  J.L. Krivine developed a new method based on realizability to construct
models of set theory where the axiom of choice fails. We attempt to recreate
his results in classical settings, i.e. symmetric extensions. We also provide a
new condition for preserving well-ordered, and other particular type of choice,
in the general settings of symmetric extensions.


  $1/f^\alpha$ noises are ubiquitous and affect many measurements. These noises
are both a nuisance and a peculiarity of several physical systems; in
dielectrics, glasses and networked liquids it is very common to study this
noise to gather useful information. Sometimes it happens that the noise has a
power-law shape only in a certain frequency range, and contains other important
features, that are however difficult to study because simple fits often fail.
Here I propose a model-based fit procedure that performs well on spectra
obtained in a molecular dynamics simulation.


  Under two-phase designs, the outcome and several covariates and confounders
are measured in the first phase, and a new predictor of interest, which may be
costly to collect, can be measured on a subsample in the second phase, without
incurring the costs of recruiting subjects. By using the information gathered
in the first phase, the second-phase subsample can be selected to enhance the
efficiency of testing and estimating the effect of the new predictor on the
outcome. Past studies have focused on optimal two-phase sampling schemes for
statistical inference on local ($\beta = o(1)$) effects of the predictor of
interest. In this study, we propose an extension of the two-phase designs that
employs an optimal sampling scheme for estimating predictor effects with pseudo
conditional likelihood estimators in case-control studies. This approach is
applicable to both local and non-local effects. We demonstrate the
effectiveness of the proposed sampling scheme through simulation studies and
analysis of data from 170 patients hospitalized for treatment of COVID-19. The
results show a significant improvement in the estimation of the parameter of
interest.


  Several low-bandwidth distributable black-box optimization algorithms in the
family of finite differences such as Evolution Strategies have recently been
shown to perform nearly as well as tailored Reinforcement Learning methods in
some Reinforcement Learning domains. One shortcoming of these black-box methods
is that they must collect information about the structure of the return
function at every update, and can often employ only information drawn from a
distribution centered around the current parameters. As a result, when these
algorithms are distributed across many machines, a significant portion of total
runtime may be spent with many machines idle, waiting for a final return and
then for an update to be calculated. In this work we introduce a novel method
to use older data in finite difference algorithms, which produces a scalable
algorithm that avoids significant idle time or wasted computation.


  In this work we consider the problem of anomaly detection in heterogeneous,
multivariate, variable-length time series datasets. Our focus is on the
aviation safety domain, where data objects are flights and time series are
sensor readings and pilot switches. In this context the goal is to detect
anomalous flight segments, due to mechanical, environmental, or human factors
in order to identifying operationally significant events and provide insights
into the flight operations and highlight otherwise unavailable potential safety
risks and precursors to accidents. For this purpose, we propose a framework
which represents each flight using a semi-Markov switching vector
autoregressive (SMS-VAR) model. Detection of anomalies is then based on
measuring dissimilarities between the model's prediction and data observation.
The framework is scalable, due to the inherent parallel nature of most
computations, and can be used to perform online anomaly detection. Extensive
experimental results on simulated and real datasets illustrate that the
framework can detect various types of anomalies along with the key parameters
involved.


  Neuromorphic computing has emphasized the need for memristors with
non-volatile, multiple conductance levels. This paper demonstrates the
potential of hexagonal boron nitride (hBN)/graphene heterostructures to act as
memristors with multiple resistance states that can be optically tuned using
visible light. The number of resistance levels in graphene can be controlled by
modulating doping levels, achieved by varying the electric field strength or
adjusting the duration of optical illumination. Our measurements show that this
photodoping of graphene results from the optical excitation of charge carriers
from the nitrogen-vacancy levels of hBN to its conduction band, with these
carriers then being transferred to graphene by the gate-induced electric field.
We develop a quantitative model to describe our observations. Additionally,
utilizing our device architecture, we propose a memristive crossbar array for
vector-matrix multiplications.


  Purpose: Implanted fiducial markers are often used in radiotherapy to
facilitate accurate visualization and localization of tumors. Typically, such
markers are used to aid daily patient positioning and to verify the target's
position during treatment. This work introduces a novel, automated method for
identifying fiducial markers in planar x-ray imaging.
  Methods: In brief, the method consists of automated filtration and
reconstruction steps that generate 3D templates of marker positions. The
normalized cross-correlation was the used to identify fiducial markers in
projection images. To quantify the accuracy of the technique, a phantom study
was performed. 75 pre-treatment CBCT scans of 15 pancreatic cancer patients
were analyzed to test the automated technique under real life conditions,
including several challenging scenarios for tracking fiducial markers.
  Results: In phantom and patient studies, the method automatically tracked
visible marker clusters in 100% of projection images. For scans in which a
phantom exhibited 0D, 1D, and 3D motion, the automated technique showed median
errors of 39 $\mu$m, 53 $\mu$m, and 93 $\mu$m, respectively. Human precision
was worse in comparison. Automated tracking was performed accurately despite
the presence of other metallic objects. Additionally, transient differences in
the cross-correlation score identified instances where markers disappeared from
view.
  Conclusions: A novel, automated method for producing dynamic templates of
fiducial marker clusters has been developed. Production of these templates
automatically provides measurements of tumor motion that occurred during the
CBCT scan that was used to produce them. Additionally, using these templates
with intra-fractional images could potentially allow for more robust real-time
target tracking in radiotherapy.


  MeetEval is an open-source toolkit to evaluate all kinds of meeting
transcription systems. It provides a unified interface for the computation of
commonly used Word Error Rates (WERs), specifically cpWER, ORC-WER and MIMO-WER
along other WER definitions. We extend the cpWER computation by a temporal
constraint to ensure that only words are identified as correct when the
temporal alignment is plausible. This leads to a better quality of the matching
of the hypothesis string to the reference string that more closely resembles
the actual transcription quality, and a system is penalized if it provides poor
time annotations. Since word-level timing information is often not available,
we present a way to approximate exact word-level timings from segment-level
timings (e.g., a sentence) and show that the approximation leads to a similar
WER as a matching with exact word-level annotations. At the same time, the time
constraint leads to a speedup of the matching algorithm, which outweighs the
additional overhead caused by processing the time stamps.


  Nonadiabatic effects that arise from the concerted motion of electrons and
atoms at comparable energy and time scales are omnipresent in thermal and
light-driven chemistry at metal surfaces. Excited (hot) electrons can
measurably affect molecule-metal reactions by contributing to state-dependent
reaction probabilities. Vibrational state-to-state scattering of NO on Au(111)
has been one of the most studied examples in this regard, providing a testing
ground for developing various nonadiabatic theories. This system is often cited
as the prime example for the failure of electronic friction theory, a very
efficient model accounting for dissipative forces on metal-adsorbed molecules
due to the creation of hot electrons in the metal. However, the exact failings
compared to experiment and their origin from theory are not established for any
system, because dynamic properties are affected by many compounding simulation
errors of which the quality of nonadiabatic treatment is just one. We use a
high-dimensional machine learning representation of electronic structure theory
to minimize errors that arise from quantum chemistry. This allows us to perform
a comprehensive quantitative analysis of the performance of nonadiabatic
molecular dynamics in describing vibrational state-to-state scattering of NO on
Au(111) and compare directly to adiabatic results. We find that electronic
friction theory accurately predicts elastic and single-quantum energy loss, but
underestimates multi-quantum energy loss and overestimates molecular trapping
at high vibrational excitation. Our analysis reveals that multi-quantum energy
loss can potentially be remedied within friction theory, whereas the
overestimation of trapping constitutes a genuine breakdown of electronic
friction theory. Addressing this overestimation for dynamic processes in
catalysis and surface chemistry will require more sophisticated theories.


  Limited by the difficulty in acceleration synchronization, it has been a
long-term challenge for on-chip dielectric laser-based accelerators (DLA) to
bridge the gap between non-relativistic and relativistic regimes. Here, we
propose a DLA based on a spatio-temporal coupling (STC) controlled laser pulse,
which enables the acceleration of a non-relativistic electron to a sub-MeV
level in a single acceleration structure (chirped spatial grating). It provides
high precision temporal and spatial tuning of the driving laser via the
dispersion manipulation, leading to a synchronous acceleration of the velocity
increasing electrons over a large energy range. Additionally, the STC scheme is
a general method and can be extended to driving fields of other wavelengths
such as terahertz pulses. Our results bring new possibilities to MeV-scale
portable electron sources and table-top acceleration experiments.


  The requirement of general covariance of quantum field theory (QFT) naturally
leads to quantization based on the manifestly covariant De Donder-Weyl
formalism. To recover the standard noncovariant formalism without violating
covariance, fields need to depend on time in a specific deterministic manner.
This deterministic evolution of quantum fields is recognized as a covariant
version of the Bohmian hidden-variable interpretation of QFT.


  The fifth generation of mobile networks (5G) represents the latest
development in mobile communications. It has been designed to support several
types of data traffic and to meet more performance requirements than ever
before. These characteristics make 5G very attractive for current but also
novel public and private industries and services. However, because of coverage,
regulatory, business, and security reasons, many of these novel applications
can only be deployed as part of a private network. The cost of licensed
frequencies makes such approach prohibitive for many stakeholders, and
therefore unlicensed frequency bands represent a more affordable option. Even
so, private 5G networks for use in globally unlicensed frequency bands do not
yet exist. In this paper we present the first end-to-end private 5G network
operating in a globally unlicensed frequency band, using general purpose
computers, open-source software and software-defined radio. We evidence its
working and show that the choice of the hardware can significantly affect the
performance of the network.


  Efficient measures to determine similarity of quantum states, such as the
fidelity metric, have been widely studied. In this paper, we address the
problem of defining a similarity measure for quantum operations that can be
\textit{efficiently estimated}. Given two quantum operations, $U_1$ and $U_2$,
represented in their circuit forms, we first develop a quantum sampling circuit
to estimate the normalized Schatten 2-norm of their difference ($\| U_1-U_2
\|_{S_2}$) with precision $\epsilon$, using only one clean qubit and one
classical random variable. We prove a Poly$(\frac{1}{\epsilon})$ upper bound on
the sample complexity, which is independent of the size of the quantum system.
We then show that such a similarity metric is directly related to a functional
definition of similarity of unitary operations using the conventional fidelity
metric of quantum states ($F$): If $\| U_1-U_2 \|_{S_2}$ is sufficiently small
(e.g. $ \leq \frac{\epsilon}{1+\sqrt{2(1/\delta - 1)}}$) then the fidelity of
states obtained by processing the same randomly and uniformly picked pure
state, $|\psi \rangle$, is as high as needed ($F({U}_1 |\psi \rangle, {U}_2
|\psi \rangle)\geq 1-\epsilon$) with probability exceeding $1-\delta$. We
provide example applications of this efficient similarity metric estimation
framework to quantum circuit learning tasks, such as finding the square root of
a given unitary operation.


