  We present a detailed model describing the effects of wire corrugation on the
trapping potential experienced by a cloud of atoms above a current carrying
micro wire. We calculate the distortion of the current distribution due to
corrugation and then derive the corresponding roughness in the magnetic field
above the wire. Scaling laws are derived for the roughness as a function of
height above a ribbon shaped wire. We also present experimental data on micro
wire traps using cold atoms which complement some previously published
measurements and which demonstrate that wire corrugation can satisfactorily
explain our observations of atom cloud fragmentation above electroplated gold
wires. Finally, we present measurements of the corrugation of new wires
fabricated by electron beam lithography and evaporation of gold. These wires
appear to be substantially smoother than electroplated wires.


  We propose a means of omni-directional contact detection using accelerometers
instead of tactile sensors for object shape estimation using touch. Unlike
tactile sensors, our contact-based detection method tends to induce a degree of
uncertainty with false-positive contact data because the sensors may react not
only to actual contact but also to the unstable behavior of the robot.
Therefore, it is crucial to consider a robust shape estimation method capable
of handling such false-positive contact data. To realize this, we introduce the
concept of heteroscedasticity into the contact data and propose a robust shape
estimation algorithm based on Gaussian process implicit surfaces (GPIS). We
confirmed that our algorithm not only reduces shape estimation errors caused by
false-positive contact data but also distinguishes false-positive contact data
more clearly than the GPIS through simulations and actual experiments using a
quadcopter.


  This paper proposes a decision-making approach for the control of
distribution systems with distributed energy resources (DERs) equipped with
photovoltaic (PV) units and battery energy storage systems (BESS). The
objective is to minimize the total operational cost of the distribution system
while satisfying the system operating constraints. The method is based on the
discrete-time finite-horizon Markov Decision Process (MDP) framework. Different
aspects of the operation of the distribution system operation are considered,
such as the possibilities of curtailment of PV generation, managing battery
storage, reactive power injection, load shedding, and providing a flexibility
service for the transmission system. The model is tested for the IEEE 33-bus
system with two added DERs and the study cases involve various unexpected
events. The experimental results show that this method enables the attainment
of relatively low total cost values compared to the reference deterministic
approach. The benefits of applying this approach are particularly evident when
there is a significant difference between the predicted and actual PV power
generation.


  The Quantum Reverse Shannon Theorem states that any quantum channel can be
simulated by an unlimited amount of shared entanglement and an amount of
classical communication equal to the channel's entanglement assisted classical
capacity. In this paper, we provide a new proof of this theorem, which has
previously been proved by Bennett, Devetak, Harrow, Shor, and Winter. Our proof
has a clear structure being based on two recent information-theoretic results:
one-shot Quantum State Merging and the Post-Selection Technique for quantum
channels.


  The Spitzer Space Telescope provides a unique view of the Universe at
infrared wavelengths. Improved sensitivity and angular resolution over previous
missions enable detailed studies of astrophysical objects, both in imaging and
spectroscopic modes. Spitzer observations of active galactic nuclei can help
shed light on the physical conditions of the central regions of these active
glalaxies.
  The nearby radio galaxy Cygnus A is one of the most luminous radio sources in
the local Universe. In addition to the high radio power, it is also very
luminous in the infrared. New Spitzer spectroscopy and photometry of Cygnus A
is combined with data from the literature at radio and sub-mm wavelengths. The
resulting complication is modeled with a combination of: a synchrotron emitting
jet, a burst of star formation, and emission from an AGN torus.
  The infrared emission in Cyngus A shows contributions from all three
processes and the models are able to reproduce the observed emission over
almost 5 dex in frequency. The bolometric AGN luminosity is found to be ~10^45
erg s^-1, with a clumpy torus size of ~7 pc. Evidence is seen for a break in
the synchrotron spectrum in the mid-infrared. The relevant component of the
infrared emission suggests Cygnus A has a star formation rate of ~20 M_sun
yr^-1. Even in the absence of the AGN, it would still be a luminous infrared
source.


  Understanding the microscopic mechanism of coexisting long-range orders (such
as lattice supersolidity) in strongly correlated systems is a subject of
immense interest. We study the possible manifestations of long-range orders,
including lattice-supersolid phases with differently broken symmetry, in a
two-dimensional square lattice system of hard-core bosons (HCBs) coupled to
archetypal cooperative/coherent normal-mode distortions such as those in
perovskites. At strong HCB-phonon coupling, using a duality transformation to
map the strong-coupling problem to a weak-coupling one, we obtain an effective
Hamiltonian involving nearest-neighbor, next-nearest-neighbor, and
next-to-next-nearest-neighbor hoppings and repulsions. Using stochastic series
expansion quantum Monte Carlo, we construct the phase diagram of the system. As
coupling strength is increased, we find that the system undergoes a first-order
quantum phase transition from a superfluid to a checkerboard solid at half
filling and from a superfluid to a diagonal striped solid [with crystalline
ordering wavevector $\vec{Q}=(2\pi/3,2\pi/3)$ or $(2\pi/3,4\pi/3)$] at
one-third filling without showing any evidence of supersolidity. On tuning the
system away from these commensurate fillings, checkerboard supersolid is
generated near half filling whereas a rare diagonal striped supersolid is
realized near one-third filling. Interestingly, there is an asymmetry in the
extent of supersolidity about one-third filling. Within our framework, we also
provide an explanation for the observed checkerboard and stripe formations in
${\rm La}_{2-x}{\rm Sr}_x{\rm NiO_4}$ at $x=1/2$ and $x=1/3$.


  High-resolution spectroscopic studies of solar-type stars have revealed
higher iron abundances derived from singly ionized species compared to neutral,
violating the ionization equilibrium under the assumption of local
thermodynamic equilibrium. In this work, we investigate the overabundances of
FeII lines reported in our previous work for a sample of 451 solar-type HARPS
stars in the solar neighborhood. The spectroscopic surface gravities of this
sample which emerge from the ionization balance, appear underestimated for the
K-type stars. In order to understand this behavior, we search our FeII line
list for unresolved blends and outliers. First, we use the VALD to identify
possible unresolved blends around our lines and calculate which ones are strong
enough to cause overestimations in the equivalent width measurements. Second,
for our sample we use reference parameters (effective temperature and
metallicity) and the Gaia DR2 parallaxes to derive surface gravities
(trigonometric gravities) and calculate the FeI and FeII abundances from
different line lists. We exclude the FeII lines which produce overabundances
above 0.10 dex. The derived surface gravities from the clean line list are now
in agreement with the trigonometric. Moreover, the difference between FeI and
FeII abundance does not show now a correlation with the effective temperature.
Finally, we show that the ionization balance of Ti can provide better estimates
of surface gravities than iron. With this analysis, we provide a solution to
the ionization balance problem observed in the atmospheres of cool dwarfs.


  It is now well established that a Josephson junction made from conventional
spin-singlet superconductors containing ferromagnetic layers can carry
spin-triplet supercurrent under certain conditions. The first experimental
signature of that fact is the propagation of such supercurrent over long
distances through strong ferromagnetic materials. Surprisingly, one of the most
salient predictions of the theory has yet to be verified experimentally --
namely that a Josephson junction containing three magnetic layers with coplanar
magnetizations should exhibit a ground-state phase shift of either zero or pi
depending on the relative orientations of those magnetizations. Here we
demonstrate this property using Josephson junctions containing three different
types of magnetic layers, chosen so that the magnetization of one layer can be
switched by 180 degrees without disturbing the other two. Phase-sensitive
detection is accomplished using a superconducting quantum interference device,
or SQUID. Such a phase-controllable junction could be used as the memory
element in a fully-superconducting computer.


  Shot-noise and fractional Poisson processes are instances of filtered Poisson
processes. We here prove Girsanov theorem for this kind of processes and give
an application to an estimate problem.


  We consider the scattering of neutrons and photons on solid volume
rectangular targets. It is common to treat this problem using the Maxwell
Boltzmann Transport Equation and to use underlying symmetries to simplify the
calculation. For isotropic scattering centers we can introduce a direct
Fredholm integral equation approach to finding the flux. Here we compare a
Monte Carlo evaluation of the resulting Fredholm equation to a deterministic
iteration method of solution. We include a kernel based method for estimating
the overall angular dependence. We find that for simple geometries utilized in
studies of radiative dosimetry, of neutron shielding assessments, and
indirectly of criticality that we get reasonably rapid convergence of flux and
current values.


  The evolution of a two-phase Hele-Shaw problem, a Muskat problem, under
assumption of a negligible surface tension is considered. We use the Schwarz
function approach and allow the sinks and sources to be line distributions with
disjoint supports located in the exterior and the interior domains, a two-phase
mother body. We give examples of exact solutions when the interface belongs to
a certain family of algebraic curves, defined by the initial shape of the
boundary, and the cusp formation does not occur.


  Using a version of density-functional theory which combines Onsager
approximation and fundamental-measure theory for spatially nonuniform phases,
we have studied the phase diagram of freely rotating hard rectangles and hard
discorectangles. We find profound differences in the phase behavior of these
models, which can be attributed to their different packing properties.
Interestingly, bimodal orientational distribution functions are found in the
nematic phase of hard rectangles, which cause a certain degree of biaxial
order, albeit metastable with respect to spatially ordered phases. This feature
is absent in discorectangles, which always show unimodal behavior. This result
may be relevant in the light of recent experimental results which have
confirmed the existence of biaxial phases. We expect that some perturbation of
the particle shapes (either a certain degree of polydispersity or even bimodal
dispersity in the aspect ratios) may actually destabilize spatially ordered
phases thereby stabilizing the biaxial phase.


  This paper is a sequel to our previous paper arXiv:1105.1554, where we
defined two types of intermediate Diophantine exponents, connected them to
Schmidt exponents and split Dyson's transference inequality into a chain of
inequalities for intermediate exponents. Here we present splitting of some
other transference inequalities involving both regular and uniform Diophantine
exponents.


  We linearize vacuum Einstein field equations with a cosmological constant
around a curved background to elaborate on the reconstruction of the
Abbott-Deser charges and incorporate a spin connection into the definition
using the algebra of differential forms on a given curved background spacetime.


