  As reinforcement learning methods increasingly amass accomplishments, the
need for comprehending their solutions becomes more crucial. Most explainable
reinforcement learning (XRL) methods generate a static explanation depicting
their developers' intuition of what should be explained and how. In contrast,
literature from the social sciences proposes that meaningful explanations are
structured as a dialog between the explainer and the explainee, suggesting a
more active role for the user and her communication with the agent. In this
paper, we present ASQ-IT -- an interactive tool that presents video clips of
the agent acting in its environment based on queries given by the user that
describe temporal properties of behaviors of interest. Our approach is based on
formal methods: queries in ASQ-IT's user interface map to a fragment of Linear
Temporal Logic over finite traces (LTLf), which we developed, and our algorithm
for query processing is based on automata theory. User studies show that
end-users can understand and formulate queries in ASQ-IT, and that using ASQ-IT
assists users in identifying faulty agent behaviors.


  Functional brain connectivity, as revealed through distant correlations in
the signals measured by functional Magnetic Resonance Imaging (fMRI), is a
promising source of biomarkers of brain pathologies. However, establishing and
using diagnostic markers requires probabilistic inter-subject comparisons.
Principled comparison of functional-connectivity structures is still a
challenging issue. We give a new matrix-variate probabilistic model suitable
for inter-subject comparison of functional connectivity matrices on the
manifold of Symmetric Positive Definite (SPD) matrices. We show that this model
leads to a new algorithm for principled comparison of connectivity coefficients
between pairs of regions. We apply this model to comparing separately
post-stroke patients to a group of healthy controls. We find
neurologically-relevant connection differences and show that our model is more
sensitive that the standard procedure. To the best of our knowledge, these
results are the first report of functional connectivity differences between a
single-patient and a group and thus establish an important step toward using
functional connectivity as a diagnostic tool.


  We first extend Generalized Differential Calculus (GDC) to higher structures
and create generalized G-invariant bilinear forms. In addition, we also focus
on developing generalized 2- and 3-connection theories in the framework of GDC.
Then, we derive the higher Bianchi-Identities and study the gauge
transformations for those generalized higher connections. Finally, we establish
the generalized 2- and 3-form Yang-Mills theories based on GDC and obtain the
corresponding fields equations.


  In this work, we observe that the model, which is trained on vast general
images using masking strategy, has been naturally embedded with the
distribution knowledge regarding natural images, and thus spontaneously attains
the underlying potential for strong image denoising. Based on this observation,
we propose a novel zero-shot denoising paradigm, i.e., Masked Pre-train then
Iterative fill (MPI). MPI pre-trains a model with masking and fine-tunes it for
denoising of a single image with unseen noise degradation. Concretely, the
proposed MPI comprises two key procedures: 1) Masked Pre-training involves
training a model on multiple natural images with random masks to gather
generalizable representations, allowing for practical applications in varying
noise degradation and even in distinct image types. 2) Iterative filling is
devised to efficiently fuse pre-trained knowledge for denoising. Similar to but
distinct from pre-training, random masking is retained to bridge the gap, but
only the predicted parts covered by masks are assembled for efficiency, which
enables high-quality denoising within a limited number of iterations.
Comprehensive experiments across various noisy scenarios underscore the notable
advances of proposed MPI over previous approaches with a marked reduction in
inference time. Code is available at https://github.com/krennic999/MPI.git.


  We realize a hybrid superconductor-semiconductor transmon device in which the
Josephson effect is controlled by a gate-defined quantum dot in an InAs/Al
nanowire. Microwave spectroscopy of the transmon's transition spectrum allows
us to probe the ground state parity of the quantum dot as a function of gate
voltages, external magnetic flux, and magnetic field applied parallel to the
nanowire. The measured parity phase diagram is in agreement with that predicted
by a single-impurity Anderson model with superconducting leads. Through
continuous time monitoring of the circuit we furthermore resolve the
quasiparticle dynamics of the quantum dot Josephson junction across the phase
boundaries. Our results can facilitate the realization of semiconductor-based
$0-\pi$ qubits and Andreev qubits.


  Bitcoin has emerged as a revolutionary payment system with its decentralized
ledger concept; however it has significant problems such as high transaction
fees and low throughput. Lightning Network (LN), which was introduced much
later, solves most of these problems with an innovative concept called
off-chain payments. With this advancement, Bitcoin has become an attractive
venue to perform micro-payments which can also be adopted in many IoT
applications (e.g., toll payments). Nevertheless, it is not feasible to host LN
and Bitcoin on IoT devices due to the storage, memory, and processing
restrictions. Therefore, in this paper, we propose a secure and efficient
protocol that enables an IoT device to use LN's functions through an untrusted
gateway node. Through this gateway which hosts the LN and Bitcoin nodes, the
IoT device can open & close LN channels and send & receive LN payments. This
delegation approach is powered by a threshold cryptography based scheme that
requires the IoT device and the LN gateway to jointly perform all LN
operations. Specifically, we propose thresholdizing LN's Bitcoin public and
private keys as well as its public and private keys for the new channel states
(i.e., commitment points). We prove with a game theoretical security analysis
that the IoT device is secure against collusion attacks. We implemented the
proposed protocol by changing LN's source code and thoroughly evaluated its
performance using several Raspberry Pis. Our evaluation results show that the
protocol; is fast, does not bring extra cost overhead, can be run on low data
rate wireless networks, is scalable and has negligible energy consumption
overhead. To the best of our knowledge, this is the first work that implemented
threshold cryptography in LN.


  The nature of coarse graining is intuitively "obvious", but it is rather
difficult to find explicit and calculable models of the coarse graining process
(and the resulting entropy flow) discussed in the literature. What we would
like to have at hand is some explicit and calculable process that takes an
arbitrary system, with specified initial entropy S, and monotonically and
controllably drives the entropy to its maximum value. This does not have to be
a physical process, in fact for some purposes it is better to deal with a
gedanken-process, since then it is more obvious how the "hidden information" is
hiding in the fine-grain correlations that one is simply agreeing not to look
at. We shall present several simple mathematically well-defined and easy to
work with conceptual models for coarse graining. We shall consider both the
classical Shannon and quantum von Neumann entropies, including models based on
quantum decoherence, and analyze the entropy flow in some detail. When
coarse-graining the quantum von Neumann entropy, we find it extremely useful to
introduce an adaptation of Hawking's super-scattering matrix. These explicit
models that we shall construct allow us to quantify and keep clear track of the
entropy that appears when coarse-graining the system, and the information that
can be hidden in unobserved correlations. (While not the focus of the current
article, in the longer run these considerations are of interest when addressing
black hole information puzzle.)


  We present Newtonian three-dimensional hydrodynamical simulations of the
merger of quark stars with black holes. The initial conditions correspond to
non-spinning stars in Keplerian orbits, the code includes gravitational
radiation reaction in the quadrupole approximation for point masses. We find
that the quark star is disrupted, forming transient accretion structures around
the black hole, but 0.03 of the original stellar mass survives the initial
encounter and remains in an elongated orbit as a rapidly rotating quark
starlet, in all cases. No resolvable amount of mass is dynamically ejected
during the encounters--the black hole eventually accretes 99.99% +/- 0.01% of
the quark matter initially present.


  We propose an experiment for an accurate measurement of the proton radius. A
key feature of our proposal is an iron-free magnetic spectrometer. Projected
systematics uncertainties will allow a 1% level accuracy for the $r_p$ value.


  This thesis investigates low-dimensional models of nonperturbative quantum
gravity, with a special focus on Causal Dynamical Triangulations (CDT). We
define the so-called curvature profile, a new quantum gravitational observable
based on the quantum Ricci curvature. We subsequently study its coarse-graining
capabilities on a class of regular, two-dimensional polygons with isolated
curvature singularities, and we determine the curvature profile of
(1+1)-dimensional CDT with toroidal topology. Next, we focus on CDT in 2+1
dimensions, intvestigating the behavior of the two-dimensional spatial slice
geometries. We then turn our attention to matrix models, exploring a
differential reformulation of the integrals over one- and two-matrix ensembles.
Finally, we provide a hands-on introduction to computer simulations of CDT
quantum gravity.


  We present FengWu, an advanced data-driven global medium-range weather
forecast system based on Artificial Intelligence (AI). Different from existing
data-driven weather forecast methods, FengWu solves the medium-range forecast
problem from a multi-modal and multi-task perspective. Specifically, a deep
learning architecture equipped with model-specific encoder-decoders and
cross-modal fusion Transformer is elaborately designed, which is learned under
the supervision of an uncertainty loss to balance the optimization of different
predictors in a region-adaptive manner. Besides this, a replay buffer mechanism
is introduced to improve medium-range forecast performance. With 39-year data
training based on the ERA5 reanalysis, FengWu is able to accurately reproduce
the atmospheric dynamics and predict the future land and atmosphere states at
37 vertical levels on a 0.25{\deg} latitude-longitude resolution. Hindcasts of
6-hourly weather in 2018 based on ERA5 demonstrate that FengWu performs better
than GraphCast in predicting 80\% of the 880 reported predictands, e.g.,
reducing the root mean square error (RMSE) of 10-day lead global z500
prediction from 733 to 651 $m^{2}/s^2$. In addition, the inference cost of each
iteration is merely 600ms on NVIDIA Tesla A100 hardware. The results suggest
that FengWu can significantly improve the forecast skill and extend the
skillful global medium-range weather forecast out to 10.75 days lead (with ACC
of z500 > 0.6) for the first time.


  We consider diffusion-controlled evolution of a $d$-dimensional $A$-particle
island in the $B$-particle sea at propagation of the sharp reaction front
$A+B\to 0$ at equal species diffusivities. The $A$-particle island is formed by
a localized (point)$A$-source with a strength $\lambda$ that acts for a finite
time $T$. We reveal the conditions under which the island collapse time $t_{c}$
becomes much longer than the injection period $T$ (long-living island) and
demonstrate that regardless of $d$ the evolution of the long-living island
radius $r_{f}(t)$ is described by the universal law
$\zeta_{f}=r_{f}/r_{f}^{M}=\sqrt{e\tau|\ln\tau|}$ where $\tau=t/t_{c}$ and
$r_{f}^{M}$ is the maximal island expansion radius at the front turning point
$t_{M}=t_{c}/e$. We find that in the long-living island regime the ratio
$t_{c}/T$ changes with the increase of the injection period $T$ by the law
$\propto (\lambda^{2}T^{2-d})^{1/d}$ i.e. increases with the increase of $T$ in
the one-dimensional (1D) case, does not change with the increase of $T$ in the
2D case and decreases with the increase of $T$ in the 3D case. We derive the
scaling laws for particles death in the long-living island and determine the
limits of their applicability. We demonstrate also that these laws describe
asymptotically the evolution of the $d$-dimensional spherical island with a
uniform initial particle distribution generalizing the results obtained earlier
for the quasi-one-dimensional geometry. As striking results we present a
systematic analysis of the front relative width evolution for fluctuation,
logarithmically modified and mean-field regimes and demonstrate that in a wide
range of parameters the front remains sharp up to a narrow vicinity of the
collapse point.


