Abstract:
  Independently exploring unknown spaces or finding objects in an indoor
environment is a daily but challenging task for visually impaired people.
However, common 2D assistive systems lack depth relationships between various
objects, resulting in difficulty to obtain accurate spatial layout and relative
positions of objects. To tackle these issues, we propose HIDA, a lightweight
assistive system based on 3D point cloud instance segmentation with a
solid-state LiDAR sensor, for holistic indoor detection and avoidance. Our
entire system consists of three hardware components, two interactive
functions~(obstacle avoidance and object finding) and a voice user interface.
Based on voice guidance, the point cloud from the most recent state of the
changing indoor environment is captured through an on-site scanning performed
by the user. In addition, we design a point cloud segmentation model with dual
lightweight decoders for semantic and offset predictions, which satisfies the
efficiency of the whole system. After the 3D instance segmentation, we
post-process the segmented point cloud by removing outliers and projecting all
points onto a top-view 2D map representation. The system integrates the
information above and interacts with users intuitively by acoustic feedback.
The proposed 3D instance segmentation model has achieved state-of-the-art
performance on ScanNet v2 dataset. Comprehensive field tests with various tasks
in a user study verify the usability and effectiveness of our system for
assisting visually impaired people in holistic indoor understanding, obstacle
avoidance and object search.


Abstract:
  We address the problem of computing the smallest symplectic eigenvalues and
the corresponding eigenvectors of symmetric positive-definite matrices in the
sense of Williamson's theorem. It is formulated as minimizing a trace cost
function over the symplectic Stiefel manifold. We first investigate various
theoretical aspects of this optimization problem such as characterizing the
sets of critical points, saddle points, and global minimizers as well as
proving that non-global local minimizers do not exist. Based on our recent
results on constructing Riemannian structures on the symplectic Stiefel
manifold and the associated optimization algorithms, we then propose solving
the symplectic eigenvalue problem in the framework of Riemannian optimization.
Moreover, a connection of the sought solution with the eigenvalues of a special
class of Hamiltonian matrices is discussed. Numerical examples are presented.


Abstract:
  Coma Berenices (Coma Ber), an open cluster about the same age as Praesepe and
the Hyades (700-800 Myr) is, despite being only 85 pc away, less well studied
than its famous cousins. This is due principally to its sparseness and low
proper motion, which together made Coma Ber's membership challenging to
establish pre-Gaia. We have curated a new list of its members based on Gaia DR2
astrometry, derived its metallicity and interstellar reddening using LAMOST
data, and inferred the cluster's age by fitting PARSEC isochrones to its
color$-$magnitude diagram. We then measured rotation periods for Coma Ber's
low-mass members using TESS and ZTF photometry. Our isochrone fitting and the
TESS- and ZTF-derived rotation periods confirm that Coma Ber is coeval with the
Hyades and Praesepe. This work is the first step toward re-establishing Coma
Ber as another valuable benchmark cluster for age$-$rotation$-$activity
studies.


Abstract:
  The thermal modeling of biological systems has increasing importance in
developing more advanced, more precise techniques such as ultrasound surgery.
One of the primary barriers is the complexity of biological materials: the
geometrical, structural, and material properties vary in a wide range, and they
depend on many factors. Despite these difficulties, there is a tremendous
effort to develop a reliable and implementable thermal model. In the present
paper, we focus on the continuum modeling of heterogeneous materials with
biological origin. There are numerous examples in the literature for
non-Fourier thermal models. However, as we realized, they are associated with a
few common misconceptions. Therefore, we first aim to clarify the basic
concepts of non-Fourier thermal models. These concepts are demonstrated by
revisiting two experiments from the literature in which the Cattaneo-Vernotte
and the dual phase lag models are utilized. Our investigation revealed that
using these non-Fourier models is based on misinterpretations of the measured
data, and the seeming deviation from Fourier's law originates in the source
terms and boundary conditions.


Abstract:
  Blockchain technologies have been rapidly enhanced in recent years. However,
its scalability still has limitations in terms of throughput and broadcast
delay as the network and the amount of transaction data increase. To improve
scalability of blockchain networks, we propose a novel approach named PiChu
that accelerates block propagation in blockchain networks by pipelining and
verifying chunks of a block in parallel. Accelerating block propagation reduces
the mining interval and chance of fork occurring, which in turn increases
throughput. Our approach can be applied to the blockchain networks either
directly or with a minor modification to the consensus. Through an extensive
and large scale simulations, we validate that the proposed PiChu scheme
significantly enhances the scalability of blockchain networks. For instance, a
64 MB block can be broadcasted in just 80 seconds in a blockchain network with
a million nodes. The efficiency of PiChu broadcasting increases with bigger
block sizes and a larger number of nodes in the network.


Abstract:
  Topic classification systems on spoken documents usually consist of two
modules: an automatic speech recognition (ASR) module to convert speech into
text and a text topic classification (TTC) module to predict the topic class
from the decoded text. In this paper, instead of using the ASR transcripts, the
fusion of deep acoustic and linguistic features is used for topic
classification on spoken documents. More specifically, a conventional CTC-based
acoustic model (AM) using phonemes as output units is first trained, and the
outputs of the layer before the linear phoneme classifier in the trained AM are
used as the deep acoustic features of spoken documents. Furthermore, these deep
acoustic features are fed to a phoneme-to-word (P2W) module to obtain deep
linguistic features. Finally, a local multi-head attention module is proposed
to fuse these two types of deep features for topic classification. Experiments
conducted on a subset selected from Switchboard corpus show that our proposed
framework outperforms the conventional ASR+TTC systems and achieves a 3.13%
improvement in ACC.


Abstract:
  We present a method for localizing a single camera with respect to a point
cloud map in indoor and outdoor scenes. The problem is challenging because
correspondences of local invariant features are inconsistent across the domains
between image and 3D. The problem is even more challenging as the method must
handle various environmental conditions such as illumination, weather, and
seasonal changes. Our method can match equirectangular images to the 3D range
projections by extracting cross-domain symmetric place descriptors. Our key
insight is to retain condition-invariant 3D geometry features from limited data
samples while eliminating the condition-related features by a designed
Generative Adversarial Network. Based on such features, we further design a
spherical convolution network to learn viewpoint-invariant symmetric place
descriptors. We evaluate our method on extensive self-collected datasets, which
involve \textit{Long-term} (variant appearance conditions),
\textit{Large-scale} (up to $2km$ structure/unstructured environment), and
\textit{Multistory} (four-floor confined space). Our method surpasses other
current state-of-the-arts by achieving around $3$ times higher place retrievals
to inconsistent environments, and above $3$ times accuracy on online
localization. To highlight our method's generalization capabilities, we also
evaluate the recognition across different datasets. With a single trained
model, i3dLoc can demonstrate reliable visual localization in random
conditions.


Abstract:
  The efficient and accurate calculation of how ionic quantum and thermal
fluctuations impact the free energy of a crystal, its atomic structure, and
phonon spectrum is one of the main challenges of solid state physics,
especially when strong anharmonicy invalidates any perturbative approach. To
tackle this problem, we present the implementation on a modular Python code of
the stochastic self-consistent harmonic approximation method. This technique
rigorously describes the full thermodyamics of crystals accounting for nuclear
quantum and thermal anharmonic fluctuations. The approach requires the
evaluation of the Born-Oppenheimer energy, as well as its derivatives with
respect to ionic positions (forces) and cell parameters (stress tensor) in
supercells, which can be provided, for instance, by first principles
density-functional-theory codes. The method performs crystal geometry
relaxation on the quantum free energy landscape, optimizing the free energy
with respect to all degrees of freedom of the crystal structure. It can be used
to determine the phase diagram of any crystal at finite temperature. It enables
the calculation of phase boundaries for both first-order and second-order phase
transitions from the Hessian of the free energy. Finally, the code can also
compute the anharmonic phonon spectra, including the phonon linewidths, as well
as phonon spectral functions. We review the theoretical framework of the
stochastic self-consistent harmonic approximation and its dynamical extension,
making particular emphasis on the physical interpretation of the variables
present in the theory that can enlighten the comparison with any other
anharmonic theory. A modular and flexible Python environment is used for the
implementation, which allows for a clean interaction with other packages. We
briefly present a toy-model calculation to illustrate the potential of the
code.


Abstract:
  Recent works of multi-source domain adaptation focus on learning a
domain-agnostic model, of which the parameters are static. However, such a
static model is difficult to handle conflicts across multiple domains, and
suffers from a performance degradation in both source domains and target
domain. In this paper, we present dynamic transfer to address domain conflicts,
where the model parameters are adapted to samples. The key insight is that
adapting model across domains is achieved via adapting model across samples.
Thus, it breaks down source domain barriers and turns multi-source domains into
a single-source domain. This also simplifies the alignment between source and
target domains, as it only requires the target domain to be aligned with any
part of the union of source domains. Furthermore, we find dynamic transfer can
be simply modeled by aggregating residual matrices and a static convolution
matrix. Experimental results show that, without using domain labels, our
dynamic transfer outperforms the state-of-the-art method by more than 3% on the
large multi-source domain adaptation datasets -- DomainNet. Source code is at
https://github.com/liyunsheng13/DRT.


Abstract:
  Human mobility similarity comparison plays a critical role in mobility
estimation/prediction model evaluation, mobility clustering and mobility
matching, which exerts an enormous impact on improving urban mobility,
accessibility, and reliability. By expanding origin-destination matrix, we
propose a concept named mobility tableau, which is an aggregated tableau
representation to the population flow distributed between different location
pairs of a study site and can be represented by a vector graph. Compared with
traditional OD matrix-based mobility comparison, mobility tableau comparison
provides high-dimensional similarity information, including volume similarity,
spatial similarity, mass inclusiveness and structure similarity. A novel
mobility tableaus similarity measurement method is proposed by optimizing the
least spatial cost of transforming the vector graph for one mobility tableau
into the other and is optimized to be efficient. The robustness of the measure
is supported through several sensitive analysis on GPS based mobility tableau.
The better performance of the approach compared with traditional mobility
comparison methods in two case studies demonstrate the practicality and
superiority, while one study is estimated mobility tableaus validation and the
other is different cities' mobility tableaus comparison.


