Abstract:
  The interplay between set-theoretic solutions of the Yang--Baxter equation of
Mathematical Physics, skew braces, regular subgroups, and Hopf--Galois
structures has spawned a considerable body of literature in recent years. In a
recent paper, Alan Koch generalised a construction of Lindsay N.~Childs,
showing how one can obtain bi-skew braces $(G, \cdot, \circ)$ from an
endomorphism of a group $(G, \cdot)$ whose image is abelian. In this paper, we
characterise the endomorphisms of a group $(G, \cdot)$ for which Koch's
construction, and a variation on it, yield (bi-)skew braces. We show how the
set-theoretic solutions of the Yang--Baxter equation derived by Koch's
construction carry over to our more general situation, and discuss the related
Hopf--Galois structures.


Abstract:
  We calculate the NNLO QCD corrections to diphoton production with an
additional jet at the LHC. Our calculation represents the first NNLO-accurate
prediction for the transverse momentum distribution of the diphoton system. The
improvement in the accuracy of the theoretical prediction is significant, by a
factor of up to four relative to NLO QCD. Our calculation is exact except for
the finite remainder of the two-loop amplitude which is included at leading
color. The numerical impact of this approximated contribution is small. The
results of this work are expected to further our understanding of the Higgs
boson sector and of the behavior of higher-order corrections to LHC processes.


Abstract:
  Image captioning model is a cross-modality knowledge discovery task, which
targets at automatically describing an image with an informative and coherent
sentence. To generate the captions, the previous encoder-decoder frameworks
directly forward the visual vectors to the recurrent language model, forcing
the recurrent units to generate a sentence based on the visual features.
Although these sentences are generally readable, they still suffer from the
lack of details and highlights, due to the fact that the substantial gap
between the image and text modalities is not sufficiently addressed. In this
work, we explicitly build a Modality Transition Module (MTM) to transfer visual
features into semantic representations before forwarding them to the language
model. During the training phase, the modality transition network is optimised
by the proposed modality loss, which compares the generated preliminary textual
encodings with the target sentence vectors from a pre-trained text
auto-encoder. In this way, the visual vectors are transited into the textual
subspace for more contextual and precise language generation. The novel MTM can
be incorporated into most of the existing methods. Extensive experiments have
been conducted on the MS-COCO dataset demonstrating the effectiveness of the
proposed framework, improving the performance by 3.4% comparing to the
state-of-the-arts.


Abstract:
  In this paper, firstly, we introduce the concept of convexity in A-metric
spaces and show that Mann iteration process converges to the unique fixed point
of Zamfirescu type contractions in this newly defined convex A-metric space.
Secondly, we define the concept of stability in convex A-metric spaces and
establish stability result for the Mann iteration process considered in such
spaces. Our results carry some well-known results from the literature to convex
A-metric spaces.


Abstract:
  Compressive sensing (CS) has been applied to estimate the direction of
arrival (DOA) in underwater acoustics. However, the key problem needed to be
resolved in a {multipath} propagation environment is to suppress the
interferences between the raypaths. Thus, in this paper, {a subspace-based
compressive sensing algorithm that formulates the statistic information of the
signal subspace in a CS framework is proposed.} The experiment results show
that (1) the proposed algorithm enables the separation of raypaths that arrive
closely at the {receiver} array and (2) the existing algorithms fail,
especially in a low signal-to-noise ratio (SNR) environment.


Abstract:
  We propose a new concept of two-dimensional (2D) Dirac semiconductor which is
characterized by the emergence of fourfold degenerate band crossings near the
band edge and provide a generic approach to realize this novel semiconductor in
the community of material science. Based on the first-principle calculations
and symmetry analysis, we discover recently synthesised triple-layer (TL)-BiOS2
is such Dirac semiconductor that features Dirac cone at X/Y point, protected by
nonsymmorphic symmetry. Due to sandwich-like structure, each Dirac fermion in
TL-BiOS2 can be regarded as a combination of two Weyl fermions with opposite
chiralities, degenerate in momentum-energy space but separated in real space.
Such Dirac semiconductor carries layer-dependent helical spin textures that
never been reported before. Moreover, novel topological phase transitions are
flexibly achieved in TL-BiOS2: (i) an vertical electric field can drive it into
Weyl semiconductor with switchable spin polarization direction, (ii) an
extensive strain is able to generate ferroelectric polarization and actuate it
into Weyl nodal ring around X point and into another type of four-fold
degenerate point at Y point. Our work extends the Dirac fermion into
semiconductor systems and provides a promising avenue to integrate spintronics
and optoelectronics in topological materials.


Abstract:
  At the Planck scale the distinction between elementary particles and black
holes becomes fuzzy. The very definition of a "quantum black hole" (QBH) is an
open issue. Starting from the idea that, at the Planck scale, the radius of the
event horizon undergoes quantum oscillations, we introduce a black hole
mass-radius Generalised Uncertainty Principle (GUP) and derive a corresponding
gravitational wavelength. Next we recover a GUP encoding effective geometry.
This semi-classical gravitational description admits black hole configurations
only for masses higher than the Planck mass. Quantum corrections lead to a
vanishing Hawking temperature when the Planck mass is approached from above.
Finally we replace our semi-classical model by a relativistic wave equation for
the "horizon wave function". The solution admits a discrete mass spectrum which
is bounded from below by a stable ground state with energy close to the Planck
mass. Interestingly higher angular momentum states fit onto Regge trajectories
indicating their stringy nature.


Abstract:
  In this paper, we introduce the notion of Grothendieck enriched categories
for categories enriched over a sufficiently nice Grothendieck monoidal category
$\mathcal{V}$, generalizing the classical notion of Grothendieck categories.
Then we establish the Gabriel-Popescu type theorem for Grothendieck enriched
categories. We also prove that the property of being Grothendieck enriched
categories is preserved under the change of the base monoidal categories by a
monoidal right adjoint functor. In particular, if we take as $\mathcal{V}$ the
monoidal category of complexes of abelian groups, we obtain the notion of
Grothendieck dg categories. As an application of the main results, we see that
the dg category of complexes of quasi-coherent sheaves on a quasi-compact and
quasi-separated scheme is an example of Grothendieck dg categories.


Abstract:
  We study the propagation coherence for neutrino oscillations in media with
different density profiles. For each profile, we find the dependence of the
coherence length, $L_{coh}$, on neutrino energy and address the issue of
correspondence of results in the distance and energy-momentum representations.
The key new feature in matter is existence of energy ranges with enhanced
coherence around the energies $E_0$ of "infinite coherence" at which $L_{coh}
\rightarrow \infty$. In the configuration space, the infinite coherence
corresponds to equality of the (effective) group velocities of the eigenstates.
In constant density medium, there is a unique $E_0$, which coincides with the
MSW resonance energy of oscillations of mass states and is close to the MSW
resonance energy of flavor states. In the case of massless neutrinos or
negligible masses in a very dense medium the coherence persists continuously.
In the adiabatic case, the infinite coherence is realized for periodic density
change. Adiabaticity violation changes the shape factors of the wave packets
(WPs) and leads to their spread. In a medium with sharp density changes
(jumps), splitting of the eigenstates occurs at crossing of each jump. We study
the increase of the coherence length in a single jump and periodic density
jumps - castle-wall (CW) profiles. For the CW profile, there are several $E_0$
corresponding to parametric resonances. We outlined applications of the results
for supernova neutrinos. In particular, we show that coherence between two
shock wave fronts leads to observable oscillation effects, and our analysis
suggests that the decoherence can be irrelevant for flavor transformations in
the central parts of collapsing stars.


Abstract:
  Gathering manually annotated images for the purpose of training a predictive
model is far more challenging in the medical domain than for natural images as
it requires the expertise of qualified radiologists. We therefore propose to
take advantage of past radiological exams (specifically, knee X-ray
examinations) and formulate a framework capable of learning the correspondence
between the images and reports, and hence be capable of generating diagnostic
reports for a given X-ray examination consisting of an arbitrary number of
image views. We demonstrate how aggregating the image features of individual
exams and using them as conditional inputs when training a language generation
model results in auto-generated exam reports that correlate well with
radiologist-generated reports.


Abstract:
  We develop a method to generate prediction intervals that have a
user-specified coverage level across all regions of feature-space, a property
called conditional coverage. A typical approach to this task is to estimate the
conditional quantiles with quantile regression -- it is well-known that this
leads to correct coverage in the large-sample limit, although it may not be
accurate in finite samples. We find in experiments that traditional quantile
regression can have poor conditional coverage. To remedy this, we modify the
loss function to promote independence between the size of the intervals and the
indicator of a miscoverage event. For the true conditional quantiles, these two
quantities are independent (orthogonal), so the modified loss function
continues to be valid. Moreover, we empirically show that the modified loss
function leads to improved conditional coverage, as evaluated by several
metrics. We also introduce two new metrics that check conditional coverage by
looking at the strength of the dependence between the interval size and the
indicator of miscoverage.


Abstract:
  We compute the decomposition numbers of the unipotent characters lying in the
principal $\ell$-block of a finite group of Lie type $B_{2n}(q)$ or $C_{2n}(q)$
when $q$ is an odd prime power and $\ell$ is an odd prime number such that the
order of $q$ mod $\ell$ is $2n$. Along the way, we extend to these finite
groups the results of \cite{DVV19} on the branching graph for Harish-Chandra
induction and restriction.


Abstract:
  Experimentally observed complex networks are often scale-free, small-world
and have unexpectedly large number of small cycles. Apollonian network is one
notable example of a model network respecting simultaneously having all three
of these properties. This network is constructed by a deterministic procedure
of consequentially splitting a triangle into smaller and smaller triangles.
Here we present a similar construction based on consequential splitting of
tetragons and other polygons with even number of edges. The suggested procedure
is stochastic and results in the ensemble of planar scale-free graphs, in the
limit of large number of splittings the degree distribution of the graph
converges to a true power law with exponent, which is smaller than 3 in the
case of tetragons, and larger than 3 for polygons with larger number of edges.
We show that it is possible to stochastically mix tetragon-based and
hexagon-based constructions to obtain an ensemble of graphs with tunable
exponent of degree distribution. Other possible planar generalizations of the
Apollonian procedure are also briefly discussed.


