  We study dissipation in holographic superfluids at finite temperature and
zero chemical potential. The zero overlap with the heat current allows us to
isolate the physics of the conserved current corresponding to the broken global
$U(1)$. By using analytic techniques we write constitutive relations including
the first non-trivial dissipative terms. The corresponding transport
coefficients are determined in terms of thermodynamic quantities and the black
hole horizon data. By analysing their behaviour close to the phase transition
we show explicitly the breakdown of the hydrodynamic expansion. Finally, we
study the pseudo-Goldstone mode that emerges upon introducing a perturbative
symmetry breaking source and we determine its resonant frequency and decay
rate.


  We consider the two-dimensional shallow water model derived by Levermore and
Sammartino (Nonlinearity 14,2001), describing the motion of an incompressible
fluid, confined in a shallow basin, with varying bottom topography. We
construct the approximate inertial manifolds for the associated dynamical
system and estimate its order. Finally, considering the whole domain R^2 and
under suitable conditions on the time dependent forcing term, we prove the L^2
asymptotic decay of the weak solutions.


  Financial markets are of much interest to researchers due to their dynamic
and stochastic nature. With their relations to world populations, global
economies and asset valuations, understanding, identifying and forecasting
trends and regimes are highly important. Attempts have been made to forecast
market trends by employing machine learning methodologies, while statistical
techniques have been the primary methods used in developing market regime
switching models used for trading and hedging. In this paper we present a novel
framework for the detection of regime switches within the US financial markets.
Principal component analysis is applied for dimensionality reduction and the
k-means algorithm is used as a clustering technique. Using a combination of
cluster analysis and classification, we identify regimes in financial markets
based on publicly available economic data. We display the efficacy of the
framework by constructing and assessing the performance of two trading
strategies based on detected regimes.


  There have been many proposals for access control models and authorization
policy languages, which are used to inform the design of access control
systems. Most, if not all, of these proposals impose restrictions on the
implementation of access control systems, thereby limiting the type of
authorization requests that can be processed or the structure of the
authorization policies that can be specified. In this paper, we develop a
formal characterization of the features of an access control model that imposes
few restrictions of this nature. Our characterization is intended to be a
generic framework for access control, from which we may derive access control
models and reason about the properties of those models. In this paper, we
consider the properties of monotonicity and completeness, the first being
particularly important for attribute-based access control systems. XACML, an
XML-based language and architecture for attribute-based access control, is
neither monotonic nor complete. Using our framework, we define attribute-based
access control models, in the style of XACML, that are, respectively, monotonic
and complete.


  A quantum causal topology is presented. This is modeled after a
non-commutative scheme type of theory for the curved finitary spacetime sheaves
of the non-abelian incidence Rota algebras that represent `gravitational
quantum causal sets'. The finitary spacetime primitive algebra scheme
structures for quantum causal sets proposed here are interpreted as the
kinematics of a curved and reticular local quantum causality. Dynamics for
quantum causal sets is then represented by appropriate scheme morphisms, thus
it has a purely categorical description that is manifestly `gauge-independent'.
Hence, a schematic version of the Principle of General Covariance of General
Relativity is formulated for the dynamically variable quantum causal sets. We
compare our non-commutative scheme-theoretic curved quantum causal topology
with some recent $C^{*}$-quantale models for non-abelian generalizations of
classical commutative topological spaces or locales, as well as with some
relevant recent results obtained from applying sheaf and topos-theoretic ideas
to quantum logic proper. Motivated by the latter, we organize our finitary
spacetime primitive algebra schemes of curved quantum causal sets into a
topos-like structure, coined `quantum topos', and argue that it is a sound
model of a structure that Selesnick has anticipated to underlie Finkelstein's
reticular and curved quantum causal net. At the end we conjecture that the
fundamental quantum time-asymmetry that Penrose has expected to be the main
characteristic of the elusive `true quantum gravity' is possibly of a
kinematical or structural rather than of a dynamical character, and we also
discuss the possibility of a unified description of quantum logic and quantum
gravity in quantum topos-theoretic terms.


  Up until now, a consistent causal theory of point charged particles (for
example electrons) interacting with electromagnetic field is not known. The
well-known problem is that the standard Lorentz force alone (in the case of
point particles) does not lead to a theory in which momentum and energy are
conserved. The need of radiation reaction force (or self-force) thus arises.
The well known candidate for such force, the Lorentz-Abraham-Dirac reaction
force, gives non-causal particle behavior, i.e., the particle starts to move
before the arrival of external electromagnetic fields. Alternative, causal
proposals provide non-physical behavior of the particle -- the particle moves
with non-zero acceleration long after any external forces acted on it. Below,
we question the existence of a causal theory. We show that for certain
electromagnetic pulse of radiation and point particle being initially at rest,
there {\it does not exists} a causal particle trajectory, such that the
particle ends up moving with constant velocity (when no external
electromagnetic fields act on it for very long time). This shows that the
proper, causal electrodynamics of point particles does not exist.


  We study the properties of the right-handed sneutrino and its viability as a
WIMP dark matter candidate in an extended version of the NMSSM in which a
right-handed neutrino superfield is included with a coupling to the singlet
Higgs in order to provide non-vanishing Majorana neutrino masses. We perform a
systematic study of the parameter space, including LEP constraints and
experimental bounds on low-energy observables. We investigate the conditions
under which the right-handed sneutrino has the correct relic abundance and the
dominant annihilation channels. Next we calculate the theoretical predictions
for the sneutrino-proton elastic scattering cross section and compare it with
present and future experimental sensitivities. We find that sneutrinos with a
mass in the range of 5-200 GeV can reproduce the observed dark matter relic
density without being excluded by direct dark matter searches and for natural
values of the input parameters. Interestingly, the predicted scattering cross
section is generally within the reach of future experiments. Finally, we
comment on the possible implications for collider physics.


  We propose a novel formal framework (called 3D-nCDC-ASP) to represent and
reason about cardinal directions between extended objects in 3-dimensional (3D)
space, using Answer Set Programming (ASP). 3D-nCDC-ASP extends Cardinal
Directional Calculus (CDC) with a new type of default constraints, and nCDC-ASP
to 3D. 3D-nCDC-ASP provides a flexible platform offering different types of
reasoning: Nonmonotonic reasoning with defaults, checking consistency of a set
of constraints on 3D cardinal directions between objects, explaining
inconsistencies, and inferring missing CDC relations. We prove the soundness of
3D-nCDC-ASP, and illustrate its usefulness with applications. This paper is
under consideration for acceptance in TPLP.


  We study semifinite harmonic functions on the zigzag graph, which corresponds
to Pieri's rule for the fundamental quasisymmetric functions $\{F_{\lambda}\}$.
The main problem, which we solve here, is to classify the indecomposable
semifinite harmonic functions on this graph. We describe the set of
classification parameters and an explicit construction that produces a
semifinite indecomposable harmonic function out of every point of this set. We
also establish a semifinite analog of the Vershik-Kerov ring theorem.


  The remarkable capability of large language models (LLMs) for in-context
learning (ICL) needs to be activated by demonstration examples. Prior work has
extensively explored the selection of examples for ICL, predominantly following
the "select then organize" paradigm, such approaches often neglect the internal
relationships between examples and exist an inconsistency between the training
and inference. In this paper, we formulate the problem as a $Se$quential
$Se$lection problem and introduce $Se^2$, a sequential-aware method that
leverages the LLM's feedback on varying context, aiding in capturing
inter-relationships and sequential information among examples, significantly
enriching the contextuality and relevance of ICL prompts. Meanwhile, we utilize
beam search to seek and construct example sequences, enhancing both quality and
diversity. Extensive experiments across 23 NLP tasks from 8 distinct categories
illustrate that $Se^2$ markedly surpasses competitive baselines and achieves
42\% relative improvement over random selection. Further in-depth analysis
shows the effectiveness of proposed strategies, highlighting $Se^2$'s
exceptional stability and adaptability across various scenarios. Code available
at https://github.com/microsoft/LMOps.


  We define the intrinsic scale at which a network begins to reveal its
identity as the scale at which subgraphs in the network (created by a random
walk) are distinguishable from similar sized subgraphs in a perturbed copy of
the network. We conduct an extensive study of intrinsic scale for several
networks, ranging from structured (e.g. road networks) to ad-hoc and
unstructured (e.g. crowd sourced information networks), to biological. We find:
(a) The intrinsic scale is surprisingly small (7-20 vertices), even though the
networks are many orders of magnitude larger. (b) The intrinsic scale
quantifies ``structure'' in a network -- networks which are explicitly
constructed for specific tasks have smaller intrinsic scale. (c) The structure
at different scales can be fragile (easy to disrupt) or robust.


  In order to handle modern convolutional neural networks (CNNs) efficiently, a
hardware architecture of CNN inference accelerator is proposed to handle
depthwise convolutions and regular convolutions, which are both essential
building blocks for embedded-computer-vision algorithms. Different from related
works, the proposed architecture can support filter kernels with different
sizes with high flexibility since it does not require extra costs for
intra-kernel parallelism, and it can generate convolution results faster than
the architecture of the related works. The experimental results show the
importance of supporting depthwise convolutions and dilated convolutions with
the proposed hardware architecture. In addition to depthwise convolutions with
large-kernels, a new structure called DDC layer, which includes the combination
of depthwise convolutions and dilated convolutions, is also analyzed in this
paper. For face detection, the computational costs decrease by 30%, and the
model size decreases by 20% when the DDC layers are applied to the network. For
image classification, the accuracy is increased by 1% by simply replacing $3
\times 3$ filters with $5 \times 5$ filters in depthwise convolutions.


  The study of genome rearrangement has many flavours, but they all are somehow
tied to edit distances on variations of a multi-graph called the breakpoint
graph. We study a weighted 2-break distance on Eulerian 2-edge-colored
multi-graphs, which generalizes weighted versions of several Double Cut and
Join problems, including those on genomes with unequal gene content. We affirm
the connection between cycle decompositions and edit scenarios first discovered
with the Sorting By Reversals problem. Using this we show that the problem of
finding a parsimonious scenario of minimum cost on an Eulerian 2-edge-colored
multi-graph - with a general cost function for 2-breaks - can be solved by
decomposing the problem into independent instances on simple alternating
cycles. For breakpoint graphs, and a more constrained cost function, based on
coloring the vertices, we give a polynomial-time algorithm for finding a
parsimonious 2-break scenario of minimum cost, while showing that finding a
non-parsimonious 2-break scenario of minimum cost is NP-Hard.


