Abstract:
  How do galaxies transform from blue, star-forming spirals to red, quiescent
early-type galaxies? To answer this question, we analyzed a set of 26 gas-rich,
shocked post-starburst galaxies with Hubble Space Telescope (HST) imaging in B,
I, and H bands, and Sloan Digital Sky Survey (SDSS) i-band imaging of similar
depth but lower resolution. We found that post-starbursts in our sample have
intermediate morphologies between disk- and bulge-dominated (S\'ersic
n$=1.7^{+0.3}_{-0.0}$) and have red bulges, likely due to dust obscuration in
the cores.
  Majority of galaxies in our sample are more morphologically disturbed than
regular galaxies (88%, corresponding to >3$\sigma$ significance) when observed
with HST, with asymmetry and S\'ersic residual flux fraction being the most
successful measures of disturbance. Most disturbances are undetected at the
lower resolution of SDSS imaging. Although ~27% galaxies are clear merger
remnants, we found that disturbances in another ~30% of the sample are
internal, caused by small-scale perturbations or dust substructures rather than
tidal features, and require high-resolution imaging to detect. We found a
2.8$\sigma$ evidence that asymmetry features fade on timescales ~200 Myr, and
may vanish entirely after ~750 Myr, so we do not rule out a possible merger
origin of all post-starbursts given that asymmetric features may have already
faded. This work highlights the importance of small-scale disturbances,
detected only in high-resolution imaging, in understanding structural evolution
of transitioning galaxies.


Abstract:
  We propose a new coloring game on a graph, called the independence coloring
game, which is played by two players with opposite goals. The result of the
game is a proper coloring of vertices of a graph $G$, and Alice's goal is that
as few colors as possible are used during the game, while Bob wants to maximize
the number of colors. The game consists of rounds, and in round $i$, where
$i=1,2,,\ldots$, the players are taking turns in selecting a previously
unselected vertex of $G$ and giving it color $i$ (hence, in each round the
selected vertices form an independent set). The game ends when all vertices of
$G$ are selected (and thus colored), and the total number of rounds during the
game when both players are playing optimally with respect to their goals, is
called the independence game chromatic number, $\chi_{ig}(G)$, of $G$. In fact,
four different versions of the independence game chromatic number are
considered, which depend on who starts a game and who starts next rounds. We
prove that the new invariants lie between the chromatic number of a graph and
the maximum degree plus $1$, and characterize the graphs in which each of the
four versions of the game invariant equals $2$. We compare the versions of the
independence game chromatic number among themselves and with the classical game
chromatic number. In addition, we prove that the independence game chromatic
number of a tree can be arbitrarily large.


Abstract:
  The description of chiral quantum incompressible fluids by the W-infinity
symmetry can be extended from the edge, where it encompasses the conformal
field theory approach, to the non-conformal bulk. The two regimes are
characterized by excitations with different sizes, energies and momenta within
the disk geometry. In particular, the bulk quantities have a finite limit for
large droplets. We obtain analytic results for the radial shape of excitations,
the edge reconstruction phenomenon and the energy spectrum of density
fluctuations in Laughlin states.


Abstract:
  Deep reinforcement learning has been applied for a variety of wireless tasks,
which is however known with high training and inference complexity. In this
paper, we resort to deep deterministic policy gradient (DDPG) algorithm to
optimize predictive power allocation among K mobile users requesting video
streaming, which minimizes the energy consumption of the network under the
no-stalling constraint of each user. To reduce the sampling complexity and
model size of the DDPG, we exploit a kind of symmetric prior inherent in the
actor and critic networks: permutation invariant and equivariant properties, to
design the neural networks. Our analysis shows that the free model parameters
of the DDPG can be compressed by 2/K^2. Simulation results demonstrate that the
episodes required by the learning model with the symmetric prior to achieve the
same performance as the vanilla policy reduces by about one third when K = 10.


Abstract:
  Previous studies on nonspherical particle-fluid interaction were mostly
confined to elongated fiber-like particles, which were observed to induce
turbulence drag reduction. However, with the presence of tiny disk-like
particles how wall turbulence is modulated and whether drag reduction occurs
are still unknown. Motivated by those open questions, we performed two-way
coupled direct numerical simulations of inertialess spheroids in turbulent
channel flow by an Eulerian-Lagrangian approach. The additional stress accounts
for the feedback from inertialess spheroids on the fluid phase. The results
demonstrate that both rigid elongated fibers (prolate spheroids) and thin disks
(oblate spheroids) can lead to significant turbulence modulations and drag
reduction. However, the disk-induced drag reduction is less pronounced than
that of rigid fibers with the same volume fraction. Typical features of
drag-reduced flows by additives are observed in both flow statistics and
turbulence coherent structures. Moreover, in contrast to one-way simulations,
the two-way coupled results of spheroidal particles exhibit stronger
preferential alignments and lower rotation rates. At the end we propose a drag
reduction mechanism by inertialess spheroids and explain the different
performance for drag reduction by fibers and disks. We find that the spheroidal
particles weaken the quasistreamwise vortices through negative work and,
therefore, the Reynolds shear stress is reduced. However, the mean shear stress
generated by particles, which is shape-dependent, partly compensates for the
reduction of Reynolds shear stress and thus affects the efficiency of drag
reduction. The present study implies that tiny disk-like particles can be an
alternative drag reduction agent in wall turbulence.


Abstract:
  This work, presents a study of lifetime of carriers due to intrinsic
scattering mechanisms $viz.$ electron-electron (EEI), electron-phonon (EPI) and
phonon-phonon interactions (PPI) in a promising half-Heusler thermoelectric
FeVSb. Using the full-$GW$ method, the effect of EEI and temperature on the
valence and conduction band extrema and band gap are studied. The lifetime of
carriers with temperature are estimated at these band extrema. At 300 K,
estimated value of lifetime at VBM (CBM) is $\sim$1.91 x10$^{-14}s$ ($\sim$2.05
x10$^{-14}s$). The estimated ground state band gap considering EEI is $\sim$378
meV. Next, the effect of EPI on the lifetime of electrons and phonons with
temperature are discussed. The comparison of two electron lifetimes suggests
that EEI should be considered in transport calculations along with EPI. The
average acoustic, optical and overall phonon lifetimes due to EPI are studied
with temperature. Further, the effect of PPI is studied by computing average
phonon lifetime for acoustic and optical phonon branches. The lifetime of the
acoustic phonons are higher compared to optical phonons which indicates
acoustic phonons contribute more to lattice thermal conductivity
($\kappa_{ph}$). The comparison of phonon lifetime due to EPI and PPI suggests
that, above 500 K EPI is the dominant phonon scattering mechanism and cannot be
ignored in $\kappa_{ph}$ calculations. Lastly, a prediction of the power factor
and figure of merit of n-type and p-type FeVSb is made by considering the
temperature dependent carrier lifetime for the electronic transport terms. This
study shows the importance of considering EEI in electronic transport
calculations and EPI in phonon transport calculations. Our study is expected to
provide results to further explore the thermoelectric transport in this
material.


Abstract:
  In this paper, we estimate epidemiological data of the COVID-19 pandemic in
Hungary using only the daily number of hospitalized patients, and applying
well-known techniques from systems and control theory. We use a previously
published and validated compartmental model for the description of epidemic
spread. Exploiting the fact that an important subsystem of the model is linear,
first we compute the number of latent infected persons in time. Then an
estimate can be given for the number of people in other compartments. From
these data, it is possible to track the time dependent reproduction numbers via
a recursive least squares estimate. The credibility of the obtained results is
discussed using available data from the literature.


Abstract:
  We investigate the relationship between ideal membership of an operator and
its pieces relative to several canonical types of partitions of the entries of
its matrix representation with respect to a given orthonormal basis. Our main
theorems establish that if $T$ lies in an ideal $\mathcal{I}$, then $\sum P_n T
P_n$ (or more generally $\sum Q_n T P_n$) lies in the arithmetic mean closure
of $\mathcal{I}$ whenever $\{P_n\}$ (and also $\{Q_n\}$) is a sequence of
mutually orthogonal projections; and in any basis for which $T$ is a block band
matrix, in particular, when in Patnaik--Petrovic--Weiss universal block
tridiagonal form, then all the sub/super/main-block diagonals of $T$ are in
$\mathcal{I}$. And in particular, the principal ideal generated by this $T$ is
the finite sum of the principal ideals generated by each sub/super/main-block
diagonals.


Abstract:
  Modeling the arrival process to an Emergency Department (ED) is the first
step of all studies dealing with the patient flow within the ED. Many of them
focus on the increasing phenomenon of ED overcrowding, which is afflicting
hospitals all over the world. Since Discrete Event Simulation models are often
adopted with the aim to assess solutions for reducing the impact of this
problem, proper nonstationary processes are taken into account to reproduce
time-dependent arrivals. Accordingly, an accurate estimation of the unknown
arrival rate is required to guarantee the reliability of the results. In this
work, an integer nonlinear black-box optimization problem is solved to
determine the best piecewise constant approximation of the time-varying arrival
rate function, by finding the optimal partition of the 24 hours into a suitable
number of non equally spaced intervals. The black-box constraints of the
optimization problem make the feasible solutions satisfy proper statistical
hypotheses; these ensure the validity of the nonhomogeneous Poisson assumption
about the arrival process, commonly adopted in the literature, and prevent
mixing overdispersed data for model estimation. The cost function includes a
fit error term for the solution accuracy and a penalty term to select an
adequate degree of regularity of the optimal solution. To show the
effectiveness of this methodology, real data from one of the largest Italian
hospital EDs are used.


Abstract:
  We initiate the study of Dyson equations of perturbative QFT in AdS and their
consequences for large-N CFT. We show that the dressed one-particle AdS
propagator features wavefunction renormalization and operator mixing, giving
rise to finite corrections to OPE data. We show how the resummation of 1/N
effects in the CFT emerges from the dressing in AdS. When a boundary-to-bulk
propagator is dressed by propagators whose sum of conformal dimensions is lower
than the main dimension, it cannot map onto a CFT source following the standard
prescription; we relate this to an AdS/CFT version of particle instability. We
investigate the dressing of the two-particle propagator and obtain a conformal
Bethe-Salpeter equation for the conformal partial wave of a "bound state''
operator. In case of a ladder kernel, we briefly show that a bound state with
conformal dimension equal to the sum of its constituents plus a 1/N-suppressed
"binding energy'' emerges. Resummation of the Dyson equations is essential to
deriving these results.