Abstract:
  Session-based recommenders, used for making predictions out of users'
uninterrupted sequences of actions, are attractive for many applications. Here,
for this task we propose using metric learning, where a common embedding space
for sessions and items is created, and distance measures dissimilarity between
the provided sequence of users' events and the next action. We discuss and
compare metric learning approaches to commonly used learning-to-rank methods,
where some synergies exist. We propose a simple architecture for problem
analysis and demonstrate that neither extensively big nor deep architectures
are necessary in order to outperform existing methods. The experimental results
against strong baselines on four datasets are provided with an ablation study.


Abstract:
  We present and analyze a parallel implementation of a parallel-in-time method
based on $\alpha$-circulant preconditioned Richardson iterations. While there
are a lot of papers exploring this new class of single-level, time-parallel
integrators from many perspectives, performance results of actual parallel runs
are still missing. This leaves a critical gap, because the efficiency and
applicability heavily rely on the actual parallel performance, with only
limited guidance from theoretical considerations. Also, many challenges like
selecting good parameters, finding suitable communication strategies, and
performing a fair comparison to sequential time-stepping methods can be easily
missed. In this paper, we first extend the original idea by using a collocation
method of arbitrary order, which adds another level of parallelization in time.
We derive an adaptive strategy to select a new $\alpha$-circulant
preconditioner for each iteration during runtime for balancing convergence
rates, round-off errors and inexactness in the individual time-steps. After
addressing these more theoretical challenges, we present an open-source space-
and doubly-time-parallel implementation and evaluate its performance for two
different test problems.


Abstract:
  Betweenness centrality is a popular centrality measure with applications in
several domains, and whose exact computation is impractical for modern-sized
networks. We present SILVAN, a novel, efficient algorithm to compute, with high
probability, accurate estimates of the betweenness centrality of all nodes of a
graph and a high-quality approximation of the k most central nodes of a graph.
SILVAN follows a progressive sampling approach, and builds on recently improved
bounds on Monte-Carlo Empirical Rademacher Averages, a fundamental tool from
statistical learning theory. SILVAN relies on a novel estimation scheme that
leads to non-uniform bounds on the deviation of the estimates from the true
values of the between centrality of all the nodes, providing tight guarantees
on the quality of the approximation. Our extensive experimental evaluation
shows that SILVAN extracts high-quality approximations while outperforming, in
terms of number of samples and accuracy, the state-of-the-art approximation
algorithm with comparable quality guarantees.


Abstract:
  A charged particle in a magnetic field possesses discrete energy levels
associated with particle rotation around the field lines. A bound complex of
particles with a nonzero net charge possesses an analogous levels associated
with its center-of-mass motion and, in addition, the levels associated with
internal degrees of freedom, that is with relative motions of its constituent
particles. The center-of-mass and internal motions are mutually dependent,
which complicates theoretical studies of the binding energies, radiative
transitions and other properties of the complex ions moving in quantizing
magnetic fields. In this work, we present a detailed derivation of practical
expressions for the numerical treatment of such properties of the hydrogenlike
ions moving in strong quantizing magnetic fields, which follows and supplements
the previous works of Bezchastnov et al. Second, we derive asymptotic analytic
expressions for the binding energies, oscillator strengths, and photoionization
cross sections of the moving hydrogenlike ions in the limit of an ultra-strong
magnetic field.


Abstract:
  It is shown that a magnetic monopole appears as the tension state of the
primary electric charge at its motion through each section of the path equal to
the particle's de Broglie wavelength. This conclusion is followed from a
submicroscopic consideration of particles and their motion in the framework of
the theory of physical space in the form of a tessellattice developed by Michel
Bounias and the author. The periodical change of the particle's charged state
to its monopole state can easily be introduced in the conventional Maxwell
equations and the magnetic monopole automatically shows up in the structure of
Maxwell's equations. The monopole is also presented in any quark system as a
quark obeys dynamics that are also characterized by the appropriate de Broglie
wavelength and hence the electric charge changes periodically to the magnetic
monopole. A (anti)neutrino emerges as the typical electron's monopole. When the
charged particle becomes the monopole, it also loses its mass (the mass passes
to the particle's inerton cloud) and thus the neutrino is a massless particle,
or more correctly massless monopole.


Abstract:
  In a two-ion-species plasma with disparate ion masses, heavy ions tend to
concentrate in the low-temperature region of collisionally magnetized plasma
and in the high-temperature region of collisionally unmagnetized plasma,
respectively. Moreover, collisional magnetization can be determined as the
ratio of the light ion gyrofrequency to the collision frequency of light and
heavy ion species, and the behavior of this effect in the intermediate regime
of partially magnetized plasma is predominantly dependent on this Hall
parameter. Multi-ion cross-field transport has been described before in the
collisionally magnetized plasma regime, and generalized pinch relations, which
describe densities of ion species in equilibrium in that plasma, are found in
the literature. In this paper, the role of collisional magnetization and Larmor
magnetization in multi-ion collisional transport is clarified and generalized
pinch relations are extended to the partially magnetized regime, in which the
ion Hall parameter may be small, as long as electrons remain collisionally
magnetized. Equilibrium ion density profiles have the same dependence on
external forces and on each other regardless of collisional magnetization of
ions. The expansion of the range of validity of multi-ion collisional transport
models makes them applicable to a wider range of laboratory plasma conditions.
In particular, ion density profiles evolve sufficiently fast for radial
impurity transport to be observable around stagnation on MagLIF, leading to
expulsion of heavy ion impurities from the hotspot as long as plasma becomes
sufficiently collisionally magnetized during the implosion.


Abstract:
  This work proposes a Prolog-dialect for the found and prioritised problems on
expressibility and automation. Given some given C-like program, if dynamic
memory is allocated, altered and freed on runtime, then a description of
desired dynamic memory is a heap specification. The check of calculated memory
state against a given specification is dynamic memory verification. This
contribution only considers formal specification and verification in a Hoare
calculus. Issues found include: invalid assignment, (temporary) unavailable
data in memory cells, excessive memory allocation, (accidental) heap alteration
in unexpected regions and others. Excessive memory allocation is nowadays
successfully resolved by memory analysers like Valgrind. Essentially, papers in
those areas did not bring any big breakthrough. Possible reasons may also
include the decrease of tension due to more available memory and parallel
threads. However, starting with Apt, problems related to variable modes have
not yet been resolved -- neither entirely nor in an acceptable way. Research
contributions over the last decades show again and again that heap issues
remain and remain complex and still important. A significant contribution was
reached in 2016 by Peter O'Hearn, who accepted the G\"{o}del prize for his
parallel approach on a spatial heap operation.


Abstract:
  If a single line of sight (LOS) intercepts multiple dust clouds of different
spectral energy distributions and magnetic field orientations, the frequency
scaling of each of the Stokes $Q$ and $U$ parameters of thermal dust emission
may be different ("LOS frequency decorrelation"). We present first evidence for
LOS frequency decorrelation in $Planck$ data. We use independent,
neutral-hydrogen--measurements of the number of clouds per LOS and the magnetic
field orientation in each cloud to select two sets of sightlines: (i) a target
sample (pixels likely to exhibit LOS frequency decorrelation); (ii) a control
sample (pixels lacking complex LOS structure). We test the null hypothesis that
LOS frequency decorrelation is not detectable in $Planck$ 353 and 217~GHz
polarization data at high Galactic latitudes. The data reject this hypothesis
at high significance. The detection is robust against choice of CMB map and
map-making pipeline. The observed change in polarization angle due to LOS
frequency decorrelation is detectable above the $Planck$ noise level. The
probability that the detected effect is due to noise alone ranges from $5\times
10^{-2}$ to $4\times 10^{-7}$, depending on the CMB subtraction algorithm and
treatment of residual systematics; correcting for residual systematics
increases the significance of the effect. The LOS decorrelation effect is
stronger for sightlines with more misaligned magnetic fields, as expected. We
estimate that an intrinsic variation of $\sim15\%$ in the ratio of 353 to
217~GHz polarized emission between clouds is sufficient to reproduce the
measured effect. Our finding underlines the importance of ongoing studies to
map the 3D structure of the magnetized dusty ISM that could help component
separation methods to account for frequency decorrelation effects in CMB
polarization studies.


Abstract:
  To overcome the limitations of original local binary patterns (LBP), this
article proposes a new texture descriptor aided by complex networks (CN) and
LBP, named CN-LBP. Specifically, we first abstract a texture image (TI) as
directed graphs over different bands with the help of pixel distance,
intensity, and gradient (magnitude and angle). Second, several CN-based feature
measurements (including clustering coefficient, in-degree centrality,
out-degree centrality, and eigenvector centrality) are selected to further
decipher the texture features, which generates four feature images that can
retain the image information as much as possible. Third, given the original
TIs, gradient images (GI), and generated feature images, we can obtain the
discriminative representation of texture images based on uniform LBP (ULBP).
Finally, the feature vector is obtained by jointly calculating and
concatenating the spatial histograms. In contrast to original LBP, the proposed
texture descriptor contains more detailed image information, and shows
resistance to imaging and noise. Experiment results on four datasets
demonstrate that the proposed texture descriptor can significantly improve the
classification accuracies compared with the state-of-the-art LBP-based variants
and deep learning-based methods.


Abstract:
  In a target communication system, a delicately designed frequency offset
estimation scheme is required to meet certain decoding performance. In this
paper, we proposed at wo-step estimation scheme, coarse and residual, with
different value of an time interval parameter. A result of RF conduction test
shows that the proposed method has an 1dB gain of SNR compared to coarse-only
estimator. A result of the commercial test also indicates the proposed method
outperforms coarse-only estimator especially in low SNR condition.