  We analyze high signal-to-noise spectrophotometric observations acquired
simultaneously with TWIN, a double-arm spectrograph, from 3400 to 10400 \AA of
three star-forming regions in the HII galaxy SDSS J165712.75+321141.4. We have
measured four line temperatures: Te([OIII]), Te([SIII]), Te([OII]), and
Te([SII]), with high precision, rms errors of order 2%, 5%, 6% and 6%,
respectively, for the brightest region, and slightly worse for the other two.
The temperature measurements allowed the direct derivation of ionic abundances
of oxygen, sulphur, nitrogen, neon and argon.
  We have computed CLOUDY tailor-made models which reproduce the O2+ measured
thermal and ionic structures within the errors in the three knots, with
deviations of only 0.1 dex in the case of O+ and S2+ ionic abundances. In the
case of the electron temperature and the ionic abundances of S+/H+, we find
major discrepancies which could be consequence of the presence of colder
diffuse gas. The star formation history derived using STARLIGHT shows a similar
age distribution of the ionizing population among the three star-forming
regions. This fact suggests a similar evolutionary history which is probably
related to a process of interaction with a companion galaxy that triggered the
star formation in the different regions almost at the same time. The hardness
of the radiation field mapped through the use of the softness parameter $\eta$
is the same within the observational errors for all three regions, implying
that the equivalent effective temperature of the radiation fields are very
similar for all the studied regions of the galaxy, in spite of some small
differences in the ionization state of different elements.


  There is a large diversity in the C IV broad absorption line (BAL) profile
among BAL quasars (BALQs). We quantify this diversity by exploring the
distribution of the C IV BAL properties, FWHM, maximum depth of absorption and
its velocity shift ($v_{\rm md}$), using the SDSS DR7 quasar catalogue. We find
the following: (i) Although the median C IV BAL profile in the quasar
rest-frame becomes broader and shallower as the UV continuum slope
($\alpha_{\rm UV}$ at 1700-3000 A) gets bluer, the median individual profile in
the absorber rest-frame remains identical, and is narrow (FWHM = 3500 km/s) and
deep. Only 4 per cent of BALs have FWHM > 10,000 km/s. (ii) As the He II
emission equivalent-width (EW) decreases, the distributions of FWHM and $v_{\rm
md}$ extend to larger values, and the median maximum depth increases. These
trends are consistent with theoretical models in which softer ionizing continua
reduce overionization, and allow radiative acceleration of faster BAL outflows.
(iii) As $\alpha_{\rm UV}$ becomes bluer, the distribution of $v_{\rm md}$
extends to larger values. This trend may imply faster outflows at higher
latitudes above the accretion disc plane. (iv) For non-BALQs, the C IV emission
line decreases with decreasing He II EW, and becomes more asymmetric and
blueshifted. This suggests an increasing relative contribution of emission from
the BAL outflow to the C IV emission line as the ionizing spectral energy
distribution (SED) gets softer, which is consistent with the increasing
fraction of BALQs as the ionizing SED gets softer.


  Oscillatory behavior of cumulant moments obtained from the experimental data
in $pp$ collisions and $\bar{p}p$ collisions are analyzed by the modified
negative binomial distribution (MNBD) and the negative binomial distribution
(NBD). Both distributions well describe the cumulant moments obtained from the
data. This fact shows sharp contrast to the result in $e^+e^-$ collisions,
which is described by the the MNBD much better than by the NBD.


  Numerical simulations of planet-disk interactions are usually performed with
hydro-codes that -- because they consider only an annulus of the disk, over a
2D grid -- can not take into account the global evolution of the disk. However,
the latter governs planetary migration of type II, so that the accuracy of the
planetary evolution can be questioned.
  To develop an algorithm that models the local planet-disk interactions
together with the global viscous evolution of the disk, we surround the usual
2D grid with a 1D grid ranging over the real extension of the disk. The 1D and
2D grids are coupled at their common boundaries via ghost rings, paying
particular attention to the fluxes at the interface, especially the flux of
angular momentum carried by waves. The computation is done in the frame
centered on the center of mass to ensure angular momentum conservation.
  The global evolution of the disk and the local planet-disk interactions are
both well described and the feedback of one on the other can be studied with
this algorithm, for a negligible additional computing cost with respect to
usual algorithms.


  This is an introductory review to localization techniques in supersymmetric
two-dimensional gauge theories. In particular we describe how to construct
Lagrangians of N=(2,2) theories on curved spaces, and how to compute their
partition functions and certain correlators on the sphere, the hemisphere and
other curved backgrounds. We also describe how to evaluate the partition
function of N=(0,2) theories on the torus, known as the elliptic genus. Finally
we summarize some of the applications, in particular to probe mirror symmetry
and other non-perturbative dualities.


  We present an analytical and numerical solution of the calculation of the
transition moments for the exact semi-classical light-matter interaction for
wavefunctions expanded in a Gaussian basis. By a simple manipulation we show
that the exact semi-classical light-matter interaction of a plane wave can be
compared to a Fourier transformation of a Gaussian where analytical recursive
formulas are well known and hence making the difficulty in the implementation
of the exact semi-classical light-matter interaction comparable to the
transition dipole. Since the evaluation of the analytical expression involves a
new Gaussian we instead have chosen to evaluate the integrals using a standard
Gau{\ss}-Hermite quadrature since this is faster. A brief discussion of the
numerical advantages of the exact semi-classical light-matter interaction in
comparison to the multipole expansion along with the unphysical interpretation
of the multipole expansion is discussed. Numerical examples on
[CuCl$_4$]$^{2-}$ to show that the usual features of the multipole expansion is
immediately visible also for the exact semi-classical light-matter interaction
and that this can be used to distinguish between symmetries. Calculation on
[FeCl$_4$]$^{1-}$ is presented to demonstrate the better numerical stability
with respect to the choice of basis set in comparison to the multipole
expansion and finally Fe-O-Fe to show origin independence is a given for the
exact operator. The implementation is freely available in OpenMolcas.


  In many sequential tasks, a model needs to remember relevant events from the
distant past to make correct predictions. Unfortunately, a straightforward
application of gradient based training requires intermediate computations to be
stored for every element of a sequence. This requires to store prohibitively
large intermediate data if a sequence consists of thousands or even millions
elements, and as a result, makes learning of very long-term dependencies
infeasible. However, the majority of sequence elements can usually be predicted
by taking into account only temporally local information. On the other hand,
predictions affected by long-term dependencies are sparse and characterized by
high uncertainty given only local information. We propose MemUP, a new training
method that allows to learn long-term dependencies without backpropagating
gradients through the whole sequence at a time. This method can potentially be
applied to any recurrent architecture. LSTM network trained with MemUP performs
better or comparable to baselines while requiring to store less intermediate
data.


  Accurately modeling and verifying the correct operation of systems
interacting in dynamic environments is challenging. By leveraging parametric
uncertainty within the model description, one can relax the requirement to
describe exactly the interactions with the environment; however, one must still
guarantee that the model, despite uncertainty, behaves acceptably. This paper
presents a convex optimization method to efficiently compute the set of
configurations of a polynomial dynamical system that are able to safely reach a
user defined target set despite parametric uncertainty in the model. Since
planning in the presence of uncertainty can lead to undesirable conservatives,
this paper computes those trajectories of the uncertain nonlinear systems which
are $\alpha$-probable of reaching the desired configuration. The presented
approach uses the notion of occupation measures to describe the evolution of
trajectories of a nonlinear system with parametric uncertainty as a linear
equation over measures whose supports coincide with the trajectories under
investigation. This linear equation is approximated with vanishing conservatism
using a hierarchy of semidefinite programs each of which is proven to compute
an approximation to the set of initial conditions that are $\alpha$-probable of
reaching the user defined target set safely in spite of uncertainty. The
efficacy of this method is illustrated on four systems with parametric
uncertainty.


  We study the stability of zero-vorticity and vortex lattice quantum droplets
(LQDs), which are described by a two-dimensional (2D) Gross-Pitaevskii (GP)
equation with a periodic potential and Lee-Huang-Yang (LHY) term. The LQDs are
divided in two types: onsite-centered and offsitecentered LQDs, the centers of
which are located at the minimum and the maximum of the potential,
respectively. The stability areas of these two types of LQDs with different
number of sites for zerovorticity and vorticity with S = 1 are given. We found
that the u-N relationship of the stable LQDs with a fixed number of sites can
violate the Vakhitov-Kolokolov (VK) criterion, which is a necessary stability
condition for nonlinear modes with an attractive interaction. Moreover, the u-N
relationship shows that two types of vortex LQDs with the same number of sites
are degenerated, while the zero-vorticity LQDs are not degenerated. It is worth
mentioning that the offsite-centered LQDs with zero-vorticity and vortex LQDs
with S = 1 are heterogeneous.


  In the present paper, we present some numerical methods for computing
approximate solutions to some large differential linear matrix equations. In
the first part of this work, we deal with differential generalized Sylvester
matrix equations with full rank right-hand sides using a global Galerkin and a
norm-minimization approaches. In the second part, we consider large
differential Lyapunov matrix equations with low rank right-hand sides and use
the extended global Arnoldi process to produce low rank approximate solutions.
We give some theoretical results and present some numerical experiments.


  String Fusion Model Code results for Pb--Pb central collisions at SPS
energies including the string fusion interaction mechanism are compared to the
last experimental data. Predictions for RHIC energies are also presented. On
the other hand, the evolution of the strangeness enhancement ratio
$E_s=\frac{<\Lambda>+4<K_s^0>}{3<\pi^->}$ with the energy and with the atomic
number of participant nuclei is discussed.


  In this paper we concentrate on the relations between the structure of small
Galois groups, arithmetic of fields, Bloch-Kato conjecture, and Galois groups
of maximal pro-$p$-quotients of absolute Galois groups.


