  Emotional voice conversion aims to transform emotional prosody in speech
while preserving the linguistic content and speaker identity. Prior studies
show that it is possible to disentangle emotional prosody using an
encoder-decoder network conditioned on discrete representation, such as one-hot
emotion labels. Such networks learn to remember a fixed set of emotional
styles. In this paper, we propose a novel framework based on variational
auto-encoding Wasserstein generative adversarial network (VAW-GAN), which makes
use of a pre-trained speech emotion recognition (SER) model to transfer
emotional style during training and at run-time inference. In this way, the
network is able to transfer both seen and unseen emotional style to a new
utterance. We show that the proposed framework achieves remarkable performance
by consistently outperforming the baseline framework. This paper also marks the
release of an emotional speech dataset (ESD) for voice conversion, which has
multiple speakers and languages.


  This paper considers a communication system where a source sends
time-sensitive information to its destination. We assume that both arrival and
service processes of the messages are memoryless and the source has a single
server with no buffer. Besides, we consider that the service is interrupted by
an independent random process, which we model using the On-Off process. For
this setup, we study the age of information for two queueing disciplines: 1)
non-preemptive, where the messages arriving while the server is occupied are
discarded, and 2) preemptive, where the in-service messages are replaced with
newly arriving messages in the Off states. For these disciplines, we derive
closed-form expressions for the mean peak age and mean age.


  In this paper, we examine holomorphic Segre preserving maps between the
complexifications of real hypersurfaces in $\mathbb{C}^{n+1}$. In particular,
we find several sufficient conditions ensuring that Segre transversality and
total Segre nondegeneracy of the maps must hold.


  Coded caching is a technique for achieving increased throughput in cached
networks during peak hours. Placement delivery arrays (PDAs) capture both
placement and delivery scheme requirements in coded caching in a single array.
Lifting is a method of constructing PDAs, where entries in a small base PDA are
replaced with constituent PDAs that satisfy a property called
Blackburn-compatibility. We propose two new constructions for
Blackburn-compatible PDAs including a novel method for lifting
Blackburn-compatible PDAs to obtain new sets of Blackburn-compatible PDAs. Both
of these constructions improve upon previous tradeoffs between rate, memory and
subpacketization. We generalize lifting constructions by defining partial
Blackburn-compatibility between two PDAs w.r.t. a third PDA. This is a wider
notion of Blackburn-compatibility making the original definition a special
case. We show that some popular coded caching schemes can be defined as lifting
constructions in terms of this extended notion.


  Memtranstor that correlates charge and magnetic flux via nonlinear
magnetoelectric effects has a great potential in developing next-generation
nonvolatile devices. In addition to multi-level nonvolatile memory, we
demonstrate here that nonvolatile logic gates such as NOR and NAND can be
implemented in a single memtranstor made of the Ni/PMN-PT/Ni heterostructure.
After applying two sequent voltage pulses (X1, X2) as the logic inputs on the
memtranstor, the output magnetoelectric voltage can be positive high (logic
"1"), positive low (logic "0"), or negative (logic "0"), depending on the
levels of X1 and X2. The underlying physical mechanism is related to the
complete or partial reversal of ferroelectric polarization controlled by
inputting selective voltage pulses, which determines the magnitude and sign of
the magnetoelectric voltage coefficient. The combined functions of both memory
and logic could enable the memtranstor as a promising candidate for future
computing systems beyond von Neumann architecture.


  We propose Additive Powers-of-Two~(APoT) quantization, an efficient
non-uniform quantization scheme for the bell-shaped and long-tailed
distribution of weights and activations in neural networks. By constraining all
quantization levels as the sum of Powers-of-Two terms, APoT quantization enjoys
high computational efficiency and a good match with the distribution of
weights. A simple reparameterization of the clipping function is applied to
generate a better-defined gradient for learning the clipping threshold.
Moreover, weight normalization is presented to refine the distribution of
weights to make the training more stable and consistent. Experimental results
show that our proposed method outperforms state-of-the-art methods, and is even
competitive with the full-precision models, demonstrating the effectiveness of
our proposed APoT quantization. For example, our 4-bit quantized ResNet-50 on
ImageNet achieves 76.6% top-1 accuracy without bells and whistles; meanwhile,
our model reduces 22% computational cost compared with the uniformly quantized
counterpart. The code is available at
https://github.com/yhhhli/APoT_Quantization.


  Consider an infinite tree. A hierarchomorphism (spheromorphism) is a
homeomorphism of the absolute which can be extended to the tree except a finite
subtree. Examples of groups of hierarchomorphisms: groups of locally analitic
diffeomorphisms of $p$-adic line; also Richard Thompson groups. The groups of
hierarchomorphisms have some properties similar to the group of diffeomorphisms
of the circle. We discuss actions of groups of ierarchomorphisms in some
natural Hilbert spaces associated with trees.


  The remarkable achievements of Large Language Models (LLMs) have led to the
emergence of a novel recommendation paradigm -- Recommendation via LLM
(RecLLM). Nevertheless, it is important to note that LLMs may contain social
prejudices, and therefore, the fairness of recommendations made by RecLLM
requires further investigation. To avoid the potential risks of RecLLM, it is
imperative to evaluate the fairness of RecLLM with respect to various sensitive
attributes on the user side. Due to the differences between the RecLLM paradigm
and the traditional recommendation paradigm, it is problematic to directly use
the fairness benchmark of traditional recommendation. To address the dilemma,
we propose a novel benchmark called Fairness of Recommendation via LLM
(FaiRLLM). This benchmark comprises carefully crafted metrics and a dataset
that accounts for eight sensitive attributes1 in two recommendation scenarios:
music and movies. By utilizing our FaiRLLM benchmark, we conducted an
evaluation of ChatGPT and discovered that it still exhibits unfairness to some
sensitive attributes when generating recommendations. Our code and dataset can
be found at https://github.com/jizhi-zhang/FaiRLLM.


  Federated learning has recently emerged as a decentralized approach to learn
a high-performance model without access to user data. Despite its
effectiveness, federated learning gives malicious users opportunities to
manipulate the model by uploading poisoned model updates to the server. In this
paper, we propose a review mechanism called FedReview to identify and decline
the potential poisoned updates in federated learning. Under our mechanism, the
server randomly assigns a subset of clients as reviewers to evaluate the model
updates on their training datasets in each round. The reviewers rank the model
updates based on the evaluation results and count the number of the updates
with relatively low quality as the estimated number of poisoned updates. Based
on review reports, the server employs a majority voting mechanism to integrate
the rankings and remove the potential poisoned updates in the model aggregation
process. Extensive evaluation on multiple datasets demonstrate that FedReview
can assist the server to learn a well-performed global model in an adversarial
environment.


  Adversarial examples have gained tons of attention in recent years. Many
adversarial attacks have been proposed to attack image classifiers, but few
work shift attention to object detectors. In this paper, we propose Sparse
Adversarial Attack (SAA) which enables adversaries to perform effective evasion
attack on detectors with bounded \emph{l$_{0}$} norm perturbation. We select
the fragile position of the image and designed evasion loss function for the
task. Experiment results on YOLOv4 and FasterRCNN reveal the effectiveness of
our method. In addition, our SAA shows great transferability across different
detectors in the black-box attack setting. Codes are available at
\emph{https://github.com/THUrssq/Tianchi04}.


  This paper empirically argues for a closer examination of what we wish to
retain when we speak of "retention" in engineering [1]. We present and
interpret data from clinical interviews and classroom video of "Michael," a
student who feels marginalized by an engineering program that undervalues him
because of his stance toward knowledge [2],[3]. Michael is a sophomore
Electrical Engineering and Mathematics major in a Basic Circuits course. In his
own words, he's a "fringe" student because of his robust tendency to try making
sense of the concepts being taught rather than memorizing formulae. He also
feels alienated because he views learning in terms of argument and intuition,
not algorithm and rote acceptance. Furthermore, for Michael the practice of
sense-making defines him; it's an integral aspect of his identity [4]. Thus,
Michael's self-reported sense of alienation resonates strongly with existing
identity-based accounts of students leaving the field [5],[6]. We contend the
field of engineering suffers if individuals like Michael don't pursue it.
Through this case study of Michael, we urge the retention discussion to
consider not just the demographic categories of people we hope to keep, but
also the approaches to knowledge, learning, and problem-solving we aim to
support.


  We discuss a (2+1) dimensional topological superconductor with $N_f$ left-
and right-moving Majorana edge modes and a $\mathbb{Z}_2\times \mathbb{Z}_2$
symmetry. In the absence of interactions, these phases are distinguished by an
integral topological invariant $N_f$. With interactions, the edge state in the
case $N_f=8$ is unstable against interactions, and a $\mathbb{Z}_2\times
\mathbb{Z}_2$ invariant mass gap can be generated dynamically. We show that
this phenomenon is closely related to the modular invariance of type II
superstring theory. More generally, we show that the global gravitational
anomaly of the non-chiral Majorana edge states is the physical manifestation of
the bulk topological superconductors classified by $\mathbb{Z}_8$.


  Spatio-temporal analysis typically performed in botany and in statistical
physics reveals persistent correlations of olive yields which range depend on
the size of the averaging region. Mapping spatially correlated regions reveals
areas which mimic historical spread of Olea europaea. These yield patterns are
remarkable given the intensive nature of modern agriculture, and cannot be
attributed to weather due to inability of weather variables to predict yields.
Long-range correlations between olive trees may indicate long-range
communications.


  Projection mapping (PM) is a technique that enhances the appearance of
real-world surfaces using projected images, enabling multiple people to view
augmentations simultaneously, thereby facilitating communication and
collaboration. However, PM typically requires a dark environment to achieve
high-quality projections, limiting its practicality. In this paper, we overcome
this limitation by replacing conventional room lighting with heterogeneous
projectors. These projectors replicate environmental lighting by selectively
illuminating the scene, excluding the projection target. Our contributions
include a distributed projector optimization framework designed to effectively
replicate environmental lighting and the incorporation of a large-aperture
projector, in addition to standard projectors, to reduce high-luminance emitted
rays and hard shadows -- undesirable factors for collaborative tasks in PM. We
conducted a series of quantitative and qualitative experiments, including user
studies, to validate our approach. Our findings demonstrate that our
projector-based lighting system significantly enhances the contrast and realism
of PM results even under environmental lighting compared to typical lights.
Furthermore, our method facilitates a substantial shift in the perceived color
mode from the undesirable aperture-color mode, where observers perceive the
projected object as self-luminous, to the surface-color mode in PM.


