  For Y$_2$C$_3$ with a superconducting critical temperature (T$_c$) $\sim$18
K, zone-center imaginary optical phonon modes have been found for the
high-symmetry $I$-$43d$ structure due to C dimer wobbling motion and electronic
instability from a flat band near Fermi energy. After lattice distortion to the
more stable lowest symmetry $P1$ structure, these stabilized low-energy phonon
modes with a mixed C and Y character carry a strong electron-phonon coupling to
give arise to the observed sizable T$_c$. Our work shows that compounds with
the calculated dynamical instability should not be simply excluded in
high-throughput search for new phonon-mediated superconductors. Moreover, we
have studied the phase stability of the $I$-$43d$ structure by calculating the
enthalpy of different structural motifs of binary compounds containing group IV
elements at the 2:3 composition and also exploring the energy landscapes via
$ab$ $initio$ molecular dynamics near and out of the $I$-$43d$ structure. Our
results show that the $I$-$43d$ type structures with C dimers are preferred in
the low to medium pressure range. Because of the wobbling motion of the C
dimers, there are many local energy minimums with degenerated energies. Thus,
the ensemble average of many $I$-$43d$-distorted structures with C dimer
wobbling motion at finite temperature still gives an overall $I$-$43d$
structure.


  Clifford algebras are used for constructing spin groups, and are therefore of
particular importance in the theory of quantum mechanics. But the spin group is
not the only subgroup of the Clifford algebra. An algebraist's perspective on
these groups and algebras may suggest ways in which they might be applied more
widely to describe the fundamental properties of matter. I do not claim to
build a physical theory on top of the fundamental algebra, and my suggestions
for possible physical interpretations are indicative only, and may not work.
Nevertheless, both the existence of three generations of fermions and the
symmetry-breaking of the weak interaction seem to emerge naturally from an
extension of the Dirac algebra from complex numbers to quaternions.


  Functional data often arise from measurements on fine time grids and are
obtained by separating an almost continuous time record into natural
consecutive intervals, for example, days. The functions thus obtained form a
functional time series, and the central issue in the analysis of such data
consists in taking into account the temporal dependence of these functional
observations. Examples include daily curves of financial transaction data and
daily patterns of geophysical and environmental data. For scalar and vector
valued stochastic processes, a large number of dependence notions have been
proposed, mostly involving mixing type distances between $\sigma$-algebras. In
time series analysis, measures of dependence based on moments have proven most
useful (autocovariances and cumulants). We introduce a moment-based notion of
dependence for functional time series which involves $m$-dependence. We show
that it is applicable to linear as well as nonlinear functional time series.
Then we investigate the impact of dependence thus quantified on several
important statistical procedures for functional data. We study the estimation
of the functional principal components, the long-run covariance matrix, change
point detection and the functional linear model. We explain when temporal
dependence affects the results obtained for i.i.d. functional observations and
when these results are robust to weak dependence.


  El Ni\~{n}o is a typical example of a coupled atmosphere--ocean phenomenon,
but it is unclear whether it can be described quantitatively by a correlation
between relevant climate events. To provide clarity on this issue, we developed
a machine learning-based El Ni\~{n}o prediction model that uses the time series
of climate indices. By transforming the multidimensional time series into the
path signature, the model is able to properly evaluate the order and
nonlinearity of climate events, which allowed us to achieve good forecasting
skill (mean square error = 0.596 for 6-month prediction). In addition, it is
possible to provide information about the sequence of climate events that tend
to change the future NINO3.4 sea surface temperatures. In forecasting
experiments conducted, changes in the North Pacific Index and several NINO
indices were found to be important precursors. The results suggest that El
Ni\~{n}o is predictable to some extent based on the correlation of climate
events.


  The propensity of molecules to spatially align along the polarization vector
of intense, pulsed light fields is related to readily-accessible parameters
(molecular polarizabilities, moment of inertia, peak intensity of the light and
its pulse duration). Predictions can now be made of which molecules can be
spatially aligned, and under what circumstances, upon irradiation by intense
light. Accounting for both enhanced ionization and hyperpolarizability, it is
shown that {\it all} molecules can be aligned, even those with the smallest
static polarizability, when subjected to the shortest available laser pulses
(of sufficient intensity).


  Road obstacle detection is an important problem for vehicle driving safety.
In this paper, we aim to obtain robust road obstacle detection based on
spatio-temporal context modeling. Firstly, a data-driven spatial context model
of the driving scene is constructed with the layouts of the training data.
Then, obstacles in the input image are detected via the state-of-the-art object
detection algorithms, and the results are combined with the generated scene
layout. In addition, to further improve the performance and robustness,
temporal information in the image sequence is taken into consideration, and the
optical flow is obtained in the vicinity of the detected objects to track the
obstacles across neighboring frames. Qualitative and quantitative experiments
were conducted on the Small Obstacle Detection (SOD) dataset and the Lost and
Found dataset. The results indicate that our method with spatio-temporal
context modeling is superior to existing methods for road obstacle detection.


  Within a decade, probably less, we will need to find ways to work effectively
with ever growing numbers of intelligent things, including robots and
intelligent agents. The networked workforce of the near future will thus
consist of not only interconnected and interdependent humans but also of
intelligent things. This raises a number of challenging issues, none more
compelling and urgent than finding an answer to the question "How to manage
this new organizational form?" We consider these issues in a particularly
challenging domain of human endeavor -- warfare. Command and Control (C2) is
the term applied to management or governance of military organizations and
endeavors. We consider how human and other intelligent entities can best
contribute to ensuring that the decision makers, whether human or machine, have
the information they require and make good use of this information to
accomplish C2 functions. Commanders or managers of mixed human-thing
organizations will face several challenges that the discussion above has
highlighted. Things are challenged in a number of areas and will need humans to
provide these capabilities. These include their ability to explain, build
trust, bond, understand personal agendas, emotions, politics, and negotiate.
Things and people both to some extent have difficulty anticipating and coping
with the unusual and unexpected and to think of out-of-the-box solutions.


  Einstein's gravity has been extensively tested in the weak field regime,
primarily with experiments in the Solar System and observations of binary
pulsars, and current data agree well with theoretical predictions. On the other
hand, strong gravity is largely unexplored and there are a number of theories
beyond Einstein's gravity that make the same predictions for weak fields and
present deviations only when gravity becomes strong. The best laboratory for
testing strong gravity is the spacetime around astrophysical black holes. X-ray
reflection spectroscopy can be a powerful tool to probe the strong gravity
region around astrophysical black holes and test the nature of these objects.
In this paper, we will introduce RELXILL_NK, which is the first XSPEC
reflection model to test Einstein's gravity in the strong field regime, and we
will present the constraints on possible deviations from Einstein's gravity
that we have obtained by analyzing Suzaku data of Ark 564.


  The declining price anomaly states that the price weakly decreases when
multiple copies of an item are sold sequentially over time. The anomaly has
been observed in a plethora of practical applications. On the theoretical side,
Gale and Stegeman proved that the anomaly is guaranteed to hold in full
information sequential auctions with exactly two buyers. We prove that the
declining price anomaly is not guaranteed in full information sequential
auctions with three or more buyers. This result applies to both first-price and
second-price sequential auctions. Moreover, it applies regardless of the
tie-breaking rule used to generate equilibria in these sequential auctions. To
prove this result we provide a refined treatment of subgame perfect equilibria
that survive the iterative deletion of weakly dominated strategies and use this
framework to experimentally generate a very large number of random sequential
auction instances. In particular, our experiments produce an instance with
three bidders and eight items that, for a specific tie-breaking rule, induces a
non-monotonic price trajectory. Theoretic analyses are then applied to show
that this instance can be used to prove that for every possible tie-breaking
rule there is a sequential auction on which it induces a non-monotonic price
trajectory. On the other hand, our experiments show that non-monotonic price
trajectories are extremely rare. In over six million experiments only a
0.000183 proportion of the instances violated the declining price anomaly.


  As an efficient recurrent neural network (RNN) model, reservoir computing
(RC) models, such as Echo State Networks, have attracted widespread attention
in the last decade. However, while they have had great success with time series
data [1], [2], many time series have a multiscale structure, which a
single-hidden-layer RC model may have difficulty capturing. In this paper, we
propose a novel hierarchical reservoir computing framework we call Deep Echo
State Networks (Deep-ESNs). The most distinctive feature of a Deep-ESN is its
ability to deal with time series through hierarchical projections.
Specifically, when an input time series is projected into the high-dimensional
echo-state space of a reservoir, a subsequent encoding layer (e.g., a PCA,
autoencoder, or a random projection) can project the echo-state representations
into a lower-dimensional space. These low-dimensional representations can then
be processed by another ESN. By using projection layers and encoding layers
alternately in the hierarchical framework, a Deep-ESN can not only attenuate
the effects of the collinearity problem in ESNs, but also fully take advantage
of the temporal kernel property of ESNs to explore multiscale dynamics of time
series. To fuse the multiscale representations obtained by each reservoir, we
add connections from each encoding layer to the last output layer. Theoretical
analyses prove that stability of a Deep-ESN is guaranteed by the echo state
property (ESP), and the time complexity is equivalent to a conventional ESN.
Experimental results on some artificial and real world time series demonstrate
that Deep-ESNs can capture multiscale dynamics, and outperform both standard
ESNs and previous hierarchical ESN-based models.


  We report a measurement of CP-violating asymmetries in
$B^0\to(\rho\pi)^0\to\pi^+\pi^-\pi^0$ decays using a time-dependent Dalitz plot
analysis. The results are obtained from a data sample of 347 million
$\Upsilon(4S) \to B\bar{B}$ decays, collected by the BaBar detector at the
PEP-II asymmetric-energy B Factory at SLAC. We measure 26 coefficients of the
bilinear form factor terms occurring in the time-dependent decay rate of the
\Bz meson and derive the physically relevant quantities from these
coefficients. In particular we find a three standard deviation evidence of
direct CP-violation in $B^0\to\rho^\pm\pi^\mp$ decays, with systematic
uncertainties included. We also achieve a constraint of the angle $\alpha$ of
the Unitarity Triangle. All results presented are preliminary.