Abstract:
  Learned indices have been proposed to replace classic index structures like
B-Tree with machine learning (ML) models. They require to replace both the
indices and query processing algorithms currently deployed by the databases,
and such a radical departure is likely to encounter challenges and obstacles.
In contrast, we propose a fundamentally different way of using ML techniques to
improve on the query performance of the classic R-Tree without the need of
changing its structure or query processing algorithms. Specifically, we develop
reinforcement learning (RL) based models to decide how to choose a subtree for
insertion and how to split a node when building an R-Tree, instead of relying
on hand-crafted heuristic rules currently used by R-Tree and its variants.
Experiments on real and synthetic datasets with up to more than 100 million
spatial objects clearly show that our RL based index outperforms R-Tree and its
variants in terms of query processing time.


Abstract:
  We develop new algebraic tools to reason about concurrent behaviours modelled
as languages of Mazurkiewicz traces and asynchronous automata. These tools
reflect the distributed nature of traces and the underlying causality and
concurrency between events, and can be said to support true concurrency. They
generalize the tools that have been so efficient in understanding, classifying
and reasoning about word languages. In particular, we introduce an asynchronous
version of the wreath product operation and we describe the trace languages
recognized by such products (the so-called asynchronous wreath product
principle). We then propose a decomposition result for recognizable trace
languages, analogous to the Krohn-Rhodes theorem, and we prove this
decomposition result in the special case of acyclic architectures. Finally, we
introduce and analyze two distributed automata-theoretic operations. One, the
local cascade product, is a direct implementation of the asynchronous wreath
product operation. The other, global cascade sequences, although conceptually
and operationally similar to the local cascade product, translates to a more
complex asynchronous implementation which uses the gossip automaton of Mukund
and Sohoni. This leads to interesting applications to the characterization of
trace languages definable in first-order logic: they are accepted by a
restricted local cascade product of the gossip automaton and 2-state
asynchronous reset automata, and also by a global cascade sequence of 2-state
asynchronous reset automata. Over distributed alphabets for which the
asynchronous Krohn-Rhodes theorem holds, a local cascade product of such
automata is sufficient and this, in turn, leads to the identification of a
simple temporal logic which is expressively complete for such alphabets.


Abstract:
  The rise of the Internet of Things (IoT) has opened new research lines that
focus on applying IoT applications to domains further beyond basic user-grade
applications, such as Industry or Healthcare. These domains demand a very high
Quality of Service (QoS), mainly a very short response time. In order to meet
these demands, some works are evaluating how to modularize and deploy IoT
applications in different nodes of the infrastructure (edge, fog, cloud), as
well as how to place the network controllers, since these decisions affect the
response time of the application. Some works in the literature have approached
this problem by providing separate plans for deployment and placing of
controllers. However, this approach makes sub-optimal decisions, that
complicate guaranteeing the demanded response time. To guarantee an optimal
response time, it is crucial to solve the problem in a single effort that
considers both, the networking and computing dimensions. In this work, we
analyze the influences between the response time of computing and networking in
edge computing environments with SDN networks, merging both optimization
efforts into a single one and proposing a solution to the joint problem. Our
evaluation shows that our proposal can shorten response time by up to 28.97%


Abstract:
  The B-emission line stars are rapid rotators that were probably spun up by
mass and angular momentum accretion through mass transfer in an interacting
binary. Mass transfer will strip the donor star of its envelope to create a
small and hot subdwarf remnant. Here we report on Hubble Space Telescope/STIS
far-ultraviolet spectroscopy of a sample of Be stars that reveals the presence
of the hot sdO companion through the calculation of cross-correlation functions
of the observed and model spectra. We clearly detect the spectral signature of
the sdO star in 10 of the 13 stars in the sample, and the spectral signals
indicate that the sdO stars are hot, relatively faint, and slowly rotating as
predicted by models. A comparison of their temperatures and radii with
evolutionary tracks indicates that the sdO stars occupy the relatively
long-lived, He-core burning stage. Only one of the ten detections was a known
binary prior to this investigation, which emphasizes the difficulty of finding
such Be+sdO binaries through optical spectroscopy. However, these results and
others indicate that many Be stars probably host hot subdwarf companions.


Abstract:
  In this study, we investigate an extreme ultraviolet (EUV) wave event on 2010
February 11, which occurred as a limb event from the Earth viewpoint and a disk
event from the STEREO--B viewpoint. We use the data obtained by the Atmospheric
Imaging Assembly (AIA) aboard the Solar Dynamics Observatory (SDO) in various
EUV channels. The EUV wave event was launched by a partial prominence eruption.
Similar to some EUV wave events in previous works, this EUV wave event contains
a faster wave with a speed of $\sim$445$\pm$6 km s$^{-1}$, which we call
coronal Moreton wave, and a slower wave with a speed of $\sim$298$\pm$5 km
s$^{-1}$, which we call "EIT wave". The coronal Moreton wave is identified as a
fast-mode wave and the "EIT wave" is identified as an apparent propagation due
to successive field-line stretching. We also observe a stationary front
associated with the fast mode EUV wave. This stationary front is explained as
mode conversion from the coronal Moreton wave to a slow-mode wave near a
streamer.


Abstract:
  We present a framework for deterministically rounding a dynamic fractional
matching. Applying our framework in a black-box manner on top of existing
fractional matching algorithms, we derive the following new results: (1) The
first deterministic algorithm for maintaining a $(2-\delta)$-approximate
maximum matching in a fully dynamic bipartite graph, in arbitrarily small
polynomial update time. (2) The first deterministic algorithm for maintaining a
$(1+\delta)$-approximate maximum matching in a decremental bipartite graph, in
polylogarithmic update time. (3) The first deterministic algorithm for
maintaining a $(2+\delta)$-approximate maximum matching in a fully dynamic
general graph, in small polylogarithmic (specifically, $O(\log^4 n)$) update
time. These results are respectively obtained by applying our framework on top
of the fractional matching algorithms of Bhattacharya et al. [STOC'16],
Bernstein et al. [FOCS'20], and Bhattacharya and Kulkarni [SODA'19].
  Prior to our work, there were two known general-purpose rounding schemes for
dynamic fractional matchings. Both these schemes, by Arar et al. [ICALP'18] and
Wajc [STOC'20], were randomized.
  Our rounding scheme works by maintaining a good {\em matching-sparsifier}
with bounded arboricity, and then applying the algorithm of Peleg and Solomon
[SODA'16] to maintain a near-optimal matching in this low arboricity graph. To
the best of our knowledge, this is the first dynamic matching algorithm that
works on general graphs by using an algorithm for low-arboricity graphs as a
black-box subroutine. This feature of our rounding scheme might be of
independent interest.


Abstract:
  We present a biologically inspired design for swarm foraging based on ant's
pheromone deployment, where the swarm is assumed to have very restricted
capabilities. The robots do not require global or relative position
measurements and the swarm is fully decentralized and needs no infrastructure
in place. Additionally, the system only requires one-hop communication over the
robot network, we do not make any assumptions about the connectivity of the
communication graph and the transmission of information and computation is
scalable versus the number of agents. This is done by letting the agents in the
swarm act as foragers or as guiding agents (beacons). We present experimental
results computed for a swarm of Elisa-3 robots on a simulator, and show how the
swarm self-organizes to solve a foraging problem over an unknown environment,
converging to trajectories around the shortest path. At last, we discuss the
limitations of such a system and propose how the foraging efficiency can be
increased.


Abstract:
  There is recurrent debate about how useful science is for technological
development, but we know little about what kinds of science are more useful for
technology. This paper fills this gap in the literature by exploring how the
value of a patent (as measured by patent forward citations and the stock market
response to the issuing of the patent) depends on the characteristics of the
scientific papers that it builds on, specifically, basicness,
interdisciplinarity, novelty, and scientific citations. Using a dataset of
33,337 USPTO biotech utility patents and their 860,879 in-text references to
Web of Science journal articles, we find (1) a positive effect of the number of
referenced scientific papers, (2) an inverted U-shaped effect of basicness, (3)
an insignificant effect of interdisciplinarity, (4) a discontinuous and
nonlinear effect of novelty, and (5) a positive effect of scientific citations
for patent market value but an insignificant effect on patent citations. In
addition, in-text referenced papers have a higher chance of being listed on the
front-page of the same patent when they are moderately basic, less
interdisciplinary, less novel, and more highly cited. Accordingly, using
front-page reference yields substantially different results than using in-text
references.


Abstract:
  A graph $G$ is $F$-saturated if it contains no copy of $F$ as a subgraph but
the addition of any new edge to $G$ creates a copy of $F$. We prove that for $s
\geq 3$ and $t \geq 2$, the minimum number of copies of $K_{1,t}$ in a
$K_s$-saturated graph is $\Theta ( n^{t/2})$. More precise results are obtained
when $t = 2$ where the problem is related to Moore graphs with diameter 2 and
girth 5. We prove that for $s \geq 4$ and $t \geq 3$, the minimum number of
copies of $K_{2,t}$ in an $n$-vertex $K_s$-saturated graph is at least $\Omega(
n^{t/5 + 8/5})$ and at most $O(n^{t/2 + 3/2})$. These results answer a question
of Chakraborti and Loh. General estimates on the number of copies of $K_{a,b}$
in a $K_s$-saturated graph are also obtained, but finding an asymptotic formula
remains open.


Abstract:
  This paper identifies a model of software evolution that is prevalent in
large, long-lived academic research tool suites (3L-ARTS). This model results
in an "archipelago" of related but haphazardly organized architectural
"islands", and inherently induces technical debt. We illustrate the archipelago
model with examples from two 3L-ARTS archipelagos identified in literature.


Abstract:
  The dominant approach in probing neural networks for linguistic properties is
to train a new shallow multi-layer perceptron (MLP) on top of the model's
internal representations. This approach can detect properties encoded in the
model, but at the cost of adding new parameters that may learn the task
directly. We instead propose a subtractive pruning-based probe, where we find
an existing subnetwork that performs the linguistic task of interest. Compared
to an MLP, the subnetwork probe achieves both higher accuracy on pre-trained
models and lower accuracy on random models, so it is both better at finding
properties of interest and worse at learning on its own. Next, by varying the
complexity of each probe, we show that subnetwork probing Pareto-dominates MLP
probing in that it achieves higher accuracy given any budget of probe
complexity. Finally, we analyze the resulting subnetworks across various tasks
to locate where each task is encoded, and we find that lower-level tasks are
captured in lower layers, reproducing similar findings in past work.