  We discuss how systems with a large number of degrees of freedom and disorder
in their mass matrix can play a role in particle physics. We derive results on
their mass spectra using, where applicable, QFT techniques. We study concrete
realizations of these scenarios in the context of the LHC and HL-LHC, showing
that collider events with a large number of soft b-quark jets can be common.
Such final states can hide these models from current searches at the LHC. This
motivates the ongoing effort aimed at lowering trigger thresholds and expanding
data scouting.


  We present an analysis of high-resolution far-ultraviolet archival spectra
obtained with the Space Telescope Imaging Spectrograph on the Hubble Space
Telescope of the star HD 75309, which probes high-velocity shocked gas in the
Vela supernova remnant (SNR). We examine high-velocity features from
intrinsically strong absorption lines of O I, Si II, Si II*, C II, C II*, and
Si III. We also detect high-velocity components in the N V doublet and compare
these features to observations of high-velocity O VI absorption, available from
archival Far Ultraviolet Spectroscopic Explorer data. Kinetic temperatures are
derived from the observed fractional abundances of the various ions, while gas
densities and thermal pressures are obtained from the relative populations in
excited fine-structure levels of C II and Si II. Our results indicate that the
highly ionized species at high velocity probe gas in a region immediately
behind a shock driven into an interstellar cloud, while the lower ionization
species trace material further downstream in the cooling region of the
post-shock flow. Low velocity N V and O VI absorption may trace gas in a
conductive boundary layer between the unshocked portion of the cloud and the
hot X-ray emitting intercloud medium. Temporal variations in high velocity Ca
II absorption features observed toward HD 75309 further confirm the highly
inhomogeneous nature of the interstellar medium interacting with the Vela SNR.


  We provide an effective ramification theorem for the ratio of canonical forms
of a weakly complete flat front in the hyperbolic three-space. Moreover we give
the two applications of this theorem, the first one is to show an analogue of
the Ahlfors islands theorem for it and the second one is to give a simple proof
of the classification of complete nonsingular flat surfaces in the hyperbolic
three-space.


  Given a graded bialgebra $H$, we let $\Delta^{\left[ k\right] }:H\rightarrow
H^{\otimes k}$ and $m^{\left[ k\right] }:H^{\otimes k}\rightarrow H$ be its
iterated (co)multiplications for all $k\in\mathbb{N}$. For any $k$-tuple
$\alpha=\left(
  \alpha_{1},\alpha_{2},\ldots,\alpha_{k}\right) \in\mathbb{N}^{k}$ of
nonnegative integers, and any permutation $\sigma$ of $\left\{
1,2,\ldots,k\right\} $, we consider the map $p_{\alpha,\sigma}:=m^{\left[
k\right] }\circ P_{\alpha}\circ\sigma^{-1}\circ\Delta^{\left[ k\right]
}:H\rightarrow H$, where $P_{\alpha}$ denotes the projection of $H^{\otimes k}$
onto its multigraded component $H_{\alpha_{1}}\otimes
H_{\alpha_{2}}\otimes\cdots\otimes H_{\alpha_{k}}$, and where
$\sigma^{-1}:H\rightarrow H$ permutes the tensor factors.
  We prove formulas for the composition $p_{\alpha,\sigma}\circ p_{\beta,\tau}$
and the convolution $p_{\alpha,\sigma}\star p_{\beta,\tau}$ of two such maps.
When $H$ is cocommutative, these generalize Patras's 1994 results (which, in
turn, generalize Solomon's Mackey formula).
  We also construct a combinatorial Hopf algebra $\operatorname*{PNSym}$
("permuted noncommutative symmetric functions") that governs the maps
$p_{\alpha,\sigma}$ for arbitrary connected graded bialgebras $H$ in the same
way as the well-known $\operatorname*{NSym}$ governs them in the cocommutative
case. We end by outlining an application to checking identities for connected
graded Hopf algebras.


  We use Young's raising operators to introduce and study double theta
polynomials, which specialize to both the theta polynomials of Buch, Kresch,
and Tamvakis, and to double (or factorial) Schur S-polynomials and
Q-polynomials. These double theta polynomials give Giambelli formulas which
represent the equivariant Schubert classes in the torus-equivariant cohomology
ring of symplectic Grassmannians, and we employ them to obtain a new
presentation of this ring in terms of intrinsic generators and relations.


  Semantic segmentation is a key technology for autonomous vehicles to
understand the surrounding scenes. The appealing performances of contemporary
models usually come at the expense of heavy computations and lengthy inference
time, which is intolerable for self-driving. Using light-weight architectures
(encoder-decoder or two-pathway) or reasoning on low-resolution images, recent
methods realize very fast scene parsing, even running at more than 100 FPS on a
single 1080Ti GPU. However, there is still a significant gap in performance
between these real-time methods and the models based on dilation backbones. To
tackle this problem, we proposed a family of efficient backbones specially
designed for real-time semantic segmentation. The proposed deep dual-resolution
networks (DDRNets) are composed of two deep branches between which multiple
bilateral fusions are performed. Additionally, we design a new contextual
information extractor named Deep Aggregation Pyramid Pooling Module (DAPPM) to
enlarge effective receptive fields and fuse multi-scale context based on
low-resolution feature maps. Our method achieves a new state-of-the-art
trade-off between accuracy and speed on both Cityscapes and CamVid dataset. In
particular, on a single 2080Ti GPU, DDRNet-23-slim yields 77.4% mIoU at 102 FPS
on Cityscapes test set and 74.7% mIoU at 230 FPS on CamVid test set. With
widely used test augmentation, our method is superior to most state-of-the-art
models and requires much less computation. Codes and trained models are
available online.


  We undertake a systematic study of the notion of fibration in the setting of
abstract simplicial complexes, where the concept of `homotopy' has been
replaced by that of `contiguity'. Then a fibration will be a simplicial map
satisfying the `contiguity lifting property'. This definition turns out to be
equivalent to a known notion introduced by G. Minian, established in terms of a
cylinder construction $K \times I_m$. This allows us to prove several
properties of simplicial fibrations which are analogous to the classical ones
in the topological setting, for instance: all the fibers of a fibration have
the same strong homotopy type, a notion that has been recently introduced by
Barmak and Minian; any fibration with a strongly collapsible base is fibrewise
trivial; and some other ones. We introduce the concept of `simplicial
finite-fibration', that is, a map which has the contiguity lifting property
only for finite complexes. Then, we prove that the path fibration $PK \to
K\times K$ is a finite-fibration, where PK is the space of Moore paths
introduced by M. Grandis. This important result allows us to prove that any
simplicial map factors through a finite-fibration, up to a P-homotopy
equivalence. Moreover, we introduce a definition of `\v{S}varc genus' of a
simplicial map and, and using the properties stated before, we are able to
compare the \v{S}varc genus of path fibrations with the notions of simplicial
LS-category and simplicial topological complexity introduced by the authors in
several previous papers. Finally, another key result is a simplicial version of
a Varadarajan result for fibrations, relating the LS-category of the total
space, the base and the generic fiber.


  Politeness plays an important role in regulating communication and enhancing
social interactions.Research suggests that people treat interactive systems as
social agents and may expect those systems to exhibit polite behavior. We
augment early research in this area by proposing a framework that is grounded
in sociolinguistics and pragmatics. The framework focuses on two complementary
concepts - clarity and politeness. We suggest that these concepts are pertinent
to various domains of interactive technologies and provide examples for the
applicability of clarity and politeness rules to human-computer interaction
(HCI). We conducted a laboratory experiment, in which politeness and clarity
served as independent factors in the context of a cooperative computer game,
based on the Peekaboom game. The manipulation of clarity failed, yet politeness
was manipulated successfully based on the framework's rules of politeness. The
results provide empirical support for the basic propositions of the framework
and may facilitate more systematic research on politeness in various domains of
social computing and other areas related to HCI.


  For a transitive countably piecewise monotone Markov interval map we consider
the question whether there exists a conjugate map of constant slope. The answer
varies depending on whether the map is continuous or only piecewise continuous,
whether it is mixing or not, what slope we consider, and whether the conjugate
map is defined on a bounded interval, half-line or the whole real line (with
the infinities included).


  We consider the time evolution of a quantized field in backgrounds that
violate the vacuum stability (particle-creating backgrounds). Our aim is to
study the exact form of the final quantum state (the density operator at a
final instant of time) that has emerged from a given arbitrary initial state
(from a given arbitrary density operator at the initial time instant) in the
course of the evolution. We find a generating functional that allows us to have
the density operators for any initial state. Averaging over states of a
subsystem of antiparticles (particles), we obtain explicit forms for reduced
density operators for subsystems of particles (antiparticles). Studying
one-particle correlation functions, we establish a one-to-one correspondence
between these functions and the reduced density operators. It is shown that in
the general case a presence of bosons (e.g. gluons) in an initial state
increases the creation rate of the same kind of bosons. We discuss the question
(and its relation to the initial stage of quark-gluon plasma formation) whether
a thermal form of one-particle distribution can appear even if the final state
of the complete system is not a thermal equilibrium. In this respect, we
discuss some cases when a pair creation by an electric-like field can mimic a
one-particle thermal distribution. We apply our technics to some QFT problems
in slowly varying electric-like backgrounds: electric, SU(3) chromoelectric,
and metric. In particular, we study the time and temperature behavior of mean
numbers of created particles provided switching on and off effects of the
external field are negligible. It is shown that at high temperatures and in
slowly varying electric fields the rate of particle creation is essentially
time-dependent.


  Zaremba's Conjecture concerns the formation of continued fractions with
partial quotients restricted to a given alphabet. In order to answer the
numerous questions that arrive from this conjecture, it is best to consider a
semi-group, often denoted $\Gamma_{A}$, which arises naturally as a subset of
$SL_2(\mathbb{Z})$ when considering finite continued fractions. To translate
back from this semi-group into rational numbers, we select a projection mapping
satisfying certain criteria to recover the numerator and denominator of the
continued fractions in rational form. The central question of our research is
to determine the multiplicity of a given denominator. To this end, we develop a
heuristic method similar to the Hardy-Littlewood Circle Method. We compare this
theoretical model to the exact data, gleaned by simulation, and demonstrate
that our formula appears to be asymptotically valid. We then evaluate different
aspects of the accuracy of our formula.