  Identifying the microscopic origins of decoherence sources prevalent in
Josephson junction based circuits is central to their use as functional quantum
devices. Focussing on so called "strongly coupled" two-level defects, we
construct a theoretical model using the atomic position of the oxygen which is
spatially delocalised in the oxide forming the Josephson junction barrier.
Using this model, we investigate which atomic configurations give rise to
two-level behaviour of the type seen in experiments. We compute experimentally
observable parameters for phase qubits and examine defect response under the
effects of applied electric field and strain.


  As the next-generation Internet paradigm, Web 3.0 encapsulates the
expectations of user-centric immersion and interaction experiences in a
decentralized manner. Metaverse, a virtual world interacting with the physical
world, is becoming one of the most potential technology to push forward with
Web 3.0. In the Metaverse, users expect to tailor immersive and interactive
experiences by customizing real-time Metaverse services (e.g.,
augmented/virtual reality and digital twin) in the three-dimensional virtual
world. Nevertheless, there are still no unified solutions for the Metaverse
services in terms of management and orchestration. It is calling for a
continuous evolution of scalable solutions and mobile systems to support
Metaverse services, and thus bring Metaverse into reality. In this paper, to
provide scalable subscription solutions for Metaverse services, we propose a
new concept, named Metaverse-as-a-Service (MaaS), in which various
physical-virtual components and technologies in the Metaverse can be delivered
as services. Furthermore, to unify the management and orchestration of MaaS, we
propose a novel framework, called Slicing4Meta, to customize Metaverse services
by intelligently integrating MaaS models and the associated multi-dimensional
resources on the components and technologies. Additionally, we propose the
classification for typical Metaverse services based on the quality of
experience (QoE) requirements and illustrate how to fulfill the QoE
requirements under the Slicing4Meta framework. We then illustrate a virtual
traveling study case, in which we examine the relationship between the QoE and
the multi-dimensional resources by quantitatively modeling the QoE of Metaverse
users. Finally, we discuss some open challenges of Slicing4Meta and propose
potential solutions to address the challenges.


  We study electronic structures of quasi-two-dimensional finite electron
systems in high magnetic fields. The solutions in the fractional quantum Hall
regime are interpreted as quantum liquids of electrons and off-electron
vortices. The ground states are classified according to the number of vortices
inside the electron droplet. The theory predicts observable effects due to
vortex formation in the chemical potentials and magnetization of electron
droplets. We compare the transitions in the theory to those found in electron
transport experiments on a quantum dot device and find significant
correspondence.


  The quantum flag manifold ${SU_q(3)/\mathbb{T}^2}$ is interpreted as a
noncommutative bundle over the quantum complex projective plane with the
quantum or Podle\'s sphere as a fibre. A connection arising from the
(associated) quantum principal $U_q(2)$-bundle is described.


  If $k$ is a field, $A$ a finite dimensional $k$-algebra, then the simple
$A$-modules form a simple minded collection in the derived category
$\operatorname{D}^b( \operatorname{mod} A )$. Their extension closure is
$\operatorname{mod} A$; in particular, it is abelian. This situation is
emulated by a general simple minded collection $\mathcal{S}$ in a suitable
triangulated category $\mathcal{C}$. In particular, the extension closure
$\langle \mathcal{S} \rangle$ is abelian, and there is a tilting theory for
such abelian subcategories of $\mathcal{C}$. These statements follow from
$\langle \mathcal{S} \rangle$ being the heart of a bounded $t$-structure.
  It is a defining characteristic of simple minded collections that their
negative self extensions vanish in every degree. Relaxing this to vanishing in
degrees $\{ -w+1, \ldots, -1 \}$ where $w$ is a positive integer leads to the
rich, parallel notion of $w$-simple minded systems, which have recently been
the subject of vigorous interest.
  If $\mathcal{S}$ is a $w$-simple minded system for some $w \geqslant 2$, then
$\langle \mathcal{S} \rangle$ is typically not the heart of a $t$-structure.
Nevertheless, using different methods, we will prove that $\langle \mathcal{S}
\rangle$ is abelian and that there is a tilting theory for such abelian
subcategories. Our theory is based on Quillen's notion of exact categories, in
particular a theorem by Dyer which provides exact subcategories of triangulated
categories.
  The theory of simple minded systems can be viewed as "negative cluster
tilting theory". In particular, the result that $\langle \mathcal{S} \rangle$
is an abelian subcategory is a negative counterpart to the result from (higher)
positive cluster tilting theory that if $\mathcal{T}$ is a cluster tilting
subcategory, then $( \mathcal{T} * \Sigma \mathcal{T} )/[ \mathcal{T} ]$ is an
abelian quotient category.


  Convolutional neural networks (CNNs) are used in many embedded applications,
from industrial robotics and automation systems to biometric identification on
mobile devices. State-of-the-art classification is typically achieved by large
networks, which are prohibitively expensive to run on mobile and embedded
devices with tightly constrained memory and energy budgets. We propose an
approach for ahead-of-time domain specific optimization of CNN models, based on
an integer linear programming (ILP) for selecting primitive operations to
implement convolutional layers. We optimize the trade-off between execution
time and memory consumption by: 1) attempting to minimize execution time across
the whole network by selecting data layouts and primitive operations to
implement each layer; and 2) allocating an appropriate workspace that reflects
the upper bound of memory footprint per layer. These two optimization
strategies can be used to run any CNN on any platform with a C compiler. Our
evaluation with a range of popular ImageNet neural architectures (GoogleNet,
AlexNet, VGG, ResNet and SqueezeNet) on the ARM Cortex-A15 yields speedups of
8x compared to a greedy algorithm based primitive selection, reduces memory
requirement by 2.2x while sacrificing only 15% of inference time compared to a
solver that considers inference time only. In addition, our optimization
approach exposes a range of optimal points for different configurations across
the Pareto frontier of memory and latency trade-off, which can be used under
arbitrary system constraints.


  Today cloud computing infrastructure is largely being deployed in healthcare
to access various healthcare services easily over the Internet on an as needed
basis. The main advantage of healthcare cloud is that it can be used as a tool
for patients, medical professionals and insurance providers, to query and
coordinate among medical departments, organizations and other healthcare
related hubs. Although healthcare cloud services can enable better medication
process with high responsiveness, but the privacy and other requirements of the
patients need to be ensured in the process. Patients medical data may be
required by the medical professionals, hospitals, diagnostic centers for
analysis and diagnosis. However, data privacy and service quality cannot be
compromised. In other words, there may exist various service providers
corresponding to a specific healthcare service. The main challenge is to find
the appropriate providers that comply best with patients requirement. In this
paper, we propose a query based optimal medication framework to support the
patients healthcare service accessibility comprehensively with considerable
response time. The framework accepts related healthcare queries in natural
language through a comprehensive user-interface and then processes the input
query through a first order logic based evaluation engine and finds all
possible services satisfying the requirements. First order logic is used for
modeling of user requirements and queries. The query evaluation engine is built
using zChaff, a Boolean logic satisfiability solver. The efficacy and usability
of the framework is evaluated with initial case studies on synthetic and real
life healthcare cloud.


  The aim of this paper is to review some work conducted in the field of user
testing that aims at specifying or clarifying the test procedures and at
defining and developing tools to help conduct user tests. The topics that have
been selected were considered relevant for evaluating applications in the field
of medical and health care informatics. These topics are: the number of
participants that should take part in a user test, the test procedure, remote
usability evaluation, usability testing tools, and evaluating mobile
applications.


  We calculate the transmission of electrons and holes between two normal-metal
electrodes (N), separated over a distance L by an impurity-free superconductor
(S) with d-wave symmetry of the order parameter. Nodal lines of vanishing
excitation gap form ballistic conduction channels for coupled electron-hole
excitations, described by an anisotropic two-dimensional Dirac equation. We
find that the transmitted electrical and thermal currents, at zero energy, both
have the pseudodiffusive 1/L scaling characteristic of massless Dirac fermions
- regardless of the presence of tunnel barriers at the NS interfaces. Tunnel
barriers reduce the slope of the 1/L scaling in the case of the electrical
current, while leaving the thermal current unaffected.


  Learning reliable motion representation between consecutive frames, such as
optical flow, has proven to have great promotion to video understanding.
However, the TV-L1 method, an effective optical flow solver, is time-consuming
and expensive in storage for caching the extracted optical flow. To fill the
gap, we propose UF-TSN, a novel end-to-end action recognition approach enhanced
with an embedded lightweight unsupervised optical flow estimator. UF-TSN
estimates motion cues from adjacent frames in a coarse-to-fine manner and
focuses on small displacement for each level by extracting pyramid of feature
and warping one to the other according to the estimated flow of the last level.
Due to the lack of labeled motion for action datasets, we constrain the flow
prediction with multi-scale photometric consistency and edge-aware smoothness.
Compared with state-of-the-art unsupervised motion representation learning
methods, our model achieves better accuracy while maintaining efficiency, which
is competitive with some supervised or more complicated approaches.


  We describe several techniques developed by the High Resolution Fly's Eye
experiment for measuring aerosol vertical optical depth, aerosol horizontal
attenuation length, and aerosol phase function. The techniques are based on
measurements of side-scattered light generated by a steerable ultraviolet laser
and collected by an optical detector designed to measure fluorescence light
from cosmic-ray air showers. We also present a technique to cross-check the
aerosol optical depth measurement using air showers observed in stereo. These
methods can be used by future air fluorescence experiments.


  In this work we study the effects of New Physics (NP) operators on the
inclusive $\bar{B} \to X_c \tau^- \bar{\nu}_\tau$ decay including power
$(\mathcal{O}(1/m_b^2))$ corrections in the NP operators. In analogy with
$R(D^{(*)})$ observables, we study the observable
$R(X_c)=\frac{\mathcal{B}(\bar{B} \to X_c \tau^-
\bar{\nu}_\tau)}{\mathcal{B}(\bar{B} \to X_c \ell^- \bar{\nu}_\ell)}$. We
present some numerical results for $R(X_c)$ and compare the results for this
observable with and without power corrections in the NP contributions.


  We construct a parametrization of the lepton energy spectrum in inclusive
semileptonic decays of B mesons, based on the available experimental
information: moments of the spectrum with cuts, their errors and their
correlations, together with kinematical constraints. The result is obtained in
the form of a Monte Carlo sample of neural networks trained on replicas of the
experimental data, which represents the probability density in the space of
lepton energy spectra. This parametrization is then used to extract the b quark
mass m_b^{1S} in a way that theoretical uncertainties are minimized, for which
the value m_b^{1S}=4.84 \pm 0.14^{exp}\pm 0.05^{th} GeV is obtained.


