  The effect of anisotropy in the energy spectrum on the binding energy and
structural properties of excitons, trions, and biexcitons is investigated. To
this end we employ the stochastic variational method with a correlated Gaussian
basis. We present results for the binding energy of different excitonic
complexes in black phosphorus (bP) and TiS$_3$ and compare them with recent
results in the literature when available, for which we find good agreement. The
binding energies of excitonic complexes in bP are larger than those in TiS$_3$.
We calculate the different average interparticle distances in bP and TiS$_3$
and show that excitonic complexes in bP are strongly anisotropic whereas in
TiS$_3$ they are almost isotropic, even though the constituent particles have
an anisotropic energy spectrum. This is also confirmed by the correlation
functions.


  We demonstrate an optomechanical phase shifter. By electrostatically
deflecting the nanofabricated mechanical structure, the effective index of a
nearby waveguide is changed and the resulting phase shift is measured using an
integrated Mach-Zehnder interferometer. Comparing to thermo-optical phase
shifters, our device does not consume power in static operation and also it can
operate over large frequency, wavelength, and power ranges. Operation in the
MHz range and sub-$\mu$s pulses are demonstrated.


  Cryptography is the study of securing information. It is the physical process
that scrambles the information by rearrangement and substitution of content, so
that it becomes difficult for anyone to understand. In today's world, security
has become an inevitable part of our day-to-day life, right from normal
browsing to performing critical payment transactions. Hackers work endlessly to
break the security present in the apps/websites on which we perform day-to-day
operations and salvage valuable information. Because of this, many illegal
activities have taken place which affect the user. One such illegal activity is
tapping the voice communication between two users. If left unencrypted, the
communication between the users is compromised, thereby causing issues. One way
to prevent this act is to encrypt the audio in that the contents cannot have
tampered with unless the receiver has the valid key to decrypt it. The proposed
solution termed "HexE" aims to create a puzzle-based algorithm which would
encrypt and decrypt the audio files without manipulating the file header, thus
securing the contents. The algorithm works on an NxN SuDoKu-based puzzle which
is accepted both by the sender and receiver. Using the timestamp of the event
(UNIX based), a grid from the puzzle is chosen which in turn will act as the
key for both encryption and decryption. If the timestamp is slightly adjusted,
the process will end up in failure during decryption, thus ensuring
confidentiality. Another approach to secure the audio files is to implement
IPFS (Inter Planetary File System) alongside the puzzle algorithm in which the
encrypted audio is stored on it and the receiver can fetch the audio provided
if the valid IPFS Hash of the file is present. In this way, the audio file is
secured.


  Online discussions, panels, talk page edits, etc., often contain harmful
conversational content i.e., hate speech, death threats and offensive language,
especially towards certain demographic groups. For example, individuals who
identify as members of the LGBTQIA+ community and/or BIPOC (Black, Indigenous,
People of Color) are at higher risk for abuse and harassment online. In this
work, we first introduce a real-world dataset that will enable us to study and
understand harmful online conversational content. Then, we conduct several
exploratory data analysis experiments to gain deeper insights from the dataset.
We later describe our approach for detecting harmful online Anti-LGBTQIA+
conversational content, and finally, we implement two baseline machine learning
models (i.e., Support Vector Machine and Logistic Regression), and fine-tune 3
pre-trained large language models (BERT, RoBERTa, and HateBERT). Our findings
verify that large language models can achieve very promising performance on
detecting online Anti-LGBTQIA+ conversational content detection tasks.


  The spectrum of excited states observed in the finite volume of lattice QCD
is governed by the discrete symmetries of the cubic group. This finite group
permits the mixing of orbital angular momentum quanta in the finite volume. As
experimental results refer to specific angular momentum in a partial-wave
decomposition, a formalism mapping the partial-wave scattering potentials to
the finite volume is required. This formalism is developed herein for
Hamiltonian effective field theory, an extension of chiral effective field
theory incorporating the L\"uscher relation linking the energy levels observed
in finite volume to the scattering phase shift. The formalism provides an
optimal set of rest-frame basis states maximally reducing the dimension of the
Hamiltonian, and it should work in any Hamiltonian formalism. As a first
example of the formalism's implementation, lattice QCD results for the spectrum
of an isospin-2 $\pi\pi$ scattering system are analyzed to determine the $s$,
$d$, and $g$ partial-wave scattering information.


  Primary vestibular neurons are categorized as either regularly or irregularly
firing afferents that use rate and temporal sensory encoding strategies,
respectively. While many factors influence firing in these neurons, recent work
in mammalian vestibular afferents has demonstrated a rich diversity in ion
channels that drive spiking regularity. Here, I review key ionic currents
studied in vitro and demonstrate how they may enable sensory encoding
strategies demonstrated in vivo.


  Bent-core trimers are a simple model system for which the competition between
crystallization and glass-formation can be tuned by varying a single
parameter:\ the bond angle $\theta_0$. Using molecular dynamics simulations, we
examine how varying $\theta_0$ affects their thermal solidification. By
examining trends with $\theta_0$, comparing these to trends in trimers' jamming
phenomenology, and then focusing on the six $\theta_0$ that are commensurable
with close-packed crystalline order, we obtain three key results: \textbf{(i)}
the increase in trimers' solidification temperature $T_s(\theta_0)$ as they
straighten (as $\theta_0 \to 0^\circ$) is driven by the same gradual loss of
\textit{effective} configurational freedom that drives athermal trimers'
decreasing $\phi_J(\theta_0)$; \textbf{(ii)} $\theta_0$ that allow formation of
both FCC and HCP order crystallize, while $\theta_0$ that only allow formation
of HCP order glass-form; \textbf{(iii)} local cluster-level structure at
temperatures slightly \textit{above} $T_s(\theta_0)$ is highly predictive of
whether trimers will crystallize or glass-form.


  This work describes an integrated solution to face a civil security problem
in the area of Search And Rescue (SAR) of missing people. This proposal is
based on the use of emerging technologies such as Unmanned Aerial Vehicles
(UAV), also known as drones, and the use of simulated beacons on smartphones.
In particular, in the presented tool, drones fly synchronously in a specific
area so that each drone uses on-board sensors to scan and detect any signal
emitted by Bluetooth Low Energy (BLE) beacons from smartphones of missing
people. This technique allows getting the GPS position of any detected missing
person. This work also includes some security issues related to possible
attacks focused on the perimeter and physical security.


  A complete classification is presented of elliptic and K3 fibrations
birational to certain mildly singular complex Fano 3-folds. Detailed proofs are
given for one example case, namely that of a general hypersurface X of degree
30 in weighted P^4 with weights 1,4,5,6,15; but our methods apply more
generally. For constructing birational maps from X to elliptic and K3
fibrations we use Kawamata blowups and Mori theory to compute anticanonical
rings; to exclude other possible fibrations we make a close examination of the
strictly canonical singularities of (X,(1/n)H), where H is the linear system
associated to the putative birational map and n is its anticanonical degree.


  Galaxy mergers are thought to be one of the main mechanisms of the mass
assembly of galaxies. Recently, many works have suggested a possible increase
in the fraction of major mergers in the early Universe, reviving the debate on
which processes (e.g., cold accretion, star formation, mergers) most contribute
to the mass build-up of galaxies through cosmic time. To estimate the
importance of major mergers in this context, we make use of the new data
collected by the ALMA Large Program to INvestigate [CII] at Early times
(ALPINE), which observed the [CII] 158 $\mu$m emission line from a sample of 75
main-sequence star-forming galaxies at 4.4 < z < 5.9. We used, for the first
time, the morpho-kinematic information provided by the [CII] emission to obtain
the fraction of major mergers ($f_{MM}$) at z~5. By adopting different
prescriptions for the merger timescales ($T_{MM}$), we converted this fraction
into the merger rate per galaxy ($R_{MM}$) and per volume ($\Gamma_{MM}$). We
then combined our results with those at lower redshifts from the literature,
computing the cosmic evolution of the merger fraction. This is described by a
rapid increase from z~0 to higher redshifts, a peak at z~3, and a slow decrease
towards earlier epochs. Depending on the timescale prescription used, this
fraction translates into a merger rate ranging between ~0.1 and ~4.0 Gyr$^{-1}$
at z~5. Finally, we compare the specific star formation and star-formation rate
density with the analogous quantities from major mergers. Our new ALPINE data
reveal the presence of a significant merging activity in the early Universe.
However, whether this population of mergers can provide a relevant contribution
to the galaxy mass assembly at these redshifts and through the cosmic epochs is
strongly dependent on the assumption of the merger timescale.


  The thermal boundary resistance (Kapitza resistance) of (001) twist grain
boundaries in silicon nanowires depends on the mismatch angle. This dependence
is systematically investigated by means of nonequilibrium molecular dynamics
simulations. Grain boundary systems with and without coincidence site lattice
are compared. The Kapitza resistance increases with twist angle up to 40{\deg}.
For larger angles, it varies only little around 1.56 $\pm$ 0.05 K m^2/GW,
except for a drop by 30% near the 90{\deg} {\Sigma} 1 grain boundary. Finite
size effects due to the fixed outer boundary conditions of the nanowire are
negligible for diameters larger than 25 nm.


  We review the selectron production mechanisms in the linear collider modes
which do not require positrons. The standard model backgrounds can be rendered
harmless or even be nearly eliminated with polarized beams. We insist on the
complementarity of these different experiments.


  We have demonstrated a novel type of superconducting transmon qubit in which
a Josephson junction has been engineered to act as its own parallel shunt
capacitor. This merged-element transmon (MET) potentially offers a smaller
footprint and simpler fabrication than conventional transmons. Because it
concentrates the electromagnetic energy inside the junction, it reduces
relative electric field participation from other interfaces. By combining
micrometer-scale Al/AlOx/Al junctions with long oxidations and novel
processing, we have produced functional devices with $E_{J}$/$E_{C}$ in the low
transmon regime ($E_{J}$/$E_{C}$ $\lesssim$30). Cryogenic I-V measurements show
sharp dI/dV structure with low sub-gap conduction. Qubit spectroscopy of
tunable versions show a small number of avoided level crossings, suggesting the
presence of two-level systems (TLS). We have observed mean T1 times typically
in the range of 10-90 microseconds, with some annealed devices exhibiting T1 >
100 microseconds over several hours. The results suggest that energy relaxation
in conventional, small-junction transmons is not limited by junction loss.


