  We present the structure theorem for the positive support of the cube of the
Grover transition matrix of the discrete-time quantum walk (the Grover walk) on
a general graph $G$ under same condition. Thus, we introduce a zeta function on
the positive support of the cube of the Grover transition matrix of $G$, and
present its Euler product and its determinant expression. As a corollary, we
give the characteristic polynomial for the positive support of the cube of the
Grover transition matrix of a regular graph, and so obtain its spectra.
Finally, we present the poles and the radius of the convergence of this zeta
function.


  We observe that for a large class of non-amenable groups $G$, one can find
bounded representations of $A(G)$ on Hilbert space which are not completely
bounded. We also consider restriction algebras obtained from $A(G)$, equipped
with the natural operator space structure, and ask whether such algebras can be
completely isomorphic to operator algebras; partial results are obtained, using
a modified notion of Helson set which takes account of operator space
structure. In particular, we show that if $G$ is virtually abelian, then the
restriction algebra $A_G(E)$ is completely isomorphic to an operator algebra if
and only if $E$ is finite.


  There has been a surge of recent interest in sociocultural diversity in
machine learning (ML) research, with researchers (i) examining the benefits of
diversity as an organizational solution for alleviating problems with
algorithmic bias, and (ii) proposing measures and methods for implementing
diversity as a design desideratum in the construction of predictive algorithms.
Currently, however, there is a gap between discussions of measures and benefits
of diversity in ML, on the one hand, and the broader research on the underlying
concepts of diversity and the precise mechanisms of its functional benefits, on
the other. This gap is problematic because diversity is not a monolithic
concept. Rather, different concepts of diversity are based on distinct
rationales that should inform how we measure diversity in a given context.
Similarly, the lack of specificity about the precise mechanisms underpinning
diversity's potential benefits can result in uninformative generalities,
invalid experimental designs, and illicit interpretations of findings. In this
work, we draw on research in philosophy, psychology, and social and
organizational sciences to make three contributions: First, we introduce a
taxonomy of different diversity concepts from philosophy of science, and
explicate the distinct epistemic and political rationales underlying these
concepts. Second, we provide an overview of mechanisms by which diversity can
benefit group performance. Third, we situate these taxonomies--of concepts and
mechanisms--in the lifecycle of sociotechnical ML systems and make a case for
their usefulness in fair and accountable ML. We do so by illustrating how they
clarify the discourse around diversity in the context of ML systems, promote
the formulation of more precise research questions about diversity's impact,
and provide conceptual tools to further advance research and practice.


  We formulate and discuss the relationship between polyhedral stress functions
and internally self-equilibrated frameworks in 2D, and a two-mesh technique for
the prediction of the stress field associated with such systems. We generalize
classical results concerned with smooth Airy stress functions to polyhedral
functions associated with arbitrary triangulations of a simply-connected
domain. We also formulate a regularization technique that smoothly projects the
stress function corresponding to an unstructured force network over a
structured triangulation. The paper includes numerical examples dealing with a
benchmark problem of plane elasticity, and the stress fields associated with
tensegrity models of a cantilever beam and an elliptical membrane.


  We have investigated the information content in reflected-starlight spectra
of exoplanets. We specify our analysis to Barnard's Star b candidate
super-Earth, for which we assume a radius 0.6 times that of Neptune, an
atmosphere dominated by H$_2$-He, and a CH$_4$ volume mixing ratio of
5$\cdot$10$^{-3}$. The main conclusions of our study are however
planet-independent. We set up a model of the exoplanet described by seven
parameters including its radius, atmospheric methane abundance and basic
properties of a cloud layer. We generate synthetic spectra at zero phase (full
disk illumination) from 500 to 900 nm and spectral resolution R$\sim$125-225.
We simulate a measured spectrum with a simplified, wavelength-independent noise
model at Signal-to-Noise ratio S/N=10. With an MCMC-based retrieval
methodology, we analyse which planet/atmosphere parameters can be inferred from
the measured spectrum and the theoretical correlations amongst them. We
consider limiting cases in which the planet radius is either known or
completely unknown, and intermediate cases in which the planet radius is partly
constrained. If the planet radius is known, we can generally discriminate
between cloud-free and cloudy atmospheres, and constrain the methane abundance
to within two orders of magnitude. If the planet radius is unknown, new
correlations between model parameters occur and the accuracy of the retrievals
decreases. Without a radius determination, it is challenging to discern whether
the planet has clouds, and the estimates on methane abundance degrade. However,
we find the planet radius is constrained to within a factor of two for all the
cases explored. Having a priori information on the planet radius, even if
approximate, helps improve the retrievals. We urge exoplanet detection efforts
to extend the population of long-period planets with mass and radius
determinations.


  A search for evidence of particle dark matter (DM) and unparticle production
at the LHC has been performed using events containing two charged leptons,
consistent with the decay of a Z boson, and large missing transverse momentum.
This study is based on data collected with the CMS detector corresponding to an
integrated luminosity of 19.7 inverse femtobarns of pp collisions at the LHC at
a center-of-mass energy of 8 TeV. No significant excess of events is observed
above the number expected from the standard model contributions. The results
are interpreted in terms of 90% confidence level limits on the DM-nucleon
scattering cross section, as a function of the DM particle mass, for both
spin-dependent and spin-independent scenarios. Limits are set on the effective
cutoff scale Lambda, and on the annihilation rate for DM particles, assuming
that their branching fraction to quarks is 100%. Additionally, the most
stringent 95% confidence level limits to date on the unparticle model
parameters are obtained.


  We present a method of localised control of chaos in Hamiltonian systems. The
aim is to modify the perturbation locally by a small control term which makes
the controlled Hamiltonian more regular. We provide an explicit expression for
the control term which is able to recreate invariant (KAM) tori without
modifying other parts of phase space. We apply this method of localised control
to a forced pendulum model, to the delta-kicked rotor (standard map) and to a
non-twist Hamiltonian.


  A working implementation of nested transactions has been produced for LOCUS,
an integrated distributed operating system which provides a high degree of
network transparency. Several aspects of our mechanism are novel. First, the
mechanism allows a transaction to access objects directly without regard to the
location of the object. Second, processes running on behalf of a single
transaction may be located at many sites. Thus there is no need to invoke a new
transaction to perform processing or access objects at a remote site. Third,
unlike other environments, LOCUS allows replication of data objects at more
than one site in the network, and this capability is incorporated into the
transaction mechanism. If the copy of an object that is currently being
accessed becomes unavailable, it is possible to continue work by using another
one of the replicated copies. Finally, an efficient orphan removal algorithm is
presented, and the problem of providing continued operation during network
partitions is addressed in detail.


  Significant fraction of barred galaxies hosts secondary bar-like structures
on optical and NIR images. The circumnuclear dynamics of double-barred objects
are still not well understood, observational data concerning kinematics are
incomplete and inconsistent. In order to compare the simulations results with
observations, we have started new spectroscopic studying of stellar kinematics
in lenticular galaxies from Peter Erwin's catalog of secondary bars. We present
first results concerning their stellar kinematics based on the observations
performed with the integral-field spectrograph MPFS at the Russian 6-m
telescope.


  Search engines play a central role in routing political information to
citizens. The algorithmic personalization of search results by large search
engines like Google implies that different users may be offered systematically
different information. However, measuring the causal effect of user
characteristics and behavior on search results in a politically relevant
context is challenging. We set up a population of 150 synthetic internet users
("bots") who are randomly located across 25 US cities and are active for
several months during the 2020 US Elections and their aftermath. These users
differ in their browsing preferences and political ideology, and they build up
realistic browsing and search histories. We run daily experiments in which all
users enter the same election-related queries. Search results to these queries
differ substantially across users. Google prioritizes previously visited
websites and local news sites. Yet, it does not generally prioritize websites
featuring the user's ideology.


  This research is about COVID-19, which is a contagious virus that reached
many countries, including Lebanon. Monitoring the outbreak, researchers have
been involved in introducing COVID-19 targeting vaccines. Already facing
financial and political issues, Lebanon was further affected by the COVID-19
outbreak. The hereby research tends to design the optimum vaccination plan to
reach the recommended herd immunity in Lebanon. The scheme is drafted per
application of the queue modeling where the essential parameters revolve around
the arrival rate (the mean of which is 536 people) and service rate. Based on a
two-stage cluster sampling method, this data is collected from six vaccination
centers located in Beirut. The outcomes were computed through R statistical
software after validating the stability condition. Per the results, the mean
number of people in the network queue ranges between 4.1 and 5.2. In contrast,
the time spent by individuals extends from around 5.7 up to 8 minutes, having
an average of 5 servers for system one and 4 servers for system two. Therefore,
the optimization of the vaccination plan followed in Lebanon necessitates the
consistency of arrivals and the decrease of the service time. Furthermore, the
service rate can be maximized by adding servers on both systems of the network
queue. Further studies might include applying queue modeling in other medical
sectors, for instance, the emergency section of hospitals.


  The year 2000 marks the centenary of the Planck function for the blackbody
spectral distribution. Here are traced the steps, over 100 years, along the
path leading to this important formula.


  Evolution of the cluster population has been recognized as a powerful
cosmological tool. While the present--day abundance of X-ray clusters is
degenerate in $\sigma_8$, $n$ and $\Omega_0$, Oukbir and Blanchard (1992, 1997)
have pointed out that the number density evolution of X-ray clusters with
redshift can be used to determine $\Omega_0$. Here, we clarify the origin of
this statement by identifying those parameters to which the evolution of
cluster number density is most sensitive. We find that the evolution is
controlled by only two parameters: the amplitude of fluctuations, $\sigma_M$,
on the scale associated with the mass under consideration, $R = 9.5h^{1/3}
\Omega_0^{-1/3}$ $ M_{15}^{1/3} h^{-1}$Mpc, and the cosmological background
density, $\Omega_0$. In contrast, evolution is remarkably insensitive to the
slope of the power spectrum. We verify that the number density evolution of
clusters is a powerful probe of the mean density of the universe, under the
condition that $\sigma_M$ is chosen to reproduce current-day abundances.
Comparison of the cluster abundance at $z \sim 0.5-0.6$, from the EMSS, to the
present-day abundance, from the ROSAT BCS sample, unambiguously reveals the
existence of significant negative evolution. This number evolution, in
conjunction with the absence of any negative evolution in the
luminosity-temperature relation, provides robust evidence in favor of a
critical density universe ($\Omega_0=1$), in agreement with the analysis by
Sadat et al. (1998).