Abstract:
  Refractory producers face many challenges in terms of producing
MgO-containing castables due to the high likelihood of magnesia to hydrate in
contact with water, resulting in Mg(OH)2 generation. The expansive feature of
this transformation affects the performance of such refractories, as (i) if
this hydrated phase is not accommodated in the formed microstructure, ceramic
linings with cracks and low green mechanical strength will be obtained; and
(ii) if crack-free pieces are prepared, they should present low porosity and
reduced permeability, which require special attention when heating these
materials. This work investigated the ability of various additives in the
optimization of the drying behavior of Al2O3-MgO castables. Vibratable
compositions were tested after incorporating polymeric fibers (PF), an organic
salt (OAS), SiO2-based additive (SM) or permeability enhancing active compound
(MP) into the dry-mixtures. Various experimental measurements were performed to
infer the role of the drying agents to prevent the samples explosion and
whether they would also influence other properties of the castables. As
observed, OAS and MP helped to inhibit the MgO-bonded samples explosion even
under severe heating conditions (2-20C/min) and increased their green
mechanical strength and slag infiltration resistance when compared to the
additive-free composition. On the other hand, the addition of polymeric fibers
(PF) or silica-based compound (SM) to the formulations was not able to prevent
the castables explosion when using a high heating rate and other side effects
could also be observed when testing these materials. Thus, the selection of
suitable drying agents is a key issue, as they may allow the development of
MgO-bonded castables with enhanced properties and lower spalling risk during
their first thermal treatment.


Abstract:
  We present new short proofs of known spanning tree enumeration formulae for
threshold and Ferrers graphs by showing that the Laplacian matrices of such
graphs admit triangular rank-one perturbations. We then characterize the set of
graphs whose Laplacian matrices admit triangular rank-one perturbations as the
class of special 2-threshold graphs, introduced by Hung, Kloks, and Villaamil.
Our work introduces (1) a new characterization of special 2-threshold graphs
that generalizes the characterization of threshold graphs in terms of isolated
and dominating vertices, and (2) a spanning tree enumeration formula for
special 2-threshold graphs that reduces to the aforementioned formulae for
threshold and Ferrers graphs. We consider both unweighted and weighted spanning
tree enumeration.


Abstract:
  We study three structurally different states of nanocrystalline 316 steel and
show that the state, where boundaries containing excess concentration of
alloying elements are combined with mobile dislocations in grain interiors,
allows maintaining extraordinarily high strength and remarkably enhanced
plasticity. Underlying mechanisms featuring interaction between the
segregations and mobile dislocations are discussed.


Abstract:
  Recently, audio-visual speech enhancement has been tackled in the
unsupervised settings based on variational auto-encoders (VAEs), where during
training only clean data is used to train a generative model for speech, which
at test time is combined with a noise model, e.g. nonnegative matrix
factorization (NMF), whose parameters are learned without supervision.
Consequently, the proposed model is agnostic to the noise type. When visual
data are clean, audio-visual VAE-based architectures usually outperform the
audio-only counterpart. The opposite happens when the visual data are corrupted
by clutter, e.g. the speaker not facing the camera. In this paper, we propose
to find the optimal combination of these two architectures through time. More
precisely, we introduce the use of a latent sequential variable with Markovian
dependencies to switch between different VAE architectures through time in an
unsupervised manner: leading to switching variational auto-encoder (SwVAE). We
propose a variational factorization to approximate the computationally
intractable posterior distribution. We also derive the corresponding
variational expectation-maximization algorithm to estimate the parameters of
the model and enhance the speech signal. Our experiments demonstrate the
promising performance of SwVAE.


Abstract:
  We propose an algorithm to compute the $C^\infty$-ring structure of arbitrary
Weil algebra. It allows us to do some analysis with higher infinitesimals
numerically and symbolically. To that end, we first give a brief description of
the (Forward-mode) automatic differentiation (AD) in terms of $C^\infty$-rings.
The notion of a $C^\infty$-ring was introduced by Lawvere and used as the
fundamental building block of smooth infinitesimal analysis and synthetic
differential geometry. We argue that interpreting AD in terms of
$C^\infty$-rings gives us a unifying theoretical framework and modular ways to
express multivariate partial derivatives. In particular, we can "package"
higher-order Forward-mode AD as a Weil algebra, and take tensor products to
compose them to achieve multivariate higher-order AD. The algorithms in the
present paper can also be used for a pedagogical purpose in learning and
studying smooth infinitesimal analysis as well.


Abstract:
  We study boson-fermion dualities in one-dimensional many-body problems of
identical particles interacting only through two-body contacts. By using the
path-integral formalism as well as the configuration-space approach to
indistinguishable particles, we find a generalization of the boson-fermion
duality between the Lieb-Liniger model and the Cheon-Shigehara model. We
present an explicit construction of $n$-boson and $n$-fermion models which are
dual to each other and characterized by $n-1$ distinct (coordinate-dependent)
coupling constants. These models enjoy the spectral equivalence, the
boson-fermion mapping, and the strong-weak duality. We also discuss a
scale-invariant generalization of the boson-fermion duality.


Abstract:
  This letter introduces a real valued summation known as Complex Conjugate
Pair Sum (CCPS). The space spanned by CCPS and its one circular downshift is
called {\em Complex Conjugate Subspace (CCS)}. For a given positive integer
$N\geq3$, there exists $\frac{\varphi(N)}{2}$ CCPSs forming
$\frac{\varphi(N)}{2}$ CCSs, where $\varphi(N)$ is the Euler's totient
function. We prove that these CCSs are mutually orthogonal and their direct sum
form a $\varphi(N)$ dimensional subspace $s_N$ of $\mathbb{C}^N$. We propose
that any signal of finite length $N$ is represented as a linear combination of
elements from a special basis of $s_d$, for each divisor $d$ of $N$. This
defines a new transform named as Complex Conjugate Periodic Transform (CCPT).
Later, we compared CCPT with DFT (Discrete Fourier Transform) and RPT
(Ramanujan Periodic Transform). It is shown that, using CCPT we can estimate
the period, hidden periods and frequency information of a signal. Whereas, RPT
does not provide the frequency information. For a complex valued input signal,
CCPT offers computational benefit over DFT. A CCPT dictionary based method is
proposed to extract non-divisor period information.


Abstract:
  Behaviors of the synthetic characters in current military simulations are
limited since they are generally generated by rule-based and reactive
computational models with minimal intelligence. Such computational models
cannot adapt to reflect the experience of the characters, resulting in brittle
intelligence for even the most effective behavior models devised via costly and
labor-intensive processes. Observation-based behavior model adaptation that
leverages machine learning and the experience of synthetic entities in
combination with appropriate prior knowledge can address the issues in the
existing computational behavior models to create a better training experience
in military training simulations. In this paper, we introduce a framework that
aims to create autonomous synthetic characters that can perform coherent
sequences of believable behavior while being aware of human trainees and their
needs within a training simulation. This framework brings together three
mutually complementary components. The first component is a Unity-based
simulation environment - Rapid Integration and Development Environment (RIDE) -
supporting One World Terrain (OWT) models and capable of running and supporting
machine learning experiments. The second is Shiva, a novel multi-agent
reinforcement and imitation learning framework that can interface with a
variety of simulation environments, and that can additionally utilize a variety
of learning algorithms. The final component is the Sigma Cognitive Architecture
that will augment the behavior models with symbolic and probabilistic reasoning
capabilities. We have successfully created proof-of-concept behavior models
leveraging this framework on realistic terrain as an essential step towards
bringing machine learning into military simulations.


Abstract:
  One of the key ways in which quantum mechanics differs from relativity is
that it requires a fixed background reference frame for spacetime. In fact,
this appears to be one of the main conceptual obstacles to uniting the two
theories. Additionally, a combination of the two theories is expected to yield
non-classical, or "indefinite", causal structures. In this paper, we present a
background-independent formulation of the process matrix formalism - a form of
quantum mechanics that allows for indefinite causal structure - while retaining
operationally well-defined measurement statistics. We do this by postulating an
arbitrary probability distribution of measurement outcomes across discrete
"chunks" of spacetime, which we think of as physical laboratories, and then
requiring that this distribution be invariant under any permutation of
laboratories. We find (a) that one still obtains nontrivial, indefinite causal
structures with background independence, (b) that we lose the idea of local
operations in distinct laboratories, but can recover it by encoding a reference
frame into the physical states of our system, and (c) that permutation
invariance imposes surprising symmetry constraints that, although formally
similar to a superselection rule, cannot be interpreted as such.


Abstract:
  In the past twenty years several events disrupted global economies and social
well-being and generally shook the confidence in the stability of western
societies such as during the 2008 financial crisis and its economic aftermath.
Here, we aim to identify and illustrate underlying drivers of such societal
instability or even consequential collapse. For this purpose we propose a
low-dimensional and stylised model of two classes of networked agents (termed
"labourers" and "administrators" hereafter) that is inspired by Joseph
Tainter's theory of collapse of complex societies. We numerically model the
dynamics of societal complexity, measured as the share of "administrators",
which is assumed to affect the productivity of energy-producing `labourers". We
show that collapse becomes increasingly likely if the model society's
complexity continuously increases in response to external stresses that emulate
Tainter's abstract notion of problems that societies need to solve. We
additionally provide an analytical approximation of the system's dominant
dynamics which matches well with the numerical experiments, thus, allowing for
a precise estimate of tipping points beyond which societal collapse is
triggered. The administration's ability for increasing productivity of labour
shows to be the most influential parameter for longer survival times which is
additionally fostered by a minimum level of social connectivity. Finally, we
show that agents' stochastic transitions between labour force and
administration, i.e. social mobility, can increase the survival time of the
modeled society even further. Our work fosters the understanding of
socio-ecological collapse and illustrates its potentially direct link to an
ever increasing complexity in response to external shocks or stress via a
self-reinforcing feedback.


