  Analysis of short text, such as social media posts, is extremely difficult
because of their inherent brevity. In addition to classifying topics of such
posts, a common downstream task is grouping the authors of these documents for
subsequent analyses. We propose a novel model that expands on the Latent
Dirichlet Allocation by modeling strong dependence among the words in the same
document, with user-level topic distributions. We also simultaneously cluster
users, removing the need for post-hoc cluster estimation and improving topic
estimation by shrinking noisy user-level topic distributions towards typical
values. Our method performs as well as -- or better -- than traditional
approaches, and we demonstrate its usefulness on a dataset of tweets from
United States Senators, recovering both meaningful topics and clusters that
reflect partisan ideology. We also develop a novel measure of echo chambers
among these politicians by characterizing insularity of topics discussed by
groups of Senators and provide uncertainty quantification.


  Gait velocity has been consistently shown to be an important indicator and
predictor of health status, especially in older adults. Gait velocity is often
assessed clinically, but the assessments occur infrequently and thus do not
allow optimal detection of key health changes when they occur. In this paper,
we show the time it takes a person to move between rooms in their home denoted
'transition times' can predict gait velocity when estimated from passive
infrared motion detectors installed in a patient's own home. Using a support
vector regression approach to model the relationship between transition times
and gait velocities, we show that velocity can be predicted with an average
error less than 2.5 cm/sec. This is demonstrated with data collected over a 5
year period from 74 older adults monitored in their own homes. This method is
simple and cost effective, and has advantages over competing approaches such
as: obtaining 20 to100x more gait velocity measurements per day, and offering
the fusion of location specific information with time stamped gait estimates.
These advantages allow stable estimates of gait parameters (maximum or average
speed, variability) at shorter time scales than current approaches. This also
provides a pervasive in home method for context aware gait velocity sensing
that allows for monitoring of gait trajectories in space and time.


  The modelling of minimum bias interactions is a crucial ingredient to learn
about the description of soft QCD processes. It has also a significant
relevance for the simulation of the environment at the LHC with many concurrent
pp interactions (pileup). The ATLAS collaboration has provided new measurements
of the inclusive charged-particle multiplicity and its dependence on transverse
momentum and pseudorapidity in special data sets with low LHC beam currents,
recorded at center of mass energies of 8 TeV and 13 TeV. The measurements use
charged-particle selections with minimum transverse momentum of both 100 MeV
and 500 MeV, and various phase-space regions characterized by low and high
charged-particle multiplicities.


  Testing Deep Learning (DL) systems is a complex task as they do not behave
like traditional systems would, notably because of their stochastic nature.
Nonetheless, being able to adapt existing testing techniques such as Mutation
Testing (MT) to DL settings would greatly improve their potential
verifiability. While some efforts have been made to extend MT to the Supervised
Learning paradigm, little work has gone into extending it to Reinforcement
Learning (RL) which is also an important component of the DL ecosystem but
behaves very differently from SL. This paper builds on the existing approach of
MT in order to propose a framework, RLMutation, for MT applied to RL. Notably,
we use existing taxonomies of faults to build a set of mutation operators
relevant to RL and use a simple heuristic to generate test cases for RL. This
allows us to compare different mutation killing definitions based on existing
approaches, as well as to analyze the behavior of the obtained mutation
operators and their potential combinations called Higher Order Mutation(s)
(HOM). We show that the design choice of the mutation killing definition can
affect whether or not a mutation is killed as well as the generated test cases.
Moreover, we found that even with a relatively small number of test cases and
operators we manage to generate HOM with interesting properties which can
enhance testing capability in RL systems.


  In this letter we present the three-loop virtual corrections to the Higgs
boson production in the gluon fusion channel where finite top quark mass
effects are taken into account. We perform an asymptotic expansion and manage
to evaluate five terms in the expansion parameter $M_H^2/M_t^2$. A good
convergence is observed almost until $M_H\approx 2M_t$.


  We give a complete description of the possible ranges of real Smirnov
functions (quotients of two bounded analytic functions on the open unit disk
where the denominator is outer and such that the radial boundary values are
real almost everywhere on the unit circle). Our techniques use the theory of
unbounded symmetric Toeplitz operators, some general theory of unbounded
symmetric operators, classical Hardy spaces, and an application of the
uniformization theorem. In addition, we completely characterize the possible
valences for these real Smirnov functions when the valence is finite. To do so
we construct Riemann surfaces we call disk trees by welding together copies of
the unit disk and its complement in the Riemann sphere. We also make use of
certain trees we call valence trees that mirror the structure of disk trees.


  In order to achieve competitive performance, abstract machines for Prolog and
related languages end up being large and intricate, and incorporate
sophisticated optimizations, both at the design and at the implementation
levels. At the same time, efficiency considerations make it necessary to use
low-level languages in their implementation. This makes them laborious to code,
optimize, and, especially, maintain and extend. Writing the abstract machine
(and ancillary code) in a higher-level language can help tame this inherent
complexity. We show how the semantics of most basic components of an efficient
virtual machine for Prolog can be described using (a variant of) Prolog. These
descriptions are then compiled to C and assembled to build a complete bytecode
emulator. Thanks to the high level of the language used and its closeness to
Prolog, the abstract machine description can be manipulated using standard
Prolog compilation and optimization techniques with relative ease. We also show
how, by applying program transformations selectively, we obtain abstract
machine implementations whose performance can match and even exceed that of
state-of-the-art, highly-tuned, hand-crafted emulators.


  We show that the Lagrangian of the free spin-5/2 field in the spinor-tensor
representation with the auxiliary spinor field depends on the three arbitrary
parameters. The first two parameters are associated with the spin-3/2 and -1/2
sector of the theory while the latter one is related to the auxiliary degrees
of freedom. We derive a corresponding propagator of the system which represents
(2 x 2) matrix in the $(\psi_{\mu\nu},\xi)$ space. The diagonal terms stand for
the propagation of the spin-5/2 and auxiliary fields whereas the non-diagonal
ones correspond to the $\psi_{\mu\nu}-\xi$ mixing. The resulting spin-5/2
propagator contains non-pole contributions coming from the spin-3/2 and -1/2
sector of the spinor-tensor representation. A general form of the interaction
vertex involving spin-5/2 field is discussed on the example of the $\pi N
N^*_{5/2}$ coupling. It is demonstrated that lower spin degrees of freedom can
be removed from the theory by using higher order derivative coupling.


  Molecular dynamics simulations are utilized to study the melting transition
in pentane (C5H12) and heptane (C7H16), physisorbed onto the basal plane of
graphite at near-monolayer coverages. Through use of the newest, optimized
version of the anisotropic united-atom model (AUA4) to simulate both systems at
two separate coverages, this study provides evidence that the melting
transition for pentane and heptane monolayers are significantly different.
Specifically, this study proposes a very rapid transition from the solid
crystalline rectangular-centered (RC) phase to a fluid phase in pentane
monolayers, whereas heptane monolayers exhibit a slower transition that
involves a more gradual loss of RC order in the solid-fluid phase transition.
Through a study of the melting behavior, encompassing variations where the
formation of gauche defects in the alkyl chains are eliminated, this study
proposes that this gradual melting behavior for heptane monolayers is a result
of less orientational mobility of the heptane molecules in the solid RC phase,
as compared to the pentane molecules. This idea is supported through a study of
a nonane monolayer, which gives the gradual melting signature that heptane
monolayers also seem to indicate. The results of this work are compared to
previous experiment over pentane and heptane monolayers, and are found to be in
good agreement.


  The local induction approximation (LIA) of the Biot-Savart law is often used
for numerical and analytical investigations of vortex dynamics (in particular
in the theory of superfluid turbulence). In this paper, using renormalization
group (RG) methods, some features of the LIA is considered. The exact
statistical solution of the LIA equation is presented. The problem of
"marginal" terms, appearing at the Wilson's approach to the RG-procedure, is
concerned.


  We consider a class of complex manifolds constructed as multiplicative quiver
varieties associated with a cyclic quiver extended by an arbitrary number of
arrows starting at a new vertex. Such varieties admit a Poisson structure,
which is obtained by quasi-Hamiltonian reduction. We construct several families
of Poisson subalgebras inside the coordinate ring of these spaces, which we use
to obtain degenerately integrable systems. We also extend the Poisson centre of
these algebras to maximal abelian Poisson algebras, hence defining Liouville
integrable systems. By considering a suitable set of local coordinates on the
multiplicative quiver varieties, we can derive the local Poisson structure
explicitly. This allows us to interpret the integrable systems that we have
constructed as new generalisations of the spin Ruijsenaars-Schneider system
with several types of spin variables.


  In this paper, we present a new technique for studying the dynamics of a
finitely generated rational semigroup. Such a semigroup can be associated
naturally to a certain holomorphic correspondence on $\mathbb{P}^1$. Then,
results on the iterative dynamics of such a correspondence can be applied to
the study of the rational semigroup. We focus on a certain invariant measure
for the aforementioned correspondence---known as the equilibrium measure. This
confers some advantages over many of the known techniques for studying the
dynamics of rational semigroups. We use the equilibrium measure to analyse the
distribution of repelling fixed points of a finitely generated rational
semigroup, and to derive a sharp bound for the Hausdorff dimension of the Julia
set of such a semigroup.


  Self-exciting point processes describe the manner in which every event
facilitates the occurrence of succeeding events. By increasing excitability,
the event occurrences start to exhibit bursts even in the absence of external
stimuli. We revealed that the transition is uniquely determined by the average
number of events added by a single event, $1-1/\sqrt{2} \approx 0.2929$,
independently of the temporal excitation profile. We further extended the
theory to multi-dimensional processes, to be able to incite or inhibit bursting
in networks of agents.


  This paper is concerned with a linearized version of the transport problem
where the Schr\"{o}dinger-Poisson operator is replaced by a non-autonomous
Hamiltonian, slowly varying in time. We consider an explicitly solvable model
where a semiclassical island is described by a flat potential barrier, while a
time dependent 'delta' interaction is used as a model for a single quantum
well. Introducing, in addition to the complex deformation, a further
modification formed by artificial interface conditions, we give a reduced
equation for the adiabatic evolution of the sheet density of charges
accumulating around the interaction point.


