  Generating music with emotion is an important task in automatic music
generation, in which emotion is evoked through a variety of musical elements
(such as pitch and duration) that change over time and collaborate with each
other. However, prior research on deep learning-based emotional music
generation has rarely explored the contribution of different musical elements
to emotions, let alone the deliberate manipulation of these elements to alter
the emotion of music, which is not conducive to fine-grained element-level
control over emotions. To address this gap, we present a novel approach
employing musical element-based regularization in the latent space to
disentangle distinct elements, investigate their roles in distinguishing
emotions, and further manipulate elements to alter musical emotions.
Specifically, we propose a novel VQ-VAE-based model named MusER. MusER
incorporates a regularization loss to enforce the correspondence between the
musical element sequences and the specific dimensions of latent variable
sequences, providing a new solution for disentangling discrete sequences.
Taking advantage of the disentangled latent vectors, a two-level decoding
strategy that includes multiple decoders attending to latent vectors with
different semantics is devised to better predict the elements. By visualizing
latent space, we conclude that MusER yields a disentangled and interpretable
latent space and gain insights into the contribution of distinct elements to
the emotional dimensions (i.e., arousal and valence). Experimental results
demonstrate that MusER outperforms the state-of-the-art models for generating
emotional music in both objective and subjective evaluation. Besides, we
rearrange music through element transfer and attempt to alter the emotion of
music by transferring emotion-distinguishable elements.


  We analysed the XMM-Newton archival observations of 16 neutron star (NS)
low-mass X-ray binaries (LMXBs) to study the Fe K emission in these objects.
The sample includes all the observations of NS LMXBs performed in EPIC pn
Timing mode with XMM-Newton publicly available until September 30, 2009. We
performed a detailed data analysis considering pile-up and background effects.
The properties of the iron lines differed from previous published analyses due
to either incorrect pile-up corrections or different continuum
parameterization. 80% of the observations for which a spectrum can be extracted
showed significant Fe line emission. We found an average line centroid of 6.67
$\pm$ 0.02 keV and a finite width, $\sigma$, of 0.33 $\pm$ 0.02 keV. The
equivalent width of the lines varied between 17 and 189 eV, with an average
weighted value of 42 $\pm$ eV. For sources where several observations were
available the Fe line parameters changed between observations whenever the
continuum changed significantly. The line parameters did not show any
correlation with luminosity. Most important, we could fit the Fe line with a
simple Gaussian component for all the sources. The lines did not show the
asymmetric profiles that were interpreted as an indication of relativistic
effects in previous analyses of these LMXBs.


  A limiting systematic effect in 21-cm interferometric experiments is the
chromaticity due to the coupling between the sky and the instrument. This
coupling is sourced by the instrument primary beam; therefore it is important
to know the beam to extremely high precision. Here we demonstrate how known
beam uncertainties can be characterized using databases of beam models. In this
introductory work, we focus on beam errors arising from physically offset
and/or broken antennas within a station. We use the public code OSKAR to
generate an "ideal" SKA beam formed from 256 antennas regularly-spaced in a
35-m circle, as well as a large database of "perturbed" beams sampling
distributions of broken/offset antennas. We decompose the beam errors ("ideal"
minus "perturbed") using Principal Component Analysis (PCA) and Kernel PCA
(KPCA). Using 20 components, we find that PCA/KPCA can reduce the residual of
the beam in our datasets by 60-90% compared with the assumption of an ideal
beam. Using a simulated observation of the cosmic signal plus foregrounds, we
find that assuming the ideal beam can result in 1% error in the EoR window and
10% in the wedge of the 2D power spectrum. When PCA/KPCA is used to
characterize the beam uncertainties, the error in the power spectrum shrinks to
below 0.01% in the EoR window and <1% in the wedge. Our framework can be used
to characterize and then marginalize over uncertainties in the beam for robust
next-generation 21-cm parameter estimation.


  We demonstrate that, due to the finite thickness of domain walls, and the
consequent ambiguity in defining their locations, the effective string
description obtained by integrating out bulk degrees of freedom contains
ambiguities in the coefficients of the various geometric terms. The only term
with unambiguous coefficient is the zeroth order Nambu-Goto term. We argue that
fermionic ghost fields which implement gauge-fixing act to balance these
ambiguities. The renormalized string tension, obtained after integrating out
both bulk and world-sheet degrees of freedom, can be defined in a scheme
independent manner; and we compute the explicit finite expressions, to
one-loop, for the case of compact quantum electrodynamics and phi-4 theory.


  Pseudo-label (PL) filtering forms a crucial part of Self-Training (ST)
methods for unsupervised domain adaptation. Dropout-based Uncertainty-driven
Self-Training (DUST) proceeds by first training a teacher model on source
domain labeled data. Then, the teacher model is used to provide PLs for the
unlabeled target domain data. Finally, we train a student on augmented labeled
and pseudo-labeled data. The process is iterative, where the student becomes
the teacher for the next DUST iteration. A crucial step that precedes the
student model training in each DUST iteration is filtering out noisy PLs that
could lead the student model astray. In DUST, we proposed a simple, effective,
and theoretically sound PL filtering strategy based on the teacher model's
uncertainty about its predictions on unlabeled speech utterances. We estimate
the model's uncertainty by computing disagreement amongst multiple samples
drawn from the teacher model during inference by injecting noise via dropout.
In this work, we show that DUST's PL filtering, as initially used, may fail
under severe source and target domain mismatch. We suggest several approaches
to eliminate or alleviate this issue. Further, we bring insights from the
research in neural network model calibration to DUST and show that a
well-calibrated model correlates strongly with a positive outcome of the DUST
PL filtering step.


  TOI-1266c is a recently discovered super-Venus in the radius valley orbiting
an early M dwarf. However, its notional bulk density ($\sim$2.2 g cm$^{-3}$) is
consistent with a large volatile fraction, suggesting that it might have
volatile reservoirs that have survived billions of years at more than twice the
Earth's insolation. On the other hand, the upper mass limit paints a picture of
a cool super Mercury dominated by >50\% iron core ($\sim$9.2 g cm$^{-3}$) that
has tiptoed up to the collisional stripping limit and into the radius gap.
Here, we examine several hypothetical states for TOI-1266c using a combination
of new and updated open-source atmospheric escape, radiative-convective, and
photochemical models. We find that water-rich atmospheres with trace amounts of
H$_{2}$ and CO$_{2}$ are potentially detectable (SNR $>\sim 5$) in less than 20
hours of JWST observing time. We also find that water vapor spectral features
are not substantially impacted by the presence of high-altitude water or ice
clouds due the presence of a significant amount of water above the cloud-deck,
although further work with self-consistent cloud models is needed. Regardless
of its mass, however, TOI-1266c represents a unique proving ground for several
hypotheses related to the evolution of sub-Neptunes and Venus-like worlds,
particularly those near the radius valley.


  Binarized neural networks, or BNNs, show great promise in edge-side
applications with resource limited hardware, but raise the concerns of reduced
accuracy. Motivated by the complex neural networks, in this paper we introduce
complex representation into the BNNs and propose Binary complex neural network
-- a novel network design that processes binary complex inputs and weights
through complex convolution, but still can harvest the extraordinary
computation efficiency of BNNs. To ensure fast convergence rate, we propose
novel BCNN based batch normalization function and weight initialization
function. Experimental results on Cifar10 and ImageNet using state-of-the-art
network models (e.g., ResNet, ResNetE and NIN) show that BCNN can achieve
better accuracy compared to the original BNN models. BCNN improves BNN by
strengthening its learning capability through complex representation and
extending its applicability to complex-valued input data. The source code of
BCNN will be released on GitHub.


  For posets $P$ and $Q$, extremal and saturation problems about weak and
strong $P$-free subposets of $Q$ have been studied mostly in the case $Q$ is
the Boolean poset $Q_n$, the poset of all subsets of an $n$-element set ordered
by inclusion. In this paper, we study some instances of the problem with $Q$
being the grid, and its connections to the Boolean case and to the forbidden
submatrix problem.


  Emerging Information-Centric Networking (ICN) architectures seek to optimally
utilize both bandwidth and storage for efficient content distribution over the
network. The Virtual Interest Packet (VIP) framework has been proposed to
enable joint design of forwarding and caching within the Named Data Networking
(NDN) architecture. The virtual plane of the VIP framework captures the
measured demand for content objects, but does not reflect interest collapse and
suppression in the NDN network. We aim to further improve the performance of
the existing VIP algorithms by using a modified virtual plane where VIP counts
are appropriately scaled to reflect interest suppression effects. We
characterize the stability region of the modified virtual plane with VIP
scaling, develop a new distributed forwarding and caching algorithm operating
on the scaled VIPs, and demonstrate the throughput optimality of the scaled VIP
algorithm in the virtual plane. Numerical experiments demonstrate significantly
enhanced performance relative to the existing VIP algorithm, as well as a
number of other baseline algorithms.


  We introduce a novel particle-in-Fourier (PIF) scheme that extends its
applicability to non-periodic boundary conditions. Our method handles free
space boundary conditions by replacing the Fourier Laplacian operator in PIF
with a mollified Green's function as first introduced by
Vico-Greengard-Ferrando. This modification yields highly accurate free space
solutions to the Vlasov-Poisson system, while still maintaining energy
conservation up to an error bounded by the time step size. We also explain how
to extend our scheme to arbitrary Dirichlet boundary conditions via standard
potential theory, which we illustrate in detail for Dirichlet boundary
conditions on a circular boundary. We support our approach with
proof-of-concept numerical results from two-dimensional plasma test cases to
demonstrate the accuracy, efficiency, and conservation properties of the
scheme.


  The Fried-Yennie gauge is a covariant gauge for which the mass-shell
renormalization procedure can be performed without introducing spurious
infrared divergences to the theory. It is usually applied in calculations in
regular Quantum-Electrodynamics (QED), but it is particularly interesting to be
employed in the framework of pseudo-QED (PQED), where fermions are constrained
to 2+1 dimensions while external fields interacting with these fermions live in
the bulk of a 3+1 space. In this context, the gauge parameter can be adjusted
to match the power of the external momentum in the denominator of the photon
propagator, simplifying the infrared region without the need of a photon mass.
In this work we apply for the first time this machinery to PQED, generalizing
the procedure to calculate the self energy in arbitrary dimensions, allowing of
course for different dimensionality of fermions and gauge fields.