Abstract:
  Non-parametric inference for functional data over two-dimensional domains
entails additional computational and statistical challenges, compared to the
one-dimensional case. Separability of the covariance is commonly assumed to
address these issues in the densely observed regime. Instead, we consider the
sparse regime, where the latent surfaces are observed only at few irregular
locations with additive measurement error, and propose an estimator of
covariance based on local linear smoothers. Consequently, the assumption of
separability reduces the intrinsically four-dimensional smoothing problem into
several two-dimensional smoothers and allows the proposed estimator to retain
the classical minimax-optimal convergence rate for two-dimensional smoothers.
Even when separability fails to hold, imposing it can be still advantageous as
a form of regularization. A simulation study reveals a favorable bias-variance
trade-off and massive speed-ups achieved by our approach. Finally, the proposed
methodology is used for qualitative analysis of implied volatility surfaces
corresponding to call options, and for prediction of the latent surfaces based
on information from the entire data set, allowing for uncertainty
quantification. Our cross-validated out-of-sample quantitative results show
that the proposed methodology outperforms the common approach of pre-smoothing
every implied volatility surface separately.


Abstract:
  We present design and realization of an ultra-broadband optical spectrometer
capable of measuring the spectral intensity of multi-octave-spanning light
sources on a single-pulse basis with a dynamic range of up to 8 orders of
magnitude. The instrument is optimized for the characterization of the temporal
structure of femtosecond long electron bunches by analyzing the emitted
coherent transition radiation (CTR) spectra. The spectrometer operates within
the spectral range of 250nm to 11.35$\mu$m, corresponding to 5.5 optical
octaves. This is achieved by dividing the signal beam into three spectral
groups, each analyzed by a dedicated spectrometer and detector unit. The
complete instrument was characterized with regard to wavelength, relative
spectral sensitivity, and absolute photo-metric sensitivity, always accounting
for the light polarization and comparing different calibration methods.
Finally, the capability of the spectrometer is demonstrated with a CTR
measurement of a laser wakefield accelerated electron bunch, enabling to
determine temporal pulse structures at unprecedented resolution.


Abstract:
  In the paper, our main aim is to generalize the width integrals to the Orlicz
space. Under the framework of Orlicz Brunn-Minkowski theory, we introduce a new
affine geometric quantity by calculating Orlicz first order variation of the
width integrals, and call as Orlicz mixed width integrals. The fundamental
notions and conclusions of the width integrals and Minkoswki and
Brunn-Minkowski inequalities for the width integrals are extended to an Orlicz
setting and the related concepts and inequalities of Lp mixed width integrals
of convex body are also derived.


Abstract:
  Atomic properties of $n=3$ states of the W$^{56+}$ $-$ W$^{61+}$ ions are
systematically investigated through two state-of-the-art methods, namely, the
second-order many-body perturbation theory, and the multi-configuration
Dirac-Hartree-Fock method combined with the relativistic configuration
interaction approach. The contributions of valence-valence and core-valence
electron correlations, the Breit interaction, the higher-order retardation
correction beyond the Breit interaction through the transverse photon
interaction, and the quantum electrodynamical corrections to the excitation
energies are studied in detail. The excitation energies and wavelengths
obtained with the two methods agree with each other within \approx 0.01 %. The
present results achieve spectroscopic accuracy and provide a benchmark test for
various applications and other theoretical calculations of W$^{56+}$ $-$
W$^{61+}$ ions. They will assist spectroscopists in their assignment and direct
identification of observed lines in complex spectra.


Abstract:
  Three typical one-dimensional (1D)/quasi-1D nanocarbons, linear carbon
chains, carbon nanotubes, and graphene nanoribbons have been proven to grow
inside single-wall carbon nanotubes. This gives rise to three types of hybrid
materials whose behaviour and properties compared among each other are far from
understood. After proving successful the synthesis of these nanostructured
materials in recently published work, we have now been able to study their
oxidation stability systematically by using resonance Raman spectroscopy.
Surprisingly, the linear carbon chains, which have been theoretically predicted
to be very unstable, are actually thermally stable up to 500 {\deg}C assisted
by the protection of the carbon nanotube hosts. Besides, longer linear carbon
chains inside narrower CNTs are more stable than the shorter ones inside larger
tubes, suggesting that the thermal stability not only depends on the length of
linear carbon chains alone, but it is correlated with the confinement of the
host tubes in a more complicated manner. In addition, graphene nanoribbons come
overall in view as the most stable confined structures. On the other hand,
peculiarities like the higher stability of the (6,5) CNT over its (6,4)
counterpart allow this study to provide a solid platform for further studies on
the application of these 1D nanocarbons (including true 1D linear carbon
chains) at ambient conditions.


Abstract:
  The central regions of star-forming barred spiral galaxies can be devoid of
star formation because of the redistribution of gas along the length of the
bar. However, there can be gas outside the length of the bar that can host star
formation. We study a sample of barred disc galaxies in the local Universe with
an aim to discriminate between centrally quenched and globally quenched
galaxies based on their positions on star-formation-rate(SFR)--stellar mass
plots and to find a connection between the SFR of quenched galaxies and the
length of their bar. We classified barred galaxies as centrally quenched and
globally quenched based on their position on SFR--stellar mass plots, with SFRs
derived from H$\alpha$ flux and spectral energy distribution fits on combined
ultraviolet and optical flux. We selected galaxies as passive based on the
distance from the main sequence relation. From a total 2514 barred galaxies
studied here, we present 651 with suppressed star formation in their central
region but hosting star formation outside. We also find a possible correlation
between bar length and SFR for the galaxies that are fully quenched because of
the stellar bar.


Abstract:
  Recent work on explainable clustering allows describing clusters when the
features are interpretable. However, much modern machine learning focuses on
complex data such as images, text, and graphs where deep learning is used but
the raw features of data are not interpretable. This paper explores a novel
setting for performing clustering on complex data while simultaneously
generating explanations using interpretable tags. We propose deep descriptive
clustering that performs sub-symbolic representation learning on complex data
while generating explanations based on symbolic data. We form good clusters by
maximizing the mutual information between empirical distribution on the inputs
and the induced clustering labels for clustering objectives. We generate
explanations by solving an integer linear programming that generates concise
and orthogonal descriptions for each cluster. Finally, we allow the explanation
to inform better clustering by proposing a novel pairwise loss with
self-generated constraints to maximize the clustering and explanation module's
consistency. Experimental results on public data demonstrate that our model
outperforms competitive baselines in clustering performance while offering
high-quality cluster-level explanations.


Abstract:
  We develop a ready-to-use comprehensive theory for (super) 2-vector bundles
over smooth manifolds. It is based on the bicategory of (super) algebras,
bimodules, and intertwiners as a model for 2-vector spaces. We show that our
2-vector bundles form a symmetric monoidal 2-stack, we discuss the dualizable,
fully dualizable, and invertible objects, and we derive a classification in
terms of non-abelian Cech cohomology. One important feature of our 2-vector
bundles is that they contain bundle gerbes as well as ordinary algebra bundles
as full sub-bicategories, and hence provide a unifying framework for these so
far distinct objects. We provide several examples of isomorphisms between
bundle gerbes and algebra bundles, coming from representation theory, twisted
K-theory, and spin geometry.


Abstract:
  The COVID-19 epidemic is the last of a long list of pandemics that have
affected humankind in the last century. In this paper, we propose a novel
mathematical epidemiological model named SUIHTER from the names of the seven
compartments that it comprises: susceptible uninfected individuals (S),
undetected (both asymptomatic and symptomatic) infected (U), isolated (I),
hospitalized (H), threatened (T), extinct (E), and recovered (R). A suitable
parameter calibration that is based on the combined use of least squares method
and Markov Chain Monte Carlo (MCMC) method is proposed with the aim of
reproducing the past history of the epidemic in Italy, surfaced in late
February and still ongoing to date, and of validating SUIHTER in terms of its
predicting capabilities. A distinctive feature of the new model is that it
allows a one-to-one calibration strategy between the model compartments and the
data that are daily made available from the Italian Civil Protection. The new
model is then applied to the analysis of the Italian epidemic with emphasis on
the second outbreak emerged in Fall 2020. In particular, we show that the
epidemiological model SUIHTER can be suitably used in a predictive manner to
perform scenario analysis at national level.


Abstract:
  In this document the density of electrically-charged positive and negative
particles in a two component plasma (TCP) will be studied. Particularly, we
focus on a two dimensional system confined in a large rectangular box for
$\Gamma=2$ in the presence of two electric impurities. A method for solution,
which will be called, {\it dimensional reduction}, will be applied in order to
study the redistribution of electrically charged particles along the line
joining both impurities. Numerical results, by means of a finite elements
method approach, show, due to the electric field generated by the impurities,
an increase in the density of charges of opposite sign in the neighborhood of
each impurity. On the other hand, the presence of charges of the same sign
diminishes in the same region due to the existing electric repulsion; some of
the repelled particles accumulate in the border of the box. Numerical
expansions around the borders of the impurities and the box show an almost
linear power law relation of the net density for the particular cases that have
been analyzed. It is also studied how the maximum and minimum values of the net
density depend on the electric charges of the impurities, under some particular
conditions.


Abstract:
  Mesh distortion optimization is a popular research topic and has wide range
of applications in computer graphics, including geometry modeling, variational
shape interpolation, UV parameterization, elastoplastic simulation, etc. In
recent years, many solvers have been proposed to solve this nonlinear
optimization efficiently, among which projected Newton has been shown to have
best convergence rate and work well in both 2D and 3D applications. Traditional
Newton approach suffers from ill conditioning and indefiniteness of local
energy approximation. A crucial step in projected Newton is to fix this issue
by projecting energy Hessian onto symmetric positive definite (SPD) cone so as
to guarantee the search direction always pointing to decrease the energy
locally. Such step relies on time consuming eigen decomposition of element
Hessian, which has been addressed by several work before on how to obtain a
conjugacy that is as diagonal as possible. In this report, we demonstrate an
analytic form of Hessian eigen system for distortion energy defined using
principal stretches, which is the most general representation. Compared with
existing projected Newton diagonalization approaches, our formulation is more
general as it doesn't require the energy to be representable by tensor
invariants. In this report, we will only show the derivation for 3D and the
extension to 2D case is straightforward.