  The origins of the bulge and disc components of galaxies are of primary
importance to understanding galaxy formation. Here bulge-disc decomposition is
performed simultaneously in B- and R-bands for 922 bright galaxies in 8 nearby
(z < 0.06) clusters with deep redshift coverage using photometry from the NOAO
Fundamental Plane Survey. The total galaxy colours follow a universal
colour-magnitude relation (CMR). The discs of L_* galaxies are 0.24 magnitudes
bluer in $B-R$ than bulges. Bulges have a significant CMR slope while the CMR
slope of discs is flat. Thus the slope of the CMR of the total light is driven
primarily (60%) by the bulge-CMR, and to a lesser extent (40%) by the change in
the bulge-to-total ratio as a function of magnitude. The colours of the bulge
and disc components do not depend on the bulge-to-total ratio, for galaxies
with bulge-to-total ratios greater than 0.2. While the colours of the bulge
components do not depend significantly on environment, the median colours of
discs vary significantly, with discs in the cluster centre redder by 0.10
magnitudes than those at the virial radius. Thus while star formation in bulges
appears to be regulated primarily by mass-dependent, and hence presumably
internal, processes, that of discs is affected by the cluster environment.


  Fine manipulation of fluid flows at the microscale has a tremendous impact on
mass transport phenomena of chemical and biological processes inside
microfluidic platforms. Fluid mixing in the laminar flow regime at low Reynolds
is poorly effective due to the inherently slow diffusive mechanism. As a
strategy to enhance mixing and prompt mass transport, here, we focus on
polyelectrolyte multilayer capsules (PMCs) embodying a catalytic
polyoxometalate as microobjects to create elastic turbulence and as micromotors
to generate chaotic flows by fuel-fed propulsions. The effects of the elastic
turbolence and of the artificial propulsion on some basic flow parameters, such
as pressure and volumetric flow rate are studied by a microfluidic set-up
including pressure and flow sensors. Numerical-handling and physical models of
the experimental data are presented and discussed to explain the measured
dependence of the pressure drop on the flow rate in presence of the PMCs. As a
practical outcome of the study, a strong decrease of the mixing time in a
serpentine microreactor is demonstrated. Unlike our previous reports dealing
with capillarity flow studies, the present paper relies on hydrodynamic pumping
experiments, that allows us to both develop a theoretical model for the
understanding of the involved phenomena and demonstrate a successfully
microfluidic mixing application. All of this is relevant in the perspective of
developing microobject based methods to overcome microscale processes purely
dominated by diffusion with potential improvements of mass trasport in
microfluidic platforms.


  In this paper the simplest singular boundary problem of Dirichlet type for
linear differential equation of the first order of general form is considered.
The main result of this paper is criterion of correct solvability of above
problem in the space L_p(R).


  In this paper, we investigate the possibility of explaining nonclassical
correlations between two quantum systems in terms of quantum interferences
between collective states of the two systems. We achieve this by mapping the
relations between different measurement contexts in the product Hilbert space
of a pair of two-level systems onto an analogous sequence of interferences
between paths in a single-particle interferometer. The relations between
different measurement outcomes are then traced to the distribution of
probability currents in the interferometer, where paradoxical relations between
the outcomes are identified with currents connecting two states that are
orthogonal and should therefore exclude each other. We show that the relation
between probability currents and correlations can be represented by continuous
conditional (quasi)probability currents through the interferometer, given by
weak values; the violation of the noncontextual assumption is expressed by
negative conditional currents in some of the paths. Since negative conditional
currents correspond to the assignment of negative conditional probabilities to
measurements results in different measurement contexts, the necessity of such
negative probability currents represents a failure of noncontextual local
realism. Our results help to explain the meaning of nonlocal correlations in
quantum mechanics, and support Feynman's claim that interference is the origin
of all quantum phenomena.


  Predictive models are being increasingly used to support consequential
decision making at the individual level in contexts such as pretrial bail and
loan approval. As a result, there is increasing social and legal pressure to
provide explanations that help the affected individuals not only to understand
why a prediction was output, but also how to act to obtain a desired outcome.
To this end, several works have proposed optimization-based methods to generate
nearest counterfactual explanations. However, these methods are often
restricted to a particular subset of models (e.g., decision trees or linear
models) and differentiable distance functions. In contrast, we build on
standard theory and tools from formal verification and propose a novel
algorithm that solves a sequence of satisfiability problems, where both the
distance function (objective) and predictive model (constraints) are
represented as logic formulae. As shown by our experiments on real-world data,
our algorithm is: i) model-agnostic ({non-}linear, {non-}differentiable,
{non-}convex); ii) data-type-agnostic (heterogeneous features); iii)
distance-agnostic ($\ell_0, \ell_1, \ell_\infty$, and combinations thereof);
iv) able to generate plausible and diverse counterfactuals for any sample
(i.e., 100% coverage); and v) at provably optimal distances.


  An asymptotic theory is established for linear functionals of the predictive
function given by kernel ridge regression, when the reproducing kernel Hilbert
space is equivalent to a Sobolev space. The theory covers a wide variety of
linear functionals, including point evaluations, evaluation of derivatives,
$L_2$ inner products, etc. We establish the upper and lower bounds of the
estimates and their asymptotic normality. It is shown that $\lambda\sim n^{-1}$
is the universal optimal order of magnitude for the smoothing parameter to
balance the variance and the worst-case bias. The theory also implies that the
optimal $L_\infty$ error of kernel ridge regression can be attained under the
optimal smoothing parameter $\lambda\sim n^{-1}\log n$. These optimal rates for
the smoothing parameter differ from the known optimal rate $\lambda\sim
n^{-\frac{2m}{2m+d}}$ that minimizes the $L_2$ error of the kernel ridge
regression.


  The gravitational wave (GW) signals from the Galactic population of
cataclysmic variables (CVs) have yet to be carefully assessed. Here we estimate
these signals and evaluate their significance for LISA. First, we find that at
least three known systems are expected to produce strong enough signals to be
individually resolved within the first four years of LISA's operation. Second,
CVs will contribute significantly to the LISA Galactic binary background,
limiting the mission's sensitivity in the relevant frequency band. Third, we
predict a spike in the unresolved GW background at a frequency corresponding to
the CV minimum orbital period. This excess noise may impact the detection of
other systems near this characteristic frequency. Fourth, we note that the
excess noise spike amplitude and location associated with
$P_{\rm{min}}\sim80~\mathrm{min}$ can be used to measure the CV space density
and period bounce location with complementary and simple GW biases compared to
the biases and selection effects plaguing samples selected from electromagnetic
signals. Our results highlight the need to explicitly include the Galactic CV
population in the LISA mission planning, both as individual GW sources and
generators of background noise, as well as the exciting prospect of
characterising the CV population through their GW emission.


  We glue four refined topological vertices to obtain the building block of 5D
$U(2)$ quiver instanton partition functions. We take the 4D limit of the result
to obtain the building block of 4D instanton partition functions which, using
the AGT correspondence, are identified with Virasoro conformal blocks.
  We show that there is a choice of the parameters of the topological vertices
that we start with, as well as the parameters and the intermediate states
involved in the gluing procedure, such that we obtain Virasoro minimal model
conformal blocks.


  The activity of cell membrane inclusions (such as ion channels) is influenced
by the host lipid membrane, to which they are elastically coupled. This
coupling concerns the hydrophobic thickness of the bilayer (imposed by the
length of the channel, as per the hydrophobic matching principle) but also its
slope at the boundary of the inclusion. However, this parameter has never been
measured so far. We combine small-angle x-ray scattering data and a complete
elastic model to measure the slope for the model gramicidin channel and show
that it is surprisingly steep, in two membrane systems with very different
elastic properties. This conclusion is confirmed and generalized by the
comparison with recent results in the simulation literature and with
conductivity measurements.


  The problem of decentralized multi-robot target tracking asks for jointly
selecting actions, e.g., motion primitives, for the robots to maximize target
tracking performance with local communications. One major challenge for
practical implementations is to make target tracking approaches scalable for
large-scale problem instances. In this work, we propose a general-purpose
learning architecture toward collaborative target tracking at scale, with
decentralized communications. Particularly, our learning architecture leverages
a graph neural network (GNN) to capture local interactions of the robots and
learns decentralized decision-making for the robots. We train the learning
model by imitating an expert solution and implement the resulting model for
decentralized action selection involving local observations and communications
only. We demonstrate the performance of our GNN-based learning approach in a
scenario of active target tracking with large networks of robots. The
simulation results show our approach nearly matches the tracking performance of
the expert algorithm, and yet runs several orders faster with up to 100 robots.
Moreover, it slightly outperforms a decentralized greedy algorithm but runs
faster (especially with more than 20 robots). The results also exhibit our
approach's generalization capability in previously unseen scenarios, e.g.,
larger environments and larger networks of robots.


  A non-relativistic quantum mechanical theory is proposed that combines
elements of Bohmian mechanics and of Everett's "many-worlds" interpretation.
The resulting theory has the advantage of resolving known issues of both
theories, as well as those of standard quantum mechanics. It has a clear
ontology and a set of precisely defined postulates from where the predictions
of standard quantum mechanics can be derived. Most importantly, the Born rule
can be derived by straightforward application of the Laplacian rule, without
reliance on a "quantum equilibrium hypothesis" that is crucial for Bohmian
mechanics, and without reliance on a "branch weight" that is crucial for
Everett-type theories. The theory describes a continuum of worlds rather than a
single world or a discrete set of worlds, so it is similar in spirit to
many-worlds interpretations based on Everett's approach, without being actually
reducible to these. In particular, there is no "splitting of worlds", which is
a typical feature of Everett-type theories. Altogether, the theory explains 1)
the subjective occurrence of probabilities, 2) their quantitative value as
given by the Born rule, 3) the identification of observables as self-adjoint
operators on Hilbert space, and 4) the apparently random "collapse of the
wavefunction" caused by the measurement, while still being an objectively
deterministic theory.


  We study the 2+1 dimensional SU(N) Yang-Mills theory on a finite two-torus
with twisted boundary conditions. Our goal is to study the interplay between
the rank of the group N, the length of the torus L and the Z_N magnetic flux.
After presenting the classical and quantum formalism, we analyze the spectrum
of the theory using perturbation theory to one-loop and using Monte Carlo
techniques on the lattice. In perturbation theory, results to all orders depend
on the combination x=\lambda NL and an angle defined in terms of the magnetic
flux (\lambda\ is 't Hooft coupling). Thus, fixing the angle, the system
exhibits a form of volume independence (NL dependence). The numerical results
interpolate between our perturbative calculations and the confinement regime.
They are consistent with x-scaling and provide interesting information about
the k-string spectrum and effective string theories. The occurrence of
tachyonic instabilities is also analysed. They seem to be avoidable in the
large N limit with a suitable scaling of the magnetic flux.