  One of the main challenges autonomous vehicles (AVs) will face is interacting
with pedestrians, especially at unmarked midblock locations where the
right-of-way is unspecified. This study investigates pedestrian crossing
behavior given different roadway centerline features (i.e., undivided, two-way
left-turn lane (TWLTL), and median) and various AV operational schemes
portrayed to pedestrians through on-vehicle signals (i.e., no signal, yellow
negotiating indication, and yellow/blue negotiating/no-yield indications). This
study employs virtual reality to simulate an urban unmarked midblock
environment where pedestrians interact with AVs. Results demonstrate that both
roadway centerline design features and AV operations and signaling
significantly impact pedestrian unmarked midblock crossing behavior, including
the waiting time at the curb, waiting time in the middle of the road, and the
total crossing time. But only the roadway centerline features significantly
impact the walking time. Participants in the undivided scene spent a longer
time waiting at the curb and walking on the road than in the median and TWLTL
scenes, but they spent a shorter time waiting in the middle. Compared to the AV
without a signal, the design of yellow signal significantly reduced pedestrian
waiting time at the curb and in the middle. But yellow/blue significantly
increased the pedestrian waiting time. Interaction effects between roadway
centerline design features and AV operations and signaling are significant only
for waiting time in the middle. For middle waiting time, yellow/blue signals
had the most impact on the median road type and the least on the undivided
road. Demographics, past behaviors, and walking exposure are also explored.
Older individuals tend to wait longer, and pedestrian past crossing behaviors
and past walking exposures do not significantly impact pedestrian walking
behavior.


  Using the methods developed for the Bianchi I case we have shown that a
boostrap argument is also suitable to treat the future non-linear stability for
reflection symmetric solutions of the Einstein-Vlasov system of Bianchi types
II and VI$_0$. These solutions are asymptotic to the Collins-Stewart solution
with dust and the Ellis-MacCallum solution respectively. We have thus
generalized the results obtained by Rendall and Uggla in the case of locally
rotationally symmetric Bianchi II spacetimes to the reflection symmetric case.
However we needed to assume small data. For Bianchi VI$_0$ there is no
analogous previous result.


  We present a noncommutative extension of Quantum Cosmology and study the
Kantowski-Sachs (KS) cosmological model requiring that the two scale factors of
the KS metric, the coordinates of the system, and their conjugate canonical
momenta do not commute. Through the ADM formalism, we obtain the Wheeler-DeWitt
(WDW) equation for the noncommutative system. The Seiberg-Witten map is used to
transform the noncommutative equation into a commutative one, i.e. into an
equation with commutative variables, which depend on the noncommutative
parameters, $\theta$ and $\eta$. Numerical solutions are found both for the
classical and the quantum formulations of the system. These solutions are used
to characterize the dynamics and the state of the universe. From the classical
solutions we obtain the behavior of quantities such as the volume expansion,
the shear and the characteristic volume. However the analysis of these
quantities does not lead to any restriction on the value of the noncommutative
parameters, $\theta$ and $\eta$. On the other hand, for the quantum system, one
can obtain, via the numerical solution of the WDW equation, the wave function
of the universe both for commutative as well as for the noncommutative models.
Interestingly, we find that the existence of suitable solutions of the WDW
equation imposes bounds on the values of the noncommutative parameters.
Moreover, the noncommutativity in the momenta leads to damping of the wave
function implying that this noncommutativity can be of relevance for the
selection of possible initial states of the early universe.


  As 2 black holes bound to each other in a close binary approach merger their
inspiral time becomes shorter than the characteristic inflow time of
surrounding orbiting matter. Using an innovative technique in which we
represent the changing spacetime in the region occupied by the orbiting matter
with a 2.5PN approximation and the binary orbital evolution with 3.5PN, we have
simulated the MHD evolution of a circumbinary disk surrounding an equal-mass
non-spinning binary. Prior to the beginning of the inspiral, the structure of
the circumbinary disk is predicted well by extrapolation from Newtonian
results. The binary opens a low-density gap whose radius is roughly two binary
separations, and matter piles up at the outer edge of this gap as inflow is
retarded by torques exerted by the binary; nonetheless, the accretion rate is
diminished relative to its value at larger radius by only about a factor of 2.
During inspiral, the inner edge of the disk at first moves inward in
coordination with the shrinking binary, but as the orbital evolution
accelerates, the rate at which the inner edge moves toward smaller radii falls
behind the rate of binary compression. In this stage, the rate of angular
momentum transfer from the binary to the disk slows substantially, but the net
accretion rate decreases by only 10-20%. When the binary separation is tens of
gravitational radii, the rest-mass efficiency of disk radiation is a few
percent, suggesting that supermassive binary black holes in galactic nuclei
could be very luminous at this stage of their evolution. If the luminosity were
optically thin, it would be modulated at a frequency that is a beat between the
orbital frequency of the disk's surface density maximum and the binary orbital
frequency. However, a disk with sufficient surface density to be luminous
should also be optically thick; as a result, the periodic modulation may be
suppressed.


  In recent years, person Re-identification (ReID) has rapidly progressed with
wide real-world applications, but also poses significant risks of adversarial
attacks. In this paper, we focus on the backdoor attack on deep ReID models.
Existing backdoor attack methods follow an all-to-one or all-to-all attack
scenario, where all the target classes in the test set have already been seen
in the training set. However, ReID is a much more complex fine-grained open-set
recognition problem, where the identities in the test set are not contained in
the training set. Thus, previous backdoor attack methods for classification are
not applicable for ReID. To ameliorate this issue, we propose a novel backdoor
attack on deep ReID under a new all-to-unknown scenario, called Dynamic
Triggers Invisible Backdoor Attack (DT-IBA). Instead of learning fixed triggers
for the target classes from the training set, DT-IBA can dynamically generate
new triggers for any unknown identities. Specifically, an identity hashing
network is proposed to first extract target identity information from a
reference image, which is then injected into the benign images by image
steganography. We extensively validate the effectiveness and stealthiness of
the proposed attack on benchmark datasets, and evaluate the effectiveness of
several defense methods against our attack.


  Attracted by team scale and function diversity, a heterogeneous multi-robot
system (HMRS), where multiple robots with different functions and numbers are
coordinated to perform tasks, has been widely used for complex and large-scale
scenarios, including disaster search and rescue, site surveillance, and social
security. However, due to the variety of the task requirements, it is
challenging to accurately compose a robot team with appropriate sizes and
functions to dynamically satisfy task needs while limiting the robot resource
cost to a low level. To solve this problem, in this paper, a novel adaptive
cooperation method, inner attention (innerATT), is developed to flexibly team
heterogeneous robots to execute tasks as task types and environment change.
innerATT is designed by integrating a novel attention mechanism into a
multi-agent actor-critic reinforcement learning architecture. With an attention
mechanism, robot capability will be analyzed to flexibly form teams to meet
task requirements. Scenarios with different task variety ("Single Task",
"Double Task", and "Mixed Task") were designed. The effectiveness of the
innerATT was validated by its accuracy in flexible cooperation.


  We show that the dipole-dipole interaction between three identical Rydberg
atoms can give rise to bound trimer states. The microscopic origin of these
states is fundamentally different from Efimov physics. Two stable trimer
configurations exist where the atoms form the vertices of an equilateral
triangle in a plane perpendicular to a static electric field. The triangle edge
length typically exceeds $R\approx 2\,\mu\text{m}$, and each configuration is
two-fold degenerate due to Kramers' degeneracy. The depth of the potential
wells and the triangle edge length can be controlled by external parameters. We
establish the Borromean nature of the trimer states, analyze the quantum
dynamics in the potential wells and describe methods for their production and
detection.


  We study approximate equivalence relations up to commensurability, in the
presence of a definable measure. As a basic framework, we give a presentation
of probability logic based on continuous logic. Hoover's normal form is valid
here; if one begins with a discrete logic structure, it reduces arbitrary
formulas of probability logic to correlations between quantifier-free formulas.
We completely classify binary correlations in terms of the Kim-Pillay space,
leading to strong results on the interpretative power of pure probability logic
over a binary language. Assuming higher amalgamation of independent types, we
prove a higher stationarity statement for such correlations.
  We also give a short model-theoretic proof of a categoricity theorem for
continuous logic structures with a measure of full support, generalizing
theorems of Gromov-Vershik and Keisler, and often providing a canonical model
for a complete pure probability logic theory. These results also apply to local
probability logic, providing in particular a canonical model for a local pure
probability logic theory with a unique 1-type and geodesic metric.
  For sequences of approximate equivalence relations with an 'approximately
unique' probability logic 1-type, we obtain a structure theorem generalizing
the `Lie model' theorem for approximate subgroups. The models here are
Riemannian homogeneous spaces, fibered over a locally finite graph.
  Specializing to definable graphs over finite fields, we show that after a
finite partition, a definable binary relation converges in finitely many
self-compositions to an equivalence relation of geometric origin.
  For NIP theories, pursuing a question of Pillay's, we prove an archimedean
finite-dimensionality statement for the automorphism groups of definable
measures, acting on a given type of definable sets.


  As a continuation of \cite{LXY}, the paper aims to justify the high Reynolds
numbers limit for the MHD system with Prandtl boundary layer expansion when
no-slip boundary condition is imposed on velocity field and perfect conducting
boundary condition on magnetic field. Under the assumption that the viscosity
and resistivity coefficients are of the same order and the initial tangential
magnetic field on the boundary is not degenerate, we justify the validity of
the Prandtl boundary layer expansion and give a $L^\infty$ estimate on the
error by multi-scale analysis.


  High-performance scientific simulations, important for comprehension of
complex systems, encounter computational challenges especially when exploring
extensive parameter spaces. There has been an increasing interest in developing
deep neural networks (DNNs) as surrogate models capable of accelerating the
simulations. However, existing approaches for training these DNN surrogates
rely on extensive simulation data which are heuristically selected and
generated with expensive computation -- a challenge under-explored in the
literature. In this paper, we investigate the potential of incorporating active
learning into DNN surrogate training. This allows intelligent and objective
selection of training simulations, reducing the need to generate extensive
simulation data as well as the dependency of the performance of DNN surrogates
on pre-defined training simulations. In the problem context of constructing DNN
surrogates for diffusion equations with sources, we examine the efficacy of
diversity- and uncertainty-based strategies for selecting training simulations,
considering two different DNN architecture. The results set the groundwork for
developing the high-performance computing infrastructure for Smart Surrogates
that supports on-the-fly generation of simulation data steered by active
learning strategies to potentially improve the efficiency of scientific
simulations.