  [abridged] WFIRST is uniquely capable of finding planets with masses as small
as Mars at separations comparable to Jupiter, i.e., beyond the current ice
lines of their stars. These planets fall between the close-in planets found by
Kepler and the wide separation gas giants seen by direct imaging and ice giants
inferred from ALMA observations. Furthermore, the smallest planets WFIRST can
detect are smaller than the planets probed by RV and Gaia at comparable
separations. Interpreting planet populations to infer the underlying formation
and evolutionary processes requires combining results from multiple detection
methods to measure the full variation of planets as a function of planet size,
orbital separation, and host star mass. Microlensing is the only way to find
planets from 0.5 to 5M_E at 1 to 5au. The case for a microlensing survey from
space has not changed in the past 20 yrs: space allows wide-field
diffraction-limited observations that resolve main-sequence stars in the bulge,
which allows the detection and characterization of the smallest signals
including those from planets with masses at least as small as Mars. What has
changed is that ground-based (GB) microlensing is reaching its limits,
underscoring the scientific necessity for a space-based survey. GB microlensing
has found a break in the mass-ratio distribution at about a Neptune, implying
that these are the most common microlensing planet and that planets smaller
than this are rare. However, GB microlensing reaches its detection limits only
slightly below the observed break. WFIRST will measure the shape of the
mass-ratio function below the break by finding numerous smaller planets: 500
Neptunes, 600 gas giants, 200 Earths, and planets as small as 0.1M_E. Because
it will also measure host masses and distances, WFIRST will also track the
behavior of the planet distribution as a function of separation and host star
mass.


  The thermographic phosphor ScVO4:Bi3+ is used to obtain time-resolved surface
temperature measurements with sub-oC precision at 5 kHz. Measurements are used
to study transient heat loss and flame-wall interactions (FWI) within a
dedicated narrow two-wall passage (crevice) in an optically accessible fixed
volume chamber. This passage emulates a crevice relevant in many technical
environments, where FWI is less understood due to lack of detailed
measurements. Chemiluminescence (CH*) imaging is performed simultaneously with
phosphor thermometry to resolve how the spatiotemporal flame features influence
the local surface temperature. ScVO4:Bi3+ is benchmarked against
Gd3Ga5O12:Cr,Ce, a common phosphor used at low-kHz rates in FWI environments.
ScVO4:Bi3+ is shown to offer higher luminescence signal levels and temperature
sensitivity as well as negligible cross dependence on the excitation laser
fluence, improving the precision and repeatability of the wall temperature
measurement. ScVO4:Bi3+ is further used to resolve transient heat loss for
variations in crevice spacing and uniquely capture temperature transients
associated with flame dynamics. Taking advantage of these precise surface
temperature measurements the wall heat flux is calculated with crevice spacing
of 1.2 mm, where flame extinction is prevalent. Wall heat flux and estimated
quenching distance are reported for flames that actively burn or extinguish at
the measurement location.


  The purpose of this article is to initiate Arakelov theory in a
noncommutative setting. More precisely, we are concerned with Arakelov theory
of noncommutative arithmetic curves. Our first main result is an arithmetic
Riemann-Roch formula in this setup. We proceed with introducing the
Grothendieck group of arithmetic vector bundles on a noncommutative arithmetic
curve and show that there is a uniquely determined degree map, which we then
use to define a height function. We prove a duality theorem for this height.


  Augmented Reality or AR filters on selfies have become very popular on social
media platforms for a variety of applications, including marketing,
entertainment and aesthetics. Given the wide adoption of AR face filters and
the importance of faces in our social structures and relations, there is
increased interest by the scientific community to analyze the impact of such
filters from a psychological, artistic and sociological perspective. However,
there are few quantitative analyses in this area mainly due to a lack of
publicly available datasets of facial images with applied AR filters. The
proprietary, close nature of most social media platforms does not allow users,
scientists and practitioners to access the code and the details of the
available AR face filters. Scraping faces from these platforms to collect data
is ethically unacceptable and should, therefore, be avoided in research. In
this paper, we present OpenFilter, a flexible framework to apply AR filters
available in social media platforms on existing large collections of human
faces. Moreover, we share FairBeauty and B-LFW, two beautified versions of the
publicly available FairFace and LFW datasets and we outline insights derived
from the analysis of these beautified datasets.


  Transport properties provide important access to a solid's quasiparticles,
such as quasiparticle density, mobility, and scattering. The transport of heat
can be particularly revealing because, in principle, all types of excitations
in a solid may contribute. Heat transport is well understood for phonons and
electrons, but relatively little is known about heat transported by magnetic
excitations. However, during the last about two decades, the magnetic heat
transport attracted increasing attention after the discovery of large and
unusual signatures of it in low-dimensional quantum magnetic cuprate materials.
Today it constitutes an important probe to otherwise often elusive, topological
quasiparticles in a broader class of quantum magnets. This review summarizes
the experimental foundation of this research, i.e. the state of the art for the
magnetic heat transport in the mentioned cuprate materials which host
prototypical low-dimensional antiferromagnetic $S=1/2$ Heisenberg models. These
comprise, in particular, the two-dimensional square lattice, and
one-dimensional spin chain and two-leg ladder spin models. It is shown, how
studying the heat transport provides direct access to the thermal occupation
and the scattering of the already quite exotic quasiparticles of these models
which range from spin-1 spin wave and triplon excitations to fractionalized
spin-1/2 spinons. Remarkable transport properties of these quasiparticles have
been revealed: the spin-heat transport often is highly efficient and in some
cases even ballistic, in agreement with theoretical predictions.


  We describe the Chow ring with rational coefficients of Mbar_{0,1}(P^n,d) as
the subring of invariants of a ring B(Mbar_{0,1}(P^n,d);Q), relative to the
action of the group of symmetries S_d. We compute B(Mbar_{0,1}(P^n,d);Q) by
following a sequence of intermediate spaces for Mbar_{0,1}(P^n,d).


  We consider a sequence of quantum states, $\rho_N$, associated with a
submanifold $\Lambda$ of product of two integral compact K\"ahler manifolds. We
show that, when $\Lambda$ is a product submanifold, then, in the semiclassical
limit, these states are not entangled. We discuss whether geometry of $\Lambda$
(specifically $\Lambda$ being a product submanifold) has any relationship with
entanglement properties of $\rho_N$.


  Business analytics and machine learning have become essential success factors
for various industries - with the downside of cost-intensive gathering and
labeling of data. Few-shot learning addresses this challenge and reduces data
gathering and labeling costs by learning novel classes with very few labeled
data. In this paper, we design a human-in-the-loop (HITL) system for few-shot
learning and analyze an extensive range of mechanisms that can be used to
acquire human expert knowledge for instances that have an uncertain prediction
outcome. We show that the acquisition of human expert knowledge significantly
accelerates the few-shot model performance given a negligible labeling effort.
We validate our findings in various experiments on a benchmark dataset in
computer vision and real-world datasets. We further demonstrate the
cost-effectiveness of HITL systems for few-shot learning. Overall, our work
aims at supporting researchers and practitioners in effectively adapting
machine learning models to novel classes at reduced costs.


  Context: The emission line of [CII] at 158 micron is one of the strongest
cooling lines of the interstellar medium (ISM) in galaxies. Aims: Disentangling
the relative contributions of the different ISM phases to [CII] emission, is a
major topic of the HerM33es program, a Herschel key project to study the ISM in
the nearby spiral galaxy M33. Methods: Using PACS, we have mapped the emission
of [CII] 158 micron, [OI] 63 micron, and other FIR lines in a 2'x2' region of
the northern spiral arm of M33, centered on the HII region BCLMP302. At the
peak of H-alpha emission, we have observed in addition a velocity resolved
[CII] spectrum using HIFI. We use scatterplots to compare these data with PACS
160 micron continuum maps, and with maps of CO and HI data, at a common
resolution of 12 arcsec or 50 pc. Maps of H-alpha and 24 micron emission
observed with Spitzer are used to estimate the SFR. We have created maps of the
[CII] and [OI] 63 micron emission and detected [NII] 122 micron and NIII 57
micron at individual positions. Results: The [CII] line observed with HIFI is
significantly broader than that of CO, and slightly blue-shifted. In addition,
there is little spatial correlation between [CII] observed with PACS and CO
over the mapped region. There is even less spatial correlation between [CII]
and the atomic gas traced by HI. Detailed comparison of the observed
intensities towards the HII region with models of photo ionization and photon
dominated regions, confirms that a significant fraction, 20--30%, of the
observed [CII] emission stems from the ionized gas and not from the molecular
cloud. The gas heating efficiency, using the ratio between [CII] and the TIR as
a proxy, varies between 0.07 and 1.5%, with the largest variations found
outside the HII region.


  Background/Introduction: Integrating cognitive radio (CR) with traditional
wireless networks is helping solve the problem of spectrum scarcity in an
efficient manner. The opportunistic and dynamic spectrum access features of CR
provide the functionality to its unlicensed users to utilize the underutilized
spectrum at the time of need because CR nodes can sense vacant bands of
spectrum and can also access them to carry out communication. Various
capabilities of CR nodes depend upon efficient and continuous reporting of data
with each other and centralized base stations, which in turn can cause leakage
in privacy. Experimental studies have shown that the privacy of CR users can be
compromised easily during the cognition cycle, because they are knowingly or
unknowingly sharing various personally identifiable information (PII), such as
location, device ID, signal status, etc. In order to preserve this privacy
leakage, various privacy preserving strategies have been developed by
researchers, and according to us differential privacy is the most significant
among them.


  In order to complete our HI survey of galaxies in the Sculptor group area,
VLA observations of NGC 24 and NGC 45 are presented. These two galaxies of
similar magnitude M_B ~ -17.4 lie in the background of the Sculptor group and
are low surface brightness galaxies, especially NGC 45. The HI distribution and
kinematics are regular for NGC 24 while NGC 45 exhibits a kinematical twist of
its major axis. A tilted-ring model shows that the position angle of the major
axis changes by ~25 degrees. A best-fit model of their mass distribution gives
mass-to-light ratios for the stellar disk of 2.5 and 5.2 for NGC 24 and NGC 45
respectively. These values are higher than the ones expected from stellar
population synthesis models. Despite the large dark matter contribution, the
galaxy mass is still dominated by the stellar component in their very inner
regions. These high mass-to-light ratios are typical of what is seen in low
surface brightness galaxies and may indicate that, in those galaxies, disks are
far from the maximum disk case. The halo parameters derived from the best-fit
models are thus lower limits.


