the
temperature. The drawbacks of such systems are the relatively low thermal
sensitivity (Sr), and the large temperature uncertainty. To overcome that,
several research groups have been seeking new functionalized materials. The
majority of the efforts have been directed towards increasing Sr with record
around 10 %{\deg}C-1, which is, however, considered unsatisfactory. We propose
the use of an automated machine learning tool to retrieve an ideal pipeline
improving the response of photoluminescence thermometers. As a
proof-of-concept, we used Nd3+-doped YAG nanoparticles, excited at 760 nm, and
the photoluminescence spectra in the range from 860 nm to 960 nm as input
parameters. In addition to the improvement in the accuracy (> 5.5x over
traditional methods), the implementation is very simple, without the
requirement of any deconvolution procedure or knowledge of any underlying
physical mechanism. Our findings demonstrate that this approach is resilient to
natural variances across various spectral acquisitions, which may otherwise
lead to an inaccurate estimation of temperature, opening the door for real-time
applications. Our open-source code is designed to be accessible to individuals
without programming experience.


  Breast histology image classification is a crucial step in the early
diagnosis of breast cancer. In breast pathological diagnosis, Convolutional
Neural Networks (CNNs) have demonstrated great success using digitized
histology slides. However, tissue classification is still challenging due to
the high visual variability of the large-sized digitized samples and the lack
of contextual information. In this paper, we propose a novel CNN, called
Multi-level Context and Uncertainty aware (MCUa) dynamic deep learning ensemble
model.MCUamodel consists of several multi-level context-aware models to learn
the spatial dependency between image patches in a layer-wise fashion. It
exploits the high sensitivity to the multi-level contextual information using
an uncertainty quantification component to accomplish a novel dynamic ensemble
model.MCUamodelhas achieved a high accuracy of 98.11% on a breast cancer
histology image dataset. Experimental results show the superior effectiveness
of the proposed solution compared to the state-of-the-art histology
classification models.


  To investigate the dynamical properties of globular clusters, the surface
brightness and kinematic data were collected and fitted to a family of lowered
isothermal models called LIMEPY models. For 18 studied globular clusters, the
amounts of concentration, truncation, and anisotropy were determined. In
addition, the cluster mass, half-mass radius, distance, and mass-to-light ratio
were also obtained. In general, LIMEPY models could describe these clusters
well. Among these 18 clusters, NGC 5139, NGC 6388, and NGC 7078 were claimed to
be candidates to host intermediate-mass black holes in literature. The models
could not appropriately fit the central proper-motion velocity dispersion of
NGC 5139 and the slope of proper-motion velocity-dispersion profile of NGC
6388. Thus, more dedicated models with intermediate-mass black holes or a group
of stellar-mass black holes at cluster centers may need to be considered.
Considering NGC 7078, our model with some degree of anisotropy can fit the
data. Finally, the strong concentration-truncation anti-correlation and
truncation-semimajor-axis correlation were revealed, which could be the
observational imprint of the dynamical evolution of globular clusters.


  We study the instability of a thin membrane (of zero bending rigidity) to
out-of-plane deflections, when the membrane is immersed in an inviscid fluid
flow and sheds a trailing vortex-sheet wake. We solve the nonlinear eigenvalue
problem iteratively with large ensembles of initial guesses, for three
canonical boundary conditions---both ends fixed, one end fixed and one free,
and both free. Over several orders of magnitude of membrane mass density, we
find instability by divergence or flutter (particularly at large mass density,
or with one or both ends free). The most unstable eigenmodes generally become
"wavier" at smaller mass density and smaller tension, but with regions of
nonmonotonic behavior. We find good quantitative agreement with unsteady
time-stepping simulations at small amplitude, but only qualitative similarities
with the eventual steady-state large-amplitude motions.


  The coefficients of the regular continued fraction for random numbers are
distributed by the Gauss-Kuzmin distribution according to Khinchin's law. Their
geometric mean converges to Khinchin's constant and their rational
approximation speed is Khinchin's speed. It is an open question whether these
theorems also apply to algebraic numbers of degree $>2$. Since they apply to
almost all numbers it is, however, commonly inferred that it is most likely
that non quadratic algebraic numbers also do so. We argue that this inference
is not well grounded. There is strong numerical evidence that Khinchin's speed
is too fast. For Khinchin's law and Khinchin's constant the numerical evidence
is unclear. We apply the Kullback Leibler Divergence (KLD) to show that the
Gauss-Kuzmin distribution does not fit well for algebraic numbers of degree
$>2$. Our suggestion to truncate the Gauss-Kuzmin distribution for finite parts
fits slightly better but its KLD is still much larger than the KLD of a random
number. So, if it converges the convergence is non uniform and each algebraic
number has its own bound. We conclude that there is no evidence to apply the
theorems that hold for random numbers to algebraic numbers.


  The SQCRAMscope is a recently realized Scanning Quantum CRyogenic Atom
Microscope that utilizes an atomic Bose-Einstein condensate to measure magnetic
fields emanating from solid-state samples. The quantum sensor does so with
unprecedented DC sensitivity at micron resolution from room-to-cryogenic
temperatures. An additional advantage of the SQCRAMscope is the preservation of
optical access to the sample: Magnetometry imaging of, e.g., electron transport
may be performed in concert with other imaging techniques. This multimodal
imaging capability can be brought to bear with great effect in the study of
nematicity in iron-pnictide high-temperature superconductors, where the
relationship between electronic and structural symmetry-breaking resulting in a
nematic phase is under debate. Here, we combine the SQCRAMscope with an in situ
microscope that measures optical birefringence near the surface. This enables
simultaneous and spatially resolved detection of both bulk and near-surface
manifestations of nematicity via transport and structural deformation channels,
respectively. By performing the first local measurement of emergent resistivity
anisotropy in iron pnictides, we observe sharp, nearly concurrent transport and
structural transitions. More broadly, these measurements demonstrate the
SQCRAMscope's ability to reveal important insights into the physics of complex
quantum materials.


  I analyze the IR fixed point structure of Landau gauge QCD. Precisely the
fixed point with a strong kinematic singularity of the quark-gluon vertex that
proved crucial for the recently proposed confinement mechanism in the quenched
approximation is absent in dynamical QCD. Therefore, the IR singularities do
not induce asymptotic quark confinement but the long-range interaction is
screened by unquenching loops at scales of the order of the quark mass. This
provides the prerequisite for a microscopic description of deconfinement and
string breaking. The fixed points determine the qualitative form of the heavy
quark potential and may be relevant for hot and dense matter.


  We report the experimental generation, acceleration and characterization of a
uniformly-filled electron bunch obtained via space-charge-driven expansion
(often referred to as "blow-out regime") in an L-band (1.3-GHz) radiofrequency
photoinjector. The beam is photoemitted from a Cesium-Telluride semiconductor
photocathode using a short ($<200$ fs) ultraviolet laser pulse. The produced
electron bunches are characterized with conventional diagnostics and the
signatures of their ellipsoidal character is observed. We especially
demonstrate the production of ellipsoidal bunches with charges up to $\sim0.5$
nC corresponding to a $\sim20$-fold increase compared to previous experiments
with metallic photocathodes.