  Generalized category discovery (GCD) aims at grouping unlabeled samples from
known and unknown classes, given labeled data of known classes. To meet the
recent decentralization trend in the community, we introduce a practical yet
challenging task, namely Federated GCD (Fed-GCD), where the training data are
distributively stored in local clients and cannot be shared among clients. The
goal of Fed-GCD is to train a generic GCD model by client collaboration under
the privacy-protected constraint. The Fed-GCD leads to two challenges: 1)
representation degradation caused by training each client model with fewer data
than centralized GCD learning, and 2) highly heterogeneous label spaces across
different clients. To this end, we propose a novel Associated Gaussian
Contrastive Learning (AGCL) framework based on learnable GMMs, which consists
of a Client Semantics Association (CSA) and a global-local GMM Contrastive
Learning (GCL). On the server, CSA aggregates the heterogeneous categories of
local-client GMMs to generate a global GMM containing more comprehensive
category knowledge. On each client, GCL builds class-level contrastive learning
with both local and global GMMs. The local GCL learns robust representation
with limited local data. The global GCL encourages the model to produce more
discriminative representation with the comprehensive category relationships
that may not exist in local data. We build a benchmark based on six visual
datasets to facilitate the study of Fed-GCD. Extensive experiments show that
our AGCL outperforms the FedAvg-based baseline on all datasets.


  Typical techniques for sequence classification are designed for
well-segmented sequences which have been edited to remove noisy or irrelevant
parts. Therefore, such methods cannot be easily applied on noisy sequences
expected in real-world applications. In this paper, we present the Temporal
Attention-Gated Model (TAGM) which integrates ideas from attention models and
gated recurrent networks to better deal with noisy or unsegmented sequences.
Specifically, we extend the concept of attention model to measure the relevance
of each observation (time step) of a sequence. We then use a novel gated
recurrent network to learn the hidden representation for the final prediction.
An important advantage of our approach is interpretability since the temporal
attention weights provide a meaningful value for the salience of each time step
in the sequence. We demonstrate the merits of our TAGM approach, both for
prediction accuracy and interpretability, on three different tasks: spoken
digit recognition, text-based sentiment analysis and visual event recognition.


  A recently derived formula for complex conductivity of the monolayer graphene
is analyzed. We show that the real and imaginary parts in this formula obey the
Kramers and Kronig dispersion relations which are a good test for validity of
the formula for complex conductivity of monolayer graphene. We consider also an
additional test for this formula, sensitive to the integral characteristic of
the conductance such as the famous f sum rule. We write it in the two
dimensional form and show that it fulfils identically if we admit the cyclotron
mass as an effective one and take the principal value of the integral. We find
a deep relation between the graphene complex optical conductivity singularities
and electrons Zitterbewegung in graphene. Namely, the value of Zitterbewegung
frequency is related with the recently found magnitudes of the inductance L and
capacitance C by the Thomson formula.


  This paper compares simulation results and experimental published data
concerning the interaction between $\alpha_2\beta_1$ integrin and collagen.This
phenomenon is characterized by a competition among steric, reciprocal
orientation constraints of the two structures and long range electrostatic
force. The published experimental results shown that the
$\textrm{k}_{\textrm{on}}$ rate, for the 42-mer collagen fragment and the
$\alpha_2\textrm{I}$ integrin domain complex, ranges in order of magnitude from
$10^4-10^5$ M$^{-1}$s$^{-1}$. This is the lower bound of the interval defined
by the diffusion limited regime and the orientational constraint. The
electrostatic affinity between 21-mer collagen fragment and the
$\alpha_2\textrm{I}$ integrin domain due to the divalent cations
($\textrm{Mg}^{2+}$, $\textrm{Co}^{2+}$, $\textrm{Mn}^{2+}$ and
$\textrm{Ca}^{2+}$) has been expressed in terms of the Pearson correlation
coefficient between the electrostatic desolvation potential of the ligand and
the electrostatic interaction potential of the receptor. The obtained values of
the Pearson coefficient clearly reveals the complementarity between the
$\alpha_2\textrm{I}$ integrin domain and 21-mer collagen in the metal ion
dependent site. Atomistic Brownian dynamics simulations applied in the
diffusion limited regime, correctly reproduce the binding kinetics, of the
$\alpha_2\textrm{I}$ integrin domain and the 21-mer collagen in presence of
$\textrm{Mg}^{2+}$, $\textrm{Co}^{2+}$ and $\textrm{Mn}^{2+}$, according to
published experimental data.


  This article consists of a brief discussion of the energy density over time
or frequency that is obtained with the wavelet transform. Also an efficient
algorithm is suggested to calculate the continuous transform with the Morlet
wavelet. The energy values of the Wavelet transform are compared with the power
spectrum of the Fourier transform. Useful definitions for power spectra are
given. The focus of the work is on simple measures to evaluate the transform
with the Morlet wavelet in an efficient way. The use of the transform and the
defined values is shown in some examples.


  We study the spectral inequalities of Schr\"odinger operator in the whole
space for different potentials, which can be power growth or continuously
vanishing at infinity. The spectral inequalities quantitatively depend on the
density of the sensor sets with positive measure, growth rate of the potentials
and spectrum (or eigenvalues). One important component in the poof is the
adaptation of propagation of smallness argument for gradients in \cite{LM18}.
As an application, we apply the spectral inequalities to obtain quantitative
observability inequalities for heat equations.


  Over the last decade, the rise of the mobile internet and the usage of mobile
devices has enabled ubiquitous traffic information. With the increased adoption
of specific smartphone applications, the number of users of routing
applications has become large enough to disrupt traffic flow patterns in a
significant manner. Similarly, but at a slightly slower pace, novel services
for freight transportation and city logistics improve the efficiency of goods
transportation and change the use of road infrastructure. The present article
provides a general four-layer framework for modeling these new trends. The main
motivation behind the development is to provide a unifying formal system
description that can at the same time encompass system physics (flow and motion
of vehicles) as well as coordination strategies under various information and
cooperation structures. To showcase the framework, we apply it to the specific
challenge of modeling and analyzing the integration of routing applications in
today's transportation systems. In this framework, at the lowest layer (flow
dynamics) we distinguish app users from non-app users. A distributed parameter
model based on a non-local partial differential equation is introduced and
analyzed. The second layer incorporates connected services (e.g., routing) and
other applications used to optimize the local performance of the system. As
inputs to those applications, we propose a third layer introducing the
incentive design and global objectives, which are typically varying over the
day depending on road and weather conditions, external events etc. The
high-level planning is handled on the fourth layer taking social long-term
objectives into account.


  We prove the following result: Let $(X,g_0)$ be a complete, connected
4-manifold with uniformly positive isotropic curvature and with bounded
geometry. Then there is a finite collection $\mathcal{F}$ of manifolds of the
form $\mathbb{S}^3 \times \mathbb{R} /G$, where $G$ is a fixed point free
discrete subgroup of the isometry group of the standard metric on
$\mathbb{S}^3\times \mathbb{R}$, such that $X$ is diffeomorphic to a (possibly
infinite) connected sum of copies of $\mathbb{S}^4,\mathbb{RP}^4$ and/or
members of $\mathcal{F}$. This extends recent work of Chen-Tang-Zhu and Huang.
We also extend the above result to the case of orbifolds. The proof uses Ricci
flow with surgery on complete orbifolds.


  A method is described for estimating effective scattering lengths via
spectroscopy on a trapped pair of atoms. The method relies on the phenomena
that the energy levels of two atoms in a harmonic trap are shifted by their
collisional interaction. The amount of shift depends on the strength of the
interaction (i.e. scattering length). By combining the spectra of the trap
state energy levels and a suitable model for the effective scattering length,
an estimate for the latter may be inferred. Two practical methods for measuring
the trap spectra are proposed and illustrated in examples. The accuracy of the
scheme is analyzed and requirements on measurement precision are given.


  The quark dynamics inside light mesons, except pseudoscalar ones, can be
quite well described by a spinless Salpeter equation supplemented by a Cornell
interaction (possibly partly vector, partly scalar). A mass formula for these
mesons can then be obtained by computing analytical approximations of the
eigenvalues of the equation. We show that such a formula can be derived by
combining the results of two methods: the dominantly orbital state description
and the Bohr-Sommerfeld quantization approach. The predictions of the mass
formula are compared with accurate solutions of the spinless Salpeter equation
computed with a Lagrange-mesh calculation method.


  In this paper we propose on continuous level several domain decomposition
methods to solve unilateral and ideal multibody contact problems of nonlinear
elasticity. We also present theorems about convergence of these methods.


  Low-rank approximation is an effective model compression technique to not
only reduce parameter storage requirements, but to also reduce computations.
For convolutional neural networks (CNNs), however, well-known low-rank
approximation methods, such as Tucker or CP decomposition, result in degraded
model accuracy because decomposed layers hinder training convergence. In this
paper, we propose a new training technique that finds a flat minimum in the
view of low-rank approximation without a decomposed structure during training.
By preserving the original model structure, 2-dimensional low-rank
approximation demanding lowering (such as im2col) is available in our proposed
scheme. We show that CNN models can be compressed by low-rank approximation
with much higher compression ratio than conventional training methods while
maintaining or even enhancing model accuracy. We also discuss various
2-dimensional low-rank approximation techniques for CNNs.


  We propose a systemic method of applying the auxiliary systems of original
equations to find the high order nonlocal symmetries of nonlinear evolution
equation. In order to validate the effectiveness of the method, some examples
are presented.


  We introduce an asymptotic Markov's exponent and show that it is equal to
Markov's exponent for a wide class of norms. As a consequence we obtain a lower
bound for the optimal exponent in Markov's inequality considered with the norms
possessing Nikolskii type property.