Abstract:
  This research devoted to the low-resource Veps and Karelian languages.
Algorithms for assigning part of speech tags to words and grammatical
properties to words are presented in the article. These algorithms use our
morphological dictionaries, where the lemma, part of speech and a set of
grammatical features (gramset) are known for each word form. The algorithms are
based on the analogy hypothesis that words with the same suffixes are likely to
have the same inflectional models, the same part of speech and gramset. The
accuracy of these algorithms were evaluated and compared. 313 thousand Vepsian
and 66 thousand Karelian words were used to verify the accuracy of these
algorithms. The special functions were designed to assess the quality of
results of the developed algorithms. 92.4% of Vepsian words and 86.8% of
Karelian words were assigned a correct part of speech by the developed
algorithm. 95.3% of Vepsian words and 90.7% of Karelian words were assigned a
correct gramset by our algorithm. Morphological and semantic tagging of texts,
which are closely related and inseparable in our corpus processes, are
described in the paper.


Abstract:
  Magnetic resonance (MR) image acquisition is an inherently prolonged process,
whose acceleration has long been the subject of research. This is commonly
achieved by obtaining multiple undersampled images, simultaneously, through
parallel imaging. In this paper, we propose the Dual-Octave Network (DONet),
which is capable of learning multi-scale spatial-frequency features from both
the real and imaginary components of MR data, for fast parallel MR image
reconstruction. More specifically, our DONet consists of a series of
Dual-Octave convolutions (Dual-OctConv), which are connected in a dense manner
for better reuse of features. In each Dual-OctConv, the input feature maps and
convolutional kernels are first split into two components (ie, real and
imaginary), and then divided into four groups according to their spatial
frequencies. Then, our Dual-OctConv conducts intra-group information updating
and inter-group information exchange to aggregate the contextual information
across different groups. Our framework provides three appealing benefits: (i)
It encourages information interaction and fusion between the real and imaginary
components at various spatial frequencies to achieve richer representational
capacity. (ii) The dense connections between the real and imaginary groups in
each Dual-OctConv make the propagation of features more efficient by feature
reuse. (iii) DONet enlarges the receptive field by learning multiple
spatial-frequency features of both the real and imaginary components. Extensive
experiments on two popular datasets (ie, clinical knee and fastMRI), under
different undersampling patterns and acceleration factors, demonstrate the
superiority of our model in accelerated parallel MR image reconstruction.


Abstract:
  The Airborne Infrared Spectrometer (AIR-Spec) was commissioned during the
2017 total solar eclipse, when it observed five infrared coronal emission lines
from the Gulfstream V High-performance Instrumented Airborne Platform for
Environmental Research (GV HIAPER), a research jet owned by the National
Science Foundation (NSF) and operated by the National Center for Atmospheric
Research (NCAR). The second AIR-Spec research flight took place during the July
2, 2019 total solar eclipse across the south Pacific. The 2019 eclipse flight
resulted in seven minutes of observations, during which the instrument measured
all four of its target emission lines: S XI 1.393 $\mu$m, Si X 1.431 $\mu$m, S
XI 1.921 $\mu$m, and Fe IX 2.853 $\mu$m. The 1.393 $\mu$m line, half of a
density-sensitive S XI line pair, was detected for the first time. The 2017
AIR-Spec detection of Fe IX was confirmed and the first observations were made
of the Fe IX intensity as a function of solar radius. Observations of S XI and
Si X were used to estimate the temperature and density above the east and west
limbs, the subject of a future paper. Atmospheric absorption was significant in
the 2019 data, and atmospheric modeling was required to extract accurate line
intensities. Telluric absorption features were used to calibrate the wavelength
mapping, instrumental broadening, and throughput of the instrument. AIR-Spec
underwent significant upgrades in preparation for the 2019 eclipse flight. The
thermal background was reduced by a factor of 30, providing a 5.5x improvement
in signal-to-noise ratio, and the pointing stability was improved by a factor
of five to $<$10 arcsec RMS after image co-alignment. In addition, two imaging
artifacts were identified and resolved, making the 2019 data easier to
interpret and improving the spectral resolution by up to 50%.


Abstract:
  We prove that the Newton product of efficient polynomial projectors is still
efficient. Various polynomial approximation theorems are established involving
Newton product projectors on spaces of holomorphic functions on a neighborhood
of a regular compact set, on spaces of entire functions of given growth and on
spaces of differentiable functions. Efficient explicit new projectors are
presented.


Abstract:
  Neutrinos drive core-collapse supernovae, launch outflows from neutron star
merger accretion disks, and set the ratio of protons to neutrons in ejecta from
both systems that generate heavy elements in the universe. Neutrinos of
different flavors interact with matter differently, and much recent work has
suggested that fast flavor instabilities are likely ubiquitous in both systems,
but the final flavor content after the instability saturates has not been well
understood. In this work we present particle-in-cell calculations which follow
the evolution of all flavors of neutrinos and antineutrinos through saturation
and kinematic decoherence. We conduct one-dimensional three-flavor simulations
of neutrino quantum kinetics to demonstrate the outcome of this instability in
a few example cases. We demonstrate the growth of both axially symmetric and
asymmetric modes whose wavelength and growth rate match predictions from linear
stability analysis. Finally, we vary the number density, flux magnitude, and
flux direction of the neutrinos and antineutrinos and demonstrate that these
factors modify both the growth rate and post-saturation neutrino flavor
abundances. Weak electron lepton number (ELN) crossings in these simulations
produce both slow growth of the instability and little difference between the
flavor abundances in the initial and final states. In all of these calculations
the same number of neutrinos and antineutrinos change flavor, making the least
abundant between them the limiting factor for post-saturation flavor change.
Many more simulations and multi-dimensional simulations are needed to fully
probe the parameter space of the initial conditions.


Abstract:
  We introduce a non-reversible generalization of the Vertex-Reinforced Jump
Process (VRJP), which we call *-Vertex-Reinforced Jump Process (*-VRJP). It can
be seen as the continuous-time counterpart of the *-Edge-Reinforced Random Walk
(*-ERRW) (see [2,4]), which is itself a non-reversible generalization of the
original ERRW introduced by Coppersmith and Diaconis in 1986. In contrast to
the classical VRJP, the *-VRJP is not exchangeable after time-change, but we
show that with some appropriate randomization of the initial local time, it
becomes partially exchangeable after time-change. We provide a representation
of the "randomized" *-VRJP as mixture of Markov jump processes with an explicit
mixing measure, and show that the "non-randomized" *-VRJP can be written as a
mixture of conditioned Markov processes. We also give a counterpart of the
$\beta$-random potential introduced in [25] for the VRJP, which leads to new
identities between integrals.


Abstract:
  Cross-lingual entity alignment, which aims to precisely connect the same
entities in different monolingual knowledge bases (KBs) together, often suffers
challenges from feature inconsistency to sequence context unawareness. This
paper presents a dual adversarial learning framework for cross-lingual entity
alignment, DAEA, with two original contributions. First, in order to address
the structural and attribute feature inconsistency between entities in two
knowledge graphs (KGs), an adversarial kernel embedding technique is proposed
to extract graph-invariant information in an unsupervised manner, and project
two KGs into the common embedding space. Second, in order to further improve
successful rate of entity alignment, we propose to produce multiple random
walks through each entity to be aligned and mask these entities in random
walks. With the guidance of known aligned entities in the context of multiple
random walks, an adversarial knowledge translation model is developed to fill
and translate masked entities in pairwise random walks from two KGs. Extensive
experiments performed on real-world datasets show that DAEA can well solve the
feature inconsistency and sequence context unawareness issues and significantly
outperforms thirteen state-of-the-art entity alignment methods.


Abstract:
  Graphene-based nanofolds (GNFs) are edge-connected 2D stacked monolayers
originated from single-layer graphene. Graphene-based nanoscrolls (GNSs) are
nanomaterials with geometry resembling graphene layers rolled up into a spiral
(papyrus-like) form. Both GNSs and GNFs structures induce significant changes
in the mechanical and optoelectronic properties of single-layer graphene,
aggregating new functionalities in carbon-based applications. Here, we carried
our fully atomistic reactive (ReaxFF) molecular dynamics simulations to study
the self-folding and self-scrolling of edge-deformed graphene sheets. We
adopted initial armchair edge-scrolled graphene (AESG($\phi$,$\theta$))
structures with similar (or different) twist angles ($\phi$,$\theta$) in each
edge, mimicking the initial configuration that was experimentally developed to
form biscrolled sheets. Results showed that AESG(0,$2\pi$) and
AESG(2$\pi$,$2\pi$) evolved to single-folded and two-folded fully stacked
morphologies, respectively. As a general trend, for twist angles higher than
$2\pi$, the self-deformation process of AESG morphologies yields GNSs. Edge
twist angles lower than $\pi$ are not enough for triggering the
self-deformation processes. In the AESG(0,3$\pi$) and AESG(3$\pi$,3$\pi$)
cases, after a relaxation period, their morphology transition towards GNS
occurred rapidly. In the AESG(3$\pi$,3$\pi$) dynamics, a metastable biscroll
was formed by the interplay between the left- and right-sided partial scrolling
in forming a unique GNS. At high-temperature perturbations, the edge folding
and scrolling transitions to GNFs and GNSs occurred within the ultrafast
period. Remarkably, the AESG(2$\pi$,3$\pi$) evolved to a dual state that
combines folded and scrolled structures in a temperature-independent process.


Abstract:
  Performing reliability analysis on complex systems is often computationally
expensive. In particular, when dealing with systems having high input
dimensionality, reliability estimation becomes a daunting task. A popular
approach to overcome the problem associated with time-consuming and expensive
evaluations is building a surrogate model. However, these computationally
efficient models often suffer from the curse of dimensionality. Hence, training
a surrogate model for high-dimensional problems is not straightforward.
Henceforth, this paper presents a framework for solving high-dimensional
reliability analysis problems. The basic premise is to train the surrogate
model on a low-dimensional manifold, discovered using the active subspace
algorithm. However, learning the low-dimensional manifold using active subspace
is non-trivial as it requires information on the gradient of the response
variable. To address this issue, we propose using sparse learning algorithms in
conjunction with the active subspace algorithm; the resulting algorithm is
referred to as the sparse active subspace (SAS) algorithm. We project the
high-dimensional inputs onto the identified low-dimensional manifold identified
using SAS. A high-fidelity surrogate model is used to map the inputs on the
low-dimensional manifolds to the output response. We illustrate the efficacy of
the proposed framework by using three benchmark reliability analysis problems
from the literature. The results obtained indicate the accuracy and efficiency
of the proposed approach compared to already established reliability analysis
methods in the literature.