Abstract:
  In this thesis, we study chiral topological phases of 2+1 dimensional quantum
matter. Such phases are abstractly characterized by their non-vanishing chiral
central charge $c$, a topological invariant which appears as the coefficient of
a gravitational Chern-Simons (gCS) action in bulk, and of corresponding
gravitational anomalies at boundaries. The chiral central charge is of
particular importance in chiral superfluids and superconductors (CSF/Cs), where
$U(1)$ particle-number symmetry is broken, and $c$ is, in some cases, the only
topological invariant characterizing the system. However, as opposed to
invariants which can be probed by gauge fields in place of gravity, the
concrete physical implications of $c$ in the context of condensed matter
physics is quite subtle, and has been the subject of ongoing research and
controversy. The first two parts of this thesis are devoted to the physical
interpretation of the gCS action and gravitational anomalies in the context of
CSF/Cs, where they are of particular importance, but have nevertheless remained
poorly understood. We then turn to a seemingly unrelated aspect of chiral
topological phases - their computational complexity. The infamous $sign\
problem$ leads to an exponential complexity in Monte Carlo simulations of
generic many-body quantum systems. Nevertheless, many phases of matter are
known to admit a sign-problem-free representative, allowing an efficient
classical simulation. The possibility of $intrinsic$ sign problems, where a
phase of matter admits no sign-problem-free representative, was recently raised
but remains largely unexplored. Here, we establish the existence of an
intrinsic sign problem in a broad class of chiral topological phases, both
bosonic and fermionic, defined by the requirement that $e^{2\pi i c/24}$ is
$not$ the topological spin of an anyon.


Abstract:
  The central region of NGC 1068 is one of the closest and most studied Active
Galactic Nucleus. It is known to be of type 2, meaning that its accretion disk
is obscured by a large amount of dust and gas. The main properties of the
obscuring structure are still to be determined. We model the inner edge of this
structure, where the hot dust responsible for the near-infrared emission
reaches its sublimation temperature. At first, we use simple geometrical models
in image reconstructions to determine the main 2D geometrical properties of the
source. In a second step, we try to reproduce the observables with K-band
images produced by 3D radiative transfer simulations of a heated dusty disk. We
explore various parameters to find an optimal solution and a model consistent
with all the observables. The three methods indicate an elongated structure
with 4 x 6 mas dimensions and its major axis along the NW/SE direction. All
three of them suggest that the object looks like an elongated ring rather than
an elongated thin disk, with its northeast edge less luminous than the
South-West one. The best 3D model is a thick disk with an inner radius r=0.21
+/- 0.03 pc and a half-opening angle $\alpha_{1/2}=21\pm8\degree$ observed with
an inclination $i=44_{-6}^{10}$\,$\degree$ and $PA=150_{-13}^{8}$\,$\degree$. A
high density of dust $n=5_{-2.5}^{+5}\ M_{\odot}.pc^{-3}$ is required to
explain the contrast between the two edges by self-absorption from the closer
one. The overall structure is obscured by a large foreground obscuration $A_V
\sim 75$. The hot dust is not responsible for the obscuration of the central
engine. The geometry and the orientation of the structure are different from
those of the previously observed maser and molecular disks. A single disk is
unable to account for these differences, and we favor a description of the
source where multiple rings are entangled around the central mass.


Abstract:
  We provide a synthesis of different topos-theoretical approaches of the
general construction of spectra, exploring jointly its purely categorical,
model theoretic and geometric aspects. We detail also the exact relation with
Diers notion of spectrum. Then we give a more detailed account of Cole's
original construction from bicategorical universal properties. We also provide
new formulas to compute the spectrum of arbitrary etale maps as pseudolimits,
as well as generalizations of results on fibered topos we then apply to compute
spectrum of a modelled topos.


Abstract:
  In this paper, we consider a three-zero texture with diagonal reflection
symmetries in the SM. The three-zero texture has two less assumptions
($(M_{u})_{11} , (M_{\nu})_{11} \neq 0$) than the universal four-zero texture
of mass matrices $(M_{f})_{11} = (M_{f})_{13,31} = 0$ for $f = u,d,\nu, e$. The
texture allows diagonal reflection symmetries to be almost exact and
$d$\,-\,$e$ unification. They reproduce the CKM and MNS matrices with
accuracies of $O(10^{-4})$ and $O(10^{-3})$. Some perturbative diagonalizations
yield two relations with good accuracy for quark mass and mixing. Although this
calculation is done in a special basis, it is a general result in a sense,
because other textures and generalized $CP$ symmetries exist by some weak basis
transformation.
  By assuming a $d$\,-\,$e$ unified relation ($M_{d} \sim M_{e}$), we obtain
the lightest neutrino mass $m_{1} \simeq 2.12\, - \, 5.64\,$[meV] and the
effective mass of the double beta decay $|m_{ee}| \simeq 1.23 - 2.15 \,$[meV].


Abstract:
  The results of Kermack-McKendrick SIR model are planned to be reproduced by
cellular automata (CA) lattice model. The CA algorithms are proposed to study
the model of epidemic, systematically. The basic goal is to capture the effects
of spreading of infection over a scale of length. This CA model can provide the
rate of growth of the infection over the space which was lacking in the
mean-field like SIR model. The motion of the circular front of an infected
cluster shows a linear behaviour in time. The correlation of a particular site
to be infected, with respect to the central site is also studied. The outcomes
of CA model are in good agreement with that obtained from SIR model. The
results of vaccination have been also incorporated in the CA algorithm with a
satisfactory degree of success. The advantage of the present model is that it
can shed considerable amount of light to the physical properties of the spread
of a typical epidemic in a simple, yet robust way.


Abstract:
  Testing for causation, defined as the preceding impact of the past values of
one variable on the current value of another one when all other pertinent
information is accounted for, is increasingly utilized in empirical research of
the time-series data in different scientific disciplines. A relatively recent
extension of this approach has been allowing for potential asymmetric impacts
since it is harmonious with the way reality operates in many cases according to
Hatemi-J (2012). The current paper maintains that it is also important to
account for the potential change in the parameters when asymmetric causation
tests are conducted, as there exists a number of reasons for changing the
potential causal connection between variables across time. The current paper
extends therefore the static asymmetric causality tests by making them dynamic
via the usage of subsamples. An application is also provided consistent with
measurable definitions of economic or financial bad as well as good news and
their potential interaction across time.


Abstract:
  We consider the problem of estimating the 3D orientation of a user, using the
downlink mmWave signals received from multiple base stations. We show that the
received signals from several base stations, having known positions, can be
used to estimate the unknown orientation of the user. We formulate the
estimation problem as a maximum likelihood estimation problem in the the
manifold of rotation matrices. In order to provide an initial estimate to solve
the non-linear non-convex optimization problem, we resort to a least squares
estimation problem that exploits the underlying geometry. Our numerical results
show that the problem of orientation estimation can be solved when the signals
from at least two base stations are received. We also provide the orientation
lower error bound, showing a narrow gap between the performance of the proposed
estimators and the bound.


Abstract:
  Low dose computed tomography (LDCT) has attracted more and more attention in
routine clinical diagnosis assessment, therapy planning, etc., which can reduce
the dose of X-ray radiation to patients. However, the noise caused by low X-ray
exposure degrades the CT image quality and then affects clinical diagnosis
accuracy. In this paper, we train a transformer-based neural network to enhance
the final CT image quality. To be specific, we first decompose the noisy LDCT
image into two parts: high-frequency (HF) and low-frequency (LF) compositions.
Then, we extract content features (X_{L_c}) and latent texture features
(X_{L_t}) from the LF part, as well as HF embeddings (X_{H_f}) from the HF
part. Further, we feed X_{L_t} and X_{H_f} into a modified transformer with
three encoders and decoders to obtain well-refined HF texture features. After
that, we combine these well-refined HF texture features with the pre-extracted
X_{L_c} to encourage the restoration of high-quality LDCT images with the
assistance of piecewise reconstruction. Extensive experiments on Mayo LDCT
dataset show that our method produces superior results and outperforms other
methods.


Abstract:
  We consider two models of interacting dark energy, both of which interact
only through momentum exchange. One is a phenomenological one-parameter
extension to $w$CDM, and the other is a coupled quintessence model described by
a Lagrangian formalism. Using a variety of high and low redshift data sets, we
perform a global fitting of cosmological parameters and compare to
$\Lambda$CDM, uncoupled quintessence, and $w$CDM. We find that the models are
competitive with $\Lambda$CDM, even obtaining a better fit when certain data
sets are included.


Abstract:
  Young stellar associations represent a key site for the study of star
formation, but to accurately compare observations to models of stellar
evolution, the age of an association must be determined. The Upper Scorpius
region is the youngest section of the Scorpius-Centaurus OB association, which
is the largest collection of nearby, young, low-mass stars. The true age of
Upper Scorpius is not clear, and an observed mass-dependent age gradient in
Upper Scorpius, as well as in other star-forming regions, complicates age
measurements. The age gradient may indicate a genuine astrophysical feature or
may be an artifact of unrecognized systematic effects in stellar age
measurements. We have conducted a synthetic red-optical low-resolution
spectroscopic survey of a simulated analog to the Upper Scorpius star-forming
region to investigate the effects of unresolved binary stars (which have
mass-dependent demographics) on age measurements of a stellar population. We
find that the observed mass-dependent age gradient in Upper Scorpius can be
explained by a population of undetected binary stars. For a simulated
population with an age of 10 (RMS = 2) Myr, we measure an age of 10.5 (RMS =
3.5) Myr for F stars, and 7.5 (RMS = 5.8) Myr for M stars. This discrepancy is
caused by the mass-dependent mass ratio distribution and the variable steepness
of the mass-luminosity relation. Our results support the previously suggested
10 Myr age for Upper Scorpius, with a small intrinsic age spread.