  The hot gas that constitutes the intracluster medium (ICM) has been studied
at X-ray and millimeter/sub-millimeter wavelengths (Sunyaev-Zeldovich effect)
for decades. Fast radio bursts (FRBs) offer an additional method of directly
measuring the ICM and gas surrounding clusters, via observables such as
dispersion measure (DM) and Faraday rotation measure (RM). We report the
discovery of two FRB sources detected with the Deep Synoptic Array (DSA-110)
whose host galaxies belong to massive galaxy clusters. In both cases, the FRBs
exhibit excess extragalactic DM, some of which likely originates in the ICM of
their respective clusters. FRB 20220914A resides in the galaxy cluster Abell
2310 at z=0.1125 with a projected offset from the cluster center of 520 kpc.
The host of a second source, FRB 20220509G, is an elliptical galaxy at z=0.0894
that belongs to the galaxy cluster Abell 2311 at projected offset of 870 kpc.
These sources represent the first time an FRB has been localized to a galaxy
cluster. We combine our FRB data with archival X-ray, SZ, and optical
observations of these clusters in order to infer properties of the ICM,
including a measurement of gas temperature from DM and ySZ of 0.8-3.9 keV. We
then compare our results to massive cluster halos from the IllustrisTNG
simulation. Finally, we describe how large samples of localized FRBs from
future surveys will constrain the ICM, particularly beyond the virial radius of
clusters.


  The $\eta^{(\prime)}$-mesons in the quark-flavor basis are mixtures of two
mesonic states $|\eta_{q}\rangle=|\bar u u+\bar d d\rangle/\sqrt 2$ and
$|\eta_{s}\rangle=|\bar s s\rangle$. In the previous work, we have made a
detailed study on the $\eta_{s}$ leading-twist distribution amplitude. As a
sequential work, in the present paper, we fix the $\eta_q$ leading-twist
distribution amplitude by using the light-cone harmonic oscillator model for
its wave function and by using the QCD sum rules within the QCD background
field to calculate its moments. The input parameters of $\eta_q$ leading-twist
distribution amplitude $\phi_{2;\eta_q}$ at an initial scale $\mu_0\sim 1$ GeV
are then fixed by using those moments. The sum rules for the $0_{\rm th}$-order
moment can also be used to fix the magnitude of $\eta_q$ decay constant, which
gives $f_{\eta_q}=0.141\pm0.005$ GeV. As an application of the present derived
$\phi_{2;\eta_q}$, we calculate the transition form factors $B(D)^+
\to\eta^{(\prime)}$ by using the QCD light-cone sum rules up to twist-4
accuracy and by including the next-to-leading order QCD corrections to the
twist-2 part, and then fix the related CKM matrix element and the decay width
for the semi-leptonic decays $B(D)^+ \to\eta^{(\prime)}\ell^+ \nu_\ell$.


  In this work, we analyze the behavior of the multivariate symmetric
uncertainty (MSU) measure through the use of statistical simulation techniques
under various mixes of informative and non-informative randomly generated
features. Experiments show how the number of attributes, their cardinalities,
and the sample size affect the MSU. In this thesis, through observation of
results, it is proposed an heuristic condition that preserves good quality in
the MSU under different combinations of these three factors, providing a new
useful criterion to help drive the process of dimension reduction.
  --
  En el presente trabajo hemos analizado el comportamiento de una versi\'on
multivariada de la incertidumbre sim\'etrica a trav\'es de t\'ecnicas de
simulaci\'on estad\'isticas sobre varias combinaciones de atributos
informativos y no-informativos generados de forma aleatoria. Los experimentos
muestran como el n\'umero de atributos, sus cardinalidades y el tama\~no
muestral afectan al MSU como medida. En esta tesis, mediante la observaci\'on
de resultados hemos propuesto una condici\'on que preserva una buena calidad en
el MSU bajo diferentes combinaciones de los tres factores mencionados, lo cual
provee un nuevo y valioso criterio para llevar a cabo el proceso de reducci\'on
de dimensionalidad.


  Neutrino mass and mixing are amongst the major discoveries of recent years.
  From the observation of flavor change in solar and atmospheric neutrino
experiments to the measurements of neutrino mixing with terrestrial neutrinos,
recent experiments have provided consistent and compelling evidence for the
mixing of massive neutrinos. The discoveries at Super-Kamiokande, SNO, and
KamLAND have solved the long-standing solar neutrino problem and demand that we
make the first significant revision of the Standard Model in decades. Searches
for neutrinoless double-beta decay probe the particle nature of neutrinos and
continue to place limits on the effective mass of the neutrino. Possible signs
of neutrinoless double-beta decay will stimulate neutrino mass searches in the
next decade and beyond. I review the recent discoveries in neutrino physics and
the current evidence for massive neutrinos.


  In Magnetic Particle Imaging (MPI), the distribution of magnetic
nanoparticles (MNPs) is imaged by moving a field free point (FFP) in space. All
MNPs in close vicinity of the FFP contribute to the signal induced on the
receive coil. The relaxation behavior of these MNPs are subject to a DC field
due to the selection field (SF). In this work, we investigate the effects of
the DC field on the relaxation behavior of the MNPs, with the goal of
understanding the differences between the measured relaxations in Magnetic
Particle Spectrometer (MPS) setups vs. MPI scanners.


  Speaker adaptation aims to estimate a speaker specific acoustic model from a
speaker independent one to minimize the mismatch between the training and
testing conditions arisen from speaker variabilities. A variety of neural
network adaptation methods have been proposed since deep learning models have
become the main stream. But there still lacks an experimental comparison
between different methods, especially when DNN-based acoustic models have been
advanced greatly. In this paper, we aim to close this gap by providing an
empirical evaluation of three typical speaker adaptation methods: LIN, LHUC and
KLD. Adaptation experiments, with different size of adaptation data, are
conducted on a strong TDNN-LSTM acoustic model. More challengingly, here, the
source and target we are concerned with are standard Mandarin speaker model and
accented Mandarin speaker model. We compare the performances of different
methods and their combinations. Speaker adaptation performance is also examined
by speaker's accent degree.


  A hyperbolic model for diffusion, nonlinear transport (or advection) and
production of a scalar quantity, is considered. The model is based on a
constitutive law of Cattaneo-Maxwell type expressing non-Fickian diffusion by
means of a relaxation time relation. The production or source term is assumed
to be of logistic type. This paper studies the existence and spectral stability
properties of spatially periodic traveling wave solutions to this system. It is
shown that a family of subcharacteristic periodic waves emerges from a local
Hopf bifurcation around a critical value of the wave speed. These waves have
bounded fundamental period and small-amplitude. In addition, it is shown that
these waves are spectrally unstable as solutions to the hyperbolic system. For
that purpose, it is proved that the Floquet spectrum of the linearized operator
around a wave can be approximated by a linear operator whose point spectrum
intersects the unstable half plane of complex numbers with positive real part.


  We study general quadratic reaction-diffusion systems with detailed balance,
in space dimension $d \leq 4$. We show that close-to-equilibrium solutions (in
an $L^2$ sense) are regular for all times, and that they relax to equilibrium
exponentially in a strong sense. That is: all detailed balance equilibria are
exponentially asymptotically stable in all $L^p$ norms, at least in dimension
$d \leq 4$. The results are given in detail for the four-species
reaction-diffusion system, where the involved constants can be estimated
explicitly. The main novelty is the regularity result and exponential
relaxation in $L^p$ norms for $p > 1$, which up to our knowledge is new in
dimensions 3 and 4.


  Controversial studies on the jet collimation profile of BL Lacertae (BL Lac),
the eponymous blazar of BL Lac objects class, complicate the scenario in this
already puzzling class of objects. Understanding the jet geometry, in
connection with the jet kinematics and the physical conditions in the
surrounding medium, is fundamental to better constrain the formation,
acceleration and collimation mechanisms in extragalactic jets. With the aim of
investigating the jet geometry in the innermost regions of the jet of BL Lac,
and solving the controversy, we explore the radio jet in this source, using
high resolution millimeter-wave VLBI data. We collect 86GHz GMVA and 43GHz VLBA
data to obtain stacked images that we use to infer the jet collimation profile
by means of two comparable methods. We analyze the kinematics at 86GHz, and we
discuss it in the context of the jet expansion. Finally we consider a possible
implication of the Bondi sphere in shaping the different expanding region
observed along the jet. We found that the jet in BL Lac expands with an overall
conical geometry. A higher expanding rate region is observed between ~5 and 10
pc (de-projected) from the black hole. Such a region is associated with the
decrease in brightness usually observed in high-frequency VLBI images of BL
Lac. The jet retrieves the original jet expansion around 17 pc, where the
presence of a recollimation shock is supported by both the jet profile and the
15GHz kinematics (MOJAVE survey). The change in the jet expansion profile
occurring at ~5 pc could be associated with a change in the external pressure
profile in correspondence of the Bondi radius (~3.3X10$^5$$R_s$).


  We study theoretically the effects of qubit inhomogeneity on the quantum
logic gate of qubit swap, which is an integral part of the operations of a
quantum computer. Our focus here is to construct a robust pulse sequence for
swap operation in the simultaneous presence of Zeeman inhomogeneity for quantum
dot trapped electron spins and the finite-time ramp-up of exchange coupling in
a double dot. We first present a geometric explanation of spin swap operation,
mapping the two-qubit operation onto a single-qubit rotation. We then show that
in this geometric picture a square-pulse-sequence can be easily designed to
perform swap in the presence of Zeeman inhomogeneity. Finally, we investigate
how finite ramp-up times for the exchange coupling $J$ negatively affect the
performance of the swap gate sequence, and show how to correct the problems
numerically.