  We prepare and detect the hyperfine state of a single 87Rb atom coupled to a
fiber-based high finesse cavity on an atom chip. The atom is extracted from a
Bose-Einstein condensate and trapped at the maximum of the cavity field,
resulting in a reproducibly strong atom-cavity coupling. We use the cavity
reflection and transmission signal to infer the atomic hyperfine state with a
fidelity exceeding 99.92% in a read-out time of 100 microseconds. The atom is
still trapped after detection.


  We study the dynamics of Blaschke products in two dimensions, particularly
the rates of growth of the degrees of iterates and the corresponding
implications for the ergodic properties of the map.


  Long-term gamma-ray variability of a non-blazar Active Galactic Nucleus (AGN)
PKS 0521-36 is investigated by using Fermi-LAT pass 8 data covering from 2008
August to 2021 March. The results show that the histogram of the gamma-ray
fluxes follows a log-normal distribution. Interestingly, in the analysis of
about 5.8-year (from MJD 56317 to 58447) LAT data between two outbursts
(occurring during 2012 October and 2019 May respectively), a quasi-periodic
oscillation (QPO) with a period of about 1.1 years (about 5 sigma of
significance) is found in the Lomb-Scargle Periodogram (LSP), the Weighted
Wavelet Z-transform (WWZ) and the REDFIT results. This quasi-periodic signal
also appears in the results of Gaussian process modeling the light curve.
Therefore, the robustness of the QPO is examined by four different methods.
This is the first gamma-ray QPO found in a mildly beamed jet. Our results imply
that the gamma-ray outbursts play an important role in the formation of the
gamma-ray QPO.


  A method to search for dark matter by studying the interaction of accelerator
beam particles with residual gas in dependence on gas pressure is proposed. The
sensitivity of the method is estimated.


  We study the four-dimensional Yang-Mills theory in the presence of a
three-dimensional membrane of fermions by lattice Monte Carlo simulations. We
analyze the phase structure of this theory at finite temperature. Below the
phase transition temperature of the pure Yang-Mills theory, we obtain an
unconventional phase with spatially-nonuniform vacuum. In this phase, the
expectation value of the Polyakov loop is finite on the membrane, and it
exponentially decays to zero outside the membrane.


  These lecture notes are intended as an introduction to the theory of rational
Dunkl operators and the associated special functions, with an emphasis on
positivity and asymptotics.
  We start with an outline of the general concepts: Dunkl operators, the
intertwining operator, the Dunkl kernel and the Dunkl transform. We point out
the connection with integrable particle systems of Calogero-Moser-Sutherland
type, and discuss some systems of orthogonal polynomials associated with them.
A major part is devoted to positivity results for the intertwining operator and
the Dunkl kernel, the Dunkl-type heat semigroup, and related probabilistic
aspects. The notes conclude with recent results on the asymptotics of the Dunkl
kernel.


  We study analytical properties of the Stuckelberg holographic superconductors
with Weyl corrections. We obtain the minimum critical temperature as a function
of the mass of the scalar field $m^2$. We show that in limit of the
$m^2=-3$,$T^{Min}_c\approx0.158047\sqrt[3]{\rho}$ which is close to the
numerical estimate $T_c^{Numerical}\approx 0.170\sqrt[3]{\rho}$. Further we
show that the mass of the scalar field in bounded from below by the $
m^2>m_c^2$ where $m_c^2=-5.40417$. This lower bound is weaker and different
from the previous lower bound $m^2=-3$ predicted by stability analysis. We show
that in the Breitenlohner-Freedman bound, the critical temperature remains
finite. Explicitly, we prove that here there is exist a linear relation between
$<O_{\Delta}>$ and the chemical potential.


  In clinical practice, medical image segmentation provides useful information
on the contours and dimensions of target organs or tissues, facilitating
improved diagnosis, analysis, and treatment. In the past few years,
convolutional neural networks (CNNs) and Transformers have dominated this area,
but they still suffer from either limited receptive fields or costly long-range
modeling. Mamba, a State Space Sequence Model (SSM), recently emerged as a
promising paradigm for long-range dependency modeling with linear complexity.
In this paper, we introduce a Large Kernel Vision Mamba U-shape Network, or
LKM-UNet, for medical image segmentation. A distinguishing feature of our
LKM-UNet is its utilization of large Mamba kernels, excelling in locally
spatial modeling compared to small kernel-based CNNs and Transformers, while
maintaining superior efficiency in global modeling compared to self-attention
with quadratic complexity. Additionally, we design a novel hierarchical and
bidirectional Mamba block to further enhance Mamba's global and neighborhood
spatial modeling capability for vision inputs. Comprehensive experiments
demonstrate the feasibility and the effectiveness of using large-size Mamba
kernels to achieve large receptive fields. Codes are available at
https://github.com/wjh892521292/LKM-UNet.


  The maximal rank of an abstract regular polytope for M24, the Mathieu group
of degree 24, is 5. There are four such polytopes of rank 5 and in this note we
describe them using Curtis's MOG. This description is then used to give an
upper bound for the diameter of the chamber graphs of these polytopes.


  Let $\mathcal{C}$ be a $k$-linear category with split idempotents, and
$\Sigma:\mathcal{C}\rightarrow\mathcal{C}$ an automorphism. We show that there
is an $n$-angulated structure on $(\mathcal{C},\Sigma)$ under certain
conditions. As an application, we obtain a class of examples of $n$-angulated
categories from self-injective algebras.


  A basic question in the PAC model of learning is whether proper learning is
harder than improper learning. In the classical case, there are examples of
concept classes with VC dimension $d$ that have sample complexity
$\Omega\left(\frac d\epsilon\log\frac1\epsilon\right)$ for proper learning with
error $\epsilon$, while the complexity for improper learning is O$\!\left(\frac
d\epsilon\right)$. One such example arises from the Coupon Collector problem.
  Motivated by the efficiency of proper versus improper learning with quantum
samples, Arunachalam, Belovs, Childs, Kothari, Rosmanis, and de Wolf (TQC 2020)
studied an analogue, the Quantum Coupon Collector problem. Curiously, they
discovered that for learning size $k$ subsets of $[n]$ the problem has sample
complexity $\Theta(k\log\min\{k,n-k+1\})$, in contrast with the complexity of
$\Theta(k\log k)$ for Coupon Collector. This effectively negates the
possibility of a separation between the two modes of learning via the quantum
problem, and Arunachalam et al.\ posed the possibility of such a separation as
an open question.
  In this work, we first present an algorithm for the Quantum Coupon Collector
problem with sample complexity that matches the sharper lower bound of
$(1-o_k(1))k\ln\min\{k,n-k+1\}$ shown recently by Bab Hadiashar, Nayak, and
Sinha (IEEE TIT 2024), for the entire range of the parameter $k$. Next, we
devise a variant of the problem, the Quantum Padded Coupon Collector. We prove
that its sample complexity matches that of the classical Coupon Collector
problem for both modes of learning, thereby exhibiting the same asymptotic
separation between proper and improper quantum learning as mentioned above. The
techniques we develop in the process can be directly applied to any form of
padded quantum data. We hope that padding can more generally lift other forms
of classical learning behaviour to the quantum setting.


  GINGER is a proposed tridimensional array of laser gyroscopes with the aim of
measuring the Lense-Thirring effect, predicted by the General Relativity
theory, in a terrestrial laboratory environment. We discuss the required
accuracy, the methods to achieve it, and the preliminary experimental work in
this direction.


  In a large E-commerce platform, all the participants compete for impressions
under the allocation mechanism of the platform. Existing methods mainly focus
on the short-term return based on the current observations instead of the
long-term return. In this paper, we formally establish the lifecycle model for
products, by defining the introduction, growth, maturity and decline stages and
their transitions throughout the whole life period. Based on such model, we
further propose a reinforcement learning based mechanism design framework for
impression allocation, which incorporates the first principal component based
permutation and the novel experiences generation method, to maximize short-term
as well as long-term return of the platform. With the power of trial-and-error,
it is possible to optimize impression allocation strategies globally which is
contribute to the healthy development of participants and the platform itself.
We evaluate our algorithm on a simulated environment built based on one of the
largest E-commerce platforms, and a significant improvement has been achieved
in comparison with the baseline solutions.


  The incredible feats of athleticism demonstrated by humans are made possible
in part by a vast repertoire of general-purpose motor skills, acquired through
years of practice and experience. These skills not only enable humans to
perform complex tasks, but also provide powerful priors for guiding their
behaviors when learning new tasks. This is in stark contrast to what is common
practice in physics-based character animation, where control policies are most
typically trained from scratch for each task. In this work, we present a
large-scale data-driven framework for learning versatile and reusable skill
embeddings for physically simulated characters. Our approach combines
techniques from adversarial imitation learning and unsupervised reinforcement
learning to develop skill embeddings that produce life-like behaviors, while
also providing an easy to control representation for use on new downstream
tasks. Our models can be trained using large datasets of unstructured motion
clips, without requiring any task-specific annotation or segmentation of the
motion data. By leveraging a massively parallel GPU-based simulator, we are
able to train skill embeddings using over a decade of simulated experiences,
enabling our model to learn a rich and versatile repertoire of skills. We show
that a single pre-trained model can be effectively applied to perform a diverse
set of new tasks. Our system also allows users to specify tasks through simple
reward functions, and the skill embedding then enables the character to
automatically synthesize complex and naturalistic strategies in order to
achieve the task objectives.


  We report on eight millisecond pulsars (MSPs) in binary systems discovered
with the Arecibo PALFA survey. Phase-coherent timing solutions derived from 2.5
to 5 years of observations carried out at Arecibo and Jodrell Bank
observatories are provided. PSR J1921+1929 is a 2.65-ms pulsar in a 39.6-day
orbit for which we detect $\gamma$-ray pulsations in archival Fermi data. PSR
J1928+1245 is a very low-mass-function system with an orbital period of 3.3
hours that belongs to the non-eclipsing black widow population. We also present
PSR J1932+1756, the longest-orbital-period (41.5 days) intermediate-mass binary
pulsar known to date. In light of the numerous discoveries of binary MSPs over
the past years, we characterize the Galactic distribution of known MSP binaries
in terms of binary class. Our results support and strengthen previous claims
that the scatter in the Galactic scale height distribution correlates inversely
with the binary mass function. We provide evidence of observational biases
against detecting the most recycled pulsars near the Galactic plane, which
overestimates the scale height of lighter systems. A possible bimodality in the
mass function of MSPs with massive white dwarfs is also reported.