Abstract:
  In this paper, we describe an open source Python toolkit named Uncertainty
Quantification 360 (UQ360) for the uncertainty quantification of AI models. The
goal of this toolkit is twofold: first, to provide a broad range of
capabilities to streamline as well as foster the common practices of
quantifying, evaluating, improving, and communicating uncertainty in the AI
application development lifecycle; second, to encourage further exploration of
UQ's connections to other pillars of trustworthy AI such as fairness and
transparency through the dissemination of latest research and education
materials. Beyond the Python package (\url{https://github.com/IBM/UQ360}), we
have developed an interactive experience (\url{http://uq360.mybluemix.net}) and
guidance materials as educational tools to aid researchers and developers in
producing and communicating high-quality uncertainties in an effective manner.


Abstract:
  We study the feasibility of strong first-order electroweak phase transition
(EWPT) in a degenerate-scalar scenario of a complex singlet extension of the
Standard Model, in which a mass of an additional scalar is nearly degenerate
with that of the Higgs boson, 125 GeV. This scenario is known to provide an
exquisite solution for circumventing constraints from dark matter direct
detection experiments due to cancellations between two scattering amplitudes
mediated by two scalars. In the analysis of EWPT, we employ two gauge-invariant
calculation schemes on the scalar potential and two familiar resummation
methods in evaluating one-loop (gauge dependent) effective potential. We point
out that one of the conditions for the strong first-order EWPT is incompatible
with the known suppression mechanism of a dark matter cross-section scattering
off the nucleons. Nevertheless, we find that strong first-order EWPT is still
possible in the degenerate-scalar scenario by dodging dark matter constraints
differently.


Abstract:
  As a new degree of freedom for optical manipulation, recently spatiotemporal
optical vortices (STOVs) carrying transverse orbital angular momentums have
been experimentally demonstrated with bulky optical systems. Here we propose a
spatiotemporal differentiator to generate STOVs with pure transverse orbital
angular momentum. In order to create phase singularity in the spatiotemporal
domain, we design a spatiotemporal differentiator by breaking spatial mirror
symmetry. In contrast to the complex bulky systems, the device we propose here
is a simple one-dimensional periodic nanostructure and thus it is much more
compact. We show that for a normal incident pulse, the differentiator generates
a transmitted STOV pulse with transverse orbital angular momentum. Furthermore,
we demonstrate that the interference of the generated STOVs can be used to
detect the sharp changes of pulse envelopes, in both spatial and temporal
dimensions.


Abstract:
  We report on a spectroscopic program to search for dual quasars using Subaru
Hyper Suprime-Cam (HSC) images of SDSS quasars which represent an important
stage during galaxy mergers. Using Subaru/FOCAS and Gemini-N/GMOS, we identify
three new physically associated quasar pairs having projected separations less
than 20 kpc, out of 26 observed candidates. These include the discovery of the
highest redshift ($z=3.1$) quasar pair with a separation $<$ 10 kpc. Based on
the sample acquired to date, the success rate of identifying physically
associated dual quasars is $19\%$ when excluding stars based on their HSC
colors. Using the full sample of six spectroscopically confirmed dual quasars,
we find that the black holes in these systems have black hole masses ($M_{BH}
\sim 10^{8-9}M_{\odot}$) similar to single SDSS quasars as well as their
bolometric luminosities and Eddington ratios. We measure the stellar mass of
their host galaxies based on 2D image decomposition of the five-band ($grizy$)
optical emission and assess the mass relation between supermassive black holes
(SMBHs) and their hosts. Dual SMBHs appear to have elevated masses relative to
their host galaxies. Thus mergers may not necessarily align such systems onto
the local mass relation, as suggested by the Horizon-AGN simulation. This study
suggests that dual luminous quasars are triggered prior to the final
coalescence of the two SMBHs, resulting in early mass growth of the black holes
relative to their host galaxies.


Abstract:
  Book covers are intentionally designed and provide an introduction to a book.
However, they typically require professional skills to design and produce the
cover images. Thus, we propose a generative neural network that can produce
book covers based on an easy-to-use layout graph. The layout graph contains
objects such as text, natural scene objects, and solid color spaces. This
layout graph is embedded using a graph convolutional neural network and then
used with a mask proposal generator and a bounding-box generator and filled
using an object proposal generator. Next, the objects are compiled into a
single image and the entire network is trained using a combination of
adversarial training, perceptual training, and reconstruction. Finally, a Style
Retention Network (SRNet) is used to transfer the learned font style onto the
desired text. Using the proposed method allows for easily controlled and unique
book covers.


Abstract:
  Background: Subgroup analyses are frequently conducted in randomized clinical
trials to assess evidence of heterogeneous treatment effect across patient
subpopulations. Although randomization balances covariates within subgroups in
expectation, chance imbalance may be amplified in small subgroups and harm the
precision. Covariate adjustment in overall analysis of RCT is often conducted,
via either ANCOVA or propensity score weighting, but for subgroup analysis has
been rarely discussed. In this article, we develop propensity score weighting
methodology for covariate adjustment to improve the precision and power of
subgroup analyses in RCTs. Methods: We extend the propensity score weighting
methodology to subgroup analyses by fitting a logistic propensity model with
pre-specified covariate-subgroup interactions. We show that, by construction,
overlap weighting exactly balances the covariates with interaction terms in
each subgroup. Extensive simulations were performed to compare the operating
characteristics of unadjusted, different propensity score weighting and the
ANCOVA estimator. We apply these methods to the HF-ACTION trial to evaluate the
effect of exercise training on 6-minute walk test in pre-specified subgroups.
Results: Standard errors of the adjusted estimators are smaller than those of
the unadjusted estimator. The propensity score weighting estimator is as
efficient as ANCOVA, and is often more efficient when subgroup sample size is
small (e.g.<125), and/or when outcome model is misspecified. The weighting
estimators with full-interaction propensity model consistently outperform the
standard main-effect propensity model. Conclusion: Propensity score weighting
is a transparent and objective method to adjust chance imbalance of important
covariates in subgroup analyses of RCTs. It is crucial to include the full
covariate-subgroup interactions in the propensity score model.


Abstract:
  Motivated by the recent findings of unconventional superconductivity in
$\mathrm{CoSi_2 / TiSi_2}$ heterostructures, we study the effect of interface
induced Rashba spin orbit coupling on the conductance of a three terminal "T"
shape superconducting device. We calculate the differential conductance for
this device within the quasi-classical formalism that includes the mixing of
triplet-singlet pairing due to the Rashba spin orbit coupling. We discuss our
result in the light of the conductance spectra reported by Chiu {\it et al.}
for $\mathrm{CoSi_2 / TiSi_2}$ heterostructures.


Abstract:
  We give a new derivation of mean-field percolation critical behaviour from
the triangle condition that is quantitatively much better than previous proofs
when the triangle diagram $\nabla_{p_c}$ is large. In contrast to earlier
methods, our approach continues to yield bounds of reasonable order when the
triangle diagram $\nabla_p$ is unbounded but diverges slowly as $p \uparrow
p_c$, as is expected to occur in percolation on $\mathbb{Z}^d$ at the
upper-critical dimension $d=6$. Indeed, we show in particular that if the
triangle diagram diverges polylogarithmically as $p \uparrow p_c$ then
mean-field critical behaviour holds to within a polylogarithmic factor. We
apply the methods we develop to deduce that for long-range percolation on the
hierarchical lattice, mean-field critical behaviour holds to within
polylogarithmic factors at the upper-critical dimension.
  As part of the proof, we introduce a new method for comparing diagrammatic
sums on general transitive graphs that may be of independent interest.


Abstract:
  In Cyber Physical Systems humans are often kept in the loop as operators
and/or service users. Yet in many cases, humans and machines collaborate and
provide services to each other. Research on service models and service
composition for CPS exist; however, humans as service providers have not been
adequately considered as part of the CPS service composition model. We provide
a classification of human-as-a-service in CPS, and we propose a Service
Oriented Architecture (SOA) ontology model for the CPS environment as part of
the Everything-as-a-Service paradigm. The model considers human characteristics
and their dynamics, as a service provider or collaborator with the machine. As
the ontology model is an enabler for engineering a self-adaptive CPS with
human-machine collaboration as service providers, we describe how a commonly
used self-adaptive reference model can be refined to benefit from the vision.
We evaluate the ontological contribution against criteria that relates to
accuracy, completeness, adaptability, clarity, and consistency. We demonstrate
the feasibility of our conceptual reference model using a use case from the
medical domain and we show how human-machine service provision is possible.


Abstract:
  Automated tools for the computation of particle physics' processes have
become the backbone of phenomenological studies beyond the standard model.
Here, we present MadDM v3.2. This release enables the fully automated
computation of loop-induced dark-matter annihilation processes, relevant for
indirect detection observables. Special emphasis lies on the annihilation into
$\gamma X$, where $X=\gamma, Z, h$ or any new particle even under the dark
symmetry. These processes lead to the sharp spectral feature of monochromatic
gamma lines - a smoking-gun signature of dark matter in our Galaxy. MadDM
provides the predictions for the respective fluxes near-Earth and derives
constraints from the gamma-ray line searches by Fermi-LAT and HESS. As an
application, we discuss the implications for the viable parameter space of a
top-philic $t$-channel mediator model and the inert doublet model.


Abstract:
  We study chiral symmetry restoration by analyzing thermal properties of QCD's
(pseudo-)Goldstone bosons, especially the pion. The meson properties are
obtained from the spectral densities of mesonic imaginary-time correlation
functions. To obtain the correlation functions, we solve the Dyson-Schwinger
equations and the inhomogeneous Bethe-Salpeter equations in the leading
symmetry-preserving rainbow-ladder approximation. In the chiral limit, the pion
and its partner sigma degenerate at the critical temperature $T_c$. At $T
\gtrsim T_c$, it is found that the pion rapidly dissociates, which signals
deconfinement phase transition. Beyond the chiral limit, the pion dissociation
temperature can be used to define the pseudo-critical temperature of chiral
phase crossover, which is consistent with that obtained by the maximum point of
the chiral susceptibility. The parallel analysis for kaon and pseudoscalar
$s\bar{s}$ suggests that heavy mesons may survive above $T_c$.


Abstract:
  The number of maximal abelian subgroups of a finite p-group is shown to be
congruent to 1 modulo p.