  The lighter superpartner of the top quark ($\tt_1$) may be considerably
lighter than squarks of the first two generations, and hence may be accessible
to Tevatron collider searches, even if the other squarks and the gluino are too
heavy. For the range of $m_{\tt_1}$ of interest at the Tevatron, the $\tt_1$
decays to a chargino via $\tt_1 \to b\tw_1$ if this is kinematically allowed;
otherwise, the flavor-changing loop decay $\tt_1 \to c\tz_1$ dominates. In the
latter case, $\tt_1$ production is signalled by jet(s) plus $\eslt$ events. If,
instead, the chargino decay is allowed, $1\ell + b$-$jets + \eslt$ and
$\ell^+\ell'^- + jet(s) + \eslt$ events from leptonic decays of $\tw_1$ provide
the most promising signals. We perform detailed simulations for each of these
signals using ISAJET 7.07 and devise cuts to enable the extraction of each of
these signals above standard model backgrounds from vector boson or top quark
production. With an integrated luminosity of 100 $pb^{-1}$, experiments should
be able to probe top squark masses up to $\sim 100$ GeV in each of these
channels; the detection of the signal in the single lepton channel, however,
requires reasonable capability to tag displaced $b$ decay vertices in the
central region.


  We provide an analytical characterization of the model flexibility of the
synthetic control method (SCM) in the familiar form of degrees of freedom. We
obtain estimable information criteria. These may be used to circumvent
cross-validation when selecting either the weighting matrix in the SCM with
covariates, or the tuning parameter in model averaging or penalized variants of
SCM. We assess the impact of car license rationing in Tianjin and make a novel
use of SCM; while a natural match is available, it and other donors are noisy,
inviting the use of SCM to average over approximately matching donors. The very
large number of candidate donors calls for model averaging or penalized
variants of SCM and, with short pre-treatment series, model selection per
information criteria outperforms that per cross-validation.


  The polarizable embedding (PE) model is a fragment-based quantum-classical
approach aimed at accurate inclusion of environment effects in
quantum-mechanical response property calculations. The aim of this tutorial is
to give insight into the practical use of the PE model. Starting from a set of
molecular structures and until you arrive at the final property, there are many
crucial details to consider in order to obtain trustworthy results in an
efficient manner. To lower the threshold for new users wanting to explore the
use of the PE model, we describe and discuss important aspects related to its
practical use. This includes directions on how to generate input files and how
to run a calculation.


  We present new JHK spectroscopy (R ~ 5000) of GQ Lup b, acquired with the
near-infrared integral field spectrograph NIFS and the adaptive optics system
ALTAIR at the Gemini North telescope. Angular differential imaging was used in
the J and H bands to suppress the speckle noise from GQ Lup A; we show that
this approach can provide improvements in signal-to-noise ratio (S/N) by a
factor of 2 - 6 for companions located at subarcsecond separations. Based on
high quality observations and GAIA synthetic spectra, we estimate the companion
effective temperature to Teff = 2400 +/- 100 K, its gravity to log g = 4.0 +/-
0.5, and its luminosity to log(L/L_s) = -2.47 +/- 0.28. Comparisons with the
predictions of the DUSTY evolutionary tracks allow us to constrain the mass of
GQ Lup b to 8 - 60 MJup, most likely in the brown dwarf regime. Compared with
the spectra published by Seifahrt and collaborators, our spectra of GQ Lup b
are significantly redder (by 15 - 50%) and do not show important Pa\beta
emission. Our spectra are in excellent agreement with the lower S/N spectra
previously published by McElwain and collaborators.


  Low Earth Orbit (LEO) satellites are being extensively researched in the
development of secure Internet of Remote Things (IoRT). In scenarios with
miniaturized terminals, the limited transmission power and long transmission
distance often lead to low Signal-to-Noise Ratio (SNR) at the satellite
receiver, which degrades communication performance. A solution to address this
issue is the utilization of cooperative satellites, which can combine signals
received from multiple satellites, thereby significantly improve SNR. However,
in order to maximize the combination gain, the signal coherent combining is
necessary, which requires the carrier frequency and phase of each receiving
signal to be aligned.
  Under low SNR circumstances, carrier parameter estimation can be a
significant challenge, especially for short burst transmission with no training
sequence. In order to tackle it, we propose an iterative code-aided estimation
algorithm for joint Carrier Frequency Offset (CFO) and Carrier Phase Offset
(CPO). The Cram\'er-Rao Lower Bound (CRLB) is suggested as the limit on the
parameter estimation performance. Simulation results demonstrate that the
proposed algorithm can approach Bit Error Rate (BER) performance bound within
0.4 dB with regards to four-satellite collaboration.


  Low-temperature scanning tunneling microscopy and spectroscopy are employed
to investigate electron tunneling from a C60-terminated tip into a Cu(111)
surface. Tunneling between a C60 orbital and the Shockley surface states of
copper is shown to produce negative differential conductance (NDC) contrary to
conventional expectations. NDC can be tuned through barrier thickness or C60
orientation up to complete extinction. The orientation dependence of NDC is a
result of a symmetry matching between the molecular tip and the surface states.


  A number which is either the square of an integer or two times the square of
an integer is called squarish. There are two main results in the literature on
graphs whose number of perfect matchings is squarish: one due to Jockusch (for
planar graphs invariant under rotation by 90 degrees) and the other due to the
second author (concerning planar graphs with two perpendicular symmetry axes).
We present in this paper a new such class, consisting of certain planar graphs
that are only required to have one symmetry axis. Our proof relies on a natural
bijection between the set of perfect matchings of two closely related (but not
isomorphic!) families of graphs, which is interesting in its own right. The
rephrasing of this bijection in terms of spanning trees turns out to be the
most natural way to present this result.
  The basic move in the construction of the above bijection (which we call
gliding) can also be used to extend Temperley's classical bijection between
spanning trees of a planar graph and perfect matchings of a closely related
graph. We present this, and as an application we answer an open question posed
by Corteel, Huang and Krattenthaler.
  We also discuss another dimer bijection (used in the proof of the second
author's result mentioned above), and deduce from a refinement of it new
results for spanning trees. These include a finitary version of an independence
result for the uniform spanning tree on $\Z^2$ due to Johansson, a counterpart
of it, and a bijective proof of an independence result on edge inclusions in
the uniform spanning tree on $\Z^2$ due to Lyons.


  We study the inbound supply mode and inventory management decision making for
a company that sells an assortment of products. Stochastic demand for each
product arrives periodically and unmet demand is backlogged. Each product has
two distinct supply modes that may be different suppliers or different
transport modes from the same supplier. These supply modes differ in terms of
their carbon emissions, speed, and costs. The company needs to decide when to
ship how much using which supply mode such that total holding, backlog, and
procurement costs are minimized while the emissions associated with different
supply modes across the assortment remains below a certain target level. Since
the optimal policy for this inventory system is highly complex, we assume that
shipment decisions for each product are governed by a dual-index policy. This
policy dynamically prescribes shipment quantities with both supply modes based
on the on-hand inventory, the backlog, and the products that are still
in-transit. We formulate this decision problem as a mixed integer linear
program that we solve through Dantzig-wolfe decomposition. We benchmark our
decision model against two state-of-the-art approaches in a large test-bed
based on real-life carbon emissions data. Relative to our decision model, the
first benchmark lacks the flexibility to dynamically ship products with two
supply modes while the second benchmark makes supply mode decisions for each
product individually rather than holistically for the entire assortment. Our
computational experiment shows that our decision model can outperform the first
and second benchmark by up to 15 and 40 percent, respectively, for realistic
targets for carbon emission reduction.


  Given a right factor and a left factor of a Linear Partial Differential
Operator (LPDO), under which conditions we can refine these two-factor
factorizations into one three-factor factorization? This problem is solved for
LPDOs of arbitrary order and number of variables. A more general result for the
incomplete factorizations of LPDOs is proved as well.


  Osteoclast cell image analysis plays a key role in osteoporosis research, but
it typically involves extensive manual image processing and hand annotations by
a trained expert. In the last few years, a handful of machine learning
approaches for osteoclast image analysis have been developed, but none have
addressed the full instance segmentation task required to produce the same
output as that of the human expert led process. Furthermore, none of the prior,
fully automated algorithms have publicly available code, pretrained models, or
annotated datasets, inhibiting reproduction and extension of their work. We
present a new dataset with ~2*10^5 expert annotated mouse osteoclast masks,
together with a deep learning instance segmentation method which works for both
in vitro mouse osteoclast cells on plastic tissue culture plates and human
osteoclast cells on bone chips. To our knowledge, this is the first work to
automate the full osteoclast instance segmentation task. Our method achieves a
performance of 0.82 mAP_0.5 (mean average precision at intersection-over-union
threshold of 0.5) in cross validation for mouse osteoclasts. We present a novel
nuclei-aware osteoclast instance segmentation training strategy (NOISe) based
on the unique biology of osteoclasts, to improve the model's generalizability
and boost the mAP_0.5 from 0.60 to 0.82 on human osteoclasts. We publish our
annotated mouse osteoclast image dataset, instance segmentation models, and
code at github.com/michaelwwan/noise to enable reproducibility and to provide a
public tool to accelerate osteoporosis research.


  We investigate wave amplification through the Electron Cyclotron Maser
mechanism.
  We derive a semi-analytic approximation formula giving the frequencies at
which the absorption coefficient is negative. The coefficients still need to be
computed to obtain the largest, and therefore the dominant, coefficient.


  Unfamiliar, large-scale virtual environments are difficult to navigate. This
paper presents design guidelines to ease navigation in such virtual
environments. The guidelines presented here focus on the design and placement
of landmarks in virtual environments. Moreover, the guidelines are based
primarily on the extensive empirical literature on navigation in the real
world. A rationale for this approach is provided by the similarities between
navigational behavior in real and virtual environments.


  Let $p$ be a prime and let $K$ be a finite extension of the field ${\bf Q}_p$
of $p$-adic numbers such that the group ${}_pK^\times$ has order $p$. The ${\bf
F}_p$-space $K^\times\!/K^{\times p}$ carries a natural filtration coming from
the valuation on $K$, and a natural bilinear pairing coming from the
reciprocity isomorphism for the exponent~$p$. We determine the orthogonal
filtration for this pairing. We also prove the analogous result for $p$-fields
of characteristic $p$.