  The purpose of this paper is to present series expansions for even powers of
the number $\pi$. This is accomplished by generalizing Euler's method for
solving the Basel Problem, which was published in 1735. We employ elementary
symmetric polynomials, transform them into nested sums, and thereby derive nice
series formulas for even powers of the number $\pi$ such as \[
\frac{\pi^2}{3!}= \sum_{\ell_1=1}^\infty\frac{1}{\ell_1^2} \;;\quad\quad
\frac{\pi^4}{5!} = \sum_{\ell_2=2}^\infty \sum_{\ell_1=1}^{\ell_2-1}
\frac{1}{\ell_1^2\cdot\ell_2^2} \;;\quad\quad \frac{\pi^6}{7!}=
\sum_{\ell_3=3}^\infty \sum_{\ell_2=2}^{\ell_3-1}
\sum_{\ell_1=1}^{\ell_2-1}\frac{1}{\ell_1^2\cdot\ell_2^2\cdot\ell_3^2}
\;;\quad\cdots \] Many of these formulas do not seem to be widely known.
  --
  In dieser Abhandlung stellen wir ein Verfahren vor, das die Berechnung von
Reihen f\"ur geradzahlige Potenzen von $\pi$ erm\"oglicht. Die Grundidee ist
eine Verallgemeinerung des Verfahrens von Euler, mit dem er 1735 das Basler
Problem l\"oste. Wir stellen elementar-symmetrische Polynome durch mehrfach
verschachtelte Summen dar und leiten davon Reihen f\"ur geradzahlige Potenzen
der Kreiszahl $\pi$ ab. Die meisten der angegebenen Reihen scheinen nicht so
bekannt zu sein.


  A state space representation of an environment is a classic and yet powerful
tool used by many autonomous robotic systems for efficient and often optimal
solution planning. However, designing these representations with high
performance is laborious and costly, necessitating an effective and versatile
tool for autonomous generation of state spaces for autonomous robots. We
present a novel state space exploration algorithm by extending the original
surprise-based partially-observable Markov Decision Processes (sPOMDP), and
demonstrate its effective long-term exploration planning performance in various
environments. Through extensive simulation experiments, we show the proposed
model significantly increases efficiency and scalability of the original sPOMDP
learning techniques with a range of 31-63% gain in training speed while
improving robustness in environments with less deterministic transitions. Our
results pave the way for extending sPOMDP solutions to a broader set of
environments.


  The analyzing powers in proton-deuteron elastic and proton-neutron
quasi-elastic scattering have been measured at small angles using a polarized
proton beam at the COSY storage ring incident on an unpolarized deuterium
target. The data were taken at 796MeV and five higher energies from 1600MeV to
2400MeV. The analyzing power in pd elastic scattering was studied by detecting
the low energy recoil deuteron in telescopes placed symmetrically in the COSY
plane to the left and right of the beam whereas for pn quasi-elastic scattering
a low energy proton was registered in one of the telescopes in coincidence with
a fast scattered proton measured in the ANKE magnetic spectrometer. Though the
experiment explores new domains, the results are consistent with the limited
published information.


  The problem of modeling and control of multi-terminal high-voltage
direct-current transmission systems is addressed in this paper, which contains
five main contributions. First, to propose a unified, physically motivated,
modeling framework - based on port-Hamiltonian representations - of the various
network topologies used in this application. Second, to prove that the system
can be globally asymptotically stabilized with a decentralized PI control, that
exploits its passivity properties. Close connections between the proposed PI
and the popular Akagi's PQ instantaneous power method are also established.
Third, to reveal the transient performance limitations of the proposed
controller that, interestingly, is shown to be intrinsic to PI passivity-based
control. Fourth, motivated by the latter, an outer-loop that overcomes the
aforementioned limitations is proposed. The performance limitation of the PI,
and its drastic improvement using outer-loop controls, are verified via
simulations on a three-terminals benchmark example. A final contribution is a
novel formulation of the power flow equations for the centralized references
calculation.


  Two-dimensional electrons in AlAs quantum wells occupy multiple
conduction-band minima at the X- points of the Brillouin zone. These valleys
have large effective mass and g-factor compared to the stan-dard GaAs
electrons, and are also highly anisotropic. With proper choice of well width
and by applying symmetry-breaking strain in the plane, one can control the
occupation of different valleys thus rendering a system with tuneable effective
mass, g-factor, Fermi contour anisotropy, and valley degeneracy. Here we review
some of the rich physics that this system has allowed us to explore.


  We present the Carath\'eodory-Reiffen metric and the inner Carath\'eodory
distance on generalized parabolas. It turns out that on such parabolas the
Carath\'eodory distance is not inner.


  Relaxation to a thermal state is the inevitable fate of non-equilibrium
interacting quantum systems without special conservation laws. While
thermalization in one-dimensional (1D) systems can often be suppressed by
integrability mechanisms, in two spatial dimensions thermalization is expected
to be far more effective due to the increased phase space. In this work we
propose a general framework for escaping or delaying the emergence of the
thermal state in two-dimensional (2D) arrays of Rydberg atoms via the mechanism
of quantum scars, i.e. initial states that fail to thermalize. The suppression
of thermalization is achieved in two complementary ways: by adding local
perturbations or by adjusting the driving Rabi frequency according to the local
connectivity of the lattice. We demonstrate that these mechanisms allow to
realize robust quantum scars in various two-dimensional lattices, including
decorated lattices with non-constant connectivity. In particular, we show that
a small decrease of the Rabi frequency at the corners of the lattice is crucial
for mitigating the strong boundary effects in two-dimensional systems. Our
results identify synchronization as an important tool for future experiments on
two-dimensional quantum scars.


  In this article, we shall develop and formulate two novel viewpoints and
properties concerning the three-point functions at weak coupling in the SU(2)
sector of the N = 4 super Yang-Mills theory. One is a double spin-chain
formulation of the spin-chain and the associated new interpretation of the
operation of Wick contraction. It will be regarded as a skew symmetric pairing
which acts as a projection onto a singlet in the entire SO(4) sector, instead
of an inner product in the spin-chain Hilbert space. This formalism allows us
to study a class of three-point functions of operators built upon more general
spin-chain vacua than the special configuration discussed so far in the
literature. Furthermore, this new viewpoint has the signicant advantage over
the conventional method: In the usual "tailoring" operation, the Wick
contraction produces inner products between off-shell Bethe states, which
cannot be in general converted into simple expressions. In contrast, our
procedure directly produces the so-called partial domain wall partition
functions, which can be expressed as determinants. Using this property, we
derive simple determinantal representation for a broader class of three-point
functions. The second new property uncovered in this work is the non-trivial
identity satisfied by the three-point functions with monodromy operators
inserted. Generically this relation connects three-point functions of different
operators and can be regarded as a kind of Schwinger-Dyson equation. In
particular, this identity reduces in the semiclassical limit to the triviality
of the product of local monodromies around the vertex operators, which played a
crucial role in providing all important global information on the three-point
function in the strong coupling regime. This structure may provide a key to the
understanding of the notion of "integrability" beyond the spectral level.


  We study the prospects of measuring the CKM matrix element $\vert
V_{ts}\vert$ at the LHC with the top quarks produced in the processes $p p \to
t\bar{t}X$ and $p p \to t/\bar{t} X$, and the subsequent decays $t \to W^+s$
and $\bar{t} \to W^- \bar{s}$. We insist on tagging the $W^\pm$ leptonically,
$W^\pm \to \ell^\pm \nu_\ell$ ($\ell =e, \mu, \tau$), and analyse the
anticipated jet profiles in the signal process $t \to W s$ and the dominant
background from the decay $t \to W b$. To that end, we analyse the $V0$ ($K^0$
and $\Lambda$) distributions in the $s$- and $b$-quark jets concentrating on
the energy and transverse momentum distributions of these particles. The $V0$s
emanating from the $t \to W b$ branch have displaced decay vertexes from the
interaction point due to the weak decays $b \to c \to s$ and the $b$-quark jets
are rich in charged leptons. Hence, the absence of secondary vertexes and of
the energetic charged leptons in the jet provide additional ($b$-jet vs.
$s$-jet) discrimination in top quark decays. These distributions are used to
train a boosted decision tree (BDT). Using the BDT classifier, and a variant of
it called BDTD, which makes use of decorrelated variables, we calculate the
BDT(D)-response functions corresponding to the signal ($t \to W s$) and
background ($t \to W b$). Detailed simulations undertaken by us with the Monte
Carlo generator PYTHIA are used to estimate the background rejection versus
signal efficiency for three representative LHC energies $\sqrt{s}=7$ TeV, 10
TeV and 14 TeV. We argue that a benchmark with 10\% signal ($t \to W s $)
efficiency and a background ($t \to W b$) rejection by a factor $10^3$
(required due to the anticipated value of the ratio $\vert V_{ts}\vert^2/\vert
V_{tb} \vert^2 \simeq 1.6 \times 10^{-3}$) can be achieved at the LHC@14 TeV
with an integrated luminosity of 10 fb$^{-1}$.


  We provide a lower complexity bound for the satisfiability problem of a
multi-agent justification logic, establishing that the general NEXP upper bound
from our previous work is tight. We then use a simple modification of the
corresponding reduction to prove that satisfiability for all multi-agent
justification logics from there is hard for the Sigma 2 p class of the second
level of the polynomial hierarchy - given certain reasonable conditions. Our
methods improve on these required conditions for the same lower bound for the
single-agent justification logics, proven by Buss and Kuznets in 2009, thus
answering one of their open questions.


  We present Sampled Weighted Min-Hashing (SWMH), a randomized approach to
automatically mine topics from large-scale corpora. SWMH generates multiple
random partitions of the corpus vocabulary based on term co-occurrence and
agglomerates highly overlapping inter-partition cells to produce the mined
topics. While other approaches define a topic as a probabilistic distribution
over a vocabulary, SWMH topics are ordered subsets of such vocabulary.
Interestingly, the topics mined by SWMH underlie themes from the corpus at
different levels of granularity. We extensively evaluate the meaningfulness of
the mined topics both qualitatively and quantitatively on the NIPS (1.7 K
documents), 20 Newsgroups (20 K), Reuters (800 K) and Wikipedia (4 M) corpora.
Additionally, we compare the quality of SWMH with Online LDA topics for
document representation in classification.