  This paper contributes to the theory of cutting planes for mixed integer
linear programs (MILPs). Minimal valid inequalities are well understood for a
relaxation of an MILP in tableau form where all the nonbasic variables are
continuous; they are derived using the gauge function of maximal lattice-free
convex sets. In this paper we study lifting functions for the nonbasic integer
variables starting from such minimal valid inequalities. We characterize
precisely when the lifted coefficient is equal to the coefficient of the
corresponding continuous variable in every minimal lifting. The answer is a
nonconvex region that can be obtained as a finite union of convex polyhedra. We
then establish a necessary and sufficient condition for the uniqueness of the
lifting function.


  We present a method for constraining the evolution of the galaxy
luminosity-velocity (LV) relation in hierarchical scenarios of structure
formation. The comoving number density of dark-matter halos with circular
velocity of 200 km/s is predicted in favored CDM cosmologies to be nearly
constant over the redshift range 0<z<5. Any observed evolution in the density
of bright galaxies implies in turn a corresponding evolution in the LV
relation. We consider several possible forms of evolution for the zero-point of
the LV relation and predict the corresponding evolution in galaxy number
density. The Hubble Deep Field suggests a large deficit of bright (M_V < -19)
galaxies at 1.4 < z < 2. If taken at face value, this implies a dimming of the
LV zero-point by roughly 2 magnitudes. Deep, wide-field, near-IR selected
surveys will provide more secure measurements to compare with our predictions.


  We demonstrate a general and efficient informational cooling technique for
atoms which is an experimental realization of a one-dimensional Maxwell's
Demon. The technique transfers atoms from a magnetic trap into an optical trap
via a single spontaneous Raman transition which is discriminatively driven near
each atom's classical turning point. In this way, nearly all of the atomic
ensemble's kinetic energy in one dimension is removed. We develop a simple
analytical model to predict the efficiency of transfer between the traps and
provide evidence that the performance is limited only by particle dynamics in
the magnetic trap. Transfer efficiencies up to 2.2% are reported. We show that
efficiency can be traded for phase-space compression, and we report compression
up to a factor of 350. Our results represent a 15-fold improvement over our
previous demonstration of the cooling technique.


  Autonomous vehicle platforms of varying spatial scales are employed within
the research and development spectrum based on space, safety and monetary
constraints. However, deploying and validating autonomy algorithms across
varying operational scales presents challenges due to scale-specific dynamics,
sensor integration complexities, computational constraints, regulatory
considerations, environmental variability, interaction with other traffic
participants and scalability concerns. In such a milieu, this work focuses on
developing a unified framework for modeling and simulating digital twins of
autonomous vehicle platforms across different scales and operational design
domains (ODDs) to help support the streamlined development and validation of
autonomy software stacks. Particularly, this work discusses the development of
digital twin representations of 4 autonomous ground vehicles, which span across
3 different scales and target 3 distinct ODDs. We study the adoption of these
autonomy-oriented digital twins to deploy a common autonomy software stack with
an aim of end-to-end map-based navigation to achieve the ODD-specific
objective(s) for each vehicle. Finally, we also discuss the flexibility of the
proposed framework to support virtual, hybrid as well as physical testing with
seamless sim2real transfer.


  In this paper, we show that the eccentricity of a planet on an inclined orbit
with respect to a disc can be pumped up to high values by the gravitational
potential of the disc, even when the orbit of the planet crosses the disc
plane. This process is an extension of the Kozai effect. If the orbit of the
planet is well inside the disc inner cavity, the process is formally identical
to the classical Kozai effect. If the planet's orbit crosses the disc but most
of the disc mass is beyond the orbit, the eccentricity of the planet grows when
the initial angle between the orbit and the disc is larger than some critical
value which may be significantly smaller than the classical value of 39
degrees. Both the eccentricity and the inclination angle then vary periodically
with time. When the period of the oscillations of the eccentricity is smaller
than the disc lifetime, the planet may be left on an eccentric orbit as the
disc dissipates.


  The property graph data model of modern graph database systems is
increasingly adapted for storing and processing heterogeneous datasets like
networks. Many challenging applications with near real-time requirements --
e.g. financial fraud detection, recommendation systems, and on-the-fly
validation -- can be captured with graph queries, which are evaluated
repeatedly. To ensure quick response time for a changing data set, these
applications would benefit from applying incremental view maintenance (IVM)
techniques, which can perform continuous evaluation of queries and calculate
the changes in the result set upon updates. However, currently, no graph
databases provide support for incremental views. While IVM problems have been
studied extensively over relational databases, views on property graph queries
require operators outside the scope of standard relational algebra. Hence,
tackling this problem requires the integration of numerous existing IVM
techniques and possibly further extensions. In this paper, we present an
approach to perform IVM on property graphs, using a nested relational algebraic
representation for property graphs and graph operations. Then we define a chain
of transformations to reduce most property graph queries to flat relational
algebra and use techniques from discrimination networks (used in rule-based
expert systems) to evaluate them. We demonstrate the approach using our
prototype tool, ingraph, which uses openCypher, an open graph query language
specified as part of an industry initiative. However, several aspects of our
approach can be generalised to other graph query languages such as G-CORE and
PGQL.


  Surveys of redshifted emission lines in the near-infrared will provide unique
information on the cosmic star formation history. Near-infrared Halpha surveys
will probe the cosmic star formation density and the evolution of the
luminosity function of star forming galaxies out to z\sim2.5, including the
important z=1.5-2.0 epoch, where according to current knowledge the cosmic star
formation density peaks. Halpha and other hydrogen recombination lines (except
Lyman alpha) and [O II] are all useful tracers of star formation provided the
luminosity function is probed to sufficiently deep levels. However, shallow
surveys or surveys of Lyman alpha or [O III] emission are not suitable for
measuring or even constraining the cosmic star formation density. A first
estimate of the star formation density at z\approx2.25 is derived, based on a
new and sensitive large-area ESO near-infrared survey for Halpha emission at
this redshift. Future observational directions, at near-infrared and longer
wavelengths, for reading the cosmic star formation history are outlined.


  We have investigated some issues relevant for the possibility to construct
physical theories on the $\kappa$-Minkowski noncommutative spacetime. The
notion of field in $\kappa$-Minkowski has been introduced by generalizing the
Weyl system/map formalism and a comparative study of the star products arising
from this generalization has been done. A line of analysis of the symmetries of
$\kappa$-Minkowski has been proposed that relies on the possibility to find a
"maximally"-symmetric action which is invariant under a 10-generator
Poincar\'e-like symmetry algebra. The equation of motion for scalar particles
has been obtained by a generalized variational principle. An extension of the
Dirac equation for spin-1/2 particles has been proposed by using a
five-dimensional differential calculus on $\kappa$-Minkowski.


  This paper describes a set of discrete R symmetries which can be used to
define the minimal supersymmetric standard model or singlet extensions of it.


  The structure of the superconducting order parameter in the iron-pnictide
superconductor BaFe$_2$(As$_{0.67}$P$_{0.33}$)$_2$ ($T_c=31$\,K) with line
nodes is studied by the angle-resolved thermal conductivity measurements in a
magnetic field rotated within the basal plane. We find that the thermal
conductivity displays distinct fourfold oscillations with minima when the field
is directed at $\pm45^\circ$ with respect to the tetragonal a-axis. We discuss
possible gap structures that can account for the data, and conclude that the
observed results are most consistent with the closed nodal loops located at the
flat parts of the electron Fermi surface with high Fermi velocity.


  In this paper, we introduce a data-compilation ensemble, primarily intended
to serve as a resource for researchers in the field of dereverberation,
particularly for data-driven approaches. It comprises speech and song samples,
together with acoustic guitar sounds, with original annotations pertinent to
emotion recognition and Music Information Retrieval (MIR). Moreover, it
includes a selection of impulse response (IR) samples with varying
Reverberation Time (RT) values, providing a wide range of conditions for
evaluation. This data-compilation can be used together with provided Python
scripts, for generating auralized data ensembles in different sizes: tiny,
small, medium and large. Additionally, the provided metadata annotations also
allow for further analysis and investigation of the performance of
dereverberation algorithms under different conditions. All data is licensed
under Creative Commons Attribution 4.0 International License.


  The Kane-Mele model is known to show a quantized spin Hall conductivity at
zero temperature. Including Hubbard interactions at each site leads to a
quantum phase transition to an XY antiferromagnet at sufficiently high
interaction strength. Here, we use the two-particle self-consistent approach
(TPSC), which we extend to include spin-orbit coupling, to investigate the
Kane-Mele-Hubbard model at finite temperature and half-filling. TPSC is a weak
to intermediate coupling approach capable of calculating a frequency- and
momentum-dependent self-energy from spin and charge fluctuations. We present
results for the spin Hall conductivity and correlation lengths for
antiferromagnetic spin fluctuations for different values of temperature,
spin-orbit coupling and Hubbard interaction. The vertex corrections, which here
are analogues of Maki-Thompson contributions, show a strong momentum dependence
and give a large contribution in the vicinity of the phase transition at all
temperatures. Their inclusion is necessary to observe the quantization of the
spin Hall conductivity for the interacting system in the zero temperature
limit. At finite temperature, increasing the Hubbard interaction leads to a
decrease of the spin Hall conductivity. This decrease can be explained by
band-gap renormalization from scattering of electrons on antiferromagnetic spin
fluctuations.