Abstract:
  The effectiveness of Machine Learning (ML) methods depend on access to large
suitable datasets. In this article, we present how we build the LS-CAT
(Large-Scale CUDA AutoTuning) dataset sourced from GitHub for the purpose of
training NLP-based ML models. Our dataset includes 19 683 CUDA kernels focused
on linear algebra. In addition to the CUDA codes, our LS-CAT dataset contains 5
028 536 associated runtimes, with different combinations of kernels, block
sizes and matrix sizes. The runtime are GPU benchmarks on both Nvidia GTX 980
and Nvidia T4 systems. This information creates a foundation upon which
NLP-based models can find correlations between source-code features and optimal
choice of thread block sizes.
  There are several results that can be drawn out of our LS-CAT database. E.g.,
our experimental results show that an optimal choice in thread block size can
gain an average of 6% for the average case. We thus also analyze how much
performance increase can be achieved in general, finding that in 10% of the
cases more than 20% performance increase can be achieved by using the optimal
block. A description of current and future work is also included.


Abstract:
  In this paper, we present a new tracking architecture with an encoder-decoder
transformer as the key component. The encoder models the global spatio-temporal
feature dependencies between target objects and search regions, while the
decoder learns a query embedding to predict the spatial positions of the target
objects. Our method casts object tracking as a direct bounding box prediction
problem, without using any proposals or predefined anchors. With the
encoder-decoder transformer, the prediction of objects just uses a simple
fully-convolutional network, which estimates the corners of objects directly.
The whole method is end-to-end, does not need any postprocessing steps such as
cosine window and bounding box smoothing, thus largely simplifying existing
tracking pipelines. The proposed tracker achieves state-of-the-art performance
on five challenging short-term and long-term benchmarks, while running at
real-time speed, being 6x faster than Siam R-CNN. Code and models are
open-sourced at https://github.com/researchmm/Stark.


Abstract:
  There has been an increasing consensus in learning based face anti-spoofing
that the divergence in terms of camera models is causing a large domain gap in
real application scenarios. We describe a framework that eliminates the
influence of inherent variance from acquisition cameras at the feature level,
leading to the generalized face spoofing detection model that could be highly
adaptive to different acquisition devices. In particular, the framework is
composed of two branches. The first branch aims to learn the camera invariant
spoofing features via feature level decomposition in the high frequency domain.
Motivated by the fact that the spoofing features exist not only in the high
frequency domain, in the second branch the discrimination capability of
extracted spoofing features is further boosted from the enhanced image based on
the recomposition of the high-frequency and low-frequency information. Finally,
the classification results of the two branches are fused together by a
weighting strategy. Experiments show that the proposed method can achieve
better performance in both intra-dataset and cross-dataset settings,
demonstrating the high generalization capability in various application
scenarios.


Abstract:
  Ensemble-based modifications of the well-known SHapley Additive exPlanations
(SHAP) method for the local explanation of a black-box model are proposed. The
modifications aim to simplify SHAP which is computationally expensive when
there is a large number of features. The main idea behind the proposed
modifications is to approximate SHAP by an ensemble of SHAPs with a smaller
number of features. According to the first modification, called ER-SHAP,
several features are randomly selected many times from the feature set, and
Shapley values for the features are computed by means of "small" SHAPs. The
explanation results are averaged to get the final Shapley values. According to
the second modification, called ERW-SHAP, several points are generated around
the explained instance for diversity purposes, and results of their explanation
are combined with weights depending on distances between points and the
explained instance. The third modification, called ER-SHAP-RF, uses the random
forest for preliminary explanation of instances and determining a feature
probability distribution which is applied to selection of features in the
ensemble-based procedure of ER-SHAP. Many numerical experiments illustrating
the proposed modifications demonstrate their efficiency and properties for
local explanation.


Abstract:
  A Lagrangian field on a symplectic manifold $M$ is a family
$\Lambda=\{\Lambda_x|x \in M\}$ of pointed Lagrangian submanifolds of $M$. This
notion is a generalization of a real Lagrangian polarization for which each
$\Lambda_x$ is the leaf containing $x$. Two Lagrangian fields $\Lambda$ and
$\tilde \Lambda$ are called transversal if $\Lambda_x$ intersects
$\tilde\Lambda_x$ transversally at $x$ for every $x$. Two transversal
Lagrangian fields determine an almost para-K\"ahler structure on $M$. We
construct a local symplectic groupoid on a neighborhood of the zero section of
$T^\ast M$ from two transversal Lagrangian fields on $M$. The Lagrangian
manifold of $n$-cycles of this groupoid in $(T^\ast M)^n$ has a generating
function whose germ around the diagonal of $M^n$ is given by the $n$-point
cyclic Calabi function of a closed (1,1)-form on a neighborhood of the diagonal
of $M^2$ obtained from the symplectic form on $M$.


Abstract:
  The Laser Interferometer Gravitational-wave Observatory Scientific
Collaboration and Virgo Collaboration (LVC) sent out 56 gravitational-wave (GW)
notices during the third observing run (O3). Japanese collaboration for
Gravitational wave ElectroMagnetic follow-up (J-GEM) performed optical and
near-infrared observations to identify and observe an electromagnetic (EM)
counterpart. We constructed web-based system which enabled us to obtain and
share information of candidate host galaxies for the counterpart, and status of
our observations. Candidate host galaxies were selected from the GLADE catalog
with a weight based on the three-dimensional GW localization map provided by
LVC. We conducted galaxy-targeted and wide-field blind surveys, real-time data
analysis, and visual inspection of observed galaxies. We performed
galaxy-targeted follow-ups to 23 GW events during O3, and the maximum
probability covered by our observations reached to 9.8%. Among them, we
successfully started observations for 10 GW events within 0.5 days after the
detection. This result demonstrates that our follow-up observation has a
potential to constrain EM radiation models for a merger of binary neutron stars
at a distance of up to $\sim$100~Mpc with a probability area of $\leq$
500~deg$^2$.


Abstract:
  Using firm-level survey- and register-data for both Sweden and Denmark we
show systematic mis-measurement in both vacancy measures. While the
register-based measure on the aggregate constitutes a quarter of the
survey-based measure, the latter is not a super-set of the former. To obtain
the full set of unique vacancies in these two databases, the number of survey
vacancies should be multiplied by approximately 1.2. Importantly, this
adjustment factor varies over time and across firm characteristics. Our
findings have implications for both the search-matching literature and policy
analysis based on vacancy measures: Observed changes in vacancies can be an
outcome of changes in mis-measurement, and are not necessarily changes in the
actual number of vacancies.


Abstract:
  Surface stress and surface energy are two fundamental parameters that
determine the surface properties of any materials. While it is commonly
believed that the surface stress and surface energy of liquids are identical,
the relationship between the two parameters in soft polymeric gels remains
debatable. In this work, we measured the surface stress and surface energy of
soft silicone gels with varying weight ratios of crosslinkers in soft wetting
experiments. Above a critical density, $k_0$, the surface stress was found to
increase significantly with crosslinking density while the surface energy
remained unchanged. In this regime, we can estimate a non-zero surface elastic
modulus that also increases with the ratio of crosslinkers. By comparing the
surface mechanics of the soft gels with their bulk rheology, the surface
properties near the critical density $k_0$ were found to be closely related to
the underlying percolation transition of the polymer networks.


Abstract:
  In order to face the complexity of business environments and detect
priorities while triggering contingency strategies, we propose a new
methodological approach that combines text mining, social network and big data
analytics, with the assessment of stakeholders' attitudes towards company core
values. This approach was applied in a case study where we considered the
Twitter discourse about core values in Italy. We collected more than 94,000
tweets related to the core values of the firms listed in Fortune's ranking of
the World's Most Admired Companies (2013-2017). For the Italian scenario, we
found three predominant core values orientations (Customers, Employees and
Excellence) - which should be at the basis of any business strategy - and three
latent ones (Economic-Financial Growth, Citizenship and Social Responsibility),
which need periodic attention. Our contribution is mostly methodological and
extends the research on text mining and on online big data analytics applied in
complex business contexts.


Abstract:
  The HIBEAM/NNBAR experiment is a free-neutron search for $n \rightarrow$
sterile $n$ and $n \rightarrow \bar{n}$ oscillations planned to be installed at
the European Spallation Source under construction in Lund, Sweden. A key
component in the experiment is the detector to identify $n-\bar{n}$
annihilation events, which will produce on average four pions with a final
state invariant mass of two nucleons, around $1.9\,$GeV. The beamline and
experiment are shielded from magnetic fields which would suppress $n
\rightarrow \bar{n}$ transitions, thus no momentum measurement will be
possible. Additionally, calorimetry for particles with kinetic energies below
$600\,$MeV is challenging, as traditional sampling calorimeters used in HEP
would suffer from poor shower statistics. A design study is underway to use a
novel approach of a hadronic range measurement in multiple plastic scintillator
layers, followed by EM calorimetery with lead glass. A prototype calorimeter
system is being built, and will eventually be installed at an ESS test beam
line for \textit{in situ} neutron background studies.


Abstract:
  We present an empirical model for nitric oxide NO in the mesosphere
($\approx$60--90 km) derived from SCIAMACHY (SCanning Imaging Absorption
spectroMeter for Atmospheric CHartoghraphY) limb scan data. This work
complements and extends the NOEM (Nitric Oxide Empirical Model; Marsh et al.,
2004) and SANOMA (SMR Acquired Nitric Oxide Model Atmosphere; Kiviranta et al.,
2018) empirical models in the lower thermosphere. The regression ansatz builds
on the heritage of studies by Hendrickx et al. (2017) and the superposed epoch
analysis by Sinnhuber et al. (2016) which estimate NO production from particle
precipitation.
  Our model relates the daily (longitudinally) averaged NO number densities
from SCIAMACHY (Bender et al., 2017a, b) as a function of geomagnetic latitude
to the solar Lyman-alpha and the geomagnetic AE (auroral electrojet) indices.
We use a non-linear regression model, incorporating a finite and seasonally
varying lifetime for the geomagnetically induced NO. We estimate the parameters
by finding the maximum posterior probability and calculate the parameter
uncertainties using Markov chain Monte Carlo sampling. In addition to providing
an estimate of the NO content in the mesosphere, the regression coefficients
indicate regions where certain processes dominate.


