Abstract:
  The very first detection of gravitational waves from a neutron star binary
merger, GW170817, exceeded all expectations. The event was relatively nearby,
which may point to a relatively high merger rate. It was possible to extract
finite-size effects from the gravitational-wave signal, which constrains the
nuclear equation of state. Also, an electromagnetic counterpart was detected at
many wavebands from radio to gamma rays marking the begin of a new
multi-messenger era involving gravitational waves. We describe how
multi-messenger observations of GW170817 are employed to constrain the nuclear
equation of state. Combining the information from the optical emission and the
mass measurement through gravitational waves leads to a lower limit on neutron
star radii. According to this conservative analysis, which employs a minimum
set of assumptions, the radii of neutron stars with typical masses should be
larger than about 10.7~km. This implies a lower limit on the tidal
deformability of about 210, while much stronger lower bounds are not supported
by the data of GW170817. The multi-messenger interpretation of GW170817 rules
out very soft nuclear matter and complements the upper bounds on NS radii which
are derived from the measurement of finite-size effects during the pre-merger
phase. We highlight the future potential of multi-messenger observations and of
GW measurements of the postmerger phase for constraining the nuclear equation
of state. Finally, we propose an observing strategy to maximize the scientific
yield of future multi-messenger observations.


Abstract:
  We describe the installation, commissioning, and characterization of the new
injection kicker system in the Muon $g-2$ Experiment (E989) at Fermilab, which
makes a precision measurement of the muon magnetic anomaly. Three Blumlein
pulsers drive each of the 1.27-m-long non-ferric kicker magnets, which reside
in a storage ring vacuum (SRV) that is subjected to a 1.45 T magnetic field.
The new system has been redesigned relative to Muon $g-2$'s predecessor
experiment, and we present those details in this manuscript.


Abstract:
  Aim: to evaluate the ability of the nonlinear 24-HRV as a predictor of MI
using Machine Learning Methods: The sample was composed of 218 patients divided
into two groups (Healthy, n=128; MI n=90). The sample dataset is part of the
Telemetric and Holter Electrocardiogram Warehouse (THEW) database, from the
University of Rochester Medical Center. We used the most common ML algorithms
for accuracy comparison with a setting of 10-fold cross-validation (briefly,
Linear Regression, Linear Discriminant Analysis, k-Nearest Neighbour, Random
Forest, Supporting Vector Machine, Na\"ive Bayes, C 5.0 and Stochastic Gradient
Boosting). Results: The main findings of this study show that the combination
of SD1nu + SD2nu has greater predictive power for MI in comparison to other HRV
indexes. Conclusion: The ML model using nonlinear HRV indexes showed to be more
effective than the linear domain, evidenced through the application of ML,
represented by a good precision of the Stochastic Gradient Boosting model.
  Keywords: heart rate variability, machine learning, nonlinear domain,
cardiovascular disease


Abstract:
  Dialect and standard language identification are crucial tasks for many
Arabic natural language processing applications. In this paper, we present our
deep learning-based system, submitted to the second NADI shared task for
country-level and province-level identification of Modern Standard Arabic (MSA)
and Dialectal Arabic (DA). The system is based on an end-to-end deep Multi-Task
Learning (MTL) model to tackle both country-level and province-level MSA/DA
identification. The latter MTL model consists of a shared Bidirectional Encoder
Representation Transformers (BERT) encoder, two task-specific attention layers,
and two classifiers. Our key idea is to leverage both the task-discriminative
and the inter-task shared features for country and province MSA/DA
identification. The obtained results show that our MTL model outperforms
single-task models on most subtasks.


Abstract:
  Given a family of null hypotheses $H_{1},\ldots,H_{s}$, we are interested in
the hypothesis $H_{s}^{\gamma}$ that at most $\gamma-1$ of these null
hypotheses are false. Assuming that the corresponding $p$-values are
independent, we are investigating combined $p$-values that are valid for
testing $H_{s}^{\gamma}$. In various settings in which $H_{s}^{\gamma}$ is
false, we determine which combined $p$-value works well in which setting. Via
simulations, we find that the Stouffer method works well if the null $p$-values
are uniformly distributed and the signal strength is low, and the Fisher method
works better if the null $p$-values are conservative, i.e. stochastically
larger than the uniform distribution. The minimum method works well if the
evidence for the rejection of $H_{s}^{\gamma}$ is focused on only a few
non-null $p$-values, especially if the null $p$-values are conservative.
Methods that incorporate the combination of $e$-values work well if the null
hypotheses $H_{1},\ldots,H_{s}$ are simple.


Abstract:
  This work is concerned with practical stabilization of nonlinear systems by
means of inf-convolution-based sample-and-hold control. It is a fairly general
stabilization technique based on a generic non-smooth control Lyapunov function
(CLF) and robust to actuator uncertainty, measurement noise, etc. The
stabilization technique itself involves computation of descent directions of
the CLF. It turns out that non-exact realization of this computation leads not
just to a quantitative, but also qualitative obstruction in the sense that the
result of the computation might fail to be a descent direction altogether and
there is also no straightforward way to relate it to a descent direction.
Disturbance, primarily measurement noise, complicate the described issue even
more. This work suggests a modified inf-convolution-based control that is
robust w. r. t. system and measurement noise, as well as computational
uncertainty. The assumptions on the CLF are mild, as, e. g., any piece-wise
smooth function, which often results from a numerical LF/CLF construction,
satisfies them. A computational study with a three-wheel robot with dynamical
steering and throttle under various tolerances w. r. t. computational
uncertainty demonstrates the relevance of the addressed issue and the necessity
of modifying the used stabilization technique. Similar analyses may be extended
to other methods which involve optimization, such as Dini aiming or steepest
descent.


Abstract:
  Heisenberg's uncertainty principle is often cited as an example of a "purely
quantum" relation with no analogue in the classical limit where $\hbar \to 0$.
However, this formulation of the classical limit is problematic for many
reasons, one of which is dimensional analysis. Since $\hbar$ is a dimensionful
constant, we may always work in natural units in which $\hbar = 1$. Dimensional
analysis teaches us that all physical laws can be expressed purely in terms of
dimensionless quantities. This indicates that the existence of a dimensionally
consistent constraint on $\Delta x \Delta p$ requires the existence of a
dimensionful parameter with units of action, and that any definition of the
classical limit must be formulated in terms of dimensionless quantities (such
as quantum numbers). Therefore, bounds on classical uncertainty (formulated in
terms of statistical ensembles) can only be written in terms of dimensionful
scales of the system under consideration, and can be readily compared to their
quantum counterparts after being non-dimensionalized. We compare the
uncertainty of certain coupled classical systems and their quantum counterparts
(such as harmonic oscillators and particles in a box), and show that they
converge in the classical limit. We find that since these systems feature
additional dimensionful scales, the uncertainty bounds are dependent on
multiple dimensionless parameters, in accordance with dimensional
considerations.


Abstract:
  Here we propose an NPT metadynamics simulation scheme for pressure-induced
structural phase transitions, using coordination number and volume as
collective variables, and apply it to the reconstructive structural
transformation $B1$-$B2$ in NaCl. By studying systems with size up to 64 000
atoms we reach a regime beyond collective mechanism and observe transformations
proceeding via nucleation and growth. We also reveal the crossover of the
transition mechanism from Buerger-like for smaller systems to
Watanabe-Tol\'{e}dano for larger ones. The scheme is likely to be applicable to
a broader class of pressure-induced structural transitions, allowing study of
complex nucleation effects and bringing simulations closer to realistic
conditions.


Abstract:
  Using a geometric formalism of elasticity theory we develop a systematic
theoretical method for controlling and manipulating the mechanical response of
slender solids to external loads. We formally express global mechanical
properties associated with non-euclidean thin sheets, and interpret the
expressions as inverse problem for designing desired mechanical properties. We
show that by wisely designing geometric frustration, extreme mechanical
properties can be encoded into a material using accessible experimental
techniques. To test the methodology we derive a family of geometries that
result with anomalous mechanical behavior such as tunable, an-harmonic, and
even vanishing rigidities. The presented formalism can be discretized and thus
opens a new pathway for the design of both continuum and discrete solids and
structures.


Abstract:
  Crossover and mutation are the two main operators that lead to new solutions
in evolutionary approaches. In this article, a new method of performing the
crossover phase is presented. The problem of choice is evolutionary decision
tree construction. The method aims at finding such individuals that together
complement each other. Hence we say that they are diversely specialized. We
propose the way of calculating the so-called complementary fitness. In several
empirical experiments, we evaluate the efficacy of the method proposed in four
variants and compare it to a fitness-rank-based approach. One variant emerges
clearly as the best approach, whereas the remaining ones are below the
baseline.


Abstract:
  Magnesium and its alloys have aroused tremendous interests because of their
promising mechanical properties and biocompatibility. However, their
excessively fast corrosion rate hinders the development of Mg alloys in the
biomedical fields. Inspired by conventional ion implantation, a less-toxic
functional group (hydroxyl) is used as the ion source to bombard the ZK60 Mg
alloy surface to form a functionalized oxide layer. This functionalized oxide
layer significantly facilitates the corrosion resistance of the ZK60 Mg alloy
substrate and the proliferation of MC3T3-E1 cells, which is confirmed by
electrochemical, immersion, and in vitro cytocompatibility tests. In comparison
with results of ZK60 alloy implanted with carboxyl ions in our previous work,
it is concluded that hydroxyl-treated alloys exhibit slightly higher corrosion
rate while better biocompatibility. In summary, less-toxic functional ion
implantation can be an effective strategy for inhibiting corrosion of Mg alloy
implants and promoting their biocompatibility.


Abstract:
  Let $G = (V, E)$ be a simple graph of order $n$. The total dominating set of
$G$ is a subset $D$ of $V$ that every vertex of $V$ is adjacent to some
vertices of $D$. The total domination number of $G$ is equal to minimum
cardinality of total dominating set in $G$ and is denoted by $\gamma_t(G)$. The
total domination polynomial of $G$ is the polynomial
$D_t(G,x)=\sum_{i=\gamma_t(G)}^n d_t(G,i)x^i$, where $d_t(G,i)$ is the number
of total dominating sets of $G$ of size $i$. Two graphs $G$ and $H$ are said to
be total dominating equivalent or simply $\mathcal{D}_t$-equivalent, if
$D_t(G,x)=D_t(H,x)$. The equivalence class of $G$, denoted $[G]$, is the set of
all graphs $\mathcal{D}_t$-equivalent to $G$. In this paper, we investigate
$\mathcal{D}_t$-equivalence classes of some graphs. Also we introduce some
families of graphs whose total domination polynomials are unimodal.