  We assess the impact of the trace element ^{22}Ne on the cooling and
seismology of a liquid C/O white dwarf (WD). Due to this elements' neutron
excess, it sinks towards the interior as the liquid WD cools. The subsequent
gravitational energy released slows the cooling of the WD by 0.25--1.6 Gyrs by
the time it has completely crystallized, depending on the WD mass and the
adopted sedimentation rate. The effects will make massive WDs or those in metal
rich clusters (such as NGC 6791) appear younger than their true age. Our
diffusion calculations show that the ^{22}Ne mass fraction in the crystallized
core actually increases outwards. The stability of this configuration has not
yet been determined. In the liquid state, the settled ^{22}Ne enhances the
internal buoyancy of the interior and changes the periods of the high radial
order g-modes by approximately 1%. Though a small adjustment, this level of
change far exceeds the accuracy of the period measurements. A full assessment
and comparison of mode frequencies for specific WDs should help constrain the
still uncertain ^{22}Ne diffusion coefficient for the liquid interior.


  For min-max optimization and variational inequalities problems (VIP)
encountered in diverse machine learning tasks, Stochastic Extragradient (SEG)
and Stochastic Gradient Descent Ascent (SGDA) have emerged as preeminent
algorithms. Constant step-size variants of SEG/SGDA have gained popularity,
with appealing benefits such as easy tuning and rapid forgiveness of initial
conditions, but their convergence behaviors are more complicated even in
rudimentary bilinear models. Our work endeavors to elucidate and quantify the
probabilistic structures intrinsic to these algorithms. By recasting the
constant step-size SEG/SGDA as time-homogeneous Markov Chains, we establish a
first-of-its-kind Law of Large Numbers and a Central Limit Theorem,
demonstrating that the average iterate is asymptotically normal with a unique
invariant distribution for an extensive range of monotone and non-monotone
VIPs. Specializing to convex-concave min-max optimization, we characterize the
relationship between the step-size and the induced bias with respect to the
Von-Neumann's value. Finally, we establish that Richardson-Romberg
extrapolation can improve proximity of the average iterate to the global
solution for VIPs. Our probabilistic analysis, underpinned by experiments
corroborating our theoretical discoveries, harnesses techniques from
optimization, Markov chains, and operator theory.


  The growing capabilities of AI raise questions about their trustworthiness in
healthcare, particularly due to opaque decision-making and limited data
availability. This paper proposes a novel approach to address these challenges,
introducing a Bayesian Monte Carlo Dropout model with kernel modelling. Our
model is designed to enhance reliability on small medical datasets, a crucial
barrier to the wider adoption of AI in healthcare. This model leverages
existing language models for improved effectiveness and seamlessly integrates
with current workflows. We demonstrate significant improvements in reliability,
even with limited data, offering a promising step towards building trust in
AI-driven medical predictions and unlocking its potential to improve patient
care.


  In this paper, we study the variation of the Turaev--Viro invariants for
$3$-manifolds with toroidal boundary under the operation of attaching a
$(p,q)$-cable space. We apply our results to a conjecture of Chen and Yang
which relates the asymptotics of the Turaev--Viro invariants to the simplicial
volume of a compact oriented $3$-manifold. For $p$ and $q$ coprime, we show
that the Chen--Yang volume conjecture is stable under
$\left(p,q\right)$-cabling. We achieve our results by studying the linear
operator $RT_r$ associated to the torus knot cable spaces by the
Reshetikhin--Turaev $SO_3$-Topological Quantum Field Theory (TQFT), where the
TQFT is well-known to be closely related to the desired Turaev--Viro
invariants. In particular, our utilized method relies on the invertibility of
the linear operator for which we provide necessary and sufficient conditions.


  Wilson flow is an effective tool for constructing renormalized composite
operators. We explore use of the Wilson flow to construct renormalized order
parameters for the deconfinement transition in SU(3) gauge theory. We discuss
renormalization of the Polyakov loop, and of gluon condensates.


  Heterogeneous Graph Neural Networks (HGNNs) have broadened the applicability
of graph representation learning to heterogeneous graphs. However, the
irregular memory access pattern of HGNNs leads to the buffer thrashing issue in
HGNN accelerators. In this work, we identify an opportunity to address buffer
thrashing in HGNN acceleration through an analysis of the topology of
heterogeneous graphs. To harvest this opportunity, we propose a graph
restructuring method and map it into a hardware frontend named GDR-HGNN.
GDR-HGNN dynamically restructures the graph on the fly to enhance data locality
for HGNN accelerators. Experimental results demonstrate that, with the
assistance of GDR-HGNN, a leading HGNN accelerator achieves an average speedup
of 14.6 times and 1.78 times compared to the state-of-the-art software
framework running on A100 GPU and itself, respectively.


  Visible light communications (VLC) is an emerging technology with a promise
of viable solution to spectrum crunch problem in conventional radio frequency
(RF) bands. In this work, we consider a downlink multiuser VLC network where
users randomly change their location and vertical orientation. In order to
increase the spectral efficiency, we consider the non-orthogonal multiple
access (NOMA) transmission to serve multiple users simultaneously. In
particular, we propose individual and group-based user ordering techniques for
NOMA with various user feedback schemes. In order to reduce the computational
complexity and link overhead, feedback on the channel quality is proposed to be
computed using mean value of the vertical angle (instead of the exact
instantaneous value), as well as the distance information. In addition, a
two-bit feedback scheme is proposed for the group-based user scheduling, which
relies on both the distance and vertical angle, and differs from the
conventional one-bit feedback of the distance only. The outage probability and
sum-rate expressions are derived analytically, which show a very good match
with the simulation data. Numerical results verify that the practical feedback
scheme with the mean vertical angle achieves a near-optimal sum-rate
performance, and the two-bit feedback significantly outperforms the one-bit
feedback.


  We propose an original and efficient method to engineer a defect between two
well-ordered silica opals by sputtering silica on the top of the first one. As
the amount of sputtered silica can be well controlled, it is also the case for
the thickness of the layer and consequently for the spectral position of the
defect mode. The optical response of these sandwich structures is studied in
terms of specular reflection and transmission spectroscopy. Tunable highly
transmitted and reflected optical modes are evidenced. The very good agreement
between the experimental results and the simulations, run without fitting
parameters, demonstrates the almost perfect order of the synthesized
structures.


  Ballistic conductance through a single atom adsorbed on a metallic surface
and probed by a scanning tunneling microscope (STM) tip can be decomposed into
eigenchannel contributions, which can be potentially obtained from shot noise
measurements. Our density functional theory calculations provide evidence that
transmission probabilities of these eigenchannels encode information on the
modifications of the adatom's local density of states caused by its interaction
with the STM tip. In the case of open shell atoms, this can be revealed in
nonmonotonic behavior of the eigenchannel's transmissions as a function of the
tip-adatom separation.


  We study the spectrum of pure massless higher spin theories in $AdS_3$. The
light spectrum is given by a tower of massless particles of spin $s=2,\cdots,N$
and their multi-particles states. Their contribution to the torus partition
function organises into the vacuum character of the ${\cal W}_N$ algebra.
Modular invariance puts constraints on the heavy spectrum of the theory, and in
particular leads to negative norm states, which would be inconsistent with
unitarity. This negativity can be cured by including additional light states,
below the black hole threshold but whose mass grows with the central charge. We
show that these states can be interpreted as conical defects with deficit angle
$2\pi(1-1/M)$. Unitarity allows the inclusion of such defects into the path
integral provided $M \geq N$.


  Various electromagnetic systems can carry an angular momentum in their {\bf
E} and {\bf B} fields. The electromagnetic field angular momentum (EMAM) of
these systems can combine with the spin angular momentum to give composite
fermions or composite bosons. In this paper we examine the possiblity that an
EMAM could provide an explanation of the fractional quantum Hall effect (FQHE)
which is complimentary to the Chern-Simons explanation. We also examine a toy
model of a non-BCS superconductor (e.g. high $T_c$ superconductors) in terms of
an EMAM. The models presented give a common, simple picture of these two
systems in terms of an EMAM. The presence of an EMAM in these systems might be
tested through the observation of the decay modes of a charged, spin zero
unstable particle inside one of these systems.


  Ensuring intelligible speech communication for hearing assistive devices in
low-latency scenarios presents significant challenges in terms of speech
enhancement, coding and transmission. In this paper, we propose novel solutions
for low-latency joint speech transmission and enhancement, leveraging deep
neural networks (DNNs). Our approach integrates two state-of-the-art DNN
architectures for low-latency speech enhancement and low-latency analog joint
source-channel-based transmission, creating a combined low-latency system and
jointly training both systems in an end-to-end approach. Due to the
computational demands of the enhancement system, this order is suitable when
high computational power is unavailable in the decoder, like hearing assistive
devices. The proposed system enables the configuration of total latency,
achieving high performance even at latencies as low as 3 ms, which is typically
challenging to attain. The simulation results provide compelling evidence that
a joint enhancement and transmission system is superior to a simple
concatenation system in diverse settings, encompassing various wireless channel
conditions, latencies, and background noise scenarios.


  We provide the first demonstration of nonlinear self-trapping of light in
solutions of food coloring dyes, leading to dirt cheap soliton waveguides. The
optical self-focusing nonlinearity observed in such solutions is attributed to
the optical absorption force acting on dye molecules, as the thermal effect at
relatively higher power results in a self-defocusing nonlinearity. We found the
nonlinear response increases when the laser wavelength is switched to near the
peak absorption for all food dyes tested. Furthermore, the absorption-dependent
nonlinearity can be tuned by making purposive samples of different food dyes.
In particular, with appropriately mixing of dye solutions, nonlinear response
arises for otherwise inactive wavelengths, leading to interaction between two
beams of different wavelengths and formation of a coupled soliton-like pair. At
milliwatt low power levels, self-trapping achieved at one wavelength in
food-dye solutions can serve as low-cost waveguides for optical beams at other
wavelengths.