  We present an analysis of three years of precision radial velocity
measurements of 160 metal-poor stars observed with HIRES on the Keck 1
telescope. We report on variability and long-term velocity trends for each star
in our sample. We identify several long-term, low-amplitude radial-velocity
variables worthy of follow-up with direct imaging techniques. We place lower
limits on the detectable companion mass as a function of orbital period. Our
survey would have detected, with a 99.5% confidence level, over 95% of all
companions on low-eccentricity orbits with velocity semi-amplitude K > 100 m/s,
or M_p*sin(i) > 3.0 M_JUP*(P/yr)^(1/3), for orbital periods P< 3 yr. None of
the stars in our sample exhibits radial-velocity variations compatible with the
presence of Jovian planets with periods shorter than the survey duration. The
resulting average frequency of gas giants orbiting metal-poor dwarfs with -2.0
< [Fe/H] < -0.6 is f_p<0.67% (at the 1-sigma confidence level). We examine the
implications of this null result in the context of the observed correlation
between the rate of occurrence of giant planets and the metallicity of their
main-sequence solar-type stellar hosts. By combining our dataset with the
Fischer & Valenti (2005) uniform sample, we confirm that the likelihood of a
star to harbor a planet more massive than Jupiter within 2 AU is a steeply
rising function of the host's metallicity. However, the data for stars with
-1.0 < [Fe/H] < 0.0 are compatible, in a statistical sense, with a constant
occurrence rate f_p~1%. Our results can usefully inform theoretical studies of
the process of giant planet formation across two orders of magnitude in
metallicity.


  This paper proposes a conceptual model for a secure and performance-efficient
workload management model in cloud environments. In this model, a resource
management unit is employed for energy and performance proficient allocation of
virtual machines while ensuring the secure processing of users' applications by
defending against data breaches due to unauthorized access to virtual machines
in real-time. The resource management unit is guided by a secure virtual
machine management unit which is designed to generate information regarding
unauthorized access or inter-communication links among active virtual machines.
Also, a workload analyzer unit operates concurrently to estimate resource
utilization information to assist the resource management unit in the
performance-efficient allocation of virtual machines. Contrary to prior works
which engage access control mechanisms, encryption, and decryption of data
before the transfer and the use of tunneling for prevention of unauthorized
access to virtual machines which raises excess computational cost overhead, the
proposed model operates diversely for efficiently serving the same purpose.


  In the present paper we study necessary and sufficient conditions for the
existence of a semicontinuous and finite Richter-Peleg multi-utility for a
preorder. It is well know that, given a preorder on a topological space, if
there is a lower (upper) semicontinuous Richter-Peleg multi-utility, then the
topology of the space must be finer than the Upper (resp. Lower) topology.
However, this condition does not guarantee the existence of a semicontinuous
representation.
  We search for finer topologies which are necessary for semicontinuity, as
well as that they could guarantee the existence of a semicontinuous
representation. As a result, we prove that Scott topology (that refines the
Upper one) must be contained in the topology of the space in case there exists
a finite lower semicontinuous Richter-Peleg multi-utility. However, as it is
shown, the existence of this representation cannot be guaranteed.


  Genetic information is increasing exponentially, doubling every 18 months.
Analyzing this information within a reasonable amount of time requires parallel
computing resources. While considerable research has addressed DNA analysis
using GPUs, so far not much attention has been paid to the Intel Xeon Phi
coprocessor. In this paper we present an algorithm for large-scale DNA analysis
that exploits thread-level and the SIMD parallelism of the Intel Xeon Phi. We
evaluate our approach for various numbers of cores and thread allocation
affinities in the context of real-world DNA sequences of mouse, cat, dog,
chicken, human and turkey. The experimental results on Intel Xeon Phi show
speed-ups of up to 10x compared to a sequential implementation running on an
Intel Xeon processor E5.


  With ChatGPT as a representative, tons of companies have began to provide
services based on large Transformers models. However, using such a service
inevitably leak users' prompts to the model provider. Previous studies have
studied secure inference for Transformer models using secure multiparty
computation (MPC), where model parameters and clients' prompts are kept secret.
Despite this, these frameworks are still limited in terms of model performance,
efficiency, and deployment. To address these limitations, we propose framework
PUMA to enable fast and secure Transformer model inference. Our framework
designs high quality approximations for expensive functions such as GeLU and
softmax, and significantly reduce the cost of secure inference while preserving
the model performance. Additionally, we design secure Embedding and LayerNorm
procedures that faithfully implement the desired functionality without
undermining the Transformer architecture. PUMA is about $2\times$ faster than
the state-of-the-art framework MPCFORMER(ICLR 2023) and has similar accuracy as
plaintext models without fine-tuning (which the previous works failed to
achieve). PUMA can even evaluate LLaMA-7B in around 5 minutes to generate 1
token. To our best knowledge, this is the first time that a model with such a
parameter size is able to be evaluated under MPC. PUMA has been open-sourced in
the Github repository of SecretFlow-SPU.


  We analyse the implications of light charginos on the Higgs boson signal
strength via gluon-gluon fusion and di-photon decay in the Minimal
Supersymmetric Standard Model (MSSM) at the Large Hadron Collider (LHC). We
show that enhancements are possible with a rate up to 25%. We also prove that
they are possible for a high scale constrained version of the MSSM with
non-universal Higgs and gaugino masses. In contrast, effects due to light
charged Higgs bosons, that we also have investigated, are generically
negligible in the \gamma\gamma decay, though they may affect the b \bar b rate,
hence the total width.


  We test the concept that seismicity prior to a large earthquake can be
understood in terms of the statistical physics of a critical phase transition.
In this model, the cumulative seismic strain release increases as a power-law
time-to-failure before the final event. Furthermore, the region of correlated
seismicity predicted by this model is much greater than would be predicted from
simple elasto-dynamic interactions. We present a systematic procedure to test
for the accelerating seismicity predicted by the critical point model and to
identify the region approaching criticality, based on a comparison between the
observed cumulative energy (Benioff strain) release and the power-law behavior
predicted by theory. This method is used to find the critical region before all
earthquakes along the San Andreas system since 1950 with M 6.5. The statistical
significance of our results is assessed by performing the same procedure on a
large number of randomly generated synthetic catalogs. The null hypothesis,
that the observed acceleration in all these earthquakes could result from
spurious patterns generated by our procedure in purely random catalogs, is
rejected with 99.5% confidence. An empirical relation between the logarithm of
the critical region radius (R) and the magnitude of the final event (M) is
found, such that log R \mu 0.5 M, suggesting that the largest probable event in
a given region scales with the size of the regional fault network.


  Bayesian neural networks (BNNs) combine the expressive power of deep learning
with the advantages of Bayesian formalism. In recent years, the analysis of
wide, deep BNNs has provided theoretical insight into their priors and
posteriors. However, we have no analogous insight into their posteriors under
approximate inference. In this work, we show that mean-field variational
inference entirely fails to model the data when the network width is large and
the activation function is odd. Specifically, for fully-connected BNNs with odd
activation functions and a homoscedastic Gaussian likelihood, we show that the
optimal mean-field variational posterior predictive (i.e., function space)
distribution converges to the prior predictive distribution as the width tends
to infinity. We generalize aspects of this result to other likelihoods. Our
theoretical results are suggestive of underfitting behavior previously
observered in BNNs. While our convergence bounds are non-asymptotic and
constants in our analysis can be computed, they are currently too loose to be
applicable in standard training regimes. Finally, we show that the optimal
approximate posterior need not tend to the prior if the activation function is
not odd, showing that our statements cannot be generalized arbitrarily.


  Double beta decay is indispensable to solve the question of the neutrino mass
matrix together with $\nu$ oscillation experiments. The most sensitive
experiment - since eight years the HEIDELBERG-MOSCOW experiment in Gran-Sasso -
already now, with the experimental limit of $<m_\nu > < 0.26$ eV practically
excludes degenerate $\nu$ mass scenarios allowing neutrinos as hot dark matter
in the universe for the smallangle MSW solution of the solar neutrino problem.
It probes cosmological models including hot dark matter already now on the
level of future satellite experiments MAP and PLANCK. It further probes many
topics of beyond SM physics at the TeV scale. Future experiments should give
access to the multi-TeV range and complement on many ways the search for new
physics at future colliders like LHC and NLC. For neutrino physics some of them
(GENIUS) will allow to test almost all neutrino mass scenarios allowed by the
present neutrino oscillation experiments.


  We introduce the notion of a restricted exchangeable partition of
$\mathbb{N}$. We obtain integral representations, consider associated
fragmentations, embeddings into continuum random trees and convergence to such
limit trees. In particular, we deduce from the general theory developed here a
limit result conjectured previously for Ford's alpha model and its extension,
the alpha-gamma model, where restricted exchangeability arises naturally.


  Three existing interpretations of quantum mechanics, given by Heisenberg,
Bohm and Madelung, are examined to describe dissipative quantum systems as
well. It is found that the Madelung quantum hydrodynamics is the only correct
approach. A new stochastic reinterpretation of the quantum mechanics is
proposed, which represents the microscopic face of the Madelung hydrodynamics.
The main idea is that the vacuum fluctuates permanently, which explains the
probabilistic character of the quantum mechanics. Thus, it is an objective
theory independent of the human beings and their measurements. The effect of
the thermal fluctuations in the surrounding is also accounted for via a
heuristic Langevin equation with two random forces. Some statistical
characteristics of these quantum and thermal noises are determined by
reproducing known results for the system phase-space dynamics.


  In this short note we study the fourth order consequences of the stochastic
stability property for mean field spin glass models introduced in previous
paper by Aizenman and Contucci. We show that due to a remarkable cancellation
mechanism it reduces to the well known second order version as predicted by
Parisi's replica-equivalent ansatz.