Abstract:
  In this paper, we propose a novel score-base generative model for
unconditional raw audio synthesis. Our proposal builds upon the latest
developments on diffusion process modeling with stochastic differential
equations, which already demonstrated promising results on image generation. We
motivate novel heuristics for the choice of the diffusion processes better
suited for audio generation, and consider the use of a conditional U-Net to
approximate the score function. While previous approaches on diffusion models
on audio were mainly designed as speech vocoders in medium resolution, our
method termed CRASH (Controllable Raw Audio Synthesis with High-resolution)
allows us to generate short percussive sounds in 44.1kHz in a controllable way.
Through extensive experiments, we showcase on a drum sound generation task the
numerous sampling schemes offered by our method (unconditional generation,
deterministic generation, inpainting, interpolation, variations,
class-conditional sampling) and propose the class-mixing sampling, a novel way
to generate "hybrid" sounds. Our proposed method closes the gap with GAN-based
methods on raw audio, while offering more flexible generation capabilities with
lighter and easier-to-train models.


Abstract:
  The Apollo 11 Eagle Lunar Module ascent stage was abandoned in lunar orbit
after the historic landing in 1969. Its fate is unknown. Numerical analysis
described here provides evidence that this object might have remained in lunar
orbit to the present day. The simulations show a periodic variation in
eccentricity of the orbit, correlated to the selenographic longitude of the
apsidal line. The rate of apsidal precession is correlated to eccentricity.
These two factors appear to interact to stabilize the orbit over the long term.


Abstract:
  Millimeter wave wireless spectrum deployments will allow vehicular
communications to share high data rate vehicular sensor data in real-time. The
highly directional nature of wireless links in millimeter spectral bands will
require continuous channel measurements to ensure the transmitter (TX) and
receiver (RX) beams are aligned to provide the best channel. Using real-world
vehicular mmWave measurement data at 28 GHz, we determine the optimal beam
sweeping period, i.e. the frequency of the channel measurements, to align the
RX beams to the best channel directions for maximizing the
vehicle-to-infrastructure (V2I) throughput. We show that in a realistic
vehicular traffic environment in Austin, TX, for a vehicle traveling at an
average speed of 10.5 mph, a beam sweeping period of 300 ms in future V2I
communication standards would maximize the V2I throughput, using a system of
four RX phased arrays that scanned the channel 360 degrees in the azimuth and
30 degrees above and below the boresight. We also investigate the impact of the
number of active RX chains controlling the steerable phased arrays on V2I
throughput. Reducing the number of RX chains controlling the phased arrays
helps reduce the cost of the vehicular mmWave hardware while multiple RX
chains, although more expensive, provide more robustness to beam direction
changes at the vehicle, allowing near maximum throughput over a wide range of
beam sweep periods. We show that the overhead of utilizing one RX chain instead
of four leads to a 10% drop in mean V2I throughput over six non-line-of-sight
runs in real traffic conditions, with each run being 10 to 20 seconds long over
a distance of 40 to 90 meters.


Abstract:
  Generating synthesised singing voice with models trained on speech data has
many advantages due to the models' flexibility and controllability. However,
since the information about the temporal relationship between segments and
beats are lacking in speech training data, the synthesised singing may sound
off-beat at times. Therefore, the availability of the information on the
temporal relationship between speech segments and music beats is crucial. The
current study investigated the segment-beat synchronisation in singing data,
with hypotheses formed based on the linguistics theories of P-centre and
sonority hierarchy. A Mandarin corpus and an English corpus of professional
singing data were manually annotated and analysed. The results showed that the
presence of musical beats was more dependent on segment duration than sonority.
However, the sonority hierarchy and the P-centre theory were highly related to
the location of beats. Mandarin and English demonstrated cross-linguistic
variations despite exhibiting common patterns.


Abstract:
  Attribution of paintings is a critical problem in art history. This study
extends machine learning analysis to surface topography of painted works. A
controlled study of positive attribution was designed with paintings produced
by a class of art students. The paintings were scanned using a confocal optical
profilometer to produce surface data. The surface data were divided into
virtual patches and used to train an ensemble of convolutional neural networks
(CNNs) for attribution. Over a range of patch sizes from 0.5 to 60 mm, the
resulting attribution was found to be 60 to 96% accurate, and, when comparing
regions of different color, was nearly twice as accurate as CNNs using color
images of the paintings. Remarkably, short length scales, as small as twice a
bristle diameter, were the key to reliably distinguishing among artists. These
results show promise for real-world attribution, particularly in the case of
workshop practice.


Abstract:
  Several challenging optimization problems arise while considering the
deployment of the space-air-ground integrated networks (SAGINs), among which
the optimal satellite gateway deployment problem is of significant importance.
Moreover, with the increasing interest in the software-defined integration of
5G networks and satellites, the existence of an effective scheme for optimal
placement of SDN controllers is essential. In this paper, we discuss the
interrelation between the two problems above and propose suitable methods to
solve them under various network design criteria. We first provide a MILP model
for solving the joint problem, and then motivate the decomposition of the model
into two disjoint MILPs. We then show that the resulting problems can be
modeled as the optimization of submodular set functions and can be solved
efficiently with provable optimality gaps.


Abstract:
  A key element in solving real-life data science problems is selecting the
types of models to use. Tree ensemble models (such as XGBoost) are usually
recommended for classification and regression problems with tabular data.
However, several deep learning models for tabular data have recently been
proposed, claiming to outperform XGBoost for some use cases. This paper
explores whether these deep models should be a recommended option for tabular
data by rigorously comparing the new deep models to XGBoost on various
datasets. In addition to systematically comparing their performance, we
consider the tuning and computation they require. Our study shows that XGBoost
outperforms these deep models across the datasets, including the datasets used
in the papers that proposed the deep models. We also demonstrate that XGBoost
requires much less tuning. On the positive side, we show that an ensemble of
deep models and XGBoost performs better on these datasets than XGBoost alone.


Abstract:
  For each $\lambda>0$ and every square-integrable infinitely-divisible (ID)
distribution there exists at least one stationary stochastic process $t\mapsto
X_t$ with the specified distribution for $X_1$ and with first-order
autoregressive (AR(1)) structure in the sense that the autocorrelation of $X_s$
and $X_t$ is $\exp(-\lambda|s-t|)$ for all indices $s,t$. For the special case
of the standard Normal distribution, the process $X_t$ is unique -- namely, the
first-order autoregressive Ornstein-Uhlenbeck velocity process. The process
$X_t$ is also uniquely determined if $X_1$ is accorded the unit rate Poisson
distribution.
  For the Gamma distribution, however, $X_t$ is \emph{not} determined uniquely.
In these lecture notes we describe six distinct processes with the same
univariate marginal distributions and AR(1) autocorrelation function. We
explore a few of their properties and describe methods of simulating their
sample paths.


Abstract:
  Predicting multiple plausible future trajectories of the nearby vehicles is
crucial for the safety of autonomous driving. Recent motion prediction
approaches attempt to achieve such multimodal motion prediction by implicitly
regularizing the feature or explicitly generating multiple candidate proposals.
However, it remains challenging since the latent features may concentrate on
the most frequent mode of the data while the proposal-based methods depend
largely on the prior knowledge to generate and select the proposals. In this
work, we propose a novel transformer framework for multimodal motion
prediction, termed as mmTransformer. A novel network architecture based on
stacked transformers is designed to model the multimodality at feature level
with a set of fixed independent proposals. A region-based training strategy is
then developed to induce the multimodality of the generated proposals.
Experiments on Argoverse dataset show that the proposed model achieves the
state-of-the-art performance on motion prediction, substantially improving the
diversity and the accuracy of the predicted trajectories. Demo video and code
are available at https://decisionforce.github.io/mmTransformer.


Abstract:
  The quest of utilizing neutral particles to simulate the behaviour of charged
particles in a magnetic field makes the generation of artificial magnetic field
of great interest. The previous and the only proposal for the production of
synthetic magnetic field for the dark state polaritons in electromagnetically
induced transparency invokes the mechanical rotation of a sample. Here, we put
forward an optical scheme to generate effective gauge potentials for
stationary-light polaritons. 5To demonstrate the capabilities of our approach,
we present recipes for having dark state polaritons in degenerate Landau levels
and in driven quantum harmonic oscillator. Our scheme paves a novel way towards
the investigation of the bosonic analogue of the fractional quantum Hall effect
by electromagnetically induced transparency.


Abstract:
  We investigate the solution landscapes of a simplified Ericksen--Leslie (sEL)
vector model for nematic liquid crystals, confined in a two-dimensional square
domain with tangent boundary conditions. An efficient numerical algorithm is
developed to construct the solution landscapes by utilizing the symmetry
properties of the model and the domain. Since the sEL model and the reduced
Landau--de Gennes (rLdG) models can be viewed as Ginzburg--Landau functionals,
we systematically compute the solution landscapes of the sEL model, for
different domain sizes, and compare with the solution landscapes of the
corresponding rLdG models. There are many similarities, including the stable
diagonal and rotated states, bifurcation behaviors, and sub-solution landscapes
with low-index saddle solutions. Significant disparities also exist between the
two models. The sEL vector model exhibits the stable solution $C\pm$ with
interior defects, high-index "fake defects" solutions, novel tessellating
solutions, and certain types of distinctive dynamical pathways. The solution
landscape approach provides a comprehensive and efficient way for model
comparison and is applicable to a wide range of mathematical models in physics.


Abstract:
  Several machine learning-based spoiler detection models have been proposed
recently to protect users from spoilers on review websites. Although dependency
relations between context words are important for detecting spoilers, current
attention-based spoiler detection models are insufficient for utilizing
dependency relations. To address this problem, we propose a new spoiler
detection model called SDGNN that is based on syntax-aware graph neural
networks. In the experiments on two real-world benchmark datasets, we show that
our SDGNN outperforms the existing spoiler detection models.