  The search for magnetic monopoles in the cosmic radiation remains one of the
main aims of non-accelerator particle astrophysics. Experiments at high
altitude allow lower mass thresholds with respect to detectors at sea level or
underground. The SLIM experiment is a large array of nuclear track detectors at
the Chacaltaya High Altitude Laboratory (5290 m a.s.l.). The results from the
analysis of 171 m$^2$ exposed for more than 3.5 y are here reported. The
completion of the analysis of the whole detector will allow to set the lowest
flux upper limit for Magnetic Monopoles in the mass range 10$^5$ - 10$^{12}$
GeV. The experiment is also sensitive to SQM nuggets and Q-balls, which are
possible Dark Matter candidates.


  A quadratic semigroup algebra is an algebra over a field given by the
generators $x_1,...,x_n$ and a finite set of quadratic relations each of which
either has the shape $x_jx_k=0$ or the shape $x_jx_k=x_lx_m$. We prove that a
quadratic semigroup algebra given by $n$ generators and $d\leq \frac{n^2+n}{4}$
relations is always infinite dimensional. This strengthens the
Golod--Shafarevich estimate for the above class of algebras. Our main result
however is that for every $n$, there is a finite dimensional quadratic
semigroup algebra with $n$ generators and $\delta_n$ relations, where
$\delta_n$ is the first integer greater than $\frac{n^2+n}{4}$. This shows that
the above Golod-Shafarevich type estimate for semigroup algebras is sharp.


  Quantized nano-objects offer a myriad of exciting possibilities for
manipulating electrons and light that impact photonics, nanoelectronics, and
quantum information. In this context, ultrashort laser pulses combined with
nanotips and field emission have permitted renewing nano-characterization and
control electron dynamics with unprecedented space and time resolution reaching
femtosecond and even attosecond regimes. A crucial missing step in these
experiments is that no signature of quantized energy levels has yet been
observed. We combine in situ nanostructuration of nanotips and ultrashort laser
pulse excitation to induce multiphoton excitation and electron emission from a
single quantized nano-object attached at the apex of a metal nanotip.
Femtosecond induced tunneling through well-defined localized confinement states
that are tunable in energy is demonstrated. This paves the way for the
development of ultrafast manipulation of electron emission from isolated
nano-objects including stereographically fixed individual molecules and high
brightness, ultrafast, coherent single electron sources for quantum optics
experiments.


  Introducing internal degrees of freedom in the description of topological
insulators has led to a myriad of theoretical and experimental advances. Of
particular interest are the effects of periodic perturbations, either in time
or space, as they considerably enrich the variety of electronic responses, with
examples such as Thouless's charge pump and its higher dimensional cousins, or,
higher-order topological insulators. Here, we develop a semiclassical approach
to transport and accumulation of general spinor degrees of freedom, such as
physical spin, valley, or atomic orbits, in adiabatically driven, weakly
inhomogeneous insulators of dimensions one, two and three under external
electromagnetic fields. Specifically, we focus on physical spins and derive the
spin current and density up to third order in the spatio-temporal modulations
of the system. We, then, relate these contributions to geometrical and
topological objects -- the spin-Chern fluxes and numbers -- defined over the
higher-dimensional phase-space of the system, i.e., its combined
momentum-position-time coordinates. Furthermore, we provide a connection
between our semiclassical analysis and the modern theory of multipole moments
by introducing spin analogues of the electric dipole, quadrupole and octapole
moments. The results are showcased in concrete tight-binding models where the
induced responses are calculated analytically.


  We combine new high sensitivity ultraviolet (UV) imaging from the Wide Field
Camera 3 (WFC3) on the Hubble Space Telescope (HST) with existing deep
HST/Advanced Camera for Surveys (ACS) optical images from the Great
Observatories Origins Deep Survey (GOODS) program to identify UV-dropouts,
which are Lyman break galaxy (LBG) candidates at z~1-3. These new HST/WFC3
observations were taken over 50 sq.arcmin in the GOODS-South field as a part of
the Early Release Science program. The uniqueness of these new UV data is that
they are observed in 3 UV/optical (WFC3 UVIS) channel filters (F225W, F275W and
F336W), which allows us to identify three different sets of UV-dropout samples.
We apply Lyman break dropout selection criteria to identify F225W-, F275W- and
F336W-dropouts, which are z~1.7, 2.1 and 2.7 LBG candidates, respectively. Our
results are as follows: (1) these WFC3 UVIS filters are very reliable in
selecting LBGs with z~2.0, which helps to reduce the gap between the well
studied z~>3 and z~0 regimes, (2) the combined number counts agrees very well
with the observed change in the surface densities as a function of redshift
when compared with the higher redshift LBG samples; and (3) the best-fit
Schechter function parameters from the rest-frame UV luminosity functions at
three different redshifts fit very well with the evolutionary trend of the
characteristic absolute magnitude, and the faint-end slope, as a function of
redshift. This is the first study to illustrate the usefulness of the WFC3 UVIS
channel observations to select z<3 LBGs. The addition of the new WFC3 on the
HST has made it possible to uniformly select LBGs from z~1 to z~9, and
significantly enhance our understanding of these galaxies using HST sensitivity
and resolution.


  It has been proposed that the recently discovered archetypical "exotic"
meson, X(3872), with M(X(3872))=3871.68+-0.17 MeV/c^2, and an extremely narrow
width, Gamma(X(3872))<1.2 MeV, is a hadronic molecule of bound D^0 and D^*0
mesons. If true, this would establish a new species of hadrons, distinct from
qqbar mesons and qqq baryons. It is put to an important experimental test by
making a high precision measurement of the proposed molecule's binding energy.
Using 818 pb-1 of e+e- annihilation data taken with the CLEO-c detector at
psi(3770), the decays D0 -> KS K+ K- and D^0(Dbar0) -> K+- pi-+ pi+ pi- have
been studied to make the highest precision measurement of D0 mass,
M(D0)=1864.851+-0.020+-0.019+-0.054 MeV/c^2, where the first error is
statistical, the second error is systematic, and the third error is due to
uncertainty in kaon masses, or 1864.851+-0.061 MeV/c^2 with all errors added in
quadrature. This leads to M(D0 + D*0)=3871.822+-0.140 MeV/c^2, and the binding
energy BE(X(3872)) = M(D0+D*0)-M(X(3872)) = +142+-220 keV. At the 90%
confidence level this leads to the conclusion that X(3872) is either unbound by
as much as 140 keV, or it is bound by less than 420 keV. If bound, X(3872) has
a very large radius; the central value of binding energy corresponds to a
radius of 12 fm, and the lower limit to 7 fm, both being uncomfortably large
for a molecule.


  This paper explores the utility of instantaneous and continuous observations
in the optimal control of quantum dynamics. Simulations of the processes are
performed on several multilevel quantum systems with the goal of population
transfer. Optimal control fields are shown to be capable of cooperating or
fighting with observations to achieve a good yield, and the nature of the
observations may be optimized to more effectively control the quantum dynamics.
Quantum observations also can break dynamical symmetries to increase the
controllability of a quantum system. The quantum Zeno and anti-Zeno effects
induced by observations are the key operating principles in these processes.
The results indicate that quantum observations can be effective tools in the
control of quantum dynamics.


  We consider colour-electric screening as expressed by the quark contribution
to the Debye mass calculated in a PNJL model with emphasis on confining and
chiral symmetry breaking effects. We observe that the screening mass is
entirely determined by the nonperturbative quark distribution function and
temperature dependent QCD running coupling. The role of the gluon background
(Polyakov loop) is to provide strong suppression of the number of charge
carriers below the transition temperature, as an effect of confinement, while
the temperature dependent dynamical quark mass contributes additional
suppression, as an effect of chiral symmetry breaking. An alternative
derivation of this result from a modified kinetic theory is given, which allows
for a slight generalization and explicit contact with perturbative QCD. This
gives the possibility to gain insights into the colour screening mechanism in
the region near the QCD pseudocritical temperature and to provide a guideline
for the interpretation of lattice QCD data.


  In this paper we present a generic framework for ontology-based information
retrieval. We focus on the recognition of semantic information extracted from
data sources and the mapping of this knowledge into ontology. In order to
achieve more scalability, we propose an approach for semantic indexing based on
entity retrieval model. In addition, we have used ontology of public
transportation domain in order to validate these proposals. Finally, we
evaluated our system using ontology mapping and real world data sources.
Experiments show that our framework can provide meaningful search results.


  Personal IoT data is a new economic asset that individuals can trade to
generate revenue on the emerging data marketplaces. Typically, marketplaces are
centralized systems that raise concerns of privacy, single point of failure,
little transparency and involve trusted intermediaries to be fair. Furthermore,
the battery-operated IoT devices limit the amount of IoT data to be traded in
real-time that affects buyer/seller satisfaction and hence, impacting the
sustainability and usability of such a marketplace. This work proposes to
utilize blockchain technology to realize a trusted and transparent
decentralized marketplace for contract compliance for trading IoT data streams
generated by battery-operated IoT devices in real-time. The contribution of
this paper is two-fold: (1) we propose an autonomous blockchain-based
marketplace equipped with essential functionalities such as agreement
framework, pricing model and rating mechanism to create an effective
marketplace framework without involving a mediator, (2) we propose a mechanism
for selection and allocation of buyers' demands on seller's devices under
quality and battery constraints. We present a proof-of-concept implementation
in Ethereum to demonstrate the feasibility of the framework. We investigated
the impact of buyer's demand on the battery drainage of the IoT devices under
different scenarios through extensive simulations. Our results show that this
approach is viable and benefits the seller and buyer for creating a sustainable
marketplace model for trading IoT data in real-time from battery-powered IoT
devices.


  Consider the restricted Hanoi graphs which correspond to the variants of the
famous Tower of Hanoi problem with multiple pegs where moves of the discs are
restricted throughout the arcs of a movement digraph whose vertices represent
the pegs of the puzzle and an arc from vertex $p$ to vertex $q$ exists if and
only if moves from peg $p$ to peg $q$ are allowed. In this paper, we gave some
notes on how to construct the restricted Hanoi graphs as well as some
combinatorial results on the number of arcs in these graphs.


  In [{\em The Numerical Range is a $(1 + \sqrt{2})$-Spectral Set}, SIAM J.
Matrix Anal. Appl. 38 (2017), pp.~649-655], Crouzeix and Palencia show that the
numerical range of a square matrix or linear operator $A$ is a $(1 +
\sqrt{2})$-spectral set for $A$; that is, for any function $f$ analytic in the
interior of the numerical range $W(A)$ and continuous on its boundary, the
inequality $\| f(A) \| \leq (1 + \sqrt{2} ) \| f \|_{W(A)}$ holds, where the
norm on the left is the operator 2-norm and $\| f \|_{W(A)}$ on the right
denotes the supremum of $| f(z) |$ over $z \in W(A)$. In this paper, we show
how the arguments in their paper can be extended to show that other regions in
the complex plane that do {\em not} necessarily contain $W(A)$ are $K$-spectral
sets for a value of $K$ that may be close to $1 + \sqrt{2}$. We also find some
special cases in which the constant $(1 + \sqrt{2})$ for $W(A)$ can be replaced
by $2$, which is the value conjectured by Crouzeix.