Abstract:
  We consider a stochastic partial differential equation (SPDE) model for
chemorepulsion, with non-linear sensitivity on the one-dimensional torus. We
show that for any suitable initial data there exists a pathwise unique, global
solution to the SPDE. Furthermore we show that the associated semi-group is
Markov and possesses a unique invariant measure, supported on a H\"older-Besov
space of positive regularity, which the solution law converges to exponentially
fast. We also establish tail bounds on the invariant measure that are heavier
than Gaussian when measured using any $L^p$ norm.


Abstract:
  Hybrid quantum systems aim at combining the advantages of different physical
systems and to produce novel quantum devices. In particular, the hybrid
combination of superconducting circuits and spins in solid-state crystals is a
versatile platform to explore many quantum electrodynamics problems. Recently,
the remote coupling of nitrogen-vacancy center spins in diamond via a
superconducting bus was demonstrated. However, a rigorous experimental test of
the quantum nature of this hybrid system and in particular entanglement is
still missing. We review the theoretical ideas to generate and detect
entanglement, and present our own scheme to achieve this.


Abstract:
  Specification synthesis is the process of deriving a model from the
input-output traces of a system. It is used extensively in test design, reverse
engineering, and system identification. One type of the resulting artifact of
this process for cyber-physical systems is hybrid automata. They are intuitive,
precise, tool independent, and at a high level of abstraction, and can model
systems with both discrete and continuous variables. In this paper, we propose
a new technique for synthesizing hybrid automaton from the input-output traces
of a non-linear cyber-physical system. Similarity detection in non-linear
behaviors is the main challenge for extracting such models. We address this
problem by utilizing the Dynamic Time Warping technique. Our approach is
passive, meaning that it does not need interaction with the system during
automata synthesis from the logged traces; and online, which means that each
input/output trace is used only once in the procedure. In other words, each new
trace can be used to improve the already synthesized automaton. We evaluated
our algorithm in two industrial and simulated case studies. The accuracy of the
derived automata show promising results.


Abstract:
  Relation classification aims to predict a relation between two entities in a
sentence. The existing methods regard all relations as the candidate relations
for the two entities in a sentence. These methods neglect the restrictions on
candidate relations by entity types, which leads to some inappropriate
relations being candidate relations. In this paper, we propose a novel
paradigm, RElation Classification with ENtity Type restriction (RECENT), which
exploits entity types to restrict candidate relations. Specially, the mutual
restrictions of relations and entity types are formalized and introduced into
relation classification. Besides, the proposed paradigm, RECENT, is
model-agnostic. Based on two representative models GCN and SpanBERT
respectively, RECENT_GCN and RECENT_SpanBERT are trained in RECENT.
Experimental results on a standard dataset indicate that RECENT improves the
performance of GCN and SpanBERT by 6.9 and 4.4 F1 points, respectively.
Especially, RECENT_SpanBERT achieves a new state-of-the-art on TACRED.


Abstract:
  The friction force on a test particle traveling through a plasma that is both
strongly coupled and strongly magnetized is studied using molecular dynamics
simulations. In addition to the usual stopping power component aligned
antiparallel to the velocity, a transverse component that is perpendicular to
both the velocity and Lorentz force is observed. This component, which was
recently discovered in weakly coupled plasmas, is found to increase in both
absolute and relative magnitude in the strongly coupled regime. Strong coupling
is also observed to induce a third component of the friction force in the
direction of the Lorentz force. These first-principles simulations reveal novel
physics associated with collisions in strongly coupled, strongly magnetized,
plasmas that are not predicted by existing kinetic theories. The effect is
expected to influence macroscopic transport in a number of laboratory
experiments and astrophysical plasmas.


Abstract:
  In this paper we consider the question of sampling for spaces of entire
functions of exponential type in several variables. The novelty resides in the
growth condition we impose, that is, that their restriction to a hypersurface
is square integrable with respect to a natural measure. The hypersurface we
consider is the boundary $b\mathcal U$ of the Siegel upper half-space $\mathcal
U$ and it is fundamental that $b\mathcal U$ can be identified with the
Heisenberg group $\mathbb H_n$. We consider entire functions in $\mathbb
C^{n+1}$ of exponential type with respect to the hypersurface $b\mathcal U$
whose restriction to $b\mathcal U$ are square integrable with respect to the
Haar measure on $\mathbb H_n$. For these functions we prove a version of the
Whittaker--Kotelnikov--Shannon Theorem. Instrumental in our work are spaces of
entire functions in $\mathbb C^{n+1}$ of exponential type with respect to the
hypersurface $b\mathcal U$ whose restrictions to $b\mathcal U$ belong to some
homogeneous Sobolev space on $\mathbb H_n$. For these spaces, using the group
Fourier transform on $\mathbb H_n$, we prove a Paley--Wiener type theorem and a
Plancherel--P\'olya type inequality.


Abstract:
  We prove a result on the asymptotic proportion of randomly chosen pairs of
permutations in the symmetric group $S_n$ which "invariably" generate a
nonsolvable subgroup, i.e., whose cycle structures cannot possibly both occur
in the same solvable subgroup of $S_n$. As an application, we obtain that for a
large degree "random" integer polynomial $f$, reduction modulo two different
primes can be expected to suffice to prove the nonsolvability of
$Gal(f/\mathbb{Q})$.


Abstract:
  We consider the influence of transverse confinement on the instability
properties of velocity and density distributions reminiscent of those
pertaining to exchange flows in stratified inclined ducts, such as the recent
experiment of Lefauve et al. (J. Fluid Mech. 848, 508-544, 2018). Using a
normal mode streamwise and temporal expansion for flows in ducts with various
aspect ratios $B$ and non-trivial transverse velocity profiles, we calculate
two-dimensional (2D) dispersion relations with associated eigenfunctions
varying in the 'crosswise' direction, in which the density varies, and the
spanwise direction, both normal to the duct walls and to the flow direction. We
also compare these 2D dispersion relations to the so-called one-dimensional
(1D) dispersion relation obtained for spanwise invariant perturbations, for
different aspect ratios $B$ and bulk Richardson numbers $Ri_b$. In this limited
parameter space, the presence of lateral walls has a stabilizing effect.
Furthermore, accounting for spanwise-varying perturbations results in a
plethora of unstable modes, the number of which increases as the aspect ratio
is increased. These modes present an odd-even regularity in their spatial
structures, which is rationalized by comparison to the so-called
one-dimensional oblique (1D-O) dispersion relation obtained for oblique waves.
Finally, we show that in most cases, the most unstable 2D mode is the one that
oscillates the least in the spanwise direction, as a consequence of viscous
damping. However, in a limited region of the parameter space and in the absence
of stratification, we show that a secondary mode with a more complex `twisted'
structure dominated by crosswise vorticity becomes more unstable than the least
oscillating Kelvin-Helmholtz mode associated with spanwise vorticity.


Abstract:
  In this paper, the $q$-th dual curvature measure is extended to convex
functions and the associated Minkowski problem is posed. A special case
includes the $q$-th dual curvature measure of convex bodies which defined by
Huang, Lutwak, Yang and Zhang. Existence for the functional dual Minkowski
problem is showed when $q\leq0$ and the uniqueness part is obtained with some
assumptions.


Abstract:
  The use of optimal transport cost for learning generative models has become
popular with Wasserstein Generative Adversarial Networks (WGAN). Training of
WGAN relies on a theoretical background: the calculation of the gradient of the
optimal transport cost with respect to the generative model parameters. We
first demonstrate that such gradient may not be defined, which can result in
numerical instabilities during gradient-based optimization. We address this
issue by stating a valid differentiation theorem in the case of entropic
regularized transport and specify conditions under which existence is ensured.
By exploiting the discrete nature of empirical data, we formulate the gradient
in a semi-discrete setting and propose an algorithm for the optimization of the
generative model parameters. Finally, we illustrate numerically the advantage
of the proposed framework.


Abstract:
  Algorithms engineered to leverage rich behavioral and biometric data to
predict individual attributes and actions continue to permeate public and
private life. A fundamental risk may emerge from misconceptions about the
sensitivity of such data, as well as the agency of individuals to protect their
privacy when fine-grained (and possibly involuntary) behavior is tracked. In
this work, we examine how individuals adjust their behavior when incentivized
to avoid the algorithmic prediction of their intent. We present results from a
virtual reality task in which gaze, movement, and other physiological signals
are tracked. Participants are asked to decide which card to select without an
algorithmic adversary anticipating their choice. We find that while
participants use a variety of strategies, data collected remains highly
predictive of choice (80% accuracy). Additionally, a significant portion of
participants became more predictable despite efforts to obfuscate, possibly
indicating mistaken priors about the dynamics of algorithmic prediction.


Abstract:
  Nonlinear networks can host spatially compact time periodic solutions called
compact breathers. Such solutions can exist accidentally (i.e. for specific
nonlinear strength values) or parametrically (i.e. for any nonlinear strength).
In this work we introduce an efficient generator scheme for one-dimensional
nonlinear lattices which support either types of compact breathers spanned over
a given number U of lattice's unit cells and any number of sites v per cell -
scheme which can be straightforwardly extended to higher dimensions. This
scheme in particular allows to show the existence and explicitly construct
examples of parametric compact breathers with inhomogeneous spatial profiles --
extending previous results which indicated that only homogeneous parametric
compact breathers exist. We provide explicit d=1 lattices with different v
supporting compact breather solutions for U=1,2.


Abstract:
  All the laws of physics are time-reversible. Time arrow emerges only when
ensembles of classical particles are treated probabilistically, outside of
physics laws, and the entropy and the second law of thermodynamics are
introduced. In quantum physics, no mechanism for a time arrow has been proposed
despite its intrinsic probabilistic nature. In consequence, one cannot explain
why an electron in an excited state will "spontaneously" transition into a
ground state as a photon is created and emitted, instead of continuing in its
reversible unitary evolution. To address such phenomena, we introduce an
entropy for quantum physics, which will conduce to the emergence of a time
arrow.
  The entropy is a measure of randomness over the degrees of freedom of a
quantum state. It is dimensionless; it is a relativistic scalar, it is
invariant under coordinate transformation of position and momentum that
maintain conjugate properties and under CPT transformations; and its minimum is
positive due to the uncertainty principle.
  To excogitate why some quantum physical processes cannot take place even
though they obey conservation laws, we partition the set of all evolutions of
an initial state into four blocks, based on whether the entropy is (i)
increasing but not a constant, (ii) decreasing but not a constant, (iii) a
constant, (iv) oscillating. We propose a law that in quantum physics entropy
(weakly) increases over time. Thus, evolutions in the set (ii) are disallowed,
and evolutions in set (iv) are barred from completing an oscillation period by
instantaneously transitioning to a new state. This law for quantum physics
limits physical scenarios beyond conservation laws, providing causality
reasoning by defining a time arrow.