  Deep Reinforcement Learning (DRL) has recently been proposed as a methodology
to discover complex Active Flow Control (AFC) strategies [Rabault, J., Kuchta,
M., Jensen, A., Reglade, U., & Cerardi, N. (2019): "Artificial neural networks
trained through deep reinforcement learning discover control strategies for
active flow control", Journal of Fluid Mechanics, 865, 281-302]. However, while
promising results were obtained on a simple 2D benchmark flow at a moderate
Reynolds number, considerable speedups will be required to investigate more
challenging flow configurations. In the case of DRL trained with Computational
Fluid Dynamics (CFD) data, it was found that the CFD part, rather than training
the Artificial Neural Network, was the limiting factor for speed of execution.
Therefore, speedups should be obtained through a combination of two approaches.
The first one, which is well documented in the literature, is to parallelize
the numerical simulation itself. The second one is to adapt the DRL algorithm
for parallelization. Here, a simple strategy is to use several independent
simulations running in parallel to collect experiences faster. In the present
work, we discuss this solution for parallelization. We illustrate that perfect
speedups can be obtained up to the batch size of the DRL agent, and slightly
suboptimal scaling still takes place for an even larger number of simulations.
This is, therefore, an important step towards enabling the study of more
sophisticated Fluid Mechanics problems through DRL.


  Using the root adjunction formalism developed in an earlier work and
logarithmic THH, we obtain a simplified computation of $T(2)_*\text{K}(ku)$ for
$p>3$. Through this, we also produce a new algebraic $K$-theory computation;
namely we obtain $T(2)_*\text{K}(ku/p)$, where $ku/p$ is the $2$-periodic
Morava $K$-theory spectrum of height $1$.


  We study hair mass distributions in noncommutative Einstein-Born-Infeld hairy
black holes with non-zero cosmological constants. We find that the larger
noncommutative parameter makes the hair easier to condense in the near horizon
area. We also show that Hod's lower bound can be evaded in the noncommutative
gravity. However, for large black holes with a non-negative cosmological
constant, Hod's lower hair mass bound almost holds in the sense that nearly
half of the hair lays above the photonsphere.


  A canonical quantization scheme applied to a classical supersymmetric system
with quadratic in momentum supercharges gives rise to a quantum anomaly problem
described by a specific term to be quadratic in Planck constant. We reveal a
close relationship between the anomaly and the Schwarzian derivative, and
specify a quantization prescription which generates the anomaly-free
supersymmetric quantum system with second order supercharges. We also discuss
the phenomenon of a coupling-constant metarmorphosis that associates quantum
systems with first-order supersymmetry to the systems with the second-order
supercharges.


  We extend a construction of Hinich to obtain a closed model category
structure on all differential graded cocommutative coalgebras over an
algebraically closed field of characteristic zero. We further show that the
Koszul duality between commutative and Lie algebras extends to a Quillen
equivalence between cocommutative coalgebras and formal coproducts of curved
Lie algebras.


  This thesis focuses on link scheduling in wireless mesh networks by taking
into account physical layer characteristics. The assumption made throughout is
that a packet is received successfully only if the Signal to Interference and
Noise Ratio (SINR) at the receiver exceeds the communication threshold. The
thesis also discusses the complementary problem of flow control. (1) We
consider various problems on centralized link scheduling in Spatial Time
Division Multiple Access (STDMA) wireless mesh networks. We motivate the use of
spatial reuse as performance metric and provide an explicit characterization of
spatial reuse. We propose link scheduling algorithms based on certain graph
models (communication graph, SINR graph) of the network. Our algorithms achieve
higher spatial reuse than that of existing algorithms, with only a slight
increase in computational complexity. (2) We investigate random access
algorithms in wireless networks. We assume that the receiver is capable of
power-based capture and propose a splitting algorithm that varies transmission
powers of users on the basis of quaternary channel feedback. We model the
algorithm dynamics by a Discrete Time Markov Chain and consequently show that
its maximum stable throughput is 0.5518. Our algorithm achieves higher maximum
stable throughput and significantly lower delay than the First Come First Serve
(FCFS) splitting algorithm with uniform transmission power. (3) We consider the
problem of flow control in packet networks from an information-theoretic
perspective. We derive the maximum entropy of a flow which conforms to traffic
constraints imposed by a generalized token bucket regulator (GTBR), by taking
into account the covert information present in randomness of packet lengths.


  In this study, we evaluated the sensing performance of silver
nanoprism/graphene oxide/silicon nanowires (AgNPr/GO/SiNWs) nanohybrid system
for effective detection of organic dye and herbicide residues in freshwater via
surface-enhanced Raman scattering (SERS). Homogenous and vertically aligned
SiNWs have been successfully synthesized using metal-assisted chemical etching
technique by varying the etching time from 10 to 30 min. AgNPr/GO/SiNW hybrids
were assembled by spin-coating of GO followed by drop-casting deposition of
AgNPr. The microstructures of AgNPr/GO/SiNWs are strongly affected by the
etching time of SiNWs, which in turn affected its SERS performance when
rhodamine 6G is used as an analyte molecule. Owing to the synergetic effects of
GO and AgNPr, SERS response of AgNPr/GO/SiNWs composites were found to be
superior compared to AgNPr/SiNWs. High efficiency of 6.1 x 1010 has been
achieved with AgNPr/GO/SiNWs based sensor fabricated with 30-min etched SiNWs
for rhodamine 6G. An effective way of rapid detection of drinking water
pollutants such as methylene blue and atrazine up to picomolar (10-12 M)
concentrations was demonstrated using these low-cost AgNPr/GO/SiNWs nanohybrids
SERS sensors.


  Determining the frequency and duration of changing--look (CL) active galactic
nuclei (AGNs) phenomena, where the optical broad emission lines appear or
disappear, is crucial to understand the evolution of the accretion flow around
supermassive black holes. We present a strategy to select new CL candidates
starting from a spectroscopic type 2 AGNs sample and searching for current type
1 photometric variability. We use the publicly available Zwicky Transient
Facility (ZTF) alert stream and the Automatic Learning for the Rapid
Classification of Events (ALeRCE) light curve classifier to produce a list of
CL candidates with a highly automated algorithm, resulting in 60 candidates.
Visual inspection reduced the sample to 30. We performed new spectroscopic
observations of six candidates of our clean sample, without further refinement,
finding the appearance of clear broad Balmer lines in four of them and
tentative evidence of type changes in the remaining two, which suggests a
promising success rate of $\geq66$ per cent for this CL selection method.


  We review the matching conditions for a collapsing anisotropic cylindrical
perfect fluid, recently discussed in the literature (2005 {\it Class. Quantum
Grav.} {\bf 22} 2407). It is shown that radial pressure vanishes on the surface
of the cylinder, contrary to what is asserted in that reference. The origin of
this discrepancy is to be found in a mistake made in one step of the
calculations. Some comments about the relevance of this result in relation to
the momentum of Einstein--Rosen waves are presented.


  We demonstrate the feasibility of performing sufficient configurational
sampling of disordered oxides directly from first principles without resorting
to the use of fitted models such as cluster expansion. This is achieved by
harnessing the power of modern-day cluster supercomputers using the replica
exchange Monte Carlo method coupled directly with structural relaxation and
energy calculation performed by density functional codes. The idea is applied
successfully to the calculation of the temperature-dependence of the degree of
inversion in the cation sublattice of MgAl$_2$O$_4$ spinel oxide. The
possibility of bypassing fitting models will lead to investigation of
disordered systems where cluster expansion is known to perform badly: for
example, systems with large lattice deformation due to defects, or systems
where long-range interactions dominate such as electrochemical interfaces.


  We consider a stochastic continuum armed bandit problem where the arms are
indexed by the $\ell_2$ ball $B_{d}(1+\nu)$ of radius $1+\nu$ in
$\mathbb{R}^d$. The reward functions $r :B_{d}(1+\nu) \rightarrow \mathbb{R}$
are considered to intrinsically depend on $k \ll d$ unknown linear parameters
so that $r(\mathbf{x}) = g(\mathbf{A} \mathbf{x})$ where $\mathbf{A}$ is a full
rank $k \times d$ matrix. Assuming the mean reward function to be smooth we
make use of results from low-rank matrix recovery literature and derive an
efficient randomized algorithm which achieves a regret bound of $O(C(k,d)
n^{\frac{1+k}{2+k}} (\log n)^{\frac{1}{2+k}})$ with high probability. Here
$C(k,d)$ is at most polynomial in $d$ and $k$ and $n$ is the number of rounds
or the sampling budget which is assumed to be known beforehand.


  When observing spectral lines in the optically-thin corona, line-of-sight
(LOS) effects can strongly affect the interpretation of the data, especially in
regions just above the limb. We present a semi-empirical forward model, called
GHOSTS, to characterize these effects. GHOSTS uses inputs from several other
models to compute non-equilibrium ionization states (which include the
solar-wind freezing-in effect) for many ions. These are used to generate
ensembles of simulated spectral lines that are examined in detail, with
emphasis on: (1) relationships between quantities derived from observables and
the radial variation of the observed quantities, (2) the behavior of thermal
and non-thermal components of the line width, and (3) relative contributions of
collisionally excited and radiatively scattered photons. We find that rapidly
changing temperatures in the low corona can cause ion populations to vary
dramatically with height. This can lead to line-width measurements that are
constant with height (a "plateau" effect) even when the temperature is
increasing rapidly, as the plane-of-sky becomes evacuated and the
foreground/background plasma dominates the observation. We find that LOS
effects often drive the velocity width to be close to the plane-of-sky value of
the wind speed, despite it flowing perpendicularly to the LOS there. The
plateau effect can also cause the non-thermal component of the line width to
greatly exceed the solar wind velocity at the observation height. Lastly, we
study how much of the LOS is significant to the observation, and the importance
of including continuum in the solar spectrum when computing the radiatively
scattered emission.


  For supervised classification problems, this paper considers estimating the
query's label probability through local regression using observed covariates.
Well-known nonparametric kernel smoother and $k$-nearest neighbor ($k$-NN)
estimator, which take label average over a ball around the query, are
consistent but asymptotically biased particularly for a large radius of the
ball. To eradicate such bias, local polynomial regression (LPoR) and multiscale
$k$-NN (MS-$k$-NN) learn the bias term by local regression around the query and
extrapolate it to the query itself. However, their theoretical optimality has
been shown for the limit of the infinite number of training samples. For
correcting the asymptotic bias with fewer observations, this paper proposes a
\emph{local radial regression (LRR)} and its logistic regression variant called
\emph{local radial logistic regression~(LRLR)}, by combining the advantages of
LPoR and MS-$k$-NN. The idea is quite simple: we fit the local regression to
observed labels by taking only the radial distance as the explanatory variable
and then extrapolate the estimated label probability to zero distance. The
usefulness of the proposed method is shown theoretically and experimentally. We
prove the convergence rate of the $L^2$ risk for LRR with reference to
MS-$k$-NN, and our numerical experiments, including real-world datasets of
daily stock indices, demonstrate that LRLR outperforms LPoR and MS-$k$-NN.