  We point out that a 4-dimensional topological manifold with an Alexandrov
metric (of curvature bounded below) and with an effective, isometric action of
the circle or the 2-torus is locally smooth. This observation implies that the
topological and equivariant classifications of compact, simply connected
Riemannian 4-manifolds with positive or nonnegative sectional curvature and an
effective isometric action of a circle or a 2-torus also hold if we consider
Alexandrov manifolds instead of Riemannian manifolds.


  The M49 group, resident outside the virial radius of the Virgo cluster, is
falling onto the cluster from the south. We report results from deep {\sl
XMM-Newton} mosaic observations of M49. Its hot gas temperature is 0.8\,keV at
the group center and rises to 1.5\,keV beyond the brightest group galaxy (BGG).
The group gas extends to radii of $\sim300$\,kpc to the north and south. The
observations reveal a cold front $\sim20$\,kpc north of the BGG center and an
X-ray bright stripped tail 70\,kpc long and 10\,kpc wide to the southwest of
the BGG. We argue that the atmosphere of the infalling group was slowed by its
encounter with the Virgo cluster gas, causing the BGG to move forward
subsonically relative to the group gas. We measure declining temperature and
metallicity gradients along the stripped tail. The tail gas can be traced back
to the cooler and enriched gas uplifted from the BGG center by buoyant bubbles,
implying that AGN outbursts may have intensified the stripping process. We
extrapolate to a virial radius of 740\,kpc and derive a virial mass of
$4.6\times10^{13}\,M_\odot$ for the M49 group. Its group atmosphere appears
truncated and deficient when compared with isolated galaxy groups of similar
temperatures. If M49 is on its first infall to Virgo, the infall region of a
cluster could have profound impacts on galaxies and groups that are being
accreted onto galaxy clusters. Alternatively, M49 may have already passed
through Virgo once.


  Community detection is a ubiquitous problem in applied network analysis, yet
efficient techniques do not yet exist for all types of network data. Most
techniques have been developed for undirected graphs, and very few exist that
handle directed and weighted networks effectively. Here we present such an
algorithm based on Voronoi partitionings. As an added benefit, this method can
directly employ edge weights that represent lengths, in contrast to algorithms
that operate with connection strengths, requiring ad-hoc transformations of
length data. We demonstrate the method on inter-areal brain connectivity, air
transportation networks, as well as on randomly generated benchmark networks.
The algorithm can handle dense graphs where weights are the main factor
determining communities. The hierarchical structure of networks can also be
detected, as shown for the brain. Its time efficiency is comparable with other
state-of-the-art algorithms, the most costly part being Dijkstra's shortest
paths algorithm.


  In this paper, we aim to provide an accessible survey to various formulae for
calculating single Hurwitz numbers. Single Hurwitz numbers count certain
classes of meromorphic functions on complex algebraic curves and have a rich
geometric structure behind them which has attracted many mathematicians and
physicists. Formulation of the enumeration problem is purely of topological
nature, but with connections to several modern areas of mathematics and
physics.


  We generalize bounds of Liu-Wan-Xiao for slopes in eigencurves for definite
unitary groups of rank $2$ to slopes in eigenvarieties for definite unitary
groups of any rank. We show that for a definite unitary group of rank $n$, the
Newton polygon of the characteristic power series of the $U_p$ Hecke operator
has exact growth rate $x^{1+\frac2{n(n-1)}}$, times a constant proportional to
the distance of the weight from the boundary of weight space. The proof goes
through the classification of forms associated to principal series
representations. We also give a consequence for the geometry of these
eigenvarieties over the boundary of weight space.


  Most of the implicit contractions introduced by Wardowski [Fixed Point Th.
Appl., 2012, 2012:94] are Matkowski type contractions.


  In this paper we investigate the vanishing of cosmological singularities by
quantization. Starting from a 5d Kaluza--Klein approach we quantize, as a first
step, the non--spherical metric part and the dilaton field. These fields which
are classically singular become smooth after quantization. In addition, we
argue that the incorporation of non perturbative quantum corrections form a
dilaton potential. Technically, the procedure corresponds to the quantization
of 2d dilaton gravity and we discuss several models. From the 4d point of view
this procedure is a semiclassical approach where only the dilaton and moduli
matter fields are quantized.


  With the rapidly growing use of Convolutional Neural Networks (CNNs) in
real-world applications related to machine learning and Artificial Intelligence
(AI), several hardware accelerator designs for CNN inference and training have
been proposed recently. In this paper, we present ATRIA, a novel bit-pArallel
sTochastic aRithmetic based In-DRAM Accelerator for energy-efficient and
high-speed inference of CNNs. ATRIA employs light-weight modifications in DRAM
cell arrays to implement bit-parallel stochastic arithmetic based acceleration
of multiply-accumulate (MAC) operations inside DRAM. ATRIA significantly
improves the latency, throughput, and efficiency of processing CNN inferences
by performing 16 MAC operations in only five consecutive memory operation
cycles. We mapped the inference tasks of four benchmark CNNs on ATRIA to
compare its performance with five state-of-the-art in-DRAM CNN accelerators
from prior work. The results of our analysis show that ATRIA exhibits only 3.5%
drop in CNN inference accuracy and still achieves improvements of up to 3.2x in
frames-per-second (FPS) and up to 10x in efficiency (FPS/W/mm2), compared to
the best-performing in-DRAM accelerator from prior work.


  In this paper, we present a method to investigate the spin-current shot noise
in mesoscopic conductors, by using scattering matrix theory and Green's
function technique. We first derive a general expression for the spin-current
noise at zero-frequency limit, and extract the shot-noise component by
discussing it at zero-temperature limit. The expression indicates that the
spin-current shot noise in one lead is caused by the transmissions to the
spin-resolved states in this lead and the interferences of these transmissions.
As an application, we simulate the spin-current shot noise in a spin
transistor, and discuss its dependence on the device parameters and the bias
voltages applied to the transistor. The knowledge we gain from this study will
help researchers to evaluate the spin-current shot noise in the future
spintronic devices.


  We have carried out a survey of optically-selected dark clouds using the
bolometer array SCUBA on the James Clerk Maxwell Telescope, at 850 microns
wavelength. The survey covers a total of 0.5 square degrees and is unbiased
with reference to cloud size, star formation activity, or the presence of
infrared emission. Several new protostars and starless cores have been
discovered; the protostars are confirmed through the detection of their
accompanying outflows in CO(2-1) emission. The survey is believed to be
complete for Class 0 and Class I protostars, and yields two important results
regarding the lifetimes of these phases. First, the ratio of Class 0 to Class
protostars in the sample is roughly unity, very different from the 1:10 ratio
that has previously been observed for the rho Ophiuchi star-forming region.
Assuming star formation to be a homogeneous process in the dark clouds, this
implies that the Class 0 lifetime is similar to the Class I phase, which from
infrared surveys has been established to be approximately 200,000 yr. It also
suggests there is no rapid initial accretion phase in Class 0 objects. A burst
of triggered star formation some 100,000 yr ago can explain the earlier results
for rho Ophiuchus. Second, the number of starless cores is approximately twice
that of the total number of protostars, indicating a starless core lifetime of
approximately 800,000 yr. These starless cores are therefore very short-lived,
surviving only two or three free-fall times. This result suggests that, on size
scales of 10,000 AU at least, the dynamical evolution of starless cores is
probably not controlled by magnetic processes.


  Deep Neural Networks have recently gained lots of success after enabling
several breakthroughs in notoriously challenging problems. Training these
networks is computationally expensive and requires vast amounts of training
data. Selling such pre-trained models can, therefore, be a lucrative business
model. Unfortunately, once the models are sold they can be easily copied and
redistributed. To avoid this, a tracking mechanism to identify models as the
intellectual property of a particular vendor is necessary.
  In this work, we present an approach for watermarking Deep Neural Networks in
a black-box way. Our scheme works for general classification tasks and can
easily be combined with current learning algorithms. We show experimentally
that such a watermark has no noticeable impact on the primary task that the
model is designed for and evaluate the robustness of our proposal against a
multitude of practical attacks. Moreover, we provide a theoretical analysis,
relating our approach to previous work on backdooring.


  The thermal and electrical transport properties of single-crystalline
LaBe$_{13}$ have been investigated by specific-heat ($C$) and
electrical-resistivity ($\rho$) measurements. The specific-heat measurements in
a wide temperature range revealed the presence of a hump anomaly near 40 K in
the $C$($T$)/$T$ curve, indicating that LaBe$_{13}$ has a low-energy
Einstein-like-phonon mode with a characteristic temperature of $\sim$ 177 K. In
addition, a superconducting transition was observed in the $\rho$ measurements
at the transition temperature of 0.53 K, which is higher than the value of 0.27
K reported previously by Bonville et al. Furthermore, an unusual $T^3$
dependence was found in $\rho$($T$) below $\sim$ 50 K, in contrast to the
behavior expected from the electron--electron scattering or the electron--Debye
phonon scattering.


  Let $a(n)$ defined by $\sum_{n=1}^{\infty}a(n)q^n :=
\prod_{n=1}^{\infty}\frac{1}{(1-q^{3n})(1-q^n)^3}.$ In this note, we prove that
for every non-negative integer $n$, a(15n+6) \equiv 0\pmod{5}, a(15n+12) \equiv
0\pmod{5}. As a corollary, we obtained some results of Ono


  This paper strives for pixel-level segmentation of actors and their actions
in video content. Different from existing works, which all learn to segment
from a fixed vocabulary of actor and action pairs, we infer the segmentation
from a natural language input sentence. This allows to distinguish between
fine-grained actors in the same super-category, identify actor and action
instances, and segment pairs that are outside of the actor and action
vocabulary. We propose a fully-convolutional model for pixel-level actor and
action segmentation using an encoder-decoder architecture optimized for video.
To show the potential of actor and action video segmentation from a sentence,
we extend two popular actor and action datasets with more than 7,500 natural
language descriptions. Experiments demonstrate the quality of the
sentence-guided segmentations, the generalization ability of our model, and its
advantage for traditional actor and action segmentation compared to the
state-of-the-art.