Abstract:
  Electronic Health Record Systems (EHR-S) are commonly developed in monolithic
architectures. This architectural style presents greater complexity and demands
more effort when we think of interoperability. A solution proposal is the
creation of Microservices that use HL7 FHIR as an interoperability strategy. In
this sense, it is presented the development of a prototype, based on a
microservices architecture, to act in a real scenario of Patient Navigation
(PN). The problem was subdivided into 3 steps: definition of architecture,
development and construction of an interface to simulate the role of the
navigator. The Patient and Appointment microservices are capable of synchronous
communication to query and record information. In general, the implemented
architectural style not only isolates information domains but can receive data
from multiple sources while maintaining essential functionality. This type of
approach plays a crucial role in a hospital environment, specifically in PN,
highlighting the importance of the standard and expanding the possibilities for
further research to be conducted.


Abstract:
  We measure diffusion of organic molecules located a few nanometers from the
diamond surface. To study molecular diffusion, we perform local detection of
nuclear magnetic resonance, based on single shallow Nitrogen-Vacancy (NV)
centers in diamond. Specifically, we demonstrate measurements of translational
diffusion coefficient of phospholipids in an artificial cell membrane by
employing correlation spectroscopy. An analysis of correlation decay curves
using different diffusion models shows, that a simple 2D diffusion model gives
satisfactory diffusion coefficients, although the choice of the model affects
the extracted numbers. We find significant differences among the diffusion
coefficients measured by different single NV centers, which we attribute to
local heterogeneities of the lipid layers, likely caused by the supporting
diamond substrate.


Abstract:
  Steel pipes are widely used in high-risk and high-pressure scenarios such as
oil, chemical, natural gas, shale gas, etc. If there is some defect in steel
pipes, it will lead to serious adverse consequences. Applying object detection
in the field of deep learning to pipe weld defect detection and identification
can effectively improve inspection efficiency and promote the development of
industrial automation. Most predecessors used traditional computer vision
methods applied to detect defects of steel pipe weld seams. However,
traditional computer vision methods rely on prior knowledge and can only detect
defects with a single feature, so it is difficult to complete the task of
multi-defect classification, while deep learning is end-to-end. In this paper,
the state-of-the-art single-stage object detection algorithm YOLOv5 is proposed
to be applied to the field of steel pipe weld defect detection, and compared
with the two-stage representative object detection algorithm Faster R-CNN. The
experimental results show that applying YOLOv5 to steel pipe weld defect
detection can greatly improve the accuracy, complete the multi-classification
task, and meet the criteria of real-time detection.


Abstract:
  The co-maximal subgroup graph $\Gamma(G)$ of a group $G$ is a graph whose
vertices are non-trivial proper subgroups of $G$ and two vertices $H$ and $K$
are adjacent if $HK=G$. In this paper, we continue the study of $\Gamma(G)$,
especially when $\Gamma(G)$ has isolated vertices. We define a new graph
$\Gamma^*(G)$, which is obtained by removing isolated vertices from
$\Gamma(G)$. We characterize when $\Gamma^*(G)$ is connected, a complete graph,
star graph, has an universal vertex etc. We also find various graph parameters
like diameter, girth, bipartiteness etc. in terms of properties of $G$.


Abstract:
  GPUReplay is a novel way for deploying GPU-accelerated computation on mobile
and embedded devices. It addresses high complexity of a modern GPU stack for
deployment ease and security. The idea is to record GPU executions on the full
GPU stack ahead of time and replay the executions on new input at run time. We
address key challenges towards making GR feasible, sound, and practical to use.
The resultant replayer is a drop-in replacement of the original GPU stack. It
is tiny (50 KB of executable), robust (replaying long executions without
divergence), portable (running in a commodity OS, in TEE, and baremetal), and
quick to launch (speeding up startup by up to two orders of magnitude). We show
that GPUReplay works with a variety of integrated GPU hardware, GPU APIs, ML
frameworks, and 33 neural network (NN) implementations for inference or
training. The code is available at https://github.com/bakhi/GPUReplay.


Abstract:
  In this paper, a mixed-integer linear programming formulation for the problem
of obtaining task-relevant, multi-resolution, graph abstractions for
resource-constrained agents is presented. The formulation leverages concepts
from information-theoretic signal compression, specifically the information
bottleneck (IB) method, to pose a graph abstraction problem as an optimal
encoder search over the space of multi-resolution trees. The abstractions
emerge in a task-relevant manner as a function of agent information-processing
constraints, and are not provided to the system a priori. We detail our
formulation and show how the problem can be realized as an integer linear
program. A non-trivial numerical example is presented to demonstrate the
utility in employing our approach to obtain hierarchical tree abstractions for
resource-limited agents.


Abstract:
  In software reverse engineering, decompilation is the process of recovering
source code from binary files. Decompilers are used when it is necessary to
understand or analyze software for which the source code is not available.
Although existing decompilers commonly obtain source code with the same
behavior as the binaries, that source code is usually hard to interpret and
certainly differs from the original code written by the programmer. Massive
codebases could be used to build supervised machine learning models aimed at
improving existing decompilers. In this article, we build different
classification models capable of inferring the high-level type returned by
functions, with significantly higher accuracy than existing decompilers. We
automatically instrument C source code to allow the association of binary
patterns with their corresponding high-level constructs. A dataset is created
with a collection of real open-source applications plus a huge number of
synthetic programs. Our system is able to predict function return types with a
79.1% F1-measure, whereas the best decompiler obtains a 30% F1-measure.
Moreover, we document the binary patterns used by our classifier to allow their
addition in the implementation of existing decompilers.


Abstract:
  We study mappings that satisfy the inverse Poletskii-type inequality in a
domain of Euclidean space. It was proved that such mappings have a continuous
extension to the boundary under some conditions on the geometry of the given
and the mapped domains if the majorant responsible for the distortion of the
families of paths has finite integrals over the set of spheres of positive
linear measure.


Abstract:
  Multi-modal delivery systems are a promising solution to the challenges posed
by the increasing demand of e-commerce. Due to the potential benefit drones can
have on logistics networks such as delivery systems, some countries have taken
steps towards integrating drones into their airspace. In this paper we aim to
quantify this potential by developing a mathematical model for a multi-modal
delivery system composed of trucks and drones. We propose an optimization
formulation that can be efficiently solved in order to design socially-optimal
routing and allocation policies. We incorporate both societal cost in terms of
road congestion and parcel delivery latency in our formulation. Our model is
able to quantify the effect drones have on mitigating road congestion, and can
solve for the path routing needed to minimize the chosen objective. To
accurately capture the effect of stopping trucks on road latency, we use SUMO
simulations and derive a mathematical latency function for roads shared by
trucks and cars. Based on this, we show that the proposed framework is
computationally feasible to scale due to its reliance on convex quadratic
optimization techniques.


Abstract:
  The size of convective cores remains uncertain, despite its substantial
influence on stellar evolution, and thus on stellar ages. The seismic modeling
of young subgiants can be used to obtain indirect constraints on the core
structure during main sequence, thanks to the high probing potential of mixed
modes. We selected the young subgiant KIC10273246, observed by Kepler, based on
its mixed-mode properties. We thoroughly modeled this star, with the aim of
placing constraints on the size of its main sequence convective core. We first
extracted the parameters of the oscillation modes of the star using the full
Kepler data set. To overcome the challenges posed by the seismic modeling of
subgiants, we proposed a method which is specifically tailored for subgiants
with mixed modes and consists in a nested optimization. We then applied this
method to perform a detailed seismic modeling of KIC10273246. We obtained
models that show good statistical agreements with the observations, both
seismic and non-seismic. We showed that including core overshooting in the
models significantly improves the quality of the seismic fit, optimal models
being found for $\alpha_{\mathrm{ov}} = 0.15$. Higher amounts of core
overshooting strongly worsen the agreement with the observations and are thus
firmly ruled out. We also found that having access to two g-dominated mixed
modes in young subgiants allows us to place stronger constraints on the
gradient of molecular weight in the core and on the central density. This study
confirms the high potential of young subgiants with mixed modes to investigate
the size of main-sequence convective cores. It paves the way for a more general
study including the subgiants observed with Kepler, TESS, and eventually PLATO.


Abstract:
  In this work, we explore the connection between the critical curves
("shadows") and the quasinormal mode frequencies (in the eikonal limit) of Kerr
black holes. This mapping has been previously established for non-rotating
black holes. We show that, the shadow seen by an distant observer at a given
inclination angle, can be mapped to a family of quasinormal modes with
$m/(\ell+1/2)$ bounded within certain range, where $m$ is is azimuthal node
number and $\ell$ is the angular node number. We discuss the possibility of
testing such relation with space-borne gravitational wave detectors and the
next-generation Event Horizon Telescope.


Abstract:
  The lack of equitable access to radiotherapy linear accelerators (LINACs) is
a substantial barrier to cancer care in Low and Middle-Income Countries
(LMICs). Aside from the issue of cost, there are also issues of robustness of
state-of-the-art LINACs in LMICs, which are subject to mechanical and
electrical breakdowns, which can result in downtimes ranging from days to
months. Recent research has identified disparities in failure frequency and
downtimes between LMICs (Nigeria, Botswana) and a High-Income Countries (HIC,
the UK), and highlighted the need for additional data and study particularly
relating to Multi-Leaf Collimators (MLCs). This study analyses data from 14
Indonesian Hospitals with a total of 19 LINACs and shows the pathways to
failure of radiotherapy LINACs and frequency of breakdowns with an additional
focus on the Multi-Leaf Collimator (MLC) subsystem. We found that LINACs
throughout Indonesia are out of operation for 7 times longer than HICs and the
Mean Time Between Failures of a LINAC in Indonesia is 341.58 Hours, or about 14
days. Furthermore, of the LINACs with an MLC fitted, $59.02^{+1.98}_{-1.61}$%
of mechanical faults are due to the MLC and $57.14^{+0.78}_{-1.27}$% of cases
requiring a replacement component are related to the MLC. These results outline
the need to reassess the current generation of RT LINACs and ultimately work
towards guiding future designs to be robust enough for all environments.


