  In the first part of this article we prove that one of the conditions
required in the original definition of nearly Frobenius algebra, the
coassociativity, is redundant. Also, we determine the Frobenius dimension of
the product and tensor product of two nearly Frobenius algebras from the
Frobenius dimension of each of them. We apply these results to semisimple
algebras.
  In the second part we introduce the notion of normalized nearly Frobenius
algebra. We prove a series of equivalences: the concept of normalized nearly
Frobenius algebra is equivalent to the concept of separable algebra, equivalent
to the fact that the algebra is projective as a bimodule on itself and,
finally, equivalent to the category of bimodules is semisimple. Also, we relate
these concepts with the property of semisimplicity of the category of modules
over the algebra.


  Human crowds base most of their behavioral decisions upon anticipated states
of their walking environment. We explore a minimal version of a lattice model
to study lanes formation in pedestrian counterflow. Using the concept of
horizon depth, our simulation results suggest that the anticipation effect
together with the presence of a small background noise play an important role
in promoting collective behaviors in a counterflow setup. These ingredients
facilitate the formation of seemingly stable lanes and ensure the ergodicity of
the system.


  Network-assisted full-duplex (NAFD) distributed massive multiple input
multiple output (M-MIMO) enables the in-band full-duplex with existing
half-duplex devices at the network level, which exceptionally improves spectral
efficiency. This paper analyzes the impact of low-resolution analog-to-digital
converters (ADCs) on NAFD distributed M-MIMO and designs an efficient bit
allocation algorithm for low-resolution ADCs. The beamforming training
mechanism relieves the heavy pilot overhead for channel estimation, which
remarkably enhances system performance by guiding the interference cancellation
and coherence detection. Furthermore, closed-form expressions for spectral and
energy efficiency with low-resolution ADCs are derived. The multi-objective
optimization problem (MOOP) for spectral and energy efficiency is solved by the
deep Q network and the non-dominated sorting genetic algorithm II. The
simulation results corroborate the theoretical derivation and verify the
effectiveness of introducing low-resolution ADCs in NAFD distributed M-MIMO
systems. Meanwhile, a set of Pareto-optimal solutions for ADC accuracy flexibly
provide guidelines for deploying in a practical NAFD distributed M-MIMO system.


  By combining our UVES-VLT spectra of a sample of four damped Ly-alpha systems
(DLAs) toward the quasars Q0100+13, Q1331+17, Q2231-00 and Q2343+12 with the
existing HIRES-Keck spectra, we covered the total spectral range from 3150 to
10000 A for the four quasars. This large wavelength coverage and the high
quality of the spectra allowed us to measure the column densities of up to 21
ions, namely of 15 elements - N, O, Mg, Al, Si, P, S, Cl, Ar, Ti, Cr, Mn, Fe,
Ni, Zn. Such a large amount of information is necessary to constrain the
photoionization and dust depletion effects, two important steps in order to
derive the intrinsic chemical abundance patterns of DLAs. We evaluated the
photoionization effects with the help of the Al+/Al++, Fe+/Fe++, N0/N+ and
Ar/Si,S ratios, and computed dust corrections. Our analysis revealed that the
DLA toward Q2343+12 requires important ionization corrections. The access to
the complete series of relatively robust intrinsic elemental abundances in the
other three DLAs allowed us to constrain their star formation history, their
age and their star formation rate by a detailed comparison with a grid of
chemical evolution models for spiral and dwarf irregular galaxies. Our results
show that the galaxies associated with these three DLAs in the redshift
interval z_abs = 1.7-2.5 are either outer regions of spiral disks (radius >= 8
kpc) or dwarf irregular galaxies (with a bursting or continuous star formation
history) with ages varying from some 50 Myr only to >~ 3.5 Gyr and with
moderate star formation rates per unit area of -2.1 < log \psi < -1.5 M_{sol}
yr^{-1} kpc^{-2}.


  We study the effect of naval nuclear reactors on the study of neutrino
oscillations. We find that the presence of naval reactors at unknown locations
and times may limit the accuracy of future very long baseline reactor-based
neutrino oscillation experiments. At the same time we argue that a nuclear
powered surface ship such as a large Russian ice-breaker may provide an ideal
source for precision experiments. While the relatively low reactor power would
in this case require a larger detector, the source could be conveniently
located at essentially any distance from a detector built at an underground
location near a shore in a region of the world far away from other nuclear
installations. The variable baseline would allow for a precise measurement of
backgrounds and greatly reduced systematics from reactor flux and detector
efficiency. In addition, once the oscillation measurement is completed, the
detector could perform geological neutrino and astrophysical measurements with
minimal reactor background.


  This work focuses on the application related to the detection of low moisture
and environmental pollutants. A novel gas sensor with inkjet printed Double
Walled Carbon Nano Tubes (DWNTs) on a Love wave sensor platform was developed
for Volatile Organic Compounds (VOCs) and humidity detection application. The
experiments were conducted in real-time at ambient conditions. Results
demonstrate the adsorption of vapor compounds on DWNTs sensitive material and
leads for example to frequency shifts of 1.97 kHz and 2.93 kHz with 120 ppm of
ethanol vapor and 6.22 % RH, respectively.


  The partition function of four-dimensional N=2 superconformal field theories
on S^4 computes the exact Kahler potential on the space of exactly marginal
couplings [1]. We present a new elementary proof of this result using
supersymmetry Ward identities. The partition function is a section rather than
a function, and is subject to ambiguities coming from Kahler transformations
acting on the Kahler potential. This ambiguity is realized by a local
supergravity counterterm in the underlying SCFT. We provide an explicit
construction of the Kahler ambiguity counterterm in the four dimensional N=2
off-shell supergravity theory that admits S^4 as a supersymmetric background.


  The persistent discrepancy of about 3.5 standard deviations between the
experimental measurement and the Standard Model prediction for the muon
anomalous magnetic moment, $a_\mu$, is one of the most promising hints for the
possible existence of new physics. Here we report on our lattice QCD
calculation of the hadronic vacuum polarisation contribution $a_\mu^{\rm hvp}$,
based on gauge ensembles with $N_f=2+1$ flavours of O($a$) improved Wilson
quarks. We address the conceptual and numerical challenges that one encounters
along the way to a sub-percent determination of the hadronic vacuum
polarisation contribution. The current status of lattice calculations of
$a_\mu^{\rm hvp}$ is presented by performing a detailed comparison with the
results from other groups.


  You & Cheng (1980) argued that, for relativistic electrons moving through a
dense gas, the Cerenkov effect will produce peculiar atomic and/or molecular
emission lines--Cerenkov lines. They presented a series of formulae to describe
the new line-mechanism. Elegant experimental confirmation has been obtained by
Xu. et al. in the laboratory which definitely verified the existence of
Cerenkov lines. Owing to the potential importance in high energy astrophysics,
in this paper we give a more detailed physical discussion of the emission
mechanisms and improve the previous formulae system into a form which is more
convenient for astrophysical application. Specifically, the extended formulae
in this paper can be applied to other species of atoms and/or ions rather than
hydrogen as in the previous paper, and also to X-ray astronomy. They can also
be used for the calculation of a nonuniform plane-parallel slab of the emissive
gas.


  We suggest a new protocol for the information reconciliation stage of quantum
key distribution based on polar codes. The suggested approach is based on the
blind technique, which is proved to be useful for low-density parity-check
(LDPC) codes. We show that the suggested protocol outperforms the blind
reconciliation with LDPC codes, especially when there are high fluctuations in
quantum bit error rate (QBER).


  We describe an algorithm that associates to each positive real number $r$ and
each finite collection $C_r$ of planar pixels of size $r$ a planar piecewise
linear set $S_r$ with the following additional property: if $C_r$ is the
collection of pixels of size $r$ that touch a given compact semialgebraic set
$S$, then the normal cycle of $S_r$ converges to the normal cycle of $S$ in the
sense of currents. In particular, in the limit we can recover the homotopy type
of $S$ and its geometric invariants such as area, perimeter and curvature
measures. At its core, this algorithm is a discretization of stratified Morse
theory.


  We show that if a simply connected manifold is almost quarter pinched then it
is diffeomorphic to a CROSS (a compact rank one symmetric space) or a sphere.


  Large-scale vision foundation models have made significant progress in visual
tasks on natural images, with vision transformers being the primary choice due
to their good scalability and representation ability. However, large-scale
models in remote sensing (RS) have not yet been sufficiently explored. In this
paper, we resort to plain vision transformers with about 100 million parameters
and make the first attempt to propose large vision models tailored to RS tasks
and investigate how such large models perform. To handle the large sizes and
objects of arbitrary orientations in RS images, we propose a new rotated
varied-size window attention to replace the original full attention in
transformers, which can significantly reduce the computational cost and memory
footprint while learning better object representation by extracting rich
context from the generated diverse windows. Experiments on detection tasks show
the superiority of our model over all state-of-the-art models, achieving 81.24%
mAP on the DOTA-V1.0 dataset. The results of our models on downstream
classification and segmentation tasks also show competitive performance
compared to existing advanced methods. Further experiments show the advantages
of our models in terms of computational complexity and data efficiency in
transferring.


  The purpose of this paper is to analyze certain statistics of a recently
introduced non-uniform random tree model, biased recursive trees. This model is
based on constructing a random tree by establishing a correspondence with
non-uniform permutations, biased riffle shuffles. The statistics that are
treated include the number of nodes with a given number of descendants, the
depth of the tree, and the number of branches. The model yields the uniform
recursive trees as a certain limit, some new results for the uniform case are
obtained as well.


  This paper aims to classify the holonomy of the conformal Tractor connection,
and relate these holonomies to the geometry of the underlying manifold. The
conformally Einstein case is dealt with through the construction of metric
cones, whose Riemmanian holonomy is the same as the Tractor holonomy of the
underlying manifold. Direct calculations in the Ricci-flat case and an
important decomposition theorem complete the classification for definitive
signature.