  The advent of large language models (LLMs) has predominantly catered to
high-resource languages, leaving a disparity in performance for low-resource
languages. Conventional Continual Training (CT) approaches to bridge this gap
often undermine a model's original linguistic proficiency when expanding to
multilingual contexts. Addressing this issue, we introduce a novel MoE-CT
architecture, a paradigm that innovatively separates the base model's learning
from the multilingual expansion process. Our design freezes the original LLM
parameters, thus safeguarding its performance in high-resource languages, while
an appended MoE module, trained on diverse language datasets, augments
low-resource language proficiency. Our approach significantly outperforms
conventional CT methods, as evidenced by our experiments, which show marked
improvements in multilingual benchmarks without sacrificing the model's
original language performance. Moreover, our MoE-CT framework demonstrates
enhanced resistance to forgetting and superior transfer learning capabilities.
By preserving the base model's integrity and focusing on strategic parameter
expansion, our methodology advances multilingual language modeling and
represents a significant step forward for low-resource language inclusion in
LLMs, indicating a fruitful direction for future research in language
technologies.


  In order to characterize the scientific output of scientists, in this paper
we define the harmonic har-index whose values are positive integers. It is
proved that $h \leq har \leq g$, where h is the Hirsch index and g is the Egghe
index. Despite the fact that the har-index is defined in a completely different
way (including sum of reciprocals of citations of a researcher), based on our
computatioanl results, we get the surprising fact that this index is highly
correlated with the hg-index ($hg=\sqrt{hg}$ ) introduced by Alonso et al.
(2010). Accordingly, we believe that the har-index keep the advantages of both
measures as well as to minimize their disadvantages. In addition, it is much
easier to calculate the values of har-index than those of g-index and hg-index.


  Based on the ideas of cyclotomic cosets, idempotents and Mattson-Solomon
polynomials, we present a new method to construct GF(2^m), where m>0 cyclic
low-density parity-check codes. The construction method produces the dual code
idempotent which is used to define the parity-check matrix of the low-density
parity-check code. An interesting feature of this construction method is the
ability to increment the code dimension by adding more idempotents and so
steadily decrease the sparseness of the parity-check matrix. We show that the
constructed codes can achieve performance very close to the
sphere-packing-bound constrained for binary transmission.


  We give a semiclassical analysis of a nonlinear eigenvalue problem arising
from the study of the failure of analytic hypoellipticity and obtain a general
family of hypoelliptic, but not analytic hypoelliptic operators.


  Supermassive black holes in the centre of galaxies dominate the gravitational
potential of their surrounding stellar clusters. In these dense environments,
stars follow nearly Keplerian orbits, which get slowly distorted as a result of
the potential fluctuations generated by the stellar cluster itself as a whole.
In particular, stars undergo a rapid relaxation of their eccentricities through
both resonant and non-resonant processes. An efficient implementation of the
resonant diffusion coefficients allows for detailed and systematic explorations
of the parameter space describing the properties of the stellar cluster. In
conjunction with recent observations of the S-cluster orbiting SgrA*, this
framework can be used to jointly constrain the distribution of the unresolved,
old, background stellar cluster and the characteristics of a putative dark
cluster. Specifically, we show how this can be used to estimate the typical
mass and cuspide exponent of intermediate-mass black holes consistent with the
relaxed state of the distribution of eccentricities in the observed S-cluster.
This should prove useful in constraining super massive black hole formation
scenarios.


  This paper proposes BRIEF, a backward reduction algorithm that explores
compact CNN-model designs from the information flow perspective. This algorithm
can remove substantial non-zero weighting parameters (redundant neural
channels) of a network by considering its dynamic behavior, which traditional
model-compaction techniques cannot achieve. With the aid of our proposed
algorithm, we achieve significant model reduction on ResNet-34 in the ImageNet
scale (32.3% reduction), which is 3X better than the previous result (10.8%).
Even for highly optimized models such as SqueezeNet and MobileNet, we can
achieve additional 10.81% and 37.56% reduction, respectively, with negligible
performance degradation.


  We calculate the forces acting upon species adsorbed on a single wall carbon
nanotube, in the presence of electric currents. We present a self consistent
real space Green function method, which enables us to calculate the current
induced forces from an ab-initio Hamiltonian. The method is applied to
calculate the force on an adsorbed O atom on a (5,5) carbon nanotube, for
different bias voltages and adsorption sites. For good contact regimes and
biases of the order of Volts, the presence of a current can affect the
potential energy surfaces considerably. Implications of these effects for the
induced diffusion of the species are analyzed. The dependence of the force with
the nanotube radius is studied. In addition, the magnitude of inelastic
electron scattering, inducing vibrational heating, and its influence on the
adsorbates' drift, is commented.


  It is known that the classical Hilbert--Schmidt theorem can be generalized to
the case of compact operators in Hilbert $A$-modules $H_A^*$ over a
$W^*$-algebra of finite type, i.e. compact operators in $H_A^*$ under slight
restrictions can be diagonalized over $A$. We show that if $B$ is a weakly
dense $C^*$-subalgebra of real rank zero in $A$ with some additional property
then the natural extension of a compact operator from $H_B$ to $H_A^*\supset
H_B$ can be diagonalized with diagonal entries being from the $C^*$-algebra
$B$.


  We describe smooth compactifications of certain families of reductive
homogeneous spaces such as group manifolds for classical Lie groups, or
pseudo-Riemannian analogues of real hyperbolic spaces and their complex and
quaternionic counterparts. We deduce compactifications of Clifford-Klein forms
of these homogeneous spaces, namely quotients by discrete groups Gamma acting
properly discontinuously, in the case that Gamma is word hyperbolic and acts
via an Anosov representation. In particular, these Clifford-Klein forms are
topologically tame.


  Animals moving together in groups are believed to interact among each other
with effective social forces, such as attraction, repulsion and alignment. Such
forces can be inferred using 'force maps', i.e. by analysing the dependency of
the acceleration of a focal individual on relevant variables. Here we introduce
a force map technique suitable for the analysis of the alignment forces
experienced by individuals. After validating it using an agent-based model, we
apply the force map to experimental data of schooling fish. We observe
signatures of an effective alignment force with faster neighbours, and an
unexpected anti-alignment with slower neighbours. Instead of an explicit
anti-alignment behaviour, we suggest that the observed pattern is the result of
a selective attention mechanism, where fish pay less attention to slower
neighbours. This mechanism implies the existence of temporal leadership
interactions based on relative speeds between neighbours. We present support
for this hypothesis both from agent-based modelling, as well as from exploring
leader-follower relationships in the experimental data.


  In this paper we study the Clifford+Toffoli universal fault-tolerant gate
set. We introduce a generating set in order to represent any unitary
implementable by this gate set and with this we derive a bound on the
Toffoli-count of arbitrary multi-qubit unitaries. We analyse the channel
representation of the generating set elements, with the help of which we infer
$|\mathcal{J}_n^{Tof}|<|\mathcal{J}_n^T|$, where $\mathcal{J}_n^{Tof}$ and
$\mathcal{J}_n^T$ are the set of unitaries exactly implementable by the
Clifford+Toffoli and Clifford+T gate set, respectively. We develop
Toffoli-count optimal synthesis algorithms for both approximately and exactly
implementable multi-qubit unitaries. With the help of these we prove
$|\mathcal{J}_n^{Tof}|=|\mathcal{J}_n^{CS}|$, where $\mathcal{J}_n^{CS}$ is the
set of unitaries exactly implementable by the Clifford+CS gate set.


  We performed Se and F-NMR measurements on single crystals of (TMTSF)2FSO3 to
characterize the electronic structures of different phases in the
Temperature-Pressure phase diagram, determined by precise transport
measurements [Jo et al., Phys. Rev. B67, 014516 (2003)]. We claim that such
varieties of electronic states in the refined phase diagram are caused by
strong couplings of the conduction electrons with FSO3 anions, especially with
the permanent electric dipoles on the anions. We suggest that as temperature
decreases, the FSO3 anions form orientational ordering through two steps; first
only the tetrahedrons form an orientational order leaving the orientations of
the electronic dipoles in random (transition I); then the dipoles form a
perfect orientational order at a lower temperature (transition II). In the
intermediate temperature range between transitions I and II, we found an
appreciable enhancement of homogeneous and inhomogeneous widths of 77Se-NMR
spectrum. From the analysis of the angular dependence of the linewidth, we
attributed these anomalies to the intramolecular charge disproportionation or
imbalance and its slow dynamics caused by the coupling with the permanent
electric dipole of anion. Results of 19F-NMR relaxation and lineshape
measurements support this picture very well. Electronic structures at higher
pressures up to 1.25 GPa are discussed on the basis of the results of the 77Se
and 19F-NMR measurements.


  We present two general approaches to obtain the strong converse rate of
quantum hypothesis testing for correlated quantum states. One approach requires
that the states satisfy a certain factorization property; typical examples of
such states are the temperature states of translation-invariant finite-range
interactions on a spin chain. The other approach requires the differentiability
of a regularized R\'enyi $\alpha$-divergence in the parameter $\alpha$; typical
examples of such states include temperature states of non-interacting fermionic
lattice systems, and classical irreducible Markov chains. In all cases, we get
that the strong converse exponent is equal to the Hoeffding anti-divergence,
which in turn is obtained from the regularized R\'enyi divergences of the two
states.


  The boom in the meal delivery industry brings growing concern about the labor
rights of riders. Current dispatch policies of meal-delivery platforms focus
mainly on satisfying consumers or minimizing the number of riders for cost
savings. There are few discussions on improving the working conditions of
riders by algorithm design. The lack of concerns on labor rights in mechanism
and dispatch design has resulted in a very large time waste for riders and
their risky driving. In this research, we propose a queuing-model-based
framework to discuss optimal dispatch policy with the goal of labor rights
protection. We apply our framework to develop an algorithm minimizing the
waiting time of food delivery riders with guaranteed user experience. Our
framework also allows us to manifest the value of restaurants' data about their
offline-order numbers on improving the benefits of riders.


  Translationnally invariant bidimensional magnetic Laplacians are considered.
Using an improved version of the harmonic approximation, we establish the
absence of point spectrum under various assumptions on the behavior of the
magnetic field.


  In this paper, it is established finite active-set identification of an
almost cyclic 2-coordinate descent method for problems with one linear coupling
constraint and simple bounds. First, general active-set identification results
are stated for non-convex objective functions. Then, under convexity and a
quadratic growth condition (satisfied by any strongly convex function),
complexity results on the number of iterations required to identify the active
set are given. In our analysis, a simple Armijo line search is used to compute
the stepsize, thus not requiring exact minimizations or additional information.