  Entanglement, a unique quantum resource with no classical counterpart,
remains at the heart of quantum information. The Greenberger-Horne-Zeilinger
(GHZ) and $W$ states are two inequivalent classes of multipartite entangled
states which can not be transformed into each other by means of local
operations and classic communication. In this paper, we present the methods to
prepare the GHZ and $W$ states via global controls on a long-range Ising spin
model. For the GHZ state, general solutions are analytically obtained for an
arbitrary-size spin system, while for the $W$ state, we find a standard way to
prepare the $W$ state that is analytically illustrated in three- and four-spin
systems and numerically demonstrated for larger-size systems. The number of
parameters required in the numerical search increases only linearly with the
size of the system.


  Semantic image editing utilizes local semantic label maps to generate the
desired content in the edited region. A recent work borrows SPADE block to
achieve semantic image editing. However, it cannot produce pleasing results due
to style discrepancy between the edited region and surrounding pixels. We
attribute this to the fact that SPADE only uses an image-independent local
semantic layout but ignores the image-specific styles included in the known
pixels. To address this issue, we propose a style-preserved modulation (SPM)
comprising two modulations processes: The first modulation incorporates the
contextual style and semantic layout, and then generates two fused modulation
parameters. The second modulation employs the fused parameters to modulate
feature maps. By using such two modulations, SPM can inject the given semantic
layout while preserving the image-specific context style. Moreover, we design a
progressive architecture for generating the edited content in a coarse-to-fine
manner. The proposed method can obtain context-consistent results and
significantly alleviate the unpleasant boundary between the generated regions
and the known pixels.


  We present a general, rigorous theory of partition function zeros for lattice
spin models depending on one complex parameter. First, we formulate a set of
natural assumptions which are verified for a large class of spin models in a
companion paper [BBCKK2, math-ph/0304007]. Under these assumptions, we derive
equations whose solutions give the location of the zeros of the partition
function with periodic boundary conditions, up to an error which we prove is
(generically) exponentially small in the linear size of the system. For
asymptotically large systems, the zeros concentrate on phase boundaries which
are simple curves ending in multiple points. For models with an Ising-like
plus-minus symmetry, we also establish a local version of the Lee-Yang Circle
Theorem. This result allows us to control situations when in one region of the
complex plane the zeros lie precisely on the unit circle, while in the
complement of this region the zeros concentrate on less symmetric curves.


  Momentum distributions, spectroscopic factors and quasi-hole wave functions
of medium-heavy doubly closed shell nuclei have been calculated in the
framework of the Correlated Basis Function theory, by using the Fermi
hypernetted chain resummation techniques. The calculations have been done by
using microscopic two-body nucleon-nucleon potentials of Argonne type, together
with three-body interactions. Operator dependent correlations, up to the tensor
channels, have been used.


  Contextual information plays a vital role for software developers when
understanding and fixing a bug. Consequently, deep learning-based program
repair techniques leverage context for bug fixes. However, existing techniques
treat context in an arbitrary manner, by extracting code in close proximity of
the buggy statement within the enclosing file, class, or method, without any
analysis to find actual relations with the bug. To reduce noise, they use a
predefined maximum limit on the number of tokens to be used as context. We
present a program slicing-based approach, in which instead of arbitrarily
including code as context, we analyze statements that have a control or data
dependency on the buggy statement. We propose a novel concept called dual
slicing, which leverages the context of both buggy and fixed versions of the
code to capture relevant repair ingredients. We present our technique and tool
called Katana, the first to apply slicing-based context for a program repair
task. The results show Katana effectively preserves sufficient information for
a model to choose contextual information while reducing noise. We compare
against four recent state-of-the-art context-aware program repair techniques.
Our results show Katana fixes between 1.5 to 3.7 times more bugs than existing
techniques.


  The possibility to construct an inflationary scenario for
renormalization-group improved potentials corresponding to the Higgs sector of
quantum field models is investigated. Taking into account quantum corrections
to the renormalization-group potential which sums all leading logs of
perturbation theory is essential for a successful realization of the
inflationary scenario, with very reasonable parameters values. The scalar
electrodynamics inflationary scenario thus obtained are seen to be in good
agreement with the most recent observational data.


  Legged locomotion on deformable terrain is a challenging and open
robo-physics problem since the uncertainty in terrain dynamics introduced by
ground deformation complicates the dynamical modelling and control methods.
Moreover, learning how (e.g. what controls and mechanisms) to move efficiently
and stably on soft ground is a bigger issue. This work seeks to control a 1D
monopod hopper to jump to desired height. To achieve this goal, I first set up
and validate a discrete element method (DEM) based soft ground simulation
environment of a spherical granular material. With this simulation environment,
I generate resistive force theory (RFT) based models of the ground reaction
force. Then I use the RFT model to develop a feedforward force control for this
robot. In the DEM simulation, I use feedback control to compensate for
variations in the ground reaction force from the RFT model predictions. With
the feedback control, the robot tracks the desired trajectories well and
reaches the desired height after five hops. It reduces the apex position errors
a lot more than a pure feedforward control. I also change the area of the
robots square foot from 1cm^2 to 49cm^2. The feedback controller is able to
deal with the ground reaction force fluctuations even when the foot dimensions
are on the order of a grain diameter.


  We derive the formalism for steady state nonequilibrium dynamical mean-field
theory in a real-time formalism along the Kadanoff-Baym contour. The resulting
equations of motion are first transformed to Wigner coordinates (average and
relative time), and then re-expressed in terms of differential operators.
Finally, we perform a Fourier transform with respect to the relative time, and
take the first-order limit in the electric field to produce the quantum
Boltzmann equation for dynamical mean-field theory. We next discuss the
structure of the equations and their solutions, describing how these equations
reduce to the Drude result in the limit of a constant relaxation time. We also
explicitly demonstrate the equivalence between the Kubo and nonequilibrium
approaches to linear response. There are a number of interesting modifications
of the conventional quantum Boltzmann equation that arise due to the underlying
bandstructure of the lattice.


  The discovery or exclusion of the fundamental standard scalar is a hot topic,
given the data of LEP, the Tevatron and the LHC, as well as the advanced status
of the pertinent theoretical calculations. With the current statistics at the
hadron colliders, the workhorse decay channel, at all relevant H masses, is H
to WW, followed by W to light leptons. Using phase-space singularity
techniques, we construct and study a plethora of "singularity variables" meant
to facilitate the difficult tasks of separating signal and backgrounds and of
measuring the mass of a putative signal. The simplest singularity variables are
not invariant under boosts along the collider's axes and the simulation of
their distributions requires a good understanding of parton distribution
functions, perhaps not a serious shortcoming during the boson hunting season.
The derivation of longitudinally boost-invariant variables, which are functions
of the four charged-lepton observables that share this invariance, is quite
elaborate. But their use is simple and they are, in a kinematical sense,
optimal.