Abstract:
  We evaluate critically the use of pulsed gradient spin-echo nuclear magnetic
resonance (PGSE NMR) to measure molecular mobility during chemical reactions.
With raw NMR spectra available in a public depository, we confirm boosted
mobility during the click chemical reaction (Science 2020, 369, 537) regardless
of the order of magnetic field gradient (linearly-increasing,
linearly-decreasing, random sequence). We also confirm boosted mobility for the
Diels-Alder chemical reaction. The conceptual advantage of the former chemical
system is that constant reaction rate implies constant catalyst concentration,
whereas that of the latter is the absence of a paramagnetic catalyst,
precluding paramagnetism as objection to the measurements. Data and discussion
in this paper show the reliability of experiments when one avoids convection,
allows decay of nuclear spin magnetization between successive pulses and
recovery of its intensity between gradients, and satisfies quasi-steady state
during the time window to acquire each datum. Especially important is to make
comparisons on the time scale of actual chemical reaction kinetics. We discuss
possible sources of mistaken conclusions that are desirable to avoid.


Abstract:
  The adoption of neural networks and deep learning in non-Euclidean domains
has been hindered until recently by the lack of scalable and efficient learning
frameworks. Existing toolboxes in this space were mainly motivated by research
and education use cases, whereas practical aspects, such as deploying and
maintaining machine learning models, were often overlooked.
  We attempt to bridge this gap by proposing TensorFlow RiemOpt, a Python
library for optimization on Riemannian manifolds in TensorFlow. The library is
designed with the aim for a seamless integration with the TensorFlow ecosystem,
targeting not only research, but also streamlining production machine learning
pipelines.


Abstract:
  A new $\gamma$-ray source with a significance level of approximately
5$\sigma$ was reported in the region of SNR G272.2-3.2, analysing the
approximately 12.4 years of observation data from the Fermi Large Area
Telescope (Fermi-LAT). Its $\gamma$-ray spatial distribution did not show
extended feature, and it had a soft spectrum with the spectral index of
2.56$\pm$0.01 of a power-law model. No significant variability of its light
curve (LC) with 10 time bins was identified, and its spatial positions in the
X-ray and GeV bands overlapped. We suggest that the new $\gamma$-ray source is
a likely counterpart of SNR G272.2-3.2. Analysing its spectrum, we discussed
the likely origins of the $\gamma$-ray emission.


Abstract:
  We describe an infinite family of graphs $G_n$, where $G_n$ has $n$ vertices,
independence number at least $n/4$, and no set of less than $\sqrt{n}/2$
vertices intersects all its maximum independent sets. This is motivated by a
question of Bollob\'as, Erd\H{o}s and Tuza, and disproves a recent conjecture
of Friedgut, Kalai and Kindler. Motivated by a related question of the last
authors, we show that for every graph $G$ on $n$ vertices with independence
number $(1/4+\eps)n$, the average independence number of an induced subgraph of
$G$ on a uniform random subset of the vertices is at most
$(1/4+\eps-\Omega(\eps^2)) n$.


Abstract:
  In the current paradigm of planet formation research, it is believed that the
first step to forming massive bodies (such as asteroids and planets) requires
that small interstellar dust grains floating through space collide with each
other and grow to larger sizes. The initial formation of these pebbles is
governed by an integro-differential equation known as the Smoluchowski
coagulation equation, to which analytical solutions are intractable for all but
the simplest possible scenarios. While brute-force methods of approximation
have been developed, they are computationally costly, currently making it
infeasible to simulate this process including other physical processes relevant
to planet formation, and across the very large range of scales on which it
occurs. In this paper, we take a machine learning approach to designing a
system for a much faster approximation. We develop a multi-output random forest
regression model trained on brute-force simulation data to approximate
distributions of dust particle sizes in protoplanetary disks at different
points in time. The performance of our random forest model is measured against
the existing brute-force models, which are the standard for realistic
simulations. Results indicate that the random forest model can generate highly
accurate predictions relative to the brute-force simulation results, with an
$R^{2}$ of 0.97, and do so significantly faster than brute-force methods.


Abstract:
  In this paper, we get an elementary and important lemma(See Lemma 3.2) which
is about pushout and pullback of modules. And we prove a weak form of a long
open conjecture on vanishing of group cohomology for blocks.


Abstract:
  A mismatch of atomic registries between single-layer transition metal
dichalcogenides (TMDs) in a two dimensional van der Waals heterostructure
produces a moir\'e superlattice with a periodic potential, which can be
fine-tuned by introducing a twist angle between the materials. This approach is
promising both for controlling the interactions between the TMDs and for
engineering their electronic band structures, yet direct observation of the
changes to the electronic structure introduced with varying twist angle has so
far been missing. Here, we probe heterobilayers comprised of single-layer
MoS$_2$ and WS$_2$ with twist angles of $(2.0 \pm 0.5)^{\circ}$, $(13.0 \pm
0.5)^{\circ}$, and $(20.0 \pm 0.5)^{\circ}$ and investigate the differences in
their electronic band structure using micro-focused angle-resolved
photoemission spectroscopy. We find strong interlayer hybridization between
MoS$_2$ and WS$_2$ electronic states at the $\bar{\mathrm{\Gamma}}$-point of
the Brillouin zone, leading to a transition from a direct bandgap in the
single-layer to an indirect gap in the heterostructure. Replicas of the
hybridized states are observed at the centre of twist angle-dependent moir\'e
mini Brillouin zones. We confirm that these replica features arise from the
inherent moir\'e potential by comparing our experimental observations with
density functional theory calculations of the superlattice dispersion. Our
direct visualization of these features underscores the potential of using
twisted heterobilayer semiconductors to engineer hybrid electronic states and
superlattices that alter the electronic and optical properties of 2D
heterostructures.


Abstract:
  We propose an efficient encoding algorithm for the binary and non-binary
low-density parity-check codes. This algorithm transforms the parity part of
the parity-check matrix into a block triangular matrix with low weight diagonal
submatrices by row and column permutations in the preprocessing stage. This
algorithm determines the parity part of a codeword by block back-substitution
to reduce the encoding complexity in the encoding stage. Numerical experiments
show that this algorithm has a lower encoding complexity than existing encoding
algorithms. Moreover, we show that this algorithm encodes the non-binary cycle
codes in linear time.


Abstract:
  We describe our single submission to task 1 of COLIEE 2021. Our vanilla BM25
got second place, well above the median of submissions. Code is available at
https://github.com/neuralmind-ai/coliee.


Abstract:
  We have applied nuclear magnetic resonance spectroscopy to study the
distinctive network of nodal lines in the Dirac semimetal ZrSiTe. The low-$T$
behavior is dominated by a symmetry-protected nodal line, with NMR providing a
sensitive probe of the diamagnetic response of the associated carriers. A sharp
low-$T$ minimum in NMR shift and $(T_1T)^{-1}$ provides a quantitative measure
of the dispersionless, quasi-2D behavior of this nodal line. We also identify a
van Hove singularity closely connected to this nodal line, and an associated
$T$-induced Lifshitz transition. A disconnect in the NMR shift and line width
at this temperature indicates the change in electronic behavior associated with
this topological change. These features have an orientation-dependent behavior
indicating a field-dependent scaling of the associated band energies.


Abstract:
  The growing success of graph signal processing (GSP) approaches relies
heavily on prior identification of a graph over which network data admit
certain regularity. However, adaptation to increasingly dynamic environments as
well as demands for real-time processing of streaming data pose major
challenges to this end. In this context, we develop novel algorithms for online
network topology inference given streaming observations assumed to be smooth on
the sought graph. Unlike existing batch algorithms, our goal is to track the
(possibly) time-varying network topology while maintaining the memory and
computational costs in check by processing graph signals sequentially-in-time.
To recover the graph in an online fashion, we leverage proximal gradient (PG)
methods to solve a judicious smoothness-regularized, time-varying optimization
problem. Under mild technical conditions, we establish that the online graph
learning algorithm converges to within a neighborhood of (i.e., it tracks) the
optimal time-varying batch solution. Computer simulations using both synthetic
and real financial market data illustrate the effectiveness of the proposed
algorithm in adapting to streaming signals to track slowly-varying network
connectivity.


Abstract:
  In autonomous driving, using a variety of sensors to recognize preceding
vehicles in middle and long distance is helpful for improving driving
performance and developing various functions. However, if only LiDAR or camera
is used in the recognition stage, it is difficult to obtain necessary data due
to the limitations of each sensor. In this paper, we proposed a method of
converting the tracking data of vision into bird's eye view (BEV) coordinates
using an equation that projects LiDAR points onto an image, and a method of
fusion between LiDAR and vision tracked data. Thus, the newly proposed method
was effective through the results of detecting closest in-path vehicle (CIPV)
in various situations. In addition, even when experimenting with the EuroNCAP
autonomous emergency braking (AEB) test protocol using the result of fusion,
AEB performance is improved through improved cognitive performance than when
using only LiDAR. In experimental results, the performance of the proposed
method was proved through actual vehicle tests in various scenarios.
Consequently, it is convincing that the newly proposed sensor fusion method
significantly improves the ACC function in autonomous maneuvering. We expect
that this improvement in perception performance will contribute to improving
the overall stability of ACC.


Abstract:
  In this paper we give a self-contained treatment of finite group quotients of
admissible (formal) schemes and adic spaces that are locally topologically
finite type over a locally strongly noetherian adic space.


Abstract:
  We present a scalable approach for simplifying the stability analysis of
cluster synchronization patterns on directed networks. When a network has
directional couplings, decomposition of the coupling matrix into independent
blocks (which in turn decouples the variational equation) is no longer adequate
to reveal the full relations among perturbation modes. Instead, it is often
necessary to introduce directional dependencies among the blocks and establish
hierarchies among perturbation modes. For this purpose, we develop an algorithm
that finds the simultaneous block upper triangularization of sets of asymmetric
matrices, which generalizes the Jordan canonical decomposition from a single
matrix to an arbitrary number of matrices. The block upper triangularization
orders subspaces of the variational equation in a directional manner, allowing
the stability of perturbation modes to be analyzed in sequence. We show that
our algorithm gives the greatest possible simplification under mild
assumptions, both in terms of the sizes of the blocks and in terms of the
number of nonzero upper triangular entries linking the blocks.