  We report on the BeppoSAX observations of four "extreme" BL Lacs, selected to
have high synchrotron peak frequencies. All have been detected also in the PDS
band. For 1ES 0120+340, PKS 0548-322 and H 2356-309 the spectrum is well fitted
by a convex broken power-law, thus locating the synchrotron peak around 1 - 4
keV. 1ES 1426+428 presents a flat energy spectral index (alpha_x= 0.92) up to
100 keV, thus constraining the synchrotron peak to lie near or above that
value. For their extreme properties, all sources could be strong TeV emitters.


  We discuss a two-parameter renormalization group (RG) consideration of a
phyllotaxis model in the framework of the ``energetic approach'' proposed by L.
Levitov in 1991. Following L. Levitov, we consider an equilibrium distribution
of strongly repulsive particles on the surface of a finite cylinder and study
the redistribution of these particles when the cylinder is squeezed along its
axis. We construct explicitly the $\beta$-function of a given system in terms
of the modular Dedekind $\eta$-function. On basis of this $\beta$-function we
derive the equations describing the RG flow in the vicinity of the bifurcation
points between different lattices. Analyzing the structure of RG equations, we
claim emergence of Berezinskii-Kosterlitz-Thouless (BKT) transitions at strong
compression of the cylinder.


  This manuscript proposes a posterior mean (PM) super-resolution (SR) method
with a compound Gaussian Markov random field (MRF) prior. SR is a technique to
estimate a spatially high-resolution image from observed multiple
low-resolution images. A compound Gaussian MRF model provides a preferable
prior for natural images that preserves edges. PM is the optimal estimator for
the objective function of peak signal-to-noise ratio (PSNR). This estimator is
numerically determined by using variational Bayes (VB). We then solve the
conjugate prior problem on VB and the exponential-order calculation cost
problem of a compound Gaussian MRF prior with simple Taylor approximations. In
experiments, the proposed method roughly overcomes existing methods.


  We revisit the harmonic approximation (HA) for a large Josephson junction
interacting with some charge qubits through the variational approach for the
quantum dynamics of the junction-qubit coupling system. By making use of
numerical calculation and analytical treatment, the conditions under which HA
works well can be precisely presented to control the parameters implementing
the two-qubit quantum logical gate through the couplings to the large junction
with harmonic oscillator (HO) Hamiltonian.


  In a recent paper entitled "Inconsistencies of Recently Proposed Citation
Impact Indicators and how to Avoid Them," Schreiber (2012, at arXiv:1202.3861)
proposed (i) a method to assess tied ranks consistently and (ii) fractional
attribution to percentile ranks in the case of relatively small samples (e.g.,
for n < 100). Schreiber's solution to the problem of how to handle tied ranks
is convincing, in my opinion (cf. Pudovkin & Garfield, 2009). The fractional
attribution, however, is computationally intensive and cannot be done manually
for even moderately large batches of documents. Schreiber attributed scores
fractionally to the six percentile rank classes used in the Science and
Engineering Indicators of the U.S. National Science Board, and thus missed, in
my opinion, the point that fractional attribution at the level of hundred
percentiles-or equivalently quantiles as the continuous random variable-is only
a linear, and therefore much less complex problem. Given the quantile-values,
the non-linear attribution to the six classes or any other evaluation scheme is
then a question of aggregation. A new routine based on these principles
(including Schreiber's solution for tied ranks) is made available as software
for the assessment of documents retrieved from the Web of Science (at
http://www.leydesdorff.net/software/i3).


  Retrieval-Augmented Generation (RAG) offers a cost-effective approach to
injecting real-time knowledge into large language models (LLMs). Nevertheless,
constructing and validating high-quality knowledge repositories require
considerable effort. We propose a pre-retrieval framework named Pseudo-Graph
Retrieval-Augmented Generation (PG-RAG), which conceptualizes LLMs as students
by providing them with abundant raw reading materials and encouraging them to
engage in autonomous reading to record factual information in their own words.
The resulting concise, well-organized mental indices are interconnected through
common topics or complementary facts to form a pseudo-graph database. During
the retrieval phase, PG-RAG mimics the human behavior in flipping through
notes, identifying fact paths and subsequently exploring the related contexts.
Adhering to the principle of the path taken by many is the best, it integrates
highly corroborated fact paths to provide a structured and refined sub-graph
assisting LLMs. We validated PG-RAG on three specialized question-answering
datasets. In single-document tasks, PG-RAG significantly outperformed the
current best baseline, KGP-LLaMA, across all key evaluation metrics, with an
average overall performance improvement of 11.6%. Specifically, its BLEU score
increased by approximately 14.3%, and the QE-F1 metric improved by 23.7%. In
multi-document scenarios, the average metrics of PG-RAG were at least 2.35%
higher than the best baseline. Notably, the BLEU score and QE-F1 metric showed
stable improvements of around 7.55% and 12.75%, respectively. Our code:
https://github.com/IAAR-Shanghai/PGRAG.


  We give a pathwise construction of a two-parameter family of
purely-atomic-measure-valued diffusions in which ranked masses of atoms are
stationary with the Poisson-Dirichlet$(\alpha,\theta)$ distributions, for
$\alpha\in (0,1)$ and $\theta\ge 0$. This resolves a conjecture of Feng and Sun
(2010). We build on our previous work on $(\alpha,0)$- and
$(\alpha,\alpha)$-interval partition evolutions. Indeed, we first extract a
self-similar superprocess from the levels of stable processes whose jumps are
decorated with squared Bessel excursions and distinct allelic types. We
complete our construction by time-change and normalisation to unit mass. In a
companion paper, we show that the ranked masses of the measure-valued processes
evolve according to a two-parameter family of diffusions introduced by Petrov
(2009), extending work of Ethier and Kurtz (1981). These ranked-mass diffusions
arise as continuum limits of up-down Markov chains on Chinese restaurant
processes.


  For point $x$ in the inverse limit space $X$ with a single unimodal bonding
map we construct, with the use of symbolic dynamics, a planar embedding such
that $x$ is accessible. It follows that there are uncountably many
non-equivalent planar embeddings of $X$.


  Unconstrained handwritten text recognition remains challenging for computer
vision systems. Paragraph text recognition is traditionally achieved by two
models: the first one for line segmentation and the second one for text line
recognition. We propose a unified end-to-end model using hybrid attention to
tackle this task. This model is designed to iteratively process a paragraph
image line by line. It can be split into three modules. An encoder generates
feature maps from the whole paragraph image. Then, an attention module
recurrently generates a vertical weighted mask enabling to focus on the current
text line features. This way, it performs a kind of implicit line segmentation.
For each text line features, a decoder module recognizes the character sequence
associated, leading to the recognition of a whole paragraph. We achieve
state-of-the-art character error rate at paragraph level on three popular
datasets: 1.91% for RIMES, 4.45% for IAM and 3.59% for READ 2016. Our code and
trained model weights are available at
https://github.com/FactoDeepLearning/VerticalAttentionOCR.


  We present a detailed investigation into the effects of galaxy environment on
their star formation rates (SFR) using galaxies observed in the Galaxy and Mass
Assembly Survey (GAMA). We use three independent volume-limited samples of
galaxies within z < 0.2 and Mr < -17.8. We investigate the known SFR-density
relationship and explore in detail the dependence of SFR on stellar mass and
density. We show that the SFR-density trend is only visible when we include the
passive galaxy population along with the star-forming population. This
SFR-density relation is absent when we consider only the star-forming
population of galaxies, consistent with previous work. While there is a strong
dependence of the EWH?a on density we find, as in previous studies, that these
trends are largely due to the passive galaxy population and this relationship
is absent when considering a "star-forming" sample of galaxies. We find that
stellar mass has the strongest influence on SFR and EWH?a with the environment
having no significant effect on the star-formation properties of the star
forming population. We also show that the SFR-density relationship is absent
for both early and late-type star-forming galaxies. We conclude that the
stellar mass has the largest impact on the current SFR of a galaxy, and any
environmental effect is not detectable. The observation that the trends with
density are due to the changing morphology fraction with density implies that
the timescales must be very short for any quenching of the SFR in infalling
galaxies. Alternatively galaxies may in fact undergo predominantly in-situ
evolution where the infall and quenching of galaxies from the field into dense
environments is not the dominant evolutionary mode.


  With recently successful applications of deep learning in computer vision and
general signal processing, deep learning has shown many unique advantages in
medical signal processing. However, data labelling quality has become one of
the most significant issues for AI applications, especially when it requires
domain knowledge (e.g. medical image labelling). In addition, there might be
noisy labels in practical datasets, which might impair the training process of
neural networks. In this work, we propose a semi-supervised algorithm for
training data samples with noisy labels by performing selected Positive
Learning (PL) and Negative Learning (NL). To verify the effectiveness of the
proposed scheme, we designed a portable ECG patch -- iRealCare -- and applied
the algorithm on a real-life dataset. Our experimental results show that we can
achieve an accuracy of 91.0 %, which is 6.2 % higher than a normal training
process with ResNet. There are 65 patients in our dataset and we randomly
picked 2 patients to perform validation.


  We show that, at the percolation threshold, two percolating AB clusters form
in the diamond structure simultaneously instead of the usual one in the common
site percolation problems. The structure of these two clusters and the related
physical properties of semiconductor mixed crystals are discussed.


  Implantable medical devices (IMDs), such as pacemakers, implanted cardiac
defibrillators, and neurostimulators are medical devices implanted into
patients' bodies for monitoring physiological signals and performing medical
treatments. Many IMDs have built-in wireless communication modules to
facilitate data collecting and device reprogramming by external programmers.
The wireless communication brings significant conveniences for advanced
applications such as real-time and remote monitoring but also introduces the
risk of unauthorized wireless access. The absence of effective access control
mechanisms exposes patients' life to cyber attacks. In this paper, we present a
lightweight and universally applicable access control system for IMDs. By
leveraging Physically Obfuscated Keys (POKs) as the hardware root of trust,
provable security is achieved based on standard cryptographic primitives while
attaining high energy efficiency. In addition, barrier-free IMD access under
emergent situations is realized by utilizing the patient's biometrical
information. We evaluate our proposed scheme through extensive security
analysis and a prototype implementation, which demonstrates our work's
superiority on security and energy efficiency.