  Azimuthal correlations of charged hadrons with direct-photon ($\gamma_{dir}$)
and neutral-pion ($\pi^{0}$) trigger particles are analyzed in central Au+Au
and minimum-bias $p+p$ collisions at $\sqrt{s_{_{NN}}} = 200$ GeV in the STAR
experiment. The charged-hadron per-trigger yields at mid-rapidity from central
Au+Au collisions are compared with $p+p$ collisions to quantify the suppression
in Au+Au collisions. The suppression of the away-side associated-particle
yields per $\gamma_{dir}$ trigger is independent of the transverse momentum of
the trigger particle ($p_{T}^{\mathrm{trig}}$), whereas the suppression is
smaller at low transverse momentum of the associated charged hadrons
($p_{T}^{\mathrm{assoc}}$). Within uncertainty, similar levels of suppression
are observed for $\gamma_{dir}$ and $\pi^{0}$ triggers as a function of $z_{T}$
($\equiv p_T^{\mathrm{assoc}}/p_T^{\mathrm{trig}}$). The results are compared
with energy-loss-inspired theoretical model predictions. Our studies support
previous conclusions that the lost energy reappears predominantly at low
transverse momentum, regardless of the trigger energy.


  Lattice simulations reveal that the deconfinement-confinement (D-C) phase
transition (PT) of the hot pure $SU(N>2)$ Yang-Mills system is first order.
This system can be described by a pool of quasigluons moving in the Polyakov
loop background, and in this picture, we establish an effective distribution
function for quasigluons, which encodes interactions among quasigluons and in
particular the confinement effect. With it, we made the first attempt to
calculate the confinement bubble wall velocity $v_w$ at the microscopical
level, and we obtained a small velocity $v_w\sim 0.04$ using two different
approaches, which is qualitatively consistent with others results like
holography.


  Forward stimulated Brillouin scattering (F-SBS) is a third-order
nonlinear-optical mechanism that couples between two co-propagating optical
fields and a guided acoustic mode in a common medium. F-SBS gives rise to
nonlinear wave mixing along optical fibers, which adds up with four-wave mixing
induced by the Kerr effect. In this work, we report the distributed mapping of
nonlinear wave mixing processes involving both mechanisms along standard
single-mode fiber, in analysis, simulation and experiment. Measurements are
based on a multi-tone, optical time-domain reflectometry setup, which is highly
frequency-selective. The results show that F-SBS leads to nonlinear wave mixing
processes that are more complex than those that are driven by the Kerr effect
alone. The dynamics are strongly dependent on the exact frequency detuning
between optical field components. When the detuning is chosen near an F-SBS
resonance, the process becomes asymmetric. Power is coupled from an
upper-frequency input pump wave to a lower-frequency one, and the amplification
of Stokes-wave sidebands is more pronounced than that of anti-Stokes-wave
sidebands. The results are applicable to a new class of distributed fiber-optic
sensors, based on F-SBS.


  The 21-cm signal from the epoch of reionization is non-Gaussian. Current
radio telescopes are focused on detecting the 21-cm power spectrum, but in the
future the Square Kilometre Array is anticipated to provide a first measurement
of the bispectrum. Previous studies have shown that the position-dependent
power spectrum is a simple and efficient way to probe the squeezed-limit
bispectrum. In this approach, the survey is divided into subvolumes and the
correlation between the local power spectrum and the corresponding mean density
of the subvolume is computed. This correlation is equivalent to an integral of
the bispectrum in the squeezed limit, but is much simpler to implement than the
usual bispectrum estimators. It also has a clear physical interpretation: it
describes how the small-scale power spectrum of tracers such as galaxies and
the 21-cm signal respond to a large-scale environment. Reionization naturally
couples large and small scales as ionizing radiation produced by galactic
sources can travel up to tens of Megaparsecs through the intergalactic medium
during this process. Here we apply the position-dependent power spectrum
approach to fluctuations in the 21-cm background from reionization. We show
that this statistic has a distinctive evolution in time that can be understood
with a simple analytic model. We also show that the statistic can easily
distinguish between simple "inside-out" and "outside-in" models of
reionization. The position-dependent power spectrum is thus a promising method
to validate the reionization signal and to extract higher-order information on
this process.


  We investigate the intersections of balls of radius $r$, called $r$-ball
bodies, in Euclidean $d$-space. An $r$-lense (resp., $r$-spindle) is the
intersection of two balls of radius $r$ (resp., balls of radius $r$ containing
a given pair of points). We prove that among $r$-ball bodies of given volume,
the $r$-lense (resp., $r$-spindle) has the smallest inradius (resp., largest
circumradius). In general, we upper (resp., lower) bound the intrinsic volumes
of $r$-ball bodies of given inradius (resp., circumradius). This complements
and extends some earlier results on volumetric estimates for $r$-ball bodies.


  The quark-gluon plasma (QGP) is a high temperature state of matter produced
in the collisions of two nuclei at relativistic energies. The properties of
this matter at short distance scales are probed using jets with high transverse
momentum ($p_T$) resulting from quarks and gluons scattered with large momentum
transfer in the earliest stages of the collisions. The Fourier harmonics for
anisotropies in the high transverse momentum particle yield, $v_n(p_T)$,
indicate the path length dependence of jet energy loss within the QGP. We
present a framework to build off of measurements of jet energy loss using
$v_n(p_T)$ by characterizing fluctuations in jet energy loss that are currently
not constrained experimentally. In this paper, we utilize a set of multivariate
moments and cumulants as new experimental observables to measure event-by-event
fluctuations in the azimuthal anisotropies of rare probes, and compare them to
the azimuthal anisotropies of soft particles. Ultimately, these fluctuations
can be used to quantify the magnitude and fluctuations of event-by-event jet
energy loss. We relate these quantities to existing multivariate cumulant
observables, highlight their unique properties, and validate their
sensitivities with a Monte Carlo simulation.


  Absolute angular rotation rate measurements with sensitivity better than
prad/s would be beneficial for fundamental science investigations. On this
regard, large frame Earth based ring laser gyroscopes are top instrumentation
as far as bandwidth, long--term operation, and sensitivity are concerned. Here,
we demonstrate that the GINGERINO active--ring laser upper limiting noise is
close to $2 \times 10^{-15}$ rad/s for $\sim 2 \times 10^5$ s of integration
time, as estimated by the Allan deviation evaluated in a differential
measurement scheme. This is more than a factor 10 better than the theoretical
prediction so far accounted for ideal ring lasers shot--noise with the two
beams counter--propagating inside the cavity considered as two independent
propagating modes. This feature is related to the peculiarity of real ring
laser system dynamics that causes phase cross--talking among the two
counter--propagating modes. In this context, the independent beam model is,
then, not applicable and the measured noise limit falls below the expected one.


  In this theoretical study, modulation techniques are developed to support the
Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS)
mission. A CW lidar system using sine waves modulated by ML pseudo random noise
codes is described for making simultaneous online/offline differential
absorption measurements. Amplitude and Phase Shift Keying (PSK) modulated IM
carriers, in addition to a hybrid pulse technique are investigated that exhibit
optimal autocorrelation properties. A method is presented to bandwidth limit
the ML sequence based on a filter implemented in terms of Jacobi theta
functions that does not significantly degrade the resolution or introduce side
lobes as a means of reducing aliasing and IM carrier bandwidth.