  Majorana zero modes have been attracting considerable attention because of
their prospective applications in fault-tolerant topological quantum computing.
In recent years, some schemes have been proposed to detect and manipulate
Majorana zero modes using superconducting qubits. However, manipulating and
reading the Majorana zero modes must be kept in the time window of
quasiparticle poisoning. In this work, we study the problem of quasiparticle
poisoning in a split transmon qubit containing hybrid Josephson junctions
involving Majorana zero modes. We show that Majorana coupling will cause parity
mixing and 4{\pi} Josephson effect. In addition, we obtained the expression of
qubit parameter-dependent parity switching rate and demonstrated that
quasiparticle poisoning can be greatly suppressed by reducing E_J/E_C via qubit
design.


  Veracity is an essential key in research and development of innovative
products. Live Emotion analysis and verification nullify deceit made to
complainers on live chat, corroborate messages of both ends in messaging apps
and promote an honest conversation between users. The main concept behind this
emotion artificial intelligent verifier is to license or decline message
accountability by comparing variegated emotions of chat app users recognized
through facial expressions and text prediction. In this paper, a proposed
emotion intelligent live detector acts as an honest arbiter who distributes
facial emotions into labels namely, Happiness, Sadness, Surprise, and Hate.
Further, it separately predicts a label of messages through text
classification. Finally, it compares both labels and declares the message as a
fraud or a bonafide. For emotion detection, we deployed Convolutional Neural
Network (CNN) using a miniXception model and for text prediction, we selected
Support Vector Machine (SVM) natural language processing probability classifier
due to receiving the best accuracy on training dataset after applying Support
Vector Machine (SVM), Random Forest Classifier, Naive Bayes Classifier, and
Logistic regression.


  This paper was withdrawn because subsequent measurements produced results not
always consistent with the ones presented in the paper.


  Quantifying biomechanical properties of the human vasculature could deepen
our understanding of cardiovascular diseases. Standard nonlinear regression in
constitutive modeling requires considerable high-quality data and an explicit
form of the constitutive model as prior knowledge. By contrast, we propose a
novel approach that combines generative deep learning with Bayesian inference
to efficiently infer families of constitutive relationships in data-sparse
regimes. Inspired by the concept of functional priors, we develop a generative
adversarial network (GAN) that incorporates a neural operator as the generator
and a fully-connected neural network as the discriminator. The generator takes
a vector of noise conditioned on measurement data as input and yields the
predicted constitutive relationship, which is scrutinized by the discriminator
in the following step. We demonstrate that this framework can accurately
estimate means and standard deviations of the constitutive relationships of the
murine aorta using data collected either from model-generated synthetic data or
ex vivo experiments for mice with genetic deficiencies. In addition, the
framework learns priors of constitutive models without explicitly knowing their
functional form, providing a new model-agnostic approach to learning hidden
constitutive behaviors from data.


  In the context of estimating local modes of a conditional density based on
kernel density estimators, we show that existing bandwidth selection methods
developed for kernel density estimation are unsuitable for mode estimation. We
propose two methods to select bandwidths tailored for mode estimation in the
regression setting. Numerical studies using synthetic data and a real-life data
set are carried out to demonstrate the performance of the proposed methods in
comparison with several well received bandwidth selection methods for density
estimation.


  We use the detection of neutrinos from a future galactic type-II supernova
event in a water Cerenkov detector like Super-Kamiokande to constrain the
possible violation of spin-statistics by neutrinos resulting in their obeying a
mixed statistics instead of Fermi-Dirac.


  Additive composition (Foltz et al, 1998; Landauer and Dumais, 1997; Mitchell
and Lapata, 2010) is a widely used method for computing meanings of phrases,
which takes the average of vector representations of the constituent words. In
this article, we prove an upper bound for the bias of additive composition,
which is the first theoretical analysis on compositional frameworks from a
machine learning point of view. The bound is written in terms of collocation
strength; we prove that the more exclusively two successive words tend to occur
together, the more accurate one can guarantee their additive composition as an
approximation to the natural phrase vector. Our proof relies on properties of
natural language data that are empirically verified, and can be theoretically
derived from an assumption that the data is generated from a Hierarchical
Pitman-Yor Process. The theory endorses additive composition as a reasonable
operation for calculating meanings of phrases, and suggests ways to improve
additive compositionality, including: transforming entries of distributional
word vectors by a function that meets a specific condition, constructing a
novel type of vector representations to make additive composition sensitive to
word order, and utilizing singular value decomposition to train word vectors.


  We develop a general theory of Markov chains realizable as random walks on
$\mathscr R$-trivial monoids. It provides explicit and simple formulas for the
eigenvalues of the transition matrix, for multiplicities of the eigenvalues via
M\"obius inversion along a lattice, a condition for diagonalizability of the
transition matrix and some techniques for bounding the mixing time. In
addition, we discuss several examples, such as Toom-Tsetlin models, an exchange
walk for finite Coxeter groups, as well as examples previously studied by the
authors, such as nonabelian sandpile models and the promotion Markov chain on
posets. Many of these examples can be viewed as random walks on quotients of
free tree monoids, a new class of monoids whose combinatorics we develop.


  This paper is a survey and an analysis of different ways of using deep
learning (deep artificial neural networks) to generate musical content. We
propose a methodology based on five dimensions for our analysis:
  Objective - What musical content is to be generated? Examples are: melody,
polyphony, accompaniment or counterpoint. - For what destination and for what
use? To be performed by a human(s) (in the case of a musical score), or by a
machine (in the case of an audio file).
  Representation - What are the concepts to be manipulated? Examples are:
waveform, spectrogram, note, chord, meter and beat. - What format is to be
used? Examples are: MIDI, piano roll or text. - How will the representation be
encoded? Examples are: scalar, one-hot or many-hot.
  Architecture - What type(s) of deep neural network is (are) to be used?
Examples are: feedforward network, recurrent network, autoencoder or generative
adversarial networks.
  Challenge - What are the limitations and open challenges? Examples are:
variability, interactivity and creativity.
  Strategy - How do we model and control the process of generation? Examples
are: single-step feedforward, iterative feedforward, sampling or input
manipulation.
  For each dimension, we conduct a comparative analysis of various models and
techniques and we propose some tentative multidimensional typology. This
typology is bottom-up, based on the analysis of many existing deep-learning
based systems for music generation selected from the relevant literature. These
systems are described and are used to exemplify the various choices of
objective, representation, architecture, challenge and strategy. The last
section includes some discussion and some prospects.


  Sensors (e.g., light, gyroscope, accelerotmeter) and sensing enabled
applications on a smart device make the applications more user-friendly and
efficient. However, the current permission-based sensor management systems of
smart devices only focus on certain sensors and any App can get access to other
sensors by just accessing the generic sensor API. In this way, attackers can
exploit these sensors in numerous ways: they can extract or leak users'
sensitive information, transfer malware, or record or steal sensitive
information from other nearby devices. In this paper, we propose 6thSense, a
context-aware intrusion detection system which enhances the security of smart
devices by observing changes in sensor data for different tasks of users and
creating a contextual model to distinguish benign and malicious behavior of
sensors. 6thSense utilizes three different Machine Learning-based detection
mechanisms (i.e., Markov Chain, Naive Bayes, and LMT) to detect malicious
behavior associated with sensors. We implemented 6thSense on a sensor-rich
Android smart device (i.e., smartphone) and collected data from typical daily
activities of 50 real users. Furthermore, we evaluated the performance of
6thSense against three sensor-based threats: (1) a malicious App that can be
triggered via a sensor (e.g., light), (2) a malicious App that can leak
information via a sensor, and (3) a malicious App that can steal data using
sensors. Our extensive evaluations show that the 6thSense framework is an
effective and practical approach to defeat growing sensor-based threats with an
accuracy above 96% without compromising the normal functionality of the device.
Moreover, our framework costs minimal overhead.


  Microwave transversal filters, which are implemented based on the transversal
filter structure in digital signal processing, offer a high reconfigurability
for achieving a variety of signal processing functions without changing
hardware. When implemented using microwave photonic (MWP) technologies, also
known as MWP transversal filters, they provide competitive advantages over
their electrical counterparts, such as low loss, large operation bandwidth, and
strong immunity to electromagnetic interference. Recent advances in high
performance optical microcombs provide compact and powerful multiwavelength
sources for MWP transversal filters that require a larger number of wavelength
channels to achieve high performance, allowing for the demonstration of a
diverse range of filter functions with improved performance and new features.
Here, we present a comprehensive performance analysis for microcomb based MWP
spectral filters based on the transversal filter approach. First, we
investigate the theoretical limitations in the filter spectral response induced
by finite tap numbers. Next, we analyze the distortions in the filter spectral
response resulting from experimental error sources. Finally, we assess the
influence of input signals bandwidth on the filtering errors. These results
provide a valuable guide for the design and optimization of microcomb-based MWP
transversal filters for a variety of applications.


  We reprove a necessary condition for the Sakuma-Weeks triangulation of a
2-bridge link complement to be minimal in terms of the mapping class describing
its alternating 4-string braid construction. For the 2-bridge links satisfying
this condition we construct explicit angle structures on the Sakuma-Weeks
triangulations and compute both multiplicative and additive lower bounds on the
complexity of the link complements via volume estimates. These lower bounds are
an improvement on existing volume estimates for the 2-bridge links examined.


  We show that the mean-field time dependent equations in the Phi^4 theory can
be put into a classical non-canonical hamiltonian framework with a Poisson
structure which is a generalization of the standard Poisson bracket. The
Heisenberg invariant appears as a structural invariant of the Poisson tensor.
(To be pubished in Annals of Physics)


  We present the Spitzer IRS spectra for 33 young stars in Tr 37 and NGC 7160.
The sample includes the high- and intermediate-mass stars with MIPS 24 microns
excess, the only known active accretor in the 12 Myr-old cluster NGC 7160, and
19 low-mass stars with disks in the 4 Myr-old cluster Tr 37. We examine the 10
microns silicate feature, present in the whole sample of low-mass star and in 3
of the high- and intermediate-mass targets, and we find that PAH emission is
detectable only in the Herbig Be star. We analyze the composition and size of
the warm photospheric silicate grains by fitting the 10 microns silicate
feature, and study the possible correlations between the silicate
characteristics and the stellar and disk properties (age, SED slope, accretion
rate, spectral type). We find indications of dust settling with age and of the
effect of turbulent enrichment of the disk atmosphere with large grains.
Crystalline grains are only small contributors to the total silicate mass in
all disks, and do not seem to correlate with any other property, except maybe
binarity. We also observe that spectra with very weak silicate emission are at
least 3 times more frequent among M stars than among earlier spectral types,
which may be an evidence of inner disk evolution. Finally, we find that 5 of
the high- and intermediate-mass stars have SEDs and IRS spectra consistent with
debris disk models involving planet formation, which could indicate debris disk
formation at ages as early as 4 Myr.