  The impressive success of style-based GANs (StyleGANs) in high-fidelity image
synthesis has motivated research to understand the semantic properties of their
latent spaces. In this paper, we approach this problem through a geometric
analysis of latent spaces as a manifold. In particular, we propose a local
dimension estimation algorithm for arbitrary intermediate layers in a
pre-trained GAN model. The estimated local dimension is interpreted as the
number of possible semantic variations from this latent variable. Moreover,
this intrinsic dimension estimation enables unsupervised evaluation of
disentanglement for a latent space. Our proposed metric, called Distortion,
measures an inconsistency of intrinsic tangent space on the learned latent
space. Distortion is purely geometric and does not require any additional
attribute information. Nevertheless, Distortion shows a high correlation with
the global-basis-compatibility and supervised disentanglement score. Our work
is the first step towards selecting the most disentangled latent space among
various latent spaces in a GAN without attribute labels.


  Many phenomena are described by bivariate signals or bidimensional vectors in
applications ranging from radar to EEG, optics and oceanography. The
time-frequency analysis of bivariate signals is usually carried out by
analyzing two separate quantities, e.g. rotary components. We show that an
adequate quaternion Fourier transform permits to build relevant time-frequency
representations of bivariate signals that naturally identify geometrical or
polarization properties. First, the quaternion embedding of bivariate signals
is introduced, similar to the usual analytic signal of real signals. Then two
fundamental theorems ensure that a quaternion short term Fourier transform and
a quaternion continuous wavelet transform are well defined and obey desirable
properties such as conservation laws and reconstruction formulas. The resulting
spectrograms and scalograms provide meaningful representations of both the
time-frequency and geometrical/polarization content of the signal. Moreover the
numerical implementation remains simply based on the use of FFT. A toolbox is
available for reproducibility. Synthetic and real-world examples illustrate the
relevance and efficiency of the proposed approach.


  Measurements of differential cross sections are presented for inclusive
isolated-photon production in $pp$ collisions at a centre-of-mass energy of 13
TeV provided by the LHC and using 139 fb$^{-1}$ of data recorded by the ATLAS
experiment. The cross sections are measured as functions of the photon
transverse energy in different regions of photon pseudorapidity. The photons
are required to be isolated by means of a fixed-cone method with two different
cone radii. The dependence of the inclusive-photon production on the photon
isolation is investigated by measuring the fiducial cross sections as functions
of the isolation-cone radius and the ratios of the differential cross sections
with different radii in different regions of photon pseudorapidity. The results
presented in this paper constitute an improvement with respect to those
published by ATLAS earlier: the measurements are provided for different
isolation radii and with a more granular segmentation in photon pseudorapidity
that can be exploited in improving the determination of the proton parton
distribution functions. These improvements provide a more in-depth test of the
theoretical predictions. Next-to-leading-order QCD predictions from JETPHOX and
SHERPA and next-to-next-to-leading-order QCD predictions from NNLOJET are
compared to the measurements, using several parameterisations of the proton
parton distribution functions. The measured cross sections are well described
by the fixed-order QCD predictions within the experimental and theoretical
uncertainties in most of the investigated phase-space region.


  One dimensional chiral Hubbard model reduces to the Haldane-Shastry spin
chain at half-filling with large but finite on-site energy $U$.In this talk, we
show that the Gutzwiller-Jastrow wavefunctions are the eigen-states of the
Hubbard model at $U=+\infty$ at less than half-filling. The full energy
spectrum and an infinite set of mutually commuting constants of motion are also
given in this limit for the system.


  A theta graph, denoted $\theta_{a,b,c}$, is a graph of order $a+b+c-1$
consisting of a pair of vertices and three independent paths between them of
lengths $a$, $b$, and $c$. We provide a complete characterization of graphs
that do not contain a large $\theta_{a,b,c}$ as a topological minor. More
specifically, we describe the structure of $\theta_{1,2,t}$-,
$\theta_{2,2,t}$-, $\theta_{1,t,t}$-, $\theta_{2,t,t}$-, and
$\theta_{t,t,t}$-free graphs where $t$ is large. The main result is a
characterization of $\theta_{t,t,t}$-free graphs for large $t$. The
$3$-connected $\theta_{t,t,t}$-free graphs are formed by $3$-summing graphs
without a long path to certain planar graphs. The $2$-connected
$\theta_{t,t,t}$-free graphs are then built up in a similar fashion by 2- and
3-sums. This result implies a well-known theorem of Robertson and Chakravarti
on graphs that do not have a bond containing three specified edges.


  We study the Gaussian MIMO wiretap channel with a transmitter, a legitimate
receiver, an eavesdropper and an external helper, each equipped with multiple
antennas. The transmitter sends confidential messages to its intended receiver,
while the helper transmits jamming signals independent of the source message to
confuse the eavesdropper. The jamming signal is assumed to be treated as noise
at both the intended receiver and the eavesdropper. We obtain a closed-form
expression for the structure of the artificial noise covariance matrix that
guarantees no decrease in the secrecy capacity of the wiretap channel. We also
describe how to find specific realizations of this covariance matrix expression
that provide good secrecy rate performance, even when there is no non-trivial
null space between the helper and the intended receiver. Unlike prior work, our
approach considers the general MIMO case, and is not restricted to SISO or MISO
scenarios.


  In simultaneous localization and mapping (SLAM), image feature point matching
process consume a lot of time. The capacity of low-power systems such as
embedded systems is almost limited. It is difficult to ensure the timely
processing of each image information. To reduce time consuming when matching
feature points in SLAM, an algorithm of using inertial measurement unit (IMU)
to optimize the efficiency of image feature point matching is proposed. When
matching two image feature points, the presented algorithm does not need to
traverse the whole image for matching feature points, just around the predicted
point within a small range traversal search to find matching feature points.
After compared with the traditional algorithm, the experimental results show
that this method has greatly reduced the consumption of image feature points
matching time. All the conclusions will help research how to use the IMU
optimize the efficiency of image feature point matching and improve the
real-time performance in SLAM.


  We study the five-quark system $uudd{\bar s}$ in the standard
non-relativistic quark model by solving the scattering problem. Using the
Gaussian Expansion Method (GEM), we perform the almost precise multi-quark
calculations by treating a very large five-body modelspace including the NK
scattering channel explicitly. Although a lot of pseudostates (discretized
continuum states) with $J^\pi={1/2}^\pm$ and $J^\pi={3/2}^\pm$ are obtained
within the bound-state approximation, all the states in $1.4-1.85$ GeV in mass
around ${\rm {\rm \Theta}}^+(1540)$ melt into non-resonant continuum states
through the coupling with the NK scattering state in the realistic case, i.e.,
there is no five-quark resonance below 1.85GeV. Instead, we predict a
five-quark resonance state of $J^\pi={1/2}^-$ with the mass of about 1.9GeV and
the width of $\Gamma \simeq$ 2.68MeV. Similar calculation is done for the
four-quark system $c{\bar c}q{\bar q}$ ($q=u,d$) in connection with
X$^0$(3872).


  The effects of a voltage pulse on the localization probability for a Xe atom
prepared in a pure state localized on the STM surface at 0 temperature is
investigated by numerically integrating the time-dependent Schroedinger
equation. In these calculations the environmental interactions are neglected,
and voltage pulses of 20 and 7 ns with symmetric triangular and trapezoidal
shapes are considered. The atom dynamics at an environmental temperature of 4 K
is studied in the frame of a stochastic, non-linear Liouville equation for the
density operator. It is shown that the irreversible transfer from surface to
tip may be explained by thermal decoherence rather than by the driving force
acting during the application of the voltage pulse.


  We construct a Cartesian product G x H for finite simple graphs. It satisfies
the Kuenneth formula: H^k(G x H) is a direct sum of tensor products H^i(G) x
H^j(G) with i+j=k and so p(G x H,x) = p(G,x) p(H,y) for the Poincare polynomial
p(G,x) and X(G x H) = X(G) X(H) for the Euler characteristic X(G)=p(G,-1). G1=G
x K1 has as vertices the simplices of G and a natural digraph structure. We
show that dim(G1) is larger or equal than dim(G) and G1 is homotopic to G. The
Kuenneth identity is proven using Hodge describing the harmonic forms by the
product f g of harmonic forms of G and H and uses a discrete de Rham theorem
given by a combinatorial chain homotopy between simplicial and de Rham
cohomology. We show dim(G x H) = dim(G1) + dim(H1) implying that dim(G x H) is
larger or equal than dim(G) + dim(H) as for Hausdorff dimension in the
continuum. The chromatic number c(G1) is smaller or equal than c(G) and c(G x
H) is bounded above by c(G)+c(H)-1. The automorphism group of G x H contains
Aut(G) x Aut(H). If G~H and U~V then (G x U) ~ (H x V) if ~ means homotopic:
homotopy classes can be multiplided. If G is k-dimensional geometric meaning
that all unit spheres S(x) in G are (k-1)-discrete homotopy spheres, then G1 is
k-dimensional geometric. If G is k-dimensional geometric and H is l-dimensional
geometric, then G x H is geometric of dimension (l+k). The product extends to a
ring of chains which unlike the category of graphs is closed under boundary
operation taking quotients G/A with A subset Aut(G). As we can glue graphs or
chains, joins or fibre bundles can be defined with the same features as in the
continuum, allowing to build isomorphism classes of bundles.


  In a circular deconvolution model we consider the fully data driven density
estimation of a circular random variable where the density of the additive
independent measurement error is unknown. We have at hand two independent iid
samples, one of the contaminated version of the variable of interest, and the
other of the additive noise. We show optimality,in an oracle and minimax sense,
of a fully data-driven weighted sum of orthogonal series density estimators.
Two shapes of random weights are considered, one motivated by a Bayesian
approach and the other by a well known model selection method. We derive
non-asymptotic upper bounds for the quadratic risk and the maximal quadratic
risk over Sobolev-like ellipsoids of the fully data-driven estimator. We
compute rates which can be obtained in different configurations for the
smoothness of the density of interest and the error density. The rates
(strictly) match the optimal oracle or minimax rates for a large variety of
cases, and feature otherwise at most a deterioration by a logarithmic factor.
We illustrate the performance of the fully data-driven weighted sum of
orthogonal series estimators by a simulation study.


  We present the results of a search for the reactivation of active asteroid
176P/LINEAR during its 2011 perihelion passage using deep optical observations
obtained before, during, and after that perihelion passage. Deep composite
images of 176P constructed from data obtained between June 2011 and December
2011 show no visible signs of activity, while photometric measurements of the
object during this period also show no significant brightness enhancements
similar to that observed for 176P between November 2005 and December 2005 when
it was previously observed to be active. An azimuthal search for dust emission
likewise reveals no evidence for directed emission (i.e., a tail, as was
previously observed for 176P), while a one-dimensional surface brightness
profile analysis shows no indication of a spherically symmetric coma at any
time in 2011. We conclude that 176P did not in fact exhibit activity in 2011,
at least not on the level on which it exhibited activity in 2005, and suggest
that this could be due to the devolatization or mantling of the active site
responsible for its activity in 2005.