  Let p be a prime number, G a finite group, and A a finite group acting on G.
The Brown poset of nonidentity p-subgroups of G is then an A-poset. We
investigate the equivariant subposet and the equivariant Euler characteristics
and establish a global relation between locally defined Euler characteristics
and the number of p-elements of G centralized by A. It is a consequence of this
relation that the equivariant version of Brown's theorem holds: The reduced
Euler characteristic of the A-equivariant Brown poset is divisible by the
p-part of the order of the centralizer of A. The second equivariant Euler
characteristic for the conjugation of G on the Brown poset for G is especially
intriguing because of its relation to the Knorr-Robinson conjecture and we
carry out a concrete numerical verification of the conjecture in case of the
smallest simple Mathieu group.


  Devising intelligent agents able to live in an environment and learn by
observing the surroundings is a longstanding goal of Artificial Intelligence.
From a bare Machine Learning perspective, challenges arise when the agent is
prevented from leveraging large fully-annotated dataset, but rather the
interactions with supervisory signals are sparsely distributed over space and
time. This paper proposes a novel neural-network-based approach to
progressively and autonomously develop pixel-wise representations in a video
stream. The proposed method is based on a human-like attention mechanism that
allows the agent to learn by observing what is moving in the attended
locations. Spatio-temporal stochastic coherence along the attention trajectory,
paired with a contrastive term, leads to an unsupervised learning criterion
that naturally copes with the considered setting. Differently from most
existing works, the learned representations are used in open-set
class-incremental classification of each frame pixel, relying on few
supervisions. Our experiments leverage 3D virtual environments and they show
that the proposed agents can learn to distinguish objects just by observing the
video stream. Inheriting features from state-of-the art models is not as
powerful as one might expect.


  The nonequilibrium dynamics of the chiral phase transition expected during
the expansion of the quark-qluon plasma produced in a high energy hadron or
heavy ion collision is studied in the O(4) linear sigma model to leading order
in a large $N$ expansion. Starting from an approximate equilibrium
configuration at an initial proper time $\tau$ in the disordered phase we study
the transition to the ordered broken symmetry phase as the system expands and
cools. We give results for the proper time evolution of the effective pion
mass, the order parameter $<\sigma>$ as well as for the pion two point
correlation function expressed in terms of a time dependent phase space number
density and pair correlation density. We determine the phase space of initial
conditions that lead to instabilities (exponentially growing long wave length
modes) as the system evolves in time. These instabilities are what eventually
lead to disoriented chiral condensates. In our simulations,we found that
instabilities that are formed during the initial phases of the expansion exist
for proper times that are at most $3 fm/c$ and lead to condensate regions that
do not contain large numbers of particles. The damping of instabilities is a
consequence of strong coupling.


  We show that the microwave (MW) spectra in semiconductor-nanowire-based
transmon qubits provide a strong signature of the presence of Majorana bound
states in the junction. This occurs as an external magnetic field tunes the
wire into the topological regime and the energy splitting of the emergent
Majorana modes oscillates around zero energy owing to their wave function
spatial overlap in finite-length wires. In particular, we discuss how these
Majorana oscillations, and the concomitant fermion parity switches in the
ground state of the junction, result in distinct spectroscopic features --in
the form of an intermitent visibility of absorption lines-- that strongly
deviate from standard transmon behavior. In contrast, non-oscillating zero
modes, such as topologically trivial Andreev bound states resulting from
sufficiently smooth potentials, exhibit an overall standard transmon response.
These differences in the MW response could help determine whether the junction
contains topological Majoranas or not.


  Liquid-liquid phase separation is ubiquitous in suspensions of nanoparticles,
proteins and colloids. With a few notable exceptions,
surface-tension-minimizing liquid droplets in bulk suspensions continuously
coalesce, increasing in size without bound until achieving macroscale phase
separation. In comparison, the phase behavior of colloids, nanoparticles or
proteins confined to interfaces, surfaces or membranes is significantly more
complex. Inclusions distort the local interface structure leading to
interactions that are fundamentally different from the well-studied
interactions mediated by isotropic solvents. Here, we investigate liquid-liquid
phase separation in monolayer membranes composed of dissimilar chiral colloidal
rods. We demonstrate that colloidal rafts are a ubiquitous feature of binary
colloidal membranes. We measure the raft free energy landscape by visualizing
its assembly kinetics. Subsequently, we quantify repulsive raft-raft
interactions and relate them to directly imaged raft-induced membrane
distortions, demonstrating that particle chirality plays a key role in this
microphase separation. At high densities, rafts assemble into cluster crystals
which constantly exchange monomeric rods with the background reservoir to
maintain a self-limited size. Lastly, we demonstrate that rafts can form bonds
to assemble into higher-order supra-structures. Our work demonstrates that
membrane-mediated liquid-liquid phase separation can be fundamentally different
from the well-characterized behavior of bulk liquids. It outlines a robust
membrane-based pathway for assembly of monodisperse liquid clusters which is
complementary to existing methods which take place in bulk suspensions.
Finally, it reveals that chiral inclusions in membranes acquire long-ranged
repulsive interactions, which might play a role in stabilizing assemblages of
finite size.


  This paper is concerned with the development of a hybrid data-driven
technique for unsteady fluid-structure interaction systems. The proposed
data-driven technique combines the deep learning framework with a
projection-based low-order modeling. While the deep learning provides
low-dimensional approximations from datasets arising from black-box solvers,
the projection-based model constructs the low-dimensional approximations by
projecting the original high-dimensional model onto a low-dimensional subspace.
Of particular interest of this paper is to predict the long time series of
unsteady flow fields of a freely vibrating bluff-body subjected to wake-body
synchronization. We consider convolutional neural networks (CNN) for the
learning dynamics of wake-body interaction, which assemble layers of linear
convolutions with nonlinear activations to automatically extract the
low-dimensional flow features. Using the high-fidelity time series data from
the stabilized finite element Navier-Stokes solver, we first project the
dataset to a low-dimensional subspace by proper orthogonal decomposition (POD)
technique. The time-dependent coefficients of the POD subspace are mapped to
the flow field via a CNN with nonlinear rectification, and the CNN is
iteratively trained using the stochastic gradient descent method to predict the
POD time coefficient when a new flow field is fed to it. The time-averaged flow
field, the POD basis vectors, and the trained CNN are used to predict the long
time series of the flow fields and the flow predictions are quantitatively
assessed with the full-order (high-dimensional) simulation data. The proposed
POD-CNN model based on the data-driven approximation has a remarkable accuracy
in the entire fluid domain including the highly nonlinear near wake region
behind a freely vibrating bluff body.


  In this study, we present the Differential Spiral Joint (DSJ) mechanism for
variable stiffness actuation in tendon-driven robots. The DSJ mechanism
semi-decouples the modulation of position and mechanical stiffness, allowing
independent trajectory tracking in different parameter space. Past studies show
that increasing the mechanical stiffness achieves the wider range of renderable
stiffness, whereas decreasing the mechanical stiffness improves the quality of
actuator decoupling and shock absorbance. Therefore, it is often useful to
modulate the mechanical stiffness to balance the required level of stiffness
and safety. In addition, the DSJ mechanism offers a compact form factor, which
is suitable for applications where the size and weight are important. The
performance of the DSJ mechanism in various areas is validated through a set of
experiments.


  We adopt the effective Lagrangian approach to study the strong decays of the
$1^-(0^{++})$ $D^\ast\bar{D}^\ast$ molecular state [denoted as
$T_{\psi0}^a(4010)$ according to the LHCb naming convention] through triangle
diagrams. The decay channels include the open-charm $D\bar{D}$, and the
hidden-charm $\eta_c\pi$, $J/\psi\rho$, and $\chi_{c1}\pi$. The coupling
between the $T_{\psi0}^a(4010)$ and its constituents $D^\ast\bar{D}^\ast$ is
obtained by solving for the residue of the scattering T-matrix at the pole. Our
calculation yields a total width of $(12.0$$-$$35.4)$ MeV for the
$T_{\psi0}^a(4010)$ state, with its main decay channels being $\eta_c\pi$ and
$\chi_{c1}\pi$. The $X(4100)$ and $X(4050)$ have similar masses and widths,
with both masses being close to the $D^\ast\bar{D}^\ast$ threshold.
Additionally, their decay final states are consistent with those of the
$T_{\psi0}^a(4010)$. Therefore, it is likely that they represent the same state
and both potentially correspond to the $T_{\psi0}^a(4010)$. We suggest that
future experiments focus on searching for the $T_{\psi0}^a(4010)$ signal in the
final states $\eta_c\pi^-$, $\chi_{c1}\pi^-$ and $D^0D^-$ of the
$B^0\to\eta_c\pi^-K^+$, $\chi_{c1}\pi^- K^+$ and $D^0D^-K^+$ processes,
respectively, as well as further investigating its resonance parameters with
Flatt\'e-like formula.


  We prove an approximation theorem on a class of domains in $\mathbb{C}^n$ on
which the $\overline{\partial}$-problem is solvable in $L^{\infty}$.
Furthermore, as a corollary, we obtain a version of the
Axler-\v{C}u\v{c}kovi\'c-Rao Theorem in higher dimensions.


  We provide some comments about the constraints on the inflationary models
inferred from the two Swampland criteria which have been recently proposed. In
particular we argue that, in the absence of any knowledge about the origin of
the adiabatic curvature perturbations, within the slow-roll single field models
of inflation there is no tension between the swampland criteria and the current
lower bound on the tensor-to-scalar ratio.


  We examine three versions of non-relativistic electrodynamics, known as the
electric and magnetic limit theories of Maxwell's equations and Galilean
electrodynamics (GED) which is the off-shell non-relativistic limit of Maxwell
plus a free scalar field. For each of these three cases we study the couplings
to non-relativistic dynamical charged matter (point particles and charged
complex scalars). The GED theory contains besides the electric and magnetic
potentials a so-called mass potential making the mass parameter a local
function. The electric and magnetic limit theories can be coupled to twistless
torsional Newton-Cartan geometry while GED can be coupled to an arbitrary
torsional Newton-Cartan background. The global symmetries of the electric and
magnetic limit theories on flat space consist in any dimension of the infinite
dimensional Galilean conformal algebra and a $U(1)$ current algebra. For the
on-shell GED theory this symmetry is reduced but still infinite dimensional,
while off-shell only the Galilei algebra plus two dilatations remain. Hence one
can scale time and space independently, allowing Lifshitz scale symmetries for
any value of the critical exponent $z$.