  We review the intimate connection between (super-)gravity close to a
spacelike singularity (the "BKL-limit") and the theory of Lorentzian Kac-Moody
algebras. We show that in this limit the gravitational theory can be
reformulated in terms of billiard motion in a region of hyperbolic space,
revealing that the dynamics is completely determined by a (possibly infinite)
sequence of reflections, which are elements of a Lorentzian Coxeter group. Such
Coxeter groups are the Weyl groups of infinite-dimensional Kac-Moody algebras,
suggesting that these algebras yield symmetries of gravitational theories. Our
presentation is aimed to be a self-contained and comprehensive treatment of the
subject, with all the relevant mathematical background material introduced and
explained in detail. We also review attempts at making the infinite-dimensional
symmetries manifest, through the construction of a geodesic sigma model based
on a Lorentzian Kac-Moody algebra. An explicit example is provided for the case
of the hyperbolic algebra E10, which is conjectured to be an underlying
symmetry of M-theory. Illustrations of this conjecture are also discussed in
the context of cosmological solutions to eleven-dimensional supergravity.


  The increasing number of RSOs has raised concerns about the risk of
collisions and catastrophic incidents for all direct and indirect users of
space. To mitigate this issue, it is essential to have a good understanding of
the various RSOs in orbit and their behaviour. A well-established taxonomy
defining several classes of RSOs is a critical step in achieving this
understanding. This taxonomy helps assign objects to specific categories based
on their main characteristics, leading to better tracking services.
Furthermore, a well-established taxonomy can facilitate research and analysis
processes by providing a common language and framework for better understanding
the factors that influence RSO behaviour in space. These factors, in turn, help
design more efficient and effective strategies for space traffic management.
Our work proposes a new taxonomy for RSOs focusing on the low Earth orbit
regime to enhance space traffic management. In addition, we present a deep
learning-based model that uses an autoencoder architecture to reduce the
features representing the characteristics of the RSOs. The autoencoder
generates a lower-dimensional space representation that is then explored using
techniques such as Uniform Manifold Approximation and Projection to identify
fundamental clusters of RSOs based on their unique characteristics. This
approach captures the complex and non-linear relationships between the features
and the RSOs' classes identified. Our proposed taxonomy and model offer a
significant contribution to the ongoing efforts to mitigate the overall risks
posed by the increasing number of RSOs in orbit.


  We present the general diagonal and, in some cases, non-diagonal solutions of
the boundary Yang-Baxter equation for a number of related
interaction-round-a-face models, including the standard and dilute A_L, D_L and
E_{6,7,8} models.


  We test the validity of Feynman's idea that a two-slit experiment performed
with classical objects (bullets) does not produce observable interference
fringes on the detection screen because the Compton's wavelength of the bullets
is so tiny, that no real detector could resolve individual interference
fringes, thus producing only an average signal which is the observed smooth
curve. To test this idea, we study the two-slit experiment in two different
situations using light to simulate both wave-like and particle-like bullets. In
the first case, we consider coherent light with short wavelengths and in the
second case incoherent light with not-so-short wavelength. While in the former
case (simulating Feynman's wave-like bullets) the interference fringes are so
dense that they cannot be resolved by a detector, therefore resulting in an
averaged smooth signal, in the latter case (simulating Feynman's particle-like
bullets), although the detector is fully capable of discriminating each fringe,
the observed classical smooth pattern limit is produced because of the lack of
spatial coherence of the impinging field.


  The Android platform was introduced by Google in 2008 as an operating system
for mobile devices. Android's SDK provides a wide support for programming and
extensive examples and documentation. Reliability is an increasing concern for
Smart Phone applications since they often feature personal information and
data. Therefore, techniques and tools for checking the correct behavior of apps
are required. This paper shows how the Event-B method can be used to reason and
to verify the design of Android apps and how this can be used to document
implementation decisions. Our approach consists in modeling the core
functionality of the app in Event-B and using the evidence shown by the Proof
Obligations generated to reason about the design and the implementation of the
app. Although we do not propose a novel approach, we prove that heavyweight
Formal Methods (FMs) techniques with Event-B can effectively be used to support
the development of correct Android apps. We present a case study in which we
design the core functionality of WhatsApp in Event-B, we encode it over three
machine refinements modeling basic functionality (chatting, deleting content,
forwarding content, deleting a chat session, etc.), read and unread status of
chat sessions, and implementation details, respectively. We report and discuss
on underlying challenges in the design and implementation of the core
functionality.


  We present our recent progress on the relativistic modeling of
neutrino-nucleus reactions for their implementation in MonteCarlo event
generators (GENIE, NEUT) employed in neutrino oscillation experiments. We
compare charged-current neutrino and antineutrino cross sections obtained
within the SuSAv2 model, which is based on the Relativistic Mean Field theory
and on the analysis of the superscaling behavior exhibited by ($e,e'$) data. We
also evaluate and discuss the impact of multi-nucleon excitations arising from
2p-2h states excited by the action of weak forces in a fully relativistic
framework, showing for the first time their implementation in GENIE and their
comparison with recent T2K data.


  Zagier proved that the traces of singular moduli, i.e., the sums of the
values of the classical j-invariant over quadratic irrationalities, are the
Fourier coefficients of a modular form of weight 3/2 with poles at the cusps.
Using the theta correspondence, we generalize this result to traces of CM
values of (weakly holomorphic) modular functions on modular curves of arbitrary
genus. We also study the theta lift for the weight 0 Eisenstein series for
SL(2,Z) and realize a certain generating series of arithmetic intersection
numbers as the derivative of Zagier's Eisenstein series of weight 3/2. This
recovers a result of Kudla, Rapoport and Yang.


  A simple Regge-eikonal model with the eikonal represented as a
single-reggeon-exchange term is applied to description of the nucleon-nucleon
elastic diffractive scattering at ultra-high energies. The range of validity of
the proposed approximation is discussed. The model predictions for the
proton-proton cross-sections at the collision energy 14 TeV are given.


  Post-training quantization reduces the computational demand of Large Language
Models (LLMs) but can weaken some of their capabilities. Since LLM abilities
emerge with scale, smaller LLMs are more sensitive to quantization. In this
paper, we explore how quantization affects smaller LLMs' ability to perform
retrieval-augmented generation (RAG), specifically in longer contexts. We chose
personalization for evaluation because it is a challenging domain to perform
using RAG as it requires long-context reasoning over multiple documents. We
compare the original FP16 and the quantized INT4 performance of multiple 7B and
8B LLMs on two tasks while progressively increasing the number of retrieved
documents to test how quantized models fare against longer contexts. To better
understand the effect of retrieval, we evaluate three retrieval models in our
experiments. Our findings reveal that if a 7B LLM performs the task well,
quantization does not impair its performance and long-context reasoning
capabilities. We conclude that it is possible to utilize RAG with quantized
smaller LLMs.


  While studying nucleic acids to reveal the weak interactions responsible for
their three-dimensional structure and for their interactions with drugs, we
also contributed to the field of biomolecular mass spectrometry, both in terms
of fundamental understanding and with new methodological developments. A first
goal was to develop mass spectrometry approaches to detect non-covalent
interactions between antitumor drugs and their DNA target. Twenty years ago,
our attention turned towards specific DNA structures such as the G-quadruplex
(a structure formed by guanine-rich strands). Mass spectrometry allows to
discern which molecules interact with one another by measuring the masses of
the complexes, and quantity the affinities by measuring their abundance. The
most important findings came from unexpected masses. For example, we showed the
formation of higher- or lower-order structures by G-quadruplexes used in
traditional biophysical assays. We can also derive complete thermodynamic and
kinetic description of G-quadruplex folding pathways by measuring cation
binding, one at a time. Getting quantitative information required accounting
for nonspecific adduct formation and for the response factors of the different
molecular forms. But with these caveats in mind, the approach is mature enough
for routine biophysical characterization of nucleic acids. A second goal is to
obtain more detailed structural information on each of the complexes separated
by the mass spectrometer. One such approaches is ion mobility spectrometry, and
even today the challenge lies in the structural interpretation of the
measurements. We showed that, although structures such as G-quadruplexes are
well-preserved in the MS conditions, double helices actually get more compact
in the gas phase. These major rearrangements forced us to challenge comfortable
assumptions. Further work is still needed to generalize how to deduce
structures in solution from ion mobility spectrometry data, and in particular
how to account for the electrospray charging mechanisms and for ion internal
energy effects. These studies also called for complementary approaches to ion
mobility. Recently, we applied isotope exchange labeling mass spectrometry to
characterize nucleic acid structures for the first time, and we reported the
first ever circular dichroism ion spectroscopy measurement on mass-selected
trapped ions. Circular dichroism plays a key role in assigning the stacking
topology, and our new method now opens the door to characterizing a wide
variety of chiral molecule by mass spectrometry. In summary, advanced mass
spectrometry approaches to characterize gas-phase structures work well for
G-quadruplexes because they are stiffened by inner cations. The next objective
will be to generalize these methodologies to a wider range of nucleic acid
structures.


  The notion of Reactive Turing machine (RTM) was proposed as an orthogonal
extension of Turing machines with interaction. RTMs are used to define the
notion of executable transition system in the same way as Turing machines are
used to define the notion of computable function on natural numbers. RTMs
inherited finiteness of all sets involved from Turing machines, and as a
consequence, in a single step, an RTM can only communicate elements from a
finite set of data. Some process calculi, such as the pi-calculus, essentially
depend on an infinite alphabet of actions, and hence it immediately follows
that transition systems specified in these calculi are not executable. On
closer inspection, however, the pi-calculus does not appear to use the infinite
data in a non-computable manner.
  In this paper, we investigate several ways to relax the finiteness
requirement. We start by considering a variant of RTMs in which all sets are
allowed to be countable, and we get a notion of infinitary RTM. Infinitary RTMs
are extremely expressive such that we can hardly use them as a expressiveness
criterion. Then, we refine the model by adding extra restrictions. As a result,
we define a notion of RTM with atoms. It is a more restricted variant of RTMs
in which the sets of actions and data symbols are still allowed to be infinite.
We propose a notion of of nominal executability based on RTMs with atoms, and
show that every effective transition system with atoms is nominally executable.
It will follow that processes definable in the pi-calculus are nominally
executable. In contrast, in the process specification language mCRL2 it is
possible to specify processes that are not nominally executable. Thus, nominal
executability provides a new expressiveness criterion for process calculi.