  Automated material handling systems are used in healthcare facilities to
optimize material flow, minimize workforce requirements, reduce the risk of
contamination, and reduce injuries. This study proposes a framework that
integrates data analysis with system simulation and optimization to address the
following research questions: (i) What are the implications of redesigning a
hospital's material handling system? (ii) What are the implications of
improving a hospital's material handling process? This paper develops a case
study using data from the Greenville Memorial Hospital (GMH) in South Carolina,
USA. The case study is focused on the delivery of surgical cases to operating
rooms at GMH via Automated Guided Vehicles (AGVs). The data analysis provides
distributions of travel times, AGV utilization, and AGV movement patterns in
the current system. The results of data analysis are integrated in a
simulation-optimization model that incorporates the size of AGV fleet and the
corresponding routes to improve system efficiency, increase AGV utilization,
and reduce congestion. To address research question (i), a redesign of AGV
pathways is evaluated to determine whether congestion is reduced. For research
question (ii), the implementation of a Kanban system is proposed to improve AGV
utilization by controlling the number of AGVs used daily, based on the volume
of surgical cases. An extensive sensitivity analysis, simulation-optimization
experiments, and a pilot study are conducted and indicate that the proposed
Kanban system leads to significant reductions in congestion and travel times
and increased utilization of AGVs.


  We describe a general operational method that can be used in the analysis of
fractional initial and boundary value problems with additional analytic
conditions. As an example, we derive analytic solutions of some fractional
generalisation of differential equations of mathematical physics. Fractionality
is obtained by substituting the ordinary integer-order derivative with the
Caputo fractional derivative. Furthermore, operational relations between
ordinary and fractional differentiation are shown and discussed in detail.
Finally, a last example concerns the application of the method to the study of
a fractional Poisson process.


  What is the productivity of Science? Can we measure an evolution of the
production of mathematicians over history? Can we predict the waiting time till
the proof of a challenging conjecture such as the P-versus-NP problem?
Motivated by these questions, we revisit a suggestion published recently and
debated in the "New Scientist" that the historical distribution of
time-to-proof's, i.e., of waiting times between formulation of a mathematical
conjecture and its proof, can be quantified and gives meaningful insights in
the future development of still open conjectures. We find however evidence that
the mathematical process of creation is too much non-stationary, with too
little data and constraints, to allow for a meaningful conclusion. In
particular, the approximate unsteady exponential growth of human population,
and arguably that of mathematicians, essentially hides the true distribution.
Another issue is the incompleteness of the dataset available. In conclusion we
cannot really reject the simplest model of an exponential rate of conjecture
proof with a rate of 0.01/year for the dataset that we have studied,
translating into an average waiting time to proof of 100 years. We hope that
the presented methodology, combining the mathematics of recurrent processes,
linking proved and still open conjectures, with different empirical
constraints, will be useful for other similar investigations probing the
productivity associated with mankind growth and creativity.


  TES-based bolometer and microcalorimeter arrays with thousands of pixels are
under development for several space-based and ground-based applications. A
linear detector response and low levels of cross talk facilitate the
calibration of the instruments. In an effort to improve the properties of
TES-based detectors, fixing the TES resistance in a resistance-locked loop
(RLL) under optical loading has recently been proposed. Earlier theoretical
work on this mode of operation has shown that the detector speed, linearity and
dynamic range should improve with respect to voltage biased operation.
  This paper presents an experimental demonstration of multiplexed readout in
this mode of operation in a TES-based detector array with noise equivalent
power values (NEP) of $3.5\cdot 10^{-19} $W/$\sqrt{\mathrm{Hz}}$. The measured
noise and dynamic properties of the detector in the RLL will be compared with
the earlier modelling work. Furthermore, the practical implementation routes
for future FDM systems for the readout of bolometer and microcalorimeter arrays
will be discussed.


  Searches at the Large Hadron Collider (LHC) for neutral, long-lived particles
have historically relied on the detection of displaced particles produced by
their decay $\textit{within}$ the detector volume. In this paper we study the
potential of the complementary signature comprising of the missing transverse
energy ($E_T^{miss}$) signal, traditionally used to look for dark matter, e.g.,
the lightest supersymmetric particle (LSP), to extend the LHC coverage to
models with long-lived (LL) particles when they decay $\textit{outside}$ the
detector. Using CMS and ATLAS analyses at the 8 TeV LHC, we set an upper limit
at the 95% confidence level (CL) on the production cross sections for two
specific scenarios: (i) a model with a heavy non-standard model Higgs boson
decaying to a LL scalar and (ii) an R-parity violating RPV SUSY model with a LL
neutralino.
  We show that this method can significantly extend the LHC sensitivity to
neutral, LL particles with arbitrary large lifetimes and that the limits
obtained from a $E_T^{miss}$ signal are comparable to those from displaced
particle searches for decay distances above a few meters. Results obtained in
this study do not not depend on the specific decay channel of the LL particle
and therefore are model-independent in this sense. We provide limits for the
whole two-dimensional plane in terms of the mass of the LL particle and the
mass of the mediator up to masses of 2 TeV including particular benchmarks
studied in the original experimental papers. We have made these limits
available in the form of a grid which can be used for the interpretation of
various other new physics models.


  The smoothed analysis of algorithms is concerned with the expected running
time of an algorithm under slight random perturbations of arbitrary inputs.
Spielman and Teng proved that the shadow-vertex simplex method has polynomial
smoothed complexity. On a slight random perturbation of an arbitrary linear
program, the simplex method finds the solution after a walk on polytope(s) with
expected length polynomial in the number of constraints n, the number of
variables d and the inverse standard deviation of the perturbation 1/sigma.
  We show that the length of walk in the simplex method is actually
polylogarithmic in the number of constraints n. Spielman-Teng's bound on the
walk was O(n^{86} d^{55} sigma^{-30}), up to logarithmic factors. We improve
this to O(log^7 n (d^9 + d^3 \s^{-4})). This shows that the tight Hirsch
conjecture n-d on the length of walk on polytopes is not a limitation for the
smoothed Linear Programming. Random perturbations create short paths between
vertices.
  We propose a randomized phase-I for solving arbitrary linear programs, which
is of independent interest. Instead of finding a vertex of a feasible set, we
add a vertex at random to the feasible set. This does not affect the solution
of the linear program with constant probability. This overcomes one of the
major difficulties of smoothed analysis of the simplex method -- one can now
statistically decouple the walk from the smoothed linear program. This yields a
much better reduction of the smoothed complexity to a geometric quantity -- the
size of planar sections of random polytopes. We also improve upon the known
estimates for that size, showing that it is polylogarithmic in the number of
vertices.


  We present a new model for the probability that the Disturbance storm time
(Dst) index exceeds -100 nT, with a lead time between 1 and 3 days. $Dst$
provides essential information about the strength of the ring current around
the Earth caused by the protons and electrons from the solar wind, and it is
routinely used as a proxy for geomagnetic storms. The model is developed using
an ensemble of Convolutional Neural Networks (CNNs) that are trained using SoHO
images (MDI, EIT and LASCO). The relationship between the SoHO images and the
solar wind has been investigated by many researchers, but these studies have
not explicitly considered using SoHO images to predict the $Dst$ index.
  This work presents a novel methodology to train the individual models and to
learn the optimal ensemble weights iteratively, by using a customized
class-balanced mean square error (CB-MSE) loss function tied to a least-squares
(LS) based ensemble.
  The proposed model can predict the probability that Dst<-100 nT 24 hours
ahead with a True Skill Statistic (TSS) of 0.62 and Matthews Correlation
Coefficient (MCC) of 0.37. The weighted TSS and MCC from Guastavino et al.
(2021) is 0.68 and 0.47, respectively. An additional validation during
non-Earth-directed CME periods is also conducted which yields a good TSS and
MCC score.


  In this article, let $\Sigma\subset\R^{2n}$ be a compact convex hypersurface
which is symmetric with respect to the origin. We prove that if $\Sg$ carries
finitely many geometrically distinct closed characteristics, then at least
$n-1$ of them must be non-hyperbolic; if $\Sg$ carries exactly $n$
geometrically distinct closed characteristics, then at least two of them must
be elliptic.


  We explore the possibility of obtaining de Sitter vacua in strongly coupled
heterotic models by adding various corrections to the supergravity potential
energy. We show that, in a generic compactification scenario, Fayet-Iliopoulos
terms can generate a de Sitter vacuum. The cosmological constant in this vacuum
can be fine tuned to be consistent with observation. We also study moduli
potentials in non-supersymmetric compactifications of $E_8 \times E_8$ theory
with anti five-branes and $E_8 \times \bar E_8$ theory. We argue that they can
be used to create a de Sitter vacuum only if some of the Kahler structure
moduli are stabilized at values much less than the Calabi-Yau scale.


  Transmitting polarization multiplexed carrier makes the receiver of a
coherent system local oscillator-less and frequency offset-free. A polarization
multiplexed carrier based self-homodyne (PMC-SH) system with an adaptive
polarization control (PC) can replace pulse amplitude modulation (PAM- 4) data
center interconnects. An adaptive PC technique is practically implemented by
using an electrically controlled PC along-with control circuitry for PMC-SH
systems. The de-multiplexing of the carrier and the modulated signal by using
this technique is validated through simulations for a 50 Gbaud PMC-SH
quadrature phase shift keying (QPSK) system with 20km standard single mode
fiber (SSMF). We successfully demonstrate 16 Gbaud PMC-SH systems with adaptive
PC for 10km SSMF channel. A bit error rate (BER) of 5.9 x 10^(-5) is achieved
with 32 Gb/s PMC-SHQPSK system without any signal processing while a BER of 8.7
x 10^(-3) is achieved with a 64 Gb/s PMC-SH quadrature amplitude modulation
(16QAM) system after equalization.