  Kagome Materials with flat bands exhibit wildly different physical properties
depending on symmetry group, and electron number. For the case of ScV$_6$Sn$_6$
in space group 191, we investigate the existence of a charge density wave (CDW)
at vector $\bar{K}=(\frac{1}{3},\frac{1}{3},\frac{1}{3})$ and its relationship
with the phonon behavior. The experimental findings reveal a $\sim$95K CDW
without nesting/peaks in the electron susceptibility at $\bar{K}$. Notably,
ScV$_6$Sn$_6$ exhibits a collapsed phonon mode at
$H=(\frac{1}{3},\frac{1}{3},\frac{1}{2})$ and an imaginary flat phonon band in
the vicinity of $H$. The soft phonon is attributed to triangular Sn ($Sn^T$)
mirror-even vibrations along the $z$-direction. We develop a simple force
constant model to describe the entire soft phonon dispersion. By employing a
new (Gaussian) approximation of the hopping parameter, we demonstrate the
renormalization of the phonon frequency and the consequent collapse of the $H$
phonon. Additionally, we propose an effective model with two order parameters
(OPs) to explain the appearance of the CDW at $\bar{K}$, which competes with
the collapsed phonon at $H$. Through comparisons with experimental data, we
show that the $H$ OP undergoes a second-order phase transition while exhibiting
substantial fluctuations, ultimately inducing the first-order transition of the
$\bar{K}$ OP. Furthermore, we extend our analysis to the similar compound
YV$_6$Sn$_6$, which lacks a CDW phase, attributing this difference to the
participation of the heavier Y atom in the out-of-plane phonon behavior. Our
comprehensive study not only elucidates the CDW in ScV$_6$Sn$_6$ but also
presents a significant advancement in modeling complex electronic systems,
fostering collaborations between ab-initio simulations and analytical
approaches.


  Topic modeling is pivotal in discerning hidden semantic structures within
texts, thereby generating meaningful descriptive keywords. While innovative
techniques like BERTopic and Top2Vec have recently emerged in the forefront,
they manifest certain limitations. Our analysis indicates that these methods
might not prioritize the refinement of their clustering mechanism, potentially
compromising the quality of derived topic clusters. To illustrate, Top2Vec
designates the centroids of clustering results to represent topics, whereas
BERTopic harnesses C-TF-IDF for its topic extraction.In response to these
challenges, we introduce "TF-RDF" (Term Frequency - Relative Document
Frequency), a distinctive approach to assess the relevance of terms within a
document. Building on the strengths of TF-RDF, we present MPTopic, a clustering
algorithm intrinsically driven by the insights of TF-RDF. Through comprehensive
evaluation, it is evident that the topic keywords identified with the synergy
of MPTopic and TF-RDF outperform those extracted by both BERTopic and Top2Vec.


  This paper explores the influence of integrating the purpose of the
translation and the target audience into prompts on the quality of translations
produced by ChatGPT. Drawing on previous translation studies, industry
practices, and ISO standards, the research underscores the significance of the
pre-production phase in the translation process. The study reveals that the
inclusion of suitable prompts in large-scale language models like ChatGPT can
yield flexible translations, a feat yet to be realized by conventional Machine
Translation (MT). The research scrutinizes the changes in translation quality
when prompts are used to generate translations that meet specific conditions.
The evaluation is conducted from a practicing translator's viewpoint, both
subjectively and qualitatively, supplemented by the use of OpenAI's word
embedding API for cosine similarity calculations. The findings suggest that the
integration of the purpose and target audience into prompts can indeed modify
the generated translations, generally enhancing the translation quality by
industry standards. The study also demonstrates the practical application of
the "good translation" concept, particularly in the context of marketing
documents and culturally dependent idioms.


  Side-channel risks of Intel's SGX have recently attracted great attention.
Under the spotlight is the newly discovered page-fault attack, in which an
OS-level adversary induces page faults to observe the page-level access
patterns of a protected process running in an SGX enclave. With almost all
proposed defense focusing on this attack, little is known about whether such
efforts indeed raise the bar for the adversary, whether a simple variation of
the attack renders all protection ineffective, not to mention an in-depth
understanding of other attack surfaces in the SGX system. In the paper, we
report the first step toward systematic analyses of side-channel threats that
SGX faces, focusing on the risks associated with its memory management. Our
research identifies 8 potential attack vectors, ranging from TLB to DRAM
modules. More importantly, we highlight the common misunderstandings about SGX
memory side channels, demonstrating that high frequent AEXs can be avoided when
recovering EdDSA secret key through a new page channel and fine-grained
monitoring of enclave programs (at the level of 64B) can be done through
combining both cache and cross-enclave DRAM channels. Our findings reveal the
gap between the ongoing security research on SGX and its side-channel
weaknesses, redefine the side-channel threat model for secure enclaves, and can
provoke a discussion on when to use such a system and how to use it securely.


  We derive a new Chambers-type formula and prove sharper upper bounds on the
measure of the spectrum of critical almost Mathieu operators with rational
frequencies.


  In this paper, we investigate the existence of fractional revival on Cayley
graphs over finite abelian groups. We give a necessary and sufficient condition
for Cayley graphs over finite abelian groups to have fractional revival. As
applications, the existence of fractional revival on circulant graphs and
cubelike graphs are characterized.


  We analyse Raman-induced self-frequency shift in two-component solitons
supported by both quadratic and cubic nonlinearities. Treating Raman terms as a
perturbation, we derive expressions for soliton velocity and frequency shifts
of the fundamental frequency and second harmonic soliton components. We find
these predictions compare well with simulations of soliton propagation. We also
show that Raman shift can cause two-component solitons to approach the boundary
of their own existence and subsequently trigger soliton instabilities. In some
cases these instabilities are accompanied by an almost complete transfer of
power to the second harmonic, and emergence of a single-component Kerr
solitonic pulse.


  It is shown that solvable mixed spin ladder models can be constructed from
su(N) permutators. Heisenberg rung interactions appear as chemical potential
terms in the Bethe Ansatz solution. Explicit examples given are a mixed
spin-1/2 spin-1 ladder, a mixed spin-1/2 spin-3/2 ladder and a spin-1 ladder
with biquadratic interactions.


  The asymmetric shape of the nebula around $\eta$-Carinae (Homunculus) can be
explained by a spherical expansion in a non-homogeneous medium. Two models are
analyzed: an exponential and an inverse power law dependence for the density as
a function of distance from the equatorial plane. The presence of a medium with
variable density along the polar direction progressively converts the original
spherical shell into a bipolar nebula. In the case of the nebula around
$\eta$-Carinae, we know the time elapsed since the great outburst in 1840. An
exact match between observed radii and velocities can be obtained by fine
tuning the parameters involved, such as initial radius, initial velocity and
the typical scale that characterizes the gradient in density. The observed
radius and velocity of the Homunculus as a function of the polar angle in
spherical coordinates can be compared with the corresponding simulated data by
introducing the efficiency in a single or multiple directions. Once the 3D
spatial structure of the Homunculus is obtained, we can compose the image by
integrating along the line of sight. In order to simulate the observed image,
we have considered a bipolar nebula with constant thickness and an optically
thin emitting layer. Some simulated cuts of the relative intensity are reported
and may represent a useful reference for the astronomical cuts.


  We investigate room temperature lasing of terahertz quantum cascade lasers
using quantum dot chains as active material suitable for wireless communication
and imaging technologies. Bandstructure calculations for such extended systems
of coupled quantum dots are made possible by a novel 'linear combination of
quantum dot orbitals'--method, based on single quantum dot wavefunctions. Our
results demonstrate strong vertical-emission of coupled quantum dots, reduced
phonon coupling and in-plane scattering, enabling room-temperature lasing with
significantly reduced threshold current densities.


  We derive a canonical analysis of a double null 2+2 Hamiltonian description
of General Relativity in terms of complex self-dual 2-forms and the associated
SO(3) connection variables. The algebra of first class constraints is obtained
and forms a Lie algebra that consists of two constraints that generate
diffeomorphisms in the two surface, a constraint that generates diffeomorphisms
along the null generators and a constraint that generates self-dual spin and
boost transformations.


  Transfer learning across different reinforcement learning (RL) tasks is
becoming an increasingly valuable area of research. We consider a goal-based
multi-task RL framework and mechanisms by which previously solved tasks can
reduce sample complexity and regret when the agent is faced with a new task.
Specifically, we introduce two metrics on the state space that encode notions
of traversibility of the state space for an agent. Using these metrics a
topological covering is constructed by way of a set of landmark states in a
fully self-supervised manner. We show that these landmark coverings confer
theoretical advantages for transfer learning within the goal-based multi-task
RL setting. Specifically, we demonstrate three mechanisms by which landmark
coverings can be used for successful transfer learning. First, we extend the
Landmark Options Via Reflection (LOVR) framework to this new topological
covering; second, we use the landmark-centric value functions themselves as
features and define a greedy zombie policy that achieves near oracle
performance on a sequence of zero-shot transfer tasks; finally, motivated by
the second transfer mechanism, we introduce a learned reward function that
provides a more dense reward signal for goal-based RL. Our novel topological
landmark covering confers beneficial theoretical results, bounding the Q values
at each state-action pair. In doing so, we introduce a mechanism that performs
action-pruning at infeasible actions which cannot possibly be part of an
optimal policy for the current goal.


  A toy model (suggested by Klauder) is analyzed from the perspective of First
Class and Second Class Dirac constrained systems. The comparison is made by
turning a First Class into a Second Class system with the introduction of
suitable auxiliary conditions. The links between Dirac's system of constraints,
the Faddeev-Popov canonical functional integral method and the Maskawa-Nakajima
procedure to reduced phase space are explicitly illustrated. The model reveals
stark contrasts and physically distinguishable results between First and Second
class routes. Physically relevant systems such as the relativistic point
particle and electrodynamics are briefly recapped. Besides its pedagogical
value, the article also advocates the route of rendering First Class into
Second Class systems prior to quantization. Second Class systems lead to
well-defined reduced phase space and physical observables; absence of
inconsistencies in the closure of quantum constraint algebra; and consistent
promotion of fundamental Dirac brackets to quantum commutators. As First Class
systems can be turned into well-defined Second Class ones, this has
implications for the soundness of "Dirac quantization" of First Class
constrained systems by simple promotion of Poisson, rather than Dirac brackets,
to commutators without proceeding through Second Class procedures.