Abstract:
  Natural language generation (NLG) benchmarks provide an important avenue to
measure progress and develop better NLG systems. Unfortunately, the lack of
publicly available NLG benchmarks for low-resource languages poses a
challenging barrier for building NLG systems that work well for languages with
limited amounts of data. Here we introduce IndoNLG, the first benchmark to
measure natural language generation (NLG) progress in three low-resource -- yet
widely spoken -- languages of Indonesia: Indonesian, Javanese, and Sundanese.
Altogether, these languages are spoken by more than 100 million native
speakers, and hence constitute an important use case of NLG systems today.
Concretely, IndoNLG covers six tasks: summarization, question answering,
chit-chat, and three different pairs of machine translation (MT) tasks. We
collate a clean pretraining corpus of Indonesian, Sundanese, and Javanese
datasets, Indo4B-Plus, which is used to pretrain our models: IndoBART and
IndoGPT. We show that IndoBART and IndoGPT achieve competitive performance on
all tasks -- despite using only one-fifth the parameters of a larger
multilingual model, mBART-LARGE (Liu et al., 2020). This finding emphasizes the
importance of pretraining on closely related, local languages to achieve more
efficient learning and faster inference for very low-resource languages like
Javanese and Sundanese.


Abstract:
  We present implicit displacement fields, a novel representation for detailed
3D geometry. Inspired by a classic surface deformation technique, displacement
mapping, our method represents a complex surface as a smooth base surface plus
a displacement along the base's normal directions, resulting in a
frequency-based shape decomposition, where the high frequency signal is
constrained geometrically by the low frequency signal. Importantly, this
disentanglement is unsupervised thanks to a tailored architectural design that
has an innate frequency hierarchy by construction. We explore implicit
displacement field surface reconstruction and detail transfer and demonstrate
superior representational power, training stability and generalizability.


Abstract:
  We study the quantum-droplet state in a 3-dimensional (3D) Bose gas in the
presence of 1D spin-orbit-coupling and Raman coupling, especially the stripe
phase with density modulation, by numerically computing the ground state energy
including the mean-field energy and Lee-HuangYang correction. In this droplet
state, the stripe can exist in a wider range of Raman coupling, compared with
the BEC-gas state. More intriguingly, both spin-orbit-coupling and Raman
coupling strengths can be used to tune the droplet density.


Abstract:
  We have studied the masse spectra for the
$cc\bar{b}\bar{b}$/$bb\bar{c}\bar{c}$ tetraquark states with quantum numbers
$J^{P}=0^{\pm},1^{\pm}$, and $2^{+}$. We systematically construct the
interpolating currents with various spin-parity quantum numbers and calculate
their two-point correlation functions in the framework of QCD moment sum rule
method. Our calculations show that the masses are about $12.3-12.4$ GeV for the
positive parity $cc\bar{b}\bar{b}$ tetraquark ground states with $J^{P}=0^+,
1^+, 2^+$, while $12.8-13.1$ GeV for the negative parity channels with
$J^{P}=0^-, 1^-$. The mass predictions for the positive parity
$cc\bar{b}\bar{b}$ ground states are lower than the $B_{c}B_{c}$ threshold,
implying that these tetraquarks can only decay via weak interaction and thus
are expected to be stable and narrow.


Abstract:
  Measles is considered as a highly contagious disease that leads to serious
complications around the world. Thus, the paper determined the trend and the
five-year forecasted data of the Measles in the Philippines. This study
utilized the time series data for trend analysis and data forecasting using the
ARIMA model to visualize the measles cases. Figures for the time-series and
forecasted results are individually presented with the use of GRETL software.
Results showed that there was an increasing pattern of the disease from 2016 to
2019. However, there was a decreasing pattern of its occurrence in the next
five years based on the five-year forecast. Nevertheless, with the results of
the study, there is still a need to improve the different intervention plans of
the authority in alleviating the occurrence of the disease though it yielded a
decreasing pattern in the future since it is evident that the figure of the
forecasted data is still approximately 15,000 and above.


Abstract:
  This paper considers an angle-domain intelligent reflecting surface (IRS)
system. We derive maximum likelihood (ML) estimators for the effective angles
from the base station (BS) to the user and the effective angles of propagation
from the IRS to the user. It is demonstrated that the accuracy of the estimated
angles improves with the number of BS antennas. Also, deploying the IRS closer
to the BS increases the accuracy of the estimated angle from the IRS to the
user. Then, based on the estimated angles, we propose a joint optimization of
BS beamforming and IRS beamforming, which achieves similar performance to two
benchmark algorithms based on full CSI and the multiple signal classification
(MUSIC) method respectively. Simulation results show that the optimized BS beam
becomes more focused towards the IRS direction as the number of reflecting
elements increases. Furthermore, we derive a closed-form approximation, upper
bound and lower bound for the achievable rate. The analytical findings indicate
that the achievable rate can be improved by increasing the number of BS
antennas or reflecting elements. Specifically, the BS-user link and the
BS-IRS-user link can obtain power gains of order $N$ and $NM^2$, respectively,
where $N$ is the antenna number and $M$ is the number of reflecting elements.


Abstract:
  Because of the unique physical properties and potential applications, the
exploration of quantum materials with diverse symmetry-protected topological
states has attracted considerable interest in the condensed-matter community in
recent years. Most of the topologically nontirvial materials identified thus
far have two-dimensional or three-dimensional structural characteristics, while
the quasi-one-dimensional (quasi-1D) analogs are rare. Here we report on
anisotropic magnetoresistance, Hall effect, and quantum de Haas$-$van Alphen
(dHvA) oscillations in TaPtTe$_5$ single crystals, which possess a layered
crystal structure with quasi-1D PtTe$_2$ chains. TaPtTe$_5$ manifests an
anisotropic magnetoresistance and a nonlinear Hall effect at low temperatures.
The analysis of the dHvA oscillations reveals two major oscillation frequencies
(63.5 T and 95.2 T). The corresponding light effective masses and the nonzero
Berry phases suggest the nontrivial band topology in TaPtTe$_5$, which is
further corroborated by the first-principles calculations. Our results suggest
that TaPtTe$_5$, in analogy with its sister compounds TaPdTe$_5$ and
TaNiTe$_5$, is another quasi-1D material hosting topological Dirac fermions.


Abstract:
  Classical results from Sturm-Liouville theory state that the number of
unstable eigenvalues of a scalar, second-order linear operator is equal to the
number of associated conjugate points. Recent work has extended these results
to a much more general setting, thus allowing for spectral stability of
nonlinear waves in a variety of contexts to be determined by counting conjugate
points. However, in practice, it is not yet clear whether it is easier to
compute conjugate points than to just directly count unstable eigenvalues. We
address this issue by developing a framework for the computation of conjugate
points using validated numerics. Moreover, we apply our method to a
parameter-dependent system of bistable equations and show that there exist both
stable and unstable standing fronts. This application can be seen as
complimentary to the classical result via Sturm-Louiville theory that in scalar
reaction-diffusion equations pulses are unstable whereas fronts are stable, and
to the more recent result of "Instability of pulses in gradient
reaction-diffusion systems: a symplectic approach," by Beck et. al., that
symmetric pulses in reaction-diffusion systems with gradient nonlinearity are
also necessarily unstable.


Abstract:
  For the classes of finite dimensional linear time-invariant semi-dissipative
Hamiltonian ordinary differential equations and differential-algebraic
equations, stability and hypocoercivity are discussed and related to concepts
from control theory. On the basis of staircase forms the solution behavior is
characterized and connected to the hypocoercivity index of these evolution
equations. The results are applied to two infinite dimensional flow problems.


Abstract:
  The Fradkin-Schwinger functional methods to represent a Green function in an
external gravitational field are used to study the eikonal and the
next-to-eikonal limit, including the nonlinear gravitational interactions, of
the scattering amplitudes of an ultra-relativistic scalar particle on a static
super-massive scalar target in the nearly forward limit. The functional
approach confirms the exponentiation of the leading eikonal which also applies
to the first non-leading power in the energy of the light particle, moreover
includes the interaction at impact parameter much larger than the Schwarzschild
radius associated with the center of mass energy in the ultra-relativistic
limit.


Abstract:
  In this paper, the problem of vertical handover in software-defined network
(SDN) based heterogeneous networks (HetNets) is studied. In the studied model,
HetNets are required to offer diverse services for mobile users. Using an SDN
controller, HetNets have the capability of managing users' access and mobility
issues but still have the problems of ping-pong effect and service interruption
during vertical handover. To solve these problems, a mobility-aware seamless
handover method based on multipath transmission control protocol (MPTCP) is
proposed. The proposed handover method is executed in the controller of the
software-defined HetNets (SDHetNets) and consists of three steps: location
prediction, network selection, and handover execution. In particular, the
method first predicts the user's location in the next moment with an echo state
network (ESN). Given the predicted location, the SDHetNet controller can
determine the candidate network set for the handover to pre-allocate network
wireless resources. Second, the target network is selected through fuzzy
analytic hierarchical process (FAHP) algorithm, jointly considering user
preferences, service requirements, network attributes, and user mobility
patterns. Then, seamless handover is realized through the proposed MPTCP-based
handover mechanism. Simulations using real-world user trajectory data from
Korea Advanced Institute of Science & Technology show that the proposed method
can reduce the handover times by 10.85% to 29.12% compared with traditional
methods. The proposed method also maintains at least one MPTCP subflow
connected during the handover process and achieves a seamless handover.


Abstract:
  A wide range of intuitionistic type theories may be presented as equational
theories within a logical framework. This method was formulated by Per
Martin-L\"{o}f in the mid-1980's and further developed by Uemura, who used it
to prove an initiality result for a class of models. Herein is presented a
logical framework for type theories that includes an extensional equality type
so that a type theory may be given by a signature of constants. The framework
is illustrated by a number of examples of type-theoretic concepts, including
identity and equality types, and a hierarchy of universes.


Abstract:
  By exploiting the superiority of non-orthogonal multiple access (NOMA),
NOMA-aided mobile edge computing (MEC) can provide scalable and low-latency
computing services for the Internet of Things. However, given the prevalent
stochasticity of wireless networks and sophisticated signal processing of NOMA,
it is critical but challenging to design an efficient task offloading algorithm
for NOMA-aided MEC, especially under a large number of devices. This paper
presents an online algorithm that jointly optimizes offloading decisions and
resource allocation to maximize the long-term system utility (i.e., a measure
of throughput and fairness). Since the optimization variables are temporary
coupled, we first apply Lyapunov technique to decouple the long-term stochastic
optimization into a series of per-slot deterministic subproblems, which does
not require any prior knowledge of network dynamics. Second, we propose to
transform the non-convex per-slot subproblem of optimizing NOMA power
allocation equivalently to a convex form by introducing a set of auxiliary
variables, whereby the time-complexity is reduced from the exponential
complexity to $\mathcal{O} (M^{3/2})$. The proposed algorithm is proved to be
asymptotically optimal, even under partial knowledge of the device states at
the base station. Simulation results validate the superiority of the proposed
algorithm in terms of system utility, stability improvement, and the overhead
reduction.