  Multivariate regression models for age estimation are a powerful tool for
assessing abnormal brain morphology associated to neuropathology. Age
prediction models are built on cohorts of healthy subjects and are built to
reflect normal aging patterns. The application of these multivariate models to
diseased subjects usually results in high prediction errors, under the
hypothesis that neuropathology presents a similar degenerative pattern as that
of accelerated aging. In this work, we propose an alternative to the idea that
pathology follows a similar trajectory than normal aging. Instead, we propose
the use of metrics which measure deviations from the mean aging trajectory. We
propose to measure these deviations using two different metrics: uncertainty in
a Gaussian process regression model and a newly proposed age weighted
uncertainty measure. Consequently, our approach assumes that pathologic brain
patterns are different to those of normal aging. We present results for
subjects with autism, mild cognitive impairment and Alzheimer's disease to
highlight the versatility of the approach to different diseases and age ranges.
We evaluate volume, thickness, and VBM features for quantifying brain
morphology. Our evaluations are performed on a large number of images obtained
from a variety of publicly available neuroimaging databases. Across all
features, our uncertainty based measurements yield a better separation between
diseased subjects and healthy individuals than the prediction error. Finally,
we illustrate differences in the disease pattern to normal aging, supporting
the application of uncertainty as a measure of neuropathology.


  Convolutional neural networks typically contain several downsampling
operators, such as strided convolutions or pooling layers, that progressively
reduce the resolution of intermediate representations. This provides some
shift-invariance while reducing the computational complexity of the whole
architecture. A critical hyperparameter of such layers is their stride: the
integer factor of downsampling. As strides are not differentiable, finding the
best configuration either requires cross-validation or discrete optimization
(e.g. architecture search), which rapidly become prohibitive as the search
space grows exponentially with the number of downsampling layers. Hence,
exploring this search space by gradient descent would allow finding better
configurations at a lower computational cost. This work introduces DiffStride,
the first downsampling layer with learnable strides. Our layer learns the size
of a cropping mask in the Fourier domain, that effectively performs resizing in
a differentiable way. Experiments on audio and image classification show the
generality and effectiveness of our solution: we use DiffStride as a drop-in
replacement to standard downsampling layers and outperform them. In particular,
we show that introducing our layer into a ResNet-18 architecture allows keeping
consistent high performance on CIFAR10, CIFAR100 and ImageNet even when
training starts from poor random stride configurations. Moreover, formulating
strides as learnable variables allows us to introduce a regularization term
that controls the computational complexity of the architecture. We show how
this regularization allows trading off accuracy for efficiency on ImageNet.


  We study the Cauchy problem for nonlocal reaction diffusion equations with
bistable nonlinearity in 1D spatial domain and investigate the asymptotic
behaviors of solutions with a one-parameter family of monotonically increasing
and compactly supported initial data. We show that for small values of the
parameter the corresponding solutions decay to 0, while for large values the
related solutions converge to 1 uniformly on compacts. Moreover, we prove that
the transition from extinction (converging to 0) to propagation (converging to
1) is sharp. Numerical results are provided to verify the theoretical results.


  We present an overview of Gromov-Witten theory and its links with string
theory compactifications, focussing on the GW potential as the generating
function for topological string amplitudes at genus $g$. Restricting to
Calabi-Yau target spaces, we give a complete derivation of the GW potential,
discuss problems of multicovers and the infinite product expression. We explain
the link with counting instantons or BPS states in type IIA and heterotic
string theories. We show why the numbers of BPS states on the heterotic side
can be a priori expressed in terms of those on the type IIA side, and vice
versa. We compute heterotic one-loop integrals to obtain the genus $g$ GW
potential, and detail two ways to obtain threshold corrections for heterotic
orbifolds, a prerequisite for the notorious work by Harvey and Moore. We review
this long and cumbersome construction in a self-contained way and make it
explicit in examples of compactifications. We also develop the relation to
Jacobi forms and automorphic forms, and clarify the meaning of the
Gopakumar-Vafa invariants.


  Parkinson's disease is an ongoing challenge to theoretical neuroscience and
to medical treatment. During the evolution of the disease, neurodegeneration
leads to physiological and anatomical changes that affect the neuronal
discharge of the Basal Ganglia to an extent that impairs normal behavioral
patterns. To investigate this problem, single Globus Pallidus pars interna
(GPi) neurons of the 6-OHDA rat model of Parkinson's disease were
extracellularly recorded at different degrees of alertness and compared to
non-Parkinson control neurons. A structure function analysis of these data
revealed that the temporal range of rate-coded information in GPi was
substantially reduced in the Parkinson animal-model, suggesting that a
dominance of small neighborhood dynamics could be the hallmark of Parkinson's
disease. A mathematical-model of the GPi circuit, where the small neighborhood
coupling is expressed in terms of a diffusion constant, corroborates this
interpretation.


  Generating text from structured inputs, such as meaning representations or
RDF triples, has often involved the use of specialized graph-encoding neural
networks. However, recent applications of pretrained transformers to
linearizations of graph inputs have yielded state-of-the-art generation results
on graph-to-text tasks. Here, we explore the ability of these linearized models
to encode local graph structures, in particular their invariance to the graph
linearization strategy and their ability to reconstruct corrupted inputs. Our
findings motivate solutions to enrich the quality of models' implicit graph
encodings via scaffolding. Namely, we use graph-denoising objectives
implemented in a multi-task text-to-text framework. We find that these
denoising scaffolds lead to substantial improvements in downstream generation
in low-resource settings.


  We describe in this article a multiagent urban traffic simulation, as we
believe individual-based modeling is necessary to encompass the complex
influence the actions of an individual vehicle can have on the overall flow of
vehicles. We first describe how we build a graph description of the network
from purely geometric data, ESRI shapefiles. We then explain how we include
traffic related data to this graph. We go on after that with the model of the
vehicle agents: origin and destination, driving behavior, multiple lanes,
crossroads, and interactions with the other vehicles in day-to-day, ?ordinary?
traffic. We conclude with the presentation of the resulting simulation of this
model on the Rouen agglomeration.


  Iceberg meltwater is a critical freshwater flux from the cryosphere to the
oceans. Global climate simulations therefore require simple and accurate
parameterisations of iceberg melting. Iceberg shape is an important but often
neglected aspect of iceberg melting. Icebergs have an enormous range of shapes
and sizes, and distinct processes dominate basal and side melting. We show how
different iceberg aspect ratios and relative ambient water velocities affect
melting using a combined experimental and numerical study. The experimental
results show significant variations in melting between different iceberg faces,
as well as within each iceberg face. These findings are reproduced and
explained with novel multiphysics numerical simulations. At high relative
ambient velocities melting is largest on the side facing the flow, and mixing
during vortex generation causes local increases in basal melt rates of over
50%. Double-diffusive buoyancy effects become significant when the relative
ambient velocity is low. Existing melting parameterisations do not reproduce
our findings. We propose several corrections to capture the influence of aspect
ratio on iceberg melting.


  The primary X-ray emission in active galactic nuclei (AGNs) is widely
believed to be due to Comptonisation of the thermal radiation from the
accretion disc in a corona of hot electrons. The resulting spectra can, in
first approximation, be modelled with a cut-off power law, the photon index and
the high-energy roll-over encoding information on the physical properties of
the X-ray-emitting region. The photon index and the high-energy curvature of
AGNs ($\Gamma$, E$_c$) have been largely studied since the launch of X-ray
satellites operating above 10 keV. However, high-precision measurements of
these two observables have only been obtained in recent years thanks to the
unprecedented sensitivity of NuSTAR up to 79 keV. We aim at deriving relations
between phenomenological parameters ($\Gamma$ and E$_c$) and the intrinsic
properties of the X-ray-emitting region (the hot corona), namely the optical
depth and temperature. We use MoCA (Monte Carlo code for Comptonisation in
Astrophysics) to produce synthetic spectra for the case of an AGN with
M$_{BH}$=1.5$\times$10$^8$ M$_{sun}$ and accretion rate of 10% and then
compared them with the widely used power-law model with an exponential
high-energy cutoff. We provide phenomenological relations relating $\Gamma$ and
E$_c$ with the opacity and temperature of the coronal electrons for the case of
spherical and slab-like coronae. These relations give origin to a well defined
parameter space which fully contains the observed values. Exploiting the
increasing number of high-energy cut-offs quoted in the literature, we report
on the comparison of physical quantities obtained using MoCA with those
estimated using commonly adopted spectral Comptonisation models. Finally, we
discuss the negligible impact of different black hole masses and accretion
rates on the inferred relations.


  A significant fraction of cosmological gamma-ray bursts (GRBs) are
characterised by a fast rise and exponential decay (FRED) temporal structure.
This is not a distinctive feature of this class, since it is observed in many
Galactic transients and is likely descriptive of a sudden release of energy
followed by a diffusion process. Possible evidence has recently been reported
by Tello et al. (2012) for a Galactic contamination in the sample of FRED GRBs
discovered with Swift. We searched for possible Galactic intruders disguised as
FRED GRBs in the Swift catalogue up to September 2014. We selected 181 FRED
GRBs (2/3 with unknown redshift) and considered different subsamples. We tested
the degree of isotropy through the dipole and the quadrupole moment
distributions, both with reference to the Galaxy and in a
coordinate-system-independent way, as well as with the two-point angular
autocovariance function. In addition, we searched for possible indicators of a
Galactic origin among the spectral and temporal properties of individual GRBs.
We found marginal (~3 sigma) evidence for an excess of FREDs with unknown
redshift towards the Galactic plane compared with what is expected for an
isotropic distribution corrected for the non-uniform sky exposure. However,
when we account for the observational bias against optical follow-up
observations of low-Galactic latitude GRBs, the evidence for anisotropy
decreases to ~2 sigma. In addition, we found no statistical evidence for
different spectral or temporal properties from the bulk of cosmological GRBs.
We found marginal evidence for the presence of a disguised Galactic population
among Swift GRBs with unknown redshift. The estimated fraction is f=(19 +-
11)%, with an upper limit of 34% (90% confidence).


  We study committee voting rules under ranked preferences, which map the
voters' preference relations to a subset of the alternatives of predefined
size. In this setting, the compatibility between proportional representation
and committee monotonicity is a fundamental open problem that has been
mentioned in several works. We design a new multi-winner voting rule called the
Solid Coalition Refinement (SCR) Rule that simultaneously satisfies committee
monotonicity and Dummett's PSC as well as one of its variants called inclusion
PSC. This is the first rule known to satisfy both of these properties.
Moreover, we show that this is effectively the best that we can hope for as
other fairness notions adapted from approval voting such as Rank-JR and
Rank-PJR+ are incompatible with committee monotonicity.