  Technology is now omnipresent around us. Especially with the recent health
crisis, many people started working remotely, bringing home an additional
computer. Combining this with our smartphones that we could never leave behind,
we are always surrounded by these technological marvels. However, they come
along with a rather dark side from which many people choose to look away,
preferring to live in denial: the surveillance. All of these devices can be
used to keep a close eye and ear on us. The modern surveillance machine has
reached a new, groundbreaking, size; and we will attempt to understand how we
ended up in this situation. To have a complete understanding of the problem, it
is important to gather some historical background to comprehend where this
issue comes from as well as a review of the different actors. Each actor has a
specific skillset it will use to acquire the desired information, and what
information they choose to gather depends strongly on their motives. We will go
over the many tricks used to gather our information, as well as its relevance
in the current surveillance climate.


  I construct a well-defined expansion in $\epsilon=2-d$ for diffusion
processes on small-world networks. The technique permits one to calculate the
average over disorder of moments of the Green's function, and is used to
calculate the average Green's function and fluctuations to first non-leading
order in $\epsilon$, giving results which agree with numerics. This technique
is also applicable to other problems of diffusion in random media.


  We study theoretically Bloch oscillations of half-matter, half-light
quasi-particles: exciton-polaritons. We propose an original structure for the
observation of this phenomenon despite the constraints imposed by the
relatively short lifetime of the particles and the limitations on the
engineered periodic potential. First, we focus on the linear regime in a
perfect lattice where regular oscillations are obtained. Second, we take into
account a realistic structural disorder known to localize non-interacting
particles, which is quite dramatic for propagation-related phenomena. In the
non-linear condensed regime the renormalization of the energy provided by
interactions between particles allows us to screen efficiently the disorder and
to recover oscillations. This effect is useful only in a precise range of
parameters outside of which the system becomes dynamically unstable. For a
large chemical potential of the order of the potential's amplitude, a strong
Landau-Zener tunneling tends to completely delocalize particles.


  Degraded broadcast channels (DBC) are a typical multiuser communication
scenario, Semantic communications over DBC still lack in-depth research. In
this paper, we design a semantic communications approach based on multi-user
semantic fusion for wireless image transmission over DBC. In the proposed
method, the transmitter extracts semantic features for two users separately. It
then effectively fuses these semantic features for broadcasting by leveraging
semantic similarity. Unlike traditional allocation of time, power, or
bandwidth, the semantic fusion scheme can dynamically control the weight of the
semantic features of the two users to balance the performance between the two
users. Considering the different channel state information (CSI) of both users
over DBC, a DBC-Aware method is developed that embeds the CSI of both users
into the joint source-channel coding encoder and fusion module to adapt to the
channel. Experimental results show that the proposed system outperforms the
traditional broadcasting schemes.


  Despite various methods are proposed to make progress in pedestrian attribute
recognition, a crucial problem on existing datasets is often neglected, namely,
a large number of identical pedestrian identities in train and test set, which
is not consistent with practical application. Thus, images of the same
pedestrian identity in train set and test set are extremely similar, leading to
overestimated performance of state-of-the-art methods on existing datasets. To
address this problem, we propose two realistic datasets
PETA\textsubscript{$zs$} and RAPv2\textsubscript{$zs$} following zero-shot
setting of pedestrian identities based on PETA and RAPv2 datasets. Furthermore,
compared to our strong baseline method, we have observed that recent
state-of-the-art methods can not make performance improvement on PETA, RAPv2,
PETA\textsubscript{$zs$} and RAPv2\textsubscript{$zs$}. Thus, through solving
the inherent attribute imbalance in pedestrian attribute recognition, an
efficient method is proposed to further improve the performance. Experiments on
existing and proposed datasets verify the superiority of our method by
achieving state-of-the-art performance.


  Condensation of water on metallic surfaces is critical for multiple energy
conversion processes. Enhancement in condensation heat transfer efficiency
often requires surface texturing and hydrophobicity, usually achieved through
coatings, to maintain dropwise condensation. However, such surface treatments
face conflicting challenges of minimal coating thermal resistance, enhanced
coating durability and scalable fabrication. Here we present a thin (~ 2
{\mu}m) polytetrafluoroethylene - carbon nanofiber nanocomposite coating which
meets these challenges and sustains coalescence-induced jumping droplet
condensation for extended periods under highly demanding condensation
conditions. Coating durability is achieved through improved substrate adhesion
by depositing a sub-micron thick aluminum primer layer. Carbon nanofibers in a
polytetrafluoroethylene matrix increase coating thermal conductivity and
promote spontaneous surface nano-texturing to achieve superhydrophobicity for
condensate microdroplets. The coating material can be deposited through direct
spraying, ensuring economical scalability and versatility for a wide range of
substrates. We know of no other coating for metallic surfaces that is able to
sustain jumping dropwise condensation under shear of steam at 111 degC flowing
at ~ 3 m s-1 over the surface for 10 hours and dropwise condensation for an
additional 50 hours. Up to ~ 900% improvement in condensation heat transfer
coefficient is achieved compared to conventional filmwise condensation.


  Understanding mixing and transport of passive scalars in active fluids is
important to many natural (e.g. algal blooms) and industrial (e.g. biofuel,
vaccine production) processes. Here, we study the mixing of a passive scalar
(dye) in dilute suspensions of swimming Escherichia coli in experiments using a
two-dimensional (2D) time-periodic flow and in a simple simulation. Results
show that the presence of bacteria hinders large scale transport and reduce
overall mixing rate. Stretching fields, calculated from experimentally measured
velocity fields, show that bacterial activity attenuates fluid stretching and
lowers flow chaoticity. Simulations suggest that this attenuation may be
attributed to a transient accumulation of bacteria along regions of high
stretching. Spatial power spectra and correlation functions of dye
concentration fields show that the transport of scalar variance across scales
is also hindered by bacterial activity, resulting in an increase in average
size and lifetime of structures. On the other hand, at small scales, activity
seems to enhance local mixing. One piece of evidence is that the probability
distribution of the spatial concentration gradients is nearly symmetric with a
vanishing skewness. Overall, our results show that the coupling between
activity and flow can lead to nontrivial effects on mixing and transport.


  Energy harvesting sensor nodes are gaining popularity due to their ability to
improve the network life time and are becoming a preferred choice supporting
'green communication'. In this paper we focus on communicating reliably over an
AWGN channel using such an energy harvesting sensor node. An important part of
this work involves appropriate modeling of the energy harvesting, as done via
various practical architectures. Our main result is the characterization of the
Shannon capacity of the communication system. The key technical challenge
involves dealing with the dynamic (and stochastic) nature of the (quadratic)
cost of the input to the channel. As a corollary, we find close connections
between the capacity achieving energy management policies and the queueing
theoretic throughput optimal policies.


  We present the X-ray timing and spectral analysis of the new Galactic X-ray
transient Swift J1658.2-4242 observed with LAXPC and SXT instruments onboard
{\it Astrosat}. We detect prominent C-type quasi-periodic oscillations (QPOs)
of frequencies varying from $\sim 1.5$ Hz to $\sim 6.6$ Hz along with distinct
2nd harmonics and sub-harmonics. The QPO detected at $\sim 1.56$ Hz drifts to a
higher centroid frequency of $\sim 1.74$ in the course of the observation,
while the QPO detected at $\sim 6.6$\,Hz disappeared during hard flarings. The
fractional rms at the QPO and the sub-harmonic frequencies increases with
photon energy, while at the 2nd harmonic frequencies the rms seems to be
constant. In addition, we have observed soft time lag at QPO and sub-harmonic
frequencies up to a time scale of $\sim 35$ ms, however, at the 2nd harmonic
frequencies there is weak/zero time lag. We attempt spectral modeling of the
broadband data in the 0.7--25 keV band using the doubly absorbed disk plus
thermal Comptonization model. Based on the spectral and timing properties, we
identified the source to be in the hard intermediate state of black hole X-ray
binaries. To quantitatively fit the energy and frequency-dependent fractional
rms and time lag, we use a single zone fluctuation propagation model and
discuss our results in the context of that model.


  In 1988, Gross proposed a conjectural congruence between Stickelberger
elements and algebraic regulators, which is often referred to as the refined
class number formula. In this paper, we prove this congruence.


  Problems for the graduate students who want to improve problem-solving skills
in geometry. Every problem has a short elegant solution -- this gives a hint
which was not available when the problem was discovered.


  We study cutoff results for parameterized verification and synthesis of
guarded protocols, as introduced by Emerson and Kahlon (2000). Guarded
protocols describe systems of processes whose transitions are enabled or
disabled depending on the existence of other processes in certain local states.
Cutoff results reduce reasoning about systems with an arbitrary number of
processes to systems of a determined, fixed size. Our work is based on the
observation that existing cutoff results for guarded protocols are often
impractical, since they scale linearly in the number of local states of
processes in the system. We provide new cutoffs that scale not with the number
of local states, but with the number of guards in the system, which is in many
cases much smaller. Furthermore, we consider natural extensions of the classes
of systems and specifications under consideration, and present results for
problems that have not been known to admit cutoffs before.


  In the NLC (Next Linear Collider) small misalignments in each of the
individual accelerator structures (or the accelerator cells) will give rise to
wakefields which kick the beam from its electrical axis. This wakefield can
cause BBU (Beam Break Up) or at the very least it will dilute the emittance of
the beam. Several Gaussian detuned structures have been designed and tested [1]
at SLAC and in this paper we explore new distributions with possibly better
damping properties. The progress of the beam through approximately 5,000
structures is monitored in phase space and results on this are presented.
  [1] J.W. Wang et al, TUA03, LINAC2000


  Macroscopic wave packets of spin-polarized exciton-polaritons in
two-dimensional microcavities experience the zitterbewegung, the effect
manifested by the appearance of the oscillatory motion of polaritons in the
direction normal to the initial propagation direction. The oscillating
trajectories of exciton-polaritons are adjustable by the control parameters:
the splitting of the longitudinal and transverse exciton-polariton modes, the
wave vector and the width of the resonant cw pump. Our theoretical analysis
supported by the numerical calculations allowed to optimize values of the
control parameters suitable for a direct experimental observation of the
zitterbewegung effect.