  In the presence of mass anisotropy, anisotropic interaction, or in-plane
magnetic field, quantum Hall droplets can exhibit shape deformation and
internal geometrical degree of freedom. We characterize the geometry of quantum
Hall states by principal component analysis, which is a statistical technique
that emphasizes variation in a dataset. We first test the method in an integer
quantum Hall droplet with dipole-dipole interaction in disk geometry. In the
subsequent application to fractional quantum Hall systems with anisotropic
Coulomb interaction in torus geometry, we demonstrate that the principal
component analysis can quantify the metric degree of freedom and predict the
collapse of a $\nu= 1/3$ state. We also calculate the metric response to
interaction anisotropy at filling fractions $\nu= 1/5$ and $2/5$ and show that
the response is roughly the same within the same Jain sequence, but can differ
at large anisotropy for different sequences.


  In this note we study energetics of Lorenz-63 system through its Lie-Poisson
structure.


  The paper develops an earlier proposition that the physical universe is a
finite system co-ordinatised by a very large finite field
$\mathrm{F}_\mathfrak{p}$ which looks like the field of complex numbers to an
observer.
  We construct a place (homomorphism) $\mathrm{lm}$ from a pseudo-finite field
$\mathrm{F}_\mathfrak{p}$ onto the compactified field of complex numbers in
such a way that certain multiplicative subgroups $'\mathbb{R}'_+$ and
$'\mathbb{S}'$ correspond to the polar coordinate system $\mathbb{R}_+$ and
$\mathbb{S}$ of $\mathbb{C}.$ Thus $\mathrm{F}_\mathfrak{p},$ $'\mathbb{R}'_+$
and $'\mathbb{S}'$ provide co-ordinates for physical universe.
  We show that the passage from the scale of units in $'\mathbb{R}'_+$ to the
scale of units of $'\mathbb{S}'$ corresponds to a multiplication (on the
logarithmic scale) by a very large integer $\mathfrak{i}$ equal approximately
to $\sqrt{\mathfrak{p}}.$ This provides an explanation to the phenomenon of
Wick rotation.
  In the same model we explain the phenomenon of phase transition in a large
finite system


  Understanding objects in terms of their individual parts is important,
because it enables a precise understanding of the objects' geometrical
structure, and enhances object recognition when the object is seen in a novel
pose or under partial occlusion. However, the manual annotation of parts in
large scale datasets is time consuming and expensive. In this paper, we aim at
discovering object parts in an unsupervised manner, i.e., without ground-truth
part or keypoint annotations. Our approach builds on the intuition that objects
of the same class in a similar pose should have their parts aligned at similar
spatial locations. We exploit the property that neural network features are
largely invariant to nuisance variables and the main remaining source of
variations between images of the same object category is the object pose.
Specifically, given a training image, we find a set of similar images that show
instances of the same object category in the same pose, through an affine
alignment of their corresponding feature maps. The average of the aligned
feature maps serves as pseudo ground-truth annotation for a supervised training
of the deep network backbone. During inference, part detection is simple and
fast, without any extra modules or overheads other than a feed-forward neural
network. Our experiments on several datasets from different domains verify the
effectiveness of the proposed method. For example, we achieve 37.8 mAP on
VehiclePart, which is at least 4.2 better than previous methods.


  A time series that represents daily values of the WIG index (the main index
of Warsaw Stock Exchange) over last 5 years is examined. Non-Gaussian features
of distributions of fluctuations, namely returns, over a time scale are
considered. Some general properties like exponents of the long range
correlation estimated by averaged volatility and detrended fluctuations
analysis (DFA) as well as exponents describing a decay of tails of the
cumulative distributions are found. Closing, the Zipf analysis for the WIG
index time series translated into three letter text is presented.


  We propose an experiment which can demonstrate quantum correlations in a
physical scenario as discussed in the seminal work of Einstein, Podolsky and
Rosen. Momentum-entangled massive particles are produced via the four-wave
mixing process of two colliding Bose-Einstein condensates. The particles'
quantum correlations can be shown in a double double-slit experiment or via
ghost interference.


  Regular vine copulas can describe a wider array of dependency patterns than
the multivariate Gaussian copula or the multivariate Student's t copula. This
paper presents two contributions related to model selection of regular vine
copulas. First, our pair copula family selection procedure extends existing
Bayesian family selection methods by allowing pair families to be chosen from
an arbitrary set of candidate families. Second, our method represents the first
Bayesian model selection approach to include the regular vine density
construction in its scope of inference. The merits of our approach are
established in a simulation study that benchmarks against methods suggested in
current literature. A real data example about forecasting of portfolio asset
returns for risk measurement and investment allocation illustrates the
viability and relevance of the proposed scheme.


  Despite the uncovered association of a high-energy neutrino with the apparent
flaring state of blazar TXS 0506+056 in 2017, the mechanisms leading to
astrophysical particle acceleration and neutrino production are still
uncertain. Recent studies found that when transparent to $\gamma$-rays,
$\gamma$-flaring blazars do not have the opacity for protons to produce
neutrinos. Here we present observational evidence for an alternative
explanation, in which $\gamma$-ray emission is suppressed during efficient
neutrino production. A large proton and target photon density help produce
neutrinos while temporarily suppress the observable $\gamma$-emission due to a
large $\gamma \gamma$ opacity. We show that the Fermi-LAT $\gamma$-flux of
blazar PKS 1502+106 was at a local minimum when IceCube recorded the coincident
high-energy neutrino IC-190730A. Using data from the OVRO 40-meter Telescope,
we find that radio emission from PKS 1502+106 at the time period of the
coincident neutrino IC-190730A was in a high state, in contrast to earlier time
periods when radio and $\gamma$ fluxes are correlated for both low and high
states. This points to an active outflow that is $\gamma$-suppressed at the
time of neutrino production. We find similar local $\gamma$-suppression in
other blazars, including in MAGIC's TeV flux of TXS\,0506+056 and Fermi-LAT's
flux of blazar PKS B1424-418 at the time of coincident IceCube neutrino
detections. Using temporary $\gamma$-suppression, neutrino-blazar coincidence
searches could be substantially more sensitive than previously assumed,
enabling the identification of the origin of IceCube's diffuse neutrino flux
possibly with already existing data.


  Traditional beam tracking methods have severe performance loss under the high
mobility and narrow beam scenario. To alleviate the tracking performance
degradation, we propose an adaptive beamwidth control for millimeter wave
(mmWave) beam tracking. The particle filter is applied to the beam tracking,
and the AoA estimation error is approximated with a posterior density function
constructed by the particles. The error approximation leads to the adaptive
beamwidth control which is implemented by the partial activation of the antenna
array. Simulation results show that the proposed algorithm aids the beam
tracking to yield a smaller AoA estimation error under the high mobility
environments.


  We derive structure formation limits on dark matter (DM) composed of
keV-scale axion-like particles (ALPs), produced via freeze-in through the
interactions with photons and Standard Model (SM) fermions. We employ
Lyman-alpha (Ly-{\alpha}) forest data sets as well as the observed number of
Milky Way (MW) subhalos. We compare results obtained using Maxwell-Boltzmann
and quantum statistics for describing the SM bath. It should be emphasized that
the presence of logarithmic divergences complicates the calculation of the
production rate, which can not be parameterized with a simple power law
behaviour. The obtained results, in combination with X-ray bounds, exclude the
possibility for a photophilic "frozen-in" ALP DM with mass below $\sim
19\,\mathrm{keV}$. For the photophobic ALP scenario, in which DM couples
primarily to SM fermions, the ALP DM distribution function is peaked at
somewhat lower momentum and hence for such realization we find weaker limits on
DM mass. Future facilities, such as the upcoming Vera C. Rubin observatory,
will provide measurements with which the current bounds can be significantly
improved to $\sim 80\,\mathrm{keV}$.


  Active Domain Adaptation (ADA) queries the labels of a small number of
selected target samples to help adapting a model from a source domain to a
target domain. The local context of queried data is important, especially when
the domain gap is large. However, this has not been fully explored by existing
ADA works. In this paper, we propose a Local context-aware ADA framework, named
LADA, to address this issue. To select informative target samples, we devise a
novel criterion based on the local inconsistency of model predictions. Since
the labeling budget is usually small, fine-tuning model on only queried data
can be inefficient. We progressively augment labeled target data with the
confident neighbors in a class-balanced manner. Experiments validate that the
proposed criterion chooses more informative target samples than existing active
selection strategies. Furthermore, our full method clearly surpasses recent ADA
arts on various benchmarks. Code is available at https://github.com/tsun/LADA.


  Using renormalization-group arguments we show that the low-temperature
thermodynamics of a three- or two-dimensional dilute Bose gas is fully
determined by a universal scaling function $\calF_d(\mu/k_BT,\tilde g(T))$ once
the mass $m$ and the s-wave scattering length $a_d$ of the bosons are known
($d$ is the space dimension). Here $\mu$ and $T$ denote the chemical potential
and temperature of the gas, and the temperature-dependent dimensionless
interaction constant $\tilde g(T)$ is a function of $ma_d^2k_BT/\hbar^2$. We
compute the scaling function $\calF_2$ using a nonperturbative
renormalization-group approach and find that both the $\mu/k_BT$ and $\tilde
g(T)$ dependencies are in very good agreement with recent experimental data
obtained for a quasi-two-dimensional Bose gas with or without optical lattice.
We also show that the nonperturbative renormalization-group estimate of the
Berezinskii-Kosterlitz-Thouless transition temperature compares well with the
result obtained from a quantum Monte Carlo simulation of an effective classical
field theory.