  In vertex operator algebra theories, most of the general theorems are proved
under the assumptions of rationality and C_2-cofiniteness. In this paper, we
obtain several general theorems without the assumption of rationality so that
we can use them for proving rationality of given C_2-cofinite vertex operator
algebras. For example, we apply them to orbifold models and show that if g is a
finite automorphism of a rational C_2-cofinite vertex operator algebra T of
CFT-type with T'\cong T and a fixed point subVOA T^g is C_2-cofinite, then T^g
is also rational.


  In this work, we investigate the problem of public data assisted
non-interactive Local Differentially Private (LDP) learning with a focus on
non-parametric classification. Under the posterior drift assumption, we for the
first time derive the mini-max optimal convergence rate with LDP constraint.
Then, we present a novel approach, the locally differentially private
classification tree, which attains the mini-max optimal convergence rate.
Furthermore, we design a data-driven pruning procedure that avoids parameter
tuning and provides a fast converging estimator. Comprehensive experiments
conducted on synthetic and real data sets show the superior performance of our
proposed methods. Both our theoretical and experimental findings demonstrate
the effectiveness of public data compared to private data, which leads to
practical suggestions for prioritizing non-private data collection.


  Applications like Big Data, Machine Learning, Deep Learning and even other
Engineering and Scientific research requires a lot of computing power; making
High-Performance Computing (HPC) an important field. But access to
Supercomputers is out of range from the majority. Nowadays Supercomputers are
actually clusters of computers usually made-up of commodity hardware. Such
clusters are called Beowulf Clusters. The history of which goes back to 1994
when NASA built a Supercomputer by creating a cluster of commodity hardware. In
recent times a lot of effort has been done in making HPC Clusters of even
single board computers (SBCs). Although the creation of clusters of commodity
hardware is possible but is a cumbersome task. Moreover, the maintenance of
such systems is also difficult and requires special expertise and time. The
concept of cloud is to provide on-demand resources that can be services,
platform or even infrastructure and this is done by sharing a big resource
pool. Cloud computing has resolved problems like maintenance of hardware and
requirement of having expertise in networking etc. An effort is made of
bringing concepts from cloud computing to HPC in order to get benefits of
cloud. The main target is to create a system which can develop a capability of
providing computing power as a service which to further be referred to as
Supercomputer as a service. A prototype was made using Raspberry Pi (RPi) 3B
and 3B+ Single Board Computers. The reason for using RPi boards was increasing
popularity of ARM processors in the field of HPC


  We describe the methods and results of numerical simulations of coalescing
black holes. The simulation in dynamical spacetime covers the inspiral, merger,
and ringdown phases. We analyze the emission of gravitational waves and
properties of a black hole being the merger product. We discuss the results in
the context of astrophysical environment of black holes that exist in the
Universe.


  Gamma camera imaging is an important technique in nuclear medicine. It is
capable of diagnostic imaging of metabolically active areas and organ function,
and it can be used to evaluate blood flow in the heart muscle, measure bone
growth, detect tumors, and perform many other medical studies. It is a real
world application that integrates concepts in medicine, nuclear physics,
geometric optics, data processing, calibration, and image formation. This paper
provides an overview of gamma camera imaging intended for an intermediate-level
undergraduate physics course for students majoring in STEM disciplines. Because
working with radioactive materials is not practical in our setting, we use an
approach involving paper-and-pencil exercises, visible light apparatus, and
computer work.


  A chained hydrologic-hydraulic model is implemented using predicted runoff
from a large-scale hydrologic model (namely ISBA-CTRIP) as inputs to local
hydrodynamic models (TELEMAC-2D) to issue forecasts of water level and flood
extent. The uncertainties in the hydrological forcing and in friction
parameters are reduced by an Ensemble Kalman Filter that jointly assimilates
in-situ water levels and flood extent maps derived from remote sensing
observations. The data assimilation framework is cycled in a real-time
forecasting configuration. A cycle consists of a reanalysis and a forecast
phase. Over the analysis, observations up to the present are assimilated. An
ensemble is then initialized from the last analyzed states and issued forecasts
for next 36 hr. Three strategies of forcing data for this forecast are
investigated: (i) using CTRIP runoff for reanalysis and forecast, (ii) using
observed discharge for analysis, then CTRIP runoff for forecast and (iii) using
observed discharge for reanalysis and keep a persistent discharge value for
forecast. It was shown that the data assimilation strategy provides a reliable
reanalysis in hindcast mode. The combination of observed discharge and CTRIP
runoff provides the most accurate results. For all strategies, the quality of
the forecast decreases as the lead time increases. When the errors in CTRIP
forcing are non-stationary, the forecast capability may be reduced. This work
demonstrates that the forcing provided by a hydrologic model, while imperfect,
can be efficiently used as input to a hydraulic model to issue reanalysis and
forecasts, thanks to the assimilation of in-situ and remote sensing
observations.


  Object placement is a fundamental task for robots, yet it remains challenging
for partially observed objects. Existing methods for object placement have
limitations, such as the requirement for a complete 3D model of the object or
the inability to handle complex shapes and novel objects that restrict the
applicability of robots in the real world. Herein, we focus on addressing the
Unseen Object Placement (UOP}=) problem. We tackled the UOP problem using two
methods: (1) UOP-Sim, a large-scale dataset to accommodate various shapes and
novel objects, and (2) UOP-Net, a point cloud segmentation-based approach that
directly detects the most stable plane from partial point clouds. Our UOP
approach enables robots to place objects stably, even when the object's shape
and properties are not fully known, thus providing a promising solution for
object placement in various environments. We verify our approach through
simulation and real-world robot experiments, demonstrating state-of-the-art
performance for placing single-view and partial objects. Robot demos, codes,
and dataset are available at https://gistailab.github.io/uop/


  Esparza and Reiter have recently conducted a systematic comparative study of
models of distributed computing consisting of a network of identical
finite-state automata that cooperate to decide if the underlying graph of the
network satisfies a given property. The study classifies models according to
four criteria, and shows that twenty initially possible combinations collapse
into seven equivalence classes with respect to their decision power, i.e. the
properties that the automata of each class can decide. However, Esparza and
Reiter only show (proper) inclusions between the classes, and so do not
characterise their decision power. In this paper we do so for labelling
properties, i.e. properties that depend only on the labels of the nodes, but
not on the structure of the graph. In particular, majority (whether more nodes
carry label $a$ than $b$) is a labelling property. Our results show that only
one of the seven equivalence classes identified by Esparza and Reiter can
decide majority for arbitrary networks. We then study the expressive power of
the classes on bounded-degree networks, and show that three classes can. In
particular, we present an algorithm for majority that works for all
bounded-degree networks under adversarial schedulers, i.e. even if the
scheduler must only satisfy that every node makes a move infinitely often, and
prove that no such algorithm can work for arbitrary networks.


  In the paper, the law of the iterated logarithm for additive functionals of
Markov chains is obtained under some weak conditions, which are weaker than the
conditions of invariance principle of additive functionals of Markov chains in
M. Maxwell and M. Woodroofe (2000). The main technique is the martingale
argument and the theory of fractional coboundaries.


  We use relative trace formula to prove a non-vanishing result and a
subconvexity result for the twisted base change $L$-functions associated to
Hilbert modular forms whose local components at ramified places are some
supercuspidal representations. This generalizes the work of Feigon and
Whitehouse.


  In modern taxi networks, large amounts of taxi occupancy status and location
data are collected from networked in-vehicle sensors in real-time. They provide
knowledge of system models on passenger demand and mobility patterns for
efficient taxi dispatch and coordination strategies. Such approaches face new
challenges: how to deal with uncertainties of predicted customer demand while
fulfilling the system's performance requirements, including minimizing taxis'
total idle mileage and maintaining service fairness across the whole city; how
to formulate a computationally tractable problem. To address this problem, we
develop a data-driven robust taxi dispatch framework to consider
spatial-temporally correlated demand uncertainties. The robust vehicle dispatch
problem we formulate is concave in the uncertain demand and convex in the
decision variables. Uncertainty sets of random demand vectors are constructed
from data based on theories in hypothesis testing, and provide a desired
probabilistic guarantee level for the performance of robust taxi dispatch
solutions. We prove equivalent computationally tractable forms of the robust
dispatch problem using the minimax theorem and strong duality. Evaluations on
four years of taxi trip data for New York City show that by selecting a
probabilistic guarantee level at 75%, the average demand-supply ratio error is
reduced by 31.7%, and the average total idle driving distance is reduced by
10.13% or about 20 million miles annually, compared with non-robust dispatch
solutions.


  Assuming minimal regularity assumptions on the data, we revisit the classical
problem of finding isometric immersions into the Minkowski spacetime for
hypersurfaces of a Lorentzian manifold. Our approach encompasses metrics having
Sobolev regularity and Riemann curvature defined in the distributional sense,
only. It applies to timelike, spacelike, or null hypersurfaces with arbitrary
signature that possibly changes from point to point.


  This paper presents a decentralized algorithm for solving distributed convex
optimization problems in dynamic networks with time-varying objectives. The
unique feature of the algorithm lies in its ability to accommodate a wide range
of communication systems, including previously unsupported ones, by abstractly
modeling the information exchange in the network. Specifically, it supports a
novel communication protocol based on the "over-the-air" function computation
(OTA-C) technology, that is designed for an efficient and truly decentralized
implementation of the consensus step of the algorithm. Unlike existing OTA-C
protocols, the proposed protocol does not require the knowledge of network
graph structure or channel state information, making it particularly suitable
for decentralized implementation over ultra-dense wireless networks with
time-varying topologies and fading channels. Furthermore, the proposed
algorithm synergizes with the "superiorization" methodology, allowing the
development of new distributed algorithms with enhanced performance for the
intended applications. The theoretical analysis establishes sufficient
conditions for almost sure convergence of the algorithm to a common
time-invariant solution for all agents, assuming such a solution exists. Our
algorithm is applied to a real-world distributed random field estimation
problem, showcasing its efficacy in terms of convergence speed, scalability,
and spectral efficiency. Furthermore, we present a superiorized version of our
algorithm that achieves faster convergence with significantly reduced energy
consumption compared to the unsuperiorized algorithm.