  This article recalls the birth of the first electron-positron storage ring
AdA, and the construction of the higher energy collider ADONE, where early
photon-photon collisions were observed. The events which led the Austrian
physicist Bruno Touschek to propose and construct AdA will be recalled,
starting with early work on the Wideroe's betatron during World War II, up to
the construction of ADONE, and the theoretical contribution to radiative
corrections to electron-positron collisions.


  Due to the significant process variations, designers have to optimize the
statistical performance distribution of nano-scale IC design in most cases.
This problem has been investigated for decades under the formulation of
stochastic optimization, which minimizes the expected value of a performance
metric while assuming that the distribution of process variation is exactly
given. This paper rethinks the variation-aware circuit design optimization from
a new perspective. First, we discuss the variation shift problem, which means
that the actual density function of process variations almost always differs
from the given model and is often unknown. Consequently, we propose to
formulate the variation-aware circuit design optimization as a distributionally
robust optimization problem, which does not require the exact distribution of
process variations. By selecting an appropriate uncertainty set for the
probability density function of process variations, we solve the shift-aware
circuit optimization problem using distributionally robust Bayesian
optimization. This method is validated with both a photonic IC and an
electronics IC. Our optimized circuits show excellent robustness against
variation shifts: the optimized circuit has excellent performance under many
possible distributions of process variations that differ from the given
statistical model. This work has the potential to enable a new research
direction and inspire subsequent research at different levels of the EDA flow
under the setting of variation shift.


  When a mixture of propylene glycol and water is deposited on a clean glass
slide, it forms a droplet of a given apparent contact angle rather than
spreading as one would expect on such a high-energy surface. The droplet is
stabilized by a Marangoni flow due to the non-uniformity of the components'
concentrations between the border and the center of the droplet, itself a
result of evaporation. These self-contracting droplets have unusual properties
such as absence of pinning and the ability to move under an external humidity
gradient. The droplets' apparent contact angle is a function of their
concentration and the external humidity. Here we study the motion of such
droplets sliding down slopes, how they deform when moving at large speeds, and
compare the results to normal non-volatile droplets. We precisely control the
external humidity and explore the influence of the volume, viscosity, surface
tension, and contact angle. We find that the droplets suffer a negligible
pinning force so that for small velocities the capillary number ($\mathrm{Ca}$)
is directly proportional to the Bond number ($\mathrm{Bo}$):
$\mathrm{Ca}=\mathrm{Bo} \sin\alpha$ with $\alpha$ the angle of the slope. When
the droplets move at larger velocities they deform when Ca exceeds a threshold,
and deposit smaller droplets when $\mathrm{Ca}$ reaches twice this threshold.


  Observations of thermal radiation from neutron stars allow one to measure the
surface temperatures and confront them with cooling scenarios. Detection of
gravitationally redshifted spectral lines can yield the mass-to-radius ratio.
In the few cases when the distance is known, one can measure the neutron star
radius, which is particularly important to constrain the equation of state of
the superdense matter in the neutron star interiors. Finally, one can infer the
chemical composition of the neutron star surface layers, which provides
information about formation of neutron stars and their interaction with the
environments. We present the observational results on thermal radiation from
active pulsars and radio-quiet neutron stars, with emphasis on the results
obtained with the Chandra X-ray Observatory and discuss some implications of
these results. In particular, we present a new interpretation of the thermal
emission detected in the broad energy range (from optical to X-rays) from the
puzzling object RX J1856.5-3754


  We show that apparently thermalized states still store relevant amounts of
information about their past, information that can be tracked by experiments
involving nonequilibrium processes. We provide a condition for the
microcanonical quantum Crook\'s theorem, and we test it by means of numerical
experiments. In the Lipkin-Meshkov-Glick model, two different procedures
leading to the same equilibrium states give rise to different statistics of
work in nonequilibrium processes. In the Dicke model, two different
trajectories for the same nonequilibrium protocol produce different statistics
of work. Microcanonical averages provide the correct results for the
expectation values of physical observables in all the cases; the microcanonical
quantum Crook\'s theorem fails in some of them. We conclude that testing
quantum fluctuation theorems is mandatory to verify if a system is properly
thermalized.


  The Liquid Drop Models (LDM) and the Independent Particle Models (IPM) have
been known to provide two conflicting pictures of the nucleus. The IPM being
quantum mechanical, is believed to provide a fundamental picture of the nucleus
and hence has been focus of the still elusive unified theory of the nucleus. It
is believed that the LDM at best is an effective and limited model of the
nucleus. Here, through a comprehensive study of one nucleon separation energy,
we give convincing evidence that actually the LDM is as fundamental and as
basic for the description of the nucleus as the IPM is. As such the LDM and the
IPM provide simultaneously co-exiting complementary duality of the nuclear
phenomena. This fundamental duality also provides solution to the decades old
Coester Band problem in the nucleus. Similarity and differences with respect to
the well known wave-particle duality, as envisaged in Bohr's Complementarity
Principle, is pointed out. Thereafter implications of this new Duality in the
nucleus for quantum mechanics is discussed.


  We studied and compared the long-term average hard X-ray (>20keV) spectra of
a sample of twelve bright low-mass X-ray binaries hosting a neutron star (NS).
Our sample comprises the six well studied Galactic Z sources and six Atoll
sources, four of which are bright ("GX") bulge sources while two are weaker
ones in the 2-10keV range (H1750-440 and H1608-55). For all the sources of our
sample, we analysed available public data and extracted average spectra from
the IBIS/ISGRI detector on board INTEGRAL. We can describe all the spectral
states in terms of the bulk motion Comptonisation scenario. We find evidence
that bulk motion is always present, its strength is related to the accretion
rate and it is suppressed only in the presence of high local luminosity. The
two low-dim Atoll source spectra are dominated by photons up-scattered
presumably due to dynamical and thermal Comptonisation in an optically thin,
hot plasma. For the first time, we extend the detection of H1750-440 up to
150keV. The Z and bright "GX" Atoll source spectra are very similar and are
dominated by Comptonised blackbody radiation of seed photons, presumably coming
from the accretion disc and NS surface, in an optically thick cloud with plasma
temperature in the range of 2.5-3keV. Six sources show a hard tail in their
average spectrum: CygX-2 (Z), GX340+0 (Z), GX17+2 (Z), GX5-1 (Z), ScoX-1 (Z)
and GX13+1 (Atoll). This is the first detection of a hard tail in the X-ray
spectrum of the peculiar GX13+1. Using radio data from the literature we find,
in all Z sources and bright "GX" Atolls, a systematic positive correlation
between the X-ray hard tail (40-100keV) and the radio luminosity. This suggests
that hard tails and energetic electrons causing the radio emission may have the
same origin, most likely the Compton cloud located inside the NS magnetosphere.