  The International Gamma-Ray Astrophysics Laboratory (INTEGRAL) satellite has
yielded unprecedented measurements of the soft gamma-ray spectrum of our
Galaxy. Here we use those measurements to set constraints on dark matter (DM)
that decays or annihilates into photons with energies $E\approx 0.02-2$ MeV.
First, we revisit the constraints on particle DM that decays or annihilates to
photon pairs. In particular, for decaying DM, we find that previous limits were
overstated by roughly an order of magnitude. Our new, conservative analysis
finds that the DM lifetime must satisfy $\tau\gtrsim 5\times 10^{26}\,{\rm
s}\times (m_{\chi}/\rm MeV)^{-1}$ for DM masses $m_{\chi}=0.054-3.6$ MeV. For
MeV-scale DM that annihilates into photons INTEGRAL sets the strongest
constraints to date. Second, we target ultralight primordial black holes (PBHs)
through their Hawking radiation. This makes them appear as decaying DM with a
photon spectrum peaking at $E\approx 5.77/(8\pi G M_{\rm PBH})$, for a PBH of
mass $M_{\rm PBH}$. We use the INTEGRAL data to demonstrate that, at 95\% C.L.,
PBHs with masses less than $1.2\times 10^{17}$ g cannot comprise all of the DM,
setting the tightest bound to date on ultralight PBHs.


  Text classification plays an important role in many practical applications.
In the real world, there are extremely small datasets. Most existing methods
adopt pre-trained neural network models to handle this kind of dataset.
However, these methods are either difficult to deploy on mobile devices because
of their large output size or cannot fully extract the deep semantic
information between phrases and clauses. This paper proposes a multimodel-based
deep learning framework for short-text multiclass classification with an
imbalanced and extremely small data set. Our framework mainly includes five
layers: The encoder layer uses DISTILBERT to obtain context-sensitive dynamic
word vectors that are difficult to represent in traditional feature engineering
methods. Since the transformer part of this layer is distilled, our framework
is compressed. Then, we use the next two layers to extract deep semantic
information. The output of the encoder layer is sent to a bidirectional LSTM
network, and the feature matrix is extracted hierarchically through the LSTM at
the word and sentence level to obtain the fine-grained semantic representation.
After that, the max-pooling layer converts the feature matrix into a
lower-dimensional matrix, preserving only the obvious features. Finally, the
feature matrix is taken as the input of a fully connected softmax layer, which
contains a function that can convert the predicted linear vector into the
output value as the probability of the text in each classification. Extensive
experiments on two public benchmarks demonstrate the effectiveness of our
proposed approach on an extremely small data set. It retains the
state-of-the-art baseline performance in terms of precision, recall, accuracy,
and F1 score, and through the model size, training time, and convergence epoch,
we can conclude that our method can be deployed faster and lighter on mobile
devices.


  This paper opens the series of articles supplemental to the series
(hep-th/9405050,q-alg/9610026,q-alg/9611003,q-alg/9611019,funct-an/9611003),
which also lies in lines of general ideology exposed in the review
(mp_arc/96-477). The main purpose of the activity, which has its origin and
motivation presumably in the author's applied researches on the interactively
controlled systems (i.e.the controlled systems, in which the control is coupled
with unknown or uncompletely known feedbacks), is to explicate the essentially
infinite-dimensional aspects of the hidden symmetries, which appear in the
representation theory of the finite dimensional Lie algebras and related
algebraic structures. The series is organized as a sequence of topics, which
illustarate this basic idea on the simple and tame examples without superfluous
difficulties and details as well as in the previous series. Many of objects,
which will appear, are somehow related to ones discussed earlier. However, the
matherial will be treated more geometrically, presumably, from the points of
view of the infinite dimensional geometry, an infinite dimensional version of
the nonlinear geometric algebra and the infinite dimensional noncommutative
geometry. The intent reader will see some ideological and concrete similarities
between the subject of the paper and the infinite dimensional geometric picture
for a second quantized string.


  This is the expanded notes of the lecture by the author in "Arrangements in
Pyrenees", June 2012. We are discussing relations of freeness and splitting
problems of vector bundles, several techniques proving freeness of hyperplane
arrangements, K. Saito's theory of primitive derivations for Coxeter
arrangements, their application to combinatorial problems and related
conjectures.


  An algorithm and its first implementation in C# are presented for assembling
arbitrary quantum circuits on the base of Hadamard and Toffoli gates and for
constructing multivariate polynomial systems over the finite field Z_2 arising
when applying the Feynman's sum-over-paths approach to quantum circuits. The
matrix elements determined by a circuit can be computed by counting the number
of common roots in Z_2 for the polynomial system associated with the circuit.
To determine the number of solutions in Z_2 for the output polynomial system,
one can use the Groebner bases method and the relevant algorithms for computing
Groebner bases.


  Any $n$-tuple of points in the plane can be moved to any other $n$-tuple by a
continuous motion with at most $\binom{n}{3}$ intermediate changes of the order
type. Even for tuples with the same order type, the cubic bound is sharp: there
exist pairs of $n$-tuples of the same order type requiring $c\binom{n}{3}$
intermediate changes.


  One weakness of machine-learning algorithms is the need to train the models
for a new task. This presents a specific challenge for biometric recognition
due to the dynamic nature of databases and, in some instances, the reliance on
subject collaboration for data collection. In this paper, we investigate the
behavior of deep representations in widely used CNN models under extreme data
scarcity for One-Shot periocular recognition, a biometric recognition task. We
analyze the outputs of CNN layers as identity-representing feature vectors. We
examine the impact of Domain Adaptation on the network layers' output for
unseen data and evaluate the method's robustness concerning data normalization
and generalization of the best-performing layer. We improved state-of-the-art
results that made use of networks trained with biometric datasets with millions
of images and fine-tuned for the target periocular dataset by utilizing
out-of-the-box CNNs trained for the ImageNet Recognition Challenge and standard
computer vision algorithms. For example, for the Cross-Eyed dataset, we could
reduce the EER by 67% and 79% (from 1.70% and 3.41% to 0.56% and 0.71%) in the
Close-World and Open-World protocols, respectively, for the periocular case. We
also demonstrate that traditional algorithms like SIFT can outperform CNNs in
situations with limited data or scenarios where the network has not been
trained with the test classes like the Open-World mode. SIFT alone was able to
reduce the EER by 64% and 71.6% (from 1.7% and 3.41% to 0.6% and 0.97%) for
Cross-Eyed in the Close-World and Open-World protocols, respectively, and a
reduction of 4.6% (from 3.94% to 3.76%) in the PolyU database for the
Open-World and single biometric case.


  \begin{abstract} Let $\F$ be the finite field of odd prime power order $q$,
We find explicit expressions for the number of triples $\{\al-1,\al,\al+1 \}$
of consecutive non-zero squares in $\F$ and similarly for the number of triples
of consecutive non-square elements. A key ingredient is the evaluation of
Jacobsthal sums over general finite fields by Katre and Rajwade. This extends
results of Monzingo(1985) to non-prime fields. Curiously, the same machinery
alows the evaluation of the number of consecutive quadruples $\{\al -1,
\al,\al+1, \al +2\}$ of square and non-squares over $\F$, when $q$ is a power
of 5. \end{abstract}


  We summarize results on finite-volume effects in the propagators of Landau
gauge Yang-Mills theory using Dyson-Schwinger equations on a 4-dimensional
torus. We demonstrate explicitly how the solutions for the gluon and the ghost
propagator tend towards their respective infinite volume forms in the
corresponding limit. We discuss the relation of our solutions with results from
lattice Monte-Carlo simulations.


  Federated Learning (FL) offers collaborative model training without data
sharing but is vulnerable to backdoor attacks, where poisoned model weights
lead to compromised system integrity. Existing countermeasures, primarily based
on anomaly detection, are prone to erroneous rejections of normal weights while
accepting poisoned ones, largely due to shortcomings in quantifying
similarities among client models. Furthermore, other defenses demonstrate
effectiveness only when dealing with a limited number of malicious clients,
typically fewer than 10%. To alleviate these vulnerabilities, we present
G$^2$uardFL, a protective framework that reinterprets the identification of
malicious clients as an attributed graph clustering problem, thus safeguarding
FL systems. Specifically, this framework employs a client graph clustering
approach to identify malicious clients and integrates an adaptive mechanism to
amplify the discrepancy between the aggregated model and the poisoned ones,
effectively eliminating embedded backdoors. We also conduct a theoretical
analysis of convergence to confirm that G$^2$uardFL does not affect the
convergence of FL systems. Through empirical evaluation, comparing G$^2$uardFL
with cutting-edge defenses, such as FLAME (USENIX Security 2022) [28] and
DeepSight (NDSS 2022) [36], against various backdoor attacks including 3DFed
(SP 2023) [20], our results demonstrate its significant effectiveness in
mitigating backdoor attacks while having a negligible impact on the aggregated
model's performance on benign samples (i.e., the primary task performance). For
instance, in an FL system with 25% malicious clients, G$^2$uardFL reduces the
attack success rate to 10.61%, while maintaining a primary task performance of
73.05% on the CIFAR-10 dataset. This surpasses the performance of the
best-performing baseline, which merely achieves a primary task performance of
19.54%.


  Spontaneous neuronal activity is a ubiquitous feature of cortex. Its
spatiotemporal organization reflects past input and modulates future network
output. Here we study whether a particular type of spontaneous activity is
generated by a network that is optimized for input processing. Neuronal
avalanches are a type of spontaneous activity observed in superficial cortical
layers in vitro and in vivo with statistical properties expected from a network
in a 'critical state'. Theory predicts that the critical state and, therefore,
neuronal avalanches are optimal for input processing, but until now, this is
untested in experiments. Here, we use cortex slice cultures grown on planar
microelectrode arrays to demonstrate that cortical networks which generate
neuronal avalanches benefit from maximized dynamic range, i.e. the ability to
respond to the greatest range of stimuli. By changing the ratio of excitation
and inhibition in the cultures, we derive a network tuning curve for stimulus
processing as a function of distance from the critical state in agreement with
predictions from our simulations. Our findings suggest that in the cortex, (1)
balanced excitation and inhibition establishes the critical state, which
maximizes the range of inputs that can be processed and (2) spontaneous
activity and input processing are unified in the context of critical phenomena.


  Exciting optical effects such as polarization control, imaging, and
holography were demonstrated at the nanoscale using the complex and irregular
structures of nanoparticles with the multipole Mie-resonances in the optical
range. The optical response of such particles can be simulated either by full
wave numerical simulations or by the widely used analytical coupled multipole
method (CMM), however, an analytical solution in the framework of CMM can be
obtained only in a limited number of cases. In this paper, a modification of
the CMM in the framework of the Born series and its applicability for
simulation of light scattering by finite nanosphere structures, maintaining
both dipole and quadrupole resonances, are investigated. The Born approximation
simplifies an analytical consideration of various systems and helps shed light
on physical processes ongoing in that systems. Using Mie theory and Green's
functions approach, we analytically formulate the rigorous coupled
dipole-quadrupole equations and their solution in the different-order Born
approximations. We analyze in detail the resonant scattering by dielectric
nanosphere structures such as dimer and ring to obtain the convergence
conditions of the Born series and investigate how the physical characteristics
such as absorption in particles, type of multipole resonance, and geometry of
ensemble influence the convergence of Born series and its accuracy.