  Fine-grained few-shot recognition often suffers from the problem of training
data scarcity for novel categories.The network tends to overfit and does not
generalize well to unseen classes due to insufficient training data. Many
methods have been proposed to synthesize additional data to support the
training. In this paper, we focus one enlarging the intra-class variance of the
unseen class to improve few-shot classification performance. We assume that the
distribution of intra-class variance generalizes across the base class and the
novel class. Thus, the intra-class variance of the base set can be transferred
to the novel set for feature augmentation. Specifically, we first model the
distribution of intra-class variance on the base set via variational inference.
Then the learned distribution is transferred to the novel set to generate
additional features, which are used together with the original ones to train a
classifier. Experimental results show a significant boost over the
state-of-the-art methods on the challenging fine-grained few-shot image
classification benchmarks.


  The bilocal effective action is developed as a viable tool to study mesons in
the large-N limit of QCD. Several results from current algebra are derived in a
new way using this action. The question of gauge invariance is discussed in an
appendix


  When the dynamical data of a system only convey dynamic information over a
limited operating range, the identification of models with good performance
over a wider operating range is very unlikely. Nevertheless, models with such
characteristic are desirable to implement modern control systems. To overcome
such a shortcoming, this paper describes a methodology to train models from
dynamical data and steady-state information, which is assumed available. The
novelty is that the procedure can be applied to models with rather complex
structures such as multilayer perceptron neural networks in a bi-objective
fashion without the need to compute fixed points neither analytically nor
numerically. As a consequence, the required computing time is greatly reduced.
The capabilities of the proposed method are explored in numerical examples and
the development of soft-sensors for downhole pressure estimation for a real
deep-water offshore oil well. The results indicate that the procedure yields
suitable soft-sensors with good dynamical and static performance and, in the
case of models that are nonlinear in the parameters, the gain in computation
time is about three orders of magnitude considering existing approaches.


  We formulate a full-counting statistics description to study energy exchange
in multi-terminal junctions. Our approach applies to quantum systems that are
coupled either additively or non-additively (cooperatively) to multiple
reservoirs. We derive a Markovian Redfield-type equation for the counting-field
dependent reduced density operator. Under the secular approximation, we confirm
that the cumulant generating function satisfies the heat exchange fluctuation
theorem. Our treatment thus respects the second law of thermodynamics. We
exemplify our formalism on a multi-terminal two-level quantum system, and apply
it to realize the smallest quantum absorption refrigerator, operating through
engineered reservoirs, and achievable only through a cooperative bath
interaction model.


  Generating natural language descriptions of images is an important capability
for a robot or other visual-intelligence driven AI agent that may need to
communicate with human users about what it is seeing. Such image captioning
methods are typically trained by maximising the likelihood of ground-truth
annotated caption given the image. While simple and easy to implement, this
approach does not directly maximise the language quality metrics we care about
such as CIDEr. In this paper we investigate training image captioning methods
based on actor-critic reinforcement learning in order to directly optimise
non-differentiable quality metrics of interest. By formulating a per-token
advantage and value computation strategy in this novel reinforcement learning
based captioning model, we show that it is possible to achieve the state of the
art performance on the widely used MSCOCO benchmark.


  Using first-principles calculations we model the out-of-plane switching of
local dipoles in CuInP$_2$S$_6$ (CIPS) that are largely induced by Cu
off-centering. Previously, a coherent switching of polarization via a
quadruple-well potential was proposed for these materials. In the super-cells
we considered, we find multiple structures with similar energies but with
different local polar order. Our results suggest that the individual dipoles
are weakly coupled in-plane and under an electric field at very low
temperatures these dipoles in CIPS should undergo incoherent disordered
switching. The barrier for switching is determined by the single Cu-ion
switching barrier. This in turn suggests a scale-free polarization with a
switching barrier of $\sim$ 203.6-258.0 meV, a factor of five smaller than that
of HfO$_2$ (1380 meV) a prototypical scale-free ferroelectric. The mechanism of
polarization switching in CIPS is mediated by the switching of each weakly
interacting dipole rather than the macroscopic polarization itself as
previously hypothesized. These findings reconcile prior observations of a
quadruple well with sloping hysteresis loops, large ionic conductivity even at
250~K well below the Curie temperature (315~K), and a significant wake-up
effects where the macroscopic polarization is slow to order and set-in under an
applied electric field. We also find that computed piezoelectric response and
the polarization show a linear dependence on the local dipolar order. This is
consistent with having scale-free polarization and other polarization-dependent
properties and opens doors for engineering tunable metastability by-design in
CIPS (and related family of materials) for neuromorphic applications.


  We study qualitative aspects of the Welschinger-like $\mathbb Z$-valued count
of real rational curves on primitively polarized real $K3$ surfaces. In
particular, we prove that with respect to the degree of the polarization, at
logarithmic scale, the rate of growth of the number of such real rational
curves is, up to a constant factor, the rate of growth of the number of complex
rational curves. We indicate a few instances when the lower bound for the
number of real rational curves provided by our count is sharp. In addition, we
exhibit various congruences between real and complex counts.


  We present here a semiconductor injection laser operating in continuous wave
with an emission covering more than one octave in frequency, and displaying
homogeneous power distribution among the lasing modes. The gain medium is based
on a heterogeneous quantum cascade structure operating in the THz range. Laser
emission in continuous wave takes place from 1.64 THz to 3.35 THz with optical
powers in the mW range and more than 80 modes above threshold. Free-running
beatnote investigations on narrow waveguides with linewidths of 980 Hz limited
by jitter indicate frequency comb operation on a spectral bandwidth as wide as
624 GHz, making such devices ideal candidates for octave-spanning
semiconductor-laser-based THz frequency combs.


  Autonomous robots are currently one of the most popular Artificial
Intelligence problems, having experienced significant advances in the last
decade, from Self-driving cars and humanoids to delivery robots and drones.
Part of the problem is to get a robot to emulate the perception of human
beings, our sense of sight, replacing the eyes with cameras and the brain with
mathematical models such as Neural Networks. Developing an AI able to drive a
car without human intervention and a small robot to deliver packages in the
city may seem like different problems, nevertheless from the point of view of
perception and vision, both problems have several similarities. The main
solutions we currently find focus on the environment perception through visual
information using Computer Vision techniques, Machine Learning, and various
algorithms to make the robot understand the environment or scene, move, adapt
its trajectory and perform its tasks (maintenance, exploration, etc.) without
the need for human intervention. In this work, we develop a small-scale
autonomous vehicle from scratch, capable of understanding the scene using only
visual information, navigating through industrial environments, detecting
people and obstacles, or performing simple maintenance tasks. We review the
state-of-the-art of fundamental problems and demonstrate that many methods
employed at small-scale are similar to the ones employed in real Self-driving
cars from companies like Tesla or Lyft. Finally, we discuss the current state
of Robotics and autonomous driving and the technological and ethical
limitations that we can find in this field.


  Long term sustainability of the high energy physics (HEP) research software
ecosystem is essential for the field. With upgrades and new facilities coming
online throughout the 2020s this will only become increasingly relevant
throughout this decade. Meeting this sustainability challenge requires a
workforce with a combination of HEP domain knowledge and advanced software
skills. The required software skills fall into three broad groups. The first is
fundamental and generic software engineering (e.g. Unix, version control,C++,
continuous integration). The second is knowledge of domain specific HEP
packages and practices (e.g., the ROOT data format and analysis framework). The
third is more advanced knowledge involving more specialized techniques. These
include parallel programming, machine learning and data science tools, and
techniques to preserve software projects at all scales. This paper dis-cusses
the collective software training program in HEP and its activities led by the
HEP Software Foundation (HSF) and the Institute for Research and Innovation in
Software in HEP (IRIS-HEP). The program equips participants with an array of
software skills that serve as ingredients from which solutions to the computing
challenges of HEP can be formed. Beyond serving the community by ensuring that
members are able to pursue research goals, this program serves individuals by
providing intellectual capital and transferable skills that are becoming
increasingly important to careers in the realm of software and computing,
whether inside or outside HEP


  We consider the model in two dimensions with boundary quadratic deformation
(BQD), which has been discussed in tachyon condensation. The partition function
of this model (BQD) on a cylinder is determined, using the method of zeta
function regularization. We show that, for closed channel partition function, a
subtraction procedure must be introduced in order to reproduce the correct
results at conformal points. The boundary entropy (g-function) is determined
from the partition function and the off-shell boundary state. We propose and
consider a supersymmetric generalization of BQD model, which includes a
boundary fermion mass term, and check the validity of the subtraction
procedure.


  An effect of the volume change upon proper ferroelastic (martensitic) phase
transitions in cubic crystals is considered. Corresponding terms in the
Ginzburg-Landau expansion of the Gibbs free energy are analyzed for the first-
as well as second-order phase transitions from cubic to tetragonal lattice
under the action of uniaxial and hydrostatic pressure. The pressure effect on
the critical temperature as well as on the phase transition anomalies of
isothermal compressibility and linear thermal expansion coefficient are studied
and recent experimental data on thermal expansion anomalies in V$_3$Si, In-Tl
and Ni-Al are discussed. The non-linearity of thermal expansion leads to the
special relation between the shear strain and volume change as a result of the
elastic energy minimization. This phenomenon can provide the transformation
from FCC lattice to BCC one, observed in the iron alloys.


  There have been claims in the literature that accretion disks in which a
centrifugally driven wind is the dominant mode of angular momentum transport
are inherently unstable. This issue is considered here by applying an
equilibrium-curve analysis to the wind-driving, ambipolar diffusion-dominated,
magnetic disk model of Wardle & Konigl (1993). The equilibrium solution curves
for this class of models typically exhibit two distinct branches. It is argued
that only one of these branches represents unstable equilibria and that a real
disk/wind system likely corresponds to a stable solution.


  A three-dimensional polynomial algebra of order $m$ is defined by the
commutation relations $[P_0, P_\pm]$ $=$ $\pm P_\pm$, $[P_+, P_-]$ $=$
$\phi^{(m)}(P_0)$ where $\phi^{(m)}(P_0)$ is an $m$-th order polynomial in
$P_0$ with the coefficients being constants or central elements of the algebra.
It is shown that two given mutually commuting polynomial algebras of orders $l$
and $m$ can be combined to give two distinct $(l+m+1)$-th order polynomial
algebras. This procedure follows from a generalization of the well known
Jordan-Schwinger method of construction of $su(2)$ and $su(1,1)$ algebras from
two mutually commuting boson algebras.