  Machine learning algorithms have recently been used to classify students as
those likely to receive an A or B or students likely to receive a C, D, or F in
a physics class. The performance metrics used in that study become unreliable
when the outcome variable is substantially unbalanced. This study seeks to
further explored the classification of students who will receive a C, D, and F
and extend those methods to predicting whether a student will receive a D or F.
The sample used for this work ($N=7184$) is substantially unbalanced with only
12\% of the students receiving a D or F. Applying the same methods as the
previous study produced a classifier that was very inaccurate, classifying only
20\% of the D or F cases correctly. This study will focus on the random forest
machine learning algorithm. By adjusting the random forest decision threshold,
the correct classification rate of the D or F outcome rose to 46\%. This study
also investigated the previous finding that demographic variables such as
gender, underrepresented minority status, and first generation status had low
variable importance for predicting class outcomes. Downsampling revealed that
this was not the result of the underrepresentation of these students. An
optimized classification model was constructed which predicted the D and F
outcome with 46\% accuracy and C, D, and F outcome with 69\% accuracy; the
accuracy of prediction of these outcomes is called "sensitivity" in the machine
learning literature. Substantial variation was detected when this
classification model was applied to predict the C, D, or F outcome for
underrepresented demographic groups with 61\% sensitivity for women, 67\% for
underrepresented minority students, and 78\% for first-generation students.
Similar variation was observed for the D and F outcome.


  Transition from steady to oscillatory buoyancy convection of air in a
laterally heated cubic box is studied numerically by straight-forward time
integration of Boussinesq equations using a series of gradually refined finite
volume grids. Horizontal and spanwise cube boundaries are assumed to be either
perfectly thermally conducting or perfectly thermally insulated, which results
in four different sets of thermal boundary conditions. Critical Grashof numbers
are obtained by interpolation of numerically extracted growth/decay rates of
oscillations amplitude to zero. Slightly supercritical flow regimes are
described by time-averaged flows, snapshots, and spatial distribution of
oscillations amplitude. Possible similarities and dissimilarities with
two-dimensional instabilities in laterally heated square cavities are
discussed. Break of symmetries and sub- or super-critical character of
bifurcations are examined. Three consequent transitions from steady to
oscillatory regime, from oscillatory to steady, and finally to oscillatory
flow, are found in the case of perfectly insulated horizontal and spanwise
boundaries. Arguments for grid and time step independence of the results are
given.


  In this paper upper bounds are given for the successive differences
$A_{n+1}-A_{n}$ and B$_{n}-B_{n-1}$ where $A_{n}=1/(n-1)
\tsum_{r=1}^{n-1}f(r/n)$, $B_{n}=1/(n+1) \tsum_{r=0}^{n}f(r/n)$ and $f$ is
superquadratic function. We obtain bounds for the successive differences of the
more general sequence $1/c_{n}\tsum_{r=1}^{n}f(a_{r}/b_{n})$ when $f$ is
superquadratic, which refine known results for convex functions. We also obtain
bounds for various successive differences when $f$ is an increasing
subquadratic function.


  In this paper, we obtain some results on the relationships between different
ideal \linebreak convergence modes namely, $\mathcal{I}^\mathcal{K}$,
$\mathcal{I}^{\mathcal{K}^*}$, $\mathcal{I}$, $\mathcal{K}$, $\mathcal{I} \cup
\mathcal{K}$ and $(\mathcal{I} \cup \mathcal{K})^*$. We introduce a topological
space namely $\mathcal{I}^\mathcal{K}$-sequential space and show that the class
of $\mathcal{I}^\mathcal{K}$-sequential spaces contain the sequential spaces.
Further $\mathcal{I}^\mathcal{K}$-notions of cluster points and limit points of
a function are also introduced here. For a given sequence in a topological
space $X$, we characterize the set of $\mathcal{I}^\mathcal{K}$-cluster points
of the sequence as closed subsets of $X$.


  Top quarks are a prime system for hunting for new physics. Nonetheless, two
decades on from their discovery few of their couplings have been measured to
high precision. We present an overview of current determinations and the
expected sensitivities with 300 fb-1 and 3000 fb-1 of 14 TeV LHC data. In
addition to direct limits on the top quark's renormalizable couplings to
Standard Model bosons, we also explore what bounds can be set on the
coefficients of higher-dimension operators, taking particular four-fermion
operators that do not interfere with QCD as a test case. Every coupling we
consider will benefit greatly from a dedicated study at the future LHC. Some
measurements, like the irrelevant operators, are systematics-limited and will
saturate in the near-term. Others, like the important ttbar+Higgs coupling,
involve rare processes and thus demand as much data as possible.


  It is generally observed that the fraction of live lines in shared last-level
caches (SLLC) is very small for chip multiprocessors (CMPs). This can be
tackled using promotion-based replacement policies like re-reference interval
prediction (RRIP) instead of LRU, dead-block predictors, or reuse-based cache
allocation schemes. In GPU systems, similar LLC issues are alleviated using
various cache bypassing techniques. These issues are worsened in heterogeneous
CPU-GPU systems because the two processors have different data access patterns
and frequencies. GPUs generally work on streaming data, but have many more
threads accessing memory as compared to CPUs. As such, most traditional cache
replacement and allocation policies prove ineffective due to the higher number
of cache accesses in GPU applications, resulting in higher allocation for GPU
cache lines, despite their minimal reuse. In this work, we implement the Reuse
Cache approach for heterogeneous CPU-GPU systems. The reuse cache is a
decoupled tag/data SLLC which is designed to only store the data that is being
accessed more than once. This design is based on the observation that most of
the cache lines in the LLC are stored but do not get reused before being
replaced. We find that the reuse cache achieves within 0.5% of the IPC gains of
a statically partitioned LLC, while decreasing the area cost of the LLC by an
average of 40%.


  Multi-dimensional arrays are among the most fundamental and most useful data
structures of all. In C++, excellent template libraries exist for arrays whose
dimension is fixed at runtime. Arrays whose dimension can change at runtime
have been implemented in C. However, a generic object-oriented C++
implementation of runtime-flexible arrays has so far been missing. In this
article, we discuss our new implementation called Marray, a package of class
templates that fills this gap. Marray is based on views as an underlying
concept. This concept brings some of the flexibility known from script
languages such as R and MATLAB to C++. Marray is free both for commercial and
non-commercial use and is publicly available from www.andres.sc/marray


  Uranium, Thorium and Potassium-40 abundances in the Earth were calculated in
the frame of Hydridic Earth model. Terrestrial heat producton from U, Th and
K40 decays was calculated also. We must admit the existance of Earth expansion
process to understand the obtained large value of terrestrial heat producton.
The geoneutrino detector with volume more than 5 kT (LENA type) must be
constructed to definitely separate between Bulk Silicat Earth model and
Hydridic Earth model. In second version of the article we assume that K40
concentration distributes in the Earth uniformly.


  As part of an optical spectroscopic survey of nearby, bright galaxies, we
have identified a sample of over 200 emission-line nuclei having optical
spectra resembling those of giant extragalactic H II regions. Such "H II
nuclei," powered by young, massive stars, are found in a substantial fraction
of nearby galaxies, especially those of late Hubble type. This paper summarizes
the observational characteristics of H II nuclei, contrasts the variation of
their properties with Hubble type, and compares the nuclear H II regions with
those found in galaxy disks. Similarities and differences between H II nuclei
and luminous starburst nuclei are additionally noted.