  We present some simple examples of smooth projective varieties in positive
characteristic, arising from linear algebra, which do not admit a lifting
neither to characteristic zero, nor to the ring of second Witt vectors. Our
first construction is the blow-up of the graph of the Frobenius morphism of a
homogeneous space. The second example is a blow-up of $\mathbb{P}^3$ in a
'purely characteristic-$p$' configuration of points and lines.


  This paper addresses lossy transmission of a common source over a broadcast
channel when there is correlated side information at the receivers, with
emphasis on the quadratic Gaussian and binary Hamming cases. A digital scheme
that combines ideas from the lossless version of the problem, i.e.,
Slepian-Wolf coding over broadcast channels, and dirty paper coding, is
presented and analyzed. This scheme uses layered coding where the common layer
information is intended for both receivers and the refinement information is
destined only for one receiver. For the quadratic Gaussian case, a quantity
characterizing the overall quality of each receiver is identified in terms of
channel and side information parameters. It is shown that it is more
advantageous to send the refinement information to the receiver with "better"
overall quality. In the case where all receivers have the same overall quality,
the presented scheme becomes optimal. Unlike its lossless counterpart, however,
the problem eludes a complete characterization.


  A holographic interpretation for some specific Ricci flat string backgrounds
of the form $A_6\times C_4$ is proposed. The conjecture is that there is a
Four-dimensional Euclidean Conformal Field Theory (ECFT) defined on a
codimension two {\em submanifold} of the manifold $A_6$ (where one of the two
remaining {\em holographic} coordinates of $A_6$ is timelike, and the other one
spacelike), with central charge proportional to the radius of curvature of the
six-dimensional manifold, $c\sim l^4$.


  We report on the observation of Faraday waves in a cigar-shaped Fermi
superfluid of $^6$Li parametrically excited by modulating the radial trap
frequency. We characterize the phenomenon as a function of the interaction
parameter by means of a Feshbach resonance. Starting from the BEC side of the
resonance we observe a drop on the visibility of the Faraday pattern as we
approach to unitarity, possibly due to the increased incompressibility of the
system. We probe the superfluid excitation spectrum by extracting an effective
1D speed of sound for different values of the interaction parameter, in good
agreement with numerical simulations. Finally, we perform a stability analysis
in the parameter space using a simplified model and we show the emergence of
the Faraday waves as unstable solutions to a Mathieu-like equation.


  Data science and informatics tools are developing at a blistering rate, but
their users often lack the educational background or resources to efficiently
apply the methods to their research. Training resources often deprecate because
their maintenance is not prioritized by funding, giving teams little time to
devote to such endeavors. Our group has developed Open-source Tools for
Training Resources (OTTR) to offer greater efficiency and flexibility for
creating and maintaining online course content. OTTR empowers creators to
customize their work and allows for a simple workflow to publish using multiple
platforms. OTTR allows content creators to publish material to multiple massive
online learner communities using familiar rendering mechanics. OTTR allows the
incorporation of pedagogical practices like formative and summative assessments
in the form of multiple choice questions and fill in the blank problems that
are automatically graded. No local installation of any software is required to
begin creating content with OTTR. Thus far, 15 courses have been created with
OTTR repository template. By using the OTTR system, the maintenance workload
for updating these courses across platforms has been drastically reduced.


  Stimulated by the results of NMR experiments with superfluid $^3$He in
"nematically ordered" aerogel [1] we report on the results of phenomenological
analysis of stability of different phases of superfluid $^3$He subjected to a
strong homogeneous uniaxial anisotropy. On a basis of this analysis we suggest
a form of the order parameter for the new ESP2 phase observed in the quoted
experiments. In the weak coupling limit the suggested order parameter
approaches that of the axi-planar phase. We discuss a possible experimental
check of the proposed identification of the new phase.


  It is shown how the evidence state space in quantum bit commitment may be
made to depend on the bit value 0 or 1 with split entangled pairs. As a
consequence, one can obtain a protocol that is perfectly concealing, but is
also $\epsilon$-binding because the bit-value dependent evidence space prevents
the committing party from cheating by means of a local transformation that is
independent of the part of evidence state space that has never been in his
possession.


  The longest microlensing events provide enough information to estimate the
mass and distance of the lens. Among hundreds of millions of stars which were
monitored for many years by the OGLE project we selected those with clear
parallax effect and derived the mass function of lensing objects in the Milky
Way. We also found candidates for microlensing stellar-mass single black holes.
We discuss how Gaia superb astrometry will help in measuring masses of remnants
in currently on-going and future microlensing events.


  Quality control lead times are one of most significant causes of loss of time
in the pharmaceutical and cosmetics industries. This is partly due to the
organization of laboratories that feature parallel multipurpose machines for
chromatographic analyses. The testing process requires long setup times and
operators are needed to launch the process. The various controls are
non-preemptive and are characterized by a release date, a due date and
available routings. These quality processes lead to significant delays, and we
therefore evaluate the total tardiness criterion. Previous heuristics were
defined for the total tardiness criterion, parallel machines, and setup such as
ATC (Apparent Tardiness Cost) and ATCS (ATC with setups). We propose new rules
and a simulated annealing procedure in order to minimize total tardiness.


  We present a novel proposal strategy for the Metropolis-Hastings algorithm
designed to efficiently sample general convex polytopes in 100 or more
dimensions. This improves upon previous sampling strategies used for free-form
reconstruction of gravitational lenses, but is general enough to be applied to
other fields. We have written a parallel implementation within the lens
modeling framework GLASS. Testing shows that we are able to produce uniform
uncorrelated random samples which are necessary for exploring the degeneracies
inherent in lens reconstruction.


In this note we classify the primitive ideals in finite W-algebras of type A.


  We examine a well resolved 8 arcsec lensed image that is symmetrically bent
in the middle by an edge-on lenticular galaxy, in the Hubble Frontier Field
(HFF) data of MACSJ0416.1-20403. The lengthy image is generated primarily by
the large tangential shear of the cluster with a local secondary deflection by
the member galaxy out to a limiting radius of about 18 kpc. The lensing
lenticular galaxy is also well resolved and evidently lies nearly edge-on in
projection. This fortuitous combination of a long arc intersecting an edge on
galaxy provides us with an opportunity to place relatively strong constraints
on the lensing effect of this galaxy. We can model the stellar lensing
contribution using the observed pixels belonging to the galaxy, in 2D, and we
add to this a standard parameterised dark halo component. Irrespective of the
detailed choice of parameters we obtain a combined total mass of about 3E11
Msun. Depending on the dark halo parameters, the stellar contribution to this
is limited to the range 5-15E10 Msun or 20-50 percent of the total mass, in
good agreement with the independent stellar mass computed from the photometry
of 5E10 Msun for a Chabrier IMF, or 8E10Msun for a Salpeter IMF. The major axis
of the DM halo is constrained to be nearly orthogonal to the plane of the
galaxy, within a range of about 15 degrees, and with an ellipticity e=015
corresponding to an axis ratio a/c=0.54. We show that these conclusions are
very weakly dependent on the model of the cluster, or the additional influence
of neighbouring galaxies or the properties of the lensed source. Alternative
theories of gravity where the radial dependence is modified to avoid the need
for DM are challenged by this finding since generically these must be tied to
the baryonic component which here is a stellar disk oriented nearly
orthogonally to the lensed image deflection.


  Graph is an important data representation which appears in a wide diversity
of real-world scenarios. Effective graph analytics provides users a deeper
understanding of what is behind the data, and thus can benefit a lot of useful
applications such as node classification, node recommendation, link prediction,
etc. However, most graph analytics methods suffer the high computation and
space cost. Graph embedding is an effective yet efficient way to solve the
graph analytics problem. It converts the graph data into a low dimensional
space in which the graph structural information and graph properties are
maximally preserved. In this survey, we conduct a comprehensive review of the
literature in graph embedding. We first introduce the formal definition of
graph embedding as well as the related concepts. After that, we propose two
taxonomies of graph embedding which correspond to what challenges exist in
different graph embedding problem settings and how the existing work address
these challenges in their solutions. Finally, we summarize the applications
that graph embedding enables and suggest four promising future research
directions in terms of computation efficiency, problem settings, techniques and
application scenarios.


  We discuss a two-parameter family of maps that generalize piecewise linear,
expanding maps of the circle. One parameter measures the effect of a
non-linearity which bends the branches of the linear map. The second parameter
rotates points by a fixed angle. For small values of the nonlinearity
parameter, we compute the invariant measure and show that it has a singular
density to first order in the nonlinearity parameter. Its Fourier modes have
forms similar to the Weierstrass function. We discuss the consequences of this
singularity on the Lyapunov exponents and on the transport properties of the
corresponding multibaker map. For larger non-linearities, the map becomes
non-hyperbolic and exhibits a series of period-adding bifurcations.


  Population extinction is a rare event which requires overcoming an effective
barrier. We show that the extinction rate can be fragile: a small change in the
system parameters leads to an exponentially strong change of the rate, with the
barrier height depending on the parameters nonanalytically. General conditions
of the fragility are established. The fragility is found in one of the
best-known models of epidemiology, the SIS model. The analytical expressions
are compared with simulations.


  Stochastic optimal control problems have a long tradition in applied
probability, with the questions addressed being of high relevance in a
multitude of fields. Even though theoretical solutions are well understood in
many scenarios, their practicability suffers from the assumption of known
dynamics of the underlying stochastic process, raising the statistical
challenge of developing purely data-driven strategies. For the mathematically
separated classes of continuous diffusion processes and L\'evy processes, we
show that developing efficient strategies for related singular stochastic
control problems can essentially be reduced to finding rate-optimal estimators
with respect to the sup-norm risk of objects associated to the invariant
distribution of ergodic processes which determine the theoretical solution of
the control problem. From a statistical perspective, we exploit the exponential
$\beta$-mixing property as the common factor of both scenarios to drive the
convergence analysis, indicating that relying on general stability properties
of Markov processes is a sufficiently powerful and flexible approach to treat
complex applications requiring statistical methods. We show moreover that in
the L\'evy case $-$ even though per se jump processes are more difficult to
handle both in statistics and control theory $-$ a fully data-driven strategy
with regret of significantly better order than in the diffusion case can be
constructed.