  Nash or Sobolev inequalities are known to be equivalent to ultracontractive
properties of Markov semigroups, hence to uniform bounds on their kernel
densities. In this work we present a simple and extremely general method, based
on weighted Nash inequalities, to obtain non-uniform bounds on the kernel
densities. Such bounds imply a control on the trace or the Hilbert-Schmidt norm
of the heat kernels. We illustrate the method on the heat kernel on $\dR$
naturally associated with the measure with density $C_a\exp(-|x|^a)$, with $1


  Compactification of type IIB theory on torus, in the presence of fluxes, is
considered. The reduced effective action is expressed in manifestly S-duality
invariant form. Cosmological solutions of the model are discussed in several
cases in the Pre-Big Bang scenario.


  We propose a general method for constructing confidence intervals and
statistical tests for single or low-dimensional components of a large parameter
vector in a high-dimensional model. It can be easily adjusted for multiplicity
taking dependence among tests into account. For linear models, our method is
essentially the same as in Zhang and Zhang [J. R. Stat. Soc. Ser. B Stat.
Methodol. 76 (2014) 217-242]: we analyze its asymptotic properties and
establish its asymptotic optimality in terms of semiparametric efficiency. Our
method naturally extends to generalized linear models with convex loss
functions. We develop the corresponding theory which includes a careful
analysis for Gaussian, sub-Gaussian and bounded correlated designs.


  Given a physical quantum system described by a Hilbert H, for any bounded
quantum observable (a bounded self-adjoint operator) T it is possible to define
several ''hidden observable'' functions f:H->R associated to T and for any
quantum mixed state (a density matrix) D it is possible to define several
''hidden mixed states'' (probability measures) m on H associated to D in such a
way that the following equality is verified:
  Trace[ b(T). D] =integral[b(f(psi)).dm(psi) whatever is the continuous
function b:R->R. This formula gives a general way to express any expectation
value computable in a quantum theory as a classical statistical mean value.


  We discuss a continuous family of non-supersymmetric AdS$_3\times
S^3\times{\rm T^4}$ vacua in heterotic and type II supergravities whose
complete Kaluza-Klein spectrum is computed and found to be free from
instabilities. This family is protected as well against some non-perturbative
decay channels, and as such it provides the first candidate for a
non-supersymmetric holographic conformal manifold in 2$d$. We also describe the
operators realising the deformations in the worldsheet and boundary CFT's.


  We address the problem of how to "obfuscate" texts by removing stylistic
clues which can identify authorship, whilst preserving (as much as possible)
the content of the text. In this paper we combine ideas from "generalised
differential privacy" and machine learning techniques for text processing to
model privacy for text documents. We define a privacy mechanism that operates
at the level of text documents represented as "bags-of-words" - these
representations are typical in machine learning and contain sufficient
information to carry out many kinds of classification tasks including topic
identification and authorship attribution (of the original documents). We show
that our mechanism satisfies privacy with respect to a metric for semantic
similarity, thereby providing a balance between utility, defined by the
semantic content of texts, with the obfuscation of stylistic clues. We
demonstrate our implementation on a "fan fiction" dataset, confirming that it
is indeed possible to disguise writing style effectively whilst preserving
enough information and variation for accurate content classification tasks.


  The widespread adoption of voice-activated systems has modified routine
human-machine interaction but has also introduced new vulnerabilities. This
paper investigates the susceptibility of automatic speech recognition (ASR)
algorithms in these systems to interference from near-ultrasonic noise.
Building upon prior research that demonstrated the ability of near-ultrasonic
frequencies (16 kHz - 22 kHz) to exploit the inherent properties of
microelectromechanical systems (MEMS) microphones, our study explores
alternative privacy enforcement means using this interference phenomenon. We
expose a critical vulnerability in the most common microphones used in modern
voice-activated devices, which inadvertently demodulate near-ultrasonic
frequencies into the audible spectrum, disrupting the ASR process. Through a
systematic analysis of the impact of near-ultrasonic noise on various ASR
systems, we demonstrate that this vulnerability is consistent across different
devices and under varying conditions, such as broadcast distance and specific
phoneme structures. Our findings highlight the need to develop robust
countermeasures to protect voice-activated systems from malicious exploitation
of this vulnerability. Furthermore, we explore the potential applications of
this phenomenon in enhancing privacy by disrupting unauthorized audio recording
or eavesdropping. This research underscores the importance of a comprehensive
approach to securing voice-activated systems, combining technological
innovation, responsible development practices, and informed policy decisions to
ensure the privacy and security of users in an increasingly connected world.


  I elaborate on the earlier suggestion that the model describing the plateaux
transition in Integer Quantum Hall effect scales to a particular point on the
line of critical points of a theory with a higher symmetry


  We define a trisection of a closed, orientable three dimensional manifold
into three handlebodies, and a notion of stabilization for these trisections.
Several examples of trisections are described in detail. We define the
trisection genus $t(M)$ of a 3-manifold, and relate it to the Heegaard genus
$g(M)$, showing that $t(M) \le g(M) \le 2t(M)$. We show moreover that the bound
$g(M) \le 2t(M)$ is tight. We define stabilizations of trisections and show
that all trisections of a 3-manifold are stably equivalent, providing an
analogue of the Reidemeister-Singer theorem for trisections. We conclude by
showing that there exist complicated trisections of $S^3$.


  Wikipedia is a huge global repository of human knowledge, that can be
leveraged to investigate interwinements between cultures. With this aim, we
apply methods of Markov chains and Google matrix, for the analysis of the
hyperlink networks of 24 Wikipedia language editions, and rank all their
articles by PageRank, 2DRank and CheiRank algorithms. Using automatic
extraction of people names, we obtain the top 100 historical figures, for each
edition and for each algorithm. We investigate their spatial, temporal, and
gender distributions in dependence of their cultural origins. Our study
demonstrates not only the existence of skewness with local figures, mainly
recognized only in their own cultures, but also the existence of global
historical figures appearing in a large number of editions. By determining the
birth time and place of these persons, we perform an analysis of the evolution
of such figures through 35 centuries of human history for each language, thus
recovering interactions and entanglement of cultures over time. We also obtain
the distributions of historical figures over world countries, highlighting
geographical aspects of cross-cultural links. Considering historical figures
who appear in multiple editions as interactions between cultures, we construct
a network of cultures and identify the most influential cultures according to
this network.


  When a birational surface map is expanding on cohomology there is a canonical
way to associate positive closed currents to the map and its inverse. In this
paper we use a version of Dirichlet energy to construct the wedge product of
these two currents under a very weak additional condition on the map. We show
that the resulting measure is invariant and mixing, and we establish that its
Lyapunov exponents are finite and non-zero.


  We construct a Poincare-Birkhoff-Witt type basis for the Weyl modules of the
current algebra of $sl_{r+1}$. As a corollary we prove a conjecture made by
Chari and Pressley on the dimension of the Weyl modules in this case.
  Further, we relate the Weyl modules to the fusion modules of the current
algebra defined by Feigin and Loktev, and to the Demazure modules in level one
representations of the corresponding affine algebra. In particular, this allows
us to establish substantial cases of the conjectures of Feigin and Loktev on
the structure and graded character of the fusion modules.


  In this paper, we study the stabilization problem for a food extrusion
process in the isothermal case. The model expresses the mass conservation in
the extruder chamber and consists of a hyperbolic Partial Differential Equation
(PDE) and a nonlinear Ordinary Differential Equation (ODE) whose dynamics
describes the evolution of a moving interface. By using a Lyapunov approach, we
obtain the exponential stabilization for the closed-loop system under natural
feedback controls through indirect measurements.


  In this paper we study consistent solutions of spherically symmetric space in
metric f(R) gravity theory. Here we inversely obtain a generic action from
metric solutions that describe flat rotation curves in spiral galaxies without
dark matter. Then we show that obtained solutions are in conformity with
Tully-Fisher relation and modified Newtonian dynamics, which are two strong
constraints in justification of flat rotation curves in spiral galaxies.


  Recent results by various authors have established a "model deformation
phenomenon" in random matrix theory. Specifically, it is possible to construct
pairs of random matrix models such that the limiting eigenvalue distributions
are connected by push-forward under an explicitly constructible map of the
plane to itself. In this paper, we argue that the analogous transformation at
the finite-$N$ level can be accomplished by applying an appropriate heat flow
to the characteristic polynomial of the first model.
  Let the "second moment" of a random polynomial $p$ denote the expectation
value of the square of the absolute value of $p.$ We find certain pairs of
random matrix models and we apply a certain heat-type operator to the
characteristic polynomial $p_{1}$ of the first model, giving a new polynomial
$q.$ We prove that the second moment of $q$ is equal to the second moment of
the characteristic polynomial $p_{2}$ of the second model. This result leads to
several conjectures of the following sort: when $N$ is large, the zeros of $q$
have the same bulk distribution as the zeros of $p_{2},$ namely the eigenvalues
of the second random matrix model. At a more refined level, we conjecture that,
as the characteristic polynomial of the first model evolves under the
appropriate heat flow, its zeros will evolve close to the characteristic curves
of a certain PDE. All conjectures are formulated in "additive" and
"multiplicative" forms.
  As a special case, suppose we apply the standard heat operator for time $1/N$
to the characteristic polynomial $p$ of an $N\times N$ GUE matrix, giving a new
polynomial $q.$ We conjecture that the zeros of $q$ will be asymptotically
uniformly distributed over the unit disk. That is, the heat operator converts
the distribution of zeros from semicircular to circular.


  Outstanding wide-bandgap semiconductor material such as gallium nitride (GaN)
has been extensively utilized in power electronics, radiofrequency amplifiers,
and harsh environment devices. Due to its quantum confinement effect in
enabling desired deep-ultraviolet emission, excitonic impact, and electronic
transport features, two-dimensional (2D) or ultrathin quasi-2D GaN
semiconductors have been one of the most remarkable candidates for future
growth of microelectronic devices. Here, for the first time, we reported a
large area, wide bandgap, and room-temperature quasi-2D GaN synthesis and
printing strategy through introducing the plasma medicated liquid metal gallium
surface-confined nitridation reaction mechanism. The developed direct
fabrication and compositional process is consistent with various electronics
manufacturing approaches and thus opens an easy going way for cost-effective
growth of the third-generation semiconductor. In particular, the fully printed
field-effect transistors relying on the GaN thus made show p-type switching
with an on/off ratio greater than 105, maximum field-effect hole mobility of 53
cm2/(V*s), and a small sub-threshold swing. As it was demonstrated, the present
method allows to produce at room temperature the GaN with thickness spanning
from 1nm to nanometers. This basic method can be further extended, generalized,
and utilized for making various electronic and photoelectronic devices in the
coming time.