Abstract:
  The performance demands of future particle-physics experiments investigating
the high-energy frontier pose a number of new challenges, forcing us to find
improved solutions for the detection, identification, and measurement of
final-state particles in subnuclear collisions. One such challenge is the
precise measurement of muon momentum at very high energy, where an estimate of
the curvature provided by conceivable magnetic fields in realistic detectors
proves insufficient for achieving good momentum resolution when detecting,
e.g., a narrow, high mass resonance decaying to a muon pair.
  In this work we show the feasibility of an entirely new avenue for the
measurement of the energy of muons based on their radiative losses in a dense,
finely segmented calorimeter. This is made possible by exploiting spatial
information of the clusters of energy from radiated photons in a regression
task. The use of a task-specific deep learning architecture based on
convolutional layers allows us to treat the problem as one akin to image
reconstruction, where images are constituted by the pattern of energy released
in successive layers of the calorimeter. A measurement of muon energy with
better than 20% relative resolution is shown to be achievable for ultra-TeV
muons.


Abstract:
  Under the global COVID-19 crisis, developing robust diagnosis algorithm for
COVID-19 using CXR is hampered by the lack of the well-curated COVID-19 data
set, although CXR data with other disease are abundant. This situation is
suitable for vision transformer architecture that can exploit the abundant
unlabeled data using pre-training. However, the direct use of existing vision
transformer that uses the corpus generated by the ResNet is not optimal for
correct feature embedding. To mitigate this problem, we propose a novel vision
Transformer by using the low-level CXR feature corpus that are obtained to
extract the abnormal CXR features. Specifically, the backbone network is
trained using large public datasets to obtain the abnormal features in routine
diagnosis such as consolidation, glass-grass opacity (GGO), etc. Then, the
embedded features from the backbone network are used as corpus for vision
transformer training. We examine our model on various external test datasets
acquired from totally different institutions to assess the generalization
ability. Our experiments demonstrate that our method achieved the state-of-art
performance and has better generalization capability, which are crucial for a
widespread deployment.


Abstract:
  We consider a model of a binary mixture of two immiscible compressible
fluids. We propose a numerical scheme and discuss its basic properties:
Stability, consistency, convergence. The convergence is established via the
method of generalized weak solutions combined with the weak-strong uniqueness
principle.


Abstract:
  In an effort to explore high-throughput processing of microscopic image data,
a method based on deep convolutional neural network is proposed. The
state-of-the-art computer vision algorithm, Faster R-CNN, was trained for the
detection of iron (II) phthalocyanines on Se-terminated Au(111) platform
resolved by scanning probe microscopy. The construction of the feature pyramid
enables the multi-scale molecule detection in images of different scales from
10 nm to 50 nm. After the detection, the orientation of each molecule is
measured by a following program. Based on the statistical distribution of the
orientation angles, the preferred adsorption configurations of iron (II)
phthalocyanine on the platform are revealed. This method yields high accuracy
and recall with F1 score close to 1 after optimization of hyperparameters and
training. It is expected to be a feasible solution in the scenarios where
autonomous and high-throughput processing of microscopic image data is needed.


Abstract:
  The conformal bridge transformation (CBT) is reviewed in the light of the
$\mathcal{PT}$ symmetry. Originally, the CBT was presented as a non-unitary
transformation (a complex canonical transformation in the classical case) that
relates two different forms of dynamics in the sense of Dirac. Namely, it maps
the asymptotically free form into the harmonically confined form of dynamics
associated with the $\mathfrak{so}(2,1)\cong \mathfrak{sl}(2,{\mathbb R})$
conformal symmetry. However, as the transformation relates the non-Hermitian
operator $i\hat{D}$, where $\hat{D}$ is the generator of dilations, with the
compact Hermitian generator $\hat{\mathcal{J}}_0$ of the
$\mathfrak{sl}(2,{\mathbb R})$ algebra, the CBT generator can be associated
with a $\mathcal{PT}$-symmetric metric. In this work we review the applications
of this transformation for one- and two-dimensional systems, as well as for
systems on a cosmic string background, and for a conformally extended charged
particle in the field of Dirac monopole. We also compare and unify the CBT with
the Darboux transformation. The latter is used to construct
$\mathcal{PT}$-symmetric solutions of the equations of the KdV hierarchy with
the properties of extreme waves. As a new result, by using a modified CBT we
relate the one-dimensional $\mathcal{PT}$-regularized asymptotically free
conformal mechanics model with the $\mathcal{PT}$-regularized version of the de
Alfaro, Fubini and Furlan system.


Abstract:
  This paper presents a framework for solving the pure-state preparation
problem using numerical optimal control. As an example, we consider the case
where a number of qubits are dispersively coupled to a readout cavity. We model
open system quantum dynamics using the Markovian Lindblad master equation,
driven by external control pulses. The main result of this paper develops a
basis of density matrices (a parameterization) where each basis element is a
density matrix itself. Utilizing a specific objective function, we show how an
ensemble of the basis elements can be used as a single initial state throughout
the optimization process - independent of the system dimension. We apply the
general framework to the specific application of ground-state reset of one and
two qubits coupled to a readout cavity.


Abstract:
  The $J_1$-$J_2$ quantum spin sawtooth chain is a paradigmatic one-dimensional
frustrated quantum spin system exhibiting unconventional ground-state and
finite-temperature properties. In particular, it exhibits a flat energy band of
one-magnon excitations accompanied by an enhanced magnetocaloric effect for two
singular ratios of the basal interactions $J_1$ and the zigzag interactions
$J_2$. In our paper, we demonstrate that one can drive the spin system into a
flat-band scenario by applying an appropriate electric field, thus overcoming
the restriction of fine-tuned exchange couplings $J_1$ and $J_2$ and allowing
one to tune more materials towards flat-band physics, that is to show a
macroscopic magnetization jump when crossing the magnetic saturation field, a
residual entropy at zero temperature as well as an enhanced magnetocaloric
effect. While the magnetic field acts on the spin system via the ordinary
Zeeman term, the coupling of an applied electric field with the spins is given
by the sophisticated Katsura-Nagaosa-Balatsky (KNB) mechanism, where the
electric field effectively acts as a Dzyaloshinskii-Moriya spin-spin
interaction. The resulting novel features are corresponding reciprocal effects:
We find a magnetization jump driven by the electric field as well as a jump of
the electric polarization driven by the magnetic field, i.e.\ the system
exhibits an extraordinarily strong magnetoelectric effect. Moreover, in analogy
to the enhanced magnetocaloric effect the system shows an enhanced
electrocaloric effect.


Abstract:
  The thermal properties are one of the key parameters to control phase purity
and microstructure of polycrystalline materials. The melting point of the
iron-based BaFe2As2 superconductor (Ba122), which foresees high-field
applications, remains controversial. In this work,
thermogravimetry-differential scanning calorimetry measurements (TG-DSC) of
undoped and Co-doped Ba122 were carried out. Mixtures of elemental metals and
pre-reacted Ba122 powders were prepared to investigate the thermal responses
during in situ and ex situ synthesis routes, respectively. In addition, the
phases and microstructures of the quenched samples were evaluated to elucidate
the observed exothermic/endothermic peaks. Our results suggest that the melting
point of Ba122 is ~1300{\deg}C.


Abstract:
  In the field of aesthetic design, log-aesthetic curves have a significant
role to meet the high industrial requirements. In this paper, we propose a new
interactive $G^1$ Hermite interpolation method based on the algorithm of
Yoshida et al. with a minor boundary condition. In this novel approach, we
compute an extended log-aesthetic curve segment that may include inflection
point (S-shaped curve) or cusp. The curve segment is defined by its endpoints,
a tangent vector at the first point, and a tangent direction at the second
point. The algorithm also determines the shape parameter of the log-aesthetic
curve based on the length of the first tangent that provides control over the
curvature of the first point and makes the method capable of joining
log-aesthetic curve segments with $G^2$ continuity.


Abstract:
  Transformers have attracted increasing interests in computer vision, but they
still fall behind state-of-the-art convolutional networks. In this work, we
show that while Transformers tend to have larger model capacity, their
generalization can be worse than convolutional networks due to the lack of the
right inductive bias. To effectively combine the strengths from both
architectures, we present CoAtNets(pronounced "coat" nets), a family of hybrid
models built from two key insights: (1) depthwise Convolution and
self-Attention can be naturally unified via simple relative attention; (2)
vertically stacking convolution layers and attention layers in a principled way
is surprisingly effective in improving generalization, capacity and efficiency.
Experiments show that our CoAtNets achieve state-of-the-art performance under
different resource constraints across various datasets: Without extra data,
CoAtNet achieves 86.0% ImageNet top-1 accuracy; When pre-trained with 13M
images from ImageNet-21K, our CoAtNet achieves 88.56% top-1 accuracy, matching
ViT-huge pre-trained with 300M images from JFT-300M while using 23x less data;
Notably, when we further scale up CoAtNet with JFT-3B, it achieves 90.88% top-1
accuracy on ImageNet, establishing a new state-of-the-art result.


Abstract:
  The decay $D^{+}_{s}\rightarrow \pi^{+}\pi^{+}\pi^{-}\eta$ is observed for
the first time, using $e^+e^-$ collision data corresponding to an integrated
luminosity of 6.32 fb$^{-1}$, collected by the BESIII detector at
center-of-mass energies between 4.178 and 4.226 GeV. The absolute branching
fraction for this decay is measured to be $\mathcal{B}(D^+_s \to \pi^+ \pi^+
\pi^- \eta) = (3.12\pm0.13_{\rm stat.}\pm0.09_{\rm syst.})$%. The first
amplitude analysis of this decay reveals the sub-structures in
$D^{+}_{s}\rightarrow \pi^{+}\pi^{+}\pi^{-}\eta$ and determines the relative
fractions and the phases among these sub-structures. The dominant intermediate
process is $D^{+}_{s}\rightarrow a_1(1260)^+ \eta, a_1(1260)^+ \rightarrow
\rho(770)^0\pi^+$ with a branching fraction of $(1.73 \pm 0.14_{\rm stat.} \pm
0.08_{\rm syst.})$%. We also observe the W-annihilation process
$D^{+}_{s}\rightarrow a_0(980)^+\rho(770)^0$, $a_0(980)^+ \to \pi^+ \eta$ with
a branching fraction of $(0.21\pm0.08_{\rm stat.}\pm0.05_{\rm syst.})$%, which
is larger than the branching fractions of other measured pure W-annihilation
decays by one order of magnitude.