  In this paper, we present a first comparison of different Adaptive Optics
(AO) concepts to reach a given scientific specification for 3D spectroscopy on
Extremely Large Telescope (ELT). We consider that a range of 30%-50% of
Ensquarred Energy (EE) in H band (1.65um) and in an aperture size from 25 to
100mas is representative of the scientific requirements. From these preliminary
choices, different kinds of AO concepts are investigated : Ground Layer
Adaptive Optics (GLAO), Multi-Object AO (MOAO) and Laser Guide Stars AO (LGS).
Using Fourier based simulations we study the performance of these AO systems
depending on the telescope diameter.


  Large-scale diffuse radio emission is observed in some clusters of galaxies.
There is ample of evidence that the emission has its origin in synchrotron
losses of relativistic electrons, accelerated in the course of clusters
mergers. In a cosmological simulation we locate the structure formation shocks
and estimate their radio emission. We proceed as follows: Introducing a novel
approach to identify strong shock fronts in an SPH simulation, we determine the
Mach number as well as the downstream density and temperature in the
MareNostrum Universe simulation which has 2x1024^3 particles in a 500 Mpc/h box
and was carried out with non-radiative physics. Then, we estimate the radio
emission using the formalism derived in Hoeft & Brueggen (2007) and produce
artificial radio maps of massive clusters. Several of our clusters show radio
objects with similar morphology to large-scale radio relics found in the sky,
whereas about half of the clusters show only very little radio emission. In
agreement with observational findings, the maximum diffuse radio emission of
our clusters depends strongly on their X-ray temperature. We find that the
so-called accretion shocks cause only very little radio emission. We conclude
that a moderate efficiency of shock acceleration, namely xi_e <= 0.005, and
moderate magnetic fields in the region of the relics, namely 0.07 to 0.8
muGauss are sufficient to reproduce the number density and luminosity of radio
relics.


  Quasinormal modes of scalar, electromagnetic, and gravitational fields in the
extreme Schwarzschild-de Sitter background are known to be expressed in
analytic form as eigenvalues of the P\"oschl-Teller wavelike equation. We show
that perturbations of fermionic fields (given by Dirac and Rarita-Schwinger
equations) do not lead to the P\"oschl-Teller effective potential.
Nevertheless, using the Frobenius method we find quasinormal modes analytically
in this case as well. We write down the analytical formula for quasinormal
frequencies of the near-extreme Schwarzschild-de Sitter black holes, which is
valid for both bosonic and fermionic fields. We further extend the analysis to
the case of charged rotating black holes and find a general analytical formula
for quasinormal modes of the fields of various spin for the near extreme
Kerr-Newman-de Sitter spacetime.


  Integrable quantum field theories in 1+1 dimensions have recently become
amenable to a rigorous construction, but many questions about the structure of
their local observables remain open. Our goal is to characterize these local
observables in terms of their expansion coefficients in a series expansion by
interacting annihilators and creators, similar to form factors. We establish a
rigorous one-to-one characterization, where locality of an observable is
reflected in analyticity properties of its expansion coefficients; this
includes detailed information about the high-energy behaviour of the observable
and the growth properties of the analytic functions. Our results hold for
generic observables, not only smeared pointlike fields, and the characterizing
conditions depend only on the localization region - we consider wedges and
double cones - and on the permissible high energy behaviour.


  Understanding the architecture is vital for effectively maintaining and
managing large software systems. However, as software systems evolve over time,
their architectures inevitably change. To keep up with the change, architects
need to track the implementation-level changes and update the architectural
documentation accordingly, which is time-consuming and error-prone. Therefore,
many automatic architecture recovery techniques have been proposed to ease this
process. Despite efforts have been made to improve the accuracy of architecture
recovery, existing solutions still suffer from two limitations. First, most of
them only use one or two type of information for the recovery, ignoring the
potential usefulness of other sources. Second, they tend to use the information
in a coarse-grained manner, overlooking important details within it. To address
these limitations, we propose SARIF, a fully automated architecture recovery
technique, which incorporates three types of comprehensive information,
including dependencies, code text and folder structure. SARIF can recover
architecture more accurately by thoroughly analyzing the details of each type
of information and adaptively fusing them based on their relevance and quality.
To evaluate SARIF, we collected six projects with published ground-truth
architectures and three open-source projects labeled by our industrial
collaborators. We compared SARIF with nine state-of-the-art techniques using
three commonly-used architecture similarity metrics and two new metrics. The
experimental results show that SARIF is 36.1% more accurate than the best of
the previous techniques on average. By providing comprehensive architecture,
SARIF can help users understand systems effectively and reduce the manual
effort of obtaining ground-truth architectures.


  We present observations of the optical ``moving lines'' in spectra of the
Galactic relativistic jet source SS433 spread over a twenty year baseline from
1979 to 1999. The red/blue-shifts of the lines reveal the apparent precession
of the jet axis in SS433, and we present a new determination of the precession
parameters based on these data. We investigate the amplitude and nature of
time- and phase-dependent deviations from the kinematic model for the jet
precession, including an upper limit on any precessional period derivative of
$\dot P < 5 \times 10^{-5}$. We also dicuss the implications of these results
for the origins of the relativistic jets in SS433.


  We prove a key result regarding the mass and angular momentum content of
linear vacuum perturbations of the Kerr metric obtained through the formalism
developed by Chrzarnowski, Cohen, and Kegeles (CCK). More precisely we prove
that the Abbott-Deser mass and angular momentum integrals of any such
perturbation vanish, when that perturbation was obtained from a regular Fourier
mode of the Hertz potential. As a corollary we obtain a generalization of
previous results on the completion of the `no string' radiation gauge metric
perturbation generated by a point particle. We find that for any bound orbit
around a Kerr black hole, the mass and angular momentum perturbations
completing the CCK metric are simply the energy and angular momentum of the
particle "outside" the orbit and vanish "inside" the orbit.


  We discuss production of two pairs of $c \bar c$ within a simple formalism of
double-parton scattering (DPS). Surprisingly very large cross sections,
comparable to single-parton scattering (SPS) contribution, are predicted for
LHC energies. Both total inclusive cross section as a function of energy and
differential distributions for $\sqrt{s}$ are shown. We discuss a perspective
how to identify the double scattering contribution.


  In this paper we prove a viability result for multidimensional, time
dependent, stochastic differential equations driven by fractional Brownian
motion with Hurst parameter1/2 < H < 1, using pathwise approach. The sufficient
condition is also an alternative global existence result for the fractional
differential equations with restrictions on the state.


  In the present investigation, we consider two new subclasses
N_{{\Sigma}}^{{\mu}}({\alpha},{\lambda}) and
N_{{\Sigma}}^{{\mu}}({\beta},{\lambda}) of bi-univalent functions defined in
the open unit disk U={z:|z|<1}. Besides, we find upper bounds for the second
and third coefficients for functions in these new subclasses.


  We establish the long-time existence of large-data weak solutions to a system
of nonlinear partial differential equations. The system of interest governs the
motion of non-Newtonian fluids described by a simplified viscoelastic rate-type
model with a stress-diffusion term. The simplified model shares many
qualitative features with more complex viscoelastic rate-type models that are
frequently used in the modeling of fluids with complicated microstructure. As
such, the simplified model provides important preliminary insight into the
mathematical properties of these more complex and practically relevant models
of non-Newtonian fluids. The simplified model that is analyzed from the
mathematical perspective is shown to be thermodynamically consistent, and we
extensively comment on the interplay between the thermodynamical background of
the model and the mathematical analysis of the corresponding
initial-boundary-value problem.


  Data visualisation is a key tool in data mining for understanding big
datasets. Many visualisation methods have been proposed, including the
well-regarded state-of-the-art method t-Distributed Stochastic Neighbour
Embedding. However, the most powerful visualisation methods have a significant
limitation: the manner in which they create their visualisation from the
original features of the dataset is completely opaque. Many domains require an
understanding of the data in terms of the original features; there is hence a
need for powerful visualisation methods which use understandable models. In
this work, we propose a genetic programming approach named GPtSNE for evolving
interpretable mappings from a dataset to highquality visualisations. A
multi-objective approach is designed that produces a variety of visualisations
in a single run which give different trade-offs between visual quality and
model complexity. Testing against baseline methods on a variety of datasets
shows the clear potential of GP-tSNE to allow deeper insight into data than
that provided by existing visualisation methods. We further highlight the
benefits of a multi-objective approach through an in-depth analysis of a
candidate front, which shows how multiple models can


  Recent works in social network stream analysis show that a user's online
persona attributes (e.g., gender, ethnicity, political interest, location,
etc.) can be accurately inferred from the topics the user writes about or
engages with. Attribute and preference inferences have been widely used to
serve personalized recommendations, directed ads, and to enhance the user
experience in social networks. However, revealing a user's sensitive attributes
could represent a privacy threat to some individuals. Microtargeting
(e.g.,Cambridge Analytica scandal), surveillance, and discriminating ads are
examples of threats to user privacy caused by sensitive attribute inference. In
this paper, we propose Multifaceted privacy, a novel privacy model that aims to
obfuscate a user's sensitive attributes while publicly preserving the user's
public persona. To achieve multifaceted privacy, we build Aegis, a prototype
client-centric social network stream processing system that helps preserve
multifaceted privacy, and thus allowing social network users to freely express
their online personas without revealing their sensitive attributes of choice.
Aegis allows social network users to control which persona attributes should be
publicly revealed and which ones should be kept private. For this, Aegis
continuously suggests topics and hashtags to social network users to post in
order to obfuscate their sensitive attributes and hence confuse content-based
sensitive attribute inferences. The suggested topics are carefully chosen to
preserve the user's publicly revealed persona attributes while hiding their
private sensitive persona attributes. Our experiments show that adding as few
as 0 to 4 obfuscation posts (depending on how revealing the original post is)
successfully hides the user specified sensitive attributes without changing the
user's public persona attributes.