  Let $A$ and $B$ be sets in a finite vector space. In this paper, we study the
magnitude of the set $A\cap f(B)$, where $f$ runs through a set of
transformations. More precisely, we will focus on the cases that the set of
transformations is given by orthogonal matrices or orthogonal projections. One
of the most important contributions of this paper is to show that if $A,
B\subset \mathbb{F}_q^d$ satisfy some natural conditions then, for almost every
$g\in O(d)$, there are at least $\gg q^d$ elements $z\in \mathbb{F}_q^d$ such
that \[|A\cap (g(B)+z)| \sim \frac{|A||B|}{q^d}.\] This infers that $|A-gB|\gg
q^d$ for almost every $g\in O(d)$. In the flavor of expanding functions, with
$|A|\le |B|$, we also show that the image $A-gB$ grows exponentially. In two
dimensions, the result simply says that if $|A|=q^x$ and $|B|=q^y$, as long as
$0<x\le y<2$, then for almost every $g\in O(2)$, we can always find
$\epsilon=\epsilon(x, y)>0$ such that $|A-gB|\gg |B|^{1+\epsilon}$. To prove
these results, we need to develop a new and robust incidence bound between
points and rigid motions by using a number of techniques including algebraic
methods and discrete Fourier analysis. Our results are essentially sharp in odd
dimensions.


  The advent of pump-probe spectroscopy techniques paved the way to the
exploration of the ultrafast dynamics of electrons and phonons in crystalline
solids. Following photo-absorption of a pump pulse and the initial electronic
thermalization, the dynamics of electronic and vibrational degrees of freedom
is dominated by electron-phonon and phonon-phonon scattering processes.The
two-temperature model (TTM) and its generalizations -- as, e.g., the
non-thermal lattice model (NLM) -- provide valuable tools to describe these
phenomena and the ensuing coupled electron-phonon dynamics over timescales
ranging between few hundreds of femtoseconds and tens of picoseconds. While
more sophisticated theoretical approaches are nowadays available, the
conceptual and computational simplicity of the TTM makes it the method of
choice to model thermalization processes in pump-probe spectroscopy, and it
keeps being widely applied in both experimental and theoretical studies. In the
domain of ab-initio methods, the time-dependent Boltzmann equation (TDBE)
ameliorates many of the shortcomings of the TTM and it enables a realistic and
parameter-free description of ultrafast phenomena with full momentum
resolution. After a pedagogical introduction to the TTM and TDBE, in this
manuscript we review their application to the description of ultrafast process
in solid-state physics and materials science as well as their theoretical
foundation.


  Off-limb observations of the solar corona after Coronal Mass Ejections (CMEs)
often show strong, compact, and persistent UV sources behind the eruption. They
are primarily observed by the SOHO/UVCS instrument in the "hot" Fe XVIII
{\lambda}974 {\AA} line and are usually interpreted as a signature of plasma
heating due to magnetic reconnection in the post-CME Current Sheet (CS).
Nevertheless, the physical process itself and the altitude of the main energy
release are currently not fully understood. In this work, we studied the
evolution of plasma heating after the CME of 2004 July 28 by comparing UV
spectra acquired by UVCS with soft X-ray (SXR) and hard X-ray (HXR)images of
the post-flare loops taken by GOES/SXI and RHESSI. The X-ray data show a
long-lasting extended source that is rising upwards, toward the
high-temperature source detected by UVCS. UVCS data show the presence of
significant non-thermal broadening in the CS (signature of turbulent motions)
and a strong density gradient across the CS region. The thermal energy released
in the HXR source is on the order of about 10^32 erg, a factor about 2-5 larger
than the energy required to explain the high-temperature plasma sampled by
UVCS. Nevertheless, the very different time evolutions of SXR and HXR sources
compared to the UV emission suggest that reconnection occurring above the
post-eruption arcades are not directly responsible for the high-temperature
plasma sampled higher up by UVCS. We conclude that an additional plasma heating
mechanism (such as turbulent reconnection) in the CS is likely required.


  In principle, zero-shot learning makes it possible to train a recognition
model simply by specifying the category's attributes. For example, with
classifiers for generic attributes like \emph{striped} and \emph{four-legged},
one can construct a classifier for the zebra category by enumerating which
properties it possesses---even without providing zebra training images. In
practice, however, the standard zero-shot paradigm suffers because attribute
predictions in novel images are hard to get right. We propose a novel random
forest approach to train zero-shot models that explicitly accounts for the
unreliability of attribute predictions. By leveraging statistics about each
attribute's error tendencies, our method obtains more robust discriminative
models for the unseen classes. We further devise extensions to handle the
few-shot scenario and unreliable attribute descriptions. On three datasets, we
demonstrate the benefit for visual category learning with zero or few training
examples, a critical domain for rare categories or categories defined on the
fly.


  Tin sulfides SnxSy are important class of materials that are actively
investigated as novel photovoltaic and water splitting materials.
First-principles evolutionary crystal structure search is performed with the
goal of constructing the complete phase diagram of SnxSy and discover new
phases as well as new compounds of varying stoichiometry at ambient conditions
and pressures up to 100 GPa. The ambient phase of SnS2 with P3m1 symmetry
remains stable up to 30 GPa. Another ambient phase, SnS experiences a series of
phase transformations: including \alpha-SnS to \beta-SnS at 9 GPa, followed by
\beta-SnS to \gamma-SnS at 40 GPa. \gamma-SnS is a new high-pressure metallic
phase with Pm3m space group symmetry stable up to 100 GPa, which becomes a
superconductor with a maximum Tc= 9.6 K at 40 GPa. Another new metallic
compound, Sn3S4 with I43d space group symmetry, is predicted to be stable at
pressures above 15 GPa, which also becomes a superconductor with a maximum
Tc=21.8 K at 30 GPa.


  In this paper we investigate the limits of control for mixed-state quantum
systems. The constraint of unitary evolution for non-dissipative quantum
systems imposes kinematical bounds on the optimization of arbitrary
observables. We summarize our previous results on kinematical bounds and show
that these bounds are dynamically realizable for completely controllable
systems. Moreover, we establish improved bounds for certain partially
controllable systems. Finally, the question of dynamical realizability of the
bounds for arbitary partially controllable systems is shown to depend on the
accessible sets of the associated control system on the unitary group U(N) and
the results of a few control computations are discussed briefly.


  Significant discrepancies between the different determinations of the Cabibbo
angle have been observed. Here, we point out that this "Cabibbo-angle anomaly"
can be explained by lepton flavour universality (LFU) violating New Physics
(NP) in the neutrino sector. However, modified neutrino couplings to Standard
Model gauge bosons also affect many other observables sensitive to LFU
violation, which have to be taken into account in order to assess the viability
of this explanation. Therefore, we perform a model-independent Bayesian global
analysis and find that non-zero modifications of electron and muon neutrino
couplings are preferred at more than $99.99\%\,{\rm C.L.}$ (corresponding to
more than $4\,\sigma$). Our results show that constructive effects in the muon
sector are necessary, meaning simple models with right-handed neutrinos are
discarded and more sophisticated NP models required.


  In this paper, convergence results on the solutions of a time and space
discrete model approximation of the Boltzmann equation for a gas of Maxwellian
particles in a bounded domain, obtained by Babovsky and Illner [1989], are
extended to approximate the solutions of the Boltzmann equation in the whole
physical space. This is done for a class of particle interactions including
Maxwell and soft cut-off potentials in the sense of Grad.
  The main result shows that the solutions of the discrete model converge in
$\mathbb{L}^1$ to the solutions of the Boltzmann equation, when the
discretization parameters go simultaneously to zero. The convergence is uniform
with respect to the discretization parameters.
  In addition, a sufficient condition for the implementation of the main result
is provided.


  Let $(S,\mathcal L)$ be a smooth, irreducible, projective, complex surface,
polarized by a very ample line bundle $\mathcal L$ of degree $d > 35$. In this
paper we prove that $K^2_S\geq -d(d-6)$. The bound is sharp, and
$K^2_S=-d(d-6)$ if and only if $d$ is even, the linear system $|H^0(S,\mathcal
L)|$ embeds $S$ in a smooth rational normal scroll $T\subset \mathbb P^5$ of
dimension $3$, and here, as a divisor, $S$ is linearly equivalent to
$\frac{d}{2}Q$, where $Q$ is a quadric on $T$.


  The Stokes-Einstein (SE) relation between the self-diffusion and shear
viscosity coefficients operates in sufficiently dense liquids not too far from
the liquid-solid phase transition. By considering four simple model systems
with very different pairwise interaction potentials (Lennard-Jones, Coulomb,
Debye-H\"uckel or screened Coulomb, and the hard sphere limit) we identify
where exactly on the respective phase diagrams the SE relation holds. It
appears that the reduced excess entropy $s_{\rm ex}$ can be used as a suitable
indicator of the validity of the SE relation. In all cases considered the onset
of SE relation validity occurs at approximately $s_{\rm ex}\lesssim -2$. In
addition, we demonstrate that the line separating gas-like and liquid-like
fluid behaviours on the phase diagram is roughly characterized by $s_{\rm
ex}\simeq -1$.


  Thermally-driven atmospheric escape evolves from an organized outflow
(hydrodynamic escape) to escape on a molecule by molecules basis (Jeans escape)
with increasing Jeans parameter, the ratio of the gravitational to thermal
energy of molecules in a planet's atmosphere. This transition is described here
using the direct simulation Monte Carlo method for a single component
spherically symmetric atmosphere. When the heating is predominantly below the
lower boundary of the simulation region, R0, and well below the exobase, this
transition is shown to occur over a surprisingly narrow range of Jeans
parameters evaluated at R0: {\lambda}0 ~ 2-3. The Jeans parameter {\lambda}0 ~
2.1 roughly corresponds to the upper limit for isentropic, supersonic outflow
and for {\lambda}0 >3 escape occurs on a molecule by molecule basis. For
{\lambda}0 > ~6, it is shown that the escape rate does not deviate
significantly from the familiar Jeans rate evaluated at the nominal exobase,
contrary to what has been suggested. Scaling by the Jeans parameter and the
Knudsen number, escape calculations for Pluto and an early Earth's atmosphere
are evaluated, and the results presented here can be applied to
thermally-induced escape from a number of solar and extrasolar planetary
bodies.