Abstract:
  Squeezed light finds many important applications in quantum information
science and quantum metrology, and has been produced in a variety of physical
systems involving optical nonlinear processes. Here, we show how a nonlinear
magnetostrictive interaction in a ferrimagnet in cavity magnomechanics can be
used to reduce quantum noise of the electromagnetic field. We show optimal
parameter regimes where a substantial and stationary squeezing of the microwave
output field can be achieved. The scheme can be realized within the reach of
current technology in cavity electromagnonics and magnomechanics. Our work
provides a new and practicable approach for producing squeezed vacuum states of
electromagnetic fields, and may find promising applications in quantum
information processing and quantum metrology.


Abstract:
  The presented compact algorithm for recognizing handwritten digits of the
MNIST database, created on the LogNNet reservoir neural network, reaches the
recognition accuracy of 82%. The algorithm was tested on a low-memory Arduino
board with 2 Kb static RAM low-power microcontroller. The dependences of the
accuracy and time of image recognition on the number of neurons in the
reservoir have been investigated. The memory allocation demonstrates that the
algorithm stores all the necessary information in RAM without using additional
data storage, and operates with original images without preliminary processing.
The simple structure of the algorithm, with appropriate training, can be
adapted for wide practical application, for example, for creating mobile
biosensors for early diagnosis of adverse events in medicine. The study results
are important for the implementation of artificial intelligence on peripheral
constrained IoT devices and for edge computing.


Abstract:
  We present results from the NIRVANDELS survey investigating the gas-phase
metallicity ($\mathrm{Z}_{\mathrm{gas}}$, tracing O/H) and stellar metallicity
($Z_{\star}$, tracing Fe/H) of 33 star-forming galaxies at redshifts $2.95 < z
< 3.80$. Based on a combined analysis of deep optical and near-IR spectra,
tracing the rest-frame far ultraviolet and rest-frame optical respectively, we
present the first simultaneous determination of the stellar and gas-phase
mass-metallicity relationships (MZRs) at $z\simeq3.4$. In both cases, we find
that metallicity increases with increasing stellar mass ($M_{\star}$), and that
the power-law slope at $M_{\star} \lesssim 10^{10} \mathrm{M}_{\odot}$ of both
MZRs scales as $Z \propto M_{\star}^{0.3}$. Comparing the stellar and gas-phase
MZRs, we present direct evidence for super-solar O/Fe ratios (i.e.,
$\alpha$-enhancement) at $z>3$, finding $\mathrm{(O/Fe)}\simeq (2.54 \pm 0.38)
\times \mathrm{(O/Fe)}_{\odot}$, with no clear dependence on $M_{\star}$.


Abstract:
  We consider the Navier-Stokes equations on the two-dimensional unit sphere
and study the linear stability of the two-jet Kolmogorov type flow which is a
stationary solution given by the zonal spherical harmonic function of degree
two. We prove the linear stability of the two-jet Kolmogorov type flow for an
arbitrary viscosity coefficient by showing the exponential decay of a solution
to the linearized equation towards an equilibrium which grows as the viscosity
coefficient tends to zero. The main result of this paper is the nonexistence of
nonzero eigenvalues of the perturbation operator appearing in the linearized
equation. By making use of the mixing property of the perturbation operator
which is expressed by a recurrence relation for the spherical harmonics, we
show that the perturbation operator does not have not only nonreal but also
nonzero real eigenvalues. As an application of this result, we get the enhanced
dissipation for the two-jet Kolmogorov type flow in the sense that a solution
to the linearized equation rescaled in time decays arbitrarily fast as the
viscosity coefficient tends to zero.


Abstract:
  Effective conservation actions require effective population monitoring.
However, accurately counting animals in the wild to inform conservation
decision-making is difficult. Monitoring populations through image sampling has
made data collection cheaper, wide-reaching and less intrusive but created a
need to process and analyse this data efficiently. Counting animals from such
data is challenging, particularly when densely packed in noisy images.
Attempting this manually is slow and expensive, while traditional computer
vision methods are limited in their generalisability. Deep learning is the
state-of-the-art method for many computer vision tasks, but it has yet to be
properly explored to count animals. To this end, we employ deep learning, with
a density-based regression approach, to count fish in low-resolution sonar
images. We introduce a large dataset of sonar videos, deployed to record wild
mullet schools (Mugil liza), with a subset of 500 labelled images. We utilise
abundant unlabelled data in a self-supervised task to improve the supervised
counting task. For the first time in this context, by introducing uncertainty
quantification, we improve model training and provide an accompanying measure
of prediction uncertainty for more informed biological decision-making.
Finally, we demonstrate the generalisability of our proposed counting framework
through testing it on a recent benchmark dataset of high-resolution annotated
underwater images from varying habitats (DeepFish). From experiments on both
contrasting datasets, we demonstrate our network outperforms the few other deep
learning models implemented for solving this task. By providing an open-source
framework along with training data, our study puts forth an efficient deep
learning template for crowd counting aquatic animals thereby contributing
effective methods to assess natural populations from the ever-increasing visual
data.


Abstract:
  Sensitivity limits are usually determined using the Cram\'er-Rao bound.
Recently this approach has been used to obtain the ultimate resolution limit
for the estimation of the separation between two incoherent point sources.
However, methods that saturate these resolution limits, usually require the
full measurement statistics, which can be challenging to access. In this work,
we introduce a simple superresolution protocol to estimate the separation
between two thermal sources which relies only on the average value of a single
accessible observable. We show how optimal observables for this technique may
be constructed for arbitrary thermal sources, and we study their sensitivities
when one has access to spatially resolved intensity measurements (direct
imaging) and photon counting after spatial mode demultiplexing. For
demultiplexing, our method is optimal, i.e. it saturates the quantum
Cram\'er-Rao bound. We also investigate the impact of noise on the optimal
observables, their measurement sensitivity and on the scaling with the number
of detected photons of the smallest resolvable separation. For low signals in
the image plane, we demonstrate that our method saturates the Cram\'er-Rao
bound even in the presence of noise.


Abstract:
  This work focuses on the problem of unraveling nonlinearly mixed latent
components in an unsupervised manner. The latent components are assumed to
reside in the probability simplex, and are transformed by an unknown
post-nonlinear mixing system. This problem finds various applications in signal
and data analytics, e.g., nonlinear hyperspectral unmixing, image embedding,
and nonlinear clustering. Linear mixture learning problems are already
ill-posed, as identifiability of the target latent components is hard to
establish in general. With unknown nonlinearity involved, the problem is even
more challenging. Prior work offered a function equation-based formulation for
provable latent component identification. However, the identifiability
conditions are somewhat stringent and unrealistic. In addition, the
identifiability analysis is based on the infinite sample (i.e., population)
case, while the understanding for practical finite sample cases has been
elusive. Moreover, the algorithm in the prior work trades model expressiveness
with computational convenience, which often hinders the learning performance.
Our contribution is threefold. First, new identifiability conditions are
derived under largely relaxed assumptions. Second, comprehensive sample
complexity results are presented -- which are the first of the kind. Third, a
constrained autoencoder-based algorithmic framework is proposed for
implementation, which effectively circumvents the challenges in the existing
algorithm. Synthetic and real experiments corroborate our theoretical analyses.


Abstract:
  Generative adversarial networks (GANs) have proven to be surprisingly
efficient for image editing by inverting and manipulating the latent code
corresponding to a natural image. This property emerges from the disentangled
nature of the latent space. In this paper, we identify two geometric
limitations of such latent space: (a) euclidean distances differ from image
perceptual distance, and (b) disentanglement is not optimal and facial
attribute separation using linear model is a limiting hypothesis. We thus
propose a new method to learn a proxy latent representation using normalizing
flows to remedy these limitations, and show that this leads to a more efficient
space for face image editing.


Abstract:
  The Dark Matter Particle Explorer (DAMPE) is a space-borne particle detector
and cosmic ray observatory in operation since 2015, designed to probe electrons
and gamma rays from a few GeV to 10 TeV energy, as well as cosmic protons and
nuclei up to 100 TeV. Among the main scientific objectives is the precise
measurement of the cosmic electron+positron flux, which due to the very large
proton background in orbit requires a powerful particle identification method.
In the past decade, the field of machine learning has provided us the needed
tools. This paper presents a neural network based approach to cosmic electron
identification and proton rejection and showcases its performances based on
simulated Monte Carlo data. The neural network reaches significantly lower
background than the classical, cut-based method for the same detection
efficiency, especially at highest energies. A good matching between simulations
and real data completes the picture.


Abstract:
  A simple yet effective achiral platform using elliptical nanoholes for
chiroptical analysis is demonstrated. Under linearly polarized excitation, an
elliptical nanohole in a thin gold film can generate a localized chiral optical
field for chiroptical analysis and simultaneously serve as a near-field optical
trap to capture dielectric and plasmonic nanospheres. In particular, the
trapping potential is enantioselective for dielectric nanospheres, i.e., the
hole traps or repels the dielectric nanoparticles depending on the sample
chirality. For plasmonic nanospheres, the trapping potential well is much
deeper than that for dielectric particles, rendering the enantioselectivity
less pronounced. This platform is suitable for chiral analysis with
nanoparticle-based solid-state extraction and pre-concentration. Compared to
plasmonic chiroptical sensing using chiral structures or circularly polarized
light, elliptical nanoholes are a simple and effective platform, which is
expected to have a relatively low background because chiroptical noise from the
structure or chiral species outside the nanohole is minimized. The usage of
linearly polarized excitation also makes the platform easily compatible with a
commercial optical microscope.


Abstract:
  This paper describes how to augment techniques such as Distributed Shared
Memory with recent trends on disaggregated Non Volatile Memory in the data
centre so that the combination can be used in an edge environment with
potentially volatile and mobile resources. This article identifies the main
advantages and challenges, and offers an architectural evolution to incorporate
recent research trends into production-ready disaggregated edges. We also
present two prototypes showing the feasibility of this proposal.


Abstract:
  We investigate the Cauchy problem and the diffusion asymptotics for a
spatially inhomogeneous kinetic model associated to a nonlinear Fokker-Planck
operator. Its solution describes the density evolution of interacting particles
whose mobility is hampered by their aggregation. When the initial data lies
below a Maxwellian, we derive the global well-posedness with instantaneous
smoothness. The proof relies on hypoelliptic analogue of the classical
parabolic theory, as well as a positivity-spreading result based on the Harnack
inequality and barrier function methods. Moreover, the scaled equation leads to
the fast diffusion flow under the low field limit. The relative phi-entropy
method enables us to see the connection between the overdamped dynamics of the
nonlinearly coupled kinetic model and the correlated fast diffusion. The global
in time quantitative diffusion asymptotics is then derived by combining
entropic hypocoercivity, relative phi-entropy and barrier function methods.