  When the planar circular restricted 3-body problem (RTBP) is periodically
perturbed, families of unstable periodic orbits break up into whiskered tori,
with most tori persisting into the perturbed system. In this study, we 1)
develop a quasi-Newton method which simultaneously solves for the tori and
their center, stable, and unstable directions; 2) implement continuation by
both perturbation as well as rotation numbers; 3) compute Fourier-Taylor
parameterizations of the stable and unstable manifolds; 4) regularize the
equations of motion; and 5) globalize these manifolds. Our methodology improves
on efficiency and accuracy compared to prior studies, and applies to a variety
of periodic perturbations. We demonstrate the tools near resonances in the
planar elliptic RTBP.


  Proteins perform a large variety of functions in living organisms, thus
playing a key role in biology. As of now, available learning algorithms to
process protein data do not consider several particularities of such data
and/or do not scale well for large protein conformations. To fill this gap, we
propose two new learning operations enabling deep 3D analysis of large-scale
protein data. First, we introduce a novel convolution operator which considers
both, the intrinsic (invariant under protein folding) as well as extrinsic
(invariant under bonding) structure, by using $n$-D convolutions defined on
both the Euclidean distance, as well as multiple geodesic distances between
atoms in a multi-graph. Second, we enable a multi-scale protein analysis by
introducing hierarchical pooling operators, exploiting the fact that proteins
are a recombination of a finite set of amino acids, which can be pooled using
shared pooling matrices. Lastly, we evaluate the accuracy of our algorithms on
several large-scale data sets for common protein analysis tasks, where we
outperform state-of-the-art methods.


  It is shown that for a central potential that is an injective function of the
radial coordinate, a second central potential can be found that leads to
trajectories in the configuration space and the momentum space coinciding,
respectively, with the trajectories in the momentum space and the configuration
space produced by the original potential.


  Observations suggest that relativistic particles play a fundamental role in
the dynamics of jets emerging from active galactic nuclei as well as in their
interaction with the intracluster medium. However, no general consensus exists
concerning the acceleration mechanism of those high energy particles. A
gravitational acceleration mechanism is here proposed, in which particles
leaving precise regions within the ergosphere of a rotating supermassive black
hole produce a highly collimated flow. These particles follow unbound geodesics
which are asymptotically parallel to the spin axis of the black hole and are
characterized by the energy $E$, the Carter constant ${\cal Q}$ and zero
angular momentum of the component $L_z$. If environmental effects are
neglected, the present model predicts at distances of about 140 kpc from the
ergosphere the presence of electrons with energies around 9.4 GeV. The present
mechanism can also accelerate protons up to the highest energies observed in
cosmic rays by the present experiments.


  We present near-infrared and optical imaging of fifteen candidate black hole
X-ray binaries. In addition to quiescent observations for all sources, we also
observed two of these sources (IGR J17451-3022 and XTE J1818-245) in outburst.
We detect the quiescent counterpart for twelve out of fifteen sources, and for
the remaining three we report limiting magnitudes. The magnitudes of the
detected counterparts range between $K_s$ = 17.59 and $K_s$ = 22.29 mag. We
provide (limits on) the absolute magnitudes and finding charts of all sources.
Of these twelve detections in quiescence, seven represent the first quiescent
reported values (for MAXI J1543-564, XTE J1726-476, IGR J17451-3022, XTE
J1818-245, MAXI J1828-249, MAXI J1836-194, Swift J1910.2-0546) and two
detections show fainter counterparts to XTE J1752-223 and XTE J2012+381 than
previously reported. We used theoretical arguments and observed trends, for
instance between the outburst and quiescent X-ray luminosity and orbital period
$P_{orb}$ to derive an expected trend between $\Delta K_s$ and $P_{orb}$ of
$\Delta K_s \propto \log P_{orb}^{0.565}$. Comparing this to observations we
find a different behaviour. We discuss possible explanations for this result.


  We show that for any constant $\epsilon > 0$ and $p \ge 1$, it is possible to
distinguish functions $f : \{0,1\}^n \to [0,1]$ that are submodular from those
that are $\epsilon$-far from every submodular function in $\ell_p$ distance
with a constant number of queries.
  More generally, we extend the testing-by-implicit-learning framework of
Diakonikolas et al. (2007) to show that every property of real-valued functions
that is well-approximated in $\ell_2$ distance by a class of $k$-juntas for
some $k = O(1)$ can be tested in the $\ell_p$-testing model with a constant
number of queries. This result, combined with a recent junta theorem of Feldman
and Vondrak (2016), yields the constant-query testability of submodularity. It
also yields constant-query testing algorithms for a variety of other natural
properties of valuation functions, including fractionally additive (XOS)
functions, OXS functions, unit demand functions, coverage functions, and
self-bounding functions.


  Inferring past human motion from RGB images is challenging due to the
inherent uncertainty of the prediction problem. Thermal images, on the other
hand, encode traces of past human-object interactions left in the environment
via thermal radiation measurement. Based on this observation, we collect the
first RGB-Thermal dataset for human motion analysis, dubbed Thermal-IM. Then we
develop a three-stage neural network model for accurate past human pose
estimation. Comprehensive experiments show that thermal cues significantly
reduce the ambiguities of this task, and the proposed model achieves remarkable
performance. The dataset is available at
https://github.com/ZitianTang/Thermal-IM.


  Formalising the pi-calculus is an illuminating test of the expressiveness of
logical frameworks and mechanised metatheory systems, because of the presence
of name binding, labelled transitions with name extrusion, bisimulation, and
structural congruence. Formalisations have been undertaken in a variety of
systems, primarily focusing on well-studied (and challenging) properties such
as the theory of process bisimulation. We present a formalisation in Agda that
instead explores the theory of concurrent transitions, residuation, and causal
equivalence of traces, which has not previously been formalised for the
pi-calculus. Our formalisation employs de Bruijn indices and dependently-typed
syntax, and aligns the "proved transitions" proposed by Boudol and Castellani
in the context of CCS with the proof terms naturally present in Agda's
representation of the labelled transition relation. Our main contributions are
proofs of the "diamond lemma" for residuation of concurrent transitions and a
formal definition of equivalence of traces up to permutation of transitions.


  In this paper, we study a massive multiple-input multiple-output (mMIMO)
relay network where multiple source-destination pairs exchange information
through a common relay equipped with a massive antenna array. The source users
perform simultaneous wireless information and power transfer (SWIPT) and the
power-splitting (PS) scheme is used at the relay to first harvest energy from
the received signals, and then, to transmit the decoded signals using the
harvested energy. Relay performs maximum ratio combining/maximum ratio
transmission (MRC/MRT) beam-forming on the received signal. Under the
three-dimensional (3D) directional channel model, we derive a closed-form lower
bound expression for the average signal-to-interference-plus-noise ratio using
results from random matrix theory, which leads to an asymptotic approximation
of the achievable sum-rate. Based on that, we study a joint optimization
problem over the tilt and PS ratio to maximize the achievable sum-rate. Grid
search algorithm is used to solve the non-convex problem. Simulation results
verify our theoretical analysis and the efficiency of our optimized design. In
particular, our optimized system outperforms a conventional system with $\pi/4$
tilt and PS of $0.5$, by at least $61\%$.


  We propose causal recurrent variational autoencoder (CR-VAE), a novel
generative model that is able to learn a Granger causal graph from a
multivariate time series x and incorporates the underlying causal mechanism
into its data generation process. Distinct to the classical recurrent VAEs, our
CR-VAE uses a multi-head decoder, in which the $p$-th head is responsible for
generating the $p$-th dimension of $\mathbf{x}$ (i.e., $\mathbf{x}^p$). By
imposing a sparsity-inducing penalty on the weights (of the decoder) and
encouraging specific sets of weights to be zero, our CR-VAE learns a sparse
adjacency matrix that encodes causal relations between all pairs of variables.
Thanks to this causal matrix, our decoder strictly obeys the underlying
principles of Granger causality, thereby making the data generating process
transparent. We develop a two-stage approach to train the overall objective.
Empirically, we evaluate the behavior of our model in synthetic data and two
real-world human brain datasets involving, respectively, the
electroencephalography (EEG) signals and the functional magnetic resonance
imaging (fMRI) data. Our model consistently outperforms state-of-the-art time
series generative models both qualitatively and quantitatively. Moreover, it
also discovers a faithful causal graph with similar or improved accuracy over
existing Granger causality-based causal inference methods. Code of CR-VAE is
publicly available at https://github.com/hongmingli1995/CR-VAE.


  The Mid-Infrared instrument (MIRI) on board the James Webb Space Telescope
will perform the first ever characterization of young giant exoplanets observed
by direct imaging in the 5-28 microns spectral range. This wavelength range is
key for both determining the bolometric luminosity of the cool known exoplanets
and for accessing the strongest ammonia bands. In conjunction with shorter
wavelength observations, MIRI will enable a more accurate characterization of
the exoplanetary atmospheric properties. Here we consider a subsample of the
currently known exoplanets detected by direct imaging and we discuss their
detectability with MIRI, either using the coronagraphic or the spectroscopic
modes. By using the Exo-REM atmosphere model we calculate the mid-infrared
emission spectra of fourteen exoplanets, and we simulate MIRI coronagraphic or
spectroscopic observations. Specifically we analyze four coronagraphic
observational setups, which depend on (i) the target-star and reference-star
offset (0, 3, 14 mas), (ii) the wave-front-error (130, 204 nm rms), (iii) the
telescope jitter amplitude (1.6, 7 mas). We then determine the signal-to-noise
and integration time values for the coronagraphic targets whose planet-to-star
contrasts range from 3.9 to 10.1 mag. We conclude that all the MIRI targets
should be observable with different degrees of difficulty, which depends on the
final in-flight instrument performances. Furthermore, we test for detection of
ammonia in the atmosphere of the coolest targets. Finally, we present the case
of HR 8799 b to discuss what MIRI observations can bring to the knowledge of a
planetary atmosphere, either alone or in combination with shorter wavelength
observations.