  In the pattern matching with $d$ wildcards problem one is given a text $T$ of
length $n$ and a pattern $P$ of length $m$ that contains $d$ wildcard
characters, each denoted by a special symbol $'?'$. A wildcard character
matches any other character. The goal is to establish for each $m$-length
substring of $T$ whether it matches $P$. In the streaming model variant of the
pattern matching with $d$ wildcards problem the text $T$ arrives one character
at a time and the goal is to report, before the next character arrives, if the
last $m$ characters match $P$ while using only $o(m)$ words of space.
  In this paper we introduce two new algorithms for the $d$ wildcard pattern
matching problem in the streaming model. The first is a randomized Monte Carlo
algorithm that is parameterized by a constant $0\leq \delta \leq 1$. This
algorithm uses $\tilde{O}(d^{1-\delta})$ amortized time per character and
$\tilde{O}(d^{1+\delta})$ words of space. The second algorithm, which is used
as a black box in the first algorithm, is a randomized Monte Carlo algorithm
which uses $O(d+\log m)$ worst-case time per character and $O(d\log m)$ words
of space.


  We study the spatial structure and sub-structure of regions rich in Hipparcos
stars with blue B_T-V_T colours. These regions, which comprise large stellar
complexes, OB associations, and young open clusters, are tracers of on-going
star formation in the Galaxy. The DBSCAN (Density-Based Spatial Clustering of
Applications with Noise) data clustering algorithm is used to look for spatial
overdensities of early-type stars. Once an overdensity, "agglomerate", is
identified, we carry out a data and bibliographic compilation of their star
member candidates. The actual membership in agglomerate of each early-type star
is studied based on its heliocentric distance, proper motion, and previous
spectro-photometric information. We identify 35 agglomerates of early-type
Hipparcos stars. Most of them are associated to previously known clusters and
OB associations. The previously unknown P Puppis agglomerate is subject of a
dedicated study with Virtual Observatory tools. It is actually a new, nearby,
young open cluster (d ~ 470 pc, age ~ 20 Ma) with a clear radial density
gradient. We list P Puppis and other six agglomerates (including NGC 2451 A,
vdBH 23, and Trumpler 10) as new sites for substellar searches because of their
youth, closeness, and spatial density. We investigate in detail the
sub-structure in the Orion, CMa-Pup and Pup-Vel OB complexes
("super-agglomerates"). We confirm or discover some stellar overdensities in
the Orion complex, like the 25 Ori group, the Horsehead region (including the
sigma Orionis cluster), and the eta Orionis agglomerate. Finally, we derive
accurate parallactic distances to the Pleiades, NGC 2451 A, and IC 2391,
describe several field early-type stars at d < 200 pc, and discuss the
incompleteness of our search.


  We have derived CNO abundances in 12 RR Lyrae stars. Four stars show [C/Fe]
near 0.0 and two stars show [C/Fe] = 0.52 and 0.65. Red giant branch stars,
which are known to be the predecessors of RR Lyrae stars, generally show a
deficiency of carbon due to proton captures during their evolution from the
main sequence up the giant branch. We suggest that the enhancement of carbon is
due to production during the helium flash combined with mixing to the surface
by vigorous convection induced by the flash itself.


  In this paper, we present an overview of the CTC 2021, a Chinese text
correction task for native speakers. We give detailed descriptions of the task
definition and the data for training as well as evaluation. We also summarize
the approaches investigated by the participants of this task. We hope the data
sets collected and annotated for this task can facilitate and expedite future
development in this research area. Therefore, the pseudo training data, gold
standards validation data, and entire leaderboard is publicly available online
at https://destwang.github.io/CTC2021-explorer/.


  We report first-principles calculations of current-induced forces in Si
atomic wires as a function of bias and wire length. We find that these forces
are strongly nonlinear as a function of bias due to the competition between the
force originating from the scattering states and the force due to bound states.
We also find that the average force in the wire is larger the shorter the wire,
suggesting that atomic wires are more difficult to break under current flow
with increasing length. The last finding is in agreement with recent
experimental data.


  A 2-categorical generalisation of elementary topos is provided and some of
the properties of the yoneda structure it generates are explored. Examples
relevant to the globular approach to higher category theory are discussed. This
paper also contains some expository material on the theory of fibrations
internal to a finitely complete 2-category as well as a self-contained
development of the necessary background on yoneda structures.


  Observing the interstellar medium (ISM) in $z \gtrsim 6$ quasars host
galaxies is essential for understanding the co-evolution between the
supermassive black holes and their hosts. To probe the gas physical conditions
and search for imprints of Active Galactic Nuclei (AGN) on the ISM, we report
ALMA observations of the $\rm [N\ II]_{122 \mu m}$ and $\rm [O\ I]_{146 \mu m}$
lines and the underlying continuum from the $z=6.003$ quasar SDSS
J231038.88+185519.7. Together with previous $\rm [C\ II]_{158 \mu m}$ and $\rm
[O\ III]_{88 \mu m}$ observations, we use the ratios of these fine-structure
lines to probe the ISM properties. Similar to other high-$z$ systems, this
object exhibits a $\rm [C\ II]_{158 \mu m}$/$\rm [O\ I]_{146 \mu m}$ ratio
comparable to the lowest values found in local (Ultra) luminous infrared
galaxies, suggesting a "warmer" and "denser" gas component compared to typical
local systems. The $\rm [O\ III]_{88 \mu m}$/$\rm [O\ I]_{146 \mu m}$ ratio is
lower than that of other local and high-$z$ systems, indicating a smaller
ionized gas fraction in this quasar. The $\rm [O\ III]_{88 \mu m}$/$\rm [N\
II]_{122 \mu m}$ ratio is comparable to that of local systems, and suggests a
metallicity of $Z/Z_{\odot}$=1.5$-$2.1. Based on the $\rm [N\ II]_{122 \mu m}$
detection, we estimate that $17\%$ of the $\rm [C\ II]_{158 \mu m}$ emission is
associated with ionized gas. The $\rm [N\ II]_{122 \mu m}$ line shows a "flux
deficit" comparable to local systems. The $\rm [O\ I]_{146 \mu m}$ line, with a
$\rm [O\ I]_{146 \mu m}$/FIR ratio $\ge 2\times$ than expected from the local
relation, indicates no $\rm [O\ I]_{\rm 146 \mu m}$ deficit. The low $\rm [C\
II]_{158 \mu m}$/$\rm [O\ I]_{146 \mu m}$ ratio, together with the high $\rm
[O\ I]_{146 \mu m}$/FIR ratio in J2310+1855, reveals that the warm and dense
gas is likely a result of AGN heating to the ISM.


  Non-gaussianity in the microwave background radiation is bound to play a key
role in giving us clues about the physics of the very early universe. However,
the associated calculations, at second and even third order in perturbation
theory, tend to be complicated to the point of obscuring simple underlying
physical processes. In this note, we present a simple analytic procedure for
approximating the non-linearity parameters f_{NL} and g_{NL} for cyclic models
in which the cosmological perturbations are generated via the entropic
mechanism. Our approach is quick, physically transparent and agrees well with
the results of numerical calculations.


  We present here an introduction to Brainstorming approach, that was recently
proposed as a consensus meta-learning technique, and used in several practical
applications in bioinformatics and chemoinformatics. The consensus learning
denotes heterogeneous theoretical classification method, where one trains an
ensemble of machine learning algorithms using different types of input training
data representations. In the second step all solutions are gathered and the
consensus is build between them. Therefore no early solution, given even by a
generally low performing algorithm, is not discarder until the late phase of
prediction, when the final conclusion is drawn by comparing different machine
learning models. This final phase, i.e. consensus learning, is trying to
balance the generality of solution and the overall performance of trained
model.


  We present 7 new tunes of the pT-ordered shower and underlying-event model in
Pythia 6.4. These "Perugia" tunes update and supersede the older "S0" family.
The new tunes include the updated LEP fragmentation and flavour parameters
reported on by H. Hoeth at this workshop. The hadron-collider specific
parameters were then retuned (manually) using Tevatron min-bias data from 630,
1800, and 1960 GeV, Tevatron Drell-Yan data at 1800 and 1960 GeV, as well as
SPS min-bias data at 200, 540, and 900 GeV. In addition to the central
parameter set, related tunes exploring systematically soft, hard, parton
density, and color structure variations are included. Based on these
variations, a best-guess prediction of the charged track multiplicity in
inelastic, nondiffractive minimum-bias events at the LHC is made.


  In this note we review the basic mathematical ideas used in finance in the
language of modern physics. We focus on discrete time formalism, derive path
integral and Green's function formulas for pricing. We also discuss various
risk mitigation methods.


  To trace the accretion and outflow processes around YSOs, diagnostic spectral
lines such as the BrG 2.166 micron line are widely used, although due to a lack
of spatial resolution, the origin of the line emission is still unclear.
Employing the AU-scale spatial resolution which can be achieved with infrared
long-baseline interferometry, we aim to distinguish between theoretical models
which associate the BrG line emission with mass infall or mass outflow
processes. Using the VLTI/AMBER instrument, we spatially and spectrally
(R=1500) resolved the inner environment of five Herbig Ae/Be stars (HD163296,
HD104237, HD98922, MWC297, V921Sco) in the BrG emission line as well as in the
adjacent continuum. All objects (except MWC297) show an increase of visibility
within the BrG emission line, indicating that the BrG-emitting region in these
objects is more compact than the dust sublimation radius. For HD98922, our
quantitative analysis reveals that the line-emitting region is compact enough
to be consistent with the magnetospheric accretion scenario. For HD163296,
HD104237, MWC297, and V921Sco we identify a stellar wind or a disk wind as the
most likely line-emitting mechanism. We search for general trends and find that
the size of the BrG-emitting region does not seem to depend on the basic
stellar parameters, but correlates with the H-alpha line profile shape. We find
evidence for at least two distinct BrG line-formation mechanisms. Stars with a
P-Cygni H-alpha line profile and a high mass-accretion rate seem to show
particularly compact BrG-emitting regions (R_BrG/R_cont<0.2), while stars with
a double-peaked or single-peaked H-alpha-line profile show a significantly more
extended BrG-emitting region (0.6<R_BrG/R_cont<1.4), possibly tracing a stellar
wind or a disk wind.