  Automatic speaker recognition algorithms typically use pre-defined
filterbanks, such as Mel-Frequency and Gammatone filterbanks, for
characterizing speech audio. However, it has been observed that the features
extracted using these filterbanks are not resilient to diverse audio
degradations. In this work, we propose a deep learning-based technique to
deduce the filterbank design from vast amounts of speech audio. The purpose of
such a filterbank is to extract features robust to non-ideal audio conditions,
such as degraded, short duration, and multi-lingual speech. To this effect, a
1D convolutional neural network is designed to learn a time-domain filterbank
called DeepVOX directly from raw speech audio. Secondly, an adaptive triplet
mining technique is developed to efficiently mine the data samples best suited
to train the filterbank. Thirdly, a detailed ablation study of the DeepVOX
filterbanks reveals the presence of both vocal source and vocal tract
characteristics in the extracted features. Experimental results on VOXCeleb2,
NIST SRE 2008, 2010 and 2018, and Fisher speech datasets demonstrate the
efficacy of the DeepVOX features across a variety of degraded, short duration,
and multi-lingual speech. The DeepVOX features also shown to improve the
performance of existing speaker recognition algorithms, such as the
xVector-PLDA and the iVector-PLDA.


  In a recent work we introduced a nonlocal version of the
Nambu--Jona-Lasinio(NJL) model that was designed to generate a quark
self-energy in Euclidean space that was similar to that obtained in lattice
simulations of QCD. In the present work we carry out related calculations in
Minkowski space, so that we can study the effects of the significant vector and
axial-vector interactions that appear in extended NJL models and which play an
important role in the study of the $\rho$, $\omega$ and $a_1$ mesons. We study
the modification of the quark self-energy in the presence of matter and find
that our model reproduces the behavior of the quark condensate predicted by the
model-independent relation $<\bar qq>_{\rho} = <\bar
qq>_0(1-\sigma_N\rho_N/f_{\pi}^2m_{\pi}^2 +...)$, where $\sigma_N$ is the
pion-nucleon sigma term and $\rho_N$ is the density of nuclear matter. (Since
we do not include a model of confinement, our study is restricted to the
analysis of quark matter. We provide some discussion of the modification of the
above formula for quark matter.) The inclusion of a quark current mass leads to
a second-order phase transition for the restoration of chiral symmetry. That
restoration is about 80% at twice nuclear matter density for the model
considered in this work. We also find that the part of the quark self-energy
that is explicitly dependent upon density has a strong negative Lorentz-scalar
term and a strong positive Lorentz-vector term, which is analogous to the
self-energy found for the nucleon in nuclear matter when one makes use of the
Dirac equation for the nucleon. In this work we calculate the nucleon self
-energy in nuclear matter using our model of the quark self-energy and obtain
satisfactory results.


  Neutron stars close to the Galactic center are expected to swim in a dense
background of dark matter. For models in which the dark matter has efficient
interactions with neutrons, they are expected to accumulate their own local
cloud of dark matter, making them appealing targets for observations seeking
signs of dark matter annihilation. For theories with very light mediators, the
dark matter may annihilate into pairs of mediators which are sufficiently
long-lived to escape the star and decay outside it into neutrinos. We examine
the bounds on the parameter space of heavy ($\sim$TeV to $\sim$PeV) dark matter
theories with long-lived mediators decaying into neutrinos based on
observations of high energy neutrino observatories, and make projections for
the future. We find that these observations provide information that is
complementary to terrestrial searches, and probe otherwise inaccessible regimes
of dark matter parameter space.


  We study theoretically nonequilibrium noise in the fractional quantum Hall
regime for an Aharonov Bohm ring which has a third contact in the middle of the
ring. We show that as a consequence of their fractional statistics the
tunneling of a Laughlin quasiparticle between the inner and outer edge of the
ring changes the effective Aharonov Bohm flux experienced by quasiparticles
going around the ring, leading to a change in the conductance across the ring.
A small current in the middle contact therefore gives rise to fluctuations in
the current flowing across the ring which resemble random telegraph noise. We
analyze this noise using the chiral Luttinger liquid model. At low frequencies
the telegraph noise varies inversely with the quasiparticle tunneling current,
and can be much larger than the shot noise. We propose that combining the
Aharonov Bohm effect with a noise measurement provides a direct method for
observing fractional statistics.


  In this paper, we address the problem of designing an experimental plan with
both discrete and continuous factors under fairly general parametric
statistical models. We propose a new algorithm, named ForLion, to search for
locally optimal approximate designs under the D-criterion. The algorithm
performs an exhaustive search in a design space with mixed factors while
keeping high efficiency and reducing the number of distinct experimental
settings. Its optimality is guaranteed by the general equivalence theorem. We
present the relevant theoretical results for multinomial logit models (MLM) and
generalized linear models (GLM), and demonstrate the superiority of our
algorithm over state-of-the-art design algorithms using real-life experiments
under MLM and GLM. Our simulation studies show that the ForLion algorithm could
reduce the number of experimental settings by 25% or improve the relative
efficiency of the designs by 17.5% on average. Our algorithm can help the
experimenters reduce the time cost, the usage of experimental devices, and thus
the total cost of their experiments while preserving high efficiencies of the
designs.


  The first step in any NLP pipeline is to split the text into individual
tokens. The most obvious and straightforward approach is to use words as
tokens. However, given a large text corpus, representing all the words is not
efficient in terms of vocabulary size. In the literature, many tokenization
algorithms have emerged to tackle this problem by creating subwords which in
turn limits the vocabulary size in a given text corpus. Most tokenization
techniques are language-agnostic i.e they don't incorporate the linguistic
features of a given language. Not to mention the difficulty of evaluating such
techniques in practice. In this paper, we introduce three new tokenization
algorithms for Arabic and compare them to three other baselines using
unsupervised evaluations. In addition to that, we compare all the six
algorithms by evaluating them on three supervised classification tasks which
are sentiment analysis, news classification and poetry classification using six
publicly available datasets. Our experiments show that none of the tokenization
technique is the best choice overall and that the performance of a given
tokenization algorithm depends on the size of the dataset, type of the task,
and the amount of morphology that exists in the dataset. However, some
tokenization techniques are better overall as compared to others on various
text classification tasks.


  Euclidean spanners are important geometric structures, having found numerous
applications over the years. Cornerstone results in this area from the late 80s
and early 90s state that for any $d$-dimensional $n$-point Euclidean space,
there exists a $(1+\epsilon)$-spanner with $nO(\epsilon^{-d+1})$ edges and
lightness $O(\epsilon^{-2d})$. Surprisingly, the fundamental question of
whether or not these dependencies on $\epsilon$ and $d$ for small $d$ can be
improved has remained elusive, even for $d = 2$. This question naturally arises
in any application of Euclidean spanners where precision is a necessity.
  The state-of-the-art bounds $nO(\epsilon^{-d+1})$ and $O(\epsilon^{-2d})$ on
the size and lightness of spanners are realized by the {\em greedy} spanner. In
2016, Filtser and Solomon proved that, in low dimensional spaces, the greedy
spanner is near-optimal. The question of whether the greedy spanner is truly
optimal remained open to date.
  The contribution of this paper is two-fold. We resolve these longstanding
questions by nailing down the exact dependencies on $\epsilon$ and $d$ and
showing that the greedy spanner is truly optimal. Specifically, for any $d=
O(1), \epsilon = \Omega({n}^{-\frac{1}{d-1}})$:
  - We show that any $(1+\epsilon)$-spanner must have $n
\Omega(\epsilon^{-d+1})$ edges, implying that the greedy (and other) spanners
achieve the optimal size.
  - We show that any $(1+\epsilon)$-spanner must have lightness
$\Omega(\epsilon^{-d})$, and then improve the upper bound on the lightness of
the greedy spanner from $O(\epsilon^{-2d})$ to $O(\epsilon^{-d})$.
  We then complement our negative result for the size of spanners with a rather
counterintuitive positive result: Steiner points lead to a quadratic
improvement in the size of spanners! Our bound for the size of Steiner spanners
is tight as well (up to lower-order terms).


  Autonomous mobile robots deployed in urban environments must be
context-aware, i.e., able to distinguish between different semantic entities,
and robust to occlusions. Current approaches like semantic scene completion
(SSC) require pre-enumerating the set of classes and costly human annotations,
while representation learning methods relax these assumptions but are not
robust to occlusions and learn representations tailored towards auxiliary
tasks. To address these limitations, we propose LSMap, a method that lifts
masks from visual foundation models to predict a continuous, open-set semantic
and elevation-aware representation in bird's eye view (BEV) for the entire
scene, including regions underneath dynamic entities and in occluded areas. Our
model only requires a single RGBD image, does not require human labels, and
operates in real time. We quantitatively demonstrate our approach outperforms
existing models trained from scratch on semantic and elevation scene completion
tasks with finetuning. Furthermore, we show that our pre-trained representation
outperforms existing visual foundation models at unsupervised semantic scene
completion. We evaluate our approach using CODa, a large-scale, real-world
urban robot dataset. Supplementary visualizations, code, data, and pre-trained
models, will be publicly available soon.


  We present three schemes to go beyond the electric-dipole approximation in
X-ray absorption spectroscopy calculations within a four-component relativistic
framework. The first is based on the full semi-classical light-matter
interaction operator, and the two others on a truncated interaction within
Coulomb gauge (velocity representation) and multipolar gauge (length
representation). We generalize the derivation of multipolar gauge to an
arbitrary expansion point and show that the potentials corresponding to
different expansion point are related by a gauge transformation, provided the
expansion is not truncated. This suggests that the observed gauge-origin
dependence in multipolar gauge is more than just a finite-basis set effect. The
simplicity of the relativistic formalism enables arbitrary-order
implementations of the truncated interactions, with and without rotational
averaging, allowing us to test their convergence behavior numerically by
comparison to the full formulation. We confirm the observation that the
oscillator strength of the electric-dipole allowed ligand K-edge transition of
TiCl$_4$, when calculated to second order in the wave vector, become negative,
but also show that inclusion of higher-order contributions allows convergence
to the result obtained using the full light-matter interaction. However, at
higher energies, the slow convergence of such expansions becomes dramatic and
renders such approaches at best impractical. When going beyond the
electric-dipole approximation, we therefore recommend the use of the full
light-matter interaction.