  The flavin reductase ActVB is involved in the last step of actinorhodin
biosynthesis in Streptomyces coelicolor. Although ActVB can be isolated with
some FMN bound, this form was not involved in the flavin reductase activity. By
studying the ferric reductase activity of ActVB, we show that its FMN-bound
form exhibits a proper enzymatic activity of reduction of iron complexes by
NADH. This shows that ActVB active site exhibits a dual property with regard to
the FMN. It can use it as a substrate that goes in and off the active site or
as a cofactor to provide an electron transferase activity to the polypeptide.


  We present the Stata commands probitfe and logitfe, which estimate probit and
logit panel data models with individual and/or time unobserved effects. Fixed
effect panel data methods that estimate the unobserved effects can be severely
biased because of the incidental parameter problem (Neyman and Scott, 1948). We
tackle this problem by using the analytical and jackknife bias corrections
derived in Fernandez-Val and Weidner (2016) for panels where the two dimensions
($N$ and $T$) are moderately large. We illustrate the commands with an
empirical application to international trade and a Monte Carlo simulation
calibrated to this application.


  The aim of the present note is to construct invariants of the Artin braid
group valued in $G_{N}^{2}$, and further study of groups related to
$G_{n}^{3}$. In the groups $G_{n}^{2}$, the word problem is solved; these
groups are much simpler than $G_{n}^{3}$.


  Normal form bisimilarities are a natural form of program equivalence resting
on open terms, first introduced by Sangiorgi in call-by-name. The literature
contains a normal form bisimilarity for Plotkin's call-by-value
$\lambda$-calculus, Lassen's \emph{enf bisimilarity}, which validates all of
Moggi's monadic laws and can be extended to validate $\eta$. It does not
validate, however, other relevant principles, such as the identification of
meaningless terms -- validated instead by Sangiorgi's bisimilarity -- or the
commutation of $\letexp$s. These shortcomings are due to issues with open terms
of Plotkin's calculus. We introduce a new call-by-value normal form
bisimilarity, deemed \emph{net bisimilarity}, closer in spirit to Sangiorgi's
and satisfying the additional principles. We develop it on top of an existing
formalism designed for dealing with open terms in call-by-value. It turns out
that enf and net bisimilarities are \emph{incomparable}, as net bisimilarity
does not validate Moggi's laws nor $\eta$. Moreover, there is no easy way to
merge them. To better understand the situation, we provide an analysis of the
rich range of possible call-by-value normal form bisimilarities, relating them
to Ehrhard's relational model.


  Bayesian neural networks provide a direct and natural way to extend standard
deep neural networks to support probabilistic deep learning through the use of
probabilistic layers that, traditionally, encode weight (and bias) uncertainty.
In particular, hybrid Bayesian neural networks utilize standard deterministic
layers together with few probabilistic layers judicially positioned in the
networks for uncertainty estimation. A major aspect and benefit of Bayesian
inference is that priors, in principle, provide the means to encode prior
knowledge for use in inference and prediction. However, it is difficult to
specify priors on weights since the weights have no intuitive interpretation.
Further, the relationships of priors on weights to the functions computed by
networks are difficult to characterize. In contrast, functions are intuitive to
interpret and are direct since they map inputs to outputs. Therefore, it is
natural to specify priors on functions to encode prior knowledge, and to use
them in inference and prediction based on functions. To support this, we
propose hybrid Bayesian neural networks with functional probabilistic layers
that encode function (and activation) uncertainty. We discuss their foundations
in functional Bayesian inference, functional variational inference, sparse
Gaussian processes, and sparse variational Gaussian processes. We further
perform few proof-of-concept experiments using GPflus, a new library that
provides Gaussian process layers and supports their use with deterministic
Keras layers to form hybrid neural network and Gaussian process models.


  The quantum states or Hilbert spaces for the quantum field theory in de
Sitter space-time are studied on ambient space formalism. In this formalism,
the quantum states are only depended $(1)$ on the topological character of the
de Sitter space-time, {\it i.e.} $\R \times S^3$, and $(2)$ on the homogeneous
spaces which are used for construction of the unitary irreducible
representation of de Sitter group. A compact homogeneous space is chosen in
this paper. The unique feature of this homogeneous space is that its total
number of quantum states, ${\cal N}$, is finite although the Hilbert space has
infinite dimensions. It is shown that ${\cal N}$ is a continuous function of
the Hubble constant $H$ and the eigenvalue of the Casimir operators of de
Sitter group. The entropy of the quantum fields on this Hilbert space have been
calculated which is finite and invariant for all inertial observers on the de
Sitter hyperboloid.


  In nonperturbative regimes, the superfluid instability in the two-dimensional
Hubbard model can be described by an emergent BCS theory with small effective
pairing constants. We compute the effective couplings using a controlled
bold-line diagrammatic Monte Carlo approach, which stochastically sums all
skeleton Feynman diagrams dressed in the one- and two-particle channels to high
expansion orders, and map out the resulting superfluid ground-state phase
diagram in a range of next-nearest-neighbor hopping $0 \leq t^{\prime} \leq
0.3t$, interaction strength $0 \leq U \leq 3t$, and lattice filling $0 \leq n
\leq 2$. The phase diagram is dramatically transformed in the hole-doped region
and becomes particularly rich at larger doping and $t'$. At $t'=0.3$, the
weak-coupling picture with the dominant triplet pairing sharply peaked at $n
\approx 0.73$ due to the Van Hove singularity is replaced by a plateau of the
singlet $d_{x^2-y^2}$ paring, while for $U \gtrsim 3t$ the effective couplings
are consistent with the $d_{x^2-y^2}$ high-temperature superconductivity in the
hole-doped region near cuprates' optimal doping.


  In this note we report an improved determination of the scaling dimensions
and OPE coefficients of the minimal supersymmetric extension of the 3d Ising
model using the conformal bootstrap. We also show how this data can be used as
input to the Lorentzian inversion formula, finding good agreement between
analytic calculations and numerical extremal spectra once mixing effects are
resolved.


  Existing Camouflaged Object Detection (COD) methods rely heavily on
large-scale pixel-annotated training sets, which are both time-consuming and
labor-intensive. Although weakly supervised methods offer higher annotation
efficiency, their performance is far behind due to the unclear visual
demarcations between foreground and background in camouflaged images. In this
paper, we explore the potential of using boxes as prompts in camouflaged scenes
and introduce the first weakly semi-supervised COD method, aiming for
budget-efficient and high-precision camouflaged object segmentation with an
extremely limited number of fully labeled images. Critically, learning from
such limited set inevitably generates pseudo labels with serious noisy pixels.
To address this, we propose a noise correction loss that facilitates the
model's learning of correct pixels in the early learning stage, and corrects
the error risk gradients dominated by noisy pixels in the memorization stage,
ultimately achieving accurate segmentation of camouflaged objects from noisy
labels. When using only 20% of fully labeled data, our method shows superior
performance over the state-of-the-art methods.


  We prove in the vector-valued spaces $L^2(\mathbb{R}_+, X)$ (where $X$ is a
finite dimensional Hilbert space) the cyclicity for the semi-group of left
translations of some particular functions with support included in a lacunary
system of intervals.


  The illustration of cosmic acceleration under two interacting dark energy
models (pilgrim dark energy with Granda and Oliveros cutoff and its generalized
ghost version) in DGP braneworld framework is presented. In the current
scenario, the equation of state parameter, deceleration parameter,
$\omega_{D}-\omega'_{D}$ plane and statefinder diagnosis are investigated. The
equation state parameter behave-like phantom era of the universe. The
deceleration parameter depicts the accelerated expansion of the universe in
both models. The cosmological planes like $\omega_{D}-\omega'_{D}$ and
statefinder corresponds to $\Lambda$CDM limit. To end, we remark that our
results support to phenomenon of pilgrim dark energy and cosmic acceleration.
Also, the results are consistent with observational data.


  Flat optics consisting of nanostructures of high-refractive-index materials
produce lenses with thin form factors that tend to operate only at specific
wavelengths. Recent attempts to achieve achromatic lenses uncover a trade-off
between the numerical aperture (NA) and bandwidth, which limits performance.
Here we propose a new approach to design high NA, broadband and
polarization-insensitive multilayer achromatic metalenses (MAM). We combine
topology optimization and full wave simulations to inversely design MAMs and
fabricate the structures in low-refractive-index materials by two-photon
polymerization lithography. MAMs measuring 20 micrometer in diameter operating
in the visible range of 400-800 nm with 0.5 NA and 0.7 NA were achieved with
efficiencies of up to 42%. We demonstrate broadband imaging performance of the
fabricated MAM under white light, and RGB narrowband illuminations. These
results highlight the potential of the 3D printed multilayer structures for
realizing broadband and multi-functional meta-devices with inverse design.


  In this work we present a theoretical investigation into an
antiferromagnetically coupled spin system, specifically ${\rm Cu}_{3}-X$
($\mathrm{X=As,Sb}$), which exhibits an isosceles triangular configuration or
slightly distorted equilateral triangular configuration, as previously
identified in reference {[}Phys. Rev. Lett. \textbf{96}, 107202 (2006){]}. This
system can be effectively represented by the Heisenberg model on a triangular
structure, taking into account the exchange interaction, the
Dzyaloshinskii-Moriya interaction, g-factors and external magnetic field, as
delineated in the aforementioned reference. By using numerical approach we
explore both zero-temperature and finite-temperature behaviors of a ${\rm
Cu}_{3}$-like antiferromagnetically coupled spin system. At zero temperature,
the system displays a 1/3 quasi-plateau magnetization, when the magnetic field
is varied. Moreover, we place particular emphasis on magnetic properties
including magnetization, magnetic susceptibility, entropy, and specific heat at
finite temperatures. Furthermore, we investigate the magnetocaloric effect as a
function of an externally imposed magnetic field, oriented both parallel and
perpendicular to the plane of the triangular structure. Interestingly, these
configurations demonstrate remarkably similar behavior for both orientations of
the magnetic field. Our investigation also includes an analysis of the
adiabatic curve, the Gr\"uneisen parameter, and the variation in entropy when
applied or removed the magnetic field. The magnetocaloric effect is found to be
more prominent in low the temperature region, typically at $T\sim1$K, for both
parallel and perpendicular magnetic fields at $\sim4.5$T and $\sim5$T,
respectively.