Abstract:
  The quantum confinement effect on the spectrum of near-field thermal
radiation by periodic and random arrays of quantum dots (QDs) is investigated.
The local density of states (LDOS) thermally emitted by QD arrays made of three
lead chalcogenides, namely, lead sulfide, lead selenide, and lead telluride, is
computed at a near-field distance from the arrays. The dielectric function of
the QDs is extracted from their absorption spectra by utilizing an optimization
technique. The thermal discrete dipole approximation is used for computing the
LDOS. It is shown that the peak wavenumber of near-field LDOS emitted by
periodic arrays of lead chalcogenide QDs can be significantly modulated (up to
4490 cm-1) by varying the size of the dots. The LDOS is proportional to the
imaginary part of the QDs' polarizability which peaks at the bandgap energy of
the QDs. The bandgap energy of the QDs (and thus the LDOS peak) is
significantly affected by the quantum confinement effect which is
size-dependent. While the magnitude of thermal radiation by random arrays of
QDs can be different from the periodic arrays with the same filling factor by
up to 26%, the LDOS spectrum and peak location are the same for both periodic
and random arrays. The peak wavenumber of near-field radiative heat transfer
between the QD arrays is also strongly affected by quantum confinement in the
QDs, and thus it can be tuned by changing the size of the QDs.


Abstract:
  Gradient descent (GD) type optimization schemes are the standard methods to
train artificial neural networks (ANNs) with rectified linear unit (ReLU)
activation. Such schemes can be considered as discretizations of gradient flows
(GFs) associated to the training of ANNs with ReLU activation and most of the
key difficulties in the mathematical convergence analysis of GD type
optimization schemes in the training of ANNs with ReLU activation seem to be
already present in the dynamics of the corresponding GF differential equations.
It is the key subject of this work to analyze such GF differential equations in
the training of ANNs with ReLU activation and three layers (one input layer,
one hidden layer, and one output layer). In particular, in this article we
prove in the case where the target function is possibly multi-dimensional and
continuous and in the case where the probability distribution of the input data
is absolutely continuous with respect to the Lebesgue measure that the risk of
every bounded GF trajectory converges to the risk of a critical point. In
addition, in this article we show in the case of a 1-dimensional affine linear
target function and in the case where the probability distribution of the input
data coincides with the standard uniform distribution that the risk of every
bounded GF trajectory converges to zero if the initial risk is sufficiently
small. Finally, in the special situation where there is only one neuron on the
hidden layer (1-dimensional hidden layer) we strengthen the above named result
for affine linear target functions by proving that that the risk of every (not
necessarily bounded) GF trajectory converges to zero if the initial risk is
sufficiently small.


Abstract:
  Hashing has been widely used in approximate nearest neighbor search for its
storage and computational efficiency. Deep supervised hashing methods are not
widely used because of the lack of labeled data, especially when the domain is
transferred. Meanwhile, unsupervised deep hashing models can hardly achieve
satisfactory performance due to the lack of reliable similarity signals. To
tackle this problem, we propose a novel deep unsupervised hashing method,
namely Distilled Smooth Guidance (DSG), which can learn a distilled dataset
consisting of similarity signals as well as smooth confidence signals. To be
specific, we obtain the similarity confidence weights based on the initial
noisy similarity signals learned from local structures and construct a priority
loss function for smooth similarity-preserving learning. Besides, global
information based on clustering is utilized to distill the image pairs by
removing contradictory similarity signals. Extensive experiments on three
widely used benchmark datasets show that the proposed DSG consistently
outperforms the state-of-the-art search methods.


Abstract:
  Mutation analysis assesses a test suite's adequacy by measuring its ability
to detect small artificial faults, systematically seeded into the tested
program. Mutation analysis is considered one of the strongest test-adequacy
criteria. Mutation testing builds on top of mutation analysis and is a testing
technique that uses mutants as test goals to create or improve a test suite.
Mutation testing has long been considered intractable because the sheer number
of mutants that can be created represents an insurmountable problem -- both in
terms of human and computational effort. This has hindered the adoption of
mutation testing as an industry standard. For example, Google has a codebase of
two billion lines of code and more than 500,000,000 tests are executed on a
daily basis. The traditional approach to mutation testing does not scale to
such an environment. To address these challenges, this paper presents a
scalable approach to mutation testing based on the following main ideas: (1)
Mutation testing is done incrementally, mutating only changed code during code
review, rather than the entire code base; (2) Mutants are filtered, removing
mutants that are likely to be irrelevant to developers, and limiting the number
of mutants per line and per code review process; (3) Mutants are selected based
on the historical performance of mutation operators, further eliminating
irrelevant mutants and improving mutant quality. Evaluation in a
code-review-based setting with more than 24,000 developers on more than 1,000
projects shows that the proposed approach produces orders of magnitude fewer
mutants and that context-based mutant filtering and selection improve mutant
quality and actionability. Overall, the proposed approach represents a mutation
testing framework that seamlessly integrates into the software development
workflow and is applicable up to large-scale industrial settings.


Abstract:
  Previous studies have verified that the functionality of black-box models can
be stolen with full probability outputs. However, under the more practical
hard-label setting, we observe that existing methods suffer from catastrophic
performance degradation. We argue this is due to the lack of rich information
in the probability prediction and the overfitting caused by hard labels. To
this end, we propose a novel hard-label model stealing method termed
\emph{black-box dissector}, which consists of two erasing-based modules. One is
a CAM-driven erasing strategy that is designed to increase the information
capacity hidden in hard labels from the victim model. The other is a
random-erasing-based self-knowledge distillation module that utilizes soft
labels from the substitute model to mitigate overfitting. Extensive experiments
on four widely-used datasets consistently demonstrate that our method
outperforms state-of-the-art methods, with an improvement of at most $8.27\%$.
We also validate the effectiveness and practical potential of our method on
real-world APIs and defense methods. Furthermore, our method promotes other
downstream tasks, \emph{i.e.}, transfer adversarial attacks.


Abstract:
  The dark photon is a new gauge boson which arises from an extra U'(1) gauge
symmetry. In this paper, a novel dark photon production mechanism based on
MeV-scale $\gamma$-$\gamma$ collider is considered: $\gamma \gamma \rightarrow
\gamma A'$. With the aid of PACKAGE-X, differential cross section of $\gamma
\gamma \rightarrow \gamma A'$ is obtained, as a function of the kinetic mixing
parameter $\varepsilon$ and dark photon mass $m_{A'}$. Taking the
light-by-light scattering as background, the constraints on the dark photon
parameter space for different time intervals in a MeV-scale $\gamma$-$\gamma$
collider are also given.


Abstract:
  The generation of robot motions in the real world is difficult by using
conventional controllersalone and requires highly intelligent processing. In
this regard, learning-based motion generations are currently being
investigated. However, the main issue has been improvements of the adaptability
to spatially varying environments, but a variation of the operating speed has
not been investigated in detail. In contact-rich tasks, it is especially
important to be able to adjust the operating speed because a nonlinear
relationship occurs between the operating speed and force (e.g., inertial and
frictional forces), and it affects the results of the tasks. Therefore, in this
study, we propose a method for generating variable operating speeds while
adapting to spatial perturbations in the environment. The proposed method can
be adapted to nonlinearities by utilizing a small amount of motion data. We
experimentally evaluated the proposed method by erasing a line using an eraser
fixed to the tip of the robot as an example of a contact-rich task.
Furthermore, the proposed method enables a robot to perform a task faster than
a human operator and is capable of operating close to the control bandwidth.


Abstract:
  The amount of information available to the general public is enormous, and it
is challenging to extract meaningful and reliable content. The availability of
news sources and their trustability are the biggest problems for selecting the
proper authority. Moreover, news providers' inability to publish good news on
social media creates a gap for young people to access trustworthy information.
This article describes an automated system that can extract the essential news
and publish them in a different form on social media networks.


Abstract:
  Understanding black hole microstructure via the thermodynamic geometry can
provide us with more deeper insight into black hole thermodynamics in modified
gravities. In this paper, we study the black hole phase transition and
Ruppeiner geometry for the $d$-dimensional charged Gauss-Bonnet anti-de Sitter
black holes. The results show that the small-large black hole phase transition
is universal in this gravity. By reducing the thermodynamic quantities with the
black hole charge, we clearly exhibit the phase diagrams in different parameter
spaces. Of particular interest is that the radius of the black hole horizon can
act as the order parameter to characterize the black hole phase transition. We
also disclose that different from the five-dimensional neutral black holes, the
charged ones allow the repulsive interaction among its microstructure for small
black hole of higher temperature. Another significant difference between them
is that the microscopic interaction changes during the small-large black hole
phase transition for the charged case, where the black hole microstructure
undergoes a sudden change. These results are helpful for peeking into the
microstructure of charged black holes in the Gauss-Bonnet gravity.


Abstract:
  The enrichment of coronal loops and the slow solar wind with elements that
have low First Ionisation Potential, known as the FIP effect, has often been
interpreted as the tracer of a common origin. A current explanation for this
FIP fractionation rests on the influence of ponderomotive forces and turbulent
mixing acting at the top of the chromosphere. The implied wave transport and
turbulence mechanisms are also key to wave-driven coronal heating and solar
wind acceleration models. This work makes use of a shell turbulence model run
on open and closed magnetic field lines of the solar corona to investigate with
a unified approach the influence of magnetic topology, turbulence amplitude and
dissipation on the FIP fractionation. We try in particular to assess whether
there is a clear distinction between the FIP effect on closed and open field
regions.


Abstract:
  Let $F_n$ denote the $n$-th Fibonacci number and $L_n$ the $n$-th Lucas
number. We completely solve the family of cubic Thue equations
$${(X-F_nY)(X-L_nY)X-Y^3=\pm1}$$ and show that there are no non-trivial
solutions for $n\neq 1,3$.