Abstract:
  Detecting arguments in online interactions is useful to understand how
conflicts arise and get resolved. Users often use figurative language, such as
sarcasm, either as persuasive devices or to attack the opponent by an ad
hominem argument. To further our understanding of the role of sarcasm in
shaping the disagreement space, we present a thorough experimental setup using
a corpus annotated with both argumentative moves (agree/disagree) and sarcasm.
We exploit joint modeling in terms of (a) applying discrete features that are
useful in detecting sarcasm to the task of argumentative relation
classification (agree/disagree/none), and (b) multitask learning for
argumentative relation classification and sarcasm detection using deep learning
architectures (e.g., dual Long Short-Term Memory (LSTM) with hierarchical
attention and Transformer-based architectures). We demonstrate that modeling
sarcasm improves the argumentative relation classification task
(agree/disagree/none) in all setups.


Abstract:
  Using Ginzburg-Landau formalism, we theoretically study the isothermal shear
modulus anomaly of an unconventional superconductor with a multicomponent order
parameter, when the superconducting transition is split by a symmetry-breaking
field. Experimental signatures are proposed for both chiral and nematic
superconductors. Particularly striking is the vanishing of $C_{66}$ across the
lower transition to a nematic superconducting state. Our findings can guide
future experiments and shed new lights on materials such as Sr$_2$RuO$_4$ and
$M_x$Bi$_2$Se$_3$.


Abstract:
  We develop a systematic and unified approach to estimate all possible
secondary (i.e. non-primordial) nonlinear effects to the cosmic microwave
background (CMB) polarization, named curve-of-sight integration approach. In
this approach, the Boltzmann equation for polarized photons is rewritten in a
line-of-sight integral along an exact geodesic in the perturbed universe,
rather than a geodesic in the background universe used in the linear-order CMB
calculation. This approach resolves the difficulty to solve the Boltzmann
hierarchy with the nonlinear gravitational effects in the photon free-streaming
regime and thus unifies the standard remapping approach for CMB lensing into
the direct approach solving the Boltzmann equation for the nonlinear
collisional effects. In this paper, we derive formulae that: (i) include all
the nonlinear effects; (ii) can treat extended sources such as the
contributions after the reionization. It offers a solid framework to discuss
possible systematics in the standard estimation of CMB lensing by the remapping
approach. As an explicit demonstration, we estimate the secondary B-mode power
spectrum induced by all foreground gravitational effects: lensing, redshift,
time-delay, emission-angle, and polarization-rotation effects. We define these
effects properly so that they do not have any overlap, also without overlooking
any effect. Then, we show that these effects only give corrections of the order
of 0.001-0.01% to the standard lensing-induced B-mode power spectrum in the
concordance $\Lambda$ cold dark matter model. Our result confirms the
reliability of using the remapping approach in upcoming CMB experiments aiming
to detect the primordial gravitational waves with the tensor-to-scalar ratio of
$r \sim 10^{-3}$.


Abstract:
  We provide explicit sequence space representations for the test function and
distribution spaces occurring in the Valdivia-Vogt structure tables by making
use of Wilson bases generated by compactly supported smooth windows.
Furthermore, we show that these kind of bases are common unconditional Schauder
bases for all separable spaces occurring in these tables. Our work implies that
the Valdivia-Vogt structure tables for test functions and distributions may be
interpreted as one large commutative diagram.


Abstract:
  We study the quantum chaos in the Bose-Fermi Kondo model in which the
impurity spin interacts with conduction electrons and a bosonic bath at the
intermediate temperature in the large $N$ limit. The out-of-time-ordered
correlator is calculated based on the Bethe-Salpeter equation and the Lyapunov
exponent $\lambda_L$ is extracted. Our calculation shows that the Lyapunov
exponent monotonically increases as the Kondo coupling $J_K$ increases, and it
can reach an order of $\lambda_L\sim T$ as $J_K$ approaches the $MCK$ point.
Furthermore, we also demonstrate that $\lambda_L$ decreases monotonously as the
impurity and bosonic bath coupling $g$ increases, which is contrary to the
general expectation that the most chaotic property occurs at the quantum
critical point with the non-Fermi liquid nature.


Abstract:
  An excitation of a large-amplitude out-of-plane magnetization oscillation in
a ferromagnet by the spin Hall effect is of great interest for practical
applications such as microwave generator and neuromorphic computing. However,
both experimental and theoretical works have revealed that only small-amplitude
oscillation around an in-plane easy axis can be excited via the spin Hall
effect. Here, we propose that an out-of-plane oscillation can be excited due to
an assistance of field-like torque. We focus on an in-plane magnetized
ferromagnet with an easy axis parallel to current direction. We notice that the
field-like torque with an appropriate sign provides an additional field
modifying the dynamic trajectory of the magnetization and drives the
auto-oscillation. The condition on the sign of the field-like torque is
satisfied for typical nonmagnet used in spin Hall devices such as tungsten.


Abstract:
  We study a stochastic particle system which is motivated from grain boundary
coarsening in two-dimensional networks. Each particles lives on the positive
real line and is labeled as belonging to either Species 1 or Species 2. Species
1 particles drift at unit speed toward the origin, while Species 2 particles do
not move. When a particle in Species 1 hits the origin, it is removed, and a
randomly selected particle mutates from Species 2 to Species 1. The process
described is an example of a high-dimensional piecewise deterministic Markov
process (PDMP), in which deterministic flow is punctuated with stochastic
jumps. Our main result is a proof of exponential concentration inequalities of
the Kolmogorov-Smirnoff distance between empirical measures of the particle
system and solutions of limiting nonlinear kinetic equations. Our method of
proof involves a time and space discretization of the kinetic equations, which
we compare with the particle system to derive recurrence inequalities for
comparing total numbers in small intervals. To show these recurrences occur
with high probability, we appeal to a state dependent Hoeffding type inequality
at each time increment.


Abstract:
  Analyzing particle data plays an important role in many scientific
applications such as fluid simulation, cosmology simulation and molecular
dynamics. While there exist methods that can perform feature extraction and
tracking for volumetric data, performing those tasks for particle data is more
challenging because of the lack of explicit connectivity information. Although
one may convert the particle data to volume first, this approach is at risk of
incurring error and increasing the size of the data. In this paper, we take a
deep learning approach to create feature representations for scientific
particle data to assist feature extraction and tracking. We employ a deep
learning model, which produces latent vectors to represent the relation between
spatial locations and physical attributes in a local neighborhood. With the
latent vectors, features can be extracted by clustering these vectors. To
achieve fast feature tracking, the mean-shift tracking algorithm is applied in
the feature space, which only requires inference of the latent vector for
selected regions of interest. We validate our approach using two datasets and
compare our method with other existing methods.


Abstract:
  In Disquisitiones Arithmeticae, Gauss studied binary quadratic forms and
introduced a very general version of a composition operator that allows
composing even forms of different discriminants and imprimitive forms. Section
V of Disquisitiones contains truly revolutionary ideas and involves very
complicated computations, sometimes left to the reader. With this theory, Gauss
develops a method of factorization using quadratic residues obtained by binary
quadratic forms. In this work we explore Gauss method making many of the
computations necessary to better understand his work.


Abstract:
  The {\it exchange} interaction arising from the particle indistinguishability
is of central importance to physics of many-particle quantum systems. Here we
study analytically the dynamical generation of quantum entanglement induced by
this interaction in an isolated system, namely, an ideal Fermi gas confined in
a chaotic cavity, which evolves unitarily from a non-Gaussian pure state. We
find that the breakdown of the quantum-classical correspondence of particle
motion, via dramatically changing the spatial structure of many-body
wavefunction, leads to profound changes of the entanglement structure.
Furthermore, for a class of initial states, such change leads to the approach
to thermal equilibrium everywhere in the cavity, with the well-known Ehrenfest
time in quantum chaos as the thermalization time. Specifically, the quantum
expectation values of various correlation functions at different spatial scales
are all determined by the Fermi-Dirac distribution. In addition, by using the
reduced density matrix (RDM) and the entanglement entropy (EE) as local probes,
we find that the gas inside a subsystem is at equilibrium with that outside,
and its thermal entropy is the EE, even though the whole system is in a pure
state. As a by-product of this work, we provide an analytical solution
supporting an important conjecture on thermalization, made and numerically
studied by Garrison and Grover in: Phys. Rev. X \textbf{8}, 021026 (2018), and
strengthen its statement.


Abstract:
  In this work, we develop a method to generate infinite high-resolution images
with diverse and complex content. It is based on a perfectly equivariant
generator with synchronous interpolations in the image and latent spaces.
Latent codes, when sampled, are positioned on the coordinate grid, and each
pixel is computed from an interpolation of the nearby style codes. We modify
the AdaIN mechanism to work in such a setup and train the generator in an
adversarial setting to produce images positioned between any two latent
vectors. At test time, this allows for generating complex and diverse infinite
images and connecting any two unrelated scenes into a single arbitrarily large
panorama. Apart from that, we introduce LHQ: a new dataset of \lhqsize
high-resolution nature landscapes. We test the approach on LHQ, LSUN Tower and
LSUN Bridge and outperform the baselines by at least 4 times in terms of
quality and diversity of the produced infinite images. The project page is
located at https://universome.github.io/alis.