  X-ray properties of hot interstellar gas in a starburst galaxy NGC 253 were
investigated to gain a further understanding of starburst-driven outflow
activity by XMM-Newton and Suzaku. Spectroscopic analysis for three regions of
the galaxy characterized by multiwavelength observations was conducted. The hot
gas was represented by two thin thermal plasmas with temperatures of kT ~0.2
and ~0.6 keV. Abundance ratios i.e., O/Fe, Ne/Fe, Mg/Fe and Si/Fe, are
consistent between three regions, which suggests the common origin of the hot
gas. The abundance patterns are consistent with those of type II supernova
ejecta, indicating that the starburst activity in the central region provides
metals toward the halo through a galactic-scale starburst-driven outflow. The
energetics also can support this indication on condition that 0.01-50
{\eta}^0.5 % of the total emission in the nuclear region has flowed to the halo
region. To constrain the dynamics of hot interstellar gas, surface brightness
and hardness ratio profiles which trace the density and temperature were
extracted. Assuming a simple polytropic equation of state of gas,
T{\rho}^(1-{\gamma}) = const, we constrained the physical condition. {\gamma}
is consistent with 5/3 at the hot disk and T is constant ({\gamma} = 1) in the
halo. It is suggested that the hot gas expands adiabatically from the central
region towards the halo region while it moves as free expansion from the inner
part of the halo towards the outer part of the halo as the outflow. We
constrained the outflow velocity to be >100 km s^-1 from the observed
temperature gradient in the halo. In comparison with the escape velocity of
~220 km s^-1 for NGC 253, it is indicated that the hot interstellar gas can
escape from the gravitational potential of NGC 253 by combining the outflow
velocity and the thermal velocity.


  FEbeam is a compact field emission data processing interface with the
capability to analyze the field emission cathode performance in an rf injector
by extracting the field enhancement factor, local field, and effective emission
area from the Fowler-Nordheim equations. It also has the capability of
processing beam imaging micrographs using its sister software, FEpic. The
current version of FEbeam was designed for the Argonne Cathode Teststand (ACT)
of the Argonne Wakefield Accelerator facility switch yard. With slight
modifications, FEbeam can work for any rf field emission injector. This
software is open-source and can be found at https://github.com/schne525/FEbeam


  In the Karlin infinite occupancy scheme, balls are thrown independently into
an infinite array of boxes $1$, $2,\ldots$, with probability $p_k$ of hitting
the box $k$. For $j,n\in\mathbb{N}$, denote by $\mathcal{K}^*_j(n)$ the number
of boxes containing exactly $j$ balls provided that $n$ balls have been thrown.
We call $\textit{small counts}$ the variables $\mathcal{K}^*_j(n)$, with $j$
fixed. Our main result is a law of the iterated logarithm (LIL) for the small
counts as the number of balls thrown becomes large. Its proof exploits a
Poissonization technique and is based on a new LIL for infinite sums of
independent indicators $\sum_{k\geq 1}\Bbb{1}_{A_k(t)}$ as $t\to\infty$, where
the family of events $(A_k(t))_{t\geq 0}$ is not necessarily monotone in $t$.
The latter LIL is an extension of a LIL obtained recently by Buraczewski,
Iksanov and Kotelnikova (2023+) in the situation that $(A_k(t))_{t\geq 0}$
forms a nondecreasing family of events.


  The Quantum Approximate Optimization Algorithm (QAOA) is an algorithmic
framework for finding approximate solutions to combinatorial optimization
problems, derived from an approximation to the Quantum Adiabatic Algorithm
(QAA). In solving combinatorial optimization problems with constraints in the
context of QAOA or QAA, one needs to find a way to encode problem constraints
into the scheme. In this paper, we formalize different constraint types to
linear equalities, linear inequalities, and arbitrary form. Based on this, we
propose constraint-encoding schemes well-fitting into the QAOA framework for
solving NP combinatorial optimization problems. The implemented algorithms
demonstrate the effectiveness and efficiency of the proposed scheme by the
testing results of varied instances of some well-known NP optimization
problems. We argue that our work leads to a generalized framework for finding,
in the context of QAOA, high-quality approximate solutions to combinatorial
problems with various types of constraints.


  We propose a self-supervised learning method for long text documents based on
contrastive learning. A key to our method is Shuffle and Divide (SaD), a simple
text augmentation algorithm that sets up a pretext task required for
contrastive updates to BERT-based document embedding. SaD splits a document
into two sub-documents containing randomly shuffled words in the entire
documents. The sub-documents are considered positive examples, leaving all
other documents in the corpus as negatives. After SaD, we repeat the
contrastive update and clustering phases until convergence. It is naturally a
time-consuming, cumbersome task to label text documents, and our method can
help alleviate human efforts, which are most expensive resources in AI. We have
empirically evaluated our method by performing unsupervised text classification
on the 20 Newsgroups, Reuters-21578, BBC, and BBCSport datasets. In particular,
our method pushes the current state-of-the-art, SS-SB-MT, on 20 Newsgroups by
20.94% in accuracy. We also achieve the state-of-the-art performance on
Reuters-21578 and exceptionally-high accuracy performances (over 95%) for
unsupervised classification on the BBC and BBCSport datasets.


  The presence of planets in binary systems poses interesting problems for
planet formation theories, both in cases where planets must have formed in very
compact discs around the individual stars and where they are located near the
edge of the stable circumbinary region, where in situ formation is challenging.
Dust dynamics is expected to play an important role in such systems, since dust
trapping at the inner edge of circumbinary discs could aid in situ formation,
but would simultaneously starve the circumstellar discs of the solid material
needed to form planets. Here we investigate the dynamics of dust in binary
systems using Smooth Particle Hydrodynamics. We find that all our simulations
tend towards dust trapping in the circumbinary disc, but the timescale on which
trapping begins depends on binary mass ratio ($q$) and eccentricity as well as
the angular momentum of the infalling material. For $q \gtrsim 0.1$, we find
that dust can initially accrete onto the circumstellar discs, but as the
circumbinary cavity grows in radius, dust eventually becomes trapped in the
circumbinary disc. For $q = 0.01$, we find that increasing the binary
eccentricity increases the time required for dust trapping to begin. However,
even this longer timescale is likely to be shorter than the planet formation
timescale in the inner disc and is insufficient to explain the observed
pre-transitional discs. This indicates that increase in companion eccentricity
alone is not enough to allow significant transfer of solids from the outer to
the inner disc.


  A general method for obtaining the decoherence time in self-induced
decoherence is presented. In particular, it is shown that such a time can be
computed from the poles of the resolvent or of the initial conditions in the
complex extension of the Hamiltonian's spectrum. Several decoherence times are
estimated: $10^{-13}-$ $10^{-15}s$ for microscopic systems, and
$10^{-37}-10^{-39}s$ for macroscopic bodies. For the particular case of a
thermal bath, our results agree with those obtained by the einselection
(environment-induced decoherence) approach.


  We calculate nuclear-polarization energy shifts for hydrogenlike
$^{208}_{~82}$Pb$^{81+}$. A retarded transverse part as well as the Coulomb
part is taken into account as the electromagnetic interaction between an
electron and the nucleus. With a finite charge distribution for the nuclear
ground state and the random-phase approximation to describe the nuclear
excitations, we obtain nuclear polarization energy of the $1s_{1/2}$ state as
--38.2 (--37.0) meV in the Feynman (Coulomb) gauge. For the $2s_{1/2}$,
$2p_{1/2}$ and $2p_{3/2}$ states, they are --6.7 (--6.4), --0.2 (--0.2) and
+0.0 (+0.0) meV, respectively. The seagull term in the two-photon exchange
diagrams is shown to be quite important to obtain the gauge invariance of the
nuclear polarization energies.


  We present a semi-empirical effective Hamiltonian to capture effects of
disorder associated with Ba and Sr cations occupying $A$ sites in
(Ba$_{x}$Sr$_{1-x}$)TiO$_3$ on its ferroelectric phase transition. Averaging
between the parameters of first-principles effective Hamiltonians of end
members BaTiO$_3$ and SrTiO$_3$, we include a term with an empirical parameter
to capture the local polarization and strains arising from the difference
between ionic radii of Ba and Sr. Using mixed-space molecular dynamics of the
effective Hamiltonian, we determine $T$-dependent ferroelectric phase
transitions in (Ba$_{x}$Sr$_{1-x}$)TiO$_3$ which are in good agreement with
experiment. Our scheme of determination of semi-empirical parameters in
effective Hamiltonian should be applicable to other perovskite-type
ferroelectric solid solutions.


  In this paper, we propose a selection region based multihop routing protocol
with directional antennas for wireless mobile ad hoc networks, where the
selection region is defined by two parameters: a reference distance and the
beamwidth of the directional antenna. At each hop, we choose the nearest node
to the transmitter within the selection region as the next hop relay. By
maximizing the expected density of progress, we present an upper bound for the
optimum reference distance and derive the relationship between the optimum
reference distance and the optimum transmission probability. Compared with the
results with routing strategy using omnidirectional antennas in
\cite{Di:Relay-Region}, we find interestingly that the optimum transmission
probability is a constant independent of the beamwidth, the expected density of
progress with the new routing strategy is increased significantly, and the
computational complexity involved in the relay selection is also greatly
reduced.


  This paper introduces hyperspherical prototype networks, which unify
classification and regression with prototypes on hyperspherical output spaces.
For classification, a common approach is to define prototypes as the mean
output vector over training examples per class. Here, we propose to use
hyperspheres as output spaces, with class prototypes defined a priori with
large margin separation. We position prototypes through data-independent
optimization, with an extension to incorporate priors from class semantics. By
doing so, we do not require any prototype updating, we can handle any training
size, and the output dimensionality is no longer constrained to the number of
classes. Furthermore, we generalize to regression, by optimizing outputs as an
interpolation between two prototypes on the hypersphere. Since both tasks are
now defined by the same loss function, they can be jointly trained for
multi-task problems. Experimentally, we show the benefit of hyperspherical
prototype networks for classification, regression, and their combination over
other prototype methods, softmax cross-entropy, and mean squared error
approaches.


  We consider two isotonic smooth estimators for a monotone baseline hazard in
the Cox model, a maximum smooth likelihood estimator and a Grenander-type
estimator based on the smoothed Breslow estimator for the cumulative baseline
hazard. We show that they are both asymptotically normal at rate
$n^{m/(2m+1)}$, where $m\geq 2$ denotes the level of smoothness considered, and
we relate their limit behavior to kernel smoothed isotonic estimators studied
in Lopuha\"a and Musta (2016). It turns out that the Grenander-type estimator
is asymptotically equivalent to the kernel smoothed isotonic estimators, while
the maximum smoothed likelihood estimator exhibits the same asymptotic variance
but a different bias. Finally, we present numerical results on pointwise
confidence intervals that illustrate the comparable behavior of the two
methods.