  Spectral Clustering(SC) is a prominent data clustering technique of recent
times which has attracted much attention from researchers. It is a highly
data-driven method and makes no strict assumptions on the structure of the data
to be clustered. One of the central pieces of spectral clustering is the
construction of an affinity matrix based on a similarity measure between data
points. The way the similarity measure is defined between data points has a
direct impact on the performance of the SC technique. Several attempts have
been made in the direction of strengthening the pairwise similarity measure to
enhance the spectral clustering. In this work, we have defined a novel affinity
measure by employing the concept of non-conformity used in Conformal
Prediction(CP) framework. The non-conformity based affinity captures the
relationship between neighborhoods of data points and has the power to
generalize the notion of contextual similarity. We have shown that this
formulation of affinity measure gives good results and compares well with the
state of the art methods.


  We show that a positive linear map preserves local continuity (convergence)
of the entropy if and only if it preserves finiteness of the entropy, i.e.
transforms operators with finite entropy to operators with finite entropy. The
last property is equivalent to the boundedness of the output entropy of a map
on the set of pure states.


  We calculate theoretical isochrones in a consistent way for five filters (K,
K', Ks, F205W, and F222M) in the atmospheric window between 1.9 and 2.5 micron
using the Padova stellar evolutionary models by Girardi et al. Even when
displayed in the same Vega magnitude system, the near-infrared colors of the
same isochrone can differ by up to 0.18 mag at its bright end, depending on the
filter. We present magnitude transformations between K-band filters as a
function of color from H and K band filters. Isochrones with extinction at K of
up to 6 mag are also presented. We find that care is needed when comparing
extinction values that are estimated using different filter sets in the K band,
in particular when comparing those of atmospheric and space filter sets:
extinction values for space filters can be in error by up to 0.3 mag. To reduce
this error, we introduce an "effective extinction slope" for each filter set
and isochrone model, which describes the extinction behavior of isochrones in
the color-magnitude diagram more correctly than the actual extinction law. Our
calculation also suggests that the extinction law implied by the observations
of Rieke et al. for wavelengths between the H and K bands is better described
by a power-law function with an exponent of 1.61 instead of 1.55, which is
commonly used with an assumption that the transmission functions of H and K
filters are Dirac delta functions.


  We study the distinguishability of a particular type of maximally entangled
states -- the "lattice states" using a new approach of semidefinite program.
With this, we successfully construct all sets of four ququad-ququad orthogonal
maximally entangled states that are locally indistinguishable and find some
curious sets of six states having interesting property of distinguishability.
Also, some of the problems arose from \cite{CosentinoR14} about the
PPT-distinguishability of "lattice" maximally entangled states can be answered.


  We propose a "matter-antimatter coexistence method" for finite-density
lattice QCD, aiming at a possible solution of the sign problem. In this method,
we consider matter and anti-matter systems on two parallel ${\bf R}^4$-sheets
in five-dimensional Euclidean space-time. For the matter system $M$ with a
chemical potential $\mu \in {\bf C}$ on a ${\bf R}^4$-sheet, we also prepare
the anti-matter system $\bar M$ with $-\mu^*$ on the other ${\bf R}^4$-sheet
shifted in the fifth direction. In the lattice QCD formalism, we introduce a
correlation term between the gauge variables $U_\nu \equiv e^{iagA_\nu}$ in $M$
and $\tilde U_\nu \equiv e^{iag \tilde A_\nu}$ in $\bar M$, such as $S_\lambda
\equiv \sum_{x,\nu} 2\lambda \{N_c-{\rm Re~tr} [U_\nu(x) \tilde
U_\nu^\dagger(x)]\} \simeq \sum_x \frac{1}{2}\lambda a^2 \{A_\nu^a(x)-\tilde
A_\nu^a(x)\}^2$ with a real parameter $\lambda$. In the limit of $\lambda
\rightarrow \infty$, a strong constraint $\tilde U_\nu(x)=U_\nu(x)$ is
realized, and the total fermionic determinant is real and non-negative. In the
limit of $\lambda \rightarrow 0$, this system goes to two separated ordinary
QCD systems with the chemical potential of $\mu$ and $-\mu^*$. On a
finite-volume lattice, if one takes an enough large value of $\lambda$, $\tilde
U_\nu(x) \simeq U_\nu(x)$ is realized and there occurs a phase cancellation
approximately between two fermionic determinants in $M$ and $\bar M$, which is
expected to suppress the sign problem and to make the lattice calculation
possible. For the obtained gauge configurations of the coexistence system,
matter-side quantities are evaluated through their measurement only for the
matter part $M$. By the calculations with gradually decreasing $\lambda$ and
their extrapolation to $\lambda=0$, physical quantities in finite density QCD
are expected to be estimated.


  A critical component in knowledge distillation is the means of coupling the
teacher and student. The predominant sequence knowledge distillation method
involves supervised learning of the student against teacher-decoded outputs,
and is exemplified by the current state of the art, which incorporates minimum
Bayes risk (MBR) decoding. In this paper we seek to integrate MBR more tightly
in distillation training, specifically by using several high scoring MBR
translations, rather than a single selected sequence, thus capturing a rich
diversity of teacher outputs. Our experiments on English to German and English
to Japanese translation show consistent improvements over strong baseline
methods for both tasks and with varying model sizes. Additionally, we conduct a
detailed analysis focusing on data efficiency and capacity curse aspects to
elucidate MBR-n and explore its further potential.


  Emotion recognition in conversation (ERC) has attracted much attention in
recent years for its necessity in widespread applications. Existing ERC methods
mostly model the self and inter-speaker context separately, posing a major
issue for lacking enough interaction between them. In this paper, we propose a
novel Speaker and Position-Aware Graph neural network model for ERC (S+PAGE),
which contains three stages to combine the benefits of both Transformer and
relational graph convolution network (R-GCN) for better contextual modeling.
Firstly, a two-stream conversational Transformer is presented to extract the
coarse self and inter-speaker contextual features for each utterance. Then, a
speaker and position-aware conversation graph is constructed, and we propose an
enhanced R-GCN model, called PAG, to refine the coarse features guided by a
relative positional encoding. Finally, both of the features from the former two
stages are input into a conditional random field layer to model the emotion
transfer.


  Processing data streams arriving at high speed requires the development of
models that can provide fast and accurate predictions. Although deep neural
networks are the state-of-the-art for many machine learning tasks, their
performance in real-time data streaming scenarios is a research area that has
not yet been fully addressed. Nevertheless, there have been recent efforts to
adapt complex deep learning models for streaming tasks by reducing their
processing rate. The design of the asynchronous dual-pipeline deep learning
framework allows to predict over incoming instances and update the model
simultaneously using two separate layers. The aim of this work is to assess the
performance of different types of deep architectures for data streaming
classification using this framework. We evaluate models such as multi-layer
perceptrons, recurrent, convolutional and temporal convolutional neural
networks over several time-series datasets that are simulated as streams. The
obtained results indicate that convolutional architectures achieve a higher
performance in terms of accuracy and efficiency.


  Using a nondegenerate four-wave mixing process based on a double-$\Lambda$
scheme in hot cesium vapor, we generate quantum correlated twin beams with a
maximum intensity-difference squeezing of 6.5 dB. The substantially improved
squeezing can be mainly attributed to very good frequency and phase-difference
stability between the pump and probe beams in our experiment.
Intensity-difference squeezing can be observed within a wide experimental
parameter range, which guarantees its robust generation. Since this scheme
produces multi-spatial-mode twin beams at the Cs $D_{1}$ line, it is of
interest for experiments involving quantum imaging and coherent interfaces
between atomic and solid-state systems.


  Humans draw to facilitate reasoning: we draw auxiliary lines when solving
geometry problems; we mark and circle when reasoning on maps; we use sketches
to amplify our ideas and relieve our limited-capacity working memory. However,
such actions are missing in current multimodal language models (LMs). Current
chain-of-thought and tool-use paradigms only use text as intermediate reasoning
steps. In this work, we introduce Sketchpad, a framework that gives multimodal
LMs a visual sketchpad and tools to draw on the sketchpad. The LM conducts
planning and reasoning according to the visual artifacts it has drawn.
Different from prior work, which uses text-to-image models to enable LMs to
draw, Sketchpad enables LMs to draw with lines, boxes, marks, etc., which is
closer to human sketching and better facilitates reasoning. Sketchpad can also
use specialist vision models during the sketching process (e.g., draw bounding
boxes with object detection models, draw masks with segmentation models), to
further enhance visual perception and reasoning. We experiment with a wide
range of math tasks (including geometry, functions, graphs, and chess) and
complex visual reasoning tasks. Sketchpad substantially improves performance on
all tasks over strong base models with no sketching, yielding an average gain
of 12.7% on math tasks, and 8.6% on vision tasks. GPT-4o with Sketchpad sets a
new state of the art on all tasks, including V*Bench (80.3%), BLINK spatial
reasoning (83.9%), and visual correspondence (80.8%). All codes and data are in
https://visualsketchpad.github.io/.


  We study the holographic complexity of a pair of asymptotically dS universes
in the presence of axion matter, to characterize these observables in more
general spacetimes. The system is prepared in a two-copy Hartle-Hawking state
by slicing an Euclidean wormhole, which entangles the two universes. We derive
the evolution of codimension-1 Complexity=Anything proposals by anchoring the
probes to a worldline observer in each of the universes and connecting them
through the Euclidean wormhole. We investigate how the axion charge competes
with the cosmological constant in the time evolution of complexity. When the
complexity proposal equals the volume of an extremal surface, its evolution is
determined by the scale factor of the axion-dS universe, and as a result, the
observable might increase nearly exponentially for low axion charge, while it
decreases to a vanishing value as one approaches the maximal axion charge
allowed by de Sitter space.


  Efficient resource allocation (RA) strategies within massive and dense
Internet of Things (IoT) networks is one of the major challenges in the
deployment of IoT-network based smart ecosystems involving heterogeneous
power-constrained IoT devices operating in varied radio and environmental
conditions. In this paper, we focus on the transmit power minimization problem
for IoT devices while maintaining a threshold channel throughput. The
established optimization literature is not robust against the fast-fading
channel and the interaction among different transmit signals in each instance.
Besides, realistically, each IoT node possesses incomplete channel state
information (CSI) on its neighbors, such as the channel gain being private
information for the node itself. In this work, we resort to Bayesian game
theoretic strategies for solving the transmit power optimization problem
exploiting incomplete CSIs within massive IoT networks. We provide a steady
discussion on the rationale for selecting the game theory, particularly the
Bayesian scheme, with a graphical visualization of our formulated problem. We
take advantage of the property of the existence and uniqueness of the Bayesian
Nash equilibrium (BNE), which exhibits reduced computational complexity while
optimizing transmit power and maintaining target throughput within networks
comprised of heterogeneous devices.