Abstract:
  Few-shot learning deals with the fundamental and challenging problem of
learning from a few annotated samples, while being able to generalize well on
new tasks. The crux of few-shot learning is to extract prior knowledge from
related tasks to enable fast adaptation to a new task with a limited amount of
data. In this paper, we propose meta-learning kernels with random Fourier
features for few-shot learning, we call MetaKernel. Specifically, we propose
learning variational random features in a data-driven manner to obtain
task-specific kernels by leveraging the shared knowledge provided by related
tasks in a meta-learning setting. We treat the random feature basis as the
latent variable, which is estimated by variational inference. The shared
knowledge from related tasks is incorporated into a context inference of the
posterior, which we achieve via a long-short term memory module. To establish
more expressive kernels, we deploy conditional normalizing flows based on
coupling layers to achieve a richer posterior distribution over random Fourier
bases. The resultant kernels are more informative and discriminative, which
further improves the few-shot learning. To evaluate our method, we conduct
extensive experiments on both few-shot image classification and regression
tasks. A thorough ablation study demonstrates that the effectiveness of each
introduced component in our method. The benchmark results on fourteen datasets
demonstrate MetaKernel consistently delivers at least comparable and often
better performance than state-of-the-art alternatives.


Abstract:
  Observations and modelling of stars with near-solar masses in their early
phases of evolution is critical for a better understanding of how dynamos of
solar-type stars evolve. We examine the chemical composition and the spot
distribution of the pre-main-sequence solar analogue EK Dra. Using spectra from
the HERMES Spectrograph (La Palma), we obtain the abundances of 23 elements
with respect to the solar ones, which lead to a $[{\rm Fe/H}]=0.03$, with
significant overabundance of Li and Ba. The s-process elements Sr, Y, and Ce
are marginally overabundant, while Co, Ni, Cu, Zn are marginally deficient
compared to solar abundances. The overabundance of Ba is most likely due to the
assumption of depth-independent microturbulent velocity. Li abundance is
consistent with the age and the other abundances may indicate distinct initial
conditions of the pre-stellar nebula. We estimate a mass of 1.04 $M_\odot$ and
an age of $27^{+11}_{-8}$\,Myr using various spectroscopic and photometric
indicators. We study the surface distribution of dark spots, using 17 spectra
collected during 15 nights using the CAFE Spectrograph (Calar Alto). We also
conduct flux emergence and transport (FEAT) simulations for EK Dra's parameters
and produce 15-day-averaged synoptic maps of the likely starspot distributions.
Using Doppler imaging, we reconstruct the surface brightness distributions for
the observed spectra and FEAT simulations, which show overall agreement for
polar and mid-latitude spots, while in the simulations there is a lack of
low-latitude spots compared to the observed image. We find indications that
cross-equatorial extensions of mid-latitude spots can be artefacts of the less
visible southern-hemisphere activity.


Abstract:
  Income verification is the problem of validating a person's stated income
given basic identity information such as name, location, job title and
employer. It is widely used in the context of mortgage lending, rental
applications and other financial risk models. However, the current processes
surrounding verification involve significant human effort and document
gathering which can be both time-consuming and expensive. In this paper, we
propose a novel model for verifying an individual's income given very limited
identity information typically available in loan applications. Our model is a
combination of a deep neural network and hand-engineered features. The hand
engineered features are based upon matching the input information against
income records extracted automatically from various publicly available online
sources (e.g. payscale.com, H-1B filings, government employee salaries). We
conduct experiments on two data sets, one simulated from H-1B records and the
other from a real-world data set of peer-to-peer (P2P) loan applications
obtained from one of the world's largest P2P lending platform. Our results show
a significant reduction in error of 3-6% relative to several strong baselines.
We also perform ablation studies to demonstrate that a combined model is indeed
necessary to achieve state-of-the-art performance on this task.


Abstract:
  We derive constraints on scalar field theories coupled to gravity by using
recently developed positivity bounds in the presence of gravity. It is found
that a canonically-normalized real scalar cannot have an arbitrarily flat
potential unless some new physics enters well below the Planck scale. An upper
bound on the scale of new physics is determined by loop corrections to the
self-energy. Our result provides a swampland condition for scalar potentials.


Abstract:
  Let $F(x)$ be an integer coefficient polynomial with degree at least $2.$
Define the sequence $a_n$ by $a_n=F(a_{n-1})$ for all $n\ge 1$ and $a_0=0.$ Let
$\mathscr{B}_{F,G,k}$ be the set of all positive integers $n$ such that $k\mid
\gcd(G(n),a_n)$ and if $p\mid \gcd(G(n),a_n)$ for some $p$, then $p\mid k.$ And
$\mathscr{A}_{F,G,k}$ be the subset of $\mathscr{B}_{F,G,k}$ such that
$\mathscr{A}_{F,G,k}=\{n\ge 1 : \gcd(G(n),a_n)=k\}.$ In this article, we
explain the asymptotic density of $\mathscr{A}_{F,G,k}$ and
$\mathscr{B}_{F,G,k}$ for a class of $(F,G)$ and also compute the explicit
density of $\mathscr{A}_{F,G,k}$ and $\mathscr{B}_{F,G,k}$ for $G(x)=x.$


Abstract:
  Immersive 3D media is an emerging type of media that captures, encodes and
reconstructs the 3D appearance of people and objects, with applications in
tele-presence, teleconference, entertainment, gaming and other fields. In this
paper, we discuss a novel concept of live 3D immersive media streaming in a
serverless setting. In particular, we present a novel network-centric adaptive
streaming framework which deviates from a traditional client-based adaptive
streaming used in 2D video. In our framework, the decisions for the production
of the transcoding profiles are taken in a centralized manner, by considering
consumer metrics vs provisioning costs and inferring an expected consumer
quality of experience and behaviour based on them. In addition, we demonstrate
that a naive application of the serverless paradigm might be sub optimal under
some common immersive 3D media scenarios.


Abstract:
  Acoustic holograms have promising applications in sound-field reconstruction,
particle manipulation, ultrasonic haptics and therapy. This paper reports on
the theoretical, numerical, and experimental investigation of multiplexed
acoustic holograms at both audio and ultrasonic frequencies via a rationally
designed transmission-type acoustic metamaterial. The proposed meta-hologram is
composed of two Fabry-Perot resonant channels per unit cell, which enables the
simultaneous modulation of the transmitted amplitude and phase at two desired
frequencies. In contrast to conventional acoustic metamaterial-based holograms,
the design strategy proposed here, provides a new degree of freedom (frequency)
that can actively tailor holograms that are otherwise completely passive and
hence significantly enhances the information encoded in acoustic metamaterials.
To demonstrate the multiplexed acoustic metamaterial, we first show the
projection of two different high-quality meta-holograms at 14 kHz and 17 kHz,
with the patterns of the letters, N and S. We then demonstrate two-channel
ultrasound focusing and annular beams generation for the incident ultrasonic
frequencies of 35 kHz and 42.5 kHz. These multiplexed acoustic meta-holograms
offer a technical advance to tackle the rising challenges in the fields of
acoustic metamaterials, architectural acoustics, and medical ultrasound.


Abstract:
  In this work we will focus on the existence of weak solutions for a system
describing a general compressible viscous fluid in the case of the pressure
being a linear function of the density and the viscous stress tensor being a
non-linear function of the symmetric velocity gradient. More precisely, we will
first prove the existence of dissipative solutions and study under which
conditions it is possible to guarantee the existence of weak solutions.


Abstract:
  In 2017 a definition of spiral tilings was given, thereby answering a
question posed by Gr\"unbaum and Shephard in the late 1970s. The author had the
pleasure to discuss the topic via e-mail with Branko Gr\"unbaum in his 87th
year. During this correspondence the question arose whether a spiral structure
(given a certain definition of it) could be recognized automatically or whether
"to some extent, at least, the spiral effect is psychological", as Gr\"unbaum
and Shephard had conjectured in 1987 (see exercise section of chapter 9.5 in
"Tilings and Patterns"). In this paper, an algorithm for automatic detection of
such a tiling's spiral structure and its first implementation results will be
discussed. Finally, the definitions for several types of spiral tilings are
refined based on this investigation.


Abstract:
  We propose a combination of a variational autoencoder and a transformer based
model which fully utilises graph convolutional and graph pooling layers to
operate directly on graphs. The transformer model implements a novel node
encoding layer, replacing the position encoding typically used in transformers,
to create a transformer with no position information that operates on graphs,
encoding adjacent node properties into the edge generation process. The
proposed model builds on graph generative work operating on graphs with edge
features, creating a model that offers improved scalability with the number of
nodes in a graph. In addition, our model is capable of learning a disentangled,
interpretable latent space that represents graph properties through a mapping
between latent variables and graph properties. In experiments we chose a
benchmark task of molecular generation, given the importance of both generated
node and edge features. Using the QM9 dataset we demonstrate that our model
performs strongly across the task of generating valid, unique and novel
molecules. Finally, we demonstrate that the model is interpretable by
generating molecules controlled by molecular properties, and we then analyse
and visualise the learned latent representation.


Abstract:
  We measure the transverse baryon acoustic oscillations (BAO) signal in the
local Universe using a sample of blue galaxies from the Sloan Digital Sky
Survey (SDSS) survey as a cosmological tracer. The method is weakly dependent
on a cosmological model and is suitable for 2D analyses in thin redshift bins
to investigate the SDSS data in the interval $z {\in} [0.105, 0.115]$. We
detect the transverse BAO signal ${\theta}_{BAO} = 19.8^{\deg} {\pm}
1.05^{\deg}$ at $z_{eff} = 0.11$, with a statistical significance of $2.2
{\sigma}$. Additionally, we perform tests that confirm the robustness of this
angular BAO signature. Supported by a large set of log-normal simulations, our
error analyses include statistical and systematic contributions. In addition,
considering the sound horizon scale calculated by the Planck Collaboration,
$r_{s}^{Planck}$, and the ${\theta}_{BAO}$ value obtained here, we obtain a
measurement of the angular diameter distance $D_{A}(0.11) = 258.31 {\pm} 13.71
\,Mpc/h$. Moreover, combining this ${\theta}_{BAO}$ measurement at low redshift
with other BAO angular scale data reported in the literature, we perform
statistical analyses for the cosmological parameters of some Lambda cold dark
matter (${\Lambda}$CDM) type models.