Abstract:
  Conversational search systems, such as Google Assistant and Microsoft
Cortana, enable users to interact with search systems in multiple rounds
through natural language dialogues. Evaluating such systems is very challenging
given that any natural language responses could be generated, and users
commonly interact for multiple semantically coherent rounds to accomplish a
search task. Although prior studies proposed many evaluation metrics, the
extent of how those measures effectively capture user preference remains to be
investigated. In this paper, we systematically meta-evaluate a variety of
conversational search metrics. We specifically study three perspectives on
those metrics: (1) reliability: the ability to detect "actual" performance
differences as opposed to those observed by chance; (2) fidelity: the ability
to agree with ultimate user preference; and (3) intuitiveness: the ability to
capture any property deemed important: adequacy, informativeness, and fluency
in the context of conversational search. By conducting experiments on two test
collections, we find that the performance of different metrics varies
significantly across different scenarios whereas consistent with prior studies,
existing metrics only achieve a weak correlation with ultimate user preference
and satisfaction. METEOR is, comparatively speaking, the best existing
single-turn metric considering all three perspectives. We also demonstrate that
adapted session-based evaluation metrics can be used to measure multi-turn
conversational search, achieving moderate concordance with user satisfaction.
To our knowledge, our work establishes the most comprehensive meta-evaluation
for conversational search to date.


Abstract:
  Gas morphology and kinematics in the Milky Way contain key information for
understanding the formation and evolution of our Galaxy. We present a high
resolution hydrodynamical simulation based on a realistic barred Milky Way
potential constrained by recent observations. Our model can reproduce most
features in the observed longitude-velocity diagram, including the Central
Molecular Zone, the Near and Far 3-kpc arms, the Molecular Ring, and the spiral
arm tangents. It can also explain the non-circular motions of masers obtained
by the recent BeSSeL2 survey. The central gas kinematics are consistent with a
mass of $6.9\times10^8\; {\rm M}_{\odot}$ in the Nuclear Stellar Disk. Our
model predicts the formation of an elliptical gaseous ring surrounding the bar,
which is composed of the 3-kpc arms, Norma arm, and the bar-spiral interfaces.
This ring is similar to those "inner" rings in some Milky Way analogs with a
boxy/peanut-shaped bulge. The kinematics of gas near the solar neighbourhood
are governed by the Local arm, which is induced by the four major stellar
spiral arms. The bar pattern speed constrained by our gas model is $37.5-40\;
{\rm km}\;{\rm s}^{-1}\;{\rm kpc}^{-1}$, corresponding to a corotation radius
of $R_{\rm CR}=6.0-6.4\;{\rm kpc}$. The rotation curve of our model rises
gently within the central $\sim5\;{\rm kpc}$, which is significantly less steep
than those predicted by modern zoom-in cosmological simulations such as Auriga.


Abstract:
  We study the critical level statistics at the many-body localization (MBL)
transition region in random spin systems. By employing the inter-sample
randomness as indicator, we manage to locate the MBL transition point in both
orthogonal and unitary models. We further count the $n$-th order gap ratio
distributions at the transition region up to $n=4$, and find they fit well with
the short-range plasma model (SRPM) with inverse temperature $\beta =1$ for
orthogonal model and $\beta =2$ for unitary. These critical level statistics
are argued to be universal by comparing results from systems both with and
without total $S_{z}$ conservation. We also point out that these critical
distributions can emerge from the spectrum of a Poisson ensemble, which
indicates the thermal-MBL transition point is more affected by the MBL phase
rather than thermal phase.


Abstract:
  Near-field radio holography is a common method for measuring and aligning
mirror surfaces for millimeter and sub-millimeter telescopes. In instruments
with more than a single mirror, degeneracies arise in the holography
measurement, requiring multiple measurements and new fitting methods. We
present HoloSim-ML, a Python code for beam simulation and analysis of radio
holography data from complex optical systems. This code uses machine learning
to efficiently determine the position of hundreds of mirror adjusters on
multiple mirrors with few micron accuracy. We apply this approach to the
example of the Simons Observatory 6m telescope.


Abstract:
  Among fundamental problems in the context of distributed computing by
autonomous mobile entities, one of the most representative and well studied is
{\sc Point Convergence}: given an arbitrary initial configuration of identical
entities, disposed in the Euclidean plane, move in such a way that, for all
$\eps>0$, a configuration in which the separation between all entities is at
most $\eps$ is eventually reached and maintained.
  The problem has been previously studied in a variety of settings, including
full visibility, exact measurements (like distances and angles), and
synchronous activation of entities. Our study concerns the minimal assumptions
under which entities, moving asynchronously with limited and unknown visibility
range and subject to limited imprecision in measurements, can be guaranteed to
converge in this way.
  We present an algorithm that solves {\sc Point Convergence}, for entities in
the plane, in such a setting, provided the degree of asynchrony is bounded:
while any one entity is active, any other entity can be activated at most $k$
times, for some arbitrarily large but fixed $k$. This provides a strong
positive answer to a decade old open question posed by Katreniak.
  We also prove that in a comparable setting that permits unbounded asynchrony,
{\sc Point Convergence} in the plane is impossible, contingent on the natural
assumption that algorithms maintain the (visible) connectivity among entities
present in the initial configuration. This variant, that we call {\sc Cohesive
Convergence}, serves to distinguish the power of bounded and unbounded
asynchrony in the control of autonomous mobile entities, settling at the same
time a long-standing question whether in the Euclidean plane synchronously
scheduled entities are more powerful than asynchronously scheduled entities.


Abstract:
  We present a statistical analysis of more than two thousand bipolar
electrostatic solitary waves (ESW) collected from ten quasi-perpendicular
Earth's bow shock crossings by Magnetospheric Multiscale spacecraft. We
developed and implemented a correction procedure for reconstruction of actual
electric fields, velocities, and other properties of ESW from measurements,
whose spatial scales are typically comparable with or smaller than spatial
distance between voltage-sensitive probes. We determined the optimal ratio
between frequency response factors of axial and spin plane antennas to be
around 1.65/1.8. We found that more than 95\% of the ESW in the Earth's bow
shock are of negative polarity and present an in depth analysis of properties
of these ESW. They have spatial scales of about 10--100 m that is within a
range of $\lambda_{D}$ to $10\lambda_{D}$, amplitudes typically below a few
Volts that is below 0.1 of local electron temperature, and velocities below a
few hundreds km/s in spacecraft and plasma rest frames that is on the order of
local ion-acoustic speed. The spatial scales of ESW are distinctly correlated
with local Debye length $\lambda_{D}$. ESW with amplitudes of 5--30 V or
0.1--0.3 Te have the occurrence rate of a few percent. The ESW have electric
fields generally oblique to local magnetic field and propagate highly oblique
to shock normal ${\bf N}$; more than 80\% of ESW propagate within 30$^{\circ}$
of the shock plane. In the shock plane, ESW typically propagate within a few
tens of degrees of local magnetic field projection ${\bf B}_{\rm LM}$ onto the
shock plane and preferentially opposite to ${\bf N}\times {\bf B}_{\rm LM}$. We
argue that the ESW of negative polarity are ion phase space holes produced in a
nonlinear stage of ion-ion ion-streaming instabilities. We estimated lifetimes
of the ion holes to be 10--100 ms, or 1--10 km in terms of spatial distance.


Abstract:
  Representing deep neural networks (DNNs) in low-precision is a promising
approach to enable efficient acceleration and memory reduction. Previous
methods that train DNNs in low-precision typically keep a copy of weights in
high-precision during the weight updates. Directly training with low-precision
weights leads to accuracy degradation due to complex interactions between the
low-precision number systems and the learning algorithms. To address this
issue, we develop a co-designed low-precision training framework, termed
LNS-Madam, in which we jointly design a logarithmic number system (LNS) and a
multiplicative weight update algorithm (Madam). We prove that LNS-Madam results
in low quantization error during weight updates, leading to a stable
convergence even if the precision is limited. We further propose a hardware
design of LNS-Madam that resolves practical challenges in implementing an
efficient datapath for LNS computations. Our implementation effectively reduces
energy overhead incurred by LNS-to-integer conversion and partial sum
accumulation. Experimental results show that LNS-Madam achieves comparable
accuracy to full-precision counterparts with only 8 bits on popular computer
vision and natural language tasks. Compared to a full-precision floating-point
implementation, LNS-Madam reduces the energy consumption by over 90.


Abstract:
  This paper addressed the dark matter analysis on the spiral galaxy NGC 4321
(M100) by considering the nine different dark matter profiles so far lacking in
the scientific literature, i.e. Pseudoisothermal, Burkert, NFW, Moore, Einasto,
core-modified, DC14, coreNFW and Lucky13 profiles. In this paper, we analyzed
the rotation curve analysis on the galaxy NGC 4321 by using nonlinear fitting
of star, gaseous and dark matter halo equations with selected VLA HI
observation data. Among the nine dark matter profiles, four dark matter
profiles (DC14, Lucky13, Burkert and Moore profiles) showed declining features
and hence found to be not suitable for this galaxy. This is concluded to be
mainly due to the characteristics of those dark matter profiles and also due to
the varying levels of problems within the inner regions fittings. For the
remaining five accepted dark matter profiles, we further conducted analysis by
using the reduced chi-square test. Four out of the five accepted dark matter
profiles lie within the range of 0.40 < reduced chi-square < 1.70, except for
the case of the core-modified profile. In addition, Pseudoisothermal profile
achieved the best fitting i.e. reduced chi-square nearest to 1, mainly due to
its linearity in the inner region and flatness at large radii.


Abstract:
  Extracting relevant information from data is crucial for all forms of
learning. The information bottleneck (IB) method formalizes this, offering a
mathematically precise and conceptually appealing framework for understanding
learning phenomena. However the nonlinearity of the IB problem makes it
computationally expensive and analytically intractable in general. Here we
derive a perturbation theory for the IB method and report the first complete
characterization of the learning onset, the limit of maximum relevant
information per bit extracted from data. We test our results on synthetic
probability distributions, finding good agreement with the exact numerical
solution near the onset of learning. We explore the difference and subtleties
in our derivation and previous attempts at deriving a perturbation theory for
the learning onset and attribute the discrepancy to a flawed assumption. Our
work also provides a fresh perspective on the intimate relationship between the
IB method and the strong data processing inequality.


Abstract:
  Open-domain dialog systems have a user-centric goal: to provide humans with
an engaging conversation experience. User engagement is one of the most
important metrics for evaluating open-domain dialog systems, and could also be
used as real-time feedback to benefit dialog policy learning. Existing work on
detecting user disengagement typically requires hand-labeling many dialog
samples. We propose HERALD, an efficient annotation framework that reframes the
training data annotation process as a denoising problem. Specifically, instead
of manually labeling training samples, we first use a set of labeling
heuristics to label training samples automatically. We then denoise the weakly
labeled data using the Shapley algorithm. Finally, we use the denoised data to
train a user engagement detector. Our experiments show that HERALD improves
annotation efficiency significantly and achieves 86% user disengagement
detection accuracy in two dialog corpora.