  This paper presents a number of identities for Dirichlet series and series
with Stirling numbers of the first kind. As coefficients for the Dirichlet
series we use Cauchy numbers of the first and second kinds, hyperharmonic
numbers, derangement numbers, binomial coefficients, central binomial
coefficients, and Catalan numbers.


  Non-differentiable and constrained optimization play a key role in machine
learning, signal and image processing, communications, and beyond. For
high-dimensional minimization problems involving large datasets or many
unknowns, the forward-backward splitting method provides a simple, practical
solver. Despite its apparently simplicity, the performance of the
forward-backward splitting is highly sensitive to implementation details.
  This article is an introductory review of forward-backward splitting with a
special emphasis on practical implementation concerns. Issues like stepsize
selection, acceleration, stopping conditions, and initialization are
considered. Numerical experiments are used to compare the effectiveness of
different approaches.
  Many variations of forward-backward splitting are implemented in the solver
FASTA (short for Fast Adaptive Shrinkage/Thresholding Algorithm). FASTA
provides a simple interface for applying forward-backward splitting to a broad
range of problems.


  Robustness to malicious attacks is of paramount importance for distributed
learning. Existing works often consider the classical Byzantine attacks model,
which assumes that some workers can send arbitrarily malicious messages to the
server and disturb the aggregation steps of the distributed learning process.
To defend against such worst-case Byzantine attacks, various robust aggregators
have been proven effective and much superior to the often-used mean aggregator.
In this paper, we show that robust aggregators are too conservative for a class
of weak but practical malicious attacks, as known as label poisoning attacks,
where the sample labels of some workers are poisoned. Surprisingly, we are able
to show that the mean aggregator is more robust than the state-of-the-art
robust aggregators in theory, given that the distributed data are sufficiently
heterogeneous. In fact, the learning error of the mean aggregator is proven to
be optimal in order. Experimental results corroborate our theoretical findings,
demonstrating the superiority of the mean aggregator under label poisoning
attacks.


  Video generation has increasingly gained interest in both academia and
industry. Although commercial tools can generate plausible videos, there is a
limited number of open-source models available for researchers and engineers.
In this work, we introduce two diffusion models for high-quality video
generation, namely text-to-video (T2V) and image-to-video (I2V) models. T2V
models synthesize a video based on a given text input, while I2V models
incorporate an additional image input. Our proposed T2V model can generate
realistic and cinematic-quality videos with a resolution of $1024 \times 576$,
outperforming other open-source T2V models in terms of quality. The I2V model
is designed to produce videos that strictly adhere to the content of the
provided reference image, preserving its content, structure, and style. This
model is the first open-source I2V foundation model capable of transforming a
given image into a video clip while maintaining content preservation
constraints. We believe that these open-source video generation models will
contribute significantly to the technological advancements within the
community.


  Exact solution of Schrodinger equation for the Mie potential is obtained for
an arbitrary angular momentum. The energy eigenvalues and the corresponding
wavefunctions are calculated by the use of the Nikiforov-Uvarov method.
Wavefunctions are expressed in terms of Jacobi polynomials. The bound states
are calculated numerically for some values of l and $n$ with n<5. They are
applied to several diatomic molecules.


  OpenNMT is an open-source toolkit for neural machine translation (NMT). The
system prioritizes efficiency, modularity, and extensibility with the goal of
supporting NMT research into model architectures, feature representations, and
source modalities, while maintaining competitive performance and reasonable
training requirements. The toolkit consists of modeling and translation
support, as well as detailed pedagogical documentation about the underlying
techniques. OpenNMT has been used in several production MT systems, modified
for numerous research papers, and is implemented across several deep learning
frameworks.


  The generation and control of quantum states of light constitute fundamental
tasks in cavity quantum electrodynamics (QED). The superconducting realization
of cavity QED, circuit QED, enables on-chip microwave photonics, where
superconducting qubits control and measure individual photon states. A
long-standing issue in cavity QED is the coherent transfer of photons between
two or more resonators. Here, we use circuit QED to implement a three-resonator
architecture on a single chip, where the resonators are interconnected by two
superconducting phase qubits. We use this circuit to shuffle one- and
two-photon Fock states between the three resonators, and demonstrate
qubit-mediated vacuum Rabi swaps between two resonators. This illustrates the
potential for using multi-resonator circuits as photon quantum registries and
for creating multipartite entanglement between delocalized bosonic modes.


  We present new and exact ``sign rules'' for various spin-s anisotropic
spin-lattice models. It is shown that, after a simple transformation which
utilizes these sign rules, the ground-state wave function of the transformed
Hamiltonian is positive-definite. Using these results exact statements for
various expectation values of off-diagonal operators are presented, and
transitions in the behavior of these expectation values are observed at
particular values of the anisotropy. Furthermore, the effects of sign rules in
variational calculations and quantum Monte Carlo calculations are considered.
They are illustrated by a simple variational treatment of a one-dimensional
anisotropic spin model.


  Structured Illumination Microscopy is a widespread methodology to image live
and fixed biological structures smaller than the diffraction limits of
conventional optical microscopy. Using recent advances in image up-scaling
through deep learning models, we demonstrate a method to reconstruct 3D SIM
image stacks with twice the axial resolution attainable through conventional
SIM reconstructions. We further evaluate our method for robustness to noise &
generalisability to varying observed specimens, and discuss potential adaptions
of the method to further improvements in resolution.


  In this note, we study Serre's property $(S_i)$, and its relation to
Alexander duality for monomial ideals in a polynomial ring over a field. We
describe ideals that define the non-Cohen-Macaulay- and the non-$(S_i)$-loci of
finitely generated modules over regular rings, and show that minimal prime
ideals in these loci are homogeneous, in the graded case. We show that a
square-free monomial ideal has property $(S_i)$ if and only if its Alexander
dual has a linear resolution up to homological degree $i-1$. We prove that for
square-free monomial ideals, having property $(S_2)$ is equivalent to being
locally connected in codimension 1.


  The ISM metallicity and the stellar mass are examined in a sample of 66
galaxies at 0.4<z<1, selected from the Gemini Deep Deep Survey (GDDS) and the
Canada-France Redshift Survey (CFRS). We observe a mass-metallicity relation
similar to that seen in z~0.1 SDSS galaxies, but displaced towards higher
masses and/or lower metallicities. Using this sample, and a small sample of
z~2.3 LBGs, a redshift dependent mass-metallicity relation is proposed which
describes the observed results.


  Automatic polyp detection and segmentation are highly desirable for colon
screening due to polyp miss rate by physicians during colonoscopy, which is
about 25%. However, this computerization is still an unsolved problem due to
various polyp-like structures in the colon and high interclass polyp variations
in terms of size, color, shape, and texture. In this paper, we adapt Mask R-CNN
and evaluate its performance with different modern convolutional neural
networks (CNN) as its feature extractor for polyp detection and segmentation.
We investigate the performance improvement of each feature extractor by adding
extra polyp images to the training dataset to answer whether we need deeper and
more complex CNNs or better dataset for training in automatic polyp detection
and segmentation. Finally, we propose an ensemble method for further
performance improvement. We evaluate the performance on the 2015 MICCAI polyp
detection dataset. The best results achieved are 72.59% recall, 80% precision,
70.42% dice, and 61.24% Jaccard. The model achieved state-of-the-art
segmentation performance.


  This paper uses a classical approach to feature selection: minimization of a
cost function applied on estimated joint distributions. However, the search
space in which such minimization is performed is extended. In the original
formulation, the search space is the Boolean lattice of features sets (BLFS),
while, in the present formulation, it is a collection of Boolean lattices of
ordered pairs (features, associated value) (CBLOP), indexed by the elements of
the BLFS. In this approach, we may not only select the features that are most
related to a variable Y, but also select the values of the features that most
influence the variable or that are most prone to have a specific value of Y. A
local formulation of Shanon's mutual information is applied on a CBLOP to
select features, namely, the Local Lift Dependence Scale, an scale for
measuring variable dependence in multiple resolutions. The main contribution of
this paper is to define and apply this local measure, which permits to analyse
local properties of joint distributions that are neglected by the classical
Shanon's global measure. The proposed approach is applied to a dataset
consisting of student performances on a university entrance exam, as well as on
undergraduate courses. The approach is also applied to two datasets of the UCI
Machine Learning Repository.


  We all know that Wi-Fi is presently the most commonly used technology for
data transmission and connecting devices to the Internet, at the same time due
to much reasonable concern, (such as Wi-Fi can be vulnerable when it comes to
hacking, health concern, and low latency, etc.) the concept of Li-Fi is
becoming very popular as a new way of data transmission that use light waves to
transmit data rather than radio waves. Light-emitting diodes LED are used when
transmitting the data in the visible light spectrum. Li-fi uses visible light
communication and it has a promising future. Unlike Wi-fi, Li-Fi has low
latency, high efficiency, accessible spectrum, and high data can be achieved.
It is highly secured so the data cannot be hacked. In this paper, we design a
concept of Li-fi audio signal transmission by reusing and repurposing
pre-programmed modules to simplify and discuss visible light communication
(VLC) in other to give a new researcher the idea on how the concept of LiFi and
VLC. In addition to designing the concept we experiment to test the concept and
we illustrated the result within this paper.


  Machine learning finds rich applications in Internet of Things (IoT) networks
such as information retrieval, traffic management, spectrum sensing, and signal
authentication. While there is a surge of interest to understand the security
issues of machine learning, their implications have not been understood yet for
wireless applications such as those in IoT systems that are susceptible to
various attacks due the open and broadcast nature of wireless communications.
To support IoT systems with heterogeneous devices of different priorities, we
present new techniques built upon adversarial machine learning and apply them
to three types of over-the-air (OTA) wireless attacks, namely jamming, spectrum
poisoning, and priority violation attacks. By observing the spectrum, the
adversary starts with an exploratory attack to infer the channel access
algorithm of an IoT transmitter by building a deep neural network classifier
that predicts the transmission outcomes. Based on these prediction results, the
wireless attack continues to either jam data transmissions or manipulate
sensing results over the air (by transmitting during the sensing phase) to fool
the transmitter into making wrong transmit decisions in the test phase
(corresponding to an evasion attack). When the IoT transmitter collects sensing
results as training data to retrain its channel access algorithm, the adversary
launches a causative attack to manipulate the input data to the transmitter
over the air. We show that these attacks with different levels of energy
consumption and stealthiness lead to significant loss in throughput and success
ratio in wireless communications for IoT systems. Then we introduce a defense
mechanism that systematically increases the uncertainty of the adversary at the
inference stage and improves the performance. Results provide new insights on
how to attack and defend IoT networks using deep learning.