Abstract:
  We consider the problem of guaranteeing that the transient voltages and
currents stay within prescribed bounds in Direct Current (DC) microgrids, when
the controller does not have access to accurate system dynamics due to the load
being unknown and/or time-varying. To achieve this, we propose an optimization
based controller design using control barrier functions. We show that the
proposed controller has a decentralized structure and is robust with respect to
the uncertainty in the precise values of the system parameters, such as the
load.


Abstract:
  The computational cost of ab initio nuclear structure calculations is
rendered particularly acute by the presence of (at least) three-nucleon
interactions. This feature becomes especially critical now that many-body
methods aim at extending their reach beyond mid-mass nuclei. Consequently,
state-of-the-art ab initio calculations are typically performed while
approximating three-nucleon interactions in terms of effective, i.e.
system-dependent, zero-, one- and two-nucleon operators. While straightforward
in doubly closed-shell nuclei, existing approximation methods based on
normal-ordering techniques involve either two- and three-body density matrices
or a symmetry-breaking one-body density matrix in open-shell systems. In order
to avoid such complications, a simple, flexible, universal and accurate
approximation technique involving the convolution of the initial operator with
a sole symmetry-invariant one-body matrix is presently formulated and tested
numerically. Employed with a low-resolution Hamiltonian, the novel
approximation method is shown to induce errors below $2-3\%$ across a large
range of nuclei, observables and many-body methods.


Abstract:
  We utilize simulations of electron scattering by a chain of dynamical quantum
spins, to analyze the interplay between the spin transfer effect and the
magnetization dynamics. We show that the complex interactions between the
spin-polarized electrons and the dynamical states of the local spins can be
decomposed into separate processes involving electron reflection and
transmission, as well as absorption and emission of magnons - the quanta of
magnetization dynamics. Analysis shows that these processes are substantially
constrained by the energy and momentum conversation laws, resulting in a
significant dependence of spin transfer on the electron's energy and the
dynamical state of the local spins. Our results suggest that exquisite control
of spin transfer efficiency and of the resulting dynamical magnetization states
may be achievable by tailoring the spectral characteristics of the conduction
electrons and of the magnetic systems.


Abstract:
  In this work we present a novel approach to hierarchical reinforcement
learning for linearly-solvable Markov decision processes. Our approach assumes
that the state space is partitioned, and the subtasks consist in moving between
the partitions. We represent value functions on several levels of abstraction,
and use the compositionality of subtasks to estimate the optimal values of the
states in each partition. The policy is implicitly defined on these optimal
value estimates, rather than being decomposed among the subtasks. As a
consequence, our approach can learn the globally optimal policy, and does not
suffer from the non-stationarity of high-level decisions. If several partitions
have equivalent dynamics, the subtasks of those partitions can be shared. If
the set of boundary states is smaller than the entire state space, our approach
can have significantly smaller sample complexity than that of a flat learner,
and we validate this empirically in several experiments.


Abstract:
  We consider the derivative nonlinear Schr\"odinger equation in one space
dimension, posed both on the line and on the circle. This model is known to be
completely integrable and $L^2$-critical with respect to scaling.
  The first question we discuss is whether ensembles of orbits with
$L^2$-equicontinuous initial data remain equicontinuous under evolution. We
prove that this is true under the restriction $M(q)=\int |q|^2 < 4\pi$. We
conjecture that this restriction is unnecessary.
  Further, we prove that the problem is globally well-posed for initial data in
$H^{1/6}$ under the same restriction on $M$. Moreover, we show that this
restriction would be removed by a successful resolution of our equicontinuity
conjecture.


Abstract:
  We introduce a new method permitting the analytical determination of
entanglement entropy (and related quantities) between configurations of a
quantum field, which is either free or in interaction with a classical source,
at two distinct spatial locations. We show how such a setup can be described by
a bipartite, continuous Gaussian system. This allows us to derive explicit and
exact formulas for the entanglement entropy, the mutual information and the
quantum discord, solely in terms of the Fourier-space power spectra of the
field. This contrasts with previous studies, which mostly rely on numerical
considerations. As an illustration, we apply our formalism to massless fields
in flat space, where exact expressions are derived that only involve the ratio
between the size of the regions over which the field is coarse-grained, and the
distance between these regions. In particular, we recover the well-known fact
that mutual information decays as the fourth power of this ratio at large
distances, as previously observed in numerical works. Our method leads to the
first analytical derivation of this result, and to an exact formula that also
applies to arbitrary distances. Finally, we determine the quantum discord and
find that it identically vanishes (unless coarse-graining is performed over
smeared spheres, in which case it obeys the same suppression at large distance
as mutual information).


Abstract:
  We present a planning and control framework for physics-based manipulation
under uncertainty. The key idea is to interleave robust open-loop execution
with closed-loop control. We derive robustness metrics through contraction
theory. We use these metrics to plan trajectories that are robust to both state
uncertainty and model inaccuracies. However, fully robust trajectories are
extremely difficult to find or may not exist for many multi-contact
manipulation problems. We separate a trajectory into robust and non-robust
segments through a minimum cost path search on a robustness graph. Robust
segments are executed open-loop and non-robust segments are executed with
model-predictive control. We conduct experiments on a real robotic system for
reaching in clutter. Our results suggest that the open and closed-loop approach
results in up to 35% more real-world success compared to open-loop baselines
and a 40% reduction in execution time compared to model-predictive control. We
show for the first time that partially open-loop manipulation plans generated
with our approach reach similar success rates to model-predictive control,
while achieving a more fluent/real-time execution. A video showing real-robot
executions can be found at https://youtu.be/rPOPCwHfV4g.


Abstract:
  This paper presents a personalized adaptive cruise control (PACC) design that
can learn driver behavior and adaptively control the semi-autonomous vehicle
(SAV) in the car-following scenario, and investigates its impacts on mixed
traffic. In mixed traffic where the SAV and human-driven vehicles share the
road, the SAV's driver can choose a PACC tuning that better fits the driver's
preferred driving behaviors. The individual driver's preferences are learned
through the inverse reinforcement learning (IRL) approach by recovering a
unique cost function from the driver's demonstrated driving data that best
explains the observed driving style. The proposed PACC design plans the motion
of the SAV by minimizing the learned unique cost function considering the short
preview information of the preceding human-driven vehicle. The results reveal
that the learned driver model can identify and replicate the personalized
driving behaviors accurately and consistently when following the preceding
vehicle in a variety of traffic conditions. Furthermore, we investigated the
impacts of the PACC with different drivers on mixed traffic by considering time
headway, gap distance, and fuel economy assessments. A statistical
investigation shows that the impacts of the PACC on mixed traffic vary among
tested drivers due to their intrinsic driving preferences.


Abstract:
  The quark contribution to the QCD pressure, $P_q$, is evaluated up to
next-to-leading order (NLO) within the renormalization group optimized
perturbation theory (RGOPT) resummation approach. To evaluate the complete QCD
pressure we simply add the perturbative NLO contribution from massless gluons
to the resummed $P_q$. Despite of this unsophisticated approximation our
results for $P = P_q +P_g$ at the central scale $M\sim 2\pi T$ show a
remarkable agreement with ab initio lattice predictions for $0.25 \lesssim T
\lesssim 1 \, {\rm GeV}$. We also show that by being imbued with RG properties,
the RGOPT produces a drastic reduction of the embarrassing remnant scale
dependence that plagues both standard thermal perturbative QCD and hard thermal
loop perturbation theory (HTLpt) applications.


Abstract:
  This paper studies time changes of Brownian motions by positive continuous
additive functionals. Under a certain regularity condition on the associated
Revuz measures, we prove that the resolvents of the time-changed Brownian
motions are locally H\"{o}lder continuous in the spatial components. We also
obtain lower bounds for the indice of the H\"{o}lder continuity.


Abstract:
  In this brief review, we present some cosmological models with a Hybrid Scale
Factor (HSF) in the framework of general relativity (GR). The hybrid scale
factor fosters an early deceleration as well as a late time acceleration and
mimics the present Universe. The dynamical aspects of different cosmological
models with HSF in the presence of different matter fields have been discussed.


Abstract:
  The simultaneous presence of liquid and gas in porous media increases flow
heterogeneity compared to saturated flows. However, so far the impact of
saturation on flow statistics and transport dynamics remained unclear. Here, we
develop a theoretical framework that captures the impact of flow reorganization
on the statistics of pore-scale fluid velocities, due to the presence of gas in
the pore space, which leads to the development of a highly-structured flow
field. Preferential flow is distributed spatially through the denoted backbone
and flow recirculation occurs in dead-end regions branching from it. This
induces a marked change in the scaling of the velocity PDF compared to the
saturated case, and a sharp transition to strongly anomalous transport. We
develop a transport model based on the continuous time random walk theory that
successfully predicts advective transport dynamics for all saturation degrees.
Our results provide a new modelling framework linking phase heterogeneity to
flow heterogeneity and to transport in unsaturated media.


Abstract:
  Text lines are important parts of handwritten document images and easier to
analyze by further applications. Despite recent progress in text line
detection, text line extraction from a handwritten document remains an unsolved
task. This paper proposes to use a fully convolutional network for text line
detection and energy minimization for text line extraction. Detected text lines
are represented by blob lines that strike through the text lines. These blob
lines assist an energy function for text line extraction. The detection stage
can locate arbitrarily oriented text lines. Furthermore, the extraction stage
is capable of finding out the pixels of text lines with various heights and
interline proximity independent of their orientations. Besides, it can finely
split the touching and overlapping text lines without an orientation
assumption. We evaluate the proposed method on VML-AHTE, VML-MOC, and
Diva-HisDB datasets. The VML-AHTE dataset contains overlapping, touching and
close text lines with rich diacritics. The VML-MOC dataset is very challenging
by its multiply oriented and skewed text lines. The Diva-HisDB dataset exhibits
distinct text line heights and touching text lines. The results demonstrate the
effectiveness of the method despite various types of challenges, yet using the
same parameters in all the experiments.