  We present BubbleTouch, an open source quasi-static simulator for robotic
tactile skins. BubbleTouch can be used to simulate contact with a robot's
tactile skin patches as it interacts with humans and objects. The simulator
creates detailed traces of contact forces that can be used in experiments in
tactile contact activities. We summarize the design of BubbleTouch and
highlight our recent work that uses BubbleTouch for experiments with tactile
object recognition.


  The Ayy data for deuteron inclusive breakup off hydrogen and carbon at a
deuteron momentum of 9.0 GeV/c and large Pt of emitted protons are presented.
The large values of Ayy independent of the target mass reflect the sensitivity
of the data to the deuteron spin structure. The data obtained at fixed $x$ and
plotted versus Pt clearly demonstrate the dependence of the deuteron spin
structure at short internucleonic distances on two variables. The data are
compared with the calculations using Paris, CD-Bonn and Karmanov's deuteron
wave functions.


  We present computations of nucleosynthesis in low-mass red-giant-branch and
asymptotic-giant-branch stars of Population I experiencing extended mixing. We
adopt the updated version of the FRANEC evolutionary model, a new post-process
code for non-convective mixing and the most recent revisions for solar
abundances. In this framework, we discuss the effects of recent improvements in
relevant reaction rates for proton captures on intermediate-mass nuclei (from
carbon to aluminum). For each nucleus we briefly discuss the new choices and
their motivations. The calculations are then performed on the basis of a
parameterized circulation, where the effects of the new nuclear inputs are best
compared to previous works. We find that the new rates (and notably the one for
the 14N(p,g)15O reaction) imply considerable modifications in the composition
of post-main sequence stars. In particular, the slight temperature changes due
to the reduced efficiency of proton captures on 14N induce abundance variations
at the first dredge up (especially for 17O, whose equilibrium ratio to 16O is
very sensitive to the temperature). In this new scenario presolar oxide grains
of AGB origin turn out to be produced almost exclusively by very-low mass stars
(M<=1.5-1.7Msun), never becoming C-rich. The whole population of grains with
18O/16O below 0.0015 (the limit permitted by first dredge up) is now explained.
Also, there is now no forbidden area for very low values of 17O/16O (below
0.0005), contrary to previous findings. A rather shallow type of transport
seems to be sufficient for the CNO changes in RGB stages. Both thermohaline
diffusion and magnetic-buoyancy-induced mixing might provide a suitable
physical mechanism for this. Thermohaline mixing is in any case certainly
inadequate to account for the production of 26Al on the AGB. Other transport
mechanisms must therefore be at play.


  The 1994 data published by the H1 collaboration are compared with models
based on Regge phenomenology. The $x_{\PO}$ dependence of the data can be
described in a model based on the exchange of a dominant diffractive (pomeron)
trajectory with additional sub-leading reggeon contributions. The dynamics of
the Pomeron structure is studied within the framework of perturbative QCD and
new parton distributions are obtained. These parton distributions will allow a
direct test of factorisation breaking at Tevatron.


  Determining which parameters of a non-linear model best describe a set of
experimental data is a fundamental problem in science and it has gained much
traction lately with the rise of complex large-scale simulators. The likelihood
of such models is typically intractable, which is why classical MCMC methods
can not be used. Simulation-based inference (SBI) stands out in this context by
only requiring a dataset of simulations to train deep generative models capable
of approximating the posterior distribution that relates input parameters to a
given observation. In this work, we consider a tall data extension in which
multiple observations are available to better infer the parameters of the
model. The proposed method is built upon recent developments from the
flourishing score-based diffusion literature and allows to estimate the tall
data posterior distribution, while simply using information from a score
network trained for a single context observation. We compare our method to
recently proposed competing approaches on various numerical experiments and
demonstrate its superiority in terms of numerical stability and computational
cost.


  Based on first-principle FLAPW-GGA calculations, we have investigated
structural and electronic properties of the recently synthesized tetragonal
(space group P4/nmm) nickel-based pnictide oxide superconductors: 3.3K
(Ni2P2)(Sr4Sc2O6) and 2.7K (Ni2As2)(Sr4Sc2O6). Optimized structural data,
electronic bands, total and partial densities of states, and Fermi surface
topology have been obtained and discussed in comparison with available
experiments and with their Fe-based (Fe2P2)(Sr4Sc2O6) and (Fe2As2)(Sr4Sc2O6)
analogs.


  We find solutions of Einstein's field equation for topologically stable
strings associated with the breaking of a U(1) symmetry. Strings form in many
GUTs and are expected whenever the homotopy group $\Pi_1(M_0)$ is non-trivial.
The behavior of the fields making up the string is described by the
Euler-Lagrange equations. These fields appear in the energy-momentum tensor so
we must solve simultaneously for the coupled Einstein-scalar-gauge field
equations. Numerical results are obtained using a Taylor-series method. We
obtain a 5-parameter family of solutions and discuss their physical
characteristics. Significant gravitational lensing can occur due to strings
based on this model and are shown for different solutions. Finally, we prove
that the assumption of regularity at the string axis is not necessary by
looking at the physical properties of the string solutions.


  In this paper we selected a sample of massive star forming regions from the
Red MSX Source (RMS) survey, to study star formation activities (mainly outflow
and inflow signatures).


  Raman spectroscopy is an effective, low-cost, non-intrusive technique often
used for chemical identification. Typical approaches are based on matching
observations to a reference database, which requires careful preprocessing, or
supervised machine learning, which requires a fairly large number of training
observations from each class. We propose a new machine learning technique for
Raman spectrum matching, based on contrastive representation learning, that
requires no preprocessing and works with as little as a single reference
spectrum from each class. On three datasets we demonstrate that our approach
significantly improves or is on par with the state of the art in prediction
accuracy, and we show how to compute conformal prediction sets with specified
frequentist coverage. Based on our findings, we believe contrastive
representation learning is a promising alternative to existing methods for
Raman spectrum matching.


  We consider a reinforcement learning framework where agents have to navigate
from start states to goal states. We prove convergence of a cycle-detection
learning algorithm on a class of tasks that we call reducible. Reducible tasks
have an acyclic solution. We also syntactically characterize the form of the
final policy. This characterization can be used to precisely detect the
convergence point in a simulation. Our result demonstrates that even simple
algorithms can be successful in learning a large class of nontrivial tasks. In
addition, our framework is elementary in the sense that we only use basic
concepts to formally prove convergence.


  We have developed a gravity solver based on combining the well developed
Particle-Mesh (PM) method and TREE methods. It is designed for and has been
implemented on parallel computer architectures. The new code can deal with tens
of millions of particles on current computers, with the calculation done on a
parallel supercomputer or a group of workstations. Typically, the spatial
resolution is enhanced by more than a factor of 20 over the pure PM code with
mass resolution retained at nearly the PM level. This code runs much faster
than a pure TREE code with the same number of particles and maintains almost
the same resolution in high density regions. Multiple time step integration has
also been implemented with the code, with second order time accuracy. The
performance of the code has been checked in several kinds of parallel computer
configuration, including IBM SP1, SGI Challenge and a group of workstations,
with the speedup of the parallel code on a 32 processor IBM SP2 supercomputer
nearly linear (efficiency $\approx 80\%$) in the number of processors. The
computation/communication ratio is also very high ($\sim 50$), which means the
code spends $95\%$ of its CPU time in computation.


  An $N \times N$ generalized patterned random matrix is a symmetric matrix
defined as $A=(x_{L(i,j)}\mathbf{1}_{\Delta}(i,j))$, where $\{x_k; k \in
\mathbb{Z}^d\}$ is known as the input sequence, $L:\{1,2,\ldots, N\}^2
\rightarrow \mathbb{Z}^d$ is known as the link function and $\Delta \subseteq
\{1,2,\ldots , N\}^2$. In 2008, Bose and Sen showed that under some
restrictions on the link function $L$ and for $\Delta = \{1,2,\ldots , N\}^2$,
the corresponding patterned matrices always have a sub-Gaussian limiting
spectral distribution.
  Let $N: \mathbb{N} \rightarrow \mathbb{N}$ be a strictly increasing function
and $A_{n}=\left(x_{L(i, j)} \mathbf{1}_{\Delta}(i,j) \right)_{i,j=1}^{N(n)}$
be a sequence of generalized patterned matrices. We consider the LES of
$A_n/\sqrt{N(n)}$ for the test function $\phi(x)=x^p$, given by $$
\frac{1}{\sqrt{N(n)}} \sum_{i=1}^{N(n)} \lambda_i^p=\eta_p, \mbox{ say}, $$
where $p$ is a positive integer and $\lambda_i$'s are the eigenvalues of
$A_n/\sqrt{N(n)}$. Under some restrictions on $L$ and some moment assumptions
on input entries, we show that when $p$ is even, $(\eta_p - \mathbb{E}
[\eta_p])$ converges in distribution either to a normal distribution or to the
degenerate distribution at zero. We show that under further assumptions on $L$,
the limit is always a normal distribution.
  For odd degree monomial test functions, we derive the limiting moments of
$\eta_p$ and show that the LES may not converge to a Gaussian distribution. We
also study the LES for independent Brownian motion entries. We show that under
some assumption on link function, and that if the limiting moment sequence of
the LES of the generalized patterned random matrix with independent standard
Gaussian entries is M-determinate, then the LES for Brownian motion entries
converge to a deterministic real-valued process.