  Heterogeneous systems have become one of the most common architectures today,
thanks to their excellent performance and energy consumption. However, due to
their heterogeneity they are very complex to program and even more to achieve
performance portability on different devices. This paper presents EngineCL, a
new OpenCL-based runtime system that outstandingly simplifies the co-execution
of a single massive data-parallel kernel on all the devices of a heterogeneous
system. It performs a set of low level tasks regarding the management of
devices, their disjoint memory spaces and scheduling the workload between the
system devices while providing a layered API. EngineCL has been validated in
two compute nodes (HPC and commodity system), that combine six devices with
different architectures. Experimental results show that it has excellent
usability compared with OpenCL; a maximum 2.8% of overhead compared to the
native version under loads of less than a second of execution and a tendency
towards zero for longer execution times; and it can reach an average efficiency
of 0.89 when balancing the load.


  We investigate the effect of a planet on an eccentric orbit on a two
dimensional low mass gaseous disk. At a planet eccentricity above the planet's
Hill radius divided by its semi-major axis, we find that the disk morphology
differs from that exhibited by a disk containing a planet in a circular orbit.
An eccentric gap is created with eccentricity that can exceed the planet's
eccentricity and precesses with respect to the planet's orbit. We find that a
more massive planet is required to open a gap when the planet is on an
eccentric orbit. We attribute this behavior to spiral density waves excited at
corotation resonances that increase the disk's eccentricity and exert a torque
opposite in sign to that exerted by the Lindblad resonances. The reduced torque
makes it more difficult for waves driven by the planet to overcome viscous
inflow in the disk. Spectral energy distributions of transitional disks may
reveal the presence of an eccentric planet if they are matched by an inner edge
comprising a range of temperatures set by the semi-major axis and eccentricity
of the hole.


This paper has been withdrawn by the authors as requested by the journal.


  In this paper, we propose a Boundary-aware Graph Reasoning (BGR) module to
learn long-range contextual features for semantic segmentation. Rather than
directly construct the graph based on the backbone features, our BGR module
explores a reasonable way to combine segmentation erroneous regions with the
graph construction scenario. Motivated by the fact that most hard-to-segment
pixels broadly distribute on boundary regions, our BGR module uses the boundary
score map as prior knowledge to intensify the graph node connections and
thereby guide the graph reasoning focus on boundary regions. In addition, we
employ an efficient graph convolution implementation to reduce the
computational cost, which benefits the integration of our BGR module into
current segmentation backbones. Extensive experiments on three challenging
segmentation benchmarks demonstrate the effectiveness of our proposed BGR
module for semantic segmentation.


  Privacy-preserving recommendations are recently gaining momentum, since the
decentralized user data is increasingly harder to collect, by recommendation
service providers, due to the serious concerns over user privacy and data
security. This situation is further exacerbated by the strict government
regulations such as Europe's General Data Privacy Regulations(GDPR). Federated
Learning(FL) is a newly developed privacy-preserving machine learning paradigm
to bridge data repositories without compromising data security and privacy.
Thus many federated recommendation(FedRec) algorithms have been proposed to
realize personalized privacy-preserving recommendations. However, existing
FedRec algorithms, mostly extended from traditional collaborative filtering(CF)
method, cannot address cold-start problem well. In addition, their performance
overhead w.r.t. model accuracy, trained in a federated setting, is often
non-negligible comparing to centralized recommendations. This paper studies
this issue and presents FL-MV-DSSM, a generic content-based federated
multi-view recommendation framework that not only addresses the cold-start
problem, but also significantly boosts the recommendation performance by
learning a federated model from multiple data source for capturing richer
user-level features. The new federated multi-view setting, proposed by
FL-MV-DSSM, opens new usage models and brings in new security challenges to FL
in recommendation scenarios. We prove the security guarantees of \xxx, and
empirical evaluations on FL-MV-DSSM and its variations with public datasets
demonstrate its effectiveness. Our codes will be released if this paper is
accepted.


  A limited-area survey of the eta Chamaeleontis cluster has identified 2 new
late-type members. The more significant of these is ECHA J0843.3-7905 (= IRAS
F08450-7854), a slowly-rotating (P = 12 d) M2 classical T Tauri (CTT) star with
a spectrum dominated by Balmer emission. At a distance of 97 pc and cluster age
of approx 9 Myr, the star is a nearby rare example of an 'old' CTT star and
promises to be a rewarding laboratory for the study of disk structure and
evolution in pre-main sequence (PMS) stars. The other new member is the M4
weak-lined T Tauri (WTT) star ECHA J0841.5-7853, which is the lowest mass (M =
0.2 M_sun) primary known in the cluster.


  We apply the $l_1$ minimizing technique of compressive sensing (CS) to
non-linear quadratic observations. For the example of coherent X-ray scattering
we provide the formulae for a Kalman filter approach to quadratic CS and show
how to reconstruct the scattering data from their spatial intensity
distribution.


  We investigate the limits of thermometry using quantum probes at thermal
equilibrium within the Bayesian approach. We consider the possibility of
engineering interactions between the probes in order to enhance their
sensitivity, as well as feedback during the measurement process, i.e., adaptive
protocols. On the one hand, we obtain an ultimate bound on thermometry
precision in the Bayesian setting, valid for arbitrary interactions and
measurement schemes, which lower bounds the error with a quadratic
(Heisenberg-like) scaling with the number of probes. We develop a simple
adaptive strategy that can saturate this limit. On the other hand, we derive a
no-go theorem for non-adaptive protocols that does not allow for better than
linear (shot-noise-like) scaling even if one has unlimited control over the
probes, namely access to arbitrary many-body interactions.


  The paper studies the overdamped motion of Brownian particles in a tilted
sawtooth potential. The dependencies of the diffusion coefficient and coherence
level of Brownian transport on temperature, tilting force, and the shape of the
potential are analyzed. It is demonstrated that at low temperatures the
coherence level of Brownian transport stabilizes in the extensive domain of the
tilting force where the value of the Peclet factor is Pe=2. This domain
coincides with the one where the enhancement of the diffusion coefficient
versus the tilting force is the most rapid. The necessary and sufficient
conditions for the non-monotonic behaviour of the diffusion coefficient as a
function of temperature are found. The effect of the acceleration of diffusion
by bias and temperature is demonstrated to be very sensitive to the value of
the asymmetry parameter of the potential.


  Which one is better between two representative graph summarization models
with and without edge weights? From web graphs to online social networks, large
graphs are everywhere. Graph summarization, which is an effective graph
compression technique, aims to find a compact summary graph that accurately
represents a given large graph. Two versions of the problem, where one allows
edge weights in summary graphs and the other does not, have been studied in
parallel without direct comparison between their underlying representation
models. In this work, we conduct a systematic comparison by extending three
search algorithms to both models and evaluating their outputs on eight datasets
in five aspects: (a) reconstruction error, (b) error in node importance, (c)
error in node proximity, (d) the size of reconstructed graphs, and (e)
compression ratios. Surprisingly, using unweighted summary graphs leads to
outputs significantly better in all the aspects than using weighted ones, and
this finding is supported theoretically. Notably, we show that a
state-of-the-art algorithm can be improved substantially (specifically, 8.2X,
7.8X, and 5.9X in terms of (a), (b), and (c), respectively, when (e) is fixed)
based on the observation.


  We design a controlled-phase gate for linear optical quantum computing by
using photodetectors that cannot resolve photon number. An intrinsic
error-correction circuit corrects errors introduced by the detectors. Our
controlled-phase gate has a 1/4 success probability. Recent development in
cluster-state quantum computing has shown that a two-qubit gate with non-zero
success probability can build an arbitrarily large cluster state with only
polynomial overhead. Hence, it is possible to generate optical cluster states
without number-resolving detectors and with polynomial overhead.


  The coupling to light mesons leads to large widths and shifts of the energy
of the excited states in the baryon spectrum from the predictions of the
constituent quark model with three valence quarks. This coupling may be
modelled by admixtures of sea-quark $qqq(q\bar q)^n$ configurations in the
resonances. A schematic flavor and spin dependent interaction model, similar to
the one which reproduces the low lying part of the experimental baryon spectrum
in the valence quark model, is shown to bring the lowest $qqqq\bar q$ states
with L=1 and positive parity below the states with L=0. Because of the partial
overlap with the corresponding $qqq$ states this suggests that most of the low
lying states in the baryon resonance spectrum have substantial $qqqq\bar q$
admixtures.


  Time-resolved analysis of periodically excited luminescence decays by the
phasor method in the presence of time-gating or binning is revisited.
Analytical expressions for discrete configurations of square gates are derived
and the locus of the phasors of such modified periodic single-exponential
decays is compared to the canonical uni-versal semicircle. The effects of IRF
offset, decay truncation and gate shape are also discussed. Finally, modified
expressions for the phase and modulus lifetimes are pro-vided for some simple
cases. A discussion of a modified phasor calibration approach is presented, and
illustration of the new concepts with examples from the literature conclude
this work.