  We consider the reflection equation of the N=3 Cremmer-Gervais R-matrix. The
reflection equation is shown to be equivalent to 38 equations which do not
depend on the parameter of the R-matrix, q. Solving those 38 equations. the
solution space is found to be the union of two types of spaces, each of which
is parametrized by the algebraic variety $\mathbb{P}^1(\mathbb{C}) \times
\mathbb{P}^1(\mathbb{C}) \times \mathbb{P}^2(\mathbb{C})$ and $ \mathbb{C}
\times \mathbb{P}^1(\mathbb{C}) \times \mathbb{P}^2(\mathbb{C})$.


  We study the evolution of massive mixed Dirac and Majorana neutrinos in
matter under the influence of a transversal magnetic field. The analysis is
based on relativistic quantum mechanics. We solve exactly the evolution
equation for relativistic neutrinos, find the neutrino wave functions, and
calculate the transition probability for spin-flavor oscillations. We analyze
the dependence of the transition probability on the external fields and compare
the cases of Dirac and Majorana neutrinos. The evolution of Majorana particles
in vacuum is also studied and correction terms to the standard oscillation
formula are derived and discussed. As a possible application of our results we
discuss the spin-flavor transitions in supernovae.


  We demonstrate room-temperature stabilization of dipolar magnetic skyrmions
with diameters in the range of $100$ nm in a single ultrathin layer of the
Heusler alloy Co$_2$FeAl (CFA) under moderate magnetic fields. Current-induced
skyrmion dynamics in microwires is studied with a scanning Nitrogen-Vacancy
magnetometer operating in the photoluminescence quenching mode. We first
demonstrate skyrmion nucleation by spin-orbit torque and show that its
efficiency can be significantly improved using tilted magnetic fields, an
effect which is not specific to Heusler alloys and could be advantageous for
future skyrmion-based devices. We then show that current-induced skyrmion
motion remains limited by strong pinning effects, even though CFA is a magnetic
material with a low magnetic damping parameter.


  The quantum speed limit is a fundamental concept in quantum mechanics, which
aims at finding the minimum time scale or the maximum dynamical speed for some
fixed targets. In a large number of studies in this field, the construction of
valid bounds for the evolution time is always the core mission, yet the physics
behind it and some fundamental questions like which states can really fulfill
the target, are ignored. Understanding the physics behind the bounds is at
least as important as constructing attainable bounds. Here we provide an
operational approach for the definition of the quantum speed limit, which
utilizes the set of states that can fulfill the target to define the speed
limit. Its performances in various scenarios have been investigated. For
time-independent Hamiltonians, it is inverse-proportional to the difference
between the highest and lowest energies. The fact that its attainability does
not require a zero ground-state energy suggests it can be used as an indicator
of quantum phase transitions. For time-dependent Hamiltonians, it is shown that
contrary to the results given by existing bounds, the true speed limit should
be independent of the time. Moreover, in the case of spontaneous emission, we
find a counterintuitive phenomenon that a lousy purity can benefit the
reduction of the quantum speed limit.


  Realistic linear quantum information processing necessitates the ability to
synchronously generate entangled photon pairs either at the same or at distant
locations. Here, we report the experimental realization of synchronized
generation of independent entangled photon pairs. The quality of
synchronization is confirmed by observing a violation of Bell's inequality with
3.2 standard deviations in an entanglement swapping experiment. The techniques
developed in our experiment will be of great importance for future linear
optical realization of quantum repeaters and quantum computation.


  fMRI semantic category understanding using linguistic encoding models
attempts to learn a forward mapping that relates stimuli to the corresponding
brain activation. State-of-the-art encoding models use a single global model
(linear or non-linear) to predict brain activation given the stimulus. However,
the critical assumption in these methods is that a priori different brain
regions respond the same way to all the stimuli, that is, there is no
modularity or specialization assumed for any region. This goes against the
modularity theory, supported by many cognitive neuroscience investigations
suggesting that there are functionally specialized regions in the brain. In
this paper, we achieve this by clustering similar regions together and for
every cluster we learn a different linear regression model using a mixture of
linear experts model. The key idea here is that each linear expert captures the
behaviour of similar brain regions. Given a new stimulus, the utility of the
proposed model is twofold (i) predicts the brain activation as a weighted
linear combination of the activations of multiple linear experts and (ii) to
learn multiple experts corresponding to different brain regions. We argue that
each expert captures activity patterns related to a particular region of
interest (ROI) in the human brain. This study helps in understanding the brain
regions that are activated together given different kinds of stimuli.
Importantly, we suggest that the mixture of regression experts (MoRE) framework
successfully combines the two principles of organization of function in the
brain, namely that of specialization and integration. Experiments on fMRI data
from paradigm 1 [1]where participants view linguistic stimuli show that the
proposed MoRE model has better prediction accuracy compared to that of
conventional models.


  We show that the observed ratio of the subtypes Ib,c and II core-collapse
supernovae depends on the metallicity of the host galaxy, as expected on
theoretical grounds. However, the observed relation differs considerably from
expectations based on non-rotating models of single stars with mass loss. We
argue that the predictions of recent models with rotation offer a much better
agreement with observations, at least for progenitor stars with solar
metallicity; calculations of models with higher and lower metallicities are
required in order to substantiate these conclusions. We also suggest that
systematic surveys of core collapse supernovae up to redshift of 1 with the
SNAP satellite would allow to probe the effect of metallicity on supernovae
properties during the past history of the universe.


  Let $\xi_0,\xi_1,...$ be independent identically distributed (i.i.d.) random
variables such that $\E \log (1+|\xi_0|)<\infty$. We consider random analytic
functions of the form $$ G_n(z)=\sum_{k=0}^{\infty} \xi_k f_{k,n} z^k, $$ where
$f_{k,n}$ are deterministic complex coefficients. Let $\nu_n$ be the random
measure assigning the same weight $1/n$ to each complex zero of $G_n$. Assuming
essentially that $-\frac 1n \log f_{[tn], n}\to u(t)$ as $n\to\infty$, where
$u(t)$ is some function, we show that the measure $\nu_n$ converges weakly to
some deterministic measure which is characterized in terms of the
Legendre--Fenchel transform of $u$. The limiting measure is universal, that is
it does not depend on the distribution of the $\xi_k$'s. This result is applied
to several ensembles of random analytic functions including the ensembles
corresponding to the three two-dimensional geometries of constant curvature. As
another application, we prove a random polynomial analogue of the circular law
for random matrices.


  In the recent years, there has been a huge advancement in wireless sensor
computing technology. Today, wireless sensor network (WSN) has become a key
technology for different types of smart environment. Nodes localization in WSN
has arisen as a very challenging problem in the research community. Most of the
applications for WSN are not useful without a priory known nodes positions.
Adding GPS receivers to each node is an expensive solution and inapplicable for
indoor environments. In this paper, we implemented and evaluated an algorithm
based on multidimensional scaling (MDS) technique for three-dimensional (3D)
nodes localization in WSN using improved heuristic method for distance
calculation. Using extensive simulations we investigated our approach regarding
various network parameters. We compared the results from the simulations with
other approaches for 3D-WSN localization and showed that our approach
outperforms other techniques in terms of accuracy.


  The effect of the powder average on the peak shapes and positions in neutron
pair distribution functions of polycrystalline materials is examined. It is
shown that for highly anisotropic crystals, the powder average leads to shifts
in peak positions and to non-Gaussian peak shapes. The peak shifts can be as
large as several percent of the lattice spacing.


  Quantum transport of charge or energy in networks with discrete sites is
central to diverse quantum technologies, from molecular electronics to light
harvesting and quantum opto-mechanical metamaterials. A one dimensional network
can be viewed as waveguide. We show that if such waveguide is hybridised with a
control unit that contains a few sites, then transmission through the waveguide
depends sensitively on the motion of the sites in the control unit. Together,
the hybrid waveguide and its control-unit form a Fano-Anderson chain whose
Born-Oppenheimer surfaces inherit characteristics from both components: A
bandstructure from the waveguide and potential energy steps as a function of
site coordinates from the control-unit. Using time-dependent quantum wave
packets, we reveal conditions under which the hybrid structure becomes
transmissive only if the control unit contains mobile sites that induce
non-adiabatic transitions between the surfaces. Hence, our approach provides
functional synthetic Born-Oppenheimer surfaces for hybrid quantum technologies
combining mechanic and excitonic elements, and has possible applications such
as switching and temperature sensing.


  In this work, we tackle the problem of ternary eye movement classification,
which aims to separate fixations, saccades and smooth pursuits from the raw eye
positional data. The efficient classification of these different types of eye
movements helps to better analyze and utilize the eye tracking data. Different
from the existing methods that detect eye movement by several pre-defined
threshold values, we propose a hierarchical Hidden Markov Model (HMM)
statistical algorithm for detecting fixations, saccades and smooth pursuits.
The proposed algorithm leverages different features from the recorded raw eye
tracking data with a hierarchical classification strategy, separating one type
of eye movement each time. Experimental results demonstrate the effectiveness
and robustness of the proposed method by achieving competitive or better
performance compared to the state-of-the-art methods.


  The out-of-distribution (OOD) problem generally arises when neural networks
encounter data that significantly deviates from the training data distribution,
i.e., in-distribution (InD). In this paper, we study the OOD problem from a
neuron activation view. We first formulate neuron activation states by
considering both the neuron output and its influence on model decisions. Then,
to characterize the relationship between neurons and OOD issues, we introduce
the \textit{neuron activation coverage} (NAC) -- a simple measure for neuron
behaviors under InD data. Leveraging our NAC, we show that 1) InD and OOD
inputs can be largely separated based on the neuron behavior, which
significantly eases the OOD detection problem and beats the 21 previous methods
over three benchmarks (CIFAR-10, CIFAR-100, and ImageNet-1K). 2) a positive
correlation between NAC and model generalization ability consistently holds
across architectures and datasets, which enables a NAC-based criterion for
evaluating model robustness. Compared to prevalent InD validation criteria, we
show that NAC not only can select more robust models, but also has a stronger
correlation with OOD test performance.