  Virtual Reality (VR) and Augmented Reality (AR) tools have been applied in
all engineering fields in order to avoid the use of physical prototypes, to
train in high-risk situations, and to interpret real or simulated results. In
order to complete a shared task or assign tasks to the agents in such immersive
environments, collaboration or Shared Cooperative Activities are a necessity.
Collaboration in immersive environments is an emerging field of research that
aims to study and enhance the ways in which people interact and work together
in Virtual and Augmented Reality settings. Collaboration in immersive
environments is a complex process that involves different factors such as
communication, coordination, and social presence. This paper provides an
overview of the current state of research on collaboration in immersive
environments. It discusses the different types of immersive environments,
including VR and AR, and the different forms of collaboration that can occur in
these environments. The paper also highlights the challenges and limitations of
collaboration in immersive environments, such as the lack of physical cues,
cost and usability and the need for further research in this area. Overall,
collaboration in immersive environments is a promising field with a wide range
of potential applications, from education to industry, and it can benefit both
individuals and groups by enhancing their ability to work together effectively.


  We present a comparison of data of lepton angular distributions of
Drell-Yan/$Z$ production with the fixed-order pQCD calculations by which the
baseline of pQCD effects is illustrated. As for the $Z$ production, we predict
that $A_0$ and $A_2$ for $Z$ plus single gluon-jet events are very different
from that of $Z$ plus single quark-jet events, allowing a new experimental tool
for checking various algorithms which attempt to discriminate quark jets from
gluon jets. Using an intuitive geometric approach, we show that the violation
of the Lam-Tung relation, appearing at large transverse-momentum region, is
attributed to the presence of a non-coplanarity effect. This interpretation is
consistent with the appearance of violation beyond LO-QCD effect.


  The synthesis of tactile sensing with compliance is essential to many fields,
from agricultural usages like fruit picking, to sustainability practices such
as sorting recycling, to the creation of safe home-care robots for the elderly
to age with dignity. From tactile sensing, we can discern material properties,
recognize textures, and determine softness, while with compliance, we are able
to securely and safely interact with the objects and the environment around us.
These two abilities can culminate into a useful soft robotic gripper, such as
the original GelSight Fin Ray, which is able to grasp a large variety of
different objects and also perform a simple household manipulation task: wine
glass reorientation. Although the original GelSight Fin Ray solves the problem
of interfacing a generally rigid, high-resolution sensor with a soft, compliant
structure, we can improve the robustness of the sensor and implement techniques
that make such camera-based tactile sensors applicable to a wider variety of
soft robot designs. We first integrate flexible mirrors and incorporate the
rigid electronic components into the base of the gripper, which greatly
improves the compliance of the Fin Ray structure. Then, we synthesize a
flexible and high-elongation silicone adhesive-based fluorescent paint, which
can provide good quality 2D tactile localization results for our sensor.
Finally, we incorporate all of these techniques into a new design: the Baby Fin
Ray, which we use to dig through clutter, and perform successful classification
of nuts in their shells. The supplementary video can be found here:
https://youtu.be/_oD_QFtYTPM


  Monitoring small objects against cluttered moving backgrounds is a huge
challenge to future robotic vision systems. As a source of inspiration, insects
are quite apt at searching for mates and tracking prey -- which always appear
as small dim speckles in the visual field. The exquisite sensitivity of insects
for small target motion, as revealed recently, is coming from a class of
specific neurons called small target motion detectors (STMDs). Although a few
STMD-based models have been proposed, these existing models only use motion
information for small target detection and cannot discriminate small targets
from small-target-like background features (named as fake features). To address
this problem, this paper proposes a novel visual system model (STMD+) for small
target motion detection, which is composed of four subsystems -- ommatidia,
motion pathway, contrast pathway and mushroom body. Compared to existing
STMD-based models, the additional contrast pathway extracts directional
contrast from luminance signals to eliminate false positive background motion.
The directional contrast and the extracted motion information by the motion
pathway are integrated in the mushroom body for small target discrimination.
Extensive experiments showed the significant and consistent improvements of the
proposed visual system model over existing STMD-based models against fake
features.


  In the context of the readout scheme for gravitational-wave detectors, the
"double balanced homodyne detection" proposed in [K.~Nakamura and
M.-K.~Fujimoto, arXiv:1709.01697.] is discussed in detail. This double balanced
homodyne detection enables us to measure the expectation values of the photon
creation and annihilation operators. Although it has been said that the
operator $\hat{b}_{\theta}:=\cos\theta\hat{b}_{1}+\sin\theta\hat{b}_{2}$ can be
measured through the homodyne detection in literature, we first show that the
expectation value of the operator $\hat{b}_{\theta}$ cannot be measured as the
linear combination of the upper- and lower-sidebands from the output of the
balanced homodyne detection. Here, the operators $\hat{b}_{1}$ and
$\hat{b}_{2}$ are the amplitude and phase quadrature in the two-photon
formulation, respectively. On the other hand, it is shown that the above double
balanced homodyne detection enables us to measure the expectation value of the
operator $\hat{b}_{\theta}$ if we can appropriately prepare the complex
amplitude of the coherent state from the local oscillator. It is also shown
that the interferometer set up of the eight-port homodyne detection realizes
our idea of the double balanced homodyne detection. We also evaluate the
noise-spectral density of the gravitational-wave detectors when our double
balanced homodyne detection is applied as their readout scheme. Some
requirements for the coherent state from the local oscillator to realize the
double balanced homodyne detection are also discussed.


  The rapid advances in deep generative models over the past years have led to
highly {realistic media, known as deepfakes,} that are commonly
indistinguishable from real to human eyes. These advances make assessing the
authenticity of visual data increasingly difficult and pose a misinformation
threat to the trustworthiness of visual content in general. Although recent
work has shown strong detection accuracy of such deepfakes, the success largely
relies on identifying frequency artifacts in the generated images, which will
not yield a sustainable detection approach as generative models continue
evolving and closing the gap to real images. In order to overcome this issue,
we propose a novel fake detection that is designed to re-synthesize testing
images and extract visual cues for detection. The re-synthesis procedure is
flexible, allowing us to incorporate a series of visual tasks - we adopt
super-resolution, denoising and colorization as the re-synthesis. We
demonstrate the improved effectiveness, cross-GAN generalization, and
robustness against perturbations of our approach in a variety of detection
scenarios involving multiple generators over CelebA-HQ, FFHQ, and LSUN
datasets. Source code is available at
https://github.com/SSAW14/BeyondtheSpectrum.


  We discuss the efficiency of single photon sources based on a single
quasi-monochromatic emitter (such as a semiconductor quantum dot) inserted in a
pillar microcavity. We show that their efficiency, which is in principle
excellent thanks to the Purcell effect, can be drastically limited by extrinsic
cavity losses, such as those related to the scattering by the sidewalls
roughness. We present novel design rules for micropillars in view of this
application and show that for the well-mastered GaAs/AlAs system more than 70%
of the emission can be concentrated into the collimated emission beam
associated with the fundamental cavity mode.


  This paper investigates the use of visualization to display activity data on
smartwatches by surveying the data visual presentations proposed by 80
smartwatch models currently available on the Chinese e-commerce platform JD.com
and, later, surveying the preferences of 41 users concerning these
visualizations. The results show that despite radial bar charts are the most
popular visualization for activity data on smartwatches, the users' preferences
might be influenced by their familiarity with these charts. These findings from
this survey will be valuable for designers, developers, and researchers who are
interested in creating innovative and effective solutions for activity
visualization on smartwatches.


  One of the challenges in Synthetic Biology is to design circuits with
increasing levels of complexity. While circuits in Biology are complex and
subject to natural tradeoffs, most synthetic circuits are simple in terms of
the number of regulatory regions, and have been designed to meet a single
design criterion. In this contribution we introduce a multiobjective
formulation for the design of biocircuits. We set up the basis for an advanced
optimization tool for the modular and systematic design of biocircuits capable
of handling high levels of complexity and multiple design criteria. Our
methodology combines the efficiency of global Mixed Integer Nonlinear
Programming solvers with multiobjective optimization techniques. Through a
number of examples we show the capability of the method to generate non
intuitive designs with a desired functionality setting up a priori the desired
level of complexity. The presence of more than one competing objective provides
a realistic design setting where every design solution represents a trade-off
between different criteria. The tool can be useful to explore and identify
different design principles for synthetic gene circuits.


  Multilayer relationships among entities and information about entities must
be accompanied by the means to analyze, visualize, and obtain insights from
such data. We present open-source software (muxViz) that contains a collection
of algorithms for the analysis of multilayer networks, which are an important
way to represent a large variety of complex systems throughout science and
engineering. We demonstrate the ability of muxViz to analyze and interactively
visualize multilayer data using empirical genetic, neuronal, and transportation
networks. Our software is available at https://github.com/manlius/muxViz.


  Massive multiple-input multiple-output (MIMO) is expected to be a vital
component in future 5G systems. As such, there is a need for new modeling in
order to investigate the performance of massive MIMO not only at the physical
layer, but also higher up the networking stack. In this paper, we present
general optimization models for massive MIMO, based on mixed-integer
programming and compatible sets, with both maximum ratio combing and zero
forcing precoding schemes. We then apply our models to the case of joint device
scheduling and power control for heterogeneous devices and traffic demands, in
contrast to existing power control schemes that consider only homogeneous users
and saturated scenarios. Our results show substantial benefits in terms of
energy usage can be achieved without sacrificing throughput, and that both
signalling overhead and the complexity of end devices can be reduced by
abrogating the need for uplink power control through efficient scheduling.