Abstract:
  The rules of a game of dice are extended to a "hyper-die" with
$n\in\mathbb{N}$ equally probable faces, numbered from 1 to $n$. We derive
recursive and explicit expressions for the probability mass function and the
cumulative distribution function of the gain $G_n$ for arbitrary values of $n$.
A numerical study suggests the conjecture that for $n \to \infty$ the
expectation of the scaled gain $\mathbb{E}[H_n]=\mathbb{E}[G_n/\sqrt{n}\,]$
converges to $\sqrt{\pi/\,2}$. The conjecture is proved by deriving an analytic
expression of the expected gain $\mathbb{E}[G_n]$. An analytic expression of
the variance of the gain $G_n$ is derived by a similar technique. Finally, it
is proved that $H_n$ converges weakly to the Rayleigh distribution with scale
parameter~1.


Abstract:
  We revisit the computation of the trace anomaly for Weyl fermions using
dimensional regularization. For a consistent treatment of the chiral gamma
matrix $\gamma_*$ in dimensional regularization, we work in $n$ dimensions from
the very beginning and use the Breitenlohner-Maison scheme to define
$\gamma_*$. We show that the parity-odd contribution to the trace anomaly
vanishes (for which the use of dimension-dependent identities is crucial), and
that the parity-even contribution is half the one of a Dirac fermion. To arrive
at this result, we compute the full renormalized expectation value of the
fermion stress tensor to second order in perturbations around Minkowski
spacetime, and also show that it is conserved.


Abstract:
  The chirality-induced spin selectivity (CISS) effect enables the detection of
chirality as electrical charge signals. It is often studied using a
two-terminal circuit geometry where a ferromagnet is connected to a chiral
component, and a change of electrical resistance is reported upon magnetization
reversal. This is however not expected in the linear response regime because of
compensating reciprocal processes, limiting the interpretation of experimental
results. Here we show that magnetoresistance effects can indeed appear even in
the linear response regime, either by changing the magnitude or the direction
of the magnetization or an applied magnetic field. We illustrate this in a
spin-valve device and in a chiral thin film as the CISS-induced Hanle
magnetoresistance (CHMR) effect. This effect helps to distinguish
spin-transport-related effects from other effects, and can thereby provide
further insight into the origin of CISS.


Abstract:
  Semi-supervised domain adaptation (SSDA), which aims to learn models in a
partially labeled target domain with the assistance of the fully labeled source
domain, attracts increasing attention in recent years. To explicitly leverage
the labeled data in both domains, we naturally introduce a conditional GAN
framework to transfer images without changing the semantics in SSDA. However,
we identify a label-domination problem in such an approach. In fact, the
generator tends to overlook the input source image and only memorizes
prototypes of each class, which results in unsatisfactory adaptation
performance. To this end, we propose a simple yet effective Relaxed conditional
GAN (Relaxed cGAN) framework. Specifically, we feed the image without its label
to our generator. In this way, the generator has to infer the semantic
information of input data. We formally prove that its equilibrium is desirable
and empirically validate its practical convergence and effectiveness in image
transfer. Additionally, we propose several techniques to make use of unlabeled
data in the target domain, enhancing the model in SSDA settings. We validate
our method on the well-adopted datasets: Digits, DomainNet, and Office-Home. We
achieve state-of-the-art performance on DomainNet, Office-Home and most digit
benchmarks in low-resource and high-resource settings.


Abstract:
  In many different fields interactions between objects play a critical role in
determining their behavior. Graph neural networks (GNNs) have emerged as a
powerful tool for modeling interactions, although often at the cost of adding
considerable complexity and latency. In this paper, we consider the problem of
spatial interaction modeling in the context of predicting the motion of actors
around autonomous vehicles, and investigate alternative approaches to GNNs. We
revisit convolutions and show that they can demonstrate comparable performance
to graph networks in modeling spatial interactions with lower latency, thus
providing an effective and efficient alternative in time-critical systems.
Moreover, we propose a novel interaction loss to further improve the
interaction modeling of the considered methods.


Abstract:
  This article introduces a novel method for the implementation of shape
optimisation with Lipschitz domains. We propose to use the shape derivative to
determine deformation fields which represent steepest descent directions of the
shape functional in the $W^{1,\infty}-$ topology. The idea of our approach is
demonstrated for shape optimisation of $n$-dimensional star-shaped domains,
which we represent as functions defined on the unit $(n-1)$-sphere. In this
setting we provide the specific form of the shape derivative and prove the
existence of solutions to the underlying shape optimisation problem. Moreover,
we show the existence of a direction of steepest descent in the $W^{1,\infty}-$
topology. We also note that shape optimisation in this context is closely
related to the $\infty-$Laplacian, and to optimal transport, where we highlight
the latter in the numerics section. We present several numerical experiments in
two dimensions illustrating that our approach seems to be superior over a
widely used Hilbert space method in the considered examples, in particular in
developing optimised shapes with corners.


Abstract:
  It is important to know the accurate trajectory of a free fall object in
fluid (such as a spacecraft), whose motion might be chaotic in many cases.
However, it is impossible to accurately predict its chaotic trajectory in a
long enough duration by traditional numerical algorithms in double precision.
In this paper, we give the accurate predictions of the same problem by a new
strategy, namely the Clean Numerical Simulation (CNS). Without loss of
generality, a free fall disk in water is considered, whose motion is governed
by the Andersen-Pesavento-Wang model. We illustrate that convergent and
reliable trajectories of a chaotic free fall disk in a long enough interval of
time can be obtained by means of the CNS, but different traditional algorithms
in double precision give disparate trajectories. Besides, unlike the
traditional algorithms in double precision, the CNS can predict the accurate
posture of the free fall disk near the vicinity of the bifurcation point of
some physical parameters in a long duration. Therefore, the CNS can provide
reliable prediction of chaotic systems in a long enough interval of time.


Abstract:
  We elucidate how the interplay between diffusive mobility and number
conservation can make nearly phase-ordered active XY spins on a substrate
stable. For wide-ranging model parameters, it has stable uniform phases with
orientational order logarithmically stronger or weaker than in equilibrium,
together with miniscule (i.e., hyperuniform) or giant number fluctuations,
respectively, forming a new universality class. For other parameters, it has no
stable uniformly ordered phase. Our theory sheds light on wide-ranging systems,
e.g., active superfluids on substrates, synchronization of oscillators, active
carpets of cilia and bacterial flagella, and sandblasting.


Abstract:
  Intersections where vehicles are permitted to turn and interact with
vulnerable road users (VRUs) like pedestrians and cyclists are among some of
the most challenging locations for automated and accurate recognition of road
users' behavior. In this paper, we propose a deep conditional generative model
for interaction detection at such locations. It aims to automatically analyze
massive video data about the continuity of road users' behavior. This task is
essential for many intelligent transportation systems such as traffic safety
control and self-driving cars that depend on the understanding of road users'
locomotion. A Conditional Variational Auto-Encoder based model with Gaussian
latent variables is trained to encode road users' behavior and perform
probabilistic and diverse predictions of interactions. The model takes as input
the information of road users' type, position and motion automatically
extracted by a deep learning object detector and optical flow from videos, and
generates frame-wise probabilities that represent the dynamics of interactions
between a turning vehicle and any VRUs involved. The model's efficacy was
validated by testing on real--world datasets acquired from two different
intersections. It achieved an F1-score above 0.96 at a right--turn intersection
in Germany and 0.89 at a left--turn intersection in Japan, both with very busy
traffic flows.


Abstract:
  For a root system R, a field K and an invertible element q in K let U be the
associated quantum group, defined via Lusztig's divided powers construction. We
study the irreducible characters of this algebra with integral (but not
necessarily dominant) highest weight. If the l-th cyclotomic polynomial
vanishes when evaluated at q, then these characters exhibit a certain
l-periodicity.


Abstract:
  Abraham, Dolev, Geffner, and Halpern proved that, in asynchronous systems, a
$(k,t)$-robust equilibrium for $n$ players and a trusted mediator can be
implemented without the mediator as long as $n > 4(k+t)$, where an equilibrium
is $(k,t)$-robust if, roughly speaking, no coalition of $t$ players can
decrease the payoff of any of the other players, and no coalition of $k$
players can increase their payoff by deviating. We prove that this bound is
tight, in the sense that if $n \le 4(k+t)$ there exist $(k,t)$-robust
equilibria with a mediator that cannot be implemented by the players alone.
Even though implementing $(k,t)$-robust mediators seems closely related to
implementing asynchronous multiparty $(k+t)$-secure computation \cite{BCG93},
to the best of our knowledge there is no known straightforward reduction from
one problem to another. Nevertheless, we show that there is a non-trivial
reduction from a slightly weaker notion of $(k+t)$-secure computation, which we
call $(k+t)$-strict secure computation, to implementing $(k,t)$-robust
mediators. We prove the desired lower bound by showing that there are functions
on $n$ variables that cannot be $(k+t)$-strictly securely computed if $n \le
4(k+t)$. This also provides a simple alternative proof for the well-known lower
bound of $4t+1$ on asynchronous secure computation in the presence of up to $t$
malicious agents.


Abstract:
  Artificial audition aims at providing hearing capabilities to machines,
computers and robots. Existing frameworks in robot audition offer interesting
sound source localization, tracking and separation performance, but involve a
significant amount of computations that limit their use on robots with embedded
computing capabilities. This paper presents ODAS, the Open embeddeD Audition
System framework, which includes strategies to reduce the computational load
and perform robot audition tasks on low-cost embedded computing systems. It
presents key features of ODAS, along with cases illustrating its uses in
different robots and artificial audition applications.


Abstract:
  In 1957 Feynman suggested that the quantum/classical character of gravity can
be assessed by the presence/absence of entanglement between gravitationally
interacting test masses. However, in all proposed experimental realisations
using matter-wave interferometry the extreme weakness of this interaction
requires pure initial states with extreme squeezing to achieve measurable
entanglement for reasonable interaction times. In practice, the systems that
can be prepared in such nonclassical states are limited to small masses, which
in turn limits the rate at which they get entangled. Here we address this key
challenge - the weakness of gravitational interaction - by using a massive body
as an amplifying mediator of gravitational interaction between two
test-systems. Our analysis shows, that this results in an effective interaction
between the two test-systems that grows with the mass of the mediator and is
independent of its initial state and, therefore, its temperature. This greatly
reduces the requirement on the mass and degree of delocalization of the test
systems and, while still highly challenging, brings experiments on
gravitational source masses a step closer to reality.