  We obtain several new characterizations of ultrametric spaces in terms of
roundness, generalized roundness, strict p-negative type, and p-polygonal
equalities (p > 0). This allows new insight into the isometric embedding of
ultrametric spaces into Euclidean spaces. We also consider roundness properties
additive metric spaces which are not ultrametric.


  The offline software framework of the LHCb experiment has undergone a
significant overhaul to tackle the data processing challenges that will arise
in the upcoming Run 3 and Run 4 of the Large Hadron Collider. This paper
introduces FunTuple, a novel component developed for offline data processing
within the LHCb experiment. This component enables the computation and storage
of a diverse range of observables for both reconstructed and simulated events
by leveraging on the tools initially developed for the trigger system. This
feature is crucial for ensuring consistency between trigger-computed and
offline-analysed observables. The component and its tool suite offer users
flexibility to customise stored observables, and its reliability is validated
through a full-coverage set of rigorous unit tests. This paper comprehensively
explores FunTuple's design, interface, interaction with other algorithms, and
its role in facilitating offline data processing for the LHCb experiment for
the next decade and beyond.


  We propose a general inverse formula for extracting inflationary parameters
from the observed power spectrum of cosmological perturbations. Under the
general slow-roll scheme, which helps to probe the properties of inflation in a
model independent way, we invert the leading order, single field, power
spectrum formula. We also give some physically interesting examples to
demonstrate its wide applicability and illuminate its properties.


  This report relates to a study group hosted by the EPSRC funded network,
Integrating data-driven BIOphysical models into REspiratory MEdicine (BIOREME),
and supported by The Insigneo Institute and The Knowledge Transfer Network. The
BIOREME network hosts events, including this study group, to bring together
multi-disciplinary researchers, clinicians, companies and charities to catalyse
research in the applications of mathematical modelling for respiratory
medicine. The goal of this study group was to provide an interface between
companies, clinicians, and mathematicians to develop mathematical tools to the
problems presented. The study group was held at The University of Sheffield on
the 17 - 20 April 2023 and was attended by 24 researchers from 13 different
institutions.
  This report relates to a challenge presented by Arete Medical Technologies
relating to impulse oscillometry (IOS), whereby a short pressure oscillation is
imposed at a person's mouth during normal breathing, usually by a loudspeaker.
The resulting pressure and flow rate changes can be used to the impedance of
the airways, which in turn can provide proxy measurements for
(patho)physiological changes in the small airways. Disentangling the signal so
that airway mechanics can be measured accurately (and device
properties/environmental effects can be accounted for) remains an open
challenge that has the potential to significantly improve the device and its
translation to clinic. In this report, several approaches to this problem, and
the wider problem of interpreting oscillometry resuts are explored.


  We study hydrodynamic oscillations of a cylindrical Bose gas above the
Bose-Einstein transition temperature using the hydrodynamic equations derived
by Griffin, Wu and Stringari. This extends recent studies of a cylindrical
Bose-condensed gas at T=0. Explicit normal mode solutions are obtained for
non-propagating solutions. In the classical limit, the sound velocity is shown
to be the same as a uniform classical gas. We use a variational formulation of
the hydrodynamic equations to discuss the propagating modes in the degenerate
Bose-gas limit and show there is little difference from the classical results.


  The application of local periodic heating for controlling a spatially
developing shear layer downstream of a finite-thickness splitter plate is
examined by numerically solving the two-dimensional Navier-Stokes equations. At
the trailing edge of the plate, oscillatory heat flux boundary condition is
prescribed as the thermal forcing input to the shear layer. The thermal forcing
introduces low level of oscillatory surface vorticity flux and baroclinic
vorticity at the actuation frequency in the vicinity of the trailing edge. The
produced vortical perturbations can independently excite the fundamental
instability that accounts for shear layer roll-up as well as the subharmonic
instability that encourages the vortex pairing process farther downstream. We
demonstrate that the nonlinear dynamics of a spatially developing shear layer
can be modified by local oscillatory heat flux as a control input. We believe
that this study provides a basic foundation for flow control using
thermal-energy-deposition-based actuators such as thermophones and plasma
actuators.


  In this paper, we investigate stochastic differential equations(SDEs) driven
by a class of supercritical $\alpha$-stable process(including the rotational
symmetric $\alpha-$stable process) with drift $b$. The weak well-posedness is
proved, provided that the $(1-\alpha)$-H\"older semi-norm of $b$ is sufficient
small.


  Cloud detection is an important preprocessing step for the precise
application of optical satellite imagery. In this paper, we propose a deep
learning based cloud detection method named multi-scale convolutional feature
fusion (MSCFF) for remote sensing images of different sensors. In the network
architecture of MSCFF, the symmetric encoder-decoder module, which provides
both local and global context by densifying feature maps with trainable
convolutional filter banks, is utilized to extract multi-scale and high-level
spatial features. The feature maps of multiple scales are then up-sampled and
concatenated, and a novel multi-scale feature fusion module is designed to fuse
the features of different scales for the output. The two output feature maps of
the network are cloud and cloud shadow maps, which are in turn fed to binary
classifiers outside the model to obtain the final cloud and cloud shadow mask.
The MSCFF method was validated on hundreds of globally distributed optical
satellite images, with spatial resolutions ranging from 0.5 to 50 m, including
Landsat-5/7/8, Gaofen-1/2/4, Sentinel-2, Ziyuan-3, CBERS-04, Huanjing-1, and
collected high-resolution images exported from Google Earth. The experimental
results show that MSCFF achieves a higher accuracy than the traditional
rule-based cloud detection methods and the state-of-the-art deep learning
models, especially in bright surface covered areas. The effectiveness of MSCFF
means that it has great promise for the practical application of cloud
detection for multiple types of medium and high-resolution remote sensing
images. Our established global high-resolution cloud detection validation
dataset has been made available online.


  Robust and accurate localization for Unmanned Aerial Vehicles (UAVs) is an
essential capability to achieve autonomous, long-range flights. Current methods
either rely heavily on GNSS, face limitations in visual-based localization due
to appearance variances and stylistic dissimilarities between camera and
reference imagery, or operate under the assumption of a known initial pose. In
this paper, we developed a GNSS-denied localization approach for UAVs that
harnesses both Visual-Inertial Odometry (VIO) and Visual Place Recognition
(VPR) using a foundation model. This paper presents a novel vision-based
pipeline that works exclusively with a nadir-facing camera, an Inertial
Measurement Unit (IMU), and pre-existing satellite imagery for robust, accurate
localization in varied environments and conditions. Our system demonstrated
average localization accuracy within a $20$-meter range, with a minimum error
below $1$ meter, under real-world conditions marked by drastic changes in
environmental appearance and with no assumption of the vehicle's initial pose.
The method is proven to be effective and robust, addressing the crucial need
for reliable UAV localization in GNSS-denied environments, while also being
computationally efficient enough to be deployed on resource-constrained
platforms.


  Intelligent escape is an interdisciplinary field that employs artificial
intelligence (AI) techniques to enable robots with the capacity to
intelligently react to potential dangers in dynamic, intricate, and
unpredictable scenarios. As the emphasis on safety becomes increasingly
paramount and advancements in robotic technologies continue to advance, a wide
range of intelligent escape methodologies has been developed in recent years.
This paper presents a comprehensive survey of state-of-the-art research work on
intelligent escape of robotic systems. Four main methods of intelligent escape
are reviewed, including planning-based methodologies, partitioning-based
methodologies, learning-based methodologies, and bio-inspired methodologies.
The strengths and limitations of existing methods are summarized. In addition,
potential applications of intelligent escape are discussed in various domains,
such as search and rescue, evacuation, military security, and healthcare. In an
effort to develop new approaches to intelligent escape, this survey identifies
current research challenges and provides insights into future research trends
in intelligent escape.


  The assignment problem is an essential problem in many application fields and
frequently used to optimize resource usage. The problem is well understood and
various efficient algorithms exist to solve the problem. However, it was
unclear what practical performance could be achieved for privacy preserving
implementations based on multiparty computation (MPC) by leveraging more
efficient solution strategies than MPC based simplex solvers for linear
programs. We solve this question by implementing and comparing different
optimized MPC algorithms to solve the assignment problem for reasonable problem
sizes. Our empirical approach revealed various insights to MPC based
optimization and we measured a significant (50x) speedup compared to the known
simplex based approach. Furthermore, we also study the overhead introduced by
making the results publicly verifiable by means of non-interactive
zero-knowledge proofs. By leveraging modern proof systems we also achieve
significant speedup for proof and verification times compared to the previously
proposed approaches as well as compact proof sizes.


  In 1955, Lehto showed that, for every measurable function $\psi$ on the unit
circle $\mathbb T,$ there is function $f$ holomorphic in the unit disc $\mathbb
D,$ having $\psi$ as radial limit a.e. on $\mathbb T.$ We consider an analogous
boundary value problem, where the unit disc is replaced by a Stein domain on a
complex manifold and radial approach to a boundary point $p$ is replaced by
(asymptotically) total approach to $p.$


  Despite various parallels between quantum states and ordinary information,
quantum no-go-theorems have convinced many that there is no realistic framework
that might underly quantum theory, no reality that quantum states can represent
knowledge *about*. This paper develops the case that there is a plausible
underlying reality: one actual spacetime-based history, although with behavior
that appears strange when analyzed dynamically (one time-slice at a time). By
using a simple model with no dynamical laws, it becomes evident that this
behavior is actually quite natural when analyzed "all-at-once" (as in classical
action principles). From this perspective, traditional quantum states would
represent incomplete information about possible spacetime histories,
conditional on the future measurement geometry. Without dynamical laws imposing
additional restrictions, those histories can have a classical probability
distribution, where exactly one history can be said to represent an underlying
reality.


  Including finite-temperature effects from the electronic degrees of freedom
in electronic structure calculations of semiconductors and metals is desired;
however, in practice it remains exceedingly difficult when using
zero-temperature methods, since these methods require an explicit evaluation of
multiple excited states in order to account for any finite-temperature effects.
Using a Matsubara Green's function formalism remains a viable alternative,
since in this formalism it is easier to include thermal effects and to connect
the dynamic quantities such as the self-energy with static thermodynamic
quantities such as the Helmholtz energy, entropy, and internal energy. However,
despite the promising properties of this formalism, little is know about the
multiple solutions of the non-linear equations present in the self-consistent
Matsubara formalism and only a few cases involving a full Coulomb Hamiltonian
were investigated in the past. Here, to shed some light onto the iterative
nature of the Green's function solutions, we self-consistently evaluate the
thermodynamic quantities for a one-dimensional (1D) hydrogen solid at various
interatomic separations and temperatures using the self-energy approximated to
second-order (GF2). At many points in the phase diagram of this system,
multiple phases such as a metal and an insulator exist, and we are able to
determine the most stable phase from the analysis of Helmholtz energies.
Additionally, we show the evolution of the spectrum of 1D boron nitride (BN) to
demonstrate that GF2 is capable of qualitatively describing the temperature
effects influencing the size of the band gap.