  In this study we identify 11 Kepler systems (KIC 5255552, 5653126, 5731312,
7670617, 7821010, 8023317, 10268809, 10296163, 11519226, 11558882 and 12356914)
with a "flip-flop" effect in the eclipse timing variations O-C diagrams of the
systems, report on what these systems have in common and whether these systems
are dynamically stable. These systems have previously reported high eccentric
binary stars with highly eccentric third bodies/outer companions. We find that
all of the additional bodies in the system are dynamically stable for the
configurations previously reported and are therefore likely to exist as
described. We also provide additional evidence of KIC5255552 being a quadruple
star system comprised of an eclipsing binary pair and non-eclipsing binary pair
with the possibility of a fifth body in the system. With the advent of the NASA
TESS exoplanet survey, its precision photometric monitoring offers an
opportunity to help confirm more local eclipsing binary star companions,
including planets.


  We show that the ultradifferentiable-like classes of smooth functions
introduced and studied by S. Pilipovi\'c, N. Teofanov and F. Tomi\'c are
special cases of the general framework of spaces of ultradifferentiable
functions defined in terms of weight matrices in the sense of A. Rainer and the
third author. We study classes "beyond geometric growth factors" defined in
terms of a weight sequence and an exponent sequence, prove that these new types
admit a weight matrix representation and transfer known results from the
matrix-type to such a non-standard ultradifferentiable setting.


  In this work we consider searches for dark matter made of axions or
axion-like particles (ALPs) using resonant radio frequency cavities inserted
into dipole magnets from particle accelerators, wiggler magnets developed for
accelerator based advanced light sources, and toroidal magnets similar to those
used in particle physics detectors. We investigate the expected sensitivity of
such ALP dark matter detectors and discuss the engineering aspects of building
and tuning them. Brief mention is also made of even stronger field magnets that
are becoming available due to improvements in magnetic technology. It is
concluded that new experiments utilizing already existing magnets could greatly
enlarge the mass region in searches for axion-like dark matter particles.


  In this work we give precise asymptotic expressions on the probability of the
existence of fixed-size components at the threshold of connectivity for random
geometric graphs.


  Graph convolutional learning has led to many exciting discoveries in diverse
areas. However, in some applications, traditional graphs are insufficient to
capture the structure and intricacies of the data. In such scenarios,
multigraphs arise naturally as discrete structures in which complex dynamics
can be embedded. In this paper, we develop convolutional information processing
on multigraphs and introduce convolutional multigraph neural networks (MGNNs).
To capture the complex dynamics of information diffusion within and across each
of the multigraph's classes of edges, we formalize a convolutional signal
processing model, defining the notions of signals, filtering, and frequency
representations on multigraphs. Leveraging this model, we develop a multigraph
learning architecture, including a sampling procedure to reduce computational
complexity. The introduced architecture is applied towards optimal wireless
resource allocation and a hate speech localization task, offering improved
performance over traditional graph neural networks.


  The Internet of Things (IoT) has grown significantly in popularity,
accompanied by increased capacity and lower cost of communications, and
overwhelming development of technologies. At the same time, big data and
real-time data analysis have taken on great importance and have been
accompanied by unprecedented interest in sharing data among citizens, public
administrations and other organisms, giving rise to what is known as the
Collaborative Internet of Things. This growth in data and infrastructure must
be accompanied by a software architecture that allows its exploitation.
Although there are various proposals focused on the exploitation of the IoT at
edge, fog and/or cloud levels, it is not easy to find a software solution that
exploits the three tiers together, taking maximum advantage not only of the
analysis of contextual and situational data at each tier, but also of two-way
communications between adjacent ones. In this paper, we propose an architecture
that solves these deficiencies by proposing novel technologies which are
appropriate for managing the resources of each tier: edge, fog and cloud. In
addition, the fact that two-way communications along the three tiers of the
architecture is allowed considerably enriches the contextual and situational
information in each layer, and substantially assists decision making in real
time. The paper illustrates the proposed software architecture through a case
study of respiratory disease surveillance in hospitals. As a result, the
proposed architecture permits efficient communications between the different
tiers responding to the needs of these types of IoT scenarios.


  A framework is developed to describe the two-point statistics of potential
vorticity in rotating and stratified turbulence as described by the Boussinesq
equations. The Karman-Howarth equation for the dynamics of the two-point
correlation function of potential vorticity reveals the possibility of
inertial-range dynamics in certain regimes in the Rossby, Froude, Prandtl and
Reynolds number parameters. For the case of large Rossby and Froude numbers,
and for the case of quasi-geostrophic dynamics, a linear scaling law with 2/3
prefactor is derived for the third-order mixed correlation between potential
vorticity and velocity, a result that is analogous to the Kolmogorov 4/5-law
for the third-order velocity structure function in turbulence theory.


  Evaluation of effective or macroscopic coefficients of thermal conductivity
under coupled heat and moisture transfer is presented. The paper first gives a
detailed summary on the solution of a simple steady state heat conduction
problem with an emphasis on various types of boundary conditions applied to the
representative volume element -- a periodic unit cell. Since the results
essentially suggest no superiority of any type of boundary conditions, the
paper proceeds with the coupled nonlinear heat and moisture problem subjecting
the selected representative volume element to the prescribed macroscopically
uniform heat flux. This allows for a direct use of the academic or commercially
available codes. Here, the presented results are derived with the help of the
SIFEL (SIimple Finite Elements) system.


  Nested simulation encompasses the estimation of functionals linked to
conditional expectations through simulation techniques. In this paper, we treat
conditional expectation as a function of the multidimensional conditioning
variable and provide asymptotic analyses of general Least Squared Estimators on
sieve, without imposing specific assumptions on the function's form. Our study
explores scenarios in which the convergence rate surpasses that of the standard
Monte Carlo method and the one recently proposed based on kernel ridge
regression. We also delve into the conditions that allow for achieving the best
possible square root convergence rate among all methods. Numerical experiments
are conducted to support our statements.


  Neural transducer is now the most popular end-to-end model for speech
recognition, due to its naturally streaming ability. However, it is challenging
to adapt it with text-only data. Factorized neural transducer (FNT) model was
proposed to mitigate this problem. The improved adaptation ability of FNT on
text-only adaptation data came at the cost of lowered accuracy compared to the
standard neural transducer model. We propose several methods to improve the
performance of the FNT model. They are: adding CTC criterion during training,
adding KL divergence loss during adaptation, using a pre-trained language model
to seed the vocabulary predictor, and an efficient adaptation approach by
interpolating the vocabulary predictor with the n-gram language model. A
combination of these approaches results in a relative word-error-rate reduction
of 9.48\% from the standard FNT model. Furthermore, n-gram interpolation with
the vocabulary predictor improves the adaptation speed hugely with satisfactory
adaptation performance.


  We introduce CityJSON Text Sequences (CityJSONSeq in short), a format based
on JSON Text Sequences and CityJSON. CityJSONSeq was added to the CityJSON
version 2.0 standard to allow us to stream very large 3D city models. The main
idea is to decompose a CityJSON dataset into its individual city objects (each
building, each tree, etc.) and create several independent JSON objects of a
newly defined type: 'CityJSONFeature'. We elaborate on the engineering
decisions that were taken to develop CityJSONSeq, we present the open-source
software we have developed to convert to and from CityJSONSeq, and we discuss
different aspects of the new format, eg filesize, usability, memory footprint,
etc. For several use-cases, we consider CityJSONSeq to be a better format than
CityJSON because: (1) once serialised it is about 10% more compact; (2) it
takes an order of magnitude less time to process; and (3) it uses significantly
less memory.


  Studies of the $\Delta$ baryon resonance and the $K_0^\ast(700)$ and
$a_0(980)$ meson resonances using $N_f=2+1$ lattice QCD for pion masses near
200 MeV are presented. The $s$-wave scattering lengths for both the $I=1/2$ $N
\pi$ and $I=3/2$ $N \pi$ channels and properties of the $\Delta$ resonance are
identified from the finite-volume energy levels of the lattice simulation. The
importance of a three-quark $\Delta$-operator in the $N\pi$ system and
tetraquark operators in the mesonic systems is investigated.


  Multi-objective combinatorial optimization problems (MOCOPs), one type of
complex optimization problems, widely exist in various real applications.
Although meta-heuristics have been successfully applied to address MOCOPs, the
calculation time is often much longer. Recently, a number of deep reinforcement
learning (DRL) methods have been proposed to generate approximate optimal
solutions to the combinatorial optimization problems. However, the existing
studies on DRL have seldom focused on MOCOPs. This study proposes a
single-model deep reinforcement learning framework, called multi-objective
Pointer Network (MOPN), where the input structure of PN is effectively improved
so that the single PN is capable of solving MOCOPs. In addition, two training
strategies, based on representative model and transfer learning, respectively,
are proposed to further enhance the performance of MOPN in different
application scenarios. Moreover, compared to classical meta-heuristics, MOPN
only consumes much less time on forward propagation to obtain the Pareto front.
Meanwhile, MOPN is insensitive to problem scale, meaning that a trained MOPN is
able to address MOCOPs with different scales. To verify the performance of
MOPN, extensive experiments are conducted on three multi-objective traveling
salesman problems, in comparison with one state-of-the-art model DRL-MOA and
three classical multi-objective meta-heuristics. Experimental results
demonstrate that the proposed model outperforms all the comparative methods
with only 20\% to 40\% training time of DRL-MOA.


  We give a nontechnical introduction to the problem of non-uniqueness of star
products and describe a covariant resolution of this problem. Some implications
(e.g., for noncommutative gravity) and further prospects are discussed.


  We study the complete conformal geometry of shear-free spacetimes with
spherical symmetry and do not specify the form of the matter content. The
general conformal Killing symmetry is solved and we can explicitly exhibit the
vector. The existence of a conformal symmetry places restrictions on the model.
The conditions on the gravitational potentials are expressed as a system of
integrability conditions. Timelike sectors and inheriting conformal symmetry
vectors, which map fluid flow lines conformally onto fluid flow lines, are
generated and the integrability conditions are shown to be satisfied. As an
example, a spacetime, which is expanding and accelerating, is identified which
contains a spherically symmetric conformal symmetry.


  We present a number models describing the sequential deposition of a mixture
of particles whose size distribution is determined by the power-law $p(x) \sim
\alpha x^{\alpha-1}$, $x\leq l$ . We explicitly obtain the scaling function in
the case of random sequential adsorption (RSA) and show that the pattern
created in the long time limit becomes scale invariant. This pattern can be
described by an unique exponent, the fractal dimension. In addition, we
introduce an external tuning parameter beta to describe the correlated
sequential deposition of a mixture of particles where the degree of correlation
is determined by beta, while beta=0 corresponds to random sequential deposition
of mixture. We show that the fractal dimension of the resulting pattern
increases as beta increases and reaches a constant non-zero value in the limit
$\beta \to \infty$ when the pattern becomes perfectly ordered or non-random
fractals.