  Appearance and origin of resonance phenomena have been studied in the annular
system of underdamped Josephson junctions. If no fluxon is trapped in the
system, dynamics is governed by the motion of fluxon-antifluxon pairs, while if
trapped fluxons are present, they can move solely but also simultaneously with
the pairs. Locking between the rotating excitations (fluxons and antifluxons)
and the Josephson frequency leads to the appearance of zero field steps in the
current-voltage characteristics, which can further exhibit branching due to
resonance between the rotating excitations and plasma oscillations in their
tale. The number of zero field steps and their branching are strongly
determined by the total number of excitations present in the system. High
resolution analysis further reveals not only some interesting properties of
zero field steps, but also shows that the current-voltage characteristics is
determined not only by the number, but also by the type of excitations, i.e.,
whether the dynamics is governed only by the motion of fluxon-antifluxon pairs
or the trapped fluxons, or they move simultaneously in the system.


  We prove that the Beurling dimensions of the spectra for a class of Moran
spectral measures are between $0$ and their upper entropy dimensions. Moreover,
for such a Moran spectral measure $\mu$, we show that the Beurling dimension
for the spectra of $\mu$ has the intermediate value property: let $t$ be any
value between $0$ and the upper entropy dimension of $\mu$, then there exists a
spectrum whose Beurling dimension is $t.$ In particular, this result settles
affirmatively a conjecture involving spectral Bernoulli convolution proposed by
Fu, He and Wen in [J. Math. Pures Appl. 116 (2018), 105--131]. Furthermore, we
prove that the set of the spectra whose Beurling dimensions are equal to any
fixed value between $0$ and $\ue \mu$ has the cardinality of the continuum.


  This paper investigates the power of preprocessing in the CONGEST model.
Schmid and Suomela (ACM HotSDN 2013) introduced the SUPPORTED CONGEST model to
study the application of distributed algorithms in Software-Defined Networks
(SDNs). In this paper, we show that a large class of lower bounds in the
CONGEST model still hold in the SUPPORTED model, highlighting the robustness of
these bounds. This also raises the question how much does preprocessing help in
the CONGEST model.


  The synchronization of coupled oscillators is a phenomenon found throughout
nature. Mechanical oscillators are paradigmatic among such systems, but
realising them at the nanoscale is challenging. We report synchronization of
the mechanical dynamics of a pair of optomechanical crystal cavities that are
intercoupled with a mechanical link and support independent optical modes. In
this regime they oscillate in anti-phase, which is in agreement with the
predictions of our numerical model that considers reactive coupling. Finally,
we show how to temporarily disable synchronization of the coupled system by
actuating one of the cavities with a heating laser, so that both cavities
oscillate independently. Our results can be upscaled to more than two cavities
and are thus the first step towards realizing integrated networks of
synchronized optomechanical oscillators. Such networks promise unparalleled
performances for time-keeping and sensing purposes and unveil a new route for
neuromorphic computing applications.


  With the development of image recovery models,especially those based on
adversarial and perceptual losses,the detailed texture portions of images are
being recovered more naturally.However,these restored images are similar but
not identical in detail texture to their reference images.With traditional
image quality assessment methods,results with better subjective perceived
quality often score lower in objective scoring.Assessment methods suffer from
subjective and objective inconsistencies.This paper proposes a regional
differential information entropy (RDIE) method for image quality assessment to
address this problem.This approach allows better assessment of similar but not
identical textural details and achieves good agreement with perceived
quality.Neural networks are used to reshape the process of calculating
information entropy,improving the speed and efficiency of the operation.
Experiments conducted with this study image quality assessment dataset and the
PIPAL dataset show that the proposed RDIE method yields a high degree of
agreement with people average opinion scores compared to other image quality
assessment metrics,proving that RDIE can better quantify the perceived quality
of images.


  A non-parametric method for ranking stock indices according to their mutual
causal influences is presented. Under the assumption that indices reflect the
underlying economy of a country, such a ranking indicates which countries exert
the most economic influence in an examined subset of the global economy. The
proposed method represents the indices as nodes in a directed graph, where the
edges' weights are estimates of the pair-wise causal influences, quantified
using the directed information functional. This method facilitates using a
relatively small number of samples from each index. The indices are then ranked
according to their net-flow in the estimated graph (sum of the incoming weights
subtracted from the sum of outgoing weights). Daily and minute-by-minute data
from nine indices (three from Asia, three from Europe and three from the US)
were analyzed. The analysis of daily data indicates that the US indices are the
most influential, which is consistent with intuition that the indices
representing larger economies usually exert more influence. Yet, it is also
shown that an index representing a small economy can strongly influence an
index representing a large economy if the smaller economy is indicative of a
larger phenomenon. Finally, it is shown that while inter-region interactions
can be captured using daily data, intra-region interactions require more
frequent samples.


  All instance perception tasks aim at finding certain objects specified by
some queries such as category names, language expressions, and target
annotations, but this complete field has been split into multiple independent
subtasks. In this work, we present a universal instance perception model of the
next generation, termed UNINEXT. UNINEXT reformulates diverse instance
perception tasks into a unified object discovery and retrieval paradigm and can
flexibly perceive different types of objects by simply changing the input
prompts. This unified formulation brings the following benefits: (1) enormous
data from different tasks and label vocabularies can be exploited for jointly
training general instance-level representations, which is especially beneficial
for tasks lacking in training data. (2) the unified model is
parameter-efficient and can save redundant computation when handling multiple
tasks simultaneously. UNINEXT shows superior performance on 20 challenging
benchmarks from 10 instance-level tasks including classical image-level tasks
(object detection and instance segmentation), vision-and-language tasks
(referring expression comprehension and segmentation), and six video-level
object tracking tasks. Code is available at
https://github.com/MasterBin-IIAU/UNINEXT.


  We consider fixed-point equations for probability distributions on isometry
classes of measured metric spaces. The construction is required to be recursive
and tree-like, but we allow loops for the geodesics between points in the
support of the measure: one can think of a $\beta$-stable looptree decomposed
around one loop. We study existence and uniqueness of solutions together with
the attractiveness of the fixed-points by iterating. We obtain bounds on the
Hausdorff and upper Minkowski dimension, which appear to be tight for the
studied models. This setup applies to formerly studied structures as the
$\beta$-stable trees and looptrees, of which we give a new characterization and
recover the fractal dimensions.


  Bacteria (e.g. E. Coli) are very sensitive to certain chemoattractants (e.g.
asparate) which they themselves produce. This leads to chemical instabilities
in a uniform population. We discuss here the different case of a single
bacterium, following the general scheme of Brenner, Levitov and Budrene. We
show that in one and two dimensions (in a capillary or in a thin film) the
bacterium can become self-trapped in its cloud of attractant. This should occur
if a certain coupling constant $g$ is larger than unity. We then estimate the
reduced diffusion D_eff of the bacterium in the strong coupling limit, and find
D_eff ~ 1/g.


  Characteristics of the depression contribution functions are studied for the
Stokes line profiles formed in a magnetic field. The form of the depression
functions depends mainly on the strength of splitting and the Zeeman component
intensity, and is of a complicated character with a distinctly pronounces
asymmetry. The depths of formation of magnetically sensitive lines are found by
means of these contribution functions. The calculations reveal that the steep
section of the line profile is formed higher than the profile center when a
strong longitudinal magnetic field is present. The Stokes profiles that
describe the polarization characteristics are formed only several kilometers
higher than the Stokes profile that specifies the general depression of the
unpolarized and polarized radiation. The averaged depth of formation of the
whole line profile is practically independent of the magnetic field strength.
The depths of formation of 17 photospheric lines usually used in
magnetospectroscopic observations are calculated for the models of the quiet
photosphere, a flux tube, and the sunspot umbra.


  We derive a thin film model for viscoelastic liquids under strong slip which
obey the stress tensor dynamics of corotational Jeffreys fluids.


  Errors or failures in a high-volume manufacturing environment can have
significant impact that can result in both the loss of time and money.
Identifying such failures early has been a top priority for manufacturing
industries and various rule-based algorithms have been developed over the
years. However, catching these failures is time consuming and such algorithms
cannot adapt well to changes in designs, and sometimes variations in everyday
behavior. More importantly, the number of units to monitor in a high-volume
manufacturing environment is too big for manual monitoring or for a simple
program. Here we develop a novel program that combines both rule-based
decisions and machine learning models that can not only learn and adapt to such
day-to-day variations or long-term design changes, but also can be applied at
scale to the high number of manufacturing units in use today. Using the current
state-of-the-art technologies, we then deploy this program at-scale to handle
the needs of ever-increasing demand from the manufacturing environment.


  Diquark correlations are important in baryons, which can be modeled as
quark-diquark bound states. In addition, diquarks could play a role in
non-standard hadrons such as tetraquarks and pentaquarks. Here, we obtain
properties of these diquarks from the corresponding bound state equation, using
a model for the effective quark-quark interaction that has proved successful in
the light meson sector. Subsequently, we use the same model to estimate the
masses of the lightest diquark-diquark and diquark-antidiquark states.


  Joint resource allocation involving optimization of subcarrier allocation,
subcarrier pairing (SCP), and power allocation in a cooperative secure
orthogonal frequency division multiple access (OFDMA) communication system with
untrusted users is considered. Both amplify and forward (AF), and decode and
forward (DF) modes of operations are considered with individual power budget
constraints for source and relay. After finding optimal subcarrier allocation
for an AF relayed system, we prove the joint power allocation as a generalized
convex problem, and solve it optimally. Compared to the conventional channel
gain matching view, the optimal SCP is emphasized as a novel concept of channel
gain tailoring. We prove that the optimal SCP pairs subcarriers such that the
variance among the effective channel gains is minimized. For a DF relayed
system, we show that depending on the power budgets of source and relay, SCP
can either be in a subordinate role where it improves the energy efficiency, or
in a main role where it improves the spectral efficiency of the system. In an
AF relayed system we confirm that SCP plays a crucial role, and improves the
spectral efficiency of the system. The channel gain tailoring property of SCP,
various roles of SCP in improving the spectral and the energy efficiency of a
cooperative communication system are validated with the help of simulation
results.