  Transformations performing on the dependent and/or the independent variables
are an useful method used to classify PDE in class of equivalence. In this
paper we consider a large class of U(1)-invariant nonlinear Schr\"odinger
equations containing complex nonlinearities. The U(1) symmetry implies the
existence of a continuity equation for the particle density
$\rho\equiv|\psi|^2$ where the current ${\bfm j}_{_\psi}$ has, in general, a
nonlinear structure. We introduce a nonlinear gauge transformation on the
dependent variables $\rho$ and ${\bfm j}_{\psi}$ which changes the evolution
equation in another one containing only a real nonlinearity and transforms the
particle current ${\bfm j}_{_\psi}$ in the standard bilinear form. We extend
the method to U(1)-invariant coupled nonlinear Schr\"odinger equations where
the most general nonlinearity is taken into account through the sum of an
Hermitian matrix and an anti-Hermitian matrix. By means of the nonlinear gauge
transformation we change the nonlinear system in another one containing only a
purely Hermitian nonlinearity. Finally, we consider nonlinear Schr\"odinger
equations minimally coupled with an Abelian gauge field whose dynamics is
governed, in the most general fashion, through the Maxwell-Chern-Simons
equation. It is shown that the nonlinear transformation we are introducing can
be applied, in this case, separately to the gauge field or to the matter field
with the same final result. In conclusion, some relevant examples are presented
to show the applicability of the method.


  Let $X_n=\sum_{i=1}^{\infty}a_i\epsilon_{n-i}$, where the $\epsilon_i$ are
i.i.d. with mean 0 and at least finite second moment, and the $a_i$ are assumed
to satisfy $|a_i|=O(i^{-\beta})$ with $\beta >1/2$. When $1/2<\beta<1$, $X_n$
is usually called a long-range dependent or long-memory process. For a certain
class of Borel functions $K(x_1,...,x_{d+1})$, $d\ge0$, from
${\mathcal{R}}^{d+1}$ to $\mathcal{R}$, which includes indicator functions and
polynomials, the stationary sequence $K(X_n,X_{n+1},...,X_{n+d})$ is
considered. By developing a finite orthogonal expansion of
$K(X_n,...,X_{n+d})$, the Berry--Esseen type bounds for the normalized sum
$Q_N/\sqrt{N},Q_N=\sum_{n=1}^N(K(X_
n,...,X_{n+d})-\mathrm{E}K(X_n,...,X_{n+d}))$ are obtained when $Q_N/\sqrt{N}$
obeys the central limit theorem with positive limiting variance.


  We present an updated analysis of all available solar and reactor neutrino
data, emphasizing in particular the totality of the KamLAND (314d live time)
results and including for the first time the solar $SNO$ (391d live time, phase
II NaCl-enhanced) spectrum data. As a novelty of the statistical analysis, we
study the variability of the KamLand results with respect the use of diverse
statistics. A new statistic, not used before is proposed. Moreover, in the
analysis of the SNO spectrum a novel technique is used in order to include full
correlated errors among bins. Combining all data, we obtain the following
best-fit parameters: we determine individual neutrino mixing parameters and
their errors $ \Delta m^2= 8.2\pm 0.08\times 10^{-5} \eV^2,\quad \tan^2\theta=
0.50^{+0.12}_{-0.07}.$ The impact of these results is discussed. We also
estimate the individual elements of the neutrino mass matrix. In the framework
of three neutrino oscillations we obtain the mass matrix: \begin{eqnarray}M&=&
eV \pmatrix{1.0+ 4.0\pm 3.2 10^{-5}& 4.2\pm 3.2 10^{-5} &-13.5\pm 14.0 10^{-5}
\cr 4.2\pm 3.2 10^{-5}& 1.0+4.3\pm 3.5 10^{-5} &-13.5\pm 14.5 10^{-5} \cr
13.5\pm 14.0 10^{-5}& -13.5\pm 14.5 10^{-5} &1.0+100.0\pm 30.0\
10^{-5}}.\end{eqnarray}


  The use of low resolution Analog to Digital Converters (ADCs) can
significantly reduce the power consumption for massive Multiple Input Multiple
Output (MIMO) systems. The existing literature on quantized massive MIMO
systems deals with Cyclic Prefix (CP) transmission schemes in
frequency-selective fading channels. In this paper, we propose a block
processing Frequency Domain Equalization (FDE) technique in CP-free
transmission schemes for massive MIMO systems having low resolution ADCs. The
optimal block length for FDE is found by minimizing a computational complexity
cost function and taking quantization distortion, channel impulse response and
the number of transmit and receiver antennas into account. Through numerical
simulation, it is shown that the optimal block length also guarantees good
performance in terms of the Mean Square Error (MSE) and Bit Error-Rate (BER)
criterion.


  The so-called polynomial equations play an important role both in algebra and
in the theory of functional equations. If the unknown functions in the equation
are additive, relatively many results are known. However, even in this case,
there are a lot of open questions. In some specific cases, according to
classical results, the unknown additive functions are homomorphisms,
derivations, or linear combinations of these. The question arises as to whether
the solutions can be described even if the unknown functions are not assumed to
be additive but to be generalized monomials. As a starting point, we will deal
with quadratic functions in this paper. We aim to show that quadratic functions
that are solutions to certain polynomial equations necessarily have a `special'
form. Further, we also present a method to determine these special forms.


  A Neutron Time-Of-Flight (NTOF) spectrometer based on Heavy Ion Research
Facility in Lanzhou (HIRFL) is developed for studies of neutron production of
proton induced spallation reactions related to the ADS project. After the
presentation of comparisons between calculated spallation neutron production
double-differential cross sections and the available experimental one, a
detailed description of NTOF spectrometer is given. Test beam results show that
the spectrometer works well and data analysis procedures are established. The
comparisons of the test beam neutron spectra with those of GEANT4 simulations
are presented.


  We construct a cdh-local motivic homotopy category SH_cdh(S) over an
arbitrary base scheme S, and show that there is a canonical equivalence between
SH_cdh(S) and SH(S). We learned this result from D.-C. Cisinski.


  The relation between the macroscopic quantum coherent nature of superfluids
and the coherent properties of optical interference patterns will be utilized
to examine the optical properties of superfluid hydrodynamics. A Bragg pattern
imposed on the superfluid (either holographically or using a phase mask) is
expected to induce periodic variations in the local index of refraction of the
normal and super fluid components. The altered optical properties can then be
probed by a second coherent light source. In this manner, the behavior of the
probe beam can be switched using the specific characteristics of the imposed
pattern. Acoustic modes should also manifest measurable affects on incident
coherent radiations.


  We show how (a slight modification of) the noncommutative geometry bosonic
spectral action can be obtained by the cancelation of the scale anomaly of the
fermionic action. In this sense the standard model coupled with gravity is
induced by the quantum nature of the fermions. The regularization used is very
natural in noncommutative geometry and puts the bosonic and fermionic action on
a similar footing.


  We present a Monte Carlo study of the magnetic properties of thin cylindrical
nanowires composed of a ferromagnetic core and an antiferromagnetic shell
implementing a classical spin Hamiltonian. We address systematically the impact
of interface exchange coupling on the loop characteristics and the
magnetization reversal mechanism. We study the effect of shell
polycrystallinity on the characteristic fields of the isothermal hysteresis
loop (coercivity, exchange-bias). We demonstrate that coupling to a
polycrystalline antiferromagnetic shell increases the critical core diameter
for transition from transverse to vortex domain walls.


  The Higgs boson production p p (p\bar p) -> b h +X via b g -> b h at the LHC,
which may be an important channel for testing the bottom quark Yukawa coupling,
is subject to large supersymmetric quantum corrections. In this work the
one-loop SUSY-QCD corrections to this process are evaluated and are found to be
quite sizable in some parameter space. We also study the behavior of the
corrections in the limit of heavy SUSY masses and find the remnant effects of
SUSY-QCD. These remnant effects, which are left over in the Higgs sector by the
heavy sparticles, are found to be so sizable (for a light CP-odd Higgs and
large \tan\beta) that they might be observable in the future LHC experiment.
The exploration of such remnant effects is important for probing SUSY,
especially in case that the sparticles are too heavy (above TeV) to be directly
discovered at the LHC.


  We propose double/debiased machine learning approaches to infer (at the
parametric rate) the parametric component of a logistic partially linear model
with the binary response following a conditional logistic model of a low
dimensional linear parametric function of some key (exposure) covariates and a
nonparametric function adjusting for the confounding effect of other
covariates. We consider a Neyman orthogonal (doubly robust) score equation
consisting of two nuisance functions: nonparametric component in the logistic
model and conditional mean of the exposure on the other covariates and with the
response fixed. To estimate the nuisance models, we separately consider the use
of high dimensional (HD) sparse parametric models and more general (typically
nonparametric) machine learning (ML) methods. In the HD case, we derive certain
moment equations to calibrate the first-order bias of the nuisance models and
grant our method a model double robustness property in the sense that our
estimator achieves the desirable rate when at least one of the nuisance models
is correctly specified and both of them are ultra-sparse. In the ML case, the
non-linearity of the logit link makes it substantially harder than the
partially linear setting to use an arbitrary conditional mean learning
algorithm to estimate the nuisance component of the logistic model. We handle
this obstacle through a novel full model refitting procedure that is
easy-to-implement and facilitates the use of nonparametric ML algorithms in our
framework. Our ML estimator is rate doubly robust in the same sense as
Chernozhukov et al. (2018a). We evaluate our methods through simulation studies
and apply them in assessing the effect of emergency contraceptive (EC) pill on
early gestation foetal with a policy reform in Chile in 2008 (Bentancor and
Clarke, 2017).


  U-Net, known for its simple yet efficient architecture, is widely utilized
for image processing tasks and is particularly suitable for deployment on
neuromorphic chips. This paper introduces the novel concept of Spiking-UNet for
image processing, which combines the power of Spiking Neural Networks (SNNs)
with the U-Net architecture. To achieve an efficient Spiking-UNet, we face two
primary challenges: ensuring high-fidelity information propagation through the
network via spikes and formulating an effective training strategy. To address
the issue of information loss, we introduce multi-threshold spiking neurons,
which improve the efficiency of information transmission within the
Spiking-UNet. For the training strategy, we adopt a conversion and fine-tuning
pipeline that leverage pre-trained U-Net models. During the conversion process,
significant variability in data distribution across different parts is observed
when utilizing skip connections. Therefore, we propose a connection-wise
normalization method to prevent inaccurate firing rates. Furthermore, we adopt
a flow-based training method to fine-tune the converted models, reducing time
steps while preserving performance. Experimental results show that, on image
segmentation and denoising, our Spiking-UNet achieves comparable performance to
its non-spiking counterpart, surpassing existing SNN methods. Compared with the
converted Spiking-UNet without fine-tuning, our Spiking-UNet reduces inference
time by approximately 90\%. This research broadens the application scope of
SNNs in image processing and is expected to inspire further exploration in the
field of neuromorphic engineering. The code for our Spiking-UNet implementation
is available at https://github.com/SNNresearch/Spiking-UNet.