Abstract:
  In this paper we present an adaptive deep density approximation strategy
based on KRnet (ADDA-KR) for solving the steady-state Fokker-Planck (F-P)
equations. F-P equations are usually high-dimensional and defined on an
unbounded domain, which limits the application of traditional grid based
numerical methods. With the Knothe-Rosenblatt rearrangement, our newly proposed
flow-based generative model, called KRnet, provides a family of probability
density functions to serve as effective solution candidates for the
Fokker-Planck equations, which has a weaker dependence on dimensionality than
traditional computational approaches and can efficiently estimate general
high-dimensional density functions. To obtain effective stochastic collocation
points for the approximation of the F-P equation, we develop an adaptive
sampling procedure, where samples are generated iteratively using the
approximate density function at each iteration. We present a general framework
of ADDA-KR, validate its accuracy and demonstrate its efficiency with numerical
experiments.


Abstract:
  We report on the demonstration of metalorganic vapor phase epitaxy-regrown
(MOVPE) ohmic contacts in an all MOVPE-grown $\beta$-Ga$_2$O$_3$
metal-semiconductor field effect transistor (MESFET). The low-temperature
(600$^{\circ}$C) heavy (n$^{+}$) Si-doped regrown layers exhibit extremely high
conductivity with sheet resistance of 73 $\Omega$/$\square$ and record low
metal/n$^{+}$-Ga$_2$O$_3$ contact resistance of 80 m$\Omega$.mm and specific
contact resistivity of 8.3$\times$10$^{-7}$ $\Omega$.cm$^{2}$ were achieved.
The fabricated MESFETs exhibit a maximum drain-to-source current of 130 mA/mm,
a high I$_{ON}$/I$_{OFF}$ of $>$10$^{10}$ with a high power FOM of 25
MW/cm$^{2}$ were achieved without any field plates. Nanoparticle-assisted Raman
thermometry, thermal modeling, and infrared thermography were performed to
assess the device self-heating under the high current and power conditions.
This demonstration shows the promise of MOVPE technique for the realization of
high-performance lateral $\beta$-Ga$_2$O$_3$ devices and also highlights the
need for device-level thermal management.


Abstract:
  We find a late times approximation for the SYK spectral form factor from a
large $N$ steepest descent version of the path integral over two replica
collective fields. Main ingredients are a suitable uv regularization of the two
replica kinetic operator, the property of its Fourier transform and some
spectral analysis of the four point function two replica ladder kernel.


Abstract:
  $\mathrm{Cu_2IrO_3}$ is among the newest layered honeycomb iridates and a
promising candidate to harbor a Kitaev quantum spin liquid state. Here, we
investigate the pressure and temperature dependence of its structure through a
combination of powder x-ray diffraction and x-ray absorption fine structure
measurements, as well as $ab$-$initio$ evolutionary structure search. At
ambient pressure, we revise the previously proposed $C2/c$ solution with a
related but notably more stable $P2_1/c$ structure. Pressures below 8 GPa drive
the formation of Ir-Ir dimers at both ambient and low temperatures, similar to
the case of $\mathrm{Li_2IrO_3}$. At higher pressures, the structural evolution
dramatically depends on temperature. A large discontinuous reduction of the Ir
honeycomb interplanar distance is observed around 15 GPa at room temperature,
likely driven by a collapse of the O-Cu-O dumbbells. At 15 K, pressures beyond
20 GPa first lead to an intermediate phase featuring a continuous reduction of
the interplanar distance, which then collapses at 30 GPa across yet another
phase transition. However, the resulting structure around 40 GPa is not the
same at room and low temperatures. Remarkably, the reduction in interplanar
distance leads to an apparent healing of the stacking faults at room
temperature, but not at 15 K. Possible implications on the evolution of
electronic structure of $\mathrm{Cu_2IrO_3}$ with pressure are discussed.


Abstract:
  The present paper shows a proposal of the characteristics Cloud Risk
Assessment Models should have and presents the review of the literature
considering those characteristics in order to identify current gaps. This work
shows a ranking of Cloud RA models and their degree of compliance with the
theoretical reference Cloud Risk Assessment model. The review of literature
shows that RA approaches leveraging CSA (Cloud Security Alliance) STAR Registry
that have into account organizations security requirements present higher
degree of compliance, but they still lack risk economic quantification. The
myriad of conceptual models, methodologies and frameworks although based on
current NIST SP 800:30, ISO 27001, ISO 27005, ISO 30001, ENISA standards could
be enhanced by the use of techno-economic models like UTEM, created by the
author, in order to conceive more simplified models for effective Risk
Assessment and Mitigation closer to the theoretical reference model for Cloud
Risk Assessment, available for all cloud models (IaaS, PaaS, SaaS) and easy to
use for all stakeholders.


Abstract:
  The tension between current expansion rate H$_0$ using Planck data and direct
model-independent measurements in the local universe has reached a tension
above 5$\sigma$ in the context of the $\Lambda$CDM model. The growing tension
among early time and local measurements of H$_0$ has not ameliorated and
remains a crucial and open question in cosmology. Solutions to understand this
tension are possible hidden sources of systematic error in the observable
measurements or modifications to the concordance $\Lambda$CDM model. In this
work, we investigate a solution to the H$_0$ tension by modifying $\Lambda$CDM
and we add at early times extra relativistic energy density $\rho_{ex}$. In
some context this $\rho_{ex}$ corresponds to EDE or Bound Dark Energy. We
implement our model in CAMB and perform a full COSMO-MC (MCMC) allowing to
simultaneously fit the latest data from CMB anisotropies and the value of H$_0
= 74.03\pm 1.42,\mathrm{km\, s^{-1}Mpc^{-1}}$ from A. Riess [R-19]
\ci{Riess:2019cxk}. The inclusion of $\rho_{ex}$ ameliorates the tension
between early and late time measurements only slightly and we obtain a value
H$_0 = (68.70\pm 0.45$\ksm) still in conflict with local measurements [R-19].
We follow up our analysis by proposing two forecasting standard deviation
$\sigma_H=1$ and $\sigma_H=0.5$ (in units of \ksm) for local distance
measurements H$_0 = (74.03\pm \sigma_H)$ \ksm. From MCMC we obtain a value of
H$_0 =(72.83\pm 0.47)$ \ksm\, at 68\% confidence level for $\sigma_H =0.5$,
fully consistent with [R-19], while the price to pay is a percentage increase
of $0.12\%$ in CMB $\chi_{cmb}^2$. Finally, the extra energy density
$\rho_{ex}$ leaves distinctive imprints in the matter power spectrum at scales
$k \sim k_c$ with $k_c=a_cH(a_c)$ and in the CMB power spectrum, allowing for
independent verification of our analysis.


Abstract:
  We present optical follow-up observations with the DDOTI telescope of
gravitational-wave events detected during the Advanced LIGO and Advanced Virgo
O3 observing run. DDOTI is capable of responding to an alert in a few minutes,
has an instantaneous field of about 69 deg$^{2}$, and obtains $10\sigma$ upper
limits of $w_{\rm lim}=18.5$ to 20.5 AB mag in 1000~s of exposure, depending on
the conditions. We observed 54\% (26 out of 48) of the unretracted
gravitational-wave alerts and did not find any electromagnetic counterparts. We
compare our upper limits to various possible counterparts: the kilonova
AT~2017gfo, models of radioactive- and magnetar-powered kilonovae, short
gamma-ray burst afterglows, and AGN flares. Although the large positional
uncertainties of GW sources do not allow us to place strong constraints during
O3, DDOTI observations of well-localized GW events in O4 and beyond could
meaningfully constrain models of compact binary mergers. We show that DDOTI is
able to detect kilonovae similar to AT~2017gfo up to about 200~Mpc and
magnetar-powered kilonovae up to 1~Gpc. We calculate that nearby ($\lesssim$200
Mpc) afterglows have a high chance ($\approx$70\%) to be detected by rapid
($\lesssim$3 hours) DDOTI observations if observed on-axis, whereas off-axis
afterglows are unlikely to be seen. Finally, we suggest that long-term
monitoring of massive BBH events with DDOTI could confirm or rule out late AGN
flares associated with these events.


Abstract:
  This paper proposes a equivariant filtering (EqF) framework for the
inertial-integrated state estimation problem. As the kinematic system of the
inertial-integrated navigation can be naturally modeling on the matrix Lie
group $SE_2(3)$, the symmetry of the Lie group can be exploited to design a
equivariant filter which extends the invariant extended Kalman filtering on the
group affine system. Furthermore, details of the analytic state transition
matrices for left invariant error and right invariant error are given.


Abstract:
  Let $(\pi, \mathcal{H})$ be a strongly continuous unitary representation of a
1-connected Lie group $G$ such that the Lie algebra $\mathfrak{g}$ of $G$ is
generated by the positive cone $C_\pi := \{x \in \mathfrak{g} : -i\partial
\pi(x) \geq 0\}$ and an element $h$ for which the adjoint representation of $h$
induces a 3-grading of $\mathfrak{g}$. Moreover, suppose that $(\pi,
\mathcal{H})$ extends to an antiunitary representation of the extended Lie
group $G_\tau := G \rtimes \{\mathbf{1}, \tau_G\}$, where $\tau_G$ is an
involutive automorphism of $G$ with $\mathbf{L}(\tau_G) = e^{i\pi\mathrm{ad}
h}$.
  In a recent work by Neeb and \'Olafsson, a method for constructing nets of
standard subspaces of $\mathcal{H}$ indexed by open regions of $G$ has been
introduced and applied in the case where $G$ is semisimple. In this paper, we
extend this construction to general Lie groups $G$, provided the above
assumptions are satisfied and the center of the ideal $\mathfrak{g}_C = C_\pi -
C_\pi$ of $\mathfrak{g}$ is one-dimensional. The case where the center of
$\mathfrak{g}_C$ has more than one dimension will be discussed in a separate
paper.