  Methanol masers at 6.7 GHz are the brightest of class II methanol masers and
have been found exclusively towards massive star forming regions. These masers
can thus be used as a unique tool to probe the early phases of massive star
formation. We present here a study of the spectral energy distributions of 320
6.7 GHz methanol masers chosen from the MMB catalogue, which fall in the Hi-GAL
range ($|l| \le 60^{\circ}$, $|b| \le 1^{\circ}$). The spectral energy
distributions are constructed from $870 - 70~\mu$m using data from the ATLASGAL
and Hi-GAL surveys. The emission from cold dust is modelled by a single grey
body component fit. We estimate the clump properties such as mass, FIR
luminosity and column density using the best fit parameters of the SED fits.
Considering the Kauffman criteria for massive star formation, we find that all
but a few maser hosts have the potential to harbour at least one high mass
star. The physical properties of the methanol maser hosts are also discussed.
The evolutionary stages of 6.7 GHz maser sources, explored using the mass
luminosity diagram, suggests that they are predominantly associated with high
mass stars with the majority being in the accretion phase. However, we observe
a small number of sources that could possibly be related to intermediate or
low-mass stars.


  Current cyber-physical systems (CPS) are expected to accomplish complex
tasks. To achieve this goal, high performance, but unverified controllers (e.g.
deep neural network, black-box controllers from third parties) are applied,
which makes it very challenging to keep the overall CPS safe. By sandboxing
these controllers, we are not only able to use them but also to enforce safety
properties over the controlled physical systems at the same time. However,
current available solutions for sandboxing controllers are just applicable to
deterministic (a.k.a. non-stochastic) systems, possibly affected by bounded
disturbances. In this paper, for the first time we propose a novel solution for
sandboxing unverified complex controllers for CPS operating in noisy
environments (a.k.a. stochastic CPS). Moreover, we also provide probabilistic
guarantees on their safety. Here, the unverified control input is observed at
each time instant and checked whether it violates the maximal tolerable
probability of reaching the unsafe set. If this probability exceeds a given
threshold, the unverified control input will be rejected, and the advisory
input provided by the optimal safety controller will be used to maintain the
probabilistic safety guarantee. The proposed approach is illustrated
empirically and the results indicate that the expected safety probability is
guaranteed.


  The descent polynomial of a finite $I\subseteq \mathbb{Z}^+$ is the
polynomial $d(I,n)$, for which the evaluation at $n>\max(I)$ is the number of
permutations on $n$ elements, such that $I$ is the set of indices where the
permutation is descending. In this paper we will prove some conjectures
concerning coefficient sequences of $d(I,n)$. As a corollary we will describe
some zero-free regions for the descent polynomial.


  The hydrophobic-polar (HP) model has been widely studied in the field of
protein structure prediction (PSP) both for theoretical purposes and as a
benchmark for new optimization strategies. In this work we introduce a new
heuristics based on Ant Colony Optimization (ACO) and Markov Chain Monte Carlo
(MCMC) that we called Hybrid Monte Carlo Ant Colony Optimization (HMCACO). We
describe this method and compare results obtained on well known HP instances in
the 3 dimensional cubic lattice to those obtained with standard ACO and
Simulated Annealing (SA). All methods were implemented using an unconstrained
neighborhood and a modified objective function to prevent the creation of
overlapping walks. Results show that our methods perform better than the other
heuristics in all benchmark instances.


  The ionization energy of D$_2$ has been determined experimentally from
measurements involving two-photon Doppler-free vacuum-ultraviolet pulsed laser
excitation and near-infrared continuous-wave laser excitation to yield
$E_\mathrm{I}(\mathrm{D}_2)=124\,745.393\,739(26)$ \wn. From this value, the
dissociation energy of D$_2$ is deduced to be $D_0$(D$_2$) =
36\,748.362\,282(26) \wn, representing a 25-fold improvement over previous
values, and found in good agreement (at $1.6\sigma$) with recent ab initio
calculations of the 4-particle nonadiabatic relativistic energy and of
quantum-electrodynamic corrections up to order $m\alpha^6$. This result
constitutes a test of quantum electrodynamics in the molecular domain, while a
perspective is opened to determine nuclear charge radii from molecules.


  We introduce a new probabilistic technique for finding 'almost-periods' of
convolutions of subsets of groups. This gives results similar to the
Bogolyubov-type estimates established by Fourier analysis on abelian groups but
without the need for a nice Fourier transform to exist. We also present
applications, some of which are new even in the abelian setting. These include
a probabilistic proof of Roth's theorem on three-term arithmetic progressions
and a proof of a variant of the Bourgain-Green theorem on the existence of long
arithmetic progressions in sumsets A+B that works with sparser subsets of {1,
..., N} than previously possible. In the non-abelian setting we exhibit
analogues of the Bogolyubov-Freiman-Halberstam-Ruzsa-type results of additive
combinatorics, showing that product sets A B C and A^2 A^{-2} are rather
structured, in the sense that they contain very large iterated product sets.
This is particularly so when the sets in question satisfy small-doubling
conditions or high multiplicative energy conditions. We also present results on
structures in product sets A B. Our results are 'local' in nature, meaning that
it is not necessary for the sets under consideration to be dense in the ambient
group. In particular, our results apply to finite subsets of infinite groups
provided they 'interact nicely' with some other set.


  We calculate particle spectra and continuum photon emission from the
Cassiopeia A supernova remnant (SNR). The particle spectra, ion and electron,
result from diffusive shock acceleration at the forward SNR shock and are
determined with a nonlinear Monte Carlo calculation. The calculation
self-consistently determines the shock structure under the influence of ion
pressure, and includes a simple parameterized treatment of electron injection
and acceleration. Our results are compared to photon observations,
concentrating on the connection between the Radio and GeV-TeV gamma-ray range,
and to cosmic ray ion observations. We include new upper limits from the
Cherenkov Array at Themis (CAT) imaging Cherenkov telescope and the Whipple 10m
gamma-ray telescope at > 400 GeV. These new limits support the suggestion (e.g.
Cowsik & Sarkar 1980; Allen et. al. 1997) that energetic electrons are emitting
synchrotron radiation in an extremely high magnetic field (~ 1000 microGauss),
far greater than values routinely assigned to the ISM, and help to constrain
our model. The large magnetic field allows acceleration of cosmic ray ions to
well above $10^{15}$ eV per nucleon in the ~ 300 yr lifetime of Cas A.


  We introduce T-Rex, an interactive object counting model designed to first
detect and then count any objects. We formulate object counting as an open-set
object detection task with the integration of visual prompts. Users can specify
the objects of interest by marking points or boxes on a reference image, and
T-Rex then detects all objects with a similar pattern. Guided by the visual
feedback from T-Rex, users can also interactively refine the counting results
by prompting on missing or falsely-detected objects. T-Rex has achieved
state-of-the-art performance on several class-agnostic counting benchmarks. To
further exploit its potential, we established a new counting benchmark
encompassing diverse scenarios and challenges. Both quantitative and
qualitative results show that T-Rex possesses exceptional zero-shot counting
capabilities. We also present various practical application scenarios for
T-Rex, illustrating its potential in the realm of visual prompting.


  The extensive research and development of micromechanical resonators is
trying to allow the use of these devices for highly sensitive applications.
Microcantilevers are some of the simplest MEMS structure and had been proved to
be a good platform due to its excellent mechanical properties. A cantilever
working in dynamic mode, adjust its resonance frequency depending on changes in
both the spring constant (k) and mass (m) of the resonator. The aim of this
work was to model a cantilever structure to determine the optimal dimensions in
which the resonance frequency would be a function dominated by mass changes and
not stiffness changes. In order to validate the model a set of microcantilevers
were fabricated and characterized.


  Evasion techniques allow malicious code to never be observed. This impacts
significantly the detection capabilities of tools that rely on either dynamic
or static analysis, as they never get to process the malicious code. The
dynamic nature of JavaScript, where code is often injected dynamically, makes
evasions particularly effective. Yet, we lack tools that can detect evasive
techniques in a challenging environment such as JavaScript.
  In this paper, we present FV8, a modified V8 JavaScript engine designed to
identify evasion techniques in JavaScript code. FV8 selectively enforces code
execution on APIs that conditionally inject dynamic code, thus enhancing code
coverage and consequently improving visibility into malicious code. We
integrate our tool in both the Node.js engine and the Chromium browser,
compelling code execution in npm packages and Chrome browser extensions. Our
tool increases code coverage by 11% compared to default V8 and detects 28
unique evasion categories, including five previously unreported techniques. In
data confirmed as malicious from both ecosystems, our tool identifies 1,443
(14.6%) npm packages and 164 (82%) extensions containing at least one type of
evasion. In previously unexamined extensions (39,592), our tool discovered
16,471 injected third-party scripts, and a total of 8,732,120 lines of code
executed due to our forced execution instrumentation. Furthermore, it tagged a
total of 423 extensions as both evasive and malicious and we manually verify
110 extensions (26%) to actually be malicious, impacting two million users. Our
tool is open-source and serves both as an in-browser and standalone dynamic
analysis tool, capable of detecting evasive code, bypassing obfuscation in
certain cases, offering improved access to malicious code, and supporting
recursive analysis of dynamic code injections


  We present a simple derivation of the Callan-Harvey-Naculich effect, {\it
i.e.} the compensation of charge violation on axion strings due to gauge
anomalies by accretion of charge onto the string from the surrounding space. We
then show, in the case of axion fields without a potential, that an alternative
explanation is possible in which no reference to the surrounding space is
necessary because the anomalies are cancelled by a version of the Green-Schwarz
mechanism. We prove that such an alternative explanation is always possible in
the more general context of p-brane defects in d-dimensional field theories,
and hence that there always exists an anomaly-free effective worldvolume action
whenever the spacetime theory is anomaly free. Our results have implications,
which we discuss, for heterotic and type II fivebranes.