  We present the first resolved observations of the 1.3mm polarized emission
from the disk-like structure surrounding the high-mass protostar Cepheus A HW2.
These CARMA data partially resolve the dust polarization, suggesting an uniform
morphology of polarization vectors with an average position angle of 57 degrees
and an average polarization fraction of 2.0%. The distribution of the
polarization vectors can be attributed to (1) the direct emission of
magnetically aligned grains of dust by a uniform magnetic field, or (2) the
pattern produced by the scattering of an inclined disk. We show that both
models can explain the observations, and perhaps a combination of the two
mechanisms produce the polarized emission. A third model including a toroidal
magnetic field does not match the observations. Assuming scattering is the
polarization mechanism, these observations suggest that during the first few
10000 years of high-mass star formation, grain sizes can grow from 1 to several
10s micron.


  Shannon's information entropies in position- and momentum- space and their
sum $S$ are calculated for various $s$-$p$ and $s$-$d$ shell nuclei using a
correlated one-body density matrix depending on the harmonic oscillator size
$b_0$ and the short range correlation parameter $y$ which originates from a
Jastrow correlation function. It is found that the information entropy sum for
a nucleus depends only on the correlation parameter $y$ through the simple
relation $S= s_{0A} + s_{1A} y^{-\lambda_{sA}}$, where $s_{0A}$, $s_{1A}$ and
$\lambda_{sA}$ depend on the mass number $A$. A similar approximate expression
is also valid for the root mean square radius of the nucleus as function of $y$
leading to an approximate expression which connects $S$ with the root mean
square radius. Finally, we propose a method to determine the correlation
parameter from the above property of $S$ as well as the linear dependence of
$S$ on the logarithm of the number of nucleons.


  Using an exact diagonalization technique on small clusters, we study spin and
density excitations of the triangular-lattice $t$-$J$ model with multiple-spin
exchange interactions, whereby we consider anomalous properties observed in the
doped Mott region of the two-dimensional liquid $^3$He adsorbed on a graphite
surface. We find that the double-peak structure consistent with experiment
appears in the calculated temperature dependence of the specific heat; the
low-temperature sharp peak comes from the spin excitations reflecting the
frustrated nature of the spin degrees of freedom and high-temperature broad
peak comes from the density excitations extending over the entire band width.
The clear separation in their energy scales is evident in the calculated spin
and density excitation spectra. The calculated single-particle excitation
spectra suggest the presence of fermionic quasiparticles dressed by the spin
excitations, with an enhanced effective mass consistent with experiment.


  Bose-Einstein correlations (BEC) between final state particles in the
reaction e+e- -> W+W- -> q_1 anti-q_2 q_3 anti-q_4 have been studied. Data
corresponding to a total integrated luminosity of 550 pb^{-1}, recorded by the
DELPHI detector at centre-of-mass energies ranging from 189 to 209 GeV, were
analysed. An indication for inter-W BEC between like-sign particles has been
found at the level of 2.4 standard deviations of the combined statistical and
systematic uncertainties.


  A simple thermal model with single freeze-out and flow is used to analyze the
ratios of hadron yields and the hadron transverse-mass spectra measured in 130
GeV Au+Au collisions at RHIC. An overall very good agreement between the model
predictions and the data is achieved for all measured hadron species, including
hyperons.


  Measurement-based one-way quantum computation (QC) using cluster states as
resources provides an efficient model to perform computation and information
processing of quantum codes. Arbitrary Gaussian QC can be implemented by
sufficiently long single-mode and two-mode gate sequences. However, continuous
variable (CV) gate sequences have not been realized so far due to an absence of
cluster states larger than four submodes. Here we present the first CV gate
sequence consisting of a single-mode squeezing gate and a two-mode
controlled-phase gate based on a six-mode cluster state. The quantum property
of this gate sequence is confirmed by the fidelities and the quantum
entanglement of two output modes, which depend on both the squeezing and
controlled-phase gates. The experiment demonstrates the feasibility of
implementing Gaussian QC by means of accessible gate sequences.


  Recent spectroscopic studies have revealed the presence of numerous
carbon-enhanced, metal-poor stars with [Fe/H] < -2.0 that exhibit strong
enhancements of s-process elements. These stars are believed to be the result
of a binary mass-transfer episode from a former asymptotic giant-branch (AGB)
companion that underwent s-process nucleosynthesis. However, several such stars
exhibit significantly lower Ba/Eu ratios than solar s-process abundances. This
might be explained if there were an additional contribution from the r-process,
thereby diluting the Ba/Eu ratio by extra production of Eu. We propose a model
in which the double enhancements of r-process and s-process elements originate
from a former 8-10 M_\odot companion in a wide binary system, which may undergo
s-processing during an AGB phase, followed by r-processing during its
subsequent supernova explosion. The mass of Eu (as representative of r-process
elements) captured by the secondary through the wind from the supernova is
estimated, which is assumed to be proportional to the geometric fraction of the
secondary (low-mass, main-sequence) star with respect to the primary
(exploding) star. We find that the estimated mass is in good agreement with a
constraint on the Eu yield per supernova event obtained from a Galactic
chemical evolution study, when the initial orbital separation is taken to be
\sim 1 year. If one assumes an orbital period on the order of five years, the
efficiency of wind pollution from the supernova must be enhanced by a factor of
\sim 10. This may, in fact, be realized if the expansion velocity of the
supernova's innermost ejecta, in which the r-process has taken place, is
significantly slow, resulting in an enhancement of accretion efficiency by
gravitational focusing.


  Creating content with specified identities (ID) has attracted significant
interest in the field of generative models. In the field of text-to-image
generation (T2I), subject-driven creation has achieved great progress with the
identity controlled via reference images. However, its extension to video
generation is not well explored. In this work, we propose a simple yet
effective subject identity controllable video generation framework, termed
Video Custom Diffusion (VCD). With a specified identity defined by a few
images, VCD reinforces the identity characteristics and injects frame-wise
correlation at the initialization stage for stable video outputs. To achieve
this, we propose three novel components that are essential for high-quality
identity preservation and stable video generation: 1) a noise initialization
method with 3D Gaussian Noise Prior for better inter-frame stability; 2) an ID
module based on extended Textual Inversion trained with the cropped identity to
disentangle the ID information from the background 3) Face VCD and Tiled VCD
modules to reinforce faces and upscale the video to higher resolution while
preserving the identity's features. We conducted extensive experiments to
verify that VCD is able to generate stable videos with better ID over the
baselines. Besides, with the transferability of the encoded identity in the ID
module, VCD is also working well with personalized text-to-image models
available publicly. The codes are available at
https://github.com/Zhen-Dong/Magic-Me.


  We introduce a method for deriving constraints on the symbol of Feynman
integrals from the form of their asymptotic expansions in the neighborhood of
Landau loci. In particular, we show that the behavior of these integrals near
singular points is directly related to the position in the symbol where one of
the letters vanishes or becomes infinite. We illustrate this method on
integrals with generic masses, and as a corollary prove the conjectured bound
of $\lfloor \frac {D \ell} 2\rfloor$ on the transcendental weight of
polylogarithmic $\ell$-loop integrals of this type in integer numbers of
dimensions $D$. We also derive new constraints on the kinematic dependence of
certain products of symbol letters that remain finite near singular points.