  In RF-MEMS packaging, next to the protection of movable structures,
optimization of package electrical performance plays a very important role. In
this work, a wafer-level packaging process has been investigated and optimized
in order to minimize electrical parasitic effects. The RF-MEMS package concept
used is based on a wafer-level bonding of a capping silicon substrate to an
RF-MEMS wafer. The capping silicon substrate resistivity, substrate thickness
and the geometry of through-substrate electrical interconnect vias have been
optimized using finite-element electromagnetic simulations (Ansoft HFSS). Test
structures for electrical characterization have been designed and after their
fabrication, measurement results will be compared with simulations.


  CubeSats are excellent platforms to rapidly perform simple space experiments.
Several hundreds of CubeSats have already been successfully launched in the
past few years and the number of announced launches grows every year. These
platforms provide an easy access to space for universities and organizations
which otherwise could not afford it. However, these spacecraft still rely on RF
communications, where the spectrum is already crowded and cannot support the
growing demand for data transmission to the ground. Lasercom holds the promise
to be the solution to this problem, with a potential improvement of several
orders of magnitude in the transmission capacity, while keeping a low size,
weight and power. Between 2016 and 2017, The Keck Institute for Space Studies
(KISS), a joint institute of the California Institute of Technology and the Jet
Propulsion Laboratory, brought together a group of space scientists and
lasercom engineers to address the current challenges that this technology
faces, in order to enable it to compete with RF and eventually replace it when
high-data rate is needed. After two one-week workshops, the working group
started developing a report addressing three study cases: low Earth orbit,
crosslinks and deep space. This paper presents the main points and conclusions
of these KISS workshops.


  It was proved by Elkik that, under some smoothness conditions, the Artin
functions of systems of polynomials over a Henselian pair are bounded above by
linear functions. This paper gives a stronger form of this result for the class
of excellent Henselian local rings. The linearity of Artin functions of systems
of polynomials in one variable is also studied. Explicit calculations of Artin
functions of monomials and determinantal ideals are also included.


  We review three recent results of the CDF collaboration on Bs suppressed
decays: the first search for CP--violation in the Bs-->phi phi decay, where two
CP--violating asymmetries expected to be zero in the Standard Model are
measured, and the observation and the branching ratio measurements of Bs-->jpsi
fnot and Bs-->jpsi K(*) decays.


  The Vera C. Rubin Observatory will advance many areas of astronomy over the
next decade with its unique wide-fast-deep multi-color imaging survey, the
Legacy Survey of Space and Time (LSST). The LSST will produce approximately
20TB of raw data per night, which will be automatically processed by the LSST
Science Pipelines to generate science-ready data products -- processed images,
catalogs and alerts. To ensure that these data products enable transformative
science with LSST, stringent requirements have been placed on their quality and
scientific fidelity, for example on image quality and depth, astrometric and
photometric performance, and object recovery completeness. In this paper we
introduce faro, a framework for automatically and efficiently computing
scientific performance metrics on the LSST data products for units of data of
varying granularity, ranging from single-detector to full-survey summary
statistics. By measuring and monitoring metrics, we are able to evaluate trends
in algorithmic performance and conduct regression testing during development,
compare the performance of one algorithm against another, and verify that the
LSST data products will meet performance requirements by comparing to
specifications. We present initial results using faro to characterize the
performance of the data products produced on simulated and precursor data sets,
and discuss plans to use faro to verify the performance of the LSST
commissioning data products.


  We consider the Cauchy problem $(\mathbb D_{(k)} u)(t)=\lambda u(t)$,
$u(0)=1$, where $\mathbb D_{(k)}$ is the general convolutional derivative
introduced in the paper (A. N. Kochubei, Integral Equations Oper. Theory {\bf
71} (2011), 583--600), $\lambda >0$. The solution is a generalization of the
function $t\mapsto E_\alpha (\lambda t^\alpha)$ where $0<\alpha <1$, $E_\alpha$
is the Mittag-Leffler function. The asymptotics of this solution, as $t\to
\infty$, is studied.


  We present a wide-field multi-pass implementation of label-free imaging flow
cytometry. Our technique is shown for high-speed flow imaging of ensembles of
human red blood cells with up to four passes, demonstrating x4 enhancement in
contrast and signal-to-noise. We show that our technique approaches close to
the quantum limit of measurement sensitivity, extending the range of optimal
imaging to samples in the weakly absorbing regime. This allows for near optimal
imaging sensitivity and throughput in a practical scenario of imaging a dynamic
sample under limited illumination intensity, surpassing the sensitivity
achieved with currently available quantum light sources.


  With the growing access to administrative health databases, retrospective
studies have become crucial evidence for medical treatments. Yet,
non-randomized studies frequently face selection biases, requiring mitigation
strategies. Propensity score matching (PSM) addresses these biases by selecting
comparable populations, allowing for analysis without further methodological
constraints. However, PSM has several drawbacks. Different matching methods can
produce significantly different Average Treatment Effects (ATE) for the same
task, even when meeting all validation criteria. To prevent cherry-picking the
best method, public authorities must involve field experts and engage in
extensive discussions with researchers.
  To address this issue, we introduce a novel metric, A2A, to reduce the number
of valid matches. A2A constructs artificial matching tasks that mirror the
original ones but with known outcomes, assessing each matching method's
performance comprehensively from propensity estimation to ATE estimation. When
combined with Standardized Mean Difference, A2A enhances the precision of model
selection, resulting in a reduction of up to 50% in ATE estimation errors
across synthetic tasks and up to 90% in predicted ATE variability across both
synthetic and real-world datasets. To our knowledge, A2A is the first metric
capable of evaluating outcome correction accuracy using covariates not involved
in selection.
  Computing A2A requires solving hundreds of PSMs, we therefore automate all
manual steps of the PSM pipeline. We integrate PSM methods from Python and R,
our automated pipeline, a new metric, and reproducible experiments into
popmatch, our new Python package, to enhance reproducibility and accessibility
to bias correction methods.


  We cross correlate Gamma--Ray Bursts and X--Ray selected clusters of galaxies
at z\leq0.45. We find a positive 2\sigma signal for the angular
cross--correlation function w_{bc}(\theta) on scales \theta\leq 3 deg between
short GRBs and clusters. Conversely, no correlation is found between clusters
and the population of long GRBs. The comparison with the cluster
autocorrelation function shows that short GRBs do not trace the cluster
distribution as not all short GRBs are found in clusters. A higher signal in
w_{bc}(\theta) is found if we only consider the cluster population up to z=0.1.
By comparing the short burst autocorrelation function with model predictions we
then constrain short bursts to mostly originate within \sim 270 Mpc (i.e. z\leq
0.06). Our analysis also reveals that short GRBs are better correlated with
``normal'' galaxies. The double compact object merger model for short GRBs
would associate them preferentially to early--type galaxies but the present
statistics do not allow us to exclude that at least a fraction of these events
might also take place in late--type galaxies, in agreement with recent
evidences.


  To understand applications' memory usage details, engineers use instrumented
builds and profiling tools. Both approaches are impractical for use in
production environments or deployed mobile applications. As a result,
developers can gather only high-level memory-related statistics for deployed
software. In our experience, the lack of granular field data makes fixing
performance and reliability-related defects complex and time-consuming. The
software industry needs lightweight solutions to collect detailed data about
applications' memory usage to increase developer productivity. Current research
into memory attribution-related data structures, techniques, and tools is in
the early stages and enables several new research avenues.


  While AI-assisted individual qualitative analysis has been substantially
studied, AI-assisted collaborative qualitative analysis (CQA)-a process that
involves multiple researchers working together to interpret data-remains
relatively unexplored. After identifying CQA practices and design opportunities
through formative interviews, we designed and implemented CoAIcoder, a tool
leveraging AI to enhance human-to-human collaboration within CQA through four
distinct collaboration methods. With a between-subject design, we evaluated
CoAIcoder with 32 pairs of CQA-trained participants across common CQA phases
under each collaboration method. Our findings suggest that while using a shared
AI model as a mediator among coders could improve CQA efficiency and foster
agreement more quickly in the early coding stage, it might affect the final
code diversity. We also emphasize the need to consider the independence level
when using AI to assist human-to-human collaboration in various CQA scenarios.
Lastly, we suggest design implications for future AI-assisted CQA systems.


  In this paper, we propose the USIP detector: an Unsupervised Stable Interest
Point detector that can detect highly repeatable and accurately localized
keypoints from 3D point clouds under arbitrary transformations without the need
for any ground truth training data. Our USIP detector consists of a feature
proposal network that learns stable keypoints from input 3D point clouds and
their respective transformed pairs from randomly generated transformations. We
provide degeneracy analysis of our USIP detector and suggest solutions to
prevent it. We encourage high repeatability and accurate localization of the
keypoints with a probabilistic chamfer loss that minimizes the distances
between the detected keypoints from the training point cloud pairs. Extensive
experimental results of repeatability tests on several simulated and real-world
3D point cloud datasets from Lidar, RGB-D and CAD models show that our USIP
detector significantly outperforms existing hand-crafted and deep
learning-based 3D keypoint detectors. Our code is available at the project
website. https://github.com/lijx10/USIP


  This companion paper to [D. Picconi et al., J. Chem. Phys. 150 (2019)]
presents quantum dynamical simulations, using the Gaussian-based
multiconfigurational time-dependent Hartree (G-MCTDH) method, of time-resolved
coherent Raman four-wave-mixing spectroscopic experiments for the iodine
molecule embedded in a cryogenic crystal krypton matrix. These experiments
monitor the time-evolving vibrational coherence between two wave packets
created in a quantum superposition (i. e. a 'Schr\"odinger cat state') by a
pair of pump pulses which induce electronic $B\ ^3\Pi_u\left(0^+\right)
\longleftarrow X\ ^1\Sigma_g^+$ transitions.
  A theoretical description of the spectroscopic measurement is developed,
which elucidates the connection between the nonlinear signals and the wave
packet coherence. The analysis provides an effective means to simulate the
spectra for several different optical conditions with a minimum number of
quantum dynamical propagations. The G-MCTDH method is used to calculate and
interpret the time-resolved coherent Raman spectra of two selected initial
superpositions for a $\mathrm{I_2 Kr_{18}}$ cluster embedded in a frozen Kr
cage. The time- and frequency-dependent signals carry information about the
molecular mechanisms of dissipation and decoherence, which involve vibrational
energy transfer to the stretching mode of the four 'belt' Kr atoms. The details
of these processes and the number of active solvent modes depend in a
non-trivial way on the specific initial superposition.