  We study analytical properties of the holographic superconductors with Weyl
corrections. We describe the phenomena in the probe limit neglecting
backreaction of the space-time. We observe that for the conformal dimension
$\triangle_{+}=3$, the minimum value of the critical temperature $T^{Min}_c$ at
which condensation sets, can be obtained directly from the equations of motion
as $T^{Min}_c\approx 0.170845\sqrt[3]{\rho}$, which is in very good agreement
with the numerical value $T^{Min}_c=0.170\sqrt[3]{\rho}$
[Phys.Lett.B697:153-158,2011]. This value of $T^{Min}_c$ corresponds to the
value of the Weyl's coupling $\gamma=-0.06$ in table (1) of
[Phys.Lett.B697:153-158,2011]. We calculate the $T^{Min}_c\approx
0.21408\sqrt[3]{\rho}$ for another Weyl's coupling $\gamma=0.02$ and the the
conformal dimension $\triangle_{-}=1$. Further, we show that the critical
exponent is $\beta=1/2$. We observe that there is a linear relation between the
charge density $\rho$ and the chemical potential difference $\mu-\mu_c$
qualitatively matches the numerical curves.


  We revisit the regular black hole found by Hayward in $4-$dimensional static,
spherically symmetric spacetime. To find a possible source for such a spacetime
we resort to the non-linear electrodynamics in general relativity. It is found
that a magnetic field within this context gives rise to the regular Hayward
black hole. By employing such a regular black hole we construct a thin-shell
wormhole for the case of various equations of state on the shell. We abbreviate
a general equation of state by $p=\psi \left( \sigma \right) $ where $p$ is the
surface pressure which is the function of the mass density ($\sigma $). In
particular, a linear, logarithmic, Chaplygin, etc. forms of equations of state
are considered. In each case we study the stability of the thin-shell against
linear perturbations. We plot the stability regions by tuning the parameters of
the theory. It is observed that the role of the Hayward parameter is to make
the TSW more stable. Perturbations of the throat with small velocity condition
is also studied. The matter of our TSWs, however, remains to be exotic.


  A unique description avoiding confusion is presented for all flavor
oscillation experiments in which particles of a definite flavor are emitted
from a localized source. The probability for finding a particle with the wrong
flavor must vanish at the position of the source for all times. This condition
requires flavor-time and flavor-energy factorizations which determine uniquely
the flavor mixture observed at a detector in the oscillation region; i.e. where
the overlaps between the wave packets for different mass eigenstates are almost
complete. Oscillation periods calculated for ``gedanken'' time-measurement
experiments are shown to give the correct measured oscillation wave length in
space when multiplied by the group velocity. Examples of neutrinos propagation
in a weak field and in a gravitational field are given. In these cases the
relative phase is modified differently for measurements in space and time.
Energy-momentum (frequency-wave number) and space-time descriptions are
complementary, equally valid and give the same results. The two identical phase
shifts obtained describe the same physics; adding them together to get a factor
of two is double counting.


  We analyze the Standard & Poor's 500 stock market index from the last 22
years. The probability density function of price returns exhibits two
well-distinguished regimes with self-similar structure: the first one displays
strong super-diffusion together with short-time correlations, and the second
one corresponds to weak super-diffusion with weak time correlations. Both
regimes are well-described by q-Gaussian distributions. The porous media
equation is used to derive the governing equation for these regimes, and the
Black-Scholes diffusion coefficient is explicitly obtained from the governing
equation.


  Time series data constitutes a distinct and growing problem in machine
learning. As the corpus of time series data grows larger, deep models that
simultaneously learn features and classify with these features can be
intractable or suboptimal. In this paper, we present feature learning via long
short term memory (LSTM) networks and prediction via gradient boosting trees
(XGB). Focusing on the consequential setting of electronic health record data,
we predict the occurrence of hypoxemia five minutes into the future based on
past features. We make two observations: 1) long short term memory networks are
effective at capturing long term dependencies based on a single feature and 2)
gradient boosting trees are capable of tractably combining a large number of
features including static features like height and weight. With these
observations in mind, we generate features by performing "supervised"
representation learning with LSTM networks. Augmenting the original XGB model
with these features gives significantly better performance than either
individual method.


  Site-directed insertion is an overlapping insertion operation that can be
viewed as analogous to the overlap assembly or chop operations that concatenate
strings by overlapping a suffix and a prefix of the argument strings. We
consider decision problems and language equations involving site-directed
insertion. By relying on the tools provided by semantic shuffle on trajectories
we show that one variable equations involving site-directed insertion and
regular constants can be solved. We consider also maximal and minimal variants
of the site-directed insertion operation.


  Light-to-electricity conversion is crucial for energy harvesting and
photodetection, requesting efficient electron-hole pair separation to prevent
recombination. Traditional junction-based mechanisms using built-in electric
fields fail in non-barrier regions. Homogeneous material harvesting under
photovoltaic effect is appealing but only realized in non-centrosymmetric
systems via bulk photovoltaic effect. Here we report the realization of
photovoltaic effect by employing surface acoustic waves (SAW) to generate
zero-bias photocurrent in a conventional layered semiconductor MoSe2. SAW
induces periodic modulation to electronic bands and drags the photoexcited
pairs toward the travelling direction. The photocurrent is extracted by a local
barrier. The separation of generation and extraction processes suppresses
recombination and yields large nonlocal photoresponse. We distinguish
acousto-electric drag and electron-hole pair separation effect by fabricating
devices of different configurations. The acousto-drag photovoltaic effect,
enabled by piezoelectric integration, offers an efficient light-to-electricity
conversion method, independent of semiconductor crystal symmetry.


  As persistent memory (PM) technologies emerge, hybrid memory architectures
combining DRAM with PM bring the potential to provide a tiered,
byte-addressable main memory of unprecedented capacity. Nearly a decade after
the first proposals for these hybrid architectures, the real technology has
finally reached commercial availability with Intel Optane(TM) DC Persistent
Memory (DCPMM). This raises the challenge of designing systems that realize
this potential in practice, namely through effective approaches that
dynamically decide at which memory tier should pages be placed. In this paper,
we are the first, to our knowledge, to systematically analyze tiered page
placement on real DCPMM-based systems. To this end, we start by revisiting the
assumptions of state-of-the-art proposals, and confronting them with the
idiosyncrasies of today's off-the-shelf DCPMM-equipped architectures. This
empirical study reveals that some of the key design choices in the literature
rely on important assumptions that are not verified in present-day DRAM-DCPMM
memory architectures. Based on the lessons from this study, we design and
implement HyPlacer, a tool for tiered page placement in off-the-shelf
Linux-based systems equipped with DRAM+DCPMM. In contrast to previous
proposals, HyPlacer follows an approach guided by two main practicality
principles: 1) it is tailored to the performance idiosyncrasies of off-theshelf
DRAM+DCPMM systems; and 2) it can be seamlessly integrated into Linux with
minimal kernel-mode components, while ensuring extensibility to other HMAs and
other data placement policies. Our experimental evaluation of HyPlacer shows
that it outperforms both solutions proposed in past literature and placement
options that are currently available in off-the-shelf DCPMM-equipped Linux
systems, reaching an improvement of up to 11x when compared to the default
memory policy in Linux.


  Context. Arrays of radio antennas have proven to be successful in
astroparticle physics with the observation of extensive air showers initiated
by high-energy cosmic rays in the Earth's atmosphere. Accurate determination of
the energy scale of the primary particles' energies requires an absolute
calibration of the radio antennas for which, in recent years, the utilization
of the Galactic emission as a reference source has emerged as a potential
standard. Aims. To apply the "Galactic Calibration", a proper estimation of the
systematic uncertainties on the prediction of the Galactic emission from sky
models is necessary, which we aim to determine on a global level as well as for
the specific cases of selected radio arrays. We further aim to quantify the
influence of the quiet Sun on the Galactic Calibration. Methods. We look at
four different sky models that predict the full-sky Galactic emission in the
frequency range from 30 to 408 MHz and compare them. We make an inventory of
the reference maps on which they rely and use the output of the models to
determine their global level of agreement. Next, we take the sky exposures and
frequency bands of selected radio arrays into account and repeat the comparison
for each of them. Finally, we study the relative influence of the Sun in its
quiet state by projecting it onto the sky with brightness data from recent
measurements. Results. We find systematic uncertainty of 12% on the predicted
power from the Galactic emission, which scales to approximately half of that
value as the uncertainty on the determination of the energy of cosmic
particles. When looking at the selected radio arrays, the uncertainty on the
predicted power varies between 10% and 19%. The influence of the quiet Sun
turns out to be insignificant at the lowest frequencies but increases to a
relative contribution of ~ 20% around 400 MHz.


  Several advances in deep learning have been successfully applied to the
software development process. Of recent interest is the use of neural language
models to build tools, such as Copilot, that assist in writing code. In this
paper we perform a comparative empirical analysis of Copilot-generated code
from a security perspective. The aim of this study is to determine if Copilot
is as bad as human developers. We investigate whether Copilot is just as likely
to introduce the same software vulnerabilities as human developers. Using a
dataset of C/C++ vulnerabilities, we prompt Copilot to generate suggestions in
scenarios that led to the introduction of vulnerabilities by human developers.
The suggestions are inspected and categorized in a 2-stage process based on
whether the original vulnerability or fix is reintroduced. We find that Copilot
replicates the original vulnerable code about 33% of the time while replicating
the fixed code at a 25% rate. However this behaviour is not consistent: Copilot
is more likely to introduce some types of vulnerabilities than others and is
also more likely to generate vulnerable code in response to prompts that
correspond to older vulnerabilities. Overall, given that in a significant
number of cases it did not replicate the vulnerabilities previously introduced
by human developers, we conclude that Copilot, despite performing differently
across various vulnerability types, is not as bad as human developers at
introducing vulnerabilities in code.


  Audio editing involves the arbitrary manipulation of audio content through
precise control. Although text-guided diffusion models have made significant
advancements in text-to-audio generation, they still face challenges in finding
a flexible and precise way to modify target events within an audio track. We
present a novel approach, referred to as PPAE, which serves as a general module
for diffusion models and enables precise audio editing. The editing is based on
the input textual prompt only and is entirely training-free. We exploit the
cross-attention maps of diffusion models to facilitate accurate local editing
and employ a hierarchical local-global pipeline to ensure a smoother editing
process. Experimental results highlight the effectiveness of our method in
various editing tasks.