  Effective WIMP models are minimal extensions of the standard model that
explain the relic density of dark matter by the ``WIMP miracle.'' In this paper
we consider the phenomenology of effective WIMPs with trilinear couplings to
leptons and a new ``lepton partner'' particle. The observed relic abundance
fixes the strength of the cubic coupling, so the parameters of the models are
defined by the masses of the WIMP and lepton partner particles. This gives a
simple parameter space where collider and direct detection experiments can be
compared under well-defined physical minimality assumptions. The most sensitive
collider probe is the search for leptons + MET, while the most sensitive direct
detection channel is scattering from nuclei arising from loop diagrams.
Collider and direct detection searches are highly complementary: colliders give
the only meaningful constraint when dark matter is its own antiparticle, while
direct detection is generally more sensitive if the dark matter is not its own
antiparticle.


  The main subject of this paper is the analysis of sequences of exponentially
bounded integrated semigroups which are related to Cauchy problems
\begin{equation}\label{jed} \frac{\partial}{\partial
t}u(t,x)-a(D)u(t,x)=f(t,x), \quad u(0,x)=u_0(x), \quad t\geq 0, \ x\in \mathbb
R^d, \end{equation} with a distributional initial data $u_0$ and a
distributional right hand side $f$ through a sequence of equations with
regularized $u_0$ and $f$ and a sequence of (pseudo) differential operators
$a_n(D)$ instead of $a(D)$. Comparison of sequences of infinitesimal generators
and the determination of corresponding sequences of integrated semigroups are
the main subject of the paper. For this purpose, we introduce association, the
relation of equivalence for infinitesimal generators on one side and the
corresponding relations of equivalence of integrated semigroups on another
side. The order of involved assumptions on generators essentially characterize
the mutual dependence of sequences of infinitesimal generators and the
corresponding sequences of integrated semigroups.


  Quantum effects can stabilize wormhole solutions in general relativity,
allowing information and matter to be transported between two connected
spacetimes. Here we study the revival dynamics of signals sent between two
weakly coupled quantum chaotic systems, represented as identical
Sachdev-Ye-Kitaev models, that realize holographically a traversable wormhole
in anti-de Sitter spacetime AdS$_2$ for large number $N$ of particles. In this
limit we find clear signatures of wormhole behavior: an excitation created in
one system is quickly scrambled under its unitary dynamics, and is reassembled
in the other system after a characteristic time consistent with holography
predictions. This leads to revival oscillations that at low but finite
temperature decay as a power-law in time. For small $N$ we also observe
revivals and show that they arise from a different, non-gravitational
mechanism.


  The well-known Koml\'os conjecture states that given $n$ vectors in
$\mathbb{R}^d$ with Euclidean norm at most one, there always exists a $\pm 1$
coloring such that the $\ell_{\infty}$ norm of the signed-sum vector is a
constant independent of $n$ and $d$. We prove this conjecture in a smoothed
analysis setting where the vectors are perturbed by adding a small Gaussian
noise and when the number of vectors $n =\omega(d\log d)$. The dependence of
$n$ on $d$ is the best possible even in a completely random setting.
  Our proof relies on a weighted second moment method, where instead of
considering uniformly randomly colorings we apply the second moment method on
an implicit distribution on colorings obtained by applying the Gram-Schmidt
walk algorithm to a suitable set of vectors. The main technical idea is to use
various properties of these colorings, including subgaussianity, to control the
second moment.


  I want to prove that all classical techniques of interpolation and
approximation as Lagrange, Taylor, Hermite interpolations Beziers interpolants,
Quasi interpolants, Box splines and others (radial splines, simplicial splines)
are derived from a \textbf{unique} simple hilbertian scheme. For sake of
simplicity, we shall consider only elementary examples which could be easily
generalized.


  We present further data in search of a parity-flavor-breaking phase in
simulations of dynamical QCD with two flavors of light Wilson fermions in the
strong coupling region. This is done on lattice sizes of 4^4 an up to 10^4 for
a variety of values of beta and kappa as well as the coefficient, h, of an
explicit breaking term included in the action. We confirm the existence of a
region in the beta-kappa plane where such a phase exists at beta=6/g^2 smaller
than 5.0 and above kappa_c.


  We present discovery of a radio nebula associated with the ultraluminous
X-ray source (ULX) IC 342 X-1 using the Very Large Array (VLA). Taking the
surrounding nebula as a calorimeter, one can constrain the intrinsic power of
the ULX source. We compare the obtained power that is needed to supply the
radio nebula with the W50 nebula powered by the microquasar SS433 and with
other ULXs. We find that the power required is at least two orders of magnitude
greater than that needed to power radio emission from the W50 nebula associated
with the microquasar SS433. In addition, we report the detection of a compact
radio core at the location of the X-ray source.


  We consider transverse momentum distributions in B-decays. The O(alpha_S)
coefficients for soft and collinear logarithms are computed to next-to-leading
accuracy. Resummation of large logarithmic contributions is performed in impact
parameter space within the general formalism for transverese momentum
distributions. It is shown that the shape-function approach as used for the
threshold distribution case cannot be extended to the transverse momentum one.


  Computing cost of equity for private corporations and performing comparable
company analysis (comps) for both public and private corporations is an
integral but tedious and time-consuming task, with important applications
spanning the finance world, from valuations to internal planning. Performing
comps traditionally often times include high ambiguity and subjectivity,
leading to unreliability and inconsistency. In this paper, I will present a
systematic and faster approach to compute cost of equity for private
corporations and perform comps for both public and private corporations using
spectral and agglomerative clustering. This leads to a reduction in the time
required to perform comps by orders of magnitude and entire process being more
consistent and reliable.


  We point out the peculiar form of the gravitational wave signal expected from
a gas of particles carry spin 3/2 produced during preheating. Given the very
few ways that gravitinos can manifest themselves in an experimentally
observable way, we stress the importance of improving the sensitivity of
ultra-high frequency detectors in the future. This review is based on work that
appeared in [arXiv:1811.11774 [hep-ph]].


  We consider the problem of representing, in a compressed format, a bit-vector
$S$ of $m$ bits with $n$ 1s, supporting the following operations, where $b \in
\{0, 1 \}$: $rank_b(S,i)$ returns the number of occurrences of bit $b$ in the
prefix $S[1..i]$; $select_b(S,i)$ returns the position of the $i$th occurrence
of bit $b$ in $S$. Such a data structure is called \emph{fully indexable
dictionary (FID)} [Raman et al.,2007], and is at least as powerful as
predecessor data structures. Our focus is on space-efficient FIDs on the
\textsc{ram} model with word size $\Theta(\lg m)$ and constant time for all
operations, so that the time cost is independent of the input size. Given the
bitstring $S$ to be encoded, having length $m$ and containing $n$ ones, the
minimal amount of information that needs to be stored is $B(n,m) = \lceil \log
{{m}\choose{n}} \rceil$. The state of the art in building a FID for $S$ is
given in [Patrascu,2008] using $B(m,n)+O(m / ((\log m/ t) ^t)) + O(m^{3/4}) $
bits, to support the operations in $O(t)$ time. Here, we propose a parametric
data structure exhibiting a time/space trade-off such that, for any real
constants $0 < \delta \leq 1/2$, $0 < \eps \leq 1$, and integer $s > 0$, it
uses \[ B(n,m) + O(n^{1+\delta} + n (\frac{m}{n^s})^\eps) \] bits and performs
all the operations in time $O(s\delta^{-1} + \eps^{-1})$. The improvement is
twofold: our redundancy can be lowered parametrically and, fixing $s = O(1)$,
we get a constant-time FID whose space is $B(n,m) + O(m^\eps/\poly{n})$ bits,
for sufficiently large $m$. This is a significant improvement compared to the
previous bounds for the general case.


  Given a linear dynamical system, we consider the problem of selecting (at
design-time) an optimal set of sensors (subject to certain budget constraints)
to minimize the trace of the steady state error covariance matrix of the Kalman
filter. Previous work has shown that this problem is NP-hard for certain
classes of systems and sensor costs; in this paper, we show that the problem
remains NP-hard even for the special case where the system is stable and all
sensor costs are identical. Furthermore, we show the stronger result that there
is no constant-factor (polynomial-time) approximation algorithm for this
problem. This contrasts with other classes of sensor selection problems studied
in the literature, which typically pursue constant-factor approximations by
leveraging greedy algorithms and submodularity of the cost function. Here, we
provide a specific example showing that greedy algorithms can perform
arbitrarily poorly for the problem of design-time sensor selection for Kalman
filtering.


  We describe a mathematical formalism and numerical algorithms for identifying
and tracking slowly mixing objects in nonautonomous dynamical systems. In the
autonomous setting, such objects are variously known as almost-invariant sets,
metastable sets, persistent patterns, or strange eigenmodes, and have proved to
be important in a variety of applications. In this current work, we explain how
to extend existing autonomous approaches to the nonautonomous setting. We call
the new time-dependent slowly mixing objects coherent sets as they represent
regions of phase space that disperse very slowly and remain coherent. The new
methods are illustrated via detailed examples in both discrete and continuous
time.


  The process $e^{+}e^{-}\to\Sigma^{+}\bar{\Sigma}^{-}$ is studied from
threshold up to 3.04 GeV/$c^2$ via the initial-state radiation technique using
data with an integrated luminosity of 12.0 fb$^{-1}$, collected at
center-of-mass energies between 3.773 and 4.258 GeV with the BESIII detector at
the BEPCII collider. The pair production cross sections and the effective form
factors of $\Sigma$ are measured in eleven $\Sigma^{+}\bar{\Sigma}^{-}$
invariant mass intervals from threshold to 3.04 GeV/$c^2$. The results are
consistent with the previous results from Belle and BESIII. Furthermore, the
branching fractions of the decays $J/\psi\to\Sigma^{+}\bar{\Sigma}^{-}$ and
$\psi(3686)\to\Sigma^{+}\bar{\Sigma}^{-}$ are determined and the obtained
results are consistent with the previous results of BESIII.