Abstract:
  Definite descriptions are phrases of the form 'the $x$ such that $\varphi$',
used to refer to single entities in a context. They are often more meaningful
to users than individual names alone, in particular when modelling or querying
data over ontologies. We investigate free description logics with both
individual names and definite descriptions as terms of the language, while also
accounting for their possible lack of denotation. We focus on the extensions of
$\mathcal{ALC}$ and, respectively, $\mathcal{EL}$ with nominals, the universal
role, and definite descriptions. We show that standard reasoning in these
extensions is not harder than in the original languages, and we characterise
the expressive power of concepts relative to first-order formulas using a
suitable notion of bisimulation. Moreover, we lay the foundations for automated
support for definite descriptions generation by studying the complexity of
deciding the existence of definite descriptions for an individual under an
ontology. Finally, we provide a polynomial-time reduction of reasoning in other
free description logic languages based on dual-domain semantics to the case of
partial interpretations.


Abstract:
  Long-range interactions play a central role in electron transport. At the
same time, they present a challenge for direct computer simulations, since
sufficiently large portions of the bath have to be included in the computation
to accurately compute the Coulomb potential. This article presents a
reduced-order approach, by deriving an open quantum model for the reduced
density-matrix. To treat the transient dynamics, the problem is placed in a
reduced-order framework. The dynamics, described by the Liouville von Neumann
equation, is projected to subspaces using a Petrov-Galerkin projection. In
order to recover the global electron density profile as a vehicle to compute
the Coulomb potential, we propose a domain decomposition approach, where the
computational domain also includes segments of the bath that are selected using
logarithmic grids. This approach leads to a multi-component self-energy that
enters the effective Hamiltonian. We demonstrate the accuracy of the reduced
model using a molecular junction built from a Lithium chains.


Abstract:
  Motivated by a spectral analysis of the generator of completely positive
trace-preserving semigroup, we analyze a real functional $$ A,B \in
M_n(\mathbb{C}) \to r(A,B) = \frac{1}{2}\Bigl(\langle [B,A],BA\rangle + \langle
[B,A^\ast],BA^\ast \rangle \Bigr) \in \mathbb{R} $$ where $\langle A,B\rangle
:= {\rm tr} (A^\ast B)$ is the Hilbert-Schmidt inner product, and $[A,B]:= AB -
BA$ is the commutator. In particular we discuss the upper and lower bounds of
the form $c_- \|A\|^2 \|B\|^2 \le r(A,B) \le c_+ \|A\|^2 \|B\|^2$ where $\|A\|$
is the Frobenius norm. We prove that the optimal upper and lower bounds are
given by $c_\pm = \frac{1 \pm \sqrt{2}}{2}$. If $A$ is restricted to be
traceless, the bounds are further improved to be $c_\pm = \frac{1 \pm
\sqrt{2(1-\frac{1}{n})}}{2}$. Interestingly, these upper bounds, especially the
latter one, provide new constraints on relaxation rates for the quantum
dynamical semigroup tighter than previously known constraints in the
literature. A relation with B\"{o}ttcher-Wenzel inequality is also discussed.


Abstract:
  Understanding why deep nets can classify data in large dimensions remains a
challenge. It has been proposed that they do so by becoming stable to
diffeomorphisms, yet existing empirical measurements support that it is often
not the case. We revisit this question by defining a maximum-entropy
distribution on diffeomorphisms, that allows to study typical diffeomorphisms
of a given norm. We confirm that stability toward diffeomorphisms does not
strongly correlate to performance on benchmark data sets of images. By
contrast, we find that the stability toward diffeomorphisms relative to that of
generic transformations $R_f$ correlates remarkably with the test error
$\epsilon_t$. It is of order unity at initialization but decreases by several
decades during training for state-of-the-art architectures. For CIFAR10 and 15
known architectures, we find $\epsilon_t\approx 0.2\sqrt{R_f}$, suggesting that
obtaining a small $R_f$ is important to achieve good performance. We study how
$R_f$ depends on the size of the training set and compare it to a simple model
of invariant learning.


Abstract:
  Consider the transmission eigenvalue problem \[ (\Delta+k^2\mathbf{n}^2)
w=0,\ \ (\Delta+k^2)v=0\ \ \mbox{in}\ \ \Omega;\quad w=v,\ \ \partial_\nu
w=\partial_\nu v=0\ \ \mbox{on} \ \partial\Omega. \] It is shown in [12] that
there exists a sequence of eigenfunctions $(w_m, v_m)_{m\in\mathbb{N}}$
associated with $k_m\rightarrow \infty$ such that either
$\{w_m\}_{m\in\mathbb{N}}$ or $\{v_m\}_{m\in\mathbb{N}}$ are surface-localized,
depending on $\mathbf{n}>1$ or $0<\mathbf{n}<1$. In this paper, we discover a
new type of surface-localized transmission eigenmodes by constructing a
sequence of transmission eigenfunctions $(w_m, v_m)_{m\in\mathbb{N}}$
associated with $k_m\rightarrow \infty$ such that both
$\{w_m\}_{m\in\mathbb{N}}$ and $\{v_m\}_{m\in\mathbb{N}}$ are
surface-localized, no matter $\mathbf{n}>1$ or $0<\mathbf{n}<1$. Though our
study is confined within the radial geometry, the construction is subtle and
technical.


Abstract:
  Automatically and accurately identifying user intents and filling the
associated slots from their spoken language are critical to the success of
dialogue systems. Traditional methods require manually defining the
DOMAIN-INTENT-SLOT schema and asking many domain experts to annotate the
corresponding utterances, upon which neural models are trained. This procedure
brings the challenges of information sharing hindering, out-of-schema, or data
sparsity in open-domain dialogue systems. To tackle these challenges, we
explore a new task of {\em automatic intent-slot induction} and propose a novel
domain-independent tool. That is, we design a coarse-to-fine three-step
procedure including Role-labeling, Concept-mining, And Pattern-mining (RCAP):
(1) role-labeling: extracting keyphrases from users' utterances and classifying
them into a quadruple of coarsely-defined intent-roles via sequence labeling;
(2) concept-mining: clustering the extracted intent-role mentions and naming
them into abstract fine-grained concepts; (3) pattern-mining: applying the
Apriori algorithm to mine intent-role patterns and automatically inferring the
intent-slot using these coarse-grained intent-role labels and fine-grained
concepts. Empirical evaluations on both real-world in-domain and out-of-domain
datasets show that: (1) our RCAP can generate satisfactory SLU schema and
outperforms the state-of-the-art supervised learning method; (2) our RCAP can
be directly applied to out-of-domain datasets and gain at least 76\%
improvement of F1-score on intent detection and 41\% improvement of F1-score on
slot filling; (3) our RCAP exhibits its power in generic intent-slot
extractions with less manual effort, which opens pathways for schema induction
on new domains and unseen intent-slot discovery for generalizable dialogue
systems.


Abstract:
  We investigate the evolution of spin polarization, spontaneous Hall angle
(SHA), saturation magnetization and Curie temperature of $B2$-ordered
Fe$_{60}$Al$_{40}$ thin films under varying antisite disorder, induced by
Ne$^{+}$-ion irradiation. The spin polarization increases monotonically as a
function of ion fluence. A relatively high polarization of 46 % and the SHA of
3.1 % are achieved on 40 nm thick films irradiated with 2 $\cdot$ 10$^{16}$
ions/cm$^2$ at 30 keV. An interesting divergence in the trends of the
magnetization and SHA is observed for low disorder concentrations. The high
spin polarization and its broad tunability range make ion-irradiated
Fe$_{60}$Al$_{40}$ a promising material for application in spin electronic
devices.


Abstract:
  We consider an ambient backscatter communication (AmBC) system aided by an
intelligent reflecting surface (IRS). The optimization of the IRS to assist
AmBC is extremely difficult when there is no prior channel knowledge, for which
no design solutions are currently available. We utilize a deep reinforcement
learning-based framework to jointly optimize the IRS and reader beamforming,
with no knowledge of the channels or ambient signal. We show that the proposed
framework can facilitate effective AmBC communication with a detection
performance comparable to several benchmarks under full channel knowledge.


Abstract:
  Targeted evaluations have found that machine translation systems often output
incorrect gender, even when the gender is clear from context. Furthermore,
these incorrectly gendered translations have the potential to reflect or
amplify social biases. We propose a gender-filtered self-training technique to
improve gender translation accuracy on unambiguously gendered inputs. This
approach uses a source monolingual corpus and an initial model to generate
gender-specific pseudo-parallel corpora which are then added to the training
data. We filter the gender-specific corpora on the source and target sides to
ensure that sentence pairs contain and correctly translate the specified
gender. We evaluate our approach on translation from English into five
languages, finding that our models improve gender translation accuracy without
any cost to generic translation quality. In addition, we show the viability of
our approach on several settings, including re-training from scratch,
fine-tuning, controlling the balance of the training data, forward translation,
and back-translation.


Abstract:
  We propose an end-to-end pipeline to robustly generate high-quality
quadrilateral meshes for complex CAD models. An initial quad-dominant mesh is
generated with frontal point insertion guided by a locally integrable cross
field and a scalar size map adapted to the small CAD features. After triangle
combination and midpoint-subdivision into an all-quadrilateral mesh, the
topology of the mesh is modified to reduce the number of irregular vertices.
The idea is to preserve the irregular vertices matching cross-field
singularities and to eliminate the others. The topological modifications are
either local and based on disk quadrangulations, or more global with the
remeshing of patches of quads according to predefined patterns. Validity of the
quad mesh is guaranteed by monitoring element quality during all operations and
reverting the changes when necessary. Advantages of our approach include
robustness, strict respect of the CAD features and support for user-prescribed
size constraints. The quad mesher, which is available in Gmsh, is validated and
illustrated on two datasets of CAD models.