  Computationalism provides a framework for understanding how a mathematically
describable physical world could give rise to conscious observations without
the need for dualism. A criterion is proposed for the implementation of
computations by physical systems, which has been a problem for
computationalism. Together with an independence criterion for implementations
this would allow, in principle, prediction of probabilities for various
observations based on counting implementations. Applied to quantum mechanics,
this results in a Many Computations Interpretation (MCI), which is an explicit
form of the Everett style Many Worlds Interpretation (MWI). Derivation of the
Born Rule emerges as the central problem for most realist interpretations of
quantum mechanics. If the Born Rule is derived based on computationalism and
the wavefunction it would provide strong support for the MWI; but if the Born
Rule is shown not to follow from these to an experimentally falsified extent,
it would indicate the necessity for either new physics or (more radically) new
philosophy of mind.


  We observe very small gate-voltage shifts in the transfer characteristic of
as-prepared graphene field-effect transistors (GFETs) when the pH of the buffer
is changed. This observation is in strong contrast to Si-based ion-sensitive
FETs. The low gate-shift of a GFET can be further reduced if the graphene
surface is covered with a hydrophobic fluorobenzene layer. If a thin Al-oxide
layer is applied instead, the opposite happens. This suggests that clean
graphene does not sense the chemical potential of protons. A GFET can therefore
be used as a reference electrode in an aqueous electrolyte. Our finding sheds
light on the large variety of pH-induced gate shifts that have been published
for GFETs in the recent literature.


  Interference is a major issue limiting the performance in wireless networks.
Cooperation among receivers can help mitigate interference by forming
distributed MIMO systems. The rate at which receivers cooperate, however, is
limited in most scenarios. How much interference can one bit of receiver
cooperation mitigate? In this paper, we study the two-user Gaussian
interference channel with conferencing decoders to answer this question in a
simple setting. We identify two regions regarding the gain from receiver
cooperation: linear and saturation regions. In the linear region receiver
cooperation is efficient and provides a degrees-of-freedom gain, which is
either one cooperation bit buys one more bit or two cooperation bits buy one
more bit until saturation. In the saturation region receiver cooperation is
inefficient and provides a power gain, which is at most a constant regardless
of the rate at which receivers cooperate. The conclusion is drawn from the
characterization of capacity region to within two bits. The proposed strategy
consists of two parts: (1) the transmission scheme, where superposition
encoding with a simple power split is employed, and (2) the cooperative
protocol, where one receiver quantize-bin-and-forwards its received signal, and
the other after receiving the side information decode-bin-and-forwards its
received signal.


  We use backward error analysis for differential equations to obtain modified
or distorted equations describing the behaviour of the Newmark scheme applied
to the transient structural dynamics equation. Using these results, we show how
to construct compensation terms from the original parameters of the system,
which improve the performance of Newmark simulations without changing the time
step or modifying the scheme itself. Two such compensations are given: one
eliminates numerical damping, while the other achieves fourth-order accurate
calculations using the traditionally second-order Newmark method.


  We investigate the dynamics of axion-like particle (ALP) dark matter where
the field range is enlarged by a monodromy. The monodromy potential allows
sufficient production of dark matter also at larger couplings to the Standard
Model particles. The potential typically features a number of "wiggles" that
lead to a rapid growth of fluctuations. Using classical-statistical field
theory simulations we go beyond the linear regime and treat the system in the
non-linear and even non-perturbative regime. For sufficiently strong wiggles
the initially homogeneous field is completely converted into fluctuations. The
fluctuations correspond to dark matter particles with a non-vanishing velocity
and we consider the corresponding restrictions from structure formation as well
as the effects on today's dark matter density. Since all the dark matter is
made up from these strong fluctuations, the dark matter density features large,
$\mathcal{O}(1)$ fluctuations at scales $\lesssim 10^{6}\,{\rm km}\sqrt{{\rm
eV}/m_a}$.


  We find all Ricci semi-symmetric as well as all conformally semi-symmetric
spacetimes. Neither of these properties implies the other. We verify that only
conformally flat spacetimes can be Ricci semi-symmetric without being
conformally semi-symmetric and show that only vacuum spacetimes and spacetimes
with just a $\Lambda$-term can be Ricci semi-symmetric without being
conformally semi-symmetric.


  We study quantum oscillations for a system of fermionic and bosonic dimers
and compare the results to those experimentally observed in the cuprate
superconductors in their underdoped regime. Based on gauge invariance, we argue
that the charge carriers obey the Onsager quantization condition and quantum
oscillations take on a Lifshitz-Kosevich form. We obtain the effective mass and
find good qualitative agreement with experiments if we tune the model to the
point where the observed mass divergence at optimum doping is associated to a
van Hove singularity at which four free-dimer Fermi pockets touch pairwise in
the interior of the Brillouin zone. The same van Hove singularity leads to a
maximum in the d-wave superconducting pairing amplitude when anti-ferromagnetic
interactions are included. Our combined results therefore suggest that a
quantum critical point separating the underdoped and overdoped regimes is
marked by the location of the van Hove saddle point in the fermionic dimer
dispersion.


  Pulsars (PSRs) orbiting intermediate or supermassive black holes at the
centre of galaxies and globular clusters are known as Extreme Mass Ratio
Binaries (EMRBs) and have been identified as precision probes of strong-field
GR. For appropriate orbital parameters, some of these systems may also emit
gravitational radiation in a `burst-like' pattern. The observation of this
burst radiation in conjunction with the electromagnetic radio timing signal
would allow for multimessenger astronomy in strong-field gravitational regimes.
In this work we investigate gravitational radiation from these PSR-EMRBs,
calculating the waveforms and SNRs and explore the influence of this GW on the
pulsar radio signal. We find that for typical PSR-EMRBs, gravitational burst
radiation should be detectable from both the Galactic centre and the centre of
stellar clusters, and that this radiation will not meaningfully affect the
pulsar timing signal, allowing PSR-EMRB to remain `clean' test-beds of
strong-field GR.


  Translation, rotation, and scaling are three commonly used geometric
manipulation operations in image processing. Besides, some of them are
successfully used in developing effective knowledge graph embedding (KGE)
models such as TransE and RotatE. Inspired by the synergy, we propose a new KGE
model by leveraging all three operations in this work. Since translation,
rotation, and scaling operations are cascaded to form a compound one, the new
model is named CompoundE. By casting CompoundE in the framework of group
theory, we show that quite a few scoring-function-based KGE models are special
cases of CompoundE. CompoundE extends the simple distance-based relation to
relation-dependent compound operations on head and/or tail entities. To
demonstrate the effectiveness of CompoundE, we conduct experiments on three
popular KG completion datasets. Experimental results show that CompoundE
consistently achieves the state of-the-art performance.


  We focus on the problem of black-box adversarial attacks, where the aim is to
generate adversarial examples using information limited to loss function
evaluations of input-output pairs. We use Bayesian optimization~(BO) to
specifically cater to scenarios involving low query budgets to develop query
efficient adversarial attacks. We alleviate the issues surrounding BO in
regards to optimizing high dimensional deep learning models by effective
dimension upsampling techniques. Our proposed approach achieves performance
comparable to the state of the art black-box adversarial attacks albeit with a
much lower average query count. In particular, in low query budget regimes, our
proposed method reduces the query count up to $80\%$ with respect to the state
of the art methods.


  Complete one-loop parametrization of the linear sigma model is performed and
the phase boundary between first order and crossover transition regions of the
m_pi-m_K-plane is determined using the optimized perturbation theory as a
resummation tool of perturbative series. Away from the physical point the
parameters of the model were determined by making use of chiral perturbation
theory. Along the diagonal m_pi=m_K of the mass--plane we estimate m_pi^c=110
+- 20 MeV. The location of the tricritical point on the m_pi=0 axis is
estimated in the interval m_K^TCP in (1700,1850) MeV.


  We show that the Bessel function asymptotic relation $J_{\nu}^2(z) + J_{\nu +
1}^2 (z) \sim 2/(\pi z)$ of Lommel is valid when $z$ is real but can fail
otherwise.


  Thermodynamic and transport properties of normal disordered conductors are
strongly influenced by the proximity of a superconductor. A cooperation between
mesoscopic coherence and Andreev scattering of particles from the
superconductor generates new types of interference phenomena. We introduce a
field theoretic approach capable of exploring both averaged properties and
mesoscopic fluctuations of superconductor/normal-metal systems.
  As an example the method is applied to the study of the level statistics of a
SNS-junction.


  We consider a Bayesian approach to offline model-based inverse reinforcement
learning (IRL). The proposed framework differs from existing offline
model-based IRL approaches by performing simultaneous estimation of the
expert's reward function and subjective model of environment dynamics. We make
use of a class of prior distributions which parameterizes how accurate the
expert's model of the environment is to develop efficient algorithms to
estimate the expert's reward and subjective dynamics in high-dimensional
settings. Our analysis reveals a novel insight that the estimated policy
exhibits robust performance when the expert is believed (a priori) to have a
highly accurate model of the environment. We verify this observation in the
MuJoCo environments and show that our algorithms outperform state-of-the-art
offline IRL algorithms.


  Language models (LMs) have recently flourished in natural language processing
and computer vision, generating high-fidelity texts or images in various tasks.
In contrast, the current speech generative models are still struggling
regarding speech quality and task generalization. This paper presents Vec-Tok
Speech, an extensible framework that resembles multiple speech generation
tasks, generating expressive and high-fidelity speech. Specifically, we propose
a novel speech codec based on speech vectors and semantic tokens. Speech
vectors contain acoustic details contributing to high-fidelity speech
reconstruction, while semantic tokens focus on the linguistic content of
speech, facilitating language modeling. Based on the proposed speech codec,
Vec-Tok Speech leverages an LM to undertake the core of speech generation.
Moreover, Byte-Pair Encoding (BPE) is introduced to reduce the token length and
bit rate for lower exposure bias and longer context coverage, improving the
performance of LMs. Vec-Tok Speech can be used for intra- and cross-lingual
zero-shot voice conversion (VC), zero-shot speaking style transfer
text-to-speech (TTS), speech-to-speech translation (S2ST), speech denoising,
and speaker de-identification and anonymization. Experiments show that Vec-Tok
Speech, built on 50k hours of speech, performs better than other SOTA models.
Code will be available at https://github.com/BakerBunker/VecTok .