  By investigating the convex property of the function R, appeared in computing
the entanglement of formation for isotropic states in Phys. Rev. Lett. 85, 2625
(2000), and a tight lower bound of entanglement of formation for arbitrary
bipartite mixed states in Phys. Rev. Lett. 95, 210501 (2005), we show
analytically that the very nice results in these papers are valid not only for
dimensions 2 and 3 but any dimensions.


A new definition for the notion of a (general) $\infty$-category is given.


  Solving nonlinear SMT problems over real numbers has wide applications in
robotics and AI. While significant progress is made in solving quantifier-free
SMT formulas in the domain, quantified formulas have been much less
investigated. We propose the first delta-complete algorithm for solving
satisfiability of nonlinear SMT over real numbers with universal quantification
and a wide range of nonlinear functions. Our methods combine ideas from
counterexample-guided synthesis, interval constraint propagation, and local
optimization. In particular, we show how special care is required in handling
the interleaving of numerical and symbolic reasoning to ensure
delta-completeness. In experiments, we show that the proposed algorithms can
handle many new problems beyond the reach of existing SMT solvers.


  We investigate the group velocity of edge magnetoplasmons (EMPs) in the
quantum Hall regime by means of time-of-flight measurement. The EMPs are
injected from an Ohmic contact by applying a voltage pulse, and detected at a
quantum point contact by applying another voltage pulse to its gate. We find
that the group velocity of the EMPs traveling along the edge channel defined by
a metallic gate electrode strongly depends on the voltage applied to the gate.
The observed variation of the velocity can be understood to reflect the degree
of screening caused by the metallic gate, which damps the in-plane electric
field and hence reduces the velocity. The degree of screening can be controlled
by changing the distance between the gate and the edge channel with the gate
voltage.


  Recently, applying deep neural networks in IR has become an important and
timely topic. For instance, Neural Ranking Models(NRMs) have shown promising
performance compared to the traditional ranking models. However, explaining the
ranking results has become even more difficult with NRM due to the complex
structure of neural networks. On the other hand, a great deal of research is
under progress on Interpretable Machine Learning(IML), including Grad-CAM.
Grad-CAM is an attribution method and it can visualize the input regions that
contribute to the network's output. In this paper, we adopt Grad-CAM for
interpreting the ranking results of NRM. By adopting Grad-CAM, we analyze how
each query-document term pair contributes to the matching score for a given
pair of query and document. The visualization results provide insights on why a
certain document is relevant to the given query. Also, the results show that
neural ranking model captures the subtle notion of relevance. Our
interpretation method and visualization results can be used for snippet
generation and user-query intent analysis.


  In this paper we describe an acoustic transceiver developed for the KM3NeT
positioning system. The acoustic transceiver is composed of a commercial free
flooded transducer, which works mainly in the 20-40 kHz frequency range and
withstands high pressures (up to 500 bars). A sound emission board was
developed that is adapted to the characteristics of the transducer and meets
all requirements: low power consumption, high intensity of emission, low
intrinsic noise, arbitrary signals for emission and the capacity of acquiring
the receiving signals with very good timing precision. The results of the
different tests made with the transceiver in the laboratory and shallow sea
water are described, as well as, the activities for its integration in the
Instrumentation Line of the ANTARES neutrino telescope and in a NEMO tower for
the in situ tests.


  In a warped 6D world with an extra 2-dimensional sphere, we propose an
exactly solvable model for fermion masses with zero mode. The warp factor is
given by $\phi (\theta ,\varphi )=\sin{\theta }\cos{\varphi }$, which is a
solution to the 6D Einstein equation with the bulk cosmological constant
$\Lambda $ and the energy-momentum tensor of the bulk matter fields. Our model
provides another possibility of obtaining fermion zero mode, rather than
traditional model based on Dirac's monopole.


  Mapping magnetic fields is the key to resolving what remains an unclear
physical picture of circumstellar magnetic fields in late-type evolved stars.
Observations of linearly polarized emission from thermal molecular line
transitions due to the Goldreich-Kylafis (G-K) effect provides valuable insight
into the magnetic field geometry in these sources that is complementary to
other key studies. In this paper, we present the detection of spectral-line
polarization from both the thermal $J=2-1$ CO line and the $v=1, J=5-4$ SiO
maser line toward two thermal-pulsating (TP-) AGB stars, R Crt and R Leo. The
observed fractional linear polarization in the CO emission is measured as
$m_l\sim 3.1\%$ and $m_l\sim9.7\%$ for R Crt and R Leo respectively. A
circumstellar envelope (CSE) model profile and the associated parameters are
estimated and used as input to a more detailed modeling of the predicted linear
polarization expected from the G-K effect. The observed thermal line
polarization level is consistent with the predicted results from the G-K model
for R Crt; additional effects need to be considered for R Leo.


  According to the character of Gaussian, we modify the Rank-Ordered Absolute
Differences (ROAD) to Rank-Ordered Absolute Differences of mixture of Gaussian
and impulse noises (ROADG). It will be more effective to detect impulse noise
when the impulse is mixed with Gaussian noise. Combining rightly the ROADG with
Optimal Weights Filter (OWF), we obtain a new method to deal with the mixed
noise, called Optimal Weights Mixed Filter (OWMF). The simulation results show
that the method is effective to remove the mixed noise.


  Understanding the fundamental mechanisms of single-atom catalysis (SAC) is
important to design systems with improved performance and stability. This is
problematic, however, because single-atom active sites are extremely difficult
to characterize with existing experimental techniques. Over the last 40 years,
surface science has provided the fundamental insights to understand
heterogeneous catalysis, but model systems in which metal atoms are stable on
well-characterized metal-oxide substrates at reaction temperatures are scarce.
In this perspective, I discuss what is already known about isolated metal atoms
adsorbed on model metal-oxide surfaces, and how this information can be used to
understand SAC. A key issue is that, although the highly-idealised model
systems studied in surface science may not be representative of a real working
catalyst, they do very much resemble what can be calculated using
state-of-the-art theoretical modelling. Thus, surface science offers an
opportunity to rigorously benchmark the theoretical approach to modelling SAC
in future. Perhaps more excitingly, several groups have developed model systems
where metal adatoms remain stable at elevated temperatures. To date however,
there has been no clear demonstration of catalytic activity. The perspective
closes with a brief discussion of the prospect for STM experiments under
realistic reaction conditions.


  We study the problem of computing the minimum adversarial perturbation of the
Nearest Neighbor (NN) classifiers. Previous attempts either conduct attacks on
continuous approximations of NN models or search for the perturbation by some
heuristic methods. In this paper, we propose the first algorithm that is able
to compute the minimum adversarial perturbation. The main idea is to formulate
the problem as a list of convex quadratic programming (QP) problems that can be
efficiently solved by the proposed algorithms for 1-NN models. Furthermore, we
show that dual solutions for these QP problems could give us a valid lower
bound of the adversarial perturbation that can be used for formal robustness
verification, giving us a nice view of attack/verification for NN models. For
$K$-NN models with larger $K$, we show that the same formulation can help us
efficiently compute the upper and lower bounds of the minimum adversarial
perturbation, which can be used for attack and verification.


  The light-front (LF) quantization of QCD in light-cone (l.c.) gauge is
discussed. The Dirac method is employed to construct the LF Hamiltonian and
theory quantized canonically. The Dyson-Wick perturbation theory expansion
based on LF-time ordering is constructed. The framework incorporates in it
simultaneously the Lorentz gauge condition as an operator equation as well. The
propagator of the dynamical $\psi_+$ part of the free fermionic propagator is
shown to be causal while the gauge field propagator is found to be transverse.
The interaction Hamiltonian is re-expressed in the form closely resembling the
one in covariant theory, except for additional instantaneous interactions,
which can be treated systematically. Some explicit computations in QCD are
given.


  To reveal the Schwinger effect for quarks, i.e., pair creation process of
quarks and antiquarks, we derive the vacuum decay rate at strong coupling using
AdS/CFT correspondence. Magnetic fields, in addition to the electric field
responsible for the pair creation, causes prominent effects on the rate, and is
important also in experiments such as RHIC/LHC heavy ion collisions. In this
paper, through the gravity dual we obtain the full Euler-Heisenberg Lagrangian
of N=2 supersymmetric QCD and study the Schwinger mechanism with not only a
constant electric field but also a constant magnetic field as external fields.
We determine the quark mass and temperature dependence of the Lagrangian. In
sharp contrast with the zero magnetic field case, we find that the imaginary
part, and thus the vacuum decay rate, diverges in the massless zero-temperature
limit. This may be related to a strong instability of the QCD vacuum in strong
magnetic fields. The real part of the Lagrangian serves as a generating
function for non-linear electro-magnetic responses, and is found such that the
Cotton-Mouton effect vanishes. Interestingly, our results of the Schwinger /
Cotton-Mouton effects coincide precisely with those of N=2 supersymmetric QED.


  In the theories described by a volume normalized action functional, an
arbitrary cosmological constant is eliminated from the physical picture of our
Universe, and dynamical alternatives must be responsible for today's
accelerated expansion as well as for the conjectured early-time inflation in
the Universe. A few well-known such scenarios realized by a single homogeneous
scalar field are examined in this new context, and their diverse fates are
elucidated. Typical inflationary models are not affected at the level of the
background evolution, and also give rise to the scalar perturbations equivalent
to those obtained in the standard General Relativity; however, the primordial
quantum tensor fluctuations are absent in the new framework, irrespective of
the inflationary model. As a consequence, our proposal would be ruled out
should the primordial tensor modes, or their indirect consequences, be
discovered in observations.


  The neutron total cross sections $\sigma_{tot}$ of $^{16,18}$O, $^{58,64}$Ni,
$^{103}$Rh, and $^{112,124}$Sn have been measured at the Los Alamos Neutron
Science Center (LANSCE) from low to intermediate energies (3 $\leq E_{lab}
\leq$ 450 MeV) by leveraging waveform-digitizer technology. The $\sigma_{tot}$
relative differences between isotopes are presented, revealing additional
information about the isovector components needed for an accurate optical-model
description away from stability. Digitizer-enabled $\sigma_{tot}$-measurement
techniques are discussed and a series of uncertainty-quantified dispersive
optical model (DOM) analyses using these new data is presented, validating the
use of the DOM for modeling light systems ($^{16,18}$O) and systems with open
neutron shells ($^{58,64}$Ni and $^{112,124}$Sn). The valence-nucleon
spectroscopic factors extracted for each isotope reaffirm the usefulness of
high-energy proton reaction cross sections for characterizing depletion from
the mean-field expectation.