Abstract:
  Two-dimensional (2D) multiferroics have been casted great attention owing to
their promising prospects for miniaturized electronic and memory devices.Here,
we proposed a highly stable 2D multiferroic, VOF monolayer, which is an
intrinsic ferromagnetic half semiconductor with large spin polarization ~2
$\mu_{B}/V$ atom and a significant uniaxial magnetic anisotropy along a-axis
(410 $\mu eV/V$ atom). Meanwhile, it shows excellent ferroelectricity with a
large spontaneous polarization 32.7 $\mu C/cm^{2}$ and a moderate energy
barrier (~43 meV/atom) between two ferroelectric states, which can be ascribed
to the Jahn-Teller distortion.Moreover, VOF monolayer harbors an ultra-large
negative Poisson's ratio in the in-plane direction (~-0.34). The Curie
temperature evaluated from the Monte Carlo simulations based on the Ising model
is about 215 K, which can be enhanced room temperature under -4% compressive
biaxial strain.The combination of ferromagnetism and ferroelectricity in the
VOF monolayer could provide a promising platform for future study of
multiferroic effects and next-generation multifunctional nanoelectronic device
applications.


Abstract:
  Homography estimation is often an indispensable step in many computer vision
tasks. The existing approaches, however, are not robust to illumination and/or
larger viewpoint changes. In this paper, we propose bidirectional implicit
Homography Estimation (biHomE) loss for unsupervised homography estimation.
biHomE minimizes the distance in the feature space between the warped image
from the source viewpoint and the corresponding image from the target
viewpoint. Since we use a fixed pre-trained feature extractor and the only
learnable component of our framework is the homography network, we effectively
decouple the homography estimation from representation learning. We use an
additional photometric distortion step in the synthetic COCO dataset generation
to better represent the illumination variation of the real-world scenarios. We
show that biHomE achieves state-of-the-art performance on synthetic COCO
dataset, which is also comparable or better compared to supervised approaches.
Furthermore, the empirical results demonstrate the robustness of our approach
to illumination variation compared to existing methods.


Abstract:
  A new $\beta$-metastable Ti-alloy is designed with the aim to obtain a TWIP
alloy but positioned at the limit between the TRIP/TWIP and the TWIP dominated
regime. The designed alloy exhibits a large ductility combined with an elevated
and stable work-hardening rate. Deformation occurring by formation and
multiplication of {332}<113> twins is evidenced and followed by in-situ
electron microscopy, and no primary stress induced martensite is observed.
Since microstructural investigations of the deformation mechanisms show a
highly heterogeneous deformation, the reason of the large ductility is then
investigated. The spatial strain distribution is characterized by micro-scale
digital image correlation, and the regions highly deformed are found to stand
at the crossover between twins, or at the intersection between deformation
twins and grain boundaries. Detailed electron back-scattered imaging in such
regions of interest finally allowed to evidence the formation of thin needles
of stress induced martensite. The latter is thus interpreted as an
accommodation mechanism, relaxing the local high strain fields, which ensures a
large and stable plastic deformation of this newly designed Ti-alloy.


Abstract:
  The Cadmium Zinc Telluride Imager (CZTI) onboard AstroSat is designed for
hard X-ray imaging and spectroscopy in the energy range of 20 - 100 keV. The
CZT detectors are of 5 mm thickness and hence have good efficiency for Compton
interactions beyond 100 keV. The polarisation analysis using CZTI relies on
such Compton events and have been verified experimentally. The same Compton
events can also be used to extend the spectroscopy up to 380 keV. Further, it
has been observed that about 20% pixels of the CZTI detector plane have low
gain, and they are excluded from the primary spectroscopy. If these pixels are
included, then the spectroscopic capability of CZTI can be extended up to 500
keV and further up to 700 keV with a better gain calibration in the future.
Here we explore the possibility of using the Compton events as well as the low
gain pixels to extend the spectroscopic energy range of CZTI for ON-axis bright
X-ray sources. We demonstrate this technique using Crab observations and
explore its sensitivity.


Abstract:
  Software obfuscation is a crucial technology to protect intellectual
property. Despite its importance, commercial and academic state-of-the-art
obfuscation approaches are vulnerable to a plethora of automated deobfuscation
attacks, such as symbolic execution, taint analysis, or program synthesis.
While several enhanced techniques were proposed to thwart taint analysis or
symbolic execution, they either impose a prohibitive runtime overhead or can be
removed by compiler optimizations. In general, they suffer from focusing on a
single attack vector, allowing an attacker to switch to other more effective
techniques, such as program synthesis. In this work, we present Loki, an
approach for code obfuscation that is resilient against all known automated
deobfuscation attacks. To this end, we deploy multiple techniques, including a
generic approach to synthesize formally verified expressions of arbitrary
complexity. Contrary to state-of-the-art approaches that rely on a few
hardcoded generation rules, our expressions are more diverse and harder to
pattern match against. Moreover, Loki protects against previously unaccounted
attack vectors such as program synthesis, for which it reduces the success rate
to merely 19%. Overall, our design incurs significantly less overhead while
providing a much stronger protection level.


Abstract:
  Recent progress in high-dispersion spectroscopy has revealed the presence of
vaporized heavy metals and ions in the atmosphere of hot Jupiters whose dayside
temperature is larger than 2000 K, categorized as ultra hot Jupiters (UHJs).
Using the archival data of high resolution transmission spectroscopy obtained
with the Subaru telescope, we searched for neutral metals in HD149026b, a hot
Jupiter cooler than UHJs. By removing stellar and telluric absorption and using
a cross-correlation technique, we report tentative detection of neutral
titanium with 4.4 sigma and a marginal signal of neutral iron with 2.8 sigma in
the atmosphere. This is the first detection of neutral titanium in an
exoplanetary atmosphere. In this temperature range, titanium tends to form
titanium oxide (TiO). The fact that we did not detect any signal from TiO
suggests that the C/O ratio in the atmosphere is higher than the solar value.
The detection of metals in the atmosphere of hot Jupiters cooler than UHJs will
be useful for understanding the atmospheric structure and formation history of
hot Jupiters.


Abstract:
  Nanofibers are an interesting phase into which amphiphilic molecules can
self-assemble. Described for a large number of synthetic lipids, they were
seldom reported for natural lipids like microbial amphiphiles, known as
biosurfactants. In this work, we show that the palmitic acid congener of
sophorolipids (SLC16:0), one of the most studied families of biosurfactants,
spontaneously forms a self-assembled fiber network (SAFiN) at pH below 6
through a pH jump process. pH-resolved in-situ Small Angle X-ray Scattering
(SAXS) shows a continuous micelle-to-fiber transition, characterized by an
enhanced core-shell contrast between pH 9 and pH 7 and micellar fusion into
flat membrane between pH 7 and pH 6, approximately. Below pH 6, homogeneous,
infinitely long nanofibers form by peeling off the membranes. Eventually, the
nanofiber network spontaneously forms a thixotropic hydrogel with fast recovery
rates after applying an oscillatory strain amplitude out of the linear
viscoelastic regime (LVER): after being submitted to strain amplitudes during 5
min, the hydrogel recovers about 80% and 100% of its initial elastic modulus
after, respectively, 20 s and 10 min. Finally, the strength of the hydrogel
depends on the medium's final pH, with an elastic modulus fivefold higher at pH
3 than at pH 6.


Abstract:
  The field of 2D materials has gained a lot of attention for vast range of
applications. Amongst others, the sensing ability towards harmful gases is the
application, which we explored in the present work using quantum-mechanical
simulations for the SnP3 material. Its electronic properties, namely 1 and 2
layers being semiconducting, while multilayers being metallic, offer a
possibility to build a single-material logical junction. In addition, the
harmful gases studied here show physical adsorption with charge transfer from
the substrate to the gas molecules. Calculated recovery times show promise of a
good sensing material. The I-V characteristics calculated for all cases
indicates that SnP3 could be a viable sensing material towards NO gas via
negative differential resistance.


Abstract:
  The Rouse model with internal friction (RIF), a widely used theoretical
framework to interpret the effects of internal friction on conformational
transitions in biomolecules, is shown to be an approximate treatment that is
based on preaveraging internal friction. By comparison with Brownian dynamics
simulations of an exact coarse-grained model that incorporates fluctuations in
internal friction, the accuracy of the preaveraged model predictions is
examined both at and away from equilibrium. While the two models predict
intrachain autocorrelations that approach each other for long enough chain
segments, they differ in their predictions for shorter segments. Furthermore,
the two models differ qualitatively in their predictions for the chain
extension and viscosity in shear flow, which is taken to represent a
prototypical out-of-equilibrium condition.


Abstract:
  In order to develop reliable services using machine learning, it is important
to understand the uncertainty of the model outputs. Often the probability
distribution that the prediction target follows has a complex shape, and a
mixture distribution is assumed as a distribution that uncertainty follows.
Since the output of mixture density estimation is complicated, its
interpretability becomes important when considering its use in real services.
In this paper, we propose a method for mixture density estimation that utilizes
an interpretable tree structure. Further, a fast inference procedure based on
time-invariant information cache achieves both high speed and interpretability.


Abstract:
  This is a perspective paper inspired from the study of Turing Test proposed
by A.M. Turing (23 June 1912 - 7 June 1954) in 1950. Following one important
implication of Turing Test for enabling a machine with a human-like behavior or
performance, we define human-like robustness (HLR) for AI machines. The
objective of the new definition aims to enforce AI machines with HLR, including
to evaluate them in terms of HLR. A specific task is discussed only on object
identification, because it is the most common task for every person in daily
life. Similar to the perspective, or design, position by Turing, we provide a
solution of how to achieve HLR AI machines without constructing them and
conducting real experiments. The solution should consists of three important
features in the machines. The first feature of HLR machines is to utilize
common sense from humans for realizing a causal inference. The second feature
is to make a decision from a semantic space for having interpretations to the
decision. The third feature is to include a "human-in-the-loop" setting for
advancing HLR machines. We show an "identification game" using proposed design
of HLR machines. The present paper shows an attempt to learn and explore
further from Turing Test towards the design of human-like AI machines.


Abstract:
  Let $M$ denote a finitely generated module over a Noetherian ring $R$. For an
ideal $I \subset R$ there is a study of the endomorphisms of the local
cohomology module $H^g_I(M), g = \operatorname{grade} (I,M),$ and related
results. Another subject is the study of left derived functors of the $I$-adic
completion $\Lambda^I_i(H^g_I(M))$, motivated by a characterization of
Gorenstein rings given in the book by Simon and the author. This provides
another Cohen-Macaulay criterion. The results are illustrated by several
examples. There is also an extension to the case of homomorphisms of two
different local cohomology modules.