  A Lorentz symmetry violation aether-type theoretical model is considered to
investigate the Casimir effect and the generation of topological mass
associated with a self-interacting massive scalar fields obeying Dirichlet,
Newman and mixed boundary conditions on two large and parallel plates. By
adopting the path integral approach we found the effective potential at one-
and two-loop corrections which provides both the energy density and topological
mass when taken in the ground state of the scalar field. We then analyse how
these quantities are affected by the Lorentz symmetry violation and compare the
results with previous ones found in literature.


  In this work, we provide a detailed derivation of Foldy-Wouthuysen (FW)
transformation for two-dimensional (2D) gapped Dirac fermions under external
fields and apply the formalism to study valley excitons in 2D semiconducting
materials. Similar to relativistic quantum few-body problem, the gapped Dirac
equation can be transformed into a Schr\"{o}dinger equation with "relativistic"
correction terms. In this 2D materials system, the correction terms can be
interpreted as the Berry-curvature effect. The Hamiltonian for a valley exciton
in external fields can be written based on the FW transformed Dirac
Hamiltonian. Various valley-dependent effects on excitons, such as
fine-structure splittings of exciton energy levels, valley-selected exciton
transitions, and exciton valley Zeeman effect are discussed within this
framework.


  We construct coarse moduli spaces of semiquasihomogeneous hypersurface
singularities with respect to right equivalence and contact equivalence. We
have to fix the principal part of the semiquasihomogeneous singularities. For
the moduli spaces with respect to contact equivalence we also fix the Hilbert
function of the Tjurina algebra induced by the weights.


  We introduce shape-changing swarm robots. A swarm of self-transformable
robots can both individually and collectively change their configuration to
display information, actuate objects, act as tangible controllers, visualize
data, and provide physical affordances. ShapeBots is a concept prototype of
shape-changing swarm robots. Each robot can change its shape by leveraging
small linear actuators that are thin (2.5 cm) and highly extendable (up to
20cm) in both horizontal and vertical directions. The modular design of each
actuator enables various shapes and geometries of self-transformation. We
illustrate potential application scenarios and discuss how this type of
interface opens up possibilities for the future of ubiquitous and distributed
shape-changing interfaces.


  A non-unital generalization of weak bialgebra is proposed with a
multiplier-valued comultiplication. Certain canonical subalgebras of the
multiplier algebra (named the `base algebras') are shown to carry coseparable
co-Frobenius coalgebra structures. Appropriate modules over a weak multiplier
bialgebra are shown to constitute a monoidal category via the (co)module tensor
product over the base algebra. The relation to Van Daele and Wang's (regular
and arbitrary) weak multiplier Hopf algebra is discussed.


  Public transport network constitutes for an indispensable part of a city by
providing mobility services to the general masses. To improve ease of access
and reduce infrastructural investments, public transport authorities often
adopt proof of payment system. Such a system operates by eliminating ticket
controls when boarding the vehicle and subjecting the travelers to random
ticket checks by affiliated personnel (controllers). Although cost efficient,
such a system promotes free-riders, who deliberately decide to evade fares for
the transport service. A recent survey by the association of European
transport, estimates hefty income losses due to fare evasion, highlighting that
free-riding is a serious problem that needs immediate attention. To this end,
we highlight the attack vectors which can be exploited by free-riders by
analyzing the crowdsourced data about the control-locations. Next, we propose a
framework to generate randomized control-location traces by using generative
adversarial networks (GANs) in order to minimize the attack vectors. Finally,
we propose metrics to evaluate such a system, quantified in terms of increased
risk and higher probability of being subjected to control checks across the
city.


  We consider in a Hilbert space a self-adjoint operator H and a family
Phi=(Phi_1,...,Phi_d) of mutually commuting self-adjoint operators. Under some
regularity properties of H with respect to Phi, we propose two new formulae for
a time operator for H and prove their equality. One of the expressions is based
on the time evolution of an abstract localisation operator defined in terms of
Phi while the other one corresponds to a stationary formula. Under the same
assumptions, we also conduct the spectral analysis of H by using the method of
the conjugate operator.
  Among other examples, our theory applies to Friedrichs Hamiltonians, Stark
Hamiltonians, some Jacobi operators, the Dirac operator, convolution operators
on locally compact groups, pseudodifferential operators, adjacency operators on
graphs and direct integral operators.


  We have studied photon motion around axially symmetric rotating Kerr black
hole in the presence of plasma with radial power-law density. It is shown that
in the presence of plasma the observed shape and size of shadow changes
depending on i) plasma parameters, ii) black hole spin and iii) inclination
angle between observer plane and axis of rotation of black hole. In order to
extract pure effect of plasma influence on black hole image the particular case
of the Schwarzschild black hole has also been investigated and it has been
shown that i) the photon sphere around the spherical symmetric black hole is
left unchanged under the plasma influence, ii) however the Schwarzschild black
hole shadow size in plasma is reduced due to the refraction of the
electromagnetic radiation in plasma environment of black hole. The study of the
energy emission from the black hole in plasma shows that in the presence of
plasma the maximal energy emission rate from the black hole decreases.


  Recent high precision determinations of $V_{us}$ and $V_{ud}$ indicate
towards anomalies in the first row of the CKM matrix. Namely, determination of
$V_{ud}$ from superallowed beta decays and of $V_{us}$ from kaon decays imply a
violation of first row unitarity at about $4\sigma$ level. Moreover, there is
tension between determinations of $V_{us}$ obtained from leptonic $K\mu2$ and
semileptonic $K\ell3$ kaon decays. These discrepancies can be explained if
there exist extra vector-like quarks at the TeV scale, which have large enough
mixings with the lighter quarks. In particular, extra vector-like weak singlets
quarks can be thought as a solution to the CKM unitarity problem and an extra
vector-like weak doublet can in principle resolve all tensions. The
implications of this kind of mixings are examined against the flavour changing
phenomena and SM precision tests. We consider separately the effects of an
extra down-type isosinglet, up-type isosinglet and an isodoublet containing
extra quarks of both up and down type, and determine available parameter spaces
for each case. We find that the experimental constraints on flavor changing
phenomena become more stringent with larger masses, so that the extra species
should have masses no more than few TeV. Moreover, only one type of extra
multiplet cannot entirely explain all the discrepancies, and some their
combination is required. We show that these scenarios are testable with future
experiments. Namely, if extra vector-like quarks are responsible for CKM
anomalies, then at least one of them should be found at scale of few TeV, and
anomalous weak isospin violating $Z$-boson couplings with light quarks should
be detected if the experimental precision on $Z$ hadronic decay rate is
improved by a factor of $2$ or so.


  We study the anomalous $t\bar{t}Z$ couplings in the $t\bar{t}Z$ production in
leptonic final state at the $13$ TeV LHC. We use the polarizations of top
quarks and $Z$ boson, two-body and three-body spin correlations among the top
quarks and $Z$ boson, and the cross section to probe the anomalous couplings.
We estimate one parameter and simultaneous limits on the couplings of the
effective vertex as well as the effective operators for a set of luminosities
$150$ fb$^{-1}$, $300$ fb$^{-1}$, $1000$ fb$^{-1}$, and $3000$ fb$^{-1}$. The
polarizations and the spin correlations are found to be helpful on top of the
cross section to better constrain the anomalous couplings.


  The temporal behaviors of non-Newtonian/Newtonian two-phase flows are closely
tied to flow pattern formation mechanisms, influenced significantly by the
non-Newtonian index, and exhibiting nonlinear rheological characteristics. The
rheological model parameters are model-dependent, resulting in poor
predictability of their spatio-temporal characteristics. This limitation
hinders the development of a unified analytical approach and consistent
results, confining researches to case-by-case studies. In this study, sets of
digital microfluidics experiments, along with continuous-discrete
phase-interchanging schemes, yielded 72 datasets under various non-Newtonian
fluid solution configurations, micro-channel structures, and multiple
Carreau-related models. Among these datasets, we identified consistent temporal
behaviors featuring non-Newtonian flow patterns in digital microfluidics. In
the context of significant model dependency and widespread uncertainty in model
parameters for non-Newtonian fluid characterization, this study demonstrates
that the consistent representation of the non-Newtonian behavior exit, and may
be essentially independent of specific data or models. Which finding holds
positive implications for understanding the flow behavior and morphology of
non-Newtonian fluids.


  We consider the problem of decoding corrupted error correcting codes with
NC$^0[\oplus]$ circuits in the classical and quantum settings. We show that any
such classical circuit can correctly recover only a vanishingly small fraction
of messages, if the codewords are sent over a noisy channel with positive error
rate. Previously this was known only for linear codes with large dual distance,
whereas our result applies to any code. By contrast, we give a simple quantum
circuit that correctly decodes the Hadamard code with probability
$\Omega(\varepsilon^2)$ even if a $(1/2 - \varepsilon)$-fraction of a codeword
is adversarially corrupted.
  Our classical hardness result is based on an equidistribution phenomenon for
multivariate polynomials over a finite field under biased input-distributions.
This is proved using a structure-versus-randomness strategy based on a new
notion of rank for high-dimensional polynomial maps that may be of independent
interest.
  Our quantum circuit is inspired by a non-local version of the
Bernstein-Vazirani problem, a technique to generate ``poor man's cat states''
by Watts et al., and a constant-depth quantum circuit for the OR function by
Takahashi and Tani.


  We have analyzed and compared the performance of two novel fast-neutron
imaging methods with time-of-flight spectroscopy capability. Using MCNP and
GEANT code simulations of neutron and charged-particle transport in the
detectors, key parameters such as detection efficiency, the amount of energy
deposited in the converter and the spatial resolution of both detector variants
have been evaluated.