  Over the past decade, the media landscape has seen a radical shift. As more
of the public stay informed of current events via online sources, competition
has grown as outlets vie for attention. This competition has prompted some
online outlets to publish sensationalist and alarmist content to grab readers'
attention. Such practices may threaten democracy by distorting the truth and
misleading readers about the nature of events. This paper proposes a novel
system for detecting, processing, and warning users about misleading content
online to combat the threats posed by misinformation. By training a machine
learning model on an existing dataset of 32,000 clickbait news article
headlines, the model predicts how sensationalist a headline is and then
interfaces with a web browser extension which constructs a unique content
warning notification based on existing design principles and incorporates the
models' prediction. This research makes a novel contribution to machine
learning and human-centred security with promising findings for future
research. By warning users when they may be viewing misinformation, it is
possible to prevent spontaneous reactions, helping users to take a deep breath
and approach online media with a clear mind.


  In this paper we present the design of name based access control scheme which
facilitates data confidentiality by applying end-to-end encryption to data
published on NDN with flexible fine-grained access control, which allows to
define an enforce access policies on published data. The scheme is based on
ciphertext-policy attribute-based encryption (CP-ABE). We discuss the use of
the scheme on the basis of two use-cases, and report overhead associated with
it, based on our implementation.


  In this paper we consider the Hilbert-Einstein-Dirac functional, whose
critical points are pairs, metrics-spinors, that satisfy a system coupling the
Riemannian and the spinorial part. Under some assumptions, on the sign of the
scalar curvature and the diameter, we prove a compactness result for this class
of pairs, in dimension three and four. This can be seen as the equivalent of
the study of compactness of sequences of Einstein manifolds as in
\cite{And0,N}. Indeed, we study the compactness of sequences of critical points
of the Hilbert-Einstein-Dirac functional which is an extension of the
Hilbert-Einstein functional having Einstein manifolds as critical points.
Moreover we will study the second variation of the energy, characterizing the
horizontal deformations for which the second variation vanishes. Finally we
will exhibit some explicit examples.


  The generation of initial or inflow synthetic turbulent velocity or scalar
fields reproducing statistical characteristics of realistic turbulence is still
a challenge. The synthetic eddy method, previously introduced in the context of
inflow conditions for large eddy simulations, is based on the assumption that
turbulence can be regarded as a superposition of coherent structures. In this
paper, a new type of synthetic eddy method is proposed, where the fundamental
eddy is constructed by superposing three Hill's vortices, with their axes
orthogonal to each other. A distribution of Hill's vortices is used to
synthesize an anisotropic turbulent velocity field that satisfies the
incompressibility condition and match a given Reynolds stress tensor. The
amplitudes of the three vortices that form the fundamental eddy are calculated
from known Reynolds stress profiles through a transformation from the physical
reference frame to the principal-axis reference frame. In this way,
divergence-free anisotropic turbulent velocity fields are obtained that can
reproduce a given Reynolds stress tensor. The model was tested on both
isotropic and anisotropic turbulent velocity fields, in the framework of grid
turbulence decay and turbulent channel flow, respectively. The transition from
artificial to realistic turbulence in the proximity to the inflow boundary was
found to be small in all test cases that were considered.


  We initiate an exploration of the physics and geometry of the amplituhedron,
starting with the simplest case of the integrand for four-particle scattering
in planar N=4 SYM. We show how the textbook structure of the unitarity
double-cut follows from the positive geometry. We also use the geometry to
expose the behavior of the multicollinear limit, providing a direct motivation
for studying the logarithm of the amplitude. In addition to computing the two
and three-loop integrands, we explore various lower-dimensional faces of the
amplituhedron, thereby computing non-trivial cuts of the integrand to all loop
orders.


  We study the facility location games with candidate locations from a
mechanism design perspective. Suppose there are n agents located in a metric
space whose locations are their private information, and a group of candidate
locations for building facilities. The authority plans to build some
homogeneous facilities among these candidates to serve the agents, who bears a
cost equal to the distance to the closest facility. The goal is to design
mechanisms for minimizing the total/maximum cost among the agents. For the
single-facility problem under the maximum-cost objective, we give a
deterministic 3-approximation group strategy-proof mechanism, and prove that no
deterministic (or randomized) strategy-proof mechanism can have an
approximation ratio better than 3 (or 2). For the two-facility problem on a
line, we give an anonymous deterministic group strategy-proof mechanism that is
(2n-3)-approximation for the total-cost objective, and 3-approximation for the
maximum-cost objective. We also provide (asymptotically) tight lower bounds on
the approximation ratio.


  A class of trigonometric interpolation splines depending on parameter
vectors, selected convergence factors and interpolation factors is considered.
The concept of crosslink grids and interpolation grids is introduced; these
grids can match or y not match. Parseval equality is given and some properties
of splines are discussed. The material is illustrated by a numerical example.


  We study the existence and stability of Q-balls in noncanonical scalar field
theories, $K(|\Phi|^2,X)$ where $\Phi$ is the complex scalar field and $X$ is
the kinetic term. We extend the Vakhitov-Kolokolov stability criterion to
K-field theories. We derive the condition for the perturbations to have a
well-posed Cauchy problem. We find that $K_{,X}>0$ and $K_{,X}+XK_{,XX}>0$ are
necessary but not sufficient conditions. The perturbations define a strongly
hyperbolic system if $(K_{,X}-2\phi'^2 K_{,XX})(K_{,X}+2\omega^2\phi^2 K_{,XX})
> 0$. For all modifications studied, we found that perturbations propagate at a
speed different from light. Generically, the noncanonical scalar field can
lower the charge and energy of the Q-ball and therefore improves its stability.


  Duderstadt et al. [2016b] comment that the Melott et al. [2016] study of
nitrate formation by solar proton events (SPEs) and comparison with the ice
core archive is "fundamentally flawed," because it does not include
pre-existing HNO3 in the stratosphere. We show that they exaggerate both the
enhancement predicted by our findings and pre-industrial HNO3 levels in their
model, and fail to prove this assertion. Our feasibility study matched expected
SPE nitrate production with ground truth measurements. It is not clear that
their approach is more realistic and absence of a detailed mechanism does not
disprove our results. Models can be no better than the information they are
provided and in this case there continue to be significant unknowns and
uncertainties, especially in the role of polar stratospheric clouds (PSCs) and
possible interactions with cosmic rays that constitute lower boundary
conditions. Duderstadt et al. [2014; 2016a] used incomplete, poorly-constrained
and incorrect initial and boundary conditions, and they continue to advocate on
the basis of uncertain results. Meanwhile, Smart et al. [2014] identified a
series of ice core nitrate spikes that have since been confirmed in 10Be by
McCracken and Beer [2015]. Melott et al. [2016] computationally reproduced the
ionization profile of the only major balloon measurements to date. We show that
our calculated nitrate enhancement is consistent with measured results, given
current levels of uncertainty, and that extreme SPEs can potentially produce
occasional nitrate spikes with hundreds of percent increases. Instead of
repeating old arguments to dismiss nitrates as proxies of SPEs, it is past time
for a dedicated, fine-resolution, multi-parameter, replicate ice core field
campaign to resolve this debate.


  While the automated detection of cryptographic API misuses has progressed
significantly, its precision diminishes for intricate targets due to the
reliance on manually defined patterns. Large Language Models (LLMs), renowned
for their contextual understanding, offer a promising avenue to address
existing shortcomings. However, applying LLMs in this security-critical domain
presents challenges, particularly due to the unreliability stemming from LLMs'
stochastic nature and the well-known issue of hallucination. To explore the
prevalence of LLMs' unreliable analysis and potential solutions, this paper
introduces a systematic evaluation framework to assess LLMs in detecting
cryptographic misuses, utilizing a comprehensive dataset encompassing both
manually-crafted samples and real-world projects. Our in-depth analysis of
11,940 LLM-generated reports highlights that the inherent instabilities in LLMs
can lead to over half of the reports being false positives. Nevertheless, we
demonstrate how a constrained problem scope, coupled with LLMs' self-correction
capability, significantly enhances the reliability of the detection. The
optimized approach achieves a remarkable detection rate of nearly 90%,
surpassing traditional methods and uncovering previously unknown misuses in
established benchmarks. Moreover, we identify the failure patterns that
persistently hinder LLMs' reliability, including both cryptographic knowledge
deficiency and code semantics misinterpretation. Guided by these insights, we
develop an LLM-based workflow to examine open-source repositories, leading to
the discovery of 63 real-world cryptographic misuses. Of these, 46 have been
acknowledged by the development community, with 23 currently being addressed
and 6 resolved. Reflecting on developers' feedback, we offer recommendations
for future research and the development of LLM-based security tools.


  Photon loss is one of the key challenges to overcome in complex photonic
quantum applications. Photon collection efficiencies directly impact the amount
of resources required for measurement-based quantum computation and
communication networks. Promising resources include solid-state quantum light
sources, however, efficiently coupling light from a single quantum emitter to a
guided mode remains demanding. In this work, we eliminate photon losses by
maximizing coupling efficiencies in an emitter-to-fiber interface. We develop a
waveguide-integrated 'Sawfish' photonic crystal cavity and use finite element
simulations to demonstrate that our system transfers, with 97.4% efficiency,
the zero-phonon line emission of a negatively-charged tin vacancy center in
diamond adiabatically to a single-mode fiber. A surrogate model trained by
machine learning provides quantitative estimates of sensitivities to
fabrication tolerances. Our corrugation-based design proves robust under
state-of-the-art nanofabrication parameters, maintaining an emitter-to-fiber
coupling efficiency of 88.6%. To demonstrate its potential in reducing resource
requirements, we apply the Sawfish cavity to a recent one-way quantum repeater
protocol.


  We construct the ($\beta$-deformed) higher order total derivative operators
and analyze their remarkable properties. In terms of these operators, we derive
the higher order constraints for the ($\beta$-deformed) Hermitian matrix
models. We prove that these ($\beta$-deformed) higher order constraints are
reducible to the Virasoro constraints. Meanwhile, the Itoyama-Matsuo conjecture
for the constraints of the Hermitian matrix model is proved. We also find that
through rescaling variable transformations, two sets of the constraint
operators become the $W$-operators of $W$-representations for the
($\beta$-deformed) partition function hierarchies in the literature.