  The coupled spin-valley physics in transition metal dichalcogenides (TMDs)
endows exciton states with valley degrees of freedom, making them promising for
valleytronic applications in TMDs monolayers and/or their heterostructures.
Although the valley dynamics of intralayer and interlayer excitons (IXs) have
been studied, efficient manipulation of valley pseudospins by switching exciton
states remains elusive. Therefore, it is of great importance to effectively
tune the exciton states to obtain practical valley polarization switches for
valley encoding. Here, we demonstrate the electrical switching of exciton
emission with highly variable valley polarization mediated by charged IXs
(CIXs) in the heterostructure of monolayer WS2 and two-dimensional (2D)
perovskite, irrespective of lattice constants, the rotational and translational
alignment. The formation of IXs is identified by photoluminescence excitation
(PLE) and photoluminescence (PL) studies, which can be further electrically
tuned from positively charged to negatively charged depending on the
electrostatic doping level of monolayer WS2. Importantly, we demonstrate an
electrical switching from type two to type one band alignment, manifesting as a
change in the PL profile from CIX to charged intralayer exciton emission. Such
transition induces a large contrast in valley polarization between the two
exciton states, enabling the reversible electrically regulated valley
polarization switch with a maximum ON/OFF ratio of 15.8. Our study provides an
alternative mechanism to achieve valley polarization switching with great
simplicity for valleytronics and the electrical control of exciton species and
associated valley-contrasting physics would further facilitate the development
of optoelectronic and valleytronic devices


  There is increasing demand for specialized hardware for training deep neural
networks, both in edge/IoT environments and in high-performance computing
systems. The design space of such hardware is very large due to the wide range
of processing architectures, deep neural network configurations, and dataflow
options. This makes developing deep neural network processors quite complex,
especially for training. We present TRIM, an infrastructure to help hardware
architects explore the design space of deep neural network accelerators for
both inference and training in the early design stages. The model evaluates at
the whole network level, considering both inter-layer and intra-layer
activities. Given applications, essential hardware specifications, and a design
goal, TRIM can quickly explore different hardware design options, select the
optimal dataflow and guide new hardware architecture design. We validated TRIM
with FPGA-based implementation of deep neural network accelerators and
ASIC-based architectures. We also show how to use TRIM to explore the design
space through several case studies. TRIM is a powerful tool to help architects
evaluate different hardware choices to develop efficient inference and training
architecture design.


  In this manuscript, we will discuss the construction of covariant derivative
operator in quantum gravity. We will find it is more perceptive alternative to
use affine connections more general than metric compatible connections in
quantum gravity. We will demonstrate this using the canonical quantization
procedure. This is valid irrespective of the presence and nature of sources.
The conventional Palatini and metric-affine formalisms, where the actions are
linear in the scalar curvature with metric and affine connections being the the
independent variables, are not much suitable to construct a source-free theory
of gravity with general affine connections. This is also valid for many
minimally coupled interacting theories where sources only couple with metric by
using the Levi-Civita connections exclusively. We will discuss potential
formalism of affine connections to introduce affine connections more general
than metric compatible connections in gravity. We will also discuss possible
extensions of the actions for this purpose. General affine connections
introduce new fields in gravity besides metric. In this article, we will
consider a simple potential formalism with symmetric affine connections and
symmetric Ricci tensor. Corresponding affine connections introduce two massless
scalar fields. One of these fields contributes a stress-tensor with opposite
sign to the sources of Einstein's equation when we state the equation using the
Levi-Civita connections. This means we have a massless scalar field with
negative stress-tensor in Einstein's equation. This field brings us beyond
strict local Minkowski geometries. These scalar fields can be useful to explain
inflation and dark energy.


  We describe a complete method that learns a neural network which is
guaranteed to overestimate a reference function on a given domain. The neural
network can then be used as a surrogate for the reference function. The method
involves two steps. In the first step, we construct an adaptive set of Majoring
Points. In the second step, we optimize a well-chosen neural network to
overestimate the Majoring Points. In order to extend the guarantee on the
Majoring Points to the whole domain, we necessarily have to make an assumption
on the reference function. In this study, we assume that the reference function
is monotonic. We provide experiments on synthetic and real problems. The
experiments show that the density of the Majoring Points concentrate where the
reference function varies. The learned over-estimations are both guaranteed to
overestimate the reference function and are proven empirically to provide good
approximations of it. Experiments on real data show that the method makes it
possible to use the surrogate function in embedded systems for which an
underestimation is critical; when computing the reference function requires too
many resources.


  Equity premium, the surplus returns of stocks over bonds, has been an
enduring puzzle. While numerous prior works approach the problem assuming the
utility of money is invariant across contexts, our approach implies that in
efficient markets the utility of money is polymorphic, with risk aversion
dependent on the information available in each context, i.e. the discount on
each future cash flow depends on all information available on that cash flow.
Specifically, we prove that in efficient markets, informed investors maximize
return on volatility by being risk-neutral with riskless bonds, and risk-averse
with equities, thereby resolving the puzzle. We validate our results on
historical data with surprising consistency.
  JEL Classification: C58, G00, G12, G17


  Commonly employed reconstruction algorithms in compressed sensing (CS) use
the $L_2$ norm as the metric for the residual error. However, it is well-known
that least squares (LS) based estimators are highly sensitive to outliers
present in the measurement vector leading to a poor performance when the noise
no longer follows the Gaussian assumption but, instead, is better characterized
by heavier-than-Gaussian tailed distributions. In this paper, we propose a
robust iterative hard Thresholding (IHT) algorithm for reconstructing sparse
signals in the presence of impulsive noise. To address this problem, we use a
Lorentzian cost function instead of the $L_2$ cost function employed by the
traditional IHT algorithm. We also modify the algorithm to incorporate prior
signal information in the recovery process. Specifically, we study the case of
CS with partially known support. The proposed algorithm is a fast method with
computational load comparable to the LS based IHT, whilst having the advantage
of robustness against heavy-tailed impulsive noise. Sufficient conditions for
stability are studied and a reconstruction error bound is derived. We also
derive sufficient conditions for stable sparse signal recovery with partially
known support. Theoretical analysis shows that including prior support
information relaxes the conditions for successful reconstruction. Simulation
results demonstrate that the Lorentzian-based IHT algorithm significantly
outperform commonly employed sparse reconstruction techniques in impulsive
environments, while providing comparable performance in less demanding,
light-tailed environments. Numerical results also demonstrate that the
partially known support inclusion improves the performance of the proposed
algorithm, thereby requiring fewer samples to yield an approximate
reconstruction.


  We introduce two simple two-dimensional lattice models to study traffic flow
in cities. We have found that a few basic elements give rise to the
characteristic phase diagram of a first-order phase transition from a freely
moving phase to a jammed state, with a critical point. The jammed phase
presents new transitions corresponding to structural transformations of the
jam. We discuss their relevance in the infinite size limit.


  We lay out the basis of factorization at the amplitude level for processes
involving the entire Standard Model. The factorization appears in a generalized
eikonal approximation in which we expand around a quasi-soft limit for massive
gauge bosons, fermions, and scalars. We use the chirality-flow formalism to
express loop exchanges or emissions as operators on chiral structures. This
forms the basis for amplitude evolution with parton exchange and branching in
the full Standard Model, including the electroweak sector.


  In this paper, we investigate analytically the properties of the disordered
Bernoulli model of planar matching. This model is characterized by a
topological phase transition, yielding complete planar matching solutions only
above a critical density threshold. We develop a combinatorial procedure of
arcs expansion that explicitly takes into account the contribution of short
arcs, and allows to obtain an accurate analytical estimation of the critical
value by reducing the global constrained problem to a set of local ones. As an
application to a toy representation of the RNA secondary structures, we suggest
generalized models that incorporate a one-to-one correspondence between the
contact matrix and the RNA-type sequence, thus giving sense to the notion of
effective non-integer alphabets.


  We study the quantum dynamics of a spin-orbit (SO) coupled Bose-Einstein
condensate (BEC) in a double-well potential inspired by the experimental
protocol recently developed by NIST group. We focus on the regime where the
number of atoms is very large and perform a two-mode approximation. An
analytical solution of the two-site Bose-Hubbard-like Hamiltonian is found for
several limiting cases, which range from a strong Raman coupling to a strong
Josephson coupling, ending with the complete model in the presence of weak
nonlinear interactions. Depending on the particular limit, different approaches
are chosen: a mapping onto an SU(2) spin problem together with a
Holstein-Primakoff transformation in the first two cases and a rotating wave
approximation (RWA) when dealing with the complete model. The quantum evolution
of the number difference of bosons with equal or different spin between the two
wells is investigated in a wide range of parameters; finally the corresponding
total atomic current and the spin current are computed. We show a spin
Josephson effect which could be detected in experiments and employed to build
up realistic devices.


  We argue that restricted Schur polynomials provide a useful parameterization
of the complete set of gauge invariant variables of multi-matrix models. The
two point functions of restricted Schur polynomials are evaluated exactly in
the free field theory limit. They have diagonal two point functions.


  Surface ligand exchange on all-inorganic perovskite nanocrystals of
composition CsPbBrI$_{2}$ reveals improved optoelectronic properties due to
strong interactions of the nanocrystal with mono-functionalized porphyrin
derivatives. The interaction is verified experimentally with an array of
spectroscopic measurements as well as computationally by exploiting density
functional theory calculations. The enhanced current efficiency is attributed
to a lowering of the charging energy by a factor of 2 to 3, which is determined
by combining electronic and optical measurements on a selection of ligands. The
coupled organic-inorganic nanostructures are successfully deployed in a light
emitting device with higher current efficacy and improved charge carrier
balance, magnifying the efficiency almost fivefold compared to the native
ligand.