  Density functional theory calculations are used to investigate the electronic
structures of localized states at reconstructed armchair graphene edges. We
consider graphene nanoribbons with two different edge types and obtain the
energy band structures and charge densities of the edge states. By examining
the imaginary part of the wavevector in the forbidden energy region, we reveal
the decay behavior of the wavefunctions in graphene. The complex band
structures of graphene in the armchair and zigzag directions are presented in
both tight-binding and first-principles frameworks.


  The logistic linear mixed model (LLMM) is one of the most widely used
statistical models. Generally, Markov chain Monte Carlo algorithms are used to
explore the posterior densities associated with the Bayesian LLMMs. Polson,
Scott and Windle's (2013) Polya-Gamma data augmentation (DA) technique can be
used to construct full Gibbs (FG) samplers for the LLMMs. Here, we develop
efficient block Gibbs (BG) samplers for Bayesian LLMMs using the Polya-Gamma DA
method. We compare the FG and BG samplers in the context of a real data
example, as the correlation between the fixed effects and the random effects
changes as well as when the dimensions of the design matrices vary. These
numerical examples demonstrate superior performance of the BG samplers over the
FG samplers. We also derive conditions guaranteeing geometric ergodicity of the
BG Markov chain when the popular improper uniform prior is assigned on the
regression coefficients, and proper or improper priors are placed on the
variance parameters of the random effects. This theoretical result has
important practical implications as it justifies the use of asymptotically
valid Monte Carlo standard errors for Markov chain based estimates of the
posterior quantities.


  We present new J=1-0 12-CO observations along the northern spiral arm of the
grand-design spiral galaxy M100 (NGC 4321), and study the distribution of
molecular hydrogen as derived from these observations, comparing the new data
with a set of data points on the southern arm published previously. We compare
these measurements on both spiral arms and on the interarm regions with
observations of the atomic and ionized hydrogen components. We determine
massive star formation efficiency parameters, defined as the ratio of H alpha
luminosity to total gas mass, along the arms and compare the values to those in
the interarm regions adjacent to the arms. We find that these parameters in the
spiral arms are on average a factor of 3 higher than outside the arms, a clear
indication of triggering of the star formation in the spiral arms. We discuss
possible mechanisms for this triggering, and conclude that a density wave
system is probably responsible for it. We discuss several possible systematical
effects in some detail, and infer that the conclusions on triggering are sound.
We specifically discuss the possible effects of extinction in H alpha, or a
non-standard CO to H_2 conversion factor (X), and find that our conclusions on
the enhancement of the efficiencies in the arms are reinforced rather than
weakened by these considerations. A simple star forming scheme involving
threshold densities for gravitational collapse is discussed for NGC 4321, and
comparison is made with M51. We find that the gas between the arms is generally
stable against gravitational collapse whereas the gas in the arms is not,
possibly leading to the observed arm-interarm differences in efficiency, but
also note that these results, unlike the others obtained in this paper, do
depend critically on the assumed value for the conversion factor.


  We target the problem of sparse 3D reconstruction of dynamic objects observed
by multiple unsynchronized video cameras with unknown temporal overlap. To this
end, we develop a framework to recover the unknown structure without sequencing
information across video sequences. Our proposed compressed sensing framework
poses the estimation of 3D structure as the problem of dictionary learning,
where the dictionary is defined as an aggregation of the temporally varying 3D
structures. Given the smooth motion of dynamic objects, we observe any element
in the dictionary can be well approximated by a sparse linear combination of
other elements in the same dictionary (i. e. self-expression). Moreover, the
sparse coefficients describing a locally linear 3D structural interpolation
reveal the local sequencing information. Our formulation optimizes a biconvex
cost function that leverages a compressed sensing formulation and enforces both
structural dependency coherence across video streams, as well as motion
smoothness across estimates from common video sources. We further analyze the
reconstructability of our approach under different capture scenarios, and its
comparison and relation to existing methods. Experimental results on large
amounts of synthetic data as well as real imagery demonstrate the effectiveness
of our approach.


  Recently it was argued that quantum phase transitions can be radically
different from classical phase transitions with as a highlight the 'deconfined
critical points' exhibiting fractionalization of quantum numbers due to Berry
phase effects. Such transitions are supposed to occur in frustrated
('$J_1$-$J_2$') quantum magnets. We have developed a novel renormalization
approach for such systems which is fully respecting the underlying lattice
structure. According to our findings, another profound phenomenon is around the
corner: a fluctuation induced (order-out-of-disorder) first order transition.
This has to occur for large spin and we conjecture that it is responsible for
the weakly first order behavior recently observed in numerical simulations for
frustrated $S=1/2$ systems.


  Much of the information processing performed by a neuron occurs in the
dendritic tree. For neural systems using light for communication, it is
advantageous to convert signals to the electronic domain at synaptic terminals
so dendritic computation can be performed with electrical circuits. Here we
present circuits based on Josephson junctions and mutual inductors that act as
dendrites, processing signals from synapses receiving single-photon
communication events with superconducting detectors. We show simulations of
circuits performing basic temporal filtering, logical operations, and nonlinear
transfer functions. We further show how the synaptic signal from a
single-photon can fan out locally in the electronic domain to enable the
dendrites of the receiving neuron to process a photonic synapse event or pulse
train in multiple different ways simultaneously. Such a technique makes
efficient use of photons, energy, space, and information.


  Many constraints restricting the result of some computations over an integer
sequence can be compactly represented by register automata. We improve the
propagation of the conjunction of such constraints on the same sequence by
synthesising a database of linear and non-linear invariants using their
register-automaton representation. The obtained invariants are formulae
parameterised by a function of the sequence length and proven to be true for
any long enough sequence. To assess the quality of such linear invariants, we
developed a method to verify whether a generated linear invariant is a facet of
the convex hull of the feasible points. This method, as well as the proof of
non-linear invariants, are based on the systematic generation of constant-size
deterministic finite automata that accept all integer sequences whose result
verifies some simple condition. We apply such methodology to a set of 44
time-series constraints and obtain 1400 linear invariants from which 70% are
facet defining, and 600 non-linear invariants, which were tested on short-term
electricity production problems.


  A giant planet embedded in a protoplanetary disk creates a gap. This process
is important for both theory and observations. Using results of a survey for a
wide parameter range with two-dimensional hydrodynamic simulations, we
constructed an empirical formula for the gap structure (i.e., the radial
surface density distribution), which can reproduce the gap width and depth
obtained by two-dimensional simulations. This formula enables us to judge
whether an observed gap is likely to be caused by an embedded planet or not.
The propagation of waves launched by the planet is closely connected to the gap
structure. It makes the gap wider and shallower as compared with the case where
an instantaneous wave damping is assumed. The hydrodynamic simulations shows
that the waves do not decay immediately at the launching point of waves, even
when the planet is as massive as Jupiter. Based on the results of hydrodynamic
simulations, we also obtained an empirical model of wave propagation and
damping for the cases of deep gaps. The one-dimensional gap model with our wave
propagation model is able to well reproduce the gap structures in hydrodynamic
simulations. In the case of a Jupiter-mass planet, we also found that the waves
with smaller wavenumber (e.g., $m=2$) are excited and transport the angular
momentum to the location far away from the planet. The wave with $m=2$ is
closely related with a secondary wave launched by the site opposite from the
planet.


  We modify the Zee mass matrix by adding a real one parameter perturbation
which is purely diagonal and trace-less. We show that in this way we can
explain both solar and atmospheric neutrino oscillation data. There is a
correlation between the deviation from strict maximality of $|U_{\mu 3}|=
1/\sqrt{2}$, with the emergence of a small but non-zero $U_{e3}$. We calculate
how big a value can $U_{e3}$ get when we restrict ourselves within the allowed
regions of solar and atmospheric neutrino masses and mixing angles. We also
discuss the impact of a $S_2$ permutation symmetry on our mass matrix and show
how a small $U_{e3} \ne 0$ can emerge when this $S_2$ permutation symmetry
between the second and the third generation is broken.


  We observe an unusual behavior of the low-temperature magnetoresistance of
the high-mobility two-dimensional electron gas in InGaAs/InAlAs quantum wells
in weak perpendicular magnetic fields. The observed magnetoresistance is
qualitatively similar to that expected for the weak localization and
anti-localization but its quantity exceeds significantly the scale of the
quantum corrections. The calculations show that the obtained data can be
explained by the classical effects in electron motion along the open orbits in
a quasiperiodic potential relief manifested by the presence of ridges on the
quantum well surface.


  For primes $p$, we study the maximal possible size of a $p$-core
$p'$-partition (a partition with no hook lengths or parts divisible by $p$).
McDowell recently proved that the maximum is attained by a unique partition,
say $\Lambda_p$. Using his graph theoretic description of $\Lambda_p$, we prove
for $p > 10^6$ that \[\frac{1}{24}p^6 - p^5\sqrt{p} < |\Lambda_p| <
\frac{1}{24}p^6 - \frac{1}{200}p^5\sqrt{p},\] which shows that $|\Lambda_p|
\sim p^6/24$ as $p \to \infty$.


  Millions of vulnerable consumer IoT devices in home networks are the enabler
for cyber crimes putting user privacy and Internet security at risk. Internet
service providers (ISPs) are best poised to play key roles in mitigating risks
by automatically inferring active IoT devices per household and notifying users
of vulnerable ones. Developing a scalable inference method that can perform
robustly across thousands of home networks is a non-trivial task. This paper
focuses on the challenges of developing and applying data-driven inference
models when labeled data of device behaviors is limited and the distribution of
data changes (concept drift) across time and space domains. Our contributions
are three-fold: (1) We collect and analyze network traffic of 24 types of
consumer IoT devices from 12 real homes over six weeks to highlight the
challenge of temporal and spatial concept drifts in network behavior of IoT
devices; (2) We analyze the performance of two inference strategies, namely
"global inference" (a model trained on a combined set of all labeled data from
training homes) and "contextualized inference" (several models each trained on
the labeled data from a training home) in the presence of concept drifts; and
(3) To manage concept drifts, we develop a method that dynamically applies the
``closest'' model (from a set) to network traffic of unseen homes during the
testing phase, yielding better performance in 20% of scenarios.