  Evidence for clustering within the Coma cluster is found by means of a
multiscale analysis of the combined angular-redshift distribution. We have
compiled a catalogue of 798 galaxy redshifts from published surveys from the
region of the Coma cluster. We examine the presence of substructure and of
voids at different scales ranging from $\sim 1 to \sim 16 h^{-1}$ Mpc, using
subsamples of the catalogue, ranging from $cz=3000$ km/s to $cz=28000$ km/s.
Our substructure detection method is based on the wavelet transform and on the
segmentation analysis. The wavelet transform allows us to find out structures
at different scales and the segmentation method allows us a quantitative
statistical and morphological analysis of the sample. From the whole catalogue
we select a subset of 320 galaxies, with redshifts between cz=5858 km/s and
cz=8168 km/s that we identify as belonging to the central region of Coma and on
which we have performed a deeper analysis, on scales ranging from $180 h^{-1}$
kpc to $1.44 h^{-1}$ Mpc. Our results are expressed in terms of the number of
structures or voids and their sphericity for different values of the threshold
detection and at all the scales investigated. According to our analysis, there
is strong evidence for multiple hierarchical substructure, on scales ranging
from a few hundreds of kpc to about $4 h^{-1}$ Mpc. The morphology of these
substructures is rather spherical. On the scale of $720 h^{-1}$ kpc we find two
main subclusters which where also found before, but our wavelet analysis shows
even more substructures, whose redshift position is approximatively marked by
these bright galaxies: NGC 4934 & 4840, 4889, 4898 & 4864, 4874 & 4839, 4927,
4875.


  The author constructs the moduli of representations whose images generate the
subalgebra of upper triangular matrices (up to inner automorphisms) of the full
matrix ring for any groups and any monoids.


  Given a trajectory $T$ and a distance $\Delta$, we wish to find a set $C$ of
curves of complexity at most $\ell$, such that we can cover $T$ with subcurves
that each are within Fr\'echet distance $\Delta$ to at least one curve in $C$.
We call $C$ an $(\ell,\Delta)$-clustering and aim to find an
$(\ell,\Delta)$-clustering of minimum cardinality. This problem variant was
introduced by Akitaya $et$ $al.$ (2021) and shown to be NP-complete. The main
focus has therefore been on bicriterial approximation algorithms, allowing for
the clustering to be an $(\ell, \Theta(\Delta))$-clustering of roughly optimal
size.
  We present algorithms that construct $(\ell,4\Delta)$-clusterings of
$\mathcal{O}(k \log n)$ size, where $k$ is the size of the optimal $(\ell,
\Delta)$-clustering. We use $\mathcal{O}(n \log^2 n + n \cdot (k + \ell) \log
n)$ space and $\mathcal{O}(k n^3 \log^4 n)$ time. Our algorithms significantly
improve upon the clustering quality (improving the approximation factor in
$\Delta$) and size (whenever $\ell \in \Omega(\log n)$). We offer deterministic
running times comparable to known expected bounds. Additionally, we give a
near-quadratic improvement upon the dependency on $n$ in the space usage.


  We consider non-monogenic simplest cubic fields $K=\mathbb{Q}(\rho)$ in the
family introduced by Shanks, and among these, we focus in the fields whose
generalized module index $[\mathcal{O}_K:\mathbb{Z}[\rho]]$ is a prime number
$p$. We prove that these fields arise exactly for $p=3$ or
$p\equiv1\,(\mathrm{mod}\,6)$ and we use the method introduced in
arXiv:2005.12312 to find the additive indecomposables of $\mathcal{O}_K$. We
determine the whole structure of indecomposables for the family with $p=3$ and
obtain that the behaviour is not uniform with respect to the indecomposables of
$\mathbb{Z}[\rho]$. From the knowledge of the indecomposables we derive some
arithmetical information on $K$, namely: the smallest and largest norms of
indecomposables, the Pythagoras number of $\mathcal{O}_K$ and bounds for the
minimal rank of universal quadratic forms over $K$.


  We present the results of a 0.5-0.9" FWHM imaging survey at K (2.2 micron)
and H (1.6 micron) covering 5.1' x 5.1' centered on Theta 1C Ori, the most
massive star in the Orion Nebula Cluster (ONC). At the age and distance of this
cluster, and in the absence of extinction, the hydrogen burning limit (0.08 Mo)
occurs at K~13.5 mag while an object of mass 0.02 Mo has K~16.2 mag. Our
photometry is complete for source detection at the 7 sigma level to K~17.5 mag
and thus is sensitive to objects as low-mass as 0.02 Mo seen through visual
extinction values as high as 10 magnitudes. We use the observed magnitudes,
colors, and star counts to constrain the shape of the inner ONC stellar mass
function across the hydrogen burning limit. After determining the stellar age
and near-infrared excess properties of the optically visible stars in this same
inner ONC region, we present a new technique that incorporates these
distributions when extracting the mass function from the observed density of
stars in the K-(H-K) diagram. We find that our data are inconsistent with a
mass function that rises across the stellar/sub-stellar boundary. Instead, we
find that the most likely form of the inner ONC mass function is one that rises
to a peak around 0.15 Mo, and then declines across the hydrogen-burning limit
with slope N(log M) ~ M^(0.57+/-0.05). We emphasize that our conclusions apply
to the inner 0.71 pc x 0.71 pc of the ONC only; they may not apply to the ONC
as a whole where some evidence for general mass segregation has been found.


  Chiral integer quantum Hall (QH) edge modes are immune to backscattering and
therefore are non-localized and show a vanishing longitudinal as well as
non-local resistance along with quantized 2-terminal and Hall resistance even
in the presence of sample disorder. However, this is not the case for contact
disorder, which refers to the possibility that a contact can reflect edge modes
either partially or fully. This paper shows that when all contacts are
disordered in a N-terminal quantum Hall bar, then transport via chiral QH edge
modes can have a significant localization correction. The Hall and 2-terminal
resistance in an N-terminal quantum Hall sample deviate from their values
derived while neglecting the phase acquired at disordered contacts, and this
deviation is called the quantum localization correction. This correction term
increases with the increase of disorderedness of contacts but decreases with
the increase in the number of contacts in an N-terminal Hall bar. The presence
of inelastic scattering, however, can completely destroy the quantum
localization correction.


  The aim of the Air Microwave Yield (AMY) experiment is to investigate the
Molecular Bremsstrahlung Radiation (MBR) emitted from an electron beam induced
air-shower. The measurements have been performed with a 510 MeV electron beam
at the Beam Test Facility (BTF) of Frascati INFN National Laboratories in a
wide frequency range between 1 and 20 GHz. We present the experimental
apparatus and the first results of the measurements. Contrary to what have been
reported in a previous similar experiment~\cite{Gorham-SLAC}, we have found
that the intensity of the emission is strongly influenced by the particular
time structure of the accelerator beam. This makes very difficult the
interpretation of the emission process and a realistic extrapolation of the
emission yield to the plasma generated during the development of an atmospheric
shower.


  The Central Limit Theorem does not hold for strongly correlated stochastic
variables, as is the case for statistical systems close to criticality.
Recently, the calculation of the probability distribution function (PDF) of the
magnetization mode has been performed with the functional renormalization group
in the case of the three-dimensional Ising model [Balog et al., Phys. Rev.
Lett. {\bf 129}, 210602 (2022)]. It has been shown in that article that there
exists an entire family of universal PDFs parameterized by
$\zeta=\lim_{L,\xi_\infty\rightarrow\infty} L/\xi_\infty$ which is the ratio of
the system size $L$ to the bulk correlation length $\xi_{\infty}$ with both the
thermodynamic limit and the critical limit being taken simultaneously. We show
how these PDFs or, equivalently, the rate functions which are their logarithm,
can be systematically computed perturbatively in the $\epsilon=4-d$ expansion.
We determine the whole family of universal PDFs and show that they are in good
qualitative agreement with Monte Carlo data. Finally, we conjecture on how to
significantly improve the quantitative agreement between the one-loop and the
numerical results.


  We study the flavor changing t -> c H^0 decay in the framework of the general
two Higgs doublet model, so called model III. Here, we take the Yukawa
couplings complex and switch on the CP violating effects. We predict the
branching ratio six orders larger compared to the one calculated in the SM,
namely BR ~ 10^{-7}, and observe a measurable CP asymmetry, at the order of
10^{-2}.


  We introduce a fairness-aware dataset for job recommendation in advertising,
designed to foster research in algorithmic fairness within real-world
scenarios. It was collected and prepared to comply with privacy standards and
business confidentiality. An additional challenge is the lack of access to
protected user attributes such as gender, for which we propose a solution to
obtain a proxy estimate. Despite being anonymized and including a proxy for a
sensitive attribute, our dataset preserves predictive power and maintains a
realistic and challenging benchmark. This dataset addresses a significant gap
in the availability of fairness-focused resources for high-impact domains like
advertising -- the actual impact being having access or not to precious
employment opportunities, where balancing fairness and utility is a common
industrial challenge. We also explore various stages in the advertising process
where unfairness can occur and introduce a method to compute a fair utility
metric for the job recommendations in online systems case from a biased
dataset. Experimental evaluations of bias mitigation techniques on the released
dataset demonstrate potential improvements in fairness and the associated
trade-offs with utility.


  The phase diagram of the t-J-V model is discussed using a 1/N expansion in
terms of X operators. It is shown that a flux phase of d-wave symmetry is
stabilized by the Coulomb interaction V at intermediate dopings and competes
with d-wave superconductivity. Since the flux wave instability is stronger than
the superconducting one optimal doping is essentially determined by the onset
of the flux phase. Below optimal doping the flux phase coexists with
superconductivity at low and exists as a pseudo gap phase at higher
temperatures. It is also found that the flux phase boundary is much less
sensitive to impurity scattering than the boundary for superconductivity in
agreement with experiments in Zn doped La-214 and (Y,Ca)-123.