  Let $X$ be a smooth projective variety over the complex numbers, and $\Delta
\subseteq X$ a reduced divisor with normal crossings. We present a slightly
simplified proof for the following theorem of Campana and P\u{a}un: If some
tensor power of the bundle $\Omega_X^1(\log \Delta)$ contains a subsheaf with
big determinant, then $(X, \Delta)$ is of log general type. This result is a
key step in the recent proof of Viehweg's hyperbolicity conjecture.


  The present paper reports on the assessment of different turbulence-chemistry
interaction closures for modeling turbulent combustion in the Moderate and
Intense Low oxygen Dilution combustion regime. 2D RANS simulations have been
carried out to model flames issuing from two burners DJHC burner and Adelaide
JHC burner which imitate the MILD combustion. In order to model these flames,
two different approaches of turbulence-chemistry interaction models are
considered; while in the PDF based modeling, two different variants are invoked
to understand the applicability of the PDF based models in the MILD regime: one
is based on presumed shape PDF approach and the other one is transported PDF
approach. A comprehensive study has been carried out by comparing the results
obtained from these different models. For the DJHC burner, the computations are
carried out for a jet speed corresponding to Reynolds numbers of Re=4100,
whereas the Adelaide JHC burner computations are performed for a jet speed
corresponding to Reynolds number of Re=10000. The effects of molecular
diffusion on the flame characteristics are also studied by using different
micro-mixing models. In the case of DJHC burner, it has been observed that the
mean axial velocity and the turbulent kinetic energy profiles are in good
agreement with the measurements. However, the temperature profiles are
over-predicted in the downstream region by both EDC and the PDF based models.
In the context of Adelaide JHC burner, the profiles of the temperature and the
mass fraction of major species (CH4, H2, O2, H2O, CO2) obtained using LPDF
approach are in better agreement with the measurements compared to those
obtained using EDC model; although, both the solution approaches fail to
capture CO and OH radical profiles.


  The theory of quantum algorithms promises unprecedented benefits of
harnessing the laws of quantum mechanics for solving certain computational
problems. A persistent obstacle to using such algorithms for solving a wide
range of real-world problems is the cost of loading classical data to a quantum
state. Several quantum circuit-based methods have been proposed for encoding
classical data as probability amplitudes of a quantum state. However, they
require either quantum circuit depth or width to grow linearly with the data
size, even though the other dimension of the quantum circuit grows
logarithmically. In this paper, we present a configurable bidirectional
procedure that addresses this problem by tailoring the resource trade-off
between quantum circuit width and depth. In particular, we show a configuration
that encodes an $N$-dimensional state by a quantum circuit with $O(\sqrt{N})$
width and depth and entangled information in ancillary qubits. We show a
proof-of-principle on five quantum computers and compare the results.


  We show that models with an abelian family symmetry which accounts for the
observed hierarchies of masses and mixings in the quark sector may also
accomodate quasi-degeneracies in the neutrino mass spectrum. Such approximate
degeneracies are, in this context, associated with large mixing angles. The
parameters of this class of models are constrained. We discuss their
phenomenological implications for present and foreseen neutrino experiments.


  Anomalous Hall effect (AHE) is the key transport signature unlocking
topological properties of magnetic materials. While AHE is usually proportional
to the magnetization, the nonlinearity suggests the existence of complex
magnetic and electron orders. Nonlinear AHE includes the topological Hall
effect (THE) that has been widely used to identify the presence of spin
chirality in real space. But it can in principle be induced by band structure
evolution via Berry curvatures in the reciprocal space. This effect has been
largely overlooked due to the intertwined mechanisms in both real and
reciprocal spaces. Here, we observed a giant nonlinear AHE with the resistivity
up to 383.5 uohm cm, contributing unprecedentedly 97% of the total Hall
response in EuCd2As2. Moreover, it can be further enhanced by tilting the
magnetic field 30{\deg} away from [001] direction, reaching a large anomalous
Hall angle up to 21%. Although it shows exactly the same double-peak feature as
THE, our scaling analysis and first-principles calculations reveal that the
Berry phase is extremely sensitive to the spin canting, and nonlinear AHE is a
consequence of band structure evolution under the external magnetic fields.
When the spins gradually tilt from the in-plane antiferromagnetic ground state
to out-of-plane direction, band crossing and band inversion occur, introducing
a bandgap at {\Gamma} point at a canting angle of 45{\deg}. That contributes to
the enhancement of Berry curvature and consequently a large intrinsic Hall
conductivity. Our results unequivocally reveal the sensitive dependence of band
structures on spin tilting process under external magnetic fields and its
pronounced influence on the transport properties, which also need to be taken
into consideration in other magnetic materials.


  The bigerbes introduced here give a refinement of the notion of 2-gerbes,
representing degree four integral cohomology classes of a space. Defined in
terms of bisimplicial line bundles, bigerbes have a symmetry with respect to
which they form 'bundle 2-gerbes' in two ways; this structure replaces higher
associativity conditions. We provide natural examples, including a
Brylinski-McLaughlin bigerbe associated to a principal G-bundle for a simply
connected simple Lie group. This represents the first Pontryagin class of the
bundle, and is the obstruction to the lifting problem on the associated
principal bundle over the loop space to the structure group consisting of a
central extension of the loop group; in particular, trivializations of this
bigerbe for a spin manifold are in bijection with string structures on the
original manifold. Other natural examples represent 'decomposable' 4-classes
arising as cup products, a universal bigerbe on K(Z,4) involving its based
double loop space, and the representation of any 4-class on a space by a
bigerbe involving its free double loop space. The generalization to
'multigerbes' of arbitrary degree is also described.


  A newly developed method for systematically improving the convergence of path
integrals for transition amplitudes, introduced in Phys. Rev. Lett. 94 (2005)
180403, Phys. Rev. B 72 (2005) 064302, Phys. Lett. A 344 (2005) 84, and
expectation values, introduced in Phys. Lett. A 360 (2006) 217, is here applied
to the efficient calculation of energy spectra. We show how the derived
hierarchies of effective actions lead to substantial speedup of the standard
path integral Monte Carlo evaluation of energy levels. The general results and
the ensuing increase in efficiency of several orders of magnitude are shown
using explicit Monte Carlo simulations of several distinct models.


  We study the rotational evolution of a protoneutron star with hyperons and
nucleons or solely nucleons in its core due to the escape of the trapped
neutrinos. It is found that at the early stage of its evolution, the stellar
crust contracts significantly, consequently the star spins up. At the late
stage, a the protoneutron star with hyperons, it keeps shrinking and spinning
up till all the trapped neutrinos escape. Consequently, the distribution of the
stellar initial spin periods is skewed toward shorter periods. For a
protoneutron star with only nucleons, the expansion of its core dominates, and
the stellar rotation slows down. After the neutrinos escape, the range of the
spin periods is narrower than the initial one, but the distribution is still
nearly uniform. If the hyperonic star is metastable, its rotational frequency
accelerates distinguishedly before it collapses to a black hole.


  Spectroscopic studies of planets outside of our own solar system provide some
of the most crucial information about their formation, evolution, and
atmospheric properties. In ground-based spectroscopy, the process of extracting
the planet's signal from the stellar and telluric signal has proven to be the
most difficult barrier to accurate atmospheric information. However, with novel
normalization and smoothing methods, this barrier can be minimized and the
detection significance dramatically increased over existing methods. In this
paper, we take two examples of CRIRES emission spectroscopy taken of HD 209458
b and HD 179949 b and apply SPORK (SPectral cOntinuum Refinement for telluriKs)
and iterative smoothing to boost the detection significance from 5.78 to 9.71
sigma and from 4.19 sigma to 5.90 sigma, respectively. These methods, which
largely address systematic quirks introduced by imperfect detectors or
reduction pipelines, can be employed in a wide variety of scenarios, from
archival data sets to simulations of future spectrographs.


  Pulsars steadily dissipate their rotational energy via relativistic winds.
Confinement of these outflows generates luminous pulsar wind nebulae, seen
across the electromagnetic spectrum in synchrotron and inverse Compton
emission, and in optical emission lines when they shock the surrounding medium.
These sources act as important probes of relativistic shocks, particle
acceleration and of interstellar gas. We review the many recent advances in the
study of pulsar wind nebulae, with particular focus on the evolutionary stages
through which these objects progress as they expand into their surroundings,
and on morphological structures within these nebulae which directly trace the
physical processes of particle acceleration and outflow. We conclude by
considering some exciting new probes of pulsar wind nebulae, including the
study of TeV gamma-ray emission from these sources, and observations of pulsar
winds in close binary systems.


  In this paper, we consider an inference problem for the first order
autoregressive process driven by a long memory stationary Gaussian process.
Suppose that the covariance function of the noise can be expressed as
$\abs{k}^{2H-2}$ times a function slowly varying at infinity. The fractional
Gaussian noise and the fractional ARIMA model and some others Gaussian noise
are special examples that satisfy this assumption. We propose a second moment
estimator and prove the strong consistency and give the asymptotic
distribution. Moreover, when the limit distribution is Gaussian, we give the
upper Berry-Ess\'een bound by means of Fourth moment theorem.


  Interdiffusion between the yttria stabilized zirconia (YSZ) electrolyte and
the gadolinia doped ceria (CGO) barrier layer is one of the major causes to the
degradation of solid oxide fuel cells (SOFCs). We present in this work
experimental investigations on CGO-YSZ bi-layer electrolyte sintered at 1250 C
or 1315 C and element transport as a function of sintering temperature and
dwelling time. In order to quantitatively simulate the experimental
observations, the CALPHAD-type thermodynamic assessment of the CGO-YSZ system
is performed by simplifying the system to a CeO2-ZrO2 quasi-binary system, and
the kinetic descriptions (atomic mobilities) are constructed based on critical
review of literature data. The CGO-YSZ interdiffusion is then modeled with the
DICTRA software and the simulation results are compared with the experimental
data under different sintering or long-term operating conditions. The
corresponding ohmic resistance of the bi-layer electrolyte is predicted based
on the simulated concentration profile. The results implies that the
interdiffusion across the CGO-YSZ interface happens mainly during sintering at
high temperature, while during long-term operation at relatively lower
temperature the impact of interdiffusion on cell degradation is negligible.


  Let $G$ be a finite group. We study the generalized character defined by
$\Xi(g)=|G|o(g)$, for $g\in G$, which is closely related to a function that has
been very studied recently from a group theoretical point of view.