  The interaction of a short, few-cycle light pulse and an atom which is
prepared initially in a superposition of two stationary states is shown to
exhibit strong signatures of atomic coherence. For a given waveform of the
laser pulse, appropriate quantum mechanical relative phase between the
constituents of the initial superposition can increase the ionization
probability by a factor of three. A similarly strong effect can be observed
when the waveform of the ionizing pulse is changed. These results allow for
intuitive explanations, which are in agreement with the numerical integration
of the time dependent Schr\"{o}dinger equation.


  This paper proposes a hybrid fusion-based deep learning approach based on two
different modalities, audio and video, to improve human activity recognition
and violence detection in public places. To take advantage of audiovisual
fusion, late fusion, intermediate fusion, and hybrid fusion-based deep learning
(HFBDL) are used and compared. Since the objective is to detect and recognize
human violence in public places, Real-life violence situation (RLVS) dataset is
expanded and used. Simulating results of HFBDL show 96.67\% accuracy on
validation data, which is more accurate than the other state-of-the-art methods
on this dataset. To showcase our model's ability in real-world scenarios,
another dataset of 54 sounded videos of both violent and non-violent situations
was recorded. The model could successfully detect 52 out of 54 videos
correctly. The proposed method shows a promising performance on real scenarios.
Thus, it can be used for human action recognition and violence detection in
public places for security purposes.


  We use optical trapping to continuously bend an isolated microtubule while
simultaneously measuring the applied force and the resulting filament strain,
thus allowing us to determine its elastic properties over a wide range of
applied strains. We find that, while in the low-strain regime, microtubules may
be quantitatively described in terms of the classical Euler-Bernoulli elastic
filament, above a critical strain they deviate from this simple elastic model,
showing a softening response with increasingdeformations. A three-dimensional
thin-shell model, in which the increased mechanical compliance is caused by
flattening and eventual buckling of the filament cross-section, captures this
softening effect in the high strain regime and yields quantitative values of
the effective mechanical properties of microtubules. Our results demonstrate
that properties of microtubules are highly dependent on the magnitude of the
applied strain and offer a new interpretation for the large variety in
microtubule mechanical data measured by different methods.


  Summarizes recent work on the wakefields and impedances of flat, metallic
plates with small corrugations


  We study a relativistic collisionless electron-positron shock propagating
into an unmagnetized ambient medium using 2D particle-in-cell simulations of
unprecedented duration and size. The shock generates intermittent magnetic
structures of increasingly larger size as the simulation progresses. Toward the
end of our simulation, at around 26,000 plasma times, the magnetic coherence
scale approaches $\lambda\sim 100$ plasma skin depths, both ahead and behind
the shock front. We anticipate a continued growth of $\lambda$ beyond the time
span of our simulation, as long as the shock accelerates particles to
increasingly higher energies. The post-shock field is concentrated in localized
patches, which maintain a local magnetic energy fraction $\varepsilon_B\sim
0.1$. Particles randomly sampling the downstream fields spend most of their
time in low field regions ($\varepsilon_B\ll 0.1$), but emit a large fraction
of the synchrotron power in the localized patches with strong fields
($\varepsilon_B\sim 0.1$). Our results have important implications for models
of gamma-ray burst afterglows.


  Three hard problems! In this talk I investigate the long-distance properties
of quantum chromodynamics in the presence of a topological theta term. This is
done on the lattice, using the gradient flow to isolate the long-distance modes
in the functional integral measure and tracing it over successive length
scales. It turns out that the color fields produced by quarks and gluons are
screened, and confinement is lost, for vacuum angles theta > 0, thus providing
a natural solution of the strong CP problem. This solution is compatible with
recent lattice calculations of the electric dipole moment of the neutron, while
it excludes the axion extension of the Standard Model.


  It is well-known that the degeneracy of two-phase microstructures with the
same volume fraction and two-point correlation function $S_2(\mathbf{r})$ is
generally infinite. To elucidate the degeneracy problem explicitly, we examine
Debye random media, which are entirely defined by a purely exponentially
decaying two-point correlation function $S_2(r)$. In this work, we consider
three different classes of Debye random media. First, we generate the "most
probable" class using the Yeong-Torquato construction algorithm [Yeong and
Torquato, Phys. Rev. E, 57, 495 (1998)]. A second class of Debye random media
is obtained by demonstrating that the corresponding two-point correlation
functions are effectively realized in the first three space dimensions by
certain models of overlapping, polydisperse spheres. A third class is obtained
by using the Yeong-Torquato algorithm to construct Debye random media that are
constrained to have an unusual prescribed pore-size probability density
function. We structurally discriminate these three classes of Debye random
media from one another by ascertaining their other statistical descriptors,
including the pore-size, surface correlation, chord-length probability density,
and lineal-path functions. We also compare and contrast the percolation
thresholds as well as the diffusion and fluid transport properties of these
degenerate Debye random media. We find that these three classes of Debye random
media are generally distinguished by the aforementioned descriptors and their
microstructures are also visually distinct from one another. Our work further
confirms the well-known fact that scattering information is insufficient to
determine the effective physical properties of two-phase media. Additionally,
our findings demonstrate the importance of the other two-point descriptors
considered here in the design of materials with a spectrum of physical
properties.


  The sensitivity of current and future neutrino detectors like
Super-Kamiokande (SK), JUNO, Hyper-Kamiokande (HK), and DUNE is expected to
allow for the detection of the diffuse supernova neutrino background (DSNB).
However, the DSNB model ingredients like the core-collapse supernova (CCSN)
rate, neutrino emission spectra, and the fraction of failed supernovae are not
precisely known. We quantify the uncertainty on each of these ingredients by
(i) compiling a large database of recent star formation rate density
measurements, (ii) combining neutrino emission from long-term axisymmetric
CCSNe simulations and strategies for estimating the emission from the
protoneutron star cooling phase, and (iii) assuming different models of failed
supernovae. Finally, we calculate the fluxes and event rates at multiple
experiments and perform a simplified statistical estimate of the time required
to significantly detect the DSNB at SK with the gadolinium upgrade and JUNO.
Our fiducial model predicts a flux of $5.1\pm0.4^{+0.0+0.5}_{-2.0-2.7}\,{\rm
cm^2~s^{-1}}$ at SK employing Gd-tagging, or $3.6\pm0.3^{+0.0+0.8}_{-1.6-1.9}$
events per year, where the errors represent our uncertainty from star formation
rate density measurements, uncertainty in neutrino emission, and uncertainty in
the failed-supernova scenario. In this fiducial calculation, we could see a
$3\sigma$ detection by $\sim2030$ with SK-Gd and a $5\sigma$ detection by
$\sim2035$ with a joint SK-Gd/JUNO analysis, but background reduction remains
crucial.


  A definition of frames in Krein spaces is proposed which extends the concept
of $J$-frames defined by J.I. Giribet et al., J. Math. Anal. Appl.
${\textbf{393}}$ (2012), 122-137. The principal difference consists in the fact
that a $J$-frame is related to maximal definite subspaces $\mathcal{M}_\pm$
which are not assumed to be uniformly definite. The latter allows one to extend
the collection of $J$-frames. In particular, some complete $J$-orthogonal
sequences and $J$-orthogonal Schauder bases can be interpreted as $J$-frames.


  We compute the motivic nearby cycles of functions obtained by composition
with a polynomial which is non-degenerate with respect to its Newton
polyhedron. Our result involves new convolution operators and generalized
nearby cycles.


  We show that closed starshaped hypersurfaces of space forms with almost
constant mean curvature or almost constant higher order mean curvature are
closed to geodesic spheres.


  We compare dynamical nonequilibrium molecular dynamics and continuum
simulations of the dynamics of relaxation of a fluid system characterized by a
non uniform density profile. Results match quite well as long as the
lengthscale of density nonuniformities are greater than the molecular scale (10
times the molecular size). In presence of molecular scale features some of the
continuum fields (e.g. density and momentum) are in good agreement with
atomistic counterparts, but are smoother. On the contrary, other fields, such
at the temperature field, present very large difference with respect to
reference (atomistic) ones. This is due to the limited accuracy of some of the
empirical relations used in continuum models, the equation of state of the
fluid in the example considered.


  Existing partial sequence labeling models mainly focus on max-margin
framework which fails to provide an uncertainty estimation of the prediction.
Further, the unique ground truth disambiguation strategy employed by these
models may include wrong label information for parameter learning. In this
paper, we propose structured Gaussian Processes for partial sequence labeling
(SGPPSL), which encodes uncertainty in the prediction and does not need extra
effort for model selection and hyperparameter learning. The model employs
factor-as-piece approximation that divides the linear-chain graph structure
into the set of pieces, which preserves the basic Markov Random Field structure
and effectively avoids handling large number of candidate output sequences
generated by partially annotated data. Then confidence measure is introduced in
the model to address different contributions of candidate labels, which enables
the ground-truth label information to be utilized in parameter learning. Based
on the derived lower bound of the variational lower bound of the proposed
model, variational parameters and confidence measures are estimated in the
framework of alternating optimization. Moreover, weighted Viterbi algorithm is
proposed to incorporate confidence measure to sequence prediction, which
considers label ambiguity arose from multiple annotations in the training data
and thus helps improve the performance. SGPPSL is evaluated on several sequence
labeling tasks and the experimental results show the effectiveness of the
proposed model.


  High-entropy alloys are characterized by high configurational entropy. Since
the discovery of high-entropy alloys (HEA) in 2004, entropy engineering has
provided a promising direction for exploiting composition, lattice disorder,
band structure, and microstructure effects to advance thermoelectric
performance. This review discusses the impact of entropy on thermoelectric
properties and looks back at the role of multi-principal-element alloys, a
weaker version of HEA, on the development of compositionally complex
thermoelectric alloys in achieving high thermoelectric performance. The
experimental and theoretical efforts in a wide range of material systems such
as TAGS, LAST, half-Heusler, liquid-like copper chalcogenides, SnTe, and
CuInTe2 chalcopyrites provide insights into the entropy engineering approach
and also promise an emerging paradigm of high-entropy thermoelectrics.


  The cellular adaptive immune response plays a key role in resolving influenza
infection. Experiments where individuals are successively infected with
different strains within a short timeframe provide insight into the underlying
viral dynamics and the role of a cross-reactive immune response in resolving an
acute infection. We construct a mathematical model of within-host influenza
viral dynamics including three possible factors which determine the strength of
the cross-reactive cellular adaptive immune response: the initial naive T cell
number, the avidity of the interaction between T cells and the epitopes
presented by infected cells, and the epitope abundance per infected cell. Our
model explains the experimentally observed shortening of a second infection
when cross-reactivity is present, and shows that memory in the cellular
adaptive immune response is necessary to protect against a second infection.