  Starting from a general wave function described on a set of spins/qubits, we
propose several quantum algorithms to extract the components of this state on
eigenstates of the total spin ${\bf S}^2$ and its azimuthal projection $S_z$.
The method plays the role of total spin projection and gives access to the
amplitudes of the initial state on a total spin basis. The different algorithms
have various degrees of sophistication depending on the requested tasks. They
can either solely project onto the subspace with good total spin or completely
uplift the degeneracy in this subspace. After each measurement, the state
collapses to one of the spin eigenstates that could be used for
post-processing. For this reason, we call the method Total Quantum Spin
filtering (TQSf). Possible applications ranging from many-body physics to
random number generators are discussed.


  The Aharanov-Bohm (AB) effect, which predicts that a magnetic field strongly
influences the wave function of an electrically charged particle, is
investigated in a three site system in terms of the quantum control by an
additional dephasing source. The AB effect leads to a non-monotonic dependence
of the steady-state current on the gauge phase associated with the molecular
ring. This dependence is sensitive to site energy, temperature, and dephasing,
and can be explained using the concept of the dark state. Although the phase
effect vanishes in the steady-state current for strong dephasing, the phase
dependence remains visible in an associated waiting-time distribution,
especially at short times. Interestingly, the phase rigidity (i.e., the
symmetry of the AB phase) observed in the steady-state current is now broken in
the waiting-time statistics, which can be explained by the interference between
transfer pathways.


  KnowNER is a multilingual Named Entity Recognition (NER) system that
leverages different degrees of external knowledge. A novel modular framework
divides the knowledge into four categories according to the depth of knowledge
they convey. Each category consists of a set of features automatically
generated from different information sources (such as a knowledge-base, a list
of names or document-specific semantic annotations) and is used to train a
conditional random field (CRF). Since those information sources are usually
multilingual, KnowNER can be easily trained for a wide range of languages. In
this paper, we show that the incorporation of deeper knowledge systematically
boosts accuracy and compare KnowNER with state-of-the-art NER approaches across
three languages (i.e., English, German and Spanish) performing amongst
state-of-the art systems in all of them.


  The low-frequency power spectra of the X-ray and radio emission from four
microquasars suggest that two distinct modes of energy output are at work: (i)
the `coupled' mode in which the X-ray and radio luminosities are closely
coupled and vary only weakly, and (ii) the `flaring' mode, which dramatically
boosts the radio luminosity but makes no impact on the X-ray luminosity. The
systems are in the flaring mode only a few percent of the time. However, flares
completely dominate the power spectrum of radio emission, with the consequence
that sources in which the flaring mode occurs, such as GRS 1915+105 and Cyg
X-3, have radio power spectra that lie more than an order of magnitude above
the corresponding X-ray power spectra. Of the four microquasars for which we
have examined data, in only one, Cyg X-1, is the flaring mode seemingly
inactive. While Cyg X-1 is a black-hole candidate, one of the three flaring
sources, Sco X-1, is a neutron star. Consequently, it is likely that both modes
are driven by the accretion disk rather than black-hole spin. Radio imaging
strongly suggests that the flaring mode involves relativistic jets. A typical
microquasar is in the flaring mode a few percent of the time, which is similar
to the fraction of quasars that are radio loud. Thus there may be no essential
difference between radio-loud and radio-quiet quasars; radio loudness may
simply be a function of the epoch at which the source is observed.


  The persistent current in a clean mesoscopic ring with ballistic electron
motion is calculated. The particle dynamics inside a ring is assumed to be
chaotic due to scattering at the surface irregularities of atomic size. This
allows one to use the so-called ``ballistic'' supersymmetric \sigma model for
calculation of the two-level correlation function in the presence of a nonzero
magnetic flux.


  We discuss the possibility that the electro-weak and strong interactions
arise as the low energy effective description of branes in M-theory. As a step
towards constructing such a model we show how one can naturally obtain
SU(N_1)\times SU(N_2)\times U(1) gauge theories from branes, including matter
in the bi-fundamental representation of SU(N_1)\times SU(N_2) which are
fractionally charged under U(1).


  We formulate theoretically and demonstrate experimentally an all-optical
method for reconstruction of the amplitude, phase and coherence of frequency
combs from a single-shot measurement of the spectral intensity. Our approach
exploits synthetic frequency lattices with pump-induced spectral short- and
long-range couplings between different signal components across a broad
bandwidth of of hundreds GHz in a single nonlinear fiber. When combined with
ultra-fast signal conversion techniques, this approach has the potential to
provide real-time measurement of pulse-to-pulse variations in the spectral
phase and coherence properties of exotic light sources.


  We present a brief overview of precision tests of bound state QED and mainly
pay our attention to laser spectroscopy as an appropriate tool for these tests.
We particularly consider different precision tests of bound state QED theory
based on the laser spectroscopy of optical transitions in hydrogen, muonium and
positronium and related experiments.


  We discuss the preliminary results of spectral analysis simulations involving
anticipated correlated multi-wavelength observations of gamma-ray bursts (GRBs)
using Swift's Burst Alert Telescope (BAT) and the Gamma-Ray Large Area Space
Telescope's (GLAST) Burst Monitor (GLAST-GBM), resulting in joint spectral
fits, including characteristic photon energy (Epeak) values, for a conservative
annual estimate of ~30 GRBs. The addition of BAT's spectral response will (i)
complement in-orbit calibration efforts of GBM's detector response matrices,
(ii) augment GLAST's low energy sensitivity by increasing the ~20-100 keV
effective area, (iii) facilitate ground-based follow-up efforts of GLAST GRBs
by increasing GBM's source localization precision, and (iv) help identify a
subset of non-triggered GRBs discovered via off-line GBM data analysis. Such
multi-wavelength correlative analyses, which have been demonstrated by
successful joint-spectral fits of Swift-BAT GRBs with other higher energy
detectors such as Konus-WIND and Suzaku-WAM, would enable the study of
broad-band spectral and temporal evolution of prompt GRB emission over three
energy decades, thus potentially increasing the science return without placing
additional demands upon mission resources throughout their contemporaneous
orbital tenure over the next decade.


  In the latest experimental advances, Ni-, Co- and Cu-HAB MOFs were
synthesized in 2D forms, with high electrical conductivities and capacitances
as well. Motivated by these experimental advances, we employed first-principles
simulations to explore the mechanical, thermal stability and electronic
properties of single-layer Ag-, Co-, Cr-, Cu-, Fe-, Mn-, Ni-, Pd- and Rh-HAB
MOFs. Theoretical results reveal that Co-, Cr-, Fe-, Mn-, Ni-, Pd- and Rh-HAB
nanosheets exhibit linear elasticity with considerable tensile strengths.
Ab-initio molecular dynamics results confirm the high thermal stability of all
studied nanomembranes. Co- and Fe-HAB monolayers show metallic behavior with
low spin-polarization at the Fermi level. Single-layer Ag-, Cu-, Cr-, and
Mn-HAB however yield perfect half-metallic behaviors, thus can be promising
candidates for the spintronics. In contrast, Ni-, Pd- and Rh-HAB monolayers
exhibit nonmagnetic metallic behavior. The insights provided by this
investigation confirm the stability and highlight the outstanding physics of
transition metal-HAB nanosheets, which are not only highly attractive for the
energy storage systems, but may also serve for other advanced applications,
like spintronics.


  We prove global convergence of classical projection algorithms for
feasibility problems involving union convex sets, which refer to sets
expressible as the union of a finite number of closed convex sets. We present a
unified strategy for analyzing global convergence by means of studying
fixed-point iterations of a set-valued operator that is the union of a finite
number of compact-valued upper semicontinuous maps. Such a generalized
framework permits the analysis of a class of proximal algorithms for minimizing
the sum of a piecewise smooth function and the difference between pointwise
minimum of finitely many weakly convex functions and a piecewise smooth convex
function. When realized on two-set feasibility problems, this algorithm class
recovers alternating projections and averaged projections as special cases, and
thus we obtain global convergence criterion for these projection algorithms.
Using these general results, we derive sufficient conditions to guarantee
global convergence for several projection algorithms for solving the sparse
affine feasibility problem and a feasibility reformulation of the linear
complementarity problem. Notably, we obtain global convergence of both the
alternating and the averaged projection methods to the solution set for linear
complementarity problems involving $P$-matrices. By leveraging the structures
of the classes of problems we consider, we also propose acceleration algorithms
with guaranteed global convergence. Numerical results further exemplify that
the proposed acceleration schemes significantly improve upon their
non-accelerated counterparts in efficiency.


  We represent the method of dynamic diffusion for the approximate solution of
Shroedinger equation with decoherence. Decoherence shows as the divergency of
exact solution from the dynamics of diffusion swarm, which arises when the
total number of real particles grows. The method of dnamic diffusion cannot be
reduced to the solution of differential equations, in contrast to Bohm's
quantum hydrodynamics, hence the direct computer simulation is the mandatory
step of its development. We compare the dynamic diffusion with the explicit
finite differences scheme for Shredinger equation for some standard one
dimension cases.


  Let c be a periodic Reeb orbit on the boundary S of a compact star-shaped
domain C in R4. We show that if there is an immersed symplectic disc f in C
with boundary c then the self-linking number lk(c) of c equals 2 tan(f)-1 where
tan(f) is the tangential self-intersection number of f. We also show that if C
is convex and if the principal curvatures of S are suitably pointwise pinched
then the self-linking number of a periodic Reeb orbit of Maslov index 3 equals
-1.


  An upgraded analysis of $\ep$, $x_d$ and $\epp/\ep$, using the latest
determinations of the relevant experimental and theoretical parameters, is
presented. Using the recent determination of the top quark mass, $m_t=(174 \pm
17)$ GeV, our best estimate is $\epp/\ep= 3.1 \pm 2.5 $, which lies in the
range given by E731. We describe our determination of $\epp/\ep$ and make a
comparison with other similar studies. A detailed discussion of the matching of
the full theory to the effective Hamiltonian, written in terms of lattice
operators, is also given.


  Solutions of the one dimensional Dirac equation with piece-wise constant
potentials are presented using standard methods. These solutions show that the
Klein Paradox is non-existent and represents a failure to correctly match
solutions across a step potential. Consequences of this exact solution are
studied for the step potential and a square barrier. Characteristics of
massless Dirac states and the momentum linear band energies for Graphene are
shown to have quite different current and momentum properties.