  Context: Scientific software plays an important role in critical decision
making, for example making weather predictions based on climate models, and
computation of evidence for research publications. Recently, scientists have
had to retract publications due to errors caused by software faults. Systematic
testing can identify such faults in code.
  Objective: This study aims to identify specific challenges, proposed
solutions, and unsolved problems faced when testing scientific software.
  Method: We conducted a systematic literature survey to identify and analyze
relevant literature. We identified 62 studies that provided relevant
information about testing scientific software.
  Results: We found that challenges faced when testing scientific software fall
into two main categories: (1) testing challenges that occur due to
characteristics of scientific software such as oracle problems and (2) testing
challenges that occur due to cultural differences between scientists and the
software engineering community such as viewing the code and the model that it
implements as inseparable entities. In addition, we identified methods to
potentially overcome these challenges and their limitations. Finally we
describe unsolved challenges and how software engineering researchers and
practitioners can help to overcome them.
  Conclusions: Scientific software presents special challenges for testing.
Specifically, cultural differences between scientist developers and software
engineers, along with the characteristics of the scientific software make
testing more difficult. Existing techniques such as code clone detection can
help to improve the testing process. Software engineers should consider special
challenges posed by scientific software such as oracle problems when developing
testing techniques.


  Gaussian noise induced by loss on Gaussian states may be corrected by
distributing EPR entanglement through the loss channel, purifying the
entanglement using a noiseless linear amplifier (NLA) and then using it for
continuous-variable teleportation of the input state. Linear optical
implementations of the NLA unavoidably introduce small amounts of excess noise
and detection and source efficiency will be limited in current implementations.
In this paper, we analyze the error-correction protocol with non-unit
efficiency sources and detectors and show the excess noise may be partially
compensated by adjusting the classical gain of the teleportation protocol. We
present a strong case for the potential of demonstrable error-correction with
current technology.


  There are at least three fundamental states of matter, depending upon
temperature and pressure: gas, liquid, and solid (crystal). These states are
separated by first-order phase transitions between them. In both gas and liquid
phases the complete translational and rotational symmetry exist, whereas in a
solid phase both symmetries are broken. In intermediate phases between liquid
and solid, which include liquid crystal and plastic crystal phases, only one of
the two symmetries is preserved. Among the fundamental states of matter, the
liquid state is most poorly understood. We argue that it is crucial for a
better understanding of liquid to recognize that a liquid generally has a
tendency to have local structural order and its presence is intrinsic and
universal to any liquid. Such structural ordering is a consequence of many body
correlations, more specifically, bond angle correlations, which we believe are
crucial for the description of the liquid state. We show that this physical
picture may naturally explain difficult unsolved problems associated with the
liquid state, such as anomalies of water-type liquids (water, Si, Ge, ...),
liquid-liquid transition, liquid-glass transition, crystallization and
quasicrystal formation, in a unified manner. In other words, we need a new
order parameter representing low local free-energy configuration, which is bond
orientational order parameter in many cases, in addition to density order
parameter for the physical description of these phenomena. Here we review our
two-order-parameter model of liquid and consider how transient local structural
ordering is linked to all of the above-mentioned phenomena. The relationship
between these phenomena are also discussed.


  We aim to show that K+ and K- spectra at low transverse momentum measured in
light symmetric systems at around 2AGeV depend strongly on the K potential. The
ratio of the spectra can allow therefore for a direct determination of the
strength of the K+ as well as that of the K- potential in a hadronic
environment.


  We exhibit two symmetries of one-dimensional Newtonian mechanics whereby a
solution is built from the history of another solution via a generally
nonlinear and complex potential-dependent transformation of the time. One
symmetry intertwines the square roots of the kinetic and potential energies and
connects solutions of the same dynamical problem (the potential is an invariant
function). The other symmetry connects solutions of different dynamical
problems (the potential is a scalar function). The existence of corresponding
conserved quantities is examined using Noethers theorem and it is shown that
the invariant-potential symmetry is correlated with energy conservation. In the
Hamilton-Jacobi picture the invariant-potential transformation provides an
example of a field-dependent symmetry in point mechanics. It is shown that this
transformation is not a symmetry of the Schroedinger equation.


  We study the instrumental features of photons from the peak observed at
$E_\gamma=130$ GeV in the spectrum of Fermi-LAT data. We use the {\sc sPlots}
algorithm to reconstruct -- seperately for the photons in the peak and for
background photons -- the distributions of incident angles, the recorded time,
features of the spacecraft position, the zenith angles, the conversion type and
details of the energy and direction reconstruction. The presence of a striking
feature or cluster in such a variable would suggest an instrumental cause for
the peak. In the publically available data, we find several suggestive features
which may inform further studies by instrumental experts, though the size of
the signal sample is too small to draw statistically significant conclusions.


  Display cases in supermarket systems often exhibit synchronization, in which
the expansion valves in the display cases turn on and off at exactly the same
time. The study of the influence of switching noise on synchronization in
supermarket refrigeration systems is the subject matter of this work. For this
purpose, we model it as a hybrid system, for which synchronization corresponds
to a periodic trajectory. Subsequently, we investigate the influence of
switching noise. We develop a statistical method for computing an intensity
function, which measures how often the refrigeration system stays synchronized.
By analyzing the intensity, we conclude that the increase in measurement
uncertainty yields the decrease at the prevalence of synchronization.


  We consider the classical Merton problem of lifetime consumption-portfolio
optimization problem with small proportional transaction costs. The first order
term in the asymptotic expansion is explicitly calculated through a singular
ergodic control problem which can be solved in closed form in the
one-dimensional case. Unlike the existing literature, we consider a general
utility function and general dynamics for the underlying assets. Our arguments
are based on ideas from the homogenization theory and use the convergence tools
from the theory of viscosity solutions. The multidimensional case is studied in
our accompanying paper using the same approach.


  A banana manifold is a Calabi-Yau threefold fibered by Abelian surfaces whose
singular fibers contain banana configurations: three rational curves meeting
each other in two points. A nano-manifold is a Calabi-Yau threefold $X$ with
very small Hodge numbers: $h^{1,1}(X)+h^{2,1}(X)\leq 6$. We construct four
rigid banana nano-manifolds $\tilde{X}_N$, $N\in \{5,6,8,9 \}$, each with Hodge
numbers given by $(h^{1,1},h^{2,1})=(4,0)$.
  We compute the Donaldson-Thomas partition function for banana curve classes
and show that the associated genus $g$ Gromov-Witten potential is a genus 2
meromorphic Siegel modular form of weight $2g-2$ for a certain discrete
subgroup $P^{*}_{N} \subset Sp_{4}(\mathbb{R})$.
  We also compute the weight 4 modular form whose $p$th Fourier coefficient is
given by the trace of the action of Frobenius on $H^{3}_{et }(\tilde{X}_N
,{\mathbb{Q}}_{l})$ for almost all prime $p$. We observe that it is the unique
weight 4 cusp form on $\Gamma_{0}(N)$.


  A universal quantum gate is introduced for tensors of vector spaces. By using
integer powers of such a gate and by using classical reversible gates one can
approximate any element of the unitary group to any accuracy needed. The proof
uses a version of Kronecker's theory and the structure of the Bloch sphere for
tensors.


  We consider translates of functions in $L^2(\RRd)$ along an irregular set of
points. Introducing a notion of pseudo-Gramian function for the irregular case,
we obtain conditions for a family of irregular translates to be a Bessel
sequence or Riesz sequence.
  We find a representation of the canonical dual of a frame sequence $\{\phi(
\cdot - \lambda_k)\}_{k\in \ZZ}$ - where $\phi$ is a bandlimited function - in
terms of its Fourier transform.


  In this paper we study opportunistic transmission strategies for cognitive
radios (CR) in which causal noisy observation from a primary user(s) (PU) state
is available. PU is assumed to be operating in a slotted manner, according to a
two-state Markov model. The objective is to maximize utilization ratio (UR),
i.e., relative number of the PU-idle slots that are used by CR, subject to
interference ratio (IR), i.e., relative number of the PU-active slots that are
used by CR, below a certain level. We introduce an a-posteriori LLR-based
cognitive transmission strategy and show that this strategy is optimum in the
sense of maximizing UR given a certain maximum allowed IR. Two methods for
calculating threshold for this strategy in practical situations are presented.
One of them performs well in higher SNRs but might have too large IR at low
SNRs and low PU activity levels, and the other is proven to never violate the
allowed IR at the price of a reduced UR. In addition, an upper-bound for the UR
of any CR strategy operating in the presence of Markovian PU is presented.
Simulation results have shown a more than 116% improvement in UR at SNR of -3dB
and IR level of 10% with PU state estimation. Thus, this opportunistic CR
mechanism possesses a high potential in practical scenarios in which there
exists no information about true states of PU.


  This paper presents a search for the pair production of top squarks in events
with a single isolated electron or muon, jets, large missing transverse
momentum, and large transverse mass. The data sample corresponds to an
integrated luminosity of 19.5 inverse femtobarns of pp collisions collected in
2012 by the CMS experiment at the LHC at a center-of-mass energy of sqrt(s) = 8
TeV. No significant excess in data is observed above the expectation from
standard model processes. The results are interpreted in the context of
supersymmetric models with pair production of top squarks that decay either to
a top quark and a neutralino or to a bottom quark and a chargino. For small
mass values of the lightest supersymmetric particle, top-squark mass values up
to around 650 GeV are excluded.


  The one-particle Green's function of the Tomonaga-Luttinger model for
one-dimensional interacting Fermions is discussed. Far away from the origin of
the plane of space-time coordinates the function falls off like a power law.
The exponent depends on the direction within the plane. For a certain form of
the interaction potential or within an approximated cut-off procedure the
different exponents only depend on the strength of the interaction at zero
momentum and can be expressed in terms of the Luttinger liquid parameters
$K_{\rho}$ and $K_{\sigma}$ of the model at hand. For a more general
interaction and directions which are determined by the charge velocity
$v_{\rho}$ and spin velocity $v_{\sigma}$ the exponents also depend on the
smoothness of the interaction at zero momentum and the asymptotic behavior of
the Green's function is not given by the Luttinger liquid parameters alone.
This shows that the physics of large space-time distances in Luttinger liquids
is less universal than is widely believed.