  Planar flows with a free boundary in a partially filled and nonuniformly
rotating container, with a strongly noncircular shape of the cross-section, are
investigated numerically within the ideal fluid approximation. Vorticity is
assumed constant across the fluid, thus allowing us to apply the recently
developed, highly efficient numerical method based upon exact equations of
motion of the free boundary in terms of conformal variables and on the fast
Fourier transform algorithms. It is shown that decelerated rotation of such
centrifuge leads to formation of strongly nonlinear breaking waves with sharp
crests, and the wave overturning occurs either in the rotation direction or
against it, depending on value of the (negative) angular acceleration.


  We study the d-dimensional random Ising model using a Bethe-Peierls
approximation in the framework of the replica method. We take into account the
correct interaction only inside replicated clusters of spins. Our ansatz is
that the interaction of the borders of the clusters with the external world can
be described via an effective interaction among replicas. The Bethe-Peierls
model is mapped into a single Ising model with a random gaussian field, whose
strength (related to the effective coupling between two replicas) is determined
via a self-consistency equation. This allows us to obtain analytic estimates of
the internal energy and of the critical temperature in d dimensions.


  Using the Fermat's principle in curved space-time with stationary type
metric, we have obtained the speed of light as a function of spatial
coordinates and hence the corresponding refractive index. The whole region with
space dependent gravity is divided into a number of overlapping transparent
refracting media with varying refractive index. The refractive index is found
to be increasing with the strength of gravitational field. Hence using the laws
of refraction, we have explained the gravitational bending of light. Further
using the conventional idea of total internal reflection of light while going
from denser to rarer medium, in the present scenario it is the propagation of
light from the region of ultra-strong gravitational field to relatively weaker
gravitational field region, we have proposed an alternative approach for no
emission of any kind of electromagnetic radiation from the surface of a
classical Schwarzschild Black Hole. We have further noticed that for an
observer in a uniformly accelerated frame, analogous to the Unruh radiation,
there can be emission of electromagnetic waves from the event horizon of a
classical black hole. This may be named as "black hole Unruh effect".


  Deep reinforcement learning (RL) methods often require many trials before
convergence, and no direct interpretability of trained policies is provided. In
order to achieve fast convergence and interpretability for the policy in RL, we
propose a novel RL method for text-based games with a recent neuro-symbolic
framework called Logical Neural Network, which can learn symbolic and
interpretable rules in their differentiable network. The method is first to
extract first-order logical facts from text observation and external word
meaning network (ConceptNet), then train a policy in the network with directly
interpretable logical operators. Our experimental results show RL training with
the proposed method converges significantly faster than other state-of-the-art
neuro-symbolic methods in a TextWorld benchmark.


  In this paper, we study adversarial training on datasets that obey the
long-tailed distribution, which is practical but rarely explored in previous
works. Compared with conventional adversarial training on balanced datasets,
this process falls into the dilemma of generating uneven adversarial examples
(AEs) and an unbalanced feature embedding space, causing the resulting model to
exhibit low robustness and accuracy on tail data. To combat that, we
theoretically analyze the lower bound of the robust risk to train a model on a
long-tailed dataset to obtain the key challenges in addressing the
aforementioned dilemmas. Based on it, we propose a new adversarial training
framework -- Re-balancing Adversarial Training (REAT). This framework consists
of two components: (1) a new training strategy inspired by the effective number
to guide the model to generate more balanced and informative AEs; (2) a
carefully constructed penalty function to force a satisfactory feature space.
Evaluation results on different datasets and model structures prove that REAT
can effectively enhance the model's robustness and preserve the model's clean
accuracy. The code can be found in https://github.com/GuanlinLee/REAT.


  Game-theoretic motion planners are a powerful tool for the control of
interactive multi-agent robot systems. Indeed, contrary to predict-then-plan
paradigms, game-theoretic planners do not ignore the interactive nature of the
problem, and simultaneously predict the behaviour of other agents while
considering change in one's policy. This, however, comes at the expense of
computational complexity, especially as the number of agents considered grows.
In fact, planning with more than a handful of agents can quickly become
intractable, disqualifying game-theoretic planners as possible candidates for
large scale planning. In this paper, we propose a planning algorithm enabling
the use of game-theoretic planners in robot systems with a large number of
agents. Our planner is based on the reality of locality of information and thus
deploys local games with a selected subset of agents in a receding horizon
fashion to plan collision avoiding trajectories. We propose five different
principled schemes for selecting game participants and compare their collision
avoidance performance. We observe that the use of Control Barrier Functions for
priority ranking is a potent solution to the player selection problem for
motion planning.


  Identifiability, or recovery of the true latent representations from which
the observed data originates, is de facto a fundamental goal of representation
learning. Yet, most deep generative models do not address the question of
identifiability, and thus fail to deliver on the promise of the recovery of the
true latent sources that generate the observations. Recent work proposed
identifiable generative modelling using variational autoencoders (iVAE) with a
theory of identifiability. Due to the intractablity of KL divergence between
variational approximate posterior and the true posterior, however, iVAE has to
maximize the evidence lower bound (ELBO) of the marginal likelihood, leading to
suboptimal solutions in both theory and practice. In contrast, we propose an
identifiable framework for estimating latent representations using a flow-based
model (iFlow). Our approach directly maximizes the marginal likelihood,
allowing for theoretical guarantees on identifiability, thereby dispensing with
variational approximations. We derive its optimization objective in analytical
form, making it possible to train iFlow in an end-to-end manner. Simulations on
synthetic data validate the correctness and effectiveness of our proposed
method and demonstrate its practical advantages over other existing methods.


  Spin orbit coupling changes graphene, in principle, into a two-dimensional
topological insulator, also known as quantum spin Hall insulator. One of the
expected consequences is the existence of spin-filtered edge states that carry
dissipationless spin currents and undergo no back-scattering in the presence of
non-magnetic disorder, leading to quantization of conductance. Whereas, due to
the small size of spin orbit coupling in graphene, the experimental observation
of these remarkable predictions is unlikely, the theoretical understanding of
these spin-filtered states is shedding light on the electronic properties of
edge states in other two-dimensional quantum spin Hall insulators. Here we
review the effect of a variety of perturbations, like curvature, disorder, edge
reconstruction, edge crystallographic orientation, and Coulomb interactions on
the electronic properties of these spin filtered states.


  The influence of final-state $NN$-rescattering on the beam asymmetry $\Sigma$
for linearly polarized photons in $\pi^-$ photoproduction on the deuteron in
the energy range from $\pi$-threshold through the $\Delta$(1232)-resonance has
been investigated. Numerical results for this spin observable are predicted and
compared with recent experimental data from the LEGS Spin collaboration.
Final-state $NN$-rescattering is found to be quite important and leads to a
better agreement with existing experimental data. Furthermore, the differences
with other theoretical models have been discussed.


  The quark mass function $\Sigma(p)$ in QCD is revisited, using a gluon
propagator in the form $1/(k^2 + m_g^2)$ plus $2\mu^2/ (k^2 + m_g^2)^2$, where
the second (IR) term gives linear confinement for $m_g = 0$ in the
instantaneous limit, $\mu$ being another scale. To find $\Sigma(p)$ we propose
a new (differential) form of the Dyson-Schwinger Equation (DSE) for
$\Sigma(p)$, based on an infinitesimal $subtractive$ Renormalization via a
differential operator which $lowers$ the degree of divergence in integration on
the RHS, by $TWO$ units. This warrants $\Sigma(p-k)\approx \Sigma(p)$ in the
integrand since its $k$-dependence is no longer sensitive to the principal term
$(p-k)^2$ in the quark propagator. The simplified DSE (which incorporates WT
identity in the Landau gauge) is satisfied for large $p^2$ by $\Sigma(p)$ =
$\Sigma(0)/(1 + \beta p^2)$, except for Log factors. The limit $p^2 =0$
determines $\Sigma_0$.A third limit $p^2 = -m_0^2$ defines the $dynamical$ mass
$m_0$ via $\Sigma(im_0) = + m_0$. After two checks ($f_\pi = 93\pm 1 MeV $ and
$ <q{\bar q}>$= $(280 \pm 5 MeV)^3$), for $1.5<\beta<2$ with $\Sigma_0=300
MeV$, the T- dependent DSE is used in the real time formalism to determine the
"critical" index $\gamma= 1/3$ analytically, with the IR term partly serving
for the $H$ field. We find $T_c = 180 \pm 20 MeV$ and check the vanishing of
$f_\pi$ and $<q{\bar q}>$ at $T_c$. PACS: 24.85.+p; 12.38.Lg; 12.38.Aw.


  The sedenion compounding fields and their quantum interplays can be presented
by analogy with the octonionic electromagnetic, gravitational, strong and weak
interactions. In sedenion fields which are associated with electromagnetic,
gravitational, strong and weak interactions, the study deduces some conclusions
of field source particles and intermediate particles which are consistent with
current electro-weak theory. In the sedenion fields which are associated with
the hyper-strong and strong-weak fields, the paper draws some predicts and
conclusions of the field source particles and intermediate particles. The
researches results show that there may exist some new field source particles in
the sedenion compounding fields.


  Quadratic operators are used in transforming the model Hamiltonian (H) of one
correlated and dispersive band in an unique positive semidefinite form coopting
both the kinetic and interacting part of H. The expression is used in deducing
exact ground states which are minimum energy eigenstates only of the full
Hamiltonian. It is shown in this frame that at half filling, also dispersive
bands can provide ferromagnetism in exact terms by correlation effects .


  We present a search for the rare leptonic decays $B^+ \to e^+ \nu_e$ and $B^+
\to \mu^+ \nu_\mu$, using the full $\Upsilon(4S)$ data sample of $772\times
10^6$ $B\bar{B}$ pairs collected with the Belle detector at the $\rm {\small
KEKB}$ asymmetric-energy $e^+ e^-$ collider. One of the $B$ mesons from the
$\Upsilon(4S) \to B\bar{B}$ decay is fully reconstructed in a hadronic mode,
while the recoiling side is analyzed for the signal decay. We find no evidence
of a signal in any of the decay modes. Upper limits of the corresponding
branching fractions are determined as ${\cal B}(B^+ \to e^+ \nu_e)<3.5\times
10^{-6}$ and ${\cal B}(B^+ \to \mu^+ \nu_\mu)<2.7 \times 10^{-6}$ at 90$\%$
confidence level.