Abstract:
  Alzheimer's disease (AD) is one of the most common neurodegenerative
diseases, with around 50 million patients worldwide. Accessible and
non-invasive methods of diagnosing and characterising AD are therefore urgently
required. Electroencephalography (EEG) fulfils these criteria and is often used
when studying AD. Several features derived from EEG were shown to predict AD
with high accuracy, e.g. signal complexity and synchronisation. However, the
dynamics of how the brain transitions between stable states have not been
properly studied in the case of AD and EEG data. Energy landscape analysis is a
method that can be used to quantify these dynamics. This work presents the
first application of this method to both AD and EEG. Energy landscape assigns
energy value to each possible state, i.e. pattern of activations across brain
regions. The energy is inversely proportional to the probability of occurrence.
By studying the features of energy landscapes of 20 AD patients and 20 healthy
age-matched counterparts, significant differences were found. The dynamics of
AD patients' brain networks were shown to be more constrained - with more local
minima, less variation in basin size, and smaller basins. We show that energy
landscapes can predict AD with high accuracy, performing significantly better
than baseline models.


Abstract:
  We consider here a family of singular Laplace-Beltrami operators, focussing
our attention on the problem of so-called quantum confinement on the half-plane
equipped with Riemannian metrics of Grushin type degenerate at the boundary. By
introducing a costant-fiber direct integral scheme we are able to rigorously
characterize the presence or absence of self-adjointness of these operators. We
also compare our technique and results with the already studied problem of
quantum confinement on the half-cylinder.


Abstract:
  While the body of research focusing on Intelligent Environments (IEs)
programming by adults is steadily growing, informed insights about children as
programmers of such environments are limited. Previous work already established
that young children can learn programming basics. Yet, there is still a need to
investigate whether this capability can be transferred in the context of IEs,
since encouraging children to participate in the management of their
intelligent surroundings can enhance responsibility, independence, and the
spirit of cooperation. We performed a user study (N=15) with children aged
7-12, using a block-based, gamified AR spatial coding prototype allowing to
manipulate smart artifacts in an Intelligent Living room. Our results validated
that children understand and can indeed program IEs. Based on our findings, we
contribute preliminary implications regarding the use of specific technologies
and paradigms (e.g. AR, trigger-action programming) to inspire future systems
that enable children to create enriching experiences in IEs.


Abstract:
  New autonomous driving technologies are emerging every day and some of them
have been commercially applied in the real world. While benefiting from these
technologies, autonomous trucks are facing new challenges in short-term
maintenance planning, which directly influences the truck operator's profit. In
this paper, we implement a vehicle health management system by addressing the
maintenance planning issues of autonomous trucks on a transport mission. We
also present a maintenance planning model using a risk-based decision-making
method, which identifies the maintenance decision with minimal economic risk of
the truck company. Both availability losses and maintenance costs are
considered when evaluating the economic risk. We demonstrate the proposed model
by numerical experiments illustrating real-world scenarios. In the experiments,
compared to three baseline methods, the expected economic risk of the proposed
method is reduced by up to $47\%$. We also conduct sensitivity analyses of
different model parameters. The analyses show that the economic risk
significantly decreases when the estimation accuracy of remaining useful life,
the maximal allowed time of delivery delay before order cancellation, or the
number of workshops increases. The experiment results contribute to identifying
future research and development attentions of autonomous trucks from an
economic perspective.


Abstract:
  The paper reports on a theoretical description of work function of TiN, which
is one of the most used materials for the realization of electrodes and gates
in CMOS devices. Indeed, although the work function is a fundamental quantity
in quantum mechanics and also in device physics, as it allows the understanding
of band alignment at heterostructures and gap states formation at the
metal/semiconductor interface, the role of defects and contaminants is rarely
taken into account. Here, by using first principles simulations, we present an
extensive study of the work function dependence on nitrogen vacancies and
surface oxidation for different TiN surface orientations. The results
complement and explain a number of existent experimental data, and provide a
useful tool to tailoring transport properties of TiN electrodes in device
simulations.


Abstract:
  Uncovering anomalies in attributed networks has recently gained popularity
due to its importance in unveiling outliers and flagging adversarial behavior
in a gamut of data and network science applications including {the Internet of
Things (IoT)}, finance, security, to list a few. The present work deals with
uncovering anomalous edges in attributed graphs using two distinct formulations
with complementary strengths, which can be easily distributed, and hence
efficient. The first relies on decomposing the graph data matrix into low rank
plus sparse components to markedly improve performance. The second broadens the
scope of the first by performing robust recovery of the unperturbed graph,
which enhances the anomaly identification performance. The novel methods not
only capture anomalous edges linking nodes of different communities, but also
spurious connections between any two nodes with different features. Experiments
conducted on real and synthetic data corroborate the effectiveness of both
methods in the anomaly identification task.


Abstract:
  The rapid development of social media changes the lifestyle of people and
simultaneously provides an ideal place for publishing and disseminating rumors,
which severely exacerbates social panic and triggers a crisis of social trust.
Early content-based methods focused on finding clues from the text and user
profiles for rumor detection. Recent studies combine the stances of users'
comments with news content to capture the difference between true and false
rumors. Although the user's stance is effective for rumor detection, the manual
labeling process is time-consuming and labor-intensive, which limits the
application of utilizing it to facilitate rumor detection.
  In this paper, we first finetune a pre-trained BERT model on a small labeled
dataset and leverage this model to annotate weak stance labels for users'
comment data to overcome the problem mentioned above. Then, we propose a novel
Stance-aware Reinforcement Learning Framework (SRLF) to select high-quality
labeled stance data for model training and rumor detection. Both the stance
selection and rumor detection tasks are optimized simultaneously to promote
both tasks mutually. We conduct experiments on two commonly used real-world
datasets. The experimental results demonstrate that our framework outperforms
the state-of-the-art models significantly, which confirms the effectiveness of
the proposed framework.


Abstract:
  We characterize the symbols $\Phi$ for which there exists a weight w such
that the weighted composition operator M w C $\Phi$ is compact on the weighted
Bergman space B 2 $\alpha$. We also characterize the symbols for which there
exists a weight w such that M w C $\Phi$ is bounded but not compact. We also
investigate when there exists w such that M w C $\Phi$ is Hilbert-Schmidt on B
2 $\alpha$.


Abstract:
  In this paper we prove that among pairs $K,\,K' \subset \mathbb{C}$ of
conformal dynamically defined Cantor sets with sum of Hausdorff dimensions
$HD(K)+HD(K')>2$, there is an open and dense subset of such pairs verifying
$\text{int}(K-K')\neq \emptyset$. This is motivated by the work \cite{MY},
where Moreira and Yoccoz proved a similar statement for dynamically defined
Cantor sets in the real line. Here we adapt their argument to the context of
conformal Cantor sets in the complex plane, this requires the introduction of
several new concepts and a more detailed analysis in some parts of the
argument.


Abstract:
  This paper focuses on distributed learning-based control of decentralized
multi-agent systems where the agents' dynamics are modeled by Gaussian
Processes (GPs). Two fundamental problems are considered: the optimal design of
experiment for concurrent learning of the agents' GP models, and the
distributed coordination given the learned models. Using a Distributed Model
Predictive Control (DMPC) approach, the two problems are formulated as
distributed optimization problems, where each agent's sub-problem includes both
local and shared objectives and constraints. To solve the resulting complex and
non-convex DMPC problems efficiently, we develop an algorithm called
Alternating Direction Method of Multipliers with Convexification (ADMM-C) that
combines a distributed ADMM algorithm and a Sequential Convexification method.
The computational efficiency of our proposed method comes from the facts that
the computation for solving the DMPC problem is distributed to all agents and
that efficient convex optimization solvers are used at the agents for solving
the convexified sub-problems. We also prove that, under some technical
assumptions, the ADMM-C algorithm converges to a stationary point of the
penalized optimization problem. The effectiveness of our approach is
demonstrated in numerical simulations of a multi-vehicle formation control
example.


Abstract:
  In this work, we have applied the integrals of motion method in a nonunitary
approach and so obtained the time-dependent displacement and squeezed
parameters of the coherent squeezed states (CSS). On its turn, CSS for
one-dimensional systems with general time-dependent quadratic Hamiltonian are
constructed. We discuss the properties of these states, in particular,
minimization of uncertainty relation and transition probabilities. As an
application, we calculate the CSS of an oscillator with a time-dependent
frequency and shown that the solution can be obtained from these well-known
Mathieu's equation.


Abstract:
  This paper tackles the multi-objective optimization of the cost functional of
a path-following model predictive control for vehicle longitudinal and lateral
control. While the inherent optimal character of the model predictive control
and the direct consideration of constraints gives a very powerful tool for many
applications, is the determination of an appropriate cost functional a
non-trivial task. This results on the one hand from the number of degrees of
freedom or the multitude of adjustable parameters and on the other hand from
the coupling of these. To overcome this situation a Bayesian optimization
procedure is present, which gives the possibility to determine optimal cost
functional parameters for a given desire. Moreover, a Pareto-front for a whole
set of possible configurations can be computed.


Abstract:
  Attosecond science promises to reveal the most fundamental electronic
dynamics occurring in matter and it can develop further by meeting two linked
technological goals related to high-order harmonic sources: higher photon flux
(permitting to measure low cross-section processes) and improved spectral
tunability (allowing selectivity in addressing specific electronic
transitions). New developments come through parametric waveform synthesis,
which provides control over the shape of high-energy electric field transients,
enabling the creation of highly-tunable isolated attosecond pulses via
high-harmonic generation. Here we show that central energy, spectral
bandwidth/shape and temporal duration of the attosecond pulses can be
controlled by shaping the laser pulse waveform via two key parameters: the
relative-phase between two halves of the multi-octave spanning optical
spectrum, and the overall carrier-envelope phase. These results not only
promise to expand the experimental possibilities in attosecond science, but
also demonstrate coherent strong-field control of free-electron trajectories
using tailored optical waveforms.


Abstract:
  Frequency modulation coherent anti-Stokes Raman scattering (FM CARS) is
presented, using a compact as well as fast and widely tunable fiber-based light
source. With this light source Raman resonances between 700 cm$^{-1}$ and 3200
cm$^{-1}$ can be addressed via wavelength tuning within only 5 ms, which allows
for FM CARS measurements with frame-to-frame wavelength switching. Moreover,
the functionality for high-sensitivity FM CARS measurements was integrated by
means of fiber optics to keep a stable and reliable operation. The light source
accomplished FM CARS measurements with a 40 times enhanced sensitivity at a
lock-in amplifier (LIA) bandwidth of 1 Hz. For fast imaging with frame-to-frame
wavelength switching at a LIA bandwidth of 1 MHz a 18-fold contrast enhancement
could be verified, making this light source ideal for routine and out-of-lab FM
CARS measurements for medical diagnostics or environmental sensing.