Abstract:
  In this book there will be found an introduction to transcendental number
theory, starting at the beginning and ending at the frontiers. The emphasis is
on the conceptual aspects of the subject, thus the effective theory has been
more or less completely ignored, as has been the theory of $E$-functions and
$G$-functions. Still, a fair amount of ground is covered and while I take
certain results without proof, this is done primarily so as not to get bogged
down in technicalities, otherwise the exposition is detailed and little is left
to the reader.


Abstract:
  Babies born with low and very low birthweights -- i.e., birthweights below
2,500 and 1,500 grams, respectively -- have an increased risk of complications
compared to other babies, and the proportion of babies with a low birthweight
is a common metric used when evaluating public health in a population. While
many factors increase the risk of a baby having a low birthweight, many can be
linked to the mother's socioeconomic status, which in turn contributes to large
racial disparities in the incidence of low weight births. Here, we employ
Bayesian statistical models to analyze the proportion of babies with low
birthweight in Pennsylvania counties by race/ethnicity. Due to the small number
of births -- and low weight births -- in many Pennsylvania counties when
stratified by race/ethnicity, our methods must walk a fine line. On one hand,
leveraging spatial structure can help improve the precision of our estimates.
On the other hand, we must be cautious to avoid letting the model overwhelm the
information in the data and produce spurious conclusions. As such, we first
develop a framework by which we can measure (and control) the informativeness
of our spatial model. After demonstrating the properties of our framework via
simulation, we analyze the low birthweight data from Pennsylvania and examine
the extent to which the commonly used conditional autoregressive model can lead
to oversmoothing. We then reanalyze the data using our proposed framework and
highlight its ability to detect (or not detect) evidence of racial disparities
in the incidence of low birthweight.


Abstract:
  We initiate the study of Regge theory in a bottom-up holographic model for
QCD in the Veneziano limit, where the backreaction of the quarks to the gluon
dynamics is included. We determine the parameters of the model by carrying out
a precise fit to the meson spectrum in QCD. The spectrum for spin-one and
pseudoscalar mesons is well reproduced. We then generalise the model to incluce
higher spin fields in the bulk trajectories dual to the Pomeron and meson Regge
trajectories at the boundary. With this setting, we fit the masses of the
mesons with spins $J=2$, $3$, and $4$,as well as the experimental data of the
total cross-sections $\sigma(\gamma \gamma \to X)$, $\sigma(\gamma p \to X)$
and $\sigma(p p \to X)$. For the cross sections we obtain a
$\chi^2_{\mathrm{d.o.f.}}$ of $0.74$ for a total of 199 experimental points.


Abstract:
  Absorption-selected galaxies offer an effective way to study low-mass
galaxies at high redshift. However, the physical properties of the underlying
galaxy population remains uncertain. In particular, the multiphase
circum-galactic medium is thought to hold key information on gas flows into and
out of galaxies that are vital for galaxy evolution models. Here we present
ALMA observations of CO molecular gas in host galaxies of H_2-bearing
absorbers. In our sample of six absorbers we detect molecular gas-rich galaxies
in five absorber fields although we did not target high-metallicity (>50 per
cent solar) systems for which previous studies reported the highest detection
rate. Surprisingly, we find that the majority of the absorbers are associated
with multiple galaxies rather than single haloes. Together with the large
impact parameters these results suggest that the H_2-bearing gas seen in
absorption is not part of an extended disk, but resides in dense gas pockets in
the circum-galactic and intra-group medium.


Abstract:
  We report on the development of multi-beam RF linear ion accelerators that
are formed from stacks of low cost wafers and describe the status of beam power
scale-up using an array of 120 beams. The total argon ion current extracted
from the 120-beamlet extraction column was 0.5 mA. The measured energy gain in
each RF gap reached as high as 7.25 keV. We present a path of using this
technology to achieve ion currents >1 mA and ion energies >100 keV for
applications in materials processing.


Abstract:
  The notion of a tensor captures three great ideas: equivariance,
multilinearity, separability. But trying to be three things at once makes the
notion difficult to understand. We will explain tensors in an accessible and
elementary way through the lens of linear algebra and numerical linear algebra,
elucidated with examples from computational and applied mathematics.


Abstract:
  Millions of cameras at edge are being deployed to power a variety of
different deep learning applications. However, the frames captured by these
cameras are not always pristine - they can be distorted due to lighting issues,
sensor noise, compression etc. Such distortions not only deteriorate visual
quality, they impact the accuracy of deep learning applications that process
such video streams. In this work, we introduce AQuA, to protect application
accuracy against such distorted frames by scoring the level of distortion in
the frames. It takes into account the analytical quality of frames, not the
visual quality, by learning a novel metric, classifier opinion score, and uses
a lightweight, CNN-based, object-independent feature extractor. AQuA accurately
scores distortion levels of frames and generalizes to multiple different deep
learning applications. When used for filtering poor quality frames at edge, it
reduces high-confidence errors for analytics applications by 17%. Through
filtering, and due to its low overhead (14ms), AQuA can also reduce computation
time and average bandwidth usage by 25%.


Abstract:
  A theory is NIP (resp. stable) if and only if every formula with parameters
in two single variables is NIP (resp. does not have the order property).


Abstract:
  Increasing effort is being directed to understanding the personality profiles
of highly engaged information systems (IS) developers and the impact of such
profiles on development outcomes. However, there has been a lesser degree of
attention paid to studying attitudes at a fine-grained level, and relating such
attitudes to developers' in-process activities, in spite of the fact that
social motivation theory notes the importance of such a relationship in general
group work. We have therefore applied linguistic analysis, text mining and
visualization, and statistical analysis techniques to artefacts developed by
474 developers to study these issues. Our results indicate that our sample of
IS developers conveyed a range of attitudes while working to deliver systems
features, and those practitioners who communicated the most were also the most
engaged. Additionally, of eight linguistic dimensions considered, expressions
regarding work and achievement, as well as insightful attitudes, were most
closely related to developers' engagement. Accordingly, team diversity and the
provision of active support for outcome-driven developers may contribute
positively to maintaining team balance and performance.


Abstract:
  Text summarization is the process of extracting the most important
information from the text and presenting it concisely in fewer sentences. Call
transcript is a text that involves textual description of a phone conversation
between a customer (caller) and agent(s) (customer representatives). This paper
presents an indigenously developed method that combines topic modeling and
sentence selection with punctuation restoration in condensing ill-punctuated or
un-punctuated call transcripts to produce summaries that are more readable.
Extensive testing, evaluation and comparisons have demonstrated the efficacy of
this summarizer for call transcript summarization.


Abstract:
  Axions were first introduced in connection with chiral symmetry but are now
being looked for mainly as dark matter. In this paper we introduce a nonabelian
analogue of axions which can also be potential candidates for dark matter.
Their nonabelian symmetries, which are generalizations of the Peccei-Quinn
symmetry, are interesting in their own right. Detailed analysis, using fermion
measure and zeta function approaches shows that these symmetries are not
anomalous.


Abstract:
  Concept-based explanations have emerged as a popular way of extracting
human-interpretable representations from deep discriminative models. At the
same time, the disentanglement learning literature has focused on extracting
similar representations in an unsupervised or weakly-supervised way, using deep
generative models. Despite the overlapping goals and potential synergies, to
our knowledge, there has not yet been a systematic comparison of the
limitations and trade-offs between concept-based explanations and
disentanglement approaches. In this paper, we give an overview of these fields,
comparing and contrasting their properties and behaviours on a diverse set of
tasks, and highlighting their potential strengths and limitations. In
particular, we demonstrate that state-of-the-art approaches from both classes
can be data inefficient, sensitive to the specific nature of the
classification/regression task, or sensitive to the employed concept
representation.


Abstract:
  Although Generative Adversarial Networks (GANs) are successfully applied to
diverse fields, training GANs on synthetic aperture radar (SAR) data is a
challenging task mostly due to speckle noise. On the one hands, in a learning
perspective of human's perception, it is natural to learn a task by using
various information from multiple sources. However, in the previous GAN works
on SAR target image generation, the information on target classes has only been
used. Due to the backscattering characteristics of SAR image signals, the
shapes and structures of SAR target images are strongly dependent on their pose
angles. Nevertheless, the pose angle information has not been incorporated into
such generative models for SAR target images. In this paper, we firstly propose
a novel GAN-based multi-task learning (MTL) method for SAR target image
generation, called PeaceGAN that uses both pose angle and target class
information, which makes it possible to produce SAR target images of desired
target classes at intended pose angles. For this, the PeaceGAN has two
additional structures, a pose estimator and an auxiliary classifier, at the
side of its discriminator to combine the pose and class information more
efficiently. In addition, the PeaceGAN is jointly learned in an end-to-end
manner as MTL with both pose angle and target class information, thus enhancing
the diversity and quality of generated SAR target images The extensive
experiments show that taking an advantage of both pose angle and target class
learning by the proposed pose estimator and auxiliary classifier can help the
PeaceGAN's generator effectively learn the distributions of SAR target images
in the MTL framework, so that it can better generate the SAR target images more
flexibly and faithfully at intended pose angles for desired target classes
compared to the recent state-of-the-art methods.


Abstract:
  Broad and unexplored kinematic regions can be accessed at the LHC with
fixed-target $pp$, $pA$ and $PbA$ collisions at
$\sqrt{s_{\rm{NN}}}=72-115~\rm{GeV}$. The LHCb detector is a fully-instrumented
forward spectrometer able to run in fixed-target mode, and currently hosts a
target gas cell to take data in the upcoming Run 3. The LHCspin project aims at
extending this physics program to Run 4 and to bring polarised physics at the
LHC. An overview of the physics potential and a description of the LHCspin
experimental setup are presented.


Abstract:
  Motivation: A Chemical Reaction Network (CRN) is a set of chemical reactions,
which can be very complex and difficult to analyze. Indeed, dynamical
properties of CRNs can be described by a set of non-linear differential
equations that rarely can be solved in closed-form, but that can instead be
used to reason on the system dynamics. In this context, one of the possible
approaches is to perform numerical simulations, which may require a high
computational effort. In particular, in order to investigate some dynamical
properties, such as robustness or global sensitivity, many simulations have to
be performed by varying the initial concentration of chemical species. Results:
In order to reduce the computational effort required when many simulations are
needed to assess a property, we exploit a new notion of monotonicity of the
output of the system (the concentration of a target chemical species at the
steady-state) with respect to the input (the initial concentration of another
chemical species). To assess such monotonicity behavior, we propose a new
graphical approach that allows us to state sufficient conditions for ensuring
that the monotonicity property holds. Our sufficient conditions allow us to
efficiently verify the monotonicity property by exploring a graph constructed
on the basis of the reactions involved in the network. Once established, our
monotonicity property allows us to drastically reduce the number of simulations
required to assess some dynamical properties of the CRN.