  This article considers the statistical properties of L\'evy walks possessing
a regular long-term linear scaling of the mean square displacement with time,
for which the conditions of the classical Central Limit Theorem apply.
Notwithstanding this property, their higher-order moments display anomalous
scaling properties, whenever the statistics of the transition times possesses
power-law tails. This phenomenon is perfectly consistent with the classical
Central Limit Theorem, as it involves the convergence properties towards the
normal distribution. It is closely related to the property that the higher
order moments of normalized sums of $N$ independent random variables possessing
finite variance may deviate, for $N$ tending to infinity, to those of the
normal distribution. The thermodynamic implications of these results are
thoroughly analyzed by motivating the concept of higher-order anomalous
diffusion.


  We consider the existence of solutions of the following $p(x)$-Laplacian
Dirichlet problem without the Ambrosetti-Rabinowitz condition:
$-\mbox{div}(|\nabla u|^{p(x)-2}\nabla u)=f(x,u) \text{ in }\Omega,$ and
$u=0,\text{ on }\partial \Omega.$ We give a new growth condition and we point
out its importance for checking the Cerami compactness condition. We prove the
existence of solutions of the above problem via the critical point theory, and
also provide some multiplicity properties. Our results extend previous work by
Q. Zhang and C. Zhao, Existence of strong solutions of a $p(x)$-Laplacian
Dirichlet problem without the Ambrosetti-Rabinowitz condition, Comp. Math.
Appl. 69 (2015), 1-12, and we establish the existence of solutions under weaker
hypotheses on the nonlinear term.


  GRILLBot is the winning system in the 2022 Alexa Prize TaskBot Challenge,
moving towards the next generation of multimodal task assistants. It is a voice
assistant to guide users through complex real-world tasks in the domains of
cooking and home improvement. These are long-running and complex tasks that
require flexible adjustment and adaptation. The demo highlights the core
aspects, including a novel Neural Decision Parser for contextualized semantic
parsing, a new "TaskGraph" state representation that supports conditional
execution, knowledge-grounded chit-chat, and automatic enrichment of tasks with
images and videos.


  In this paper, we investigate the fluid/gravity correspondence in the
framework of massive Einstein gravity. Treating the gravitational mass terms as
an effective energy-momentum tensor and utilizing the Petrov-like boundary
condition on a timelike hypersurface, we find that the perturbation effects of
massive gravity in bulk can be completely governed by the incompressible
Navier-Stokes equation living on the cutoff surface under the near horizon and
nonrelativistic limits. Furthermore, we have concisely computed the ratio of
dynamical viscosity to entropy density for two massive Einstein gravity
theories, and found that they still saturate the Kovtun-Son-Starinets (KSS)
bound.


  We present detailed analyses of updated eclipse timing diagrams for 32
contact binary merger candidates in the Galactic bulge. The photometric data
was obtained from 2016 to 2021 using the Korea Microlensing Telescope Network
(KMTNet) with the 1.6 m telescopes located at three southern sites (CTIO, SAAO,
and SSO). The times of minimum lights were determined by applying the binary
star model to full light curves created at half-year intervals from the
observations. The orbital period variations of the binary systems were analyzed
using the $O-C$ diagrams from our new timings with the others published in the
literature (Hong et al.), which are based on the OGLE observations from 2001 to
2015. As results, the orbital periods and period decreasing rates of 32 binary
systems were located to be in the ranges of 0.370$-$1.238 days and from $-3.0$
to $-13.1\times10^{-6}$ day yr$^{-1}$, respectively. Out of these stars, 24
systems show a combination effect of a parabola and a light travel time caused
by a third body and their outer orbital periods are in the range of 9.1$-$26.5
yr, respectively. We propose that all of our merger candidates need additional
monitoring observations to study a luminous red nova (LRN) progenitor.


  Let $F \subseteq [0,1]$ be a set that supports a probability measure $\mu$
with the property that $ |\widehat{\mu}(t)| \ll (\log |t|)^{-A}$ for some
constant $ A > 0 $. Let $\mathcal{A}= (q_n)_{n\in \mathbb{N}} $ be a sequence
of natural numbers. If $\mathcal{A}$ is lacunary and $A >2$, we establish a
quantitative inhomogeneous Khintchine-type theorem in which (i) the points of
interest are restricted to $F$ and (ii) the denominators of the `shifted'
rationals are restricted to $\mathcal{A}$. The theorem can be viewed as a
natural strengthening of the fact that the sequence $(q_nx {\rm \ mod \, }
1)_{n\in \mathbb{N}} $ is uniformly distributed for $\mu$ almost all $x \in F$.
Beyond lacunary, our main theorem implies the analogous quantitative result for
sequences $\mathcal{A}$ for which the prime divisors are restricted to a finite
set of $k$ primes and $A > 2k$.


  We present a sample of 32 stars of spectral types G and K, and luminosity
classes I to V, with moderate activity levels, covering four orders of
magnitude of surface gravity and a representative range of effective
temperature. For each star we obtained high S/N TIGRE-HEROS spectra with a
spectral resolving power of $R\approx20,000$ and have measured the Ca II K
line-widths of interest, $W_0$ and $W_1$. The main physical parameters are
determined by means of iSpec synthesis and Gaia EDR3 parallaxes. Mass estimates
are based on matching to evolution models. Using this stellar sample, that is
highly uniform in terms of spectral quality and assessment, we derive the
best-fit relation between emission line width and gravity $g$, including a
notable dependence on effective temperature $T_{\rm eff}$, of the form $W_1
\propto g^{-0.229} T_{\rm eff}^{+2.41}$. This result confirms the physical
interpretation of the Wilson-Bappu effect as a line saturation and photon
redistribution effect in the chromospheric Ca II column density, under the
assumption of hydrostatic equilibrium at the bottom of the chromosphere. While
the column density (and so $W_1$) increases towards lower gravities, the
observed temperature dependence is then understood as a simple ionization
effect -- in cooler stars, Ca II densities decrease in favor of Ca I.


  A modern approach to the evolution of a mixed (stars and gas)
self-gravitating system is the fully Lagrangian particle approach. The gaseous
(particle) phase differs from the compact because the mutual force is given by
the sum of gravity and pressure gradient. In this note we report of some
characteristics, advantages and limitations of this approach for what regards
the evaluation of forces in the system. In particular, a comparison between
classic tree-code and fast multipole algorithm to evaluate gravitational forces
is discussed.


  The digitalization of automation engineering generates large quantities of
engineering data that is interlinked in knowledge graphs. Classifying and
clustering subgraphs according to their functionality is useful to discover
functionally equivalent engineering artifacts that exhibit different graph
structures. This paper presents a new graph learning algorithm designed to
classify engineering data artifacts -- represented in the form of graphs --
according to their structure and neighborhood features. Our Structural Graph
Convolutional Neural Network (SGCNN) is capable of learning graphs and
subgraphs with a novel graph invariant convolution kernel and
downsampling/pooling algorithm. On a realistic engineering-related dataset, we
show that SGCNN is capable of achieving ~91% classification accuracy.


  This paper develops and compares two motion planning algorithms for a
tethered UAV with and without the possibility of the tether contacting the
confined and cluttered environment. Tethered aerial vehicles have been studied
due to their advantages such as power duration, stability, and safety. However,
the disadvantages brought in by the extra tether have not been well
investigated by the robotic locomotion community, especially when the tethered
agent is locomoting in a non-free space occupied with obstacles. In this work,
we propose two motion planning frameworks that (1) reduce the reachable
configuration space by taking into account the tether and (2) deliberately plan
(and relax) the contact point(s) of the tether with the environment and enable
an equivalent reachable configuration space as the non-tethered counterpart
would have. Both methods are tested on a physical robot, Fotokite Pro. With our
approaches, tethered aerial vehicles could find their applications in confined
and cluttered environments with obstacles as opposed to ideal free space, while
still maintaining the advantages from the usage of a tether. The motion
planning strategies are particularly suitable for marsupial heterogeneous
robotic teams, such as visual servoing/assisting for another mobile,
tele-operated primary robot.


  A collisionless shock wave is treated as a streaming plasma instability in
the interstellar medium (ISM). We assume that in a steady state, this
instability propagates through the ISM as a self-driven plasma instability,
whereby the parameters of the instability are determined such that it causes
density and velocity jumps as well as isotropization of the particle
velocities, which then must be in accordance with MHD theory. The process of
resonant interaction and particle scattering off such instability and their
backscattering to an upstream region is simulated. We find that some ISM
particles bounce off a shock and thus become suprathermal and eligible to enter
the process of acceleration to cosmic ray energies by the mechanism of
diffusive shock acceleration.


  Based on a $2\times 2$ mass matrix, the mixing angle of the axial vector
states $f_1(1420)$ and $f_1(1285)$ is determined to be $51.5^{\circ}$, and the
theoretical results about the decay and production of the two states are
presented. The theoretical results are in good agreement with the present
experimental results, which suggests that $f_1(1420)$ can be assigned as the
partner of $f_1(1285)$ in the $^3P_1$ $q\bar{q}$ nonet. We also suggest that
the existence of $f_1(1510)$ needs further experimental confirmation.


  The introduction of the MUStARD dataset, and its emotion recognition
extension MUStARD++, have identified sarcasm to be a multi-modal phenomenon --
expressed not only in natural language text, but also through manners of speech
(like tonality and intonation) and visual cues (facial expression). With this
work, we aim to perform a rigorous benchmarking of the MUStARD++ dataset by
considering state-of-the-art language, speech, and visual encoders, for fully
utilizing the totality of the multi-modal richness that it has to offer,
achieving a 2\% improvement in macro-F1 over the existing benchmark.
Additionally, to cure the imbalance in the `sarcasm type' category in
MUStARD++, we propose an extension, which we call \emph{MUStARD++ Balanced},
benchmarking the same with instances from the extension split across both train
and test sets, achieving a further 2.4\% macro-F1 boost. The new clips were
taken from a novel source -- the TV show, House MD, which adds to the diversity
of the dataset, and were manually annotated by multiple annotators with
substantial inter-annotator agreement in terms of Cohen's kappa and
Krippendorf's alpha. Our code, extended data, and SOTA benchmark models are
made public.