  Geometric-optical illusions (GOI) are a subclass of a vast variety of visual
illusions. A special class of GOIs originates from the superposition of a
simple geometric figure ("target") with an array of non-intersecting
curvilinear elements ("context") that elicits a perceptual distortion of the
target element. Here we specifically deal with the case of circular targets.
Starting from the fact that (half)circles are geodesics in a model of
hyperbolic geometry, we conceive of the deformations of the target as resulting
from a context-induced perturbation of that "base" geometry. We present
computational methods for predicting distorted shapes of the target in
different contexts, and we report the results of a psychophysical pilot
experiment with eight subjects and four contexts to test the predictions.
Finally, we propose a common scheme for modeling GOIs associated with more
general types of target curves, subsuming those studied previously.


  The precision obtainable in phenomenological applications of Chiral
Perturbation Theory is currently limited by our lack of knowledge on the
low-energy constants (LECs). The assumption that the most important
contributions to the LECs come from the dynamics of the low-lying resonances,
often referred to as the resonance saturation hypothesis, has stimulated the
use of large-Nc resonance lagrangians in order to obtain explicit values for
the LECs. We study the validity of the resonance saturation assumption at the
next-to-leading order in the 1/Nc expansion within the framework of Resonance
Chiral Theory (RChT). We find that, by imposing QCD short-distance constraints,
the chiral couplings can be written in terms of the resonance masses and
couplings and do not depend explicitly on the coefficients of the chiral
operators in the Goldstone boson sector of RChT. As we argue, this is the
counterpart formulation of the resonance saturation statement in the context of
the resonance lagrangian. Going beyond leading order in the 1/Nc counting allow
us to keep full control of the renormalization scale dependence of the LEC
estimates.


  We show that the general Enriques surface can be recovered from the Kuznetsov
component of its bounded derived category of coherent sheaves.


  We present a study of static and frequency-dependent diffusion with
anisotropic thermally activated transition rates in a two-dimensional bond
percolation system. The approach accounts for temperature effects on diffusion
coefficients in disordered anisotropic systems. Static diffusion shows an
Arrhenius behavior for low temperatures with an activation energy given by the
highest energy barrier of the system. From the frequency-dependent diffusion
coefficients we calculate a characteristic frequency $\omega_{c}\sim 1/t_{c}$,
related to the time $t_c$ needed to overcome a characteristic barrier. We find
that $\omega_c$ follows an Arrhenius behavior with different activation
energies in each direction.


  Silicon and Germanium monopnictides SiP, SiAs, GeP and GeAs form a family of
2D layered semiconductors. We have succeeded in growing bulk single crystals of
these compounds by melt-growth under high pressure (0.5-1 GPa) in a cubic anvil
hot press. Large (mm-size), shiny, micaceous crystals of GeP, GeAs and SiAs
were obtained, and could be exfoliated into 2D flakes. Small and brittle
crystals of SiP were yielded by this method. High-pressure sintered
polycrystalline SiP and GeAs have also been successfully used as a precursor in
the Chemical Vapor Transport growth of these crystals in the presence of
I$_{2}$ as a transport agent. All compounds are found to crystallize in the
expected layered structure and do not undergo any structural transition at low
temperature, as shown by Raman spectroscopy down to T=5K. All materials exhibit
a semiconducting behavior. The electrical resistivity of GeP, GeAs and SiAs is
found to depend on temperature following a 2D-Variable Range Hopping conduction
mechanism. The availability of bulk crystals of these compounds opens new
perspectives in the field of 2D semiconducting materials for device
applications.


  Modal expansion is an attractive technique for solving electromagnetic
scattering problems. With the one set of resonator modes, calculated once and
for all, any configuration of near-field or far-field sources can be obtained
almost instantaneously. Traditionally applied to closed systems, a simple and
rigorous generalization of modal expansion to open systems using
eigenpermittivity states is also available. These open modes are suitable for
typical nanophotonic systems, for example. However, the numerical generation of
modes is usually the most difficult and time-consuming step of modal expansion
techniques. Here, we demonstrate efficient and reliable mode generation,
expanding the target modes into the modes of a simpler open system that are
known. Such a re-expansion technique is implemented for resonators with
non-uniform permittivity profiles, demonstrating its rapid convergence. Key to
the method's success is the inclusion of a set of longitudinal basis modes.


  Regularization-based approaches for injecting constraints in Machine Learning
(ML) were introduced to improve a predictive model via expert knowledge. We
tackle the issue of finding the right balance between the loss (the accuracy of
the learner) and the regularization term (the degree of constraint
satisfaction). The key results of this paper is the formal demonstration that
this type of approach cannot guarantee to find all optimal solutions. In
particular, in the non-convex case there might be optima for the constrained
problem that do not correspond to any multiplier value.


  Electrostatic (E) fields associated with the interaction of a
well-controlled, high-power, nanosecond laser pulse with an underdense plasma
are diagnosed by proton radiography. Using a current 3D wave propagation code
equipped with nonlinear and nonlocal hydrodynamics, we can model the measured
E-fields that are driven by the laser ponderomotive force in the region where
the laser undergoes filamentation. However, strong fields of up to 110 MV/m
measured in the first millimeter of propagation cannot be reproduced in the
simulations. This could point to the presence of unexpected strong thermal
electron pressure gradients possibly linked to ion acoustic turbulence, thus
emphasizing the need for the development of full kinetic collisional
simulations in order to properly model laser-plasma interaction in these
strongly nonlinear conditions.


  In this paper we study hermitian kernels invariant under the action of a
semigroup with involution. We characterize those hermitian kernels which
realize the given action by bounded operators on a Krein space. Applications to
the GNS representation of *-algebras associated to hermitian functionals are
given. We explain the key role played by the Kolmogorov decomposition in the
construction of Weyl exponentials associated to an indefinite inner product and
in the dilation thoery of hermitian maps on C*-algebras.


  Smoothed Dissipative Particle Dynamics (SDPD) is a mesoscopic particle method
which allows to select the level of resolution at which a fluid is simulated.
The numerical integration of its equations of motion still suffers from the
lack of numerical schemes satisfying all the desired properties: energy
conservation, parallelizability and stability. The similarities between SDPD
and Dissipative Particle Dynamics with Energy conservation (DPDE), which is
another coarse-grained model, enable the adaptation of recent numerical schemes
developed for DPDE to the SDPD setting. In this article, we introduce a
Metropolis step in the integration of the fluctuation/dissipation part of SDPD
to improve its stability.


  We give a new proof of the four-color theorem by exhibiting an unavoidable
set of 2822 D-reducible configurations. The existence of such a set had been
conjectured by several researchers including Stromquist, Appel and Haken, and
Robertson, Sanders, Seymour and Thomas.


  The Oceanus Procellarum region, characterized by its vast basaltic plains and
pronounced volcanic activity, serves as a focal point for understanding the
volcanic history of the Moon. Leveraging the Gravity Recovery and Interior
Laboratory (GRAIL) mission data, we imaged the magmatic structures beneath the
Oceanus Procellarum region. Our 3D density models uncover pronounced linear
magmatic structures along the Procellarum's western border and significant
intrusions within the northern and southern Marius Hills. Crucially, they
reveal three narrow near-horizontal sheeted magmatic structures, 80-150 km
long, extending from near-surface to 6- 7 km depth, which we identified as
sill-like magmatic conduits. These magmatic conduits connect the Marius Hills'
northern and southern intrusions and bridge them with the Procellarum's western
border structures. These discoveries suggest that sill-like magmatic conduits
likely serve as central pathways facilitating magma transport across various
volcanic systems and furthermore indicate widespread magmatic connectivity
beneath the Oceanus Procellarum.


  We consider regression models involving multilayer perceptrons (MLP) with one
hidden layer and a Gaussian noise. The estimation of the parameters of the MLP
can be done by maximizing the likelihood of the model. In this framework, it is
difficult to determine the true number of hidden units because the information
matrix of Fisher is not invertible if this number is overestimated. However, if
the parameters of the MLP are in a compact set, we prove that the minimization
of a suitable information criteria leads to consistent estimation of the true
number of hidden units.


  Pomsets are a model of concurrent computations introduced by Pratt. They can
provide a syntax-oblivious description of semantics of coordination models
based on asynchronous message-passing, such as Message Sequence Charts (MSCs).
In this paper, we study conditions that ensure a specification expressed as a
set of pomsets can be faithfully realised via communicating automata. Our main
contributions are (i) the definition of a realisability condition accounting
for termination soundness, (ii) conditions for global specifications with
"multi-threaded" participants, and (iii) the definition of realisability
conditions that can be decided directly over pomsets. A positive by-product of
our approach is the efficiency gain in the verification of the realisability
conditions obtained when restricting to specific classes of choreographies
characterisable in term of behavioural types.


  For the importance of differentiation theorems in metric spaces (starting
with Pansu Rademacher type theorem in Carnot groups) and relations with
rigidity of embeddings see the section 1.2 in Cheeger and Kleiner paper
arXiv:math/0611954 and its bibliographic references.
  Here we propose another type of differentiation theorem, which does not
involve measures. It is therefore different from Rademacher type theorems.
Instead, this differentiation theorem (and the concept of uniformly topological
derivable function) is formulated in terms of filters in topological spaces.


  In this short note we consider several widely used L^2-orthogonal Helmholtz
decompositions for bounded domains in R^3. It is well known that one part of
the decompositions is a subspace of the space of functions with zero mean. We
refine this global property into a local equivalent: we show that functions
from these spaces have zero mean in every part of specific decompositions of
the domain.
  An application of the zero mean properties is presented for convex domains.
We introduce a specialized Poincare-type inequality, and estimate the related
unknown constant from above. The upper bound is derived using the upper bound
for the Poincare constant proven by Payne and Weinberger. This is then used to
obtain a small improvement of upper bounds of two Maxwell-type constants
originally proven by Pauly.
  Although the two dimensional case is not considered, all derived results can
be repeated in R^2 by similar calculations.