Abstract:
  We use machine learning to classify rational two-dimensional conformal field
theories. We first use the energy spectra of these minimal models to train a
supervised learning algorithm. We find that the machine is able to correctly
predict the nature and the value of critical points of several strongly
correlated spin models using only their energy spectra. This is in contrast to
previous works that use machine learning to classify different phases of
matter, but do not reveal the nature of the critical point between phases.
Given that the ground-state entanglement Hamiltonian of certain topological
phases of matter is also described by conformal field theories, we use
supervised learning on R\'{e}yni entropies and find that the machine is able to
identify which conformal field theory describes the entanglement Hamiltonian
with only the lowest few R\'{e}yni entropies to a high degree of accuracy.
Finally, using autoencoders, an unsupervised learning algorithm, we find a
hidden variable that has a direct correlation with the central charge and
discuss prospects for using machine learning to investigate other conformal
field theories, including higher-dimensional ones. Our results highlight that
machine learning can be used to find and characterize critical points and also
hint at the intriguing possibility to use machine learning to learn about more
complex conformal field theories.


Abstract:
  Operational urban transport models require to gather heterogeneous sets of
data and often integrate different sub-models. Their systematic validation and
reproducible application therefore remains problematic. We propose in this
contribution to build transport models from the bottom-up using scientific
workflow systems with open-source components and data. These open models are
aimed in particular at estimating congestion of public transport in all UK
urban areas. This allows us building health indicators related to public
transport density in the context of the COVID-19 crisis, and testing related
policies.


Abstract:
  Networks analysis has been commonly used to study the interactions between
units of complex systems. One problem of particular interest is learning the
network's underlying connection pattern given a single and noisy instantiation.
While many methods have been proposed to address this problem in recent years,
they usually assume that the true model belongs to a known class, which is not
verifiable in most real-world applications. Consequently, network modeling
based on these methods either suffers from model misspecification or relies on
additional model selection procedures that are not well understood in theory
and can potentially be unstable in practice. To address this difficulty, we
propose a mixing strategy that leverages available arbitrary models to improve
their individual performances. The proposed method is computationally efficient
and almost tuning-free; thus, it can be used as an off-the-shelf method for
network modeling. We show that the proposed method performs equally well as the
oracle estimate when the true model is included as individual candidates. More
importantly, the method remains robust and outperforms all current estimates
even when the models are misspecified. Extensive simulation examples are used
to verify the advantage of the proposed mixing method. Evaluation of link
prediction performance on 385 real-world networks from six domains also
demonstrates the universal competitiveness of the mixing method across multiple
domains.


Abstract:
  Vertical federated learning (VFL) attracts increasing attention due to the
emerging demands of multi-party collaborative modeling and concerns of privacy
leakage. In the real VFL applications, usually only one or partial parties hold
labels, which makes it challenging for all parties to collaboratively learn the
model without privacy leakage. Meanwhile, most existing VFL algorithms are
trapped in the synchronous computations, which leads to inefficiency in their
real-world applications. To address these challenging problems, we propose a
novel {\bf VF}L framework integrated with new {\bf b}ackward updating mechanism
and {\bf b}ilevel asynchronous parallel architecture (VF{${\textbf{B}}^2$}),
under which three new algorithms, including VF{${\textbf{B}}^2$}-SGD, -SVRG,
and -SAGA, are proposed. We derive the theoretical results of the convergence
rates of these three algorithms under both strongly convex and nonconvex
conditions. We also prove the security of VF{${\textbf{B}}^2$} under
semi-honest threat models. Extensive experiments on benchmark datasets
demonstrate that our algorithms are efficient, scalable and lossless.


Abstract:
  In this paper we present a large sample of jellyfish galaxies in low redshift
clusters (z<0.05), identified through 120-168 MHz radio continuum from the
LOFAR Two-metre Sky Survey (LoTSS). From a parent sample of 29 X-ray-detected
SDSS galaxy clusters and their spectroscopic members, we visually identify 95
star-forming, LoTSS jellyfish galaxies with 144 MHz radio tails. Star formation
rates (SFRs) and stellar masses are obtained for all galaxies from SED fits.
For each jellyfish galaxy we determine the tail orientation with respect to the
cluster centre and quantify the prominence of the radio tails with the 144 MHz
shape asymmetry. After carefully accounting for redshift-dependent selection
effects, we find that the frequency of jellyfish galaxies is relatively
constant from cluster to cluster. LoTSS jellyfish galaxies are preferentially
found at small clustercentric radius and large velocity offsets within their
host clusters and have radio tails that are oriented away from the cluster
centre. These galaxies also show enhanced star formation, relative to both
'normal' cluster galaxies and isolated field galaxies, but generally fall
within the scatter of the L144MHz - SFR relation. The properties of the LoTSS
jellyfish galaxies identified in this work are fully consistent with
expectations from ram pressure stripping. This large sample of jellyfish
galaxies will be valuable for further constraining ram pressure stripping and
star formation quenching in nearby galaxy clusters. We show that LOFAR is a
powerful instrument for identifying ram pressure stripped galaxies across
extremely wide fields. Moving forward we will push the search for jellyfish
galaxies beyond this initial cluster sample, including a comprehensive survey
of the galaxy group regime.


Abstract:
  Many techniques were proposed for detecting software misconfigurations in
cloud systems and for diagnosing unintended behavior caused by such
misconfigurations. Detection and diagnosis are steps in the right direction:
misconfigurations cause many costly failures and severe performance issues.
But, we argue that continued focus on detection and diagnosis is symptomatic of
a more serious problem: configuration design and implementation are not yet
first-class software engineering endeavors in cloud systems. Little is known
about how and why developers evolve configuration design and implementation,
and the challenges that they face in doing so.
  This paper presents a source-code level study of the evolution of
configuration design and implementation in cloud systems. Our goal is to
understand the rationale and developer practices for revising initial
configuration design/implementation decisions, especially in response to
consequences of misconfigurations. To this end, we studied 1178
configuration-related commits from a 2.5 year version-control history of four
large-scale, actively-maintained open-source cloud systems (HDFS, HBase, Spark,
and Cassandra). We derive new insights into the software configuration
engineering process. Our results motivate new techniques for proactively
reducing misconfigurations by improving the configuration design and
implementation process in cloud systems. We highlight a number of future
research directions.


Abstract:
  Region modification-based data augmentation techniques have shown to improve
performance for high level vision tasks (object detection, semantic
segmentation, image classification, etc.) by encouraging underlying algorithms
to focus on multiple discriminative features. However, as these techniques
destroy spatial relationship with neighboring regions, performance can be
deteriorated when using them to train algorithms designed for low level vision
tasks (low light image enhancement, image dehazing, deblurring, etc.) where
textural consistency between recovered and its neighboring regions is important
to ensure effective performance. In this paper, we examine the efficacy of a
simple copy-blend data augmentation technique that copies patches from noisy
images and blends onto a clean image and vice versa to ensure that an
underlying algorithm localizes and recovers affected regions resulting in
increased perceptual quality of a recovered image. To assess performance
improvement, we perform extensive experiments alongside different region
modification-based augmentation techniques and report observations such as
improved performance, reduced requirement for training dataset, and early
convergence across tasks such as low light image enhancement, image dehazing
and image deblurring without any modification to baseline algorithm.


Abstract:
  Power grids, as well as neuronal networks with synaptic plasticity, describe
real-world systems of tremendous importance for our daily life. The
investigation of these seemingly unrelated types of dynamical networks has
attracted increasing attention over the last decade. In this work, we exploit
the recently established relation between these two types of networks to gain
insights into the dynamical properties of multifrequency clusters in power grid
networks. For this, we consider the model of Kuramoto-Sakaguchi phase
oscillators with inertia and describe the emergence of multicluster states.
Building on this, we provide a new perspective on solitary states in power grid
networks by introducing the concept of pseudo coupling weights.


Abstract:
  Heisenberg's uncertainty principle tells us that it is impossible to
determine simultaneously the position of a photon crossing a telescope's
aperture and its momentum. Super-resolution imaging techniques rely on
modification of the observed sample, or on entangling photons. In astronomy we
have no access to the object, but resolution may be improved by optical
amplification. Unfortunately, spontaneous emission contributes noise and
negates the possible gain from stimulated emissions. We show that it is
possible to increase the weight of the stimulated photons by considering photon
statistics, and observe an improvement in resolution. Most importantly, we
demonstrate a method which can apply for all imaging purposes.


Abstract:
  Object insertion is a classic contact-rich manipulation task. The task
remains challenging, especially when considering general objects of unknown
geometry, which significantly limits the ability to understand the contact
configuration between the object and the environment. We study the problem of
aligning the object and environment with a tactile-based feedback insertion
policy. The insertion process is modeled as an episodic policy that iterates
between insertion attempts followed by pose corrections. We explore different
mechanisms to learn such a policy based on Reinforcement Learning. The key
contribution of this paper is to demonstrate that it is possible to learn a
tactile insertion policy that generalizes across different object geometries,
and an ablation study of the key design choices for the learning agent: 1) the
type of learning scheme: supervised vs. reinforcement learning; 2) the type of
learning schedule: unguided vs. curriculum learning; 3) the type of sensing
modality: force/torque (F/T) vs. tactile; and 4) the type of tactile
representation: tactile RGB vs. tactile flow. We show that the optimal
configuration of the learning agent (RL + curriculum + tactile flow) exposed to
4 training objects yields an insertion policy that inserts 4 novel objects with
over 85.0% success rate and within 3~4 attempts. Comparisons between F/T and
tactile sensing, shows that while an F/T-based policy learns more efficiently,
a tactile-based policy provides better generalization.


Abstract:
  The diffraction of light imposes a fundamental limit on the resolution of
light microscopes. This limit can be circumvented by creating and exploiting
independent behaviors of the sample at length scales below the diffraction
limit. In super-resolution single molecule localization microscopy (SMLM), the
independence arises from individual fluorescent labels stochastically switching
between dark and fluorescent states, which in turn allows the pinpointing of
fluorophores post experimentally using a sequence of acquired sparse image
frames. Finally, the resulting list of fluorophore coordinates is utilized to
produce high resolution images or to gain quantitative insight into the
underlying biological structures. Therefore, image processing and
post-processing are essential stages of SMLM. Here, we review the latest
progress on SMLM data processing and post-processing.