  This paper reports recent progress of ultrasonic measurements on
URu$_2$Si$_2$, including ultrasonic measurements under hydrostatic pressure, in
pulsed-magnetic fields, and the effect of Rh-substitution. The observed changes
of the elastic responses shed light on the orthorhombic-lattice instability
with $\Gamma_3$-symmetry existing within the hidden order and the hybridized
5$f$-electron states of URu$_2$Si$_2$.


  The millihertz gravitational wave band can only be accessed with a
space-based interferometer, but it is one of the richest in potential sources.
Observations in this band have amazing scientific potential. The mergers
between massive black holes with mass in the range 10 thousand to 10 million
solar masses, which are expected to occur following the mergers of their host
galaxies, produce strong millihertz gravitational radiation. Observations of
these systems will trace the hierarchical assembly of structure in the Universe
in a mass range that is very difficult to probe electromagnetically. Stellar
mass compact objects falling into such black holes in the centres of galaxies
generate detectable gravitational radiation for several years prior to the
final plunge and merger with the central black hole. Measurements of these
systems offer an unprecedented opportunity to probe the predictions of general
relativity in the strong-field and dynamical regime. Millihertz gravitational
waves are also generated by millions of ultra-compact binaries in the Milky
Way, providing a new way to probe galactic stellar populations. ESA has
recognised this great scientific potential by selecting The Gravitational
Universe as its theme for the L3 large satellite mission, scheduled for launch
in ~2034. In this article we will review the likely sources for millihertz
gravitational wave detectors and describe the wide applications that
observations of these sources could have for astrophysics, cosmology and
fundamental physics.


  We give an elementary proof that for a ring homomorphism A -> B, satisfying
the property that every ideal in A is contracted from B, the following property
holds: for every chain of prime ideals p_0 \subset ... \subset p_r in A there
exists a chain of prime ideals q_0 \subset ... \subset q_r in B such that q_i
\cap A = p_i.


  Swarms of Unmanned Aerial Vehicles (UAV) have demonstrated enormous potential
in many industrial and commercial applications. However, before deploying UAVs
in the real world, it is essential to ensure they can operate safely in complex
environments, especially with limited communication capabilities. To address
this challenge, we propose a control-aware learning-based trajectory prediction
algorithm that can enable communication-efficient UAV swarm control in a
cluttered environment. Specifically, our proposed algorithm can enable each UAV
to predict the planned trajectories of its neighbors in scenarios with various
levels of communication capabilities. The predicted planned trajectories will
serve as input to a distributed model predictive control (DMPC) approach. The
proposed algorithm combines (1) a trajectory compression and reconstruction
model based on Variational Auto-Encoder, (2) a trajectory prediction model
based on EvolveGCN, a graph convolutional network (GCN) that can handle dynamic
graphs, and (3) a KKT-informed training approach that applies the
Karush-Kuhn-Tucker (KKT) conditions in the training process to encode DMPC
information into the trained neural network. We evaluate our proposed algorithm
in a funnel-like environment. Results show that the proposed algorithm
outperforms state-of-the-art benchmarks, providing close-to-optimal control
performance and robustness to limited communication capabilities and
measurement noises.


  Based on images from the FIRST survey of the radio sky at 1.4 GHz, the
positions of $\sim$5200 hotspots in $\sim$2870 radio galaxies and quasars were
compiled, and linear sizes and radio luminosities were derived from the hosts
redshifts. For a subsample of $\sim$2100 radio sources with exactly one hotspot
in each of the two opposite lobes, their geometry in terms of asymmetry and
bending was studied. The known (weak) tendency for the brighter lobe (here
hotspot) to lie closer to the host than the fainter one, is confirmed. The
median bending angle between the two arms of radio sources is 4.8$^{\circ}$
with a significant difference in the distribution between the 627 quasar hosts
and the 1501 galaxies.


  This paper investigates whether nonlinear gravitational instability can
account for the clustering of galaxies on large and small scales, and for the
evolution of clustering with epoch. No CDM-like spectrum is consistent with the
shape of the observed nonlinear spectrum. Unbiased low-density models greatly
overpredict the small-scale correlations; high-density models would require a
bias which does not vary monotonically with scale. The true linear power
spectrum contains a primordial feature at $k\simeq 0.1 \hompc$, and must break
quite abruptly to an effective slope of $n\ls -2.3$ on smaller scales This
empirical fluctuation spectrum also fits the CFRS data on the evolution of
clustering, provided the universe is open with $\Omega\simeq 0.3$. Only this
case explains naturally how the small-scale spectrum can evolve at the observed
rate while retaining the same power-law index. An unbiased open model also
matches correctly the large-scale COBE data, and offers an attractively simple
picture for the phenomenology of galaxy clustering.


  The discovery of luminous quasars at redshift z ~ 6 indicates the presence of
supermassive black holes (SMBHs) of mass ~10^9 Msun when the Universe was less
than one billion years old. This finding presents several challenges for
theoretical models. Here, we present the first multi-scale simulations that,
together with a self-regulated model for the SMBH growth, produce a luminous
quasar at z ~ 6.5 in the LCDM paradigm. We follow the hierarchical assembly
history of the most massive halo in a ~ 3 Gpc^3 volume, and find that this halo
of ~ 8x 10^{12} Msun forming at z ~ 6.5 after several major mergers is able to
reproduce a number of observed properties of SDSS J1148+5251, the most distant
quasar detected at z =6.42 (Fan et al. 2003). Moreover, the SMBHs grow through
gas accretion below the Eddington limit in a self-regulated manner owing to
feedback. We find that the progenitors experience significant star formation
(up to 10^4 Msun/yr) preceding the major quasar phase such that the stellar
mass of the quasar host reaches 10^{12} Msun at z ~ 6.5, consistent with
observations of significant metal enrichment in SDSS J1148+5251. Our results
provide a viable formation mechanism for z ~ 6 quasars in the standard LCDM
cosmology, and demonstrate a common, merger-driven origin for the rarest
quasars and the fundamental SMBH-host correlation in a hierarchical
Universe.(Abridged)


  We review how lattice QCD can contribute to the prediction and the
comprehension of tetraquarks, pentaquarks and related exotic hadrons, with at
least one heavy quark. We include all families of exotic hadrons, except for
the quarkless glueballs and the hexaquarks which are related to nuclear
physics.
  Since the discovery of quarks and the development of the QCD theory, there
has been a large interest in exotic hadrons, initiated by the tetraquark models
developed by Jaffe in 1977. Lattice QCD, being a first principle approach to
solve non-perturbative QCD, has been crucial not only to compute precise
results, but also to inspire research in hadronic physics.
  In the new millennium, this interest exploded with several experimental
discoveries of tetraquark and pentaquark resonances with heavy quarks, starting
with the Zc and Zb. Lattice QCD has not yet been able to comprehend this Z
class of tetraquarks, and is developing new methods to determine their masses,
decay widths and decay processes.
  The interest in tetraquarks was also fuelled by the lattice QCD prediction of
a second class of tetraquarks such as the Tbb, boundstates in the sense of
having no strong decays. Recently, the Tcc tetraquark, first predicted with
quark models in 1982 by Richard et al, was observed experimentally. We expect
the lattice QCD community will be able to explore this T class of tetraquarks
in more detail and with very precise results.
  We report on all the different direct and indirect approaches that lattice
QCD, with more focus on tetraquarks, has been employing to study exotic hadrons
with at least one heavy quark. We also briefly review the experimental progress
in observing tetraquarks and pentaquarks, and some of the theoretical paradigms
of tetraquarks, including three different types of mechanisms (diquark,
molecular and s pole), comparing them with the results of lattice QCD.


  Context. More than 10000 Jupiter Trojans have been detected so far. They are
moving around the L4 and L5 triangular Lagrangian points of the Sun-Jupiter
system and their distributions can provide important clues to the early
evolution of the Solar System.
  Aims. The number asymmetry of the L4 and L5 Jupiter Trojans is a longstanding
problem. We aim to test a new mechanism in order to explain this anomalous
feature by invoking the jumping-Jupiter scenario.
  Methods. First, we introduce the orbital evolution of Jupiter caused by the
giant planet instability in the early Solar System. In this scenario, Jupiter
could undergo an outward migration at a very high speed. We then investigate
how such a jump changes the numbers of the L4 (N4) and L5 (N5) Trojans.
  Results. The outward migration of Jupiter can distort the co-orbital orbits
near the Lagrangian points, resulting in L4 Trojans being more stable than the
L5 ones. We find that, this mechanism could potentially explain the unbiased
number asymmetry of N4/N5~1.6 for the known Jupiter Trojans. The uncertainties
of the system parameters, e.g. Jupiter's eccentricity and inclination, the
inclination distribution of Jupiter Trojans, are also taken into account and
our results about the L4/L5 asymmetry have been further validated. However, the
resonant amplitudes of the simulated Trojans are excited to higher values
compared to the current population. A possible solution is that collisions
among the Trojans may reduce their resonant amplitudes.


  We compute the (center-of-mass frame) scattering angle $\chi$ of
hyperboliclike encounters of two spinning black holes, at the fourth
post-Newtonian approximation level for orbital effects, and at the
next-to-next-to-leading order for spin-dependent effects. We find it convenient
to compute the gauge-invariant scattering angle (expressed as a function of
energy, orbital angular momentum and spins) by using the Effective-One-Body
formalism. The contribution to scattering associated with nonlocal, tail
effects is computed by generalizing to the case of unbound motions the method
of time-localization of the action introduced in the case of
(small-eccentricity) bound motions by Damour, Jaranowski and Sch\"afer [Phys.\
Rev.\ D {\bf 91}, no. 8, 084024 (2015)].