  This work aims to learn how to perform complex robot manipulation tasks that
are composed of several, consecutively executed low-level sub-tasks, given as
input a few visual demonstrations of the tasks performed by a person. The
sub-tasks consist of moving the robot's end-effector until it reaches a
sub-goal region in the task space, performing an action, and triggering the
next sub-task when a pre-condition is met. Most prior work in this domain has
been concerned with learning only low-level tasks, such as hitting a ball or
reaching an object and grasping it. This paper describes a new neural
network-based framework for learning simultaneously low-level policies as well
as high-level policies, such as deciding which object to pick next or where to
place it relative to other objects in the scene. A key feature of the proposed
approach is that the policies are learned directly from raw videos of task
demonstrations, without any manual annotation or post-processing of the data.
Empirical results on object manipulation tasks with a robotic arm show that the
proposed network can efficiently learn from real visual demonstrations to
perform the tasks, and outperforms popular imitation learning algorithms.


  We address the problem of how to extract the signal of a Higgs within the
intermediate mass range at a photon-photon collider that has a wide energy
spectrum. All backgrounds from two-jets production are included: direct,
so-called resolved and twice-resolved as well as single $Z$ and $W$ production.
Uncertainties in the evaluation of the QCD-initiated processes such as the
choice of structure function and the issue of radiative corrections are
discussed. We consider various combinations of the polarizations and invariant
mass resolutions as well as jet-tagging strategies with different efficiencies.
The analysis is based on an automatized technique that, given a specific
detector and machine configuration, returns the optimal set of cuts
corresponding to the best significance one may hope to achieve for each
particular Higgs mass. We find that at a photon machine obtained from a 500GeV
\epm\ linear collider with $\int\!{\cal L}=10$~fb$^{-1}$ it will be possible to
extract a Higgs signal in the range 110--140~GeV, while with the same
luminosity, a 350~GeV option not only extends the discovery limit down to
90~GeV but gives much better significance levels.


  In the past decade, a combination of unfitted finite elements (or XFEM) with
the Nitsche method has become a popular discretization method for elliptic
interface problems. This development started with the introduction and analysis
of this Nitsche-XFEM technique in the paper [A. Hansbo, P. Hansbo, Comput.
Methods Appl. Mech. Engrg. 191 (2002)]. In general, the resulting linear
systems have very large condition numbers, which depend not only on the mesh
size $h$, but also on how the interface intersects the mesh. This paper is
concerned with the design and analysis of optimal preconditioners for such
linear systems. We propose an additive subspace preconditioner which is optimal
in the sense that the resulting condition number is independent of the mesh
size $h$ and the interface position. We further show that already the simple
diagonal scaling of the stifness matrix results in a condition number that is
bounded by $ch^{-2}$, with a constant $c$ that does not depend on the location
of the interface. Both results are proven for the two-dimensional case. Results
of numerical experiments in two and three dimensions are presented, which
illustrate the quality of the preconditioner.


  Efficient lossless coding of medical volume data with temporal axis can be
achieved by motion compensated wavelet lifting. As side benefit, a scalable bit
stream is generated, which allows for displaying the data at different
resolution layers, highly demanded for telemedicine applications. Additionally,
the similarity of the temporal base layer to the input sequence is preserved by
the use of motion compensated temporal filtering. However, for medical
sequences the overall rate is increased due to the specific noise
characteristics of the data. The use of denoising filters inside the lifting
structure can improve the compression efficiency significantly without
endangering the property of perfect reconstruction. However, the design of an
optimum filter is a crucial task. In this paper, we present a new method for
selecting the optimal filter strength for a certain denoising filter in a
rate-distortion sense. This allows to minimize the required rate based on a
single input parameter for the encoder to control the requested distortion of
the temporal base layer.


  We formulate the generic $\tau$-function of the Painlev\'e II equation as a
Fredholm determinant of an integrable (Its-Izergin-Korepin-Slavnov) operator.
The $\tau$-function depends on the isomonodromic time $t$ and two Stokes'
parameters, and the vanishing locus of the $\tau$-function, called the
Malgrange divisor is determined by the zeros of the Fredholm determinant.


  We develop a rational inattention theory of echo chambers, where players
allocate limited attention across biased primary sources and other players to
gather information about an uncertain state. The resulting Poisson attention
network transmits information from the primary source to a player either
directly or indirectly through other players. Rational inattention creates
heterogeneous information demands among players who are biased toward different
decisions. In an echo-chamber equilibrium, each player focuses on his
own-biased source and like-minded friends, who attend to the same primary
source as his and can serve as secondary sources if the attention channel from
the primary source to him is disrupted. We establish conditions for the
emergence of echo-chamber equilibria, characterize the attention networks
within echo chambers, and offer insights for designing and regulating
information platforms.


  Makespan minimization in permutation flow-shop scheduling is a well-known
hard combinatorial optimization problem. Among the 120 standard benchmark
instances proposed by E. Taillard in 1993, 23 have remained unsolved for almost
three decades. In this paper, we present our attempts to solve these instances
to optimality using parallel Branch-and-Bound tree search on the
GPU-accelerated Jean Zay supercomputer. We report the exact solution of 11
previously unsolved problem instances and improved upper bounds for 8
instances. The solution of these problems requires both algorithmic
improvements and leveraging the computing power of peta-scale high-performance
computing platforms. The challenge consists in efficiently performing parallel
depth-first traversal of a highly irregular, fine-grained search tree on
distributed systems composed of hundreds of massively parallel accelerator
devices and multi-core processors. We present and discuss the design and
implementation of our permutation-based B&B and experimentally evaluate its
parallel performance on up to 384 V100 GPUs (2 million CUDA cores) and 3840 CPU
cores. The optimality proof for the largest solved instance requires about 64
CPU-years of computation -- using 256 GPUs and over 4 million parallel search
agents, the traversal of the search tree is completed in 13 hours, exploring
339$\times 10^{12}$ nodes.


  3D object detection using point cloud (PC) data is essential for perception
pipelines of autonomous driving, where efficient encoding is key to meeting
stringent resource and latency requirements. PointPillars, a widely adopted
bird's-eye view (BEV) encoding, aggregates 3D point cloud data into 2D pillars
for fast and accurate 3D object detection. However, the state-of-the-art
methods employing PointPillars overlook the inherent sparsity of pillar
encoding where only a valid pillar is encoded with a vector of channel
elements, missing opportunities for significant computational reduction.
Meanwhile, current sparse convolution accelerators are designed to handle only
element-wise activation sparsity and do not effectively address the vector
sparsity imposed by pillar encoding.
  In this paper, we propose SPADE, an algorithm-hardware co-design strategy to
maximize vector sparsity in pillar-based 3D object detection and accelerate
vector-sparse convolution commensurate with the improved sparsity. SPADE
consists of three components: (1) a dynamic vector pruning algorithm balancing
accuracy and computation savings from vector sparsity, (2) a sparse coordinate
management hardware transforming 2D systolic array into a vector-sparse
convolution accelerator, and (3) sparsity-aware dataflow optimization tailoring
sparse convolution schedules for hardware efficiency. Taped-out with a
commercial technology, SPADE saves the amount of computation by 36.3--89.2\%
for representative 3D object detection networks and benchmarks, leading to
1.3--10.9$\times$ speedup and 1.5--12.6$\times$ energy savings compared to the
ideal dense accelerator design. These sparsity-proportional performance gains
equate to 4.1--28.8$\times$ speedup and 90.2--372.3$\times$ energy savings
compared to the counterpart server and edge platforms.


  In this short note we present a Lagrangian formulation for free bosonic
Higher Spin fields which belong to massless reducible representations of
D-dimensional Anti de Sitter group using an ambient space formalism.


  We address several issues regarding the interpretation of galactic \ggg-ray
sources. We consider powerful pulsars in binaries producing X-ray and gamma-ray
{\it unpulsed} emission from the shock interaction of relativistic pulsar winds
with circumbinary material. Nebular mass outflows from companion stars of
binary pulsars can provide the right {\it calorimeters} to transform a fraction
of the electromagnetic and kinetic energy of pulsar winds into high energy
radiation. We discuss the physics of interaction of relativistic pulsar winds
with gaseous material and show that the conditions in pulsar binary systems
might be ideal to constrain shock acceleration mechanisms and pulsar wind
composition and structure. We briefly discuss the example of the 47~ms pulsar
PSR~1259-63 orbiting around a massive Be~star companion and monitored by X-ray
and gamma-ray instruments during its recent periastron passage. In addition to
young pulsars in massive binaries, also a class of recycled millisecond pulsars
in low-mass binaries can be interesting high energy emitters.


  We study the stability of the highest symmetric solution (Wigner-solution) of
Dyson-Schwinger equations in chiral limit and at zero temperature. Our results
confirm that if the chemical potential is not very large, the QCD vacuum is in
the chiral symmetry breaking phase and the quantum phase-transition of the
chiral symmetry restoration is in first order. Meanwhile it seems that there is
not competition between chiral symmetry breaking phase and color
superconductivity phase since the color superconductivity phase appears only if
the chemical potential is very large. Moreover, we propose that chiral symmetry
breaking arises from the positive feedback with respect to the mass
perturbation.


  Deep neural networks may be susceptible to learning spurious correlations
that hold on average but not in atypical test samples. As with the recent
emergence of vision transformer (ViT) models, it remains underexplored how
spurious correlations are manifested in such architectures. In this paper, we
systematically investigate the robustness of vision transformers to spurious
correlations on three challenging benchmark datasets and compare their
performance with popular CNNs. Our study reveals that when pre-trained on a
sufficiently large dataset, ViT models are more robust to spurious correlations
than CNNs. Key to their success is the ability to generalize better from the
examples where spurious correlations do not hold. Further, we perform extensive
ablations and experiments to understand the role of the self-attention
mechanism in providing robustness under spuriously correlated environments. We
hope that our work will inspire future research on further understanding the
robustness of ViT models.


  Among solutions of the strong CP problem, the "invisible" axion in the narrow
axion window is argued to be the remaining possibility among natural solutions
on the smallness of $\bar{\theta}$. Related to the gravity spoil of global
symmetries, some prospective invisible axions from theory point of view are
discussed. In all these discussions, including the observational possibility,
cosmological constraints must be included.