  Equations describing the resistive state of a layered superconductor with
anisotropic pairing are derived. The similarity with a stack of Josephson
junctions is found at small voltages only, when current density in the
direction perpendicular to the layers can be interpreted as a sum of the
Josephson superconducting, the Ohmic dissipative and the interference currents.
In the spatially uniform state differential conductivity at higher voltages
becomes negative. Nonuniformity of the current distribution generates the
branch imbalance and violates the Josephson relation between frequency and
voltage.


  This paper presents an knowledge graph to assist in reasoning over signals
for intelligence purposes. We highlight limitations of existing knowledge
graphs and reasoning systems for this purpose, using inference of an attack
using combined data from microphones, cameras and social media as an example.
Rather than acting directly on the received signal, our approach considers
attacker behaviour, signal emission, receiver characteristics, and how signals
are summarised to support inferring the underlying cause of the signal.


  We report the results of various measurements, namely magnetization, complex
dielectric permittivity and electric polarization (P) on Dy2BaNiO5 as a
function of temperature (T) and magnetic-field (H), apart from heat-capacity
(C), with the primary motivation of exploring the existence of magnetoelectric
(ME) coupling among Haldane spin-chain systems. The M(T) and C(T) data
establish long range magnetic ordering at 58K. The most noteworthy observations
are: (i) Distinct anomalies are observed in dielectric constant ({\epsilon}')
vs T and loss (tan{\delta}) vs T at different temperatures, i.e. 12.5, 30, 50
and 58K; at low temperatures, three magnetic-field-induced transitions are
observed in {\epsilon}' vs H at 6, 40 and 60 kOe. These transition temperatures
and critical magnetic fields track those obtained from magnetization data. This
establishes the existence of strong magnetoelectric coupling in this compound.
(ii) Correspondingly, electric polarization could be observed as a function of
T and H in the magnetically ordered state, thereby indicating magnetism-induced
ferroelectricity in this compound; this result suggests that this compound is a
possible new multiferroic material among spin = 1 (nickel containing) compounds
with successive magnetic transitions and strong magnetoelectric coupling.


  Elaborating on a previous letter, we use a new approach to compute energy
levels of a non-relativistic bound-state of two constituents, with masses m and
M, by systematic expansions - one in powers of m/M and another in powers of
(1-m/M). Technical aspects of the calculations are described in detail.
Theoretical predictions are given for O(alpha(Z*alpha)^5) radiative recoil and
O((Z*alpha)^6) pure recoil corrections to the average energy shift and
hyperfine splitting relevant for hydrogen, muonic hydrogen, and muonium.


  With smart devices, particular smartphones, becoming our everyday companions,
the ubiquitous mobile Internet and computing applications pervade people daily
lives. With the surge demand on high-quality mobile services at anywhere, how
to address the ubiquitous user demand and accommodate the explosive growth of
mobile traffics is the key issue of the next generation mobile networks. The
Fog computing is a promising solution towards this goal. Fog computing extends
cloud computing by providing virtualized resources and engaged location-based
services to the edge of the mobile networks so as to better serve mobile
traffics. Therefore, Fog computing is a lubricant of the combination of cloud
computing and mobile applications. In this article, we outline the main
features of Fog computing and describe its concept, architecture and design
goals. Lastly, we discuss some of the future research issues from the
networking perspective.


  Locating the boundaries of consecutive blocks of quantum information is a
fundamental building block for advanced quantum computation and quantum
communication systems. We develop a coding theoretic method for properly
locating boundaries of quantum information without relying on external
synchronization when block synchronization is lost. The method also protects
qubits from decoherence in a manner similar to conventional quantum
error-correcting codes, seamlessly achieving synchronization recovery and error
correction. A family of quantum codes that are simultaneously synchronizable
and error-correcting is given through this approach.


  We revisit the identification of an $\varepsilon$-optimal policy in
average-reward Markov Decision Processes (MDP). In such MDPs, two measures of
complexity have appeared in the literature: the diameter, $D$, and the optimal
bias span, $H$, which satisfy $H\leq D$. Prior work have studied the complexity
of $\varepsilon$-optimal policy identification only when a generative model is
available. In this case, it is known that there exists an MDP with $D \simeq H$
for which the sample complexity to output an $\varepsilon$-optimal policy is
$\Omega(SAD/\varepsilon^2)$ where $S$ and $A$ are the sizes of the state and
action spaces. Recently, an algorithm with a sample complexity of order
$SAH/\varepsilon^2$ has been proposed, but it requires the knowledge of $H$. We
first show that the sample complexity required to estimate $H$ is not bounded
by any function of $S,A$ and $H$, ruling out the possibility to easily make the
previous algorithm agnostic to $H$. By relying instead on a diameter estimation
procedure, we propose the first algorithm for $(\varepsilon,\delta)$-PAC policy
identification that does not need any form of prior knowledge on the MDP. Its
sample complexity scales in $SAD/\varepsilon^2$ in the regime of small
$\varepsilon$, which is near-optimal. In the online setting, our first
contribution is a lower bound which implies that a sample complexity polynomial
in $H$ cannot be achieved in this setting. Then, we propose an online algorithm
with a sample complexity in $SAD^2/\varepsilon^2$, as well as a novel approach
based on a data-dependent stopping rule that we believe is promising to further
reduce this bound.


  An important development in the field of the fractional quantum Hall effect
has been the proposal that the 5/2 state observed in the Landau level with
orbital index $n = 1$ of two dimensional electrons in a GaAs quantum well
originates from a chiral $p$-wave paired state of composite fermions which are
topological bound states of electrons and quantized vortices. This state is
theoretically described by a "Pfaffian" wave function or its hole partner
called the anti-Pfaffian, whose excitations are neither fermions nor bosons but
Majorana quasiparticles obeying non-Abelian braid statistics. This has inspired
ideas on fault-tolerant topological quantum computation and has also instigated
a search for other states with exotic quasiparticles. Here we report
experiments on monolayer graphene that show clear evidence for unexpected
even-denominator fractional quantum Hall physics in the $n=3$ Landau level. We
numerically investigate the known candidate states for the even-denominator
fractional quantum Hall effect, including the Pfaffian, the particle-hole
symmetric Pfaffian, and the 221-parton states, and conclude that, among these,
the 221-parton appears a potentially suitable candidate to describe the
experimentally observed state. Like the Pfaffian, this state is believed to
harbour quasi-particles with non-Abelian braid statistics


  The recent unprecedented popularity of food recommender applications has
raised several issues related to the ethical, societal and legal implications
of relying on these applications. In this paper, in order to assess the
relevant ethical issues, we rely on the emerging principles across the
AI\&Ethics community and define them tailored context specifically. Considering
the popular Food Recommender Systems (henceforth F-RS) in the European market
cannot be regarded as personalised F-RS, we show how merely this lack of
feature shifts the relevance of the focal ethical concerns. We identify the
major challenges and propose a scheme for how explicit ethical agendas should
be explained. We also argue how a multi-stakeholder approach is indispensable
to ensure producing long-term benefits for all stakeholders. After proposing
eight ethical desiderata points for F-RS, we present a case-study and assess it
based on our proposed desiderata points.


  The damped quantum search proposed in [A. Mizel, Phys. Rev. Lett., 102 150501
(2009)] was analyzed by calculating the highest possible probability of finding
the target state in each iteration. A new damping parameter that depends on the
number of iterations was obtained, this was compared to the critical damping
parameter for different values of target to database size ratio. The result
shows that the range of the new damping parameter as a function of the target
to database size ratio increases as the number of iterations is increased.
Furthermore, application of the new damping parameter per iteration on the
damped quantum search scheme shows a significant improvement on some target to
database size ratio (i.e. greater than or equal to 50% maximum percentage
difference) over the critically damped quantum search.


  Various large-scale processes induced by triaxial potentials and able to
alter stellar and gaseous abundance profiles are reviewed. In particular,
strong bars can quickly and efficiently blend stars through chaotic mixing, and
entail intricate inflows/outflows of gas. Bar-generated abundance features
include flattenings, breaks, and plateaus in radial gradients as well as box-
or peanut-like morphologies.


  Current numerical studies suggest that the first galaxies formed a few stars
at a time and were enriched only gradually by the first heavy elements.
However, the large box sizes in these models cannot resolve primordial
supernova explosions or the mixing of their metals with ambient gas, which
could result in intervening, prompt generations of low-mass stars. We present
multiscale 1D models of Population III supernovae in cosmological halos that
evolve the blast from its earliest stages as a free expansion. We find that if
the star ionizes the halo, the ejecta strongly interacts with the dense shell
swept up by the H II region, potentially cooling and fragmenting it into clumps
that are unstable to gravitational collapse. If the star fails to ionize the
halo, the explosion propagates metals out to 20 - 40 pc and then collapses,
enriching tens of thousands of solar masses of primordial gas, in contrast to
previous models that suggest such explosions 'fizzle'. Rapid formation of
low-mass stars trapped in the gravitational potential well of the halo appears
to be inevitable in such circumstances. Consequently, it is possible that far
more stars were swept up into the first galaxies, at earlier times and with
distinct chemical signatures, than in present models. Upcoming measurements by
JWST and ALMA may discriminate between these two paradigms.


  Detailed observations of the nearest star-forming regions in the Milky Way
(MW) provide the ultimate benchmark for studying star formation. The extent to
which the results of these Galaxy-based studies can be extrapolated to
extragalactic systems depends on the overlap of the environmental conditions
probed. In this paper, we compare the properties of clouds and star-forming
regions in the MW with those in nearby galaxies and in the high-redshift
Universe. We find that in terms of their baryonic composition, kinematics, and
densities, the clouds in the solar neighbourhood are similar to those in nearby
galaxies. The clouds and regions in the Central Molecular Zone (CMZ, i.e. the
inner 250 pc) of the MW are indistinguishable from high-redshift clouds and
galaxies. The presently low star formation rate in the CMZ therefore implies
that either (1) its gas represents the initial conditions for high-redshift
starbursts or (2) some yet unidentified process consistently suppresses star
formation over >100 Myr time-scales. We conclude that the MW contains large
reservoirs of gas with properties directly comparable to most of the known
range of star formation environments and is therefore an excellent template for
studying star formation across cosmological time-scales.