  We study elastic snap-through induced by a control parameter that evolves
dynamically. In particular, we study an elastic arch subject to an
end-shortening that evolves linearly with time, i.e. at a constant rate. For
large end-shortening the arch is bistable but, below a critical end-shortening,
the arch becomes monostable. We study when and how the arch transitions between
states and show that the end-shortening at which the fast 'snap' happens
depends on the rate at which the end-shortening is reduced. This lag in
snap-through is a consequence of delayed bifurcation and occurs even in the
perfectly elastic case when viscous (and viscoelastic) effects are negligible.
We present the results of numerical simulations to determine the magnitude of
this lag as the loading rate and the importance of external viscous damping
vary. We also present an asymptotic analysis of the geometrically-nonlinear
problem that reduces the salient dynamics to that of an ordinary differential
equation; the form of this reduced equation is generic for snap-through
instabilities in which the relevant control parameter is ramped linearly in
time. Moreover, this asymptotic reduction allows us to derive analytical
results for the observed lag in snap-through that are in good agreement with
the numerical results of our simulations. Finally, we discuss scaling laws for
the lag that should be expected in other examples of delayed bifurcation in
elastic instabilities.


  A high granularity hadronic 1 m3 calorimeter prototype with semi-digital
readout has been designed and built. This calorimeter has been made using
stainless steel as absorber and Glass Resistive Plates Chambers (GRPC) as
active medium, and read out through 1x1 cm2 pads. This prototype aims to
demonstrate that this technology fulfills the physics requirements for future
linear collider experiments, and also to test the feasibility of building a
realistic detector, taking into account design aspects as for instance a fully
embedded front-end electronics based on power pulsing system, a compact and
self-supporting mechanical structure, one-side services...


  We prove maximum principles for the problem of optimal control for a jump
diffusion with infinite horizon and partial information. The results are
applied to partial information optimal consumption and portfolio problems in
infinite horizon.


  Development of sustainable insurance for cyber risks, with associated
benefits, inter alia requires reduction of ambiguity of the risk. Considering
cyber risk, and data breaches in particular, as a man-made catastrophe
clarifies the actuarial need for multiple levels of analysis - going beyond
claims-driven loss statistics alone to include exposure, hazard, breach size,
and so on - and necessitating specific advances in scope, quality, and
standards of both data and models. The prominent human element, as well as
dynamic, networked, and multi-type nature, of cyber risk makes it perhaps
uniquely challenging. Complementary top-down statistical, and bottom-up
analytical approaches are discussed. Focusing on data breach severity, measured
in private information items ('ids') extracted, we exploit relatively mature
open data for U.S. data breaches. We show that this extremely heavy-tailed risk
is worsening for external attacker ('hack') events - both in frequency and
severity. Writing in Q2-2018, the median predicted number of ids breached in
the U.S. due to hacking, for the last 6 months of 2018, is 0.5 billion. But
with a 5% chance that the figure exceeds 7 billion - doubling the historical
total. 'Fortunately' the total breach in that period turned out to be near the
median.


  The delta interaction at a vertex generalizes the Robin (generalized Neumann)
boundary condition on an interval. Study of a single vertex with N infinite
leads suffices to determine the localized effects of such a vertex on densities
of states, etc. For all the standard initial-value problems, such as that for
the wave equation, the pertinent integral kernel (Green function) on the graph
can be easily constructed from the corresponding elementary Green function on
the real line. From the results one obtains the spectral-projection kernel,
local spectral density, and local energy density. The energy density, which
refers to an interpretation of the graph as the domain of a quantized scalar
field, is a coefficient in the asymptotic expansion of the Green function for
an elliptic problem involving the graph Hamiltonian; that expansion contains
spectral/geometrical information beyond that in the much-studied heat-kernel
expansion.


  Our paper focuses on the research of the possibility for speech recognition
intelligent agents to assist the elderly and disabled people's lives, to
improve their life quality by utilizing cutting-edge technologies. After
researching the attitude of elderly and disabled people toward the household
agent, we propose a design framework: ESNI(Exploration, Segmentation,
Navigation, Instruction) that apply to mobile agent, achieve some
functionalities such as processing human commands, picking up a specified
object, and moving an object to another location. The agent starts the
exploration in an unseen environment, stores each item's information in the
grid cells to his memory and analyzes the corresponding features for each
section. We divided our indoor environment into 6 sections: Kitchen, Living
room, Bedroom, Studio, Bathroom, Balcony. The agent uses algorithms to assign
sections for each grid cell then generates a navigation trajectory base on the
section segmentation. When the user gives a command to the agent, feature words
will be extracted and processed into a sequence of sub-tasks.


  We explore several physical effects on the power spectrum of the Lyman-alpha
forest transmitted flux. The effects we investigate here are usually not part
of hydrodynamic simulations and so need to be estimated separately. The most
important effect is that of high column density absorbers with damping wings,
which add power on large scales. We compute their effect using the
observational constraints on their abundance as a function of column density.
Ignoring their effect leads to an underestimation of the slope of the linear
theory power spectrum. The second effect we investigate is that of fluctuations
in the ionizing radiation field. For this purpose we use a very large high
resolution N-body simulation, which allows us to simulate both the fluctuations
in the ionizing radiation and the small scale LyaF within the same simulation.
We find an enhancement of power on large scales for quasars and a suppression
for galaxies. The strength of the effect rapidly increases with increasing
redshift, allowing it to be uniquely identified in cases where it is
significant. We develop templates which can be used to search for this effect
as a function of quasar lifetime, quasar luminosity function, and attenuation
length. Finally, we explore the effects of galactic winds using hydrodynamic
simulations. We find the wind effects on the LyaF power spectrum to be be
degenerate with parameters related to the temperature of the gas that are
already marginalized over in cosmological fits. While more work is needed to
conclusively exclude all possible systematic errors, our results suggest that,
in the context of data analysis procedures where parameters of the LyaF model
are properly marginalized over, the flux power spectrum is a reliable tracer of
cosmological information.


  In this letter we describe an approach to the current algebra based in the
Path Integral formalism. We use this method for abelian and non-abelian quantum
field theories in 1+1 and 2+1 dimensions and the correct expressions are
obtained. Our results show the independence of the regularization of the
current algebras.


  Deep learning (DL) techniques are increasingly pervasive across various
domains, including wireless communication, where they extract insights from raw
radio signals. However, the computational demands of DL pose significant
challenges, particularly in distributed wireless networks like Cell-free
networks, where deploying DL models on edge devices becomes hard due to
heightened computational loads. These computational loads escalate with larger
input sizes, often correlating with improved model performance. To mitigate
this challenge, Early Exiting (EE) techniques have been introduced in DL,
primarily targeting the depth of the model. This approach enables models to
exit during inference based on specified criteria, leveraging entropy measures
at intermediate exits. Doing so makes less complex samples exit early, reducing
computational load and inference time. In our contribution, we propose a novel
width-wise exiting strategy for Convolutional Neural Network (CNN)-based
architectures. By selectively adjusting the input size, we aim to regulate
computational demands effectively. Our approach aims to decrease the average
computational load during inference while maintaining performance levels
comparable to conventional models. We specifically investigate Modulation
Classification, a well-established application of DL in wireless communication.
Our experimental results show substantial reductions in computational load,
with an average decrease of 28%, and particularly notable reductions of 65% in
high-SNR scenarios. Through this work, we present a practical solution for
reducing computational demands in deep learning applications, particularly
within the domain of wireless communication.


  Volumetric videos, benefiting from immersive 3D realism and interactivity,
hold vast potential for various applications, while the tremendous data volume
poses significant challenges for compression. Recently, NeRF has demonstrated
remarkable potential in volumetric video compression thanks to its simple
representation and powerful 3D modeling capabilities, where a notable work is
ReRF. However, ReRF separates the modeling from compression process, resulting
in suboptimal compression efficiency. In contrast, in this paper, we propose a
volumetric video compression method based on dynamic NeRF in a more compact
manner. Specifically, we decompose the NeRF representation into the coefficient
fields and the basis fields, incrementally updating the basis fields in the
temporal domain to achieve dynamic modeling. Additionally, we perform
end-to-end joint optimization on the modeling and compression process to
further improve the compression efficiency. Extensive experiments demonstrate
that our method achieves higher compression efficiency compared to ReRF on
various datasets.


  The finite solid-liquid interface width in phase field models results in
non-equilibrium effects, including solute trapping. Prior phase field modeling
has shown that this extra degree of freedom, when compared to sharp-interface
models, results in solute trapping that is well captured when realistic
parameters, such as interface width, are employed. However, increasing the
interface width, which is desirable for computational reasons, leads to
artificially enhanced trapping thus making it difficult to model departure from
equilibrium quantitatively. In the present work, we develop a variational phase
field model with independent kinetic equations for the solid and liquid phases.
Separate kinetic equations for the phase concentrations obviate the assumption
of point wise equality of diffusion potentials, as is done in previous works.
Non-equilibrium effects such as solute trapping, drag and interface kinetics
can be introduced in a controlled manner in the present model. In addition, the
model parameters can be tuned to obtain ``experimentally-relevant" trapping
while using significantly larger interface widths than prior efforts. A
comparison with these other phase field models suggests that interface width of
about three to twenty-five times larger than current best-in-class models can
be employed depending upon the material system at hand leading to a speed-up by
a factor of $W^{(d+2)}$, where $W$ and $d$ denote the interface width and
spatial dimension, respectively. Finally the capacity to model non-equilibrium
phenomena is demonstrated by simulating oscillatory instability leading to the
formation of solute bands.


  Surface electromyogram (sEMG) is arguably the most sought-after physiological
signal with a broad spectrum of biomedical applications, especially in
miniaturized rehabilitation robots such as multifunctional prostheses. The
widespread use of sEMG to drive pattern recognition (PR)-based control schemes
is primarily due to its rich motor information content and non-invasiveness.
Moreover, sEMG recordings exhibit non-linear and non-uniformity properties with
inevitable interferences that distort intrinsic characteristics of the signal,
precluding existing signal processing methods from yielding requisite motor
control information. Therefore, we propose a multiresolution decomposition
driven by dual-polynomial interpolation (MRDPI) technique for adequate
denoising and reconstruction of multi-class EMG signals to guarantee the
dual-advantage of enhanced signal quality and motor information preservation.
Parameters for optimal MRDPI configuration were constructed across combinations
of thresholding estimation schemes and signal resolution levels using EMG
datasets of amputees who performed up to 22 predefined upper-limb motions
acquired in-house and from the public NinaPro database. Experimental results
showed that the proposed method yielded signals that led to consistent and
significantly better decoding performance for all metrics compared to existing
methods across features, classifiers, and datasets, offering a potential
solution for practical deployment of intuitive EMG-PR-based control schemes for
multifunctional prostheses and other miniaturized rehabilitation robotic
systems that utilize myoelectric signals as control inputs.