  Despite the progress made in the field of medical imaging, it remains a large
area of open research, especially due to the variety of imaging modalities and
disease-specific characteristics. This paper is a comparative study describing
the potential of using local binary patterns (LBP), deep features and the
bag-of-visual words (BoVW) scheme for the classification of histopathological
images. We introduce a new dataset, \emph{KIMIA Path960}, that contains 960
histopathology images belonging to 20 different classes (different tissue
types). We make this dataset publicly available. The small size of the dataset
and its inter- and intra-class variability makes it ideal for initial
investigations when comparing image descriptors for search and classification
in complex medical imaging cases like histopathology. We investigate deep
features, LBP histograms and BoVW to classify the images via leave-one-out
validation. The accuracy of image classification obtained using LBP was 90.62\%
while the highest accuracy using deep features reached 94.72\%. The dictionary
approach (BoVW) achieved 96.50\%. Deep solutions may be able to deliver higher
accuracies but they need extensive training with a large number of (balanced)
image datasets.


  We consider the problem of change-point detection in multivariate
time-series. The multivariate distribution of the observations is supposed to
follow a graphical model, whose graph and parameters are affected by abrupt
changes throughout time. We demonstrate that it is possible to perform exact
Bayesian inference whenever one considers a simple class of undirected graphs
called spanning trees as possible structures. We are then able to integrate on
the graph and segmentation spaces at the same time by combining classical
dynamic programming with algebraic results pertaining to spanning trees. In
particular, we show that quantities such as posterior distributions for
change-points or posterior edge probabilities over time can efficiently be
obtained. We illustrate our results on both synthetic and experimental data
arising from biology and neuroscience.


  This paper proposes a new nonlinear approach for additive functional
regression with functional response based on kernel methods along with some
slight reformulation and implementation of the linear regression and the
spectral additive model. The latter methods have in common that the covariates
and the response are represented in a basis and so, can only be applied when
the response and the covariates belong to a Hilbert space, while the proposed
method only uses the distances among data and thus can be applied to those
situations where any of the covariates or the response is not Hilbertian
typically normed or even metric spaces with a real vector structure. A
comparison of these methods with other procedures readily available in R is
performed in a simulation study and in real datasets showing the results the
advantages of the nonlinear proposals and the small loss of efficiency when the
simulation scenario is truly linear. The comparison is done in the Hilbertian
case as it is the only scenario where all the procedures can be compared.
Finally, the supplementary material provides a visualization tool for checking
the linearity of the relationship between a single covariate and the response
and a link to a GitHub repository where the code and data are available.} %and
an example considering that the response is not Hilbertian.


  We give a randomized algorithm that approximates the number of independent
sets in a dense, regular bipartite graph -- in the language of approximate
counting, we give an FPRAS for #BIS on the class of dense, regular bipartite
graphs. Efficient counting algorithms typically apply to ``high-temperature''
problems on bounded-degree graphs, and our contribution is a notable exception
as it applies to dense graphs in a low-temperature setting. Our methods give a
counting-focused complement to the long line of work in combinatorial
optimization showing that CSPs such as Max-Cut and Unique Games are easy on
dense graphs via spectral arguments. The proof exploits the fact that dense,
regular graphs exhibit a kind of small-set expansion (i.e. bounded threshold
rank), which via subspace enumeration lets us enumerate small cuts efficiently.


  This article presents a possible inflation scenario as a consequence of
non-minimal gravitational couplings in the Conformal Standard Model. The model
consists, in comparison to the SM, of additional right-chiral neutrinos and
complex scalars coupled to the right-chiral neutrinos but not to the SM
particles. The inflation is driven by two non-minimally coupled fields one
being the usual Higgs and the other one of the sterile scalars. It turns out
that in this model the tensor to scalar ratio and spectral index can match the
current data for a wide range of parameters.


  Most works studying representation learning focus only on classification and
neglect regression. Yet, the learning objectives and, therefore, the
representation topologies of the two tasks are fundamentally different:
classification targets class separation, leading to disconnected
representations, whereas regression requires ordinality with respect to the
target, leading to continuous representations. We thus wonder how the
effectiveness of a regression representation is influenced by its topology,
with evaluation based on the Information Bottleneck (IB) principle. The IB
principle is an important framework that provides principles for learning
effective representations. We establish two connections between it and the
topology of regression representations. The first connection reveals that a
lower intrinsic dimension of the feature space implies a reduced complexity of
the representation Z. This complexity can be quantified as the conditional
entropy of Z on the target Y, and serves as an upper bound on the
generalization error. The second connection suggests a feature space that is
topologically similar to the target space will better align with the IB
principle. Based on these two connections, we introduce PH-Reg, a regularizer
specific to regression that matches the intrinsic dimension and topology of the
feature space with the target space. Experiments on synthetic and real-world
regression tasks demonstrate the benefits of PH-Reg. Code:
https://github.com/needylove/PH-Reg.


  We study the dependence of the tau function of Painlev\'e I equation on the
generalized monodromy of the associated linear problem. In particular, we
compute connection constants relating the tau function asymptotics on five
canonical rays at infinity. The result is expressed in terms of dilogarithms of
cluster type coordinates on the space of Stokes data.


  We have computed accurate 1-D solar models including both a macroscopic
mixing process in the solar tachocline as well as up-to-date microscopic
physical ingredients. Using sound speed and density profiles inferred through
primary inversion of the solar oscillation frequencies coupled with the
equation of thermal equilibrium, we have extracted the temperature and hydrogen
abundance profiles. These inferred quantities place strong constraints on our
theoretical models in terms of the extent and strength of our macroscopic
mixing, on the photospheric heavy elements abundance, on the nuclear reaction
rates such as $S_{11}$ and $S_{34}$ and on the efficiency of the microscopic
diffusion. We find a good overall agreement between the seismic Sun and our
models if we introduce a macroscopic mixing in the tachocline and allow for
variation within their uncertainties of the main physical ingredients. From our
study we deduce that the solar hydrogen abundance at the solar age is $X_{\rm
inv}=0.732\pm 0.001$ and that based on the $^9$Be photospheric depletion, the
maximum extent of mixing in the tachocline is 5% of the solar radius. The
nuclear reaction rate for the fundamental $pp$ reaction is found to be
$S_{11}(0)=4.06\pm 0.07$ $10^{-25}$ MeV barns, i.e., 1.5% higher than the
present theoretical determination. The predicted solar neutrino fluxes are
discussed in the light of the new SNO/SuperKamiokande results.


  The ferro-rotational order, whose order parameter (OP) is an axial vector
invariant under both time reversal (TR) and spatial inversion (SI) operations,
is the last remaining category of ferroics to be observed after the
ferroelectric, ferromagnetic, and ferro-toroidal orders. This order has become
increasingly popular in many new quantum materials, especially in complex
oxides, and is considered responsible for a number of novel phenomena such as
polar vortices, giant magnetoelectric coupling, and type-II multiferroics.
However, physical properties of the ferro-rotational order have been rarely
studied either theoretically or experimentally. Here, using high sensitivity
rotational anisotropy second harmonic generation (RA SHG), we have, for the
first time, exploited the electric quadrupole (EQ) contribution to the SHG to
directly couple to this centrosymmetric ferro-rotational order in an archetype
of type-II multiferroics, RbFe(MoO4)2. Surprisingly, we have found that two
types of domains with opposite ferro-rotational vectors emerge with distinct
populations at the critical temperature Tc ~195 K and gradually evolve to reach
an even ratio at lower temperatures. Moreover, we have identified the
ferro-rotational order phase transition as weak first order, and have revealed
its conjugate coupling field as a unique combination of the induced EQ SHG and
the incident fundamental electric fields. Our results on physical properties of
a ferro-rotational order provide crucial knowledge for understanding and
searching for novel phases of matter built upon the ferro-rotational order.
Further, these results open the possibility of revealing unconventional
centrosymmetric orders and identifying their conjugate coupling fields with
second order nonlinear optics.


  Geometrical confinement effect in exfoliated sheets of layered materials
leads to significant evolution of energy dispersion with decreasing layer
thickness. Molybdenum disulphide (MoS2) was recently found to exhibit indirect
to direct gap transition when the thickness is reduced to a single monolayer.
This leads to remarkable enhancement in the photoluminescence efficiency, which
opens up new opportunities for the optoelectronic applications of the material.
Here we report differential reflectance and photoluminescence (PL) spectra of
mono- to few-layer WS2 and WSe2 that indicate that the band structure of these
materials undergoes similar indirect to direct transition when thinned to a
single monolayer. Strong enhancement in PL quantum yield is observed for
monoayer WS2 and WSe2 due to exciton recombination at the direct band edge. In
contrast to natural MoS2 crystals extensively used in recent studies, few-layer
WS2 and WSe2 show comparatively strong indirect gap emission along with
distinct direct gap hot electron emission, suggesting high quality of synthetic
crystals prepared by chemical vapor transport method. Fine absorption and
emission features and their thickness dependence suggest strong effect of Se
p-orbitals on the d electron band structure as well as interlayer coupling in
WSe2.


  Effective thermal conductivity is an important property of composites for
different thermal management applications. Although physics-based methods, such
as effective medium theory and solving partial differential equation, dominate
the relevant research, there is significant interest to establish the
structure-property linkage through the machine learning method. The performance
of general machine learning methods is highly dependent on features selected to
represent the microstructures. 3D convolutional neural networks (CNNs) can
directly extract geometric features of composites, which have been demonstrated
to establish structure-property linkages with high accuracy. However, to obtain
the 3D microstructure in composite is generally challenging in reality. In this
work, we attempt to use 2D cross-section images which can be easier to obtain
in real applications as input of 2D CNNs to predict effective thermal
conductivity of 3D composites. The results show that by using multiple
cross-section images along or perpendicular to the preferred directionality of
the fillers, the prediction accuracy of 2D CNNs can be as good as 3D CNNs. Such
a result is demonstrated with the particle filled composite and a stochastic
complex composite. The prediction accuracy is dependent on the
representativeness of cross-section images used. Multiple cross-section images
can fully determine the shape and distribution of fillers. The average over
multiple images and the use of large-size images can reduce the uncertainty and
increase the prediction accuracy. Besides, since cross-section images along the
heat flow direction can distinguish between serial structures and parallel
structures, they are more representative than cross-section images
perpendicular to the heat flow direction.