  In this article, we consider a gauge-theoretic equation on compact symplectic
6-manifolds, which forms an elliptic system after gauge fixing. This can be
thought of as a higher-dimensional analogue of the Seiberg-Witten equation. By
using the virtual neighbourhood method by Ruan, we define an integer-valued
invariant, a 6-dimensional Seiberg-Witten invariant, from the moduli space of
solutions to the equations, assuming that the moduli space is compact; and it
has no reducible solutions. We prove that the moduli spaces are compact if the
underlying manifold is a compact Kahler threefold. We then compute the integers
in some cases.


  Semiclassical description, based on electrostatics and Thomas-Fermi model is
applied here to calculate dimensions of the electronic shell of a fullerene
molecule and a nanotube. The internal radius of the electronic shell of a
fullerene molecule, calculated within the framework of the model is 0.2808 nm.
The external radius is 0.4182 nm. The experimental values are 0.279 nm and
0.429 nm correspondingly. This shows that semiclassical approach provides
rather good description of the dimensions of the electronic shell in a
fullerene molecule. Two types of dipole oscillations in a fullerene molecule
are considered and their frequencies are calculated. Similar calculations are
performed for a nanotube also. For a nanotube with a radius of the cylinder of
the ions, Rn = 0.7 nm, the internal radius of the electronic shell, calculated
within the framework of the model is 0.577 nm. The external radius is 0.816 nm.
Three types of dipole oscillations in nanotube are considered and their
frequencies are calculated.


  Representation learning and feature disentanglement have recently attracted
much research interests in facial expression recognition. The ubiquitous
ambiguity of emotion labels is detrimental to those methods based on
conventional supervised representation learning. Meanwhile, directly learning
the mapping from a facial expression image to an emotion label lacks explicit
supervision signals of facial details. In this paper, we propose a novel FER
model, called Poker Face Vision Transformer or PF-ViT, to separate and
recognize the disturbance-agnostic emotion from a static facial image via
generating its corresponding poker face without the need for paired images.
Here, we regard an expressive face as the comprehensive result of a set of
facial muscle movements on one's poker face (i.e., emotionless face), inspired
by Facial Action Coding System. The proposed PF-ViT leverages vanilla Vision
Transformers, and are firstly pre-trained as Masked Autoencoders on a large
facial expression dataset without emotion labels, obtaining excellent
representations. It mainly consists of five components: 1) an encoder mapping
the facial expression to a complete representation, 2) a separator decomposing
the representation into an emotional component and an orthogonal residue, 3) a
generator that can reconstruct the expressive face and synthesize the poker
face, 4) a discriminator distinguishing the fake face produced by the
generator, trained adversarially with the encoder and generator, 5) a
classification head recognizing the emotion. Quantitative and qualitative
results demonstrate the effectiveness of our method, which trumps the
state-of-the-art methods on four popular FER testing sets.


  We exhibit an explicit formula for the spectral density of a (large) random
matrix which is a diagonal matrix whose spectral density converges, perturbated
by the addition of a symmetric matrix with Gaussian entries and a given (small)
limiting variance profile.


  In this work, we propose a class of numerical schemes for solving semilinear
Hamilton-Jacobi-Bellman-Isaacs (HJBI) boundary value problems which arise
naturally from exit time problems of diffusion processes with controlled drift.
We exploit policy iteration to reduce the semilinear problem into a sequence of
linear Dirichlet problems, which are subsequently approximated by a multilayer
feedforward neural network ansatz. We establish that the numerical solutions
converge globally in the $H^2$-norm, and further demonstrate that this
convergence is superlinear, by interpreting the algorithm as an inexact Newton
iteration for the HJBI equation. Moreover, we construct the optimal feedback
controls from the numerical value functions and deduce convergence. The
numerical schemes and convergence results are then extended to HJBI boundary
value problems corresponding to controlled diffusion processes with oblique
boundary reflection. Numerical experiments on the stochastic Zermelo navigation
problem are presented to illustrate the theoretical results and to demonstrate
the effectiveness of the method.


  We apply a many-body Wentzel-Kramers-Brillouin (WKB) approach to determine
the leading quantum corrections to the semiclassical dynamics of the Josephson
model, describing interacting bosons able to tunnel between two localized
states. The semiclassical dynamics is known to divide between regular
oscillations and self-trapped oscillations where the sign of the imbalance
remains fixed. In both cases, the WKB wave functions are matched to Airy
functions, yielding a modified Bohr-Sommerfeld quantization condition. At the
critical energy dividing normal and self-trapped oscillations, the WKB wave
functions should instead be matched to parabolic cylinder functions, leading to
a quantization formula that is not just the Bohr-Sommerfeld formula, and
recovering the known logarithmic quantum break times at this energy. This work
thus provides another illustration of the usefulness of the WKB approach in
certain many-body problems.


  The end of Moore's Law and Dennard Scaling has combined with advances in
agile hardware design to foster a golden age of domain-specific acceleration.
However, this new frontier of computing opportunities is not without pitfalls.
As computer architects approach unfamiliar domains, we have seen common themes
emerge in the challenges that can hinder progress in the development of useful
acceleration. In this work, we present the Magnificent Seven Challenges in
domain-specific accelerator design that can guide adventurous architects to
contribute meaningfully to novel application domains. Although these challenges
appear across domains ranging from ML to genomics, we examine them through the
lens of autonomous systems as a motivating example in this work. To that end,
we identify opportunities for the path forward in a successful domain-specific
accelerator design from these challenges.


Neutrino-nucleus reactions as applied to astrophysics are reviewed.


  Model uncertainty is pervasive in real world analysis situations and is an
often-neglected issue in applied statistics. However, standard approaches to
the research process do not address the inherent uncertainty in model building
and, thus, can lead to overconfident and misleading analysis interpretations.
One strategy to incorporate more flexible models is to base inferences on
predictive modeling. This approach provides an alternative to existing
explanatory models, as inference is focused on the posterior predictive
distribution of the response variable. Predictive modeling can advance
explanatory ambitions in the social sciences and in addition enrich the
understanding of social phenomena under investigation. Bayesian stacking is a
methodological approach rooted in Bayesian predictive modeling. In this paper,
we outline the method of Bayesian stacking but add to it the approach of
posterior predictive checking (PPC) as a means of assessing the predictive
quality of those elements of the stacking ensemble that are important to the
research question. Thus, we introduce a viable workflow for incorporating PPC
into predictive modeling using Bayesian stacking without presuming the
existence of a true model. We apply these tools to the PISA 2018 data to
investigate potential inequalities in reading competency with respect to gender
and socio-economic background. Our empirical example serves as rough guideline
for practitioners who want to implement the concepts of predictive modeling and
model uncertainty in their work to similar research questions.


  An algebra $A$ with identity $(a\circ b)\circ c-a\circ(b\circ c)=(a\circ
c)\circ b-a\circ(c\circ b),$ is called right-symmetric. Cohomology and
deformation theory for right-symmetric algebras are developed. Cohomologies of
$gl_n$ and half-Witt algebras $W_n^{rsym}, p=0,$ $W_n^{rsym}({\bf m}), p>0,$
are calculated. In particular, one right-symmetric central extension of
$W_1^{rsym}$ is constructed.


  Bimodality is a common feature of globular cluster (GC) color distributions
in galaxies. Although it is well known that the GC system of the Milky Way is
bimodal in metallicity, this has yet to be directly demonstrated for an
elliptical galaxy. We use Lick index measurements from the literature to derive
metallicities for 47 GCs in the giant Virgo elliptical galaxy NGC 4472. The
resulting distribution shows clear evidence for two metallicity subpopulations
of GCs.


  We introduce the beta function of a knot in euclidean three-space. This is a
meromorphic function of a complex variable which we prove admits a Bernstein
type functional equation. We determine the first residues.


  The two-pion contribution from low energies to the muon magnetic moment
anomaly, although small, has a large relative uncertainty since in this region
the experimental data on the cross sections are neither sufficient nor precise
enough. It is therefore of interest to see whether the precision can be
improved by means of additional theoretical information on the pion
electromagnetic form factor, which controls the leading order contribution. In
the present paper we address this problem by exploiting analyticity and
unitarity of the form factor in a parametrization-free approach that uses the
phase in the elastic region, known with high precision from the Fermi-Watson
theorem and Roy equations for $\pi\pi$ elastic scattering as input. The
formalism also includes experimental measurements on the modulus in the region
0.65-0.70 GeV, taken from the most recent $e^+e^-\to \pi^+\pi^-$ experiments,
and recent measurements of the form factor on the spacelike axis. By combining
the results obtained with inputs from CMD2, SND, BABAR and KLOE, we make the
predictions $a_\mu^{\pi\pi, \LO}\,[2 m_\pi,\, 0.30 \gev]=(0.553 \pm 0.004)
\times 10^{-10}$ and $a_\mu^{\pi\pi, \LO}\,[0.30 \gev,\, 0.63 \gev]=(133. 083
\pm 0.837)\times 10^{-10}$. These are consistent with the other recent
determinations, and have slightly smaller errors.


  Damped Lyman-$\alpha$ Absorber (DLA), or HI 21cm Absorber (H21A), is an
important probe to model-independently measure the acceleration of
spectroscopic velocity ($v_\mathrm{S}$) via the Sandage-Loeb (SL) effect.
Confined by the shortage of DLAs and Background Radio Sources (BRSs) with
adequate information, the detectable amount of DLAs is ambiguous in the bulk of
previous work. After differing the acceleration of scale factor ($\ddot{a}$)
from the first order time derivative of spectroscopic velocity
($\dot{v}_\mathrm{S}$), we make a statistical investigation of the amount of
potential DLAs in the most of this paper. Using Kernel Density Estimation (KDE)
to depict general redshift distributions of BRSs, observed DLAs and a DLA
detection rate with different limitations (1.4GHz flux, HI column density and
spin temperature), we provide fitted multi-Gaussian expressions of the three
components and their 1$\sigma$ regions by bootstrap, with a proportional
constant of H21As in detected DLAs, leading to the measurable number
predictions of H21As for FAST, ASKAP and SKA1-Mid in HI absorption blind
survey. In our most optimistic condition ($F_\mathrm{1.4GHz}$>10mJy,
$N_\mathrm{HI}>2\times10^{20}\mathrm{cm^{-2}}$ and $T_\mathrm{S}$>500K), the
FAST, AKSAP and SKA1-Mid would probe about 80, 500 and 600 H21As respectively.