  We will study torus actions on Cuntz--Krieger algebras trivially acting on
its canonical maximal abelian $C^*$-subalgebras from the view points of
continuous orbit equivalence of one-sided topological Markov shifts and flow
equivalence of two-sided topological Markov shifts.


  In this work, we give a presentation of the prefix expansion Pr(G) of an
inverse semigroup G as recently introduced by Lawson, Margolis and Steinberg
which is similar to the universal inverse semigroup defined by the second named
author in case G is a group. The inverse semigroup Pr(G) classifies the partial
actions of G on spaces. We extend this result and prove that Fell bundles over
G correspond bijectively to saturated Fell bundles over Pr(G). In particular,
this shows that twisted partial actions of G (on C*-algebras) correspond to
twisted (global) actions of Pr(G). Furthermore, we show that this
correspondence preserves C*-algebras crossed products.


  In order to completely eliminate, or greatly reduce the number of phase wraps
in 2D wrapped phase map, Gdeisat et al. proposed an algorithm, which uses
shifting the spectrum towards the origin. But the spectrum can be shifted only
by an integer number, meaning that the phase wraps reduction is often not
optimal. In addition, Gdeisat's method will take much time to make the Fourier
transform, inverse Fourier transform, select and shift the spectral components.
In view of the above problems, we proposed an improved method for phase wraps
elimination or reduction. First, the wrapped phase map is padded with zeros,
the carrier frequency of the projected fringe is determined by high resolution,
which can be used as the moving distance of the spectrum. And then realize
frequency shift in spatial domain. So it not only can enable the spectrum to be
shifted by a rational number when the carrier frequency is not an integer
number, but also reduce the execution time. Finally, the experimental results
demonstrated that the proposed method is feasible.


  Low-intensity light beams carrying Orbital Angular Momentum (OAM), commonly
known as vortex beams, have garnered significant attention due to promising
applications in areas ranging from optical trapping to communication. In recent
years, there has been a surge in global research exploring the potential of
high-intensity vortex laser beams and specifically their interactions with
plasmas. This paper provides a comprehensive review of recent advances in this
area. Compared to conventional laser beams, intense vortex beams exhibit unique
properties such as twisted phase fronts, OAM delivery, hollow intensity
distribution, and spatially isolated longitudinal fields. These distinct
characteristics give rise to a multitude of rich phenomena, profoundly
influencing laser-plasma interactions and offering diverse applications. The
paper also discusses future prospects and identifies promising general research
areas involving vortex beams. These areas include low-divergence particle
acceleration, instability suppression, high-energy photon delivery with OAM,
and the generation of strong magnetic fields. With growing scientific interest
and application potential, the study of intense vortex lasers is poised for
rapid development in the coming years.


  We show that the quantized free relativistic point particle can be understood
as a string in a Clifford space which generates the space-time coordinates
through its inner product. The generating algebra is preserved by a unitary
symmetry which becomes the symmetry of the quantum states. We start by
resolving the space-time canonical variables of the point particle into inner
products of Weyl spinors with components in a Clifford algebra. Next, we show
that a system of N particles has a U(N) symmetry that mixes the Clifford
coordinates and momenta belonging to different particles. The inner products of
these variables are assembled into Hermitian matrices X and P which are
employed in defining a general unitarily invariant dynamical system. When X and
P commute, this system can be gauged back into the original system of
independent particles. When they do not commute, the system becomes irreducible
and infinite and generates a space-time canonical system formally identical to
Matrix Mechanics. The continuum limit is identified as a particular
parametrization of a relativistic string in Clifford space.


  We use the CORSIKA air shower simulation program to review the method for
assigning energies to ultra-high energy (UHE) cosmic rays viewed with the air
fluorescence technique. This technique uses the atmosphere as a calorimeter,
and we determine the corrections that must be made to the calorimetric energy
to yield the primary cosmic ray energy.


  We prove the extremal function for $K_9^=$ minors, where $K_9^=$ denotes the
complete graph $K_9$ with two edges removed. In particular, we show that any
graph with $n$ vertices and at least $6n - 20$ edges either contains a $K_9^=$
minor or is isomorphic to a graph obtained from disjoint copies of $K_8$ and
$K_{2, 2, 2, 2, 2}$ by identifying cliques of size 5. We utilize computer
assistance to prove one of our lemmas.


  We present a clustering comparison of 12 galaxy formation models (including
Semi-Analytic Models (SAMs) and Halo Occupation Distribution (HOD) models) all
run on halo catalogues and merger trees extracted from a single {\Lambda}CDM
N-body simulation. We compare the results of the measurements of the mean halo
occupation numbers, the radial distribution of galaxies in haloes and the
2-Point Correlation Functions (2PCF). We also study the implications of the
different treatments of orphan (galaxies not assigned to any dark matter
subhalo) and non-orphan galaxies in these measurements. Our main result is that
the galaxy formation models generally agree in their clustering predictions but
they disagree significantly between HOD and SAMs for the orphan satellites.
Although there is a very good agreement between the models on the 2PCF of
central galaxies, the scatter between the models when orphan satellites are
included can be larger than a factor of 2 for scales smaller than 1 Mpc/h. We
also show that galaxy formation models that do not include orphan satellite
galaxies have a significantly lower 2PCF on small scales, consistent with
previous studies. Finally, we show that the 2PCF of orphan satellites is
remarkably different between SAMs and HOD models. Orphan satellites in SAMs
present a higher clustering than in HOD models because they tend to occupy more
massive haloes. We conclude that orphan satellites have an important role on
galaxy clustering and they are the main cause of the differences in the
clustering between HOD models and SAMs.


  We present a system of equations between Clifford circuits, all derivable in
the ZX-calculus, and formalised as rewrite rules in the Quantomatic proof
assistant. By combining these rules with some non-trivial simplification
procedures defined in the Quantomatic tactic language, we demonstrate the use
of Quantomatic as a circuit optimisation tool. We prove that the system always
reduces Clifford circuits of one or two qubits to their minimal form, and give
numerical results demonstrating its performance on larger Clifford circuits.


  A Full Duplex Base Station (FD-BS) can be used to serve simultaneously two
Half-Duplex (HD) Mobile Stations (MSs), one working in the uplink and one in
the downlink, respectively. The same functionality can be realized by having
two interconnected and spatially separated Half Duplex Base Stations (HD-BSs),
which is a scheme termed \emph{CoMPflex} (CoMP for In-Band Wireless Full
Duplex). A FD-BS can be seen as a special case of CoMPflex with separation
distance zero. In this paper we study the performance of CoMPflex in a
two-dimensional cellular scenario using stochastic geometry and compare it to
the one achieved by FD-BSs. By deriving the Cumulative Distribution Functions,
we show that CoMPflex brings BSs closer to the MSs they are serving, while
increasing the distance between a MS and interfering MSs. Furthermore, the
results show that CoMPflex brings benefits over FD-BS in terms of communication
reliability. Following the trend of wireless network densification, CoMPflex
can be regarded as a method with a great potential to effectively use the dense
HD deployments.