Abstract:
  The quantum spin liquid (QSL) is an exotic phase of magnetic materials where
the spins continue to fluctuate without any symmetry breaking down to zero
temperature. Among the handful reports of QSL with spin $S\ge$1, examples with
magnetic ions on a three-dimensional magnetic lattice are extremely rare since
both larger spin and higher dimension tend to suppress quantum fluctuations. In
this work, we offer a new strategy to achieve 3-D QSL with high spin by
utilizing two types of transition metal ions, both are magnetically active but
located at crystallographically inequivalent positions. We design a 3-D
magnetic system Ba$_3$NiIr$_2$O$_9$ consisting of interconnected corner shared
NiO$_6$ octahedra and face shared Ir$_2$O$_9$ dimer, both having triangular
arrangements in \textit{a-b} plane. X-ray absorption spectroscopy measurements
confirm the presence of Ni$^{2+}$ ($S$=1). Our detailed thermodynamic and
magnetic measurements reveal that this compound is a realization of gapless QSL
state down to at least 100 mK. Ab-initio calculations find a strong magnetic
exchange between Ir and Ni sublattices and in-plane antiferromagnetic coupling
between the dimers, resulting in dynamically fluctuating magnetic moments on
both the Ir and Ni sublattice.


Abstract:
  Energetic electrons inside Earth's outer Van Allen belt pose a major
radiation threat to space-borne electronics that often play vital roles in our
modern society. Ultra-relativistic electrons with energies greater than or
equal to two Megaelectron-volt (MeV) are of particular interest due to their
high penetrating ability, and thus forecasting these >=2 MeV electron levels
has significant meaning to all space sectors. Here we update the latest
development of the predictive model for MeV electrons inside the Earth's outer
radiation belt. The new version, called PreMevE-2E, focuses on forecasting
ultra-relativistic electron flux distributions across the outer radiation belt,
with no need of in-situ measurements except for at the geosynchronous (GEO)
orbit. Model inputs include precipitating electrons observed in
low-Earth-orbits by NOAA satellites, upstream solar wind conditions (speeds and
densities) from solar wind monitors, as well as ultra-relativistic electrons
measured by one Los Alamos GEO satellite. We evaluated a total of 32 supervised
machine learning models that fall into four different classes of linear and
neural network architectures, and also successfully tested ensemble forecasting
by using groups of top-performing models. All models are individually trained,
validated, and tested by in-situ electron data from NASA's Van Allen Probes
mission. It is shown that the final ensemble model generally outperforms
individual models overs L-shells, and this PreMevE-2E model provides reliable
and high-fidelity 25-hr (~1-day) and 50-hr (~2-day) forecasts with high mean
performance efficiency values. Our results also suggest this new model is
dominated by non-linear components at low L-shells (< ~4) for
ultra-relativistic electrons, which is different from the dominance of linear
components at all L-shells for 1 MeV electrons as previously discovered.


Abstract:
  This article proposes Rydberg multi-dimensional discrete-time quantum walk
(DTQW) implementation that could ideally simulate different classes of
topological insulators. Using an exchange interaction between Rydberg excited
atoms in an atomic-array with dual lattice-constants, the new setup operates
both coined and coin-less models of DTQW. Here, complicated coupling
tesselations are performed by global laser excitation. The long-range
interaction provides a new feature of designing different topologically ordered
periodic boundary conditions. Limiting the Rydberg population to 1.5
excitations, coherent QW over thousand lattice sites and steps are achievable
with the current technology. These features would improve the performance of
this quantum machine in running the quantum search algorithm over topologically
ordered databases as well as diversifying the range of topological insulators
that could be simulated.


Abstract:
  We study the ring of polyfunctions over a commutative ring $R$ with unit
element, i.e., the ring of functions $f:R\to R$ which admit a polynomial
representative $p\in R[x]$ in the sense that $f(x)= p(x)$ for all $x\in R$.
This allows to define a ring invariant $s$ which associates to a commutative
ring $R$ with unit element a value in $\mathbb N\cup\{\infty\}$. The function
$s$ generalizes the number theoretic Smarandache function. For the ring
$R=\mathbb Z/n\mathbb Z$ we provide a unique representation of polynomials
which vanish as a function. This yields a new formula for the number $\Psi(n)$
of polyfunctions over $\mathbb Z/n\mathbb Z$. We also investigate algebraic
properties of the ring of polyfunctions over $\mathbb Z/n\mathbb Z$. In
particular, we identify the additive subgroup of the ring and the ring
structure itself. Moreover we derive a new formula for the size of the ring of
polyfunctions in several variables over $\mathbb Z/n\mathbb Z$.


Abstract:
  Schubert polynomials form a basis of all polynomials and appear in the study
of cohomology rings of flag manifolds. The vanishing problem for Schubert
polynomials asks if a coefficient of a Schubert polynomial is zero. We give a
tableau criterion to solve this problem, from which we deduce the first
polynomial time algorithm. These results are obtained from new
characterizations of the Schubitope, a generalization of the permutahedron
defined for any subset of the n x n grid. In contrast, we show that computing
these coefficients explicitly is #P-complete.


Abstract:
  Topologically stable field configurations appear in many fields of physics,
from elementary particles to condensed matter and cosmology. During the last
decade, chiral liquid crystals and chiral magnets took on the role of model
objects for experimental investigation of topological solitons and
understanding of their nonsingular field configurations. Here we introduce a
paradigm of facile skyrmionic networks with mutually-orthogonal orientations of
constituent isolated skyrmions. Such networks are envisioned as a novel concept
of spintronic devices and are presumably responsible for precursor phenomena
near the ordering temperatures of bulk cubic helimagnets. In particular, we
demonstrate an interconversion between mutually orthogonal skyrmions:
horizontal skyrmions swirl into an intermediate spring-like states and
subsequently squeeze into vertical skyrmions with both polarities. Skyrmion
tubes are also considered as building blocks for particle-like states with more
involved internal structure. A family of target-skyrmions, which includes an
overlooked so far type with a multiple topological charge, is formed owing to
the tendency to minimize the interaction energy between vertical and horizontal
skyrmions. The conical phase serves as a suitable background for considered
skyrmion evolution. It substantializes the attracting skyrmion-skyrmion
interaction in the skyrmionic networks, shapes their internal structure and
guides the nucleation processes. Alternatively, intricate textural changes of
isolated skyrmions result not only in the structural deformations of a host
conical state, but may lead to the formation of an exotic skyrmion order with
pairs of merons being the core of the game. Generically, the fundamental
insights provided by this work emphasize a three-dimensional character of
skyrmion metamorphoses and can also be extended to three-dimensional solitons,
such as hopfions.


Abstract:
  With the aim to parallelize and monitor biological or biochemical phenomena,
trapping and immobilization of objects such as particles, droplets or cells in
microfluidic devices has been an intense area of research and engineering so
far. Either being passive or active, these microfluidic devices are usually
composed of arrays of elementary traps with various levels of sophistication.
For a given array, it is important to have an efficient and fast immobilization
of the highest number of objects, while optimizing the spatial homogeneity of
the trapping over the whole chip. For passive devices, this has been achieved
with two-layers structures, making the fabrication process more complex. In
this work, we designed small microfluidic traps by single-layer direct laser
writing into a photoresist, and we show that even in this simplest case, the
orientation of the main flow of particles with respect to the traps have a
drastic effect on the trapping efficiency and homogeneity. To better understand
this phenomenon, we have considered two different flow geometries: parallel and
oblique with respect to the traps array, and compared quantitatively the
immobilization of particles with various sizes and densities. Using image
analysis, we show that diagonal flows gives a spatial distribution of the trap
loading that is more homogeneous over the whole chip as compared to the
straight ones, and by performing FEM and trapping simulation, we propose a
qualitative explanation of this phenomenon.


Abstract:
  Our gargantuan ab initio all-electron fully relativistic Dirac-Fock (DF),
nonrelativistic (NR) Hartree-Fock(HF) and Dirac-Fock-Breit-Gaunt(DFBG)
molecular SCF calculations for the superheavy octahedral Oganesson
hexatenniside OgTs$_6$ predict atomization energy (Ae) of 9.47, -5.54and 9.37
eV, at the optimized Os-Ts bond distances of 3.35, 3.34 and 3.36 angstroms,
respectively. There are dramatic effects of relativity for the atomization
energy of OgTs$_6$ (with seven superheavy elements and 820 electrons) of ~ 15.0
eV each at both the DF and DFBG levels of theory, respectively. Our calculated
energy of reaction for the titled superheavy reaction Og + 3Ts$_2$ -> OgTs$_6$
at the DF, NR and DFBG levels of theory is 6.33, 8.81, and 6.26 eV,
respectively. Mulliken analysis as implemented in the DIRAC code for our DF and
NR calculations (using the dyall.ev4z basis) yields the charges Og(+0.60) and
Og(+0.96), respectively on the central Og atom indicating that our relativistic
DF calculations predict octahedral OgTs$_6$ to be less ionic. However, due
caution must be used to interpret the results of Mulliken's population
analysis, which is highly basis set dependent.


Abstract:
  Accurate photo-z measurements are important to construct a large-scale
structure map of X-ray Universe in the ongoing SRG/eROSITA All-Sky Survey. We
present machine learning Random Forest-based models for probabilistic photo-z
predictions based on information from 4 large photometric surveys (SDSS,
Pan-STARRS, DESI Legacy Imaging Survey, and WISE). Our models are trained on
the large sample of $\approx$580000 quasars and galaxies selected from the SDSS
DR14 spectral catalog and take into account Galactic extinction and
uncertainties in photometric measurements for target objects. On the Stripe82X
test sample we obtained photo-z accuracy for X-ray sources: $NMAD=0.034$
(normalized median absolute deviation) and $n_{>0.15}=0.088$ (catastrophic
outliers fraction), which is almost $\sim2$ times better than best photo-z
results available in the literature.


Abstract:
  Temporal semantic scene understanding is critical for self-driving cars or
robots operating in dynamic environments. In this paper, we propose 4D panoptic
LiDAR segmentation to assign a semantic class and a temporally-consistent
instance ID to a sequence of 3D points. To this end, we present an approach and
a point-centric evaluation metric. Our approach determines a semantic class for
every point while modeling object instances as probability distributions in the
4D spatio-temporal domain. We process multiple point clouds in parallel and
resolve point-to-instance associations, effectively alleviating the need for
explicit temporal data association. Inspired by recent advances in benchmarking
of multi-object tracking, we propose to adopt a new evaluation metric that
separates the semantic and point-to-instance association aspects of the task.
With this work, we aim at paving the road for future developments of temporal
LiDAR panoptic perception.