  Semi-supervised learning (SSL) has become an interesting research area due to
its capacity for learning in scenarios where both labeled and unlabeled data
are available. In this work, we focus on the task of transduction - when the
objective is to label all data presented to the learner - with a mean-field
approximation to the Potts model. Aiming at this particular task we study how
classification results depend on $\beta$ and find that the optimal phase
depends highly on the amount of labeled data available. In the same study, we
also observe that more stable classifications regarding small fluctuations in
$\beta$ are related to configurations of high probability and propose a tuning
approach based on such observation. This method relies on a novel parameter
$\gamma$ and we then evaluate two different values of the said quantity in
comparison with classical methods in the field. This evaluation is conducted by
changing the amount of labeled data available and the number of nearest
neighbors in the similarity graph. Empirical results show that the tuning
method is effective and allows NMF to outperform other approaches in datasets
with fewer classes. In addition, one of the chosen values for $\gamma$ also
leads to results that are more resilient to changes in the number of neighbors,
which might be of interest to practitioners in the field of SSL.


  We present a detailed study of the magnetic and electronic properties of
U$_2$Rh$_3$Si$_5$, a material that has been demonstrated to exhibit a first
order antiferromagnetic phase transition. From a high magnetic field study,
together with extensive experiments in moderate fields, we establish the
magnetic phase diagrams for all crystallographic directions. The possibility of
an electronic phase in a narrow interval above the N\'eel temperature as a
precursor of a magnetic phase is discussed.


  In the spirit of the Newtonian theory, we characterize spherically symmetric
empty space in general relativity in terms of energy density measured by a
static observer and convergence density experienced by null and timelike
congruences. It turns out that space surrounding a static particle is entirely
specified by vanishing of energy and null convergence density. The
electrograv-dual$^{1}$ to this condition would be vanishing of timelike and
null convergence density which gives the dual-vacuum solution representing a
Schwarzschild black hole with global monopole charge$^{2}$ or with cloud of
string dust$^{3}$. Here the duality$^{1}$ is defined by interchange of active
and passive electric parts of the Riemann curvature, which amounts to
interchange of the Ricci and Einstein tensors. This effective characterization
of stationary vacuum works for the Schwarzschild and NUT solutions. The most
remarkable feature of the effective characterization of empty space is that it
leads to new dual spaces and the method can also be applied to lower and higher
dimensions.


  This paper describes the corpus of sockpuppet cases we gathered from
Wikipedia. A sockpuppet is an online user account created with a fake identity
for the purpose of covering abusive behavior and/or subverting the editing
regulation process. We used a semi-automated method for crawling and curating a
dataset of real sockpuppet investigation cases. To the best of our knowledge,
this is the first corpus available on real-world deceptive writing. We describe
the process for crawling the data and some preliminary results that can be used
as baseline for benchmarking research. The dataset will be released under a
Creative Commons license from our project website: http://docsig.cis.uab.edu.


  We have studied the transmission of surface acoustic waves (SAWs) in
ferromagnetic/non-magnetic/ferromagnetic tryilayers. The SAW scattering matrix
is studied for devices with various non-magnetic spacer thickness, which
defines the strength of the interlayer exchange coupling. We find the SAW
transmission amplitude depends on their propagation direction when the two
ferromagnetic layers are coupled antiferromagnetically. The degree of such SAW
nonreciprocity increases with increasing exchange coupling strength and reaches
37 dB/mm for a device with the thinnest spacer layer. These results show the
potential of interlayer exchange coupled synthetic antiferromagnets for viable
acoustic nonreciprocal transmission devices, such as circulators and isolators.


  Auxiliary Classifier GANs (AC-GANs) are widely used conditional generative
models and are capable of generating high-quality images. Previous work has
pointed out that AC-GAN learns a biased distribution. To remedy this, Twin
Auxiliary Classifier GAN (TAC-GAN) introduces a twin classifier to the min-max
game. However, it has been reported that using a twin auxiliary classifier may
cause instability in training. To this end, we propose an Unbiased Auxiliary
GANs (UAC-GAN) that utilizes the Mutual Information Neural Estimator (MINE) to
estimate the mutual information between the generated data distribution and
labels. To further improve the performance, we also propose a novel
projection-based statistics network architecture for MINE. Experimental results
on three datasets, including Mixture of Gaussian (MoG), MNIST and CIFAR10
datasets, show that our UAC-GAN performs better than AC-GAN and TAC-GAN. Code
can be found on the project website.


  Neutrino capture on beta-decaying nuclei is currently the only known
potentially viable method of detection of cosmic background neutrinos. It is
based on the idea of separation of the spectra of electrons or positrons
produced in captures of relic neutrinos on unstable nuclei from those from the
usual $\beta$-decay and requires very high energy resolution of the detector,
comparable to the neutrino mass. In this paper we suggest an alternative method
of discrimination between neutrino capture and $\beta$-decay, based on periodic
variations of angular correlations in inverse beta decay transitions induced by
relic neutrino capture. The time variations are expected to arise due to the
peculiar motion of the Sun with respect to the C$\nu$B rest frame and the
rotation of the Earth about its axis and can be observed in experiments with
both polarized and unpolarized nuclear targets. The main advantage of the
suggested method is that it does not depend crucially on the energy resolution
of detection of the produced $\beta$-particles and can be operative even if
this resolution exceeds the largest neutrino mass.


  The X-ray emission from the jets in Active Galactic Nuclei (AGN) carries
important information on the distributions of relativistic electrons and
magnetic fields on large scales. We reanalyze archival Chandra observations on
the jet of M87 from 2000 to 2016 with a total exposure of 1460 kiloseconds to
explore the X-ray emission characteristics along the jet. We investigate the
variability behaviours of the nucleus and the inner jet component HST-1, and
confirm indications for day-scale X-ray variability in the nucleus
contemporaneous to the 2010 high TeV gamma-ray state. HST-1 shows a general
decline in X-ray flux over the last few years consistent with its synchrotron
interpretation. We extract the X-ray spectra for the nucleus and all knots in
the jet, showing that they are compatible with a single power-law within the
X-ray band. There are indications of the resultant X-ray photon index to
exhibit a trend, with slight but significant index variations ranging from
$\simeq 2.2$ (e.g. in knot D) to $\simeq 2.4-2.6$ (in the outer knots F, A, and
B). When viewed in a multi-wavelength context, a more complex situation is
arising. Fitting the radio to X-ray spectral energy distributions (SEDs)
assuming a synchrotron origin, we show that a broken power-law electron
spectrum with break energy $E_b$ around $1~(300\mu G/B)^{1/2}$ TeV allows a
satisfactorily description of the multi-band SEDs for most of the knots.
However, in the case of knots B, C and D we find indications that an additional
high energy component is needed to adequately reproduce the broadband SEDs. We
discuss the implications and suggest that a stratified jet model may account
for the differences.


  In a given hypercube, draw grid lines parallel to the edges, and consider all
hypercuboids (or hypercubes) whose edges are lying on the grid lines or the
boundary. We find the limit of the value of the ratio of the arithmetic mean of
the volumes of those hypercuboids (or hypercubes) to the entire volume as the
grid spacing becomes smaller.


  It is shown that a topological vector-potential (Berry phase) is induced by
the act of measuring angular momentum in a direction defined by a reference
particle. This vector potential appears as a consequence of the back-reaction
due to the quantum measurement.


  Signal decomposition is an effective tool to assist the identification of
modal information in time-domain signals. Two signal decomposition methods,
including the empirical wavelet transform (EWT) and Fourier decomposition
method (FDM), have been developed based on Fourier theory. However, the EWT can
suffer from a mode mixing problem for signals with closely-spaced modes and
decomposition results by FDM can suffer from an inconsistency problem. An
accurate adaptive signal decomposition method, called the empirical Fourier
decomposition (EFD), is proposed to solve the problems in this work. The
proposed EFD combines the uses of an improved Fourier spectrum segmentation
technique and an ideal filter bank. The segmentation technique can solve the
inconsistency problem by predefining the number of modes in a signal to be
decomposed and filter functions in the ideal filter bank have no transition
phases, which can solve the mode mixing problem. Numerical investigations are
conducted to study the accuracy of the EFD. It is shown that the EFD can yield
accurate and consistent decomposition results for signals with multiple
non-stationary modes and those with closely-spaced modes, compared with
decomposition results by the EWT, FDM, variational mode decomposition and
empirical mode decomposition. It is also shown that the EFD can yield accurate
time-frequency representation results and it has the highest computational
efficiency among the compared methods.


  Recently, the 1/Nc expansion has been applied to the study of exotic baryons
containing both quarks and antiquarks. We extend this approach to exotic states
with mixed symmetric spin-flavor symmetry, which correspond in the quark model
to negative parity pentaquarks, and discuss the large Nc predictions for their
mass spectrum. The heavy exotics Qbar q^4 transform as 3, 6bar,15 and 15' under
flavor SU(3), while the light states include the exotic multiplets 10bar, 27,
35. We give mass relations among these multiplets following from the 1/Nc
expansion. In the quark model, the mass splittings between these states are
given by color-spin interactions. Using the observation of an anticharmed
exotic by the H1 collaboration, we give predictions for the masses of other
expected heavy pentaquarks.


  Chiral rings of two-dimensional (2,2) theories coupled to 4d $\mathcal{N}=2$
theories with matter hypermultiplets are studied. Specifically, the vacua of
the twisted superpotential of the 2d theories with vanishing sum of matter
charges are computed by considering the resolvent of the bulk theory. The
solutions to the chiral ring equations are also obtained from the instanton
partition function via Higgsing for simple surface operators and via the
orbifold description for full surface operators. These 2d/4d coupled theories
are lifted to 3d/5d theories and vacua are found similarly in two different
methods: by solving the 3d chiral ring equations taking into account the effect
of 5d resolvent and by computing the 5d instanton partition function in the
presence of a surface operator. We also check the Seiberg-like duality for both
2d/4d and 3d/5d coupled systems with a specific Chern-Simons coefficient for
the latter.


  Stringy cosmology displays features that are different from standard
cosmology. One may be surprised in that in this scenario there is no phase
transition between the radiation dominated phase and matter dominated phase and
the universe is cyclic similar to brane cyclic cosmology.