  The pretrain-finetune paradigm has achieved great success in NLP and 2D image
fields because of the high-quality representation ability and transferability
of their pretrained models. However, pretraining such a strong model is
difficult in the 3D point cloud field due to the limited amount of point cloud
sequences. This paper introduces \textbf{E}fficient \textbf{P}oint
\textbf{C}loud \textbf{L}earning (EPCL), an effective and efficient point cloud
learner for directly training high-quality point cloud models with a frozen
CLIP transformer. Our EPCL connects the 2D and 3D modalities by semantically
aligning the image features and point cloud features without paired 2D-3D data.
Specifically, the input point cloud is divided into a series of local patches,
which are converted to token embeddings by the designed point cloud tokenizer.
These token embeddings are concatenated with a task token and fed into the
frozen CLIP transformer to learn point cloud representation. The intuition is
that the proposed point cloud tokenizer projects the input point cloud into a
unified token space that is similar to the 2D images. Comprehensive experiments
on 3D detection, semantic segmentation, classification and few-shot learning
demonstrate that the CLIP transformer can serve as an efficient point cloud
encoder and our method achieves promising performance on both indoor and
outdoor benchmarks. In particular, performance gains brought by our EPCL are
$\textbf{19.7}$ AP$_{50}$ on ScanNet V2 detection, $\textbf{4.4}$ mIoU on S3DIS
segmentation and $\textbf{1.2}$ mIoU on SemanticKITTI segmentation compared to
contemporary pretrained models. Code is available at
\url{https://github.com/XiaoshuiHuang/EPCL}.


  For a tall $n\times d$ matrix $A$ and a random $m\times n$ sketching matrix
$S$, the sketched estimate of the inverse covariance matrix $(A^\top A)^{-1}$
is typically biased: $E[(\tilde A^\top\tilde A)^{-1}]\ne(A^\top A)^{-1}$, where
$\tilde A=SA$. This phenomenon, which we call inversion bias, arises, e.g., in
statistics and distributed optimization, when averaging multiple independently
constructed estimates of quantities that depend on the inverse covariance. We
develop a framework for analyzing inversion bias, based on our proposed concept
of an $(\epsilon,\delta)$-unbiased estimator for random matrices. We show that
when the sketching matrix $S$ is dense and has i.i.d. sub-gaussian entries,
then after simple rescaling, the estimator $(\frac m{m-d}\tilde A^\top\tilde
A)^{-1}$ is $(\epsilon,\delta)$-unbiased for $(A^\top A)^{-1}$ with a sketch of
size $m=O(d+\sqrt d/\epsilon)$. This implies that for $m=O(d)$, the inversion
bias of this estimator is $O(1/\sqrt d)$, which is much smaller than the
$\Theta(1)$ approximation error obtained as a consequence of the subspace
embedding guarantee for sub-gaussian sketches. We then propose a new sketching
technique, called LEverage Score Sparsified (LESS) embeddings, which uses ideas
from both data-oblivious sparse embeddings as well as data-aware leverage-based
row sampling methods, to get $\epsilon$ inversion bias for sketch size
$m=O(d\log d+\sqrt d/\epsilon)$ in time $O(\text{nnz}(A)\log n+md^2)$, where
nnz is the number of non-zeros. The key techniques enabling our analysis
include an extension of a classical inequality of Bai and Silverstein for
random quadratic forms, which we call the Restricted Bai-Silverstein
inequality; and anti-concentration of the Binomial distribution via the
Paley-Zygmund inequality, which we use to prove a lower bound showing that
leverage score sampling sketches generally do not achieve small inversion bias.


  A general or truss distributive laws between two associative operations on
the same set are studied for cancellative and inverse semigroups.


  The existence of a 'crossover region' in glass-forming liquids has long been
considered as a general phenomenon that is as important as the glass
transition. One potential origin for the crossover behavior is a
liquid-to-liquid phase transition (LLPT). Although a LLPT is thought to exist
in all forms of liquids, structural evidence for this, particularly in
supercooled liquids, is scarce, elusive, and in many cases controversial. A key
challenge to the search for a LLPT in a supercooled liquid is the interference
of crystallization during cooling. Crystallization induces major structural
changes, which can overwhelm and therefore mask the more subtle changes
associated with a LLPT. Here, we report the results of an in-situ containerless
synchrotron study of a metallic-glass-forming liquid (Zr57Nb5Al10Cu15.4Ni12.6)
that show distinct changes in the liquid structure at ~1000 K, a temperature
well below the melting temperature of the liquid and 150 K above the
crystallization temperature. The structural transition is characterized by
growing short- and extended-range order below the transition temperature, and
is accompanied by a concurrent change in density. These data provide strong
evidence for a LLPT in the supercooled metallic liquid, particularly in the
light of a recent computer simulation study. That a LLPT is found in a metallic
liquid supports the increasingly widely held view that a LLPT may be a common
feature of a variety of liquids.


  For a risk vector $V$, whose components are shared among agents by some
random mechanism, we obtain asymptotic lower and upper bounds for the
individual agents' exposure risk and the aggregated risk in the market. Risk is
measured by Value-at-Risk or Conditional Tail Expectation. We assume Pareto
tails for the components of $V$ and arbitrary dependence structure in a
multivariate regular variation setting. Upper and lower bounds are given by
asymptotically independent and fully dependent components of $V$ with respect
to the tail index $\alpha$ being smaller or larger than 1. Counterexamples,
where for non-linear aggregation functions no bounds are available, complete
the picture.


  Bald patch (BP) is a magnetic topological feature where U-shaped field lines
turn tangent to the photosphere. Field lines threading the BP trace a
separatrix surface where reconnection preferentially occurs. Here we study the
evolution of multiple, strong-field BPs in active region 12673 during the most
intense, X9.3 flare of solar cycle 24. The central BP, located between the
initial flare ribbons, largely "disintegrated" within 35 minutes. The more
remote, southern BP survived. The disintegration manifested as a 9-degree
rotation of the median shear angle; the perpendicular component of the
horizontal field (with respect to the polarity inversion line) changed sign.
The parallel component exhibited a step-wise, permanent increase of 1 kG,
consistent with previous observations of the flare-related "magnetic imprint".
The observations suggest that magnetic reconnection during a major eruption may
involve entire BP separatrices, leading to a change of magnetic topology from
BPs to sheared arcades.


  We analyze the generation of persistent currents in Bose-Einstein condensates
of ultracold gases confined in a ring. This phenomenon has been recently
investigated in an experiment [Nature \textbf{506}, 200 (2014)], where
hysteresis loops have been observed in the activation of quantized persistent
currents by rotating weak links. In this work, we demonstrate the existence of
3D stationary currents with non-quantized angular momentum. They are generated
by families of solitary waves that show a continuous variation in the angular
momentum, and provide a bridge between different winding numbers. We show that
the size of hysteresis loops is determined by the range of existence within the
weak link region of solitary waves which configure the energy barrier
preventing phase slips. The barrier vanishes when the critical rotation leads
winding numbers and solitonic states to a matching configuration. At this
point, Landau and Feynman criteria for phase slips meet: the fluid flow reaches
the local speed of sound, and stationary vortex lines (which are the building
blocks of solitons) can be excited inside the system.