  We present simplified models for the galactic center gamma-ray excess where
Dirac dark matter annihilates into pairs or triplets of on-shell bosonic
mediators to the Standard Model. These annihilation modes allow the dark matter
mass to be heavier than those of conventional effective theories for the
gamma-ray excess. Because the annihilation rate is set by the dark
matter--mediator coupling, the Standard Model coupling can be made
parametrically small to `hide' the dark sector by suppressing direct detection
and collider signals. We explore the viability of these models as a thermal
relic and on the role of the mediators for controlling the gamma-ray spectral
shape. We comment on ultraviolet completions for these simplified models and
novel options for Standard Model final states.


  In this study, we propose a new definition of multivariate conditional
value-at-risk (MCVaR) as a set of vectors for discrete probability spaces. We
explore the properties of the vector-valued MCVaR (VMCVaR) and show the
advantages of VMCVaR over the existing definitions given for continuous random
variables when adapted to the discrete case.


  In traditional quantum optics, where the interaction between atoms and light
at optical frequencies is studied, the atoms can be approximated as point-like
when compared to the wavelength of light. So far, this relation has also been
true for artificial atoms made out of superconducting circuits or quantum dots,
interacting with microwave radiation. However, recent and ongoing experiments
using surface acoustic waves show that a single artificial atom can be coupled
to a bosonic field at several points wavelengths apart. Here, we theoretically
study this type of system. We find that the multiple coupling points give rise
to a frequency dependence in the coupling strength between the atom and its
environment, and also in the Lamb shift of the atom. The frequency dependence
is given by the discrete Fourier transform of the coupling point coordinates
and can therefore be designed. We discuss a number of possible applications for
this phenomenon, including tunable coupling, single-atom lasing, and other
effects that can be achieved by designing the relative coupling strengths of
different transitions in a multi-level atom.


  It is shown that small externally applied magnetic perturbations can
significantly alter important geometric properties of magnetic flux surfaces in
tokamaks. Through 3D shaping, experimentally relevant perturbation levels are
large enough to influence turbulent transport and MHD stability in the pedestal
region. It is shown that the dominant pitch-resonant flux surface deformations
are primarily induced by non-resonant 3D fields, particularly in the presence
of significant axisymmetric shaping. The spectral content of the applied 3D
field can be used to control these effects.


  Let $f$ be a $C^{1+bv}$ circle diffeomorphism with irrational rotation
number. As established by Douady and Yoccoz in the eighties, for any given
$s>0$ there exists a unique automorphic measure of exponent $s$ for $f$. In the
present paper we prove that the same holds for multicritical circle maps, and
we provide two applications of this result. The first one, is to prove that the
space of invariant distributions of order 1 of any given multicritical circle
map is one-dimensional, spanned by the unique invariant measure. The second
one, is an improvement over the Denjoy-Koksma inequality for multicritical
circle maps and absolutely continuous observables.


  Counterfactual fairness requires that a person would have been classified in
the same way by an AI or other algorithmic system if they had a different
protected class, such as a different race or gender. This is an intuitive
standard, as reflected in the U.S. legal system, but its use is limited because
counterfactuals cannot be directly observed in real-world data. On the other
hand, group fairness metrics (e.g., demographic parity or equalized odds) are
less intuitive but more readily observed. In this paper, we use $\textit{causal
context}$ to bridge the gaps between counterfactual fairness, robust
prediction, and group fairness. First, we motivate counterfactual fairness by
showing that there is not necessarily a fundamental trade-off between fairness
and accuracy because, under plausible conditions, the counterfactually fair
predictor is in fact accuracy-optimal in an unbiased target distribution.
Second, we develop a correspondence between the causal graph of the
data-generating process and which, if any, group fairness metrics are
equivalent to counterfactual fairness. Third, we show that in three common
fairness contexts$\unicode{x2013}$measurement error, selection on label, and
selection on predictors$\unicode{x2013}$counterfactual fairness is equivalent
to demographic parity, equalized odds, and calibration, respectively.
Counterfactual fairness can sometimes be tested by measuring relatively simple
group fairness metrics.


  We propose a traffic danger recognition model that works with arbitrary
traffic surveillance cameras to identify and predict car crashes. There are too
many cameras to monitor manually. Therefore, we developed a model to predict
and identify car crashes from surveillance cameras based on a 3D reconstruction
of the road plane and prediction of trajectories. For normal traffic, it
supports real-time proactive safety checks of speeds and distances between
vehicles to provide insights about possible high-risk areas. We achieve good
prediction and recognition of car crashes without using any labeled training
data of crashes. Experiments on the BrnoCompSpeed dataset show that our model
can accurately monitor the road, with mean errors of 1.80% for distance
measurement, 2.77 km/h for speed measurement, 0.24 m for car position
prediction, and 2.53 km/h for speed prediction.


  This paper discusses the systematic use of product feedback information to
support life-cycle design approaches and provides guidelines for developing a
design at both the product and the system levels. Design activities are
surveyed in the light of the product life cycle, and the design information
flow is interpreted from a semiotic perspective. The natural evolution of a
design is considered, the notion of design expectations is introduced, and the
importance of evaluation of these expectations in dynamic environments is
argued. Possible strategies for reconciliation of the expectations and
environmental factors are described. An Internet-enabled technology is proposed
to monitor product functionality, usage, and operational environment and supply
the designer with relevant information. A pilot study of assessing design
expectations of a refrigerator is outlined, and conclusions are drawn.


  We have used the ACS and WFC3 cameras on board HST to resolve stars in the
halo of the nearest giant elliptical (gE) galaxy NGC 5128 out to a projected
distance of 140 kpc (25 effective radii, Reff) along the major axis and 90 kpc
(16 Reff) along the minor axis. This dataset provides an unprecedented radial
coverage of the stellar halo properties in any gE galaxy. Color-magnitude
diagrams clearly reveal the presence of the red giant branch stars belonging to
the halo of NGC 5128, even in our most distant fields. The star counts
demonstrate increasing flattening of the outer halo, which is elongated along
the major axis of the galaxy. The V-I colors of the red giants enable us to
measure the metallicity distribution in each field and so map the gradient out
to ~16 Reff from the galaxy center along the major axis. A median metallicity
is obtained even for the outermost fields along both axes. We observe a smooth
transition from a metal-rich ([M/H]~0.0) inner galaxy to lower metallicity in
the outer halo, with the metallicity gradient slope along the major axis of
$\Delta$[M/H]/$\Delta$ R=-0.0054 $\pm$ 0.0006 dex/kpc. In the outer halo,
beyond ~10 Reff, the number density profile follows a power law, but also
significant field-to-field metallicity and star count variations are detected.
The metal-rich component dominates in all observed fields, and the median
metallicity is [M/H]>-1 dex in all fields.


  The rapid development of new generation radio interferometers such as the
Square Kilometer Array (SKA) has opened up unprecedented opportunities for
astronomical research. However, anthropogenic Radio Frequency Interference
(RFI) from communication technologies and other human activities severely
affects the fidelity of observational data. It also significantly reduces the
sensitivity of the telescopes. We proposed a robust Convolutional Neural
Network (CNN) model to identify RFI based on machine learning methods. We
overlaid RFI on the simulation data of SKA1-LOW to construct three visibility
function datasets. One dataset was used for modeling, and the other two were
used for validating the model's usability. The experimental results show that
the Area Under the Curve (AUC) reaches 0.93, with satisfactory accuracy and
precision. We then further investigated the effectiveness of the model by
identifying the RFI in the actual observational data from LOFAR and MeerKAT.
The results show that the model performs well. The overall effectiveness is
comparable to AOFlagger software and provides an improvement over existing
methods in some instances.


  This paper explores two techniques to improve the performance of
text-dependent speaker verification systems based on deep neural networks.
Firstly, we propose a general alignment mechanism to keep the temporal
structure of each phrase and obtain a supervector with the speaker and phrase
information, since both are relevant for a text-dependent verification. As we
show, it is possible to use different alignment techniques to replace the
global average pooling providing significant gains in performance. Moreover, we
also present a novel back-end approach to train a neural network for detection
tasks by optimizing the Area Under the Curve (AUC) as an alternative to the
usual triplet loss function, so the system is end-to-end, with a cost function
close to our desired measure of performance. As we can see in the experimental
section, this approach improves the system performance, since our triplet
neural network based on an approximation of the AUC (aAUC) learns how to
discriminate between pairs of examples from the same identity and pairs of
different identities. The different alignment techniques to produce
supervectors in addition to the new back-end approach were tested on the
RSR2015-Part I database for text-dependent speaker verification, providing
competitive results compared to similar size networks using the global average
pooling to extract supervectors and using a simple back-end or triplet loss
training.


  We model the OH megamaser emission from the luminous infrared galaxy IIIZw35
as arising from a narrow rotating starburst ring of radius 22 pc enclosing a
mass of 7 x 10^6 Msun. We show how both the compact and apparently diffuse
maser emission from this ring can arise from a single phase of unsaturated
maser clouds amplifying background radio continuum. The masing clouds are
estimated to have a diameter of <0.7 pc and internal velocity dispersion of
about 20 km/s. We find that the clouds are neither self-gravitating nor
pressure confined but are freely expanding. Their dispersal lifetimes may set
the vertical thickness of the ring. For an estimated internal density of 3 x
10^3 cm^-3, cloud masses are of order 24 Msun. The observed spectral features
and velocity gradients indicate that the clouds must be outflowing and escaping
the nucleus. The cloud mass outflow rate is estimated to be 0.8 Msun/yr, while
the star formation rate is about 19 Msun/yr. Associated ionised gas, possibly
generated from dissipated clouds, provides free-free absorption along the
source axis, explaining the observed East-West asymmetries. We show that the
clumpiness of a maser medium can have a dramatic effect on what is observed
even in a relatively low gain OH megamaser. Specifically, in IIIZw35 our clumpy
maser model naturally explains the large line to continuum ratios, the large
1667MHz:1665MHz line ratios and the wide velocity dispersions seen in the
compact maser spots. Other astrophysical masers showing both compact and
apparently diffuse emission might be explained by similar clumpy structures.