  Collaborative cybersecurity relies on organizations sharing information to
boost security, but trust management is a key concern. Decentralized solutions
like distributed ledgers, particularly blockchain, are crucial for eliminating
single points of failure. However, the existing literature on blockchain-based
collaborative cybersecurity is limited, lacking comprehensive insights. This
paper addresses this gap by surveying blockchain's role in collaborative
cybersecurity from 2016 to 2023. It explores various applications, trends, and
the evolution of blockchain technology, focusing on access control, data
validation policies, underlying tech, and consensus mechanisms. A key finding
is the fragmentation of the field with no dominant research group or venue.
Many recent projects poorly select consensus protocols for their blockchain. To
aid researchers and practitioners, this paper offers guidelines for choosing
the right blockchain for specific purposes and highlights open research areas
and lessons learned from past blockchain applications in collaborative
cybersecurity, encouraging further exploration in this field.


  We examine the evolutionary status of luminous, star-forming galaxies in
intermediate-redshift clusters by considering their star formation rates and
the chemical and ionsiation properties of their interstellar emitting gas. Our
sample consists of 17 massive, star-forming, mostly disk galaxies with
M_{B}<-20, in clusters with redshifts in the range 0.31< z <0.59, with a median
of <z>=0.42. We compare these galaxies with the identically selected and
analysed intermediate-redshift field sample of Mouhcine et al. (2006), and with
local galaxies from the Nearby Field Galaxy Survey of Jansen et al. (2000).
  From our optical spectra we measure the equivalent widths of OII, Hbeta and
OIII emission lines to determine diagnostic line ratios, oxygen abundances, and
extinction-corrected star formation rates. The star-forming galaxies in
intermediate-redshift clusters display emission line equivalent widths which
are, on average, significantly smaller than measured for field galaxies at
comparable redshifts. However, a contrasting fraction of our cluster galaxies
have equivalent widths similar to the highest observed in the field. This
tentatively suggests a bimodality in the star-formation rates per unit
luminosity for galaxies in distant clusters. We find no evidence for further
bimodalities, or differences between our cluster and field samples, when
examining additional diagnostics and the oxygen abundances of our galaxies.
This maybe because no such differences exist, perhaps because the cluster
galaxies which still display signs of star-formation have recently arrived from
the field. In order to examine this topic with more certainty, and to further
investigate the way in which any disparity varies as a function of cluster
properties, larger spectroscopic samples are needed.


  This proposal introduces a Dialogue Challenge for building end-to-end
task-completion dialogue systems, with the goal of encouraging the dialogue
research community to collaborate and benchmark on standard datasets and
unified experimental environment. In this special session, we will release
human-annotated conversational data in three domains (movie-ticket booking,
restaurant reservation, and taxi booking), as well as an experiment platform
with built-in simulators in each domain, for training and evaluation purposes.
The final submitted systems will be evaluated both in simulated setting and by
human judges.


  For any odd $k$, a connection is established between the dihedral and
supersymmetric extensions of the Tremblay-Turbiner-Winternitz Hamiltonians
$H_k$ on a plane. For this purpose, the elements of the dihedral group $D_{2k}$
are realized in terms of two independent pairs of fermionic creation and
annihilation operators and some interesting trigonometric identities are
demonstrated.


  We compute the integrals of products of Hermite functions using the
generating functions. The precise asymptotics of 4 Hermite functions are
presented below. This estimate is relevant for the corresponding cubic
nonlinear equation.


  Fingerprinting based WLAN indoor positioning system (FWIPS) provides a
promising indoor positioning solution to meet the growing interests for indoor
location-based services (e.g., indoor way finding or geo-fencing). FWIPS is
preferred because it requires no additional infrastructure for deploying an
FWIPS and achieving the position estimation by reusing the available WLAN and
mobile devices, and capable of providing absolute position estimation. For
fingerprinting based positioning (FbP), a model is created to provide reference
values of observable features (e.g., signal strength from access point (AP)) as
a function of location during offline stage. One widely applied method to build
a complete and an accurate reference database (i.e. radio map (RM)) for FWIPS
is carrying out a site survey throughout the region of interest (RoI). Along
the site survey, the readings of received signal strength (RSS) from all
visible APs at each reference point (RP) are collected. This site survey,
however, is time-consuming and labor-intensive, especially in the case that the
RoI is large (e.g., an airport or a big mall). This bottleneck hinders the wide
commercial applications of FWIPS (e.g., proximity promotions in a shopping
center). To diminish the cost of site survey, we propose a probabilistic model,
which combines fingerprinting based positioning (FbP) and RM generation based
on stochastic variational Bayesian inference (SVBI). This SVBI based position
and RSS estimation has three properties: i) being able to predict the
distribution of the estimated position and RSS, ii) treating each observation
of RSS at each RP as an example to learn for FbP and RM generation instead of
using the whole RM as an example, and iii) requiring only one time training of
the SVBI model for both localization and RSS estimation. These benefits make it
outperforms the previous proposed approaches.


  In materials science, selecting the right synthesis technique for specific
compounds is one of the most important steps. High-pressure conditions have a
significant effect on the crystal growth processes, leading to the creation of
unique structures and properties that usually are not possible under normal
conditions. The prime objective of this article is to illustrate the benefits
of using high-pressure, high-temperature (HPHT) technique when developing
two-dimensional (2D) materials. We could successfully grow bulk single crystals
of hexagonal boron nitride (hBN) and magnesium doped hexagonal boron nitride
(Mg-hBN) from Mg-B-N solvent. Further exploration of the Mg-B-N system could
lead to the crystallization of isotopically 10B and 11B enriched hBN crystals,
and other doped variants of it. Black phosphorus (b-P) and black phosphorus
doped with arsenic (b-AsP) were obtained by directly converting its elements
into melt and subsequently crystallizing them under HPHT. Germanium arsenide
(GeAs) bulk single crystals were also obtained from the melt at a pressure of 1
GPa. Upon crystallization, all these compounds exhibit the anticipated layered
structures, which makes them easy to exfoliate into 2D flakes, thus providing
opportunities to modify their electrical behavior and create new useful
devices.


  For a compact oriented 3-manifold with torus boundary the adjoint
Reidemeister torsion is defined as a function on the
$\mathrm{SL}_2(\mathbb{C})$-character variety depending on a choice of a
boundary curve. Under reasonable assumptions, it is conjectured that the
adjoint torsion satisfies a certain type of vanishing identities. In this
paper, we prove that the conjecture holds for all hyperbolic twist knot
exteriors by using Jacobi's residue theorem.


  We formulate the electroweak chiral Lagrangian in its mass eigenstates, and
study the its one-loop renormalization and provide its renormalization group
equations to the same order, so as to complete it as the low energy effective
theory of the standard model below a few TeV. In order to make our computation
consistent, we have provided a modified power counting rule to estimate the
contributions of higher loop and higher operators. As one of the application of
its renormalization group equations, we analyze the solution to the effects of
the Higgs scalar. We find that similar to the SU(2) case, that the triple
anomalous couplings are sensitive to the quartic couplings (here $\al_5$).
While the quadratic anomalous couplings are not sensitive, due to the large
leading contributions and the accidental cancellation. The differences in the
triple anomalous couplings between the direct method and renormalization group
equation method are well within the detection power of the LHC and LC, if the
Higgs scalar is not too heavy (say, 300 or 400 GeV). We also suggest a new
mechanism to generate the negative $S$ parameter through the radiative
corrections of the anomalous couplings. Comparison of the renormalization group
equation method and direct methods is provided in the full theory, the standard
model, to reveal the basic differences of them. The problem of the unitarity
violation is also addressed for our assumption in the modified power counting
rule.


  Based on a data sample of 81.7 fb^-1 collected at the Upsilon(4S) resonance
with the BABAR detector at the PEP-II asymmetric-energy B Factory at SLAC, we
present the current status of the measurement of the branching fraction of
Upsilon(4S) to B0bar B^0. Our study of the decay was performed through the
exclusive decays B0bar to D^{*+} \ell^{-} bar{\nu}_{\ell} using a partial
reconstruction method, where the D^{*+} is detected only through the soft pion
daughter from the decay D^{*+} to D^{0} pi^{+}.


  We calculate the mass of $0^{-+}$ triple-valence-gluon resonance, the
trigluon glueball, with QCD sum rules. Its mass is found to be approximately in
the region between 1.9 GeV and 2.7 GeV with some theoretical uncertainties.
Moreover, it is likely that the new BES measurement of the $p \bar{p}$
enhancement near threshold in the $J/\psi$ decays exhibits the behavior of this
trigluon state. Our analyzes favor the baryonium-gluonium mixing picture for
the BES observation.


  There have been many articles and mishaps published about the risks of
uncontrolled spreadsheets in today's business environment, including
non-compliance, operational risk, errors, and fraud all leading to significant
loss events. Spreadsheets fall into the realm of end user developed
applications and are often absent the proper safeguards and controls an IT
organization would enforce for enterprise applications. There is also an
overall lack of software programming discipline enforced in how spreadsheets
are developed. However, before an organization can apply proper controls and
discipline to critical spreadsheets, an accurate and living inventory of
spreadsheets across the enterprise must be created, and all critical
spreadsheets must be identified. As such, this paper proposes an automated
approach to the initial stages of the spreadsheet management lifecycle -
discovery, inventory and risk assessment. Without the use of technology, these
phases are often treated as a one-off project. By leveraging technology, they
become a sustainable business process.


  We show that the (Gurevich) topological entropy for the countable Markov
shift associated with an infinite transition matrix $A$ coincides with the
non-commutative topological entropy for the Exel--Laca algebra associated with
$A$, under certain conditions on $A$. An important example satisfying the
conditions is the renewal shift, which is not locally finite. We also pose
interesting questions for future research on non-commutative topological
entropy for non-locally finite transition matrices.