Abstract:
  We present an approach for implementing a formally certified loop-invariant
code motion optimization by composing an unrolling pass and a formally
certified yet efficient global subexpression elimination.This approach is
lightweight: each pass comes with a simple and independent proof of
correctness.Experiments show the approach significantly narrows the performance
gap between the CompCert certified compiler and state-of-the-art optimizing
compilers.Our static analysis employs an efficient yet verified hashed set
structure, resulting in fast compilation.


Abstract:
  The radiation magnetohydrodynamics (RMHD) system couples the ideal
magnetohydrodynamics equations with a gray radiation transfer equation. The
main challenge is that the radiation travels at the speed of light while the
magnetohydrodynamics changes with the time scale of the fluid. The time scales
of these two processes can vary dramatically. In order to use mesh sizes and
time steps that are independent of the speed of light, asymptotic preserving
(AP) schemes in both space and time are desired. In this paper, we develop an
AP scheme in both space and time for the RMHD system. Two different scalings
are considered. One results in an equilibrium diffusion limit system, while the
other results in a non-equilibrium system. The main idea is to decompose the
radiative intensity into three parts, each part is treated differently with
suitable combinations of explicit and implicit discretizations guaranteeing the
favorable stability conditionand computational efficiency. The performance of
the AP method is presented, for both optically thin and thick regions, as well
as for the radiative shock problem.


Abstract:
  The fine-tuning of the universe for life, the idea that the constants of
nature (or ratios between them) must belong to very small intervals in order
for life to exist, has been debated by scientists for several decades. Several
criticisms have emerged concerning probabilistic measurement of life-permitting
intervals. Herein, a Bayesian statistical approach is used to assign an upper
bound for the probability of tuning, which is invariant with respect to change
of physical units, and under certain assumptions it is small whenever the
life-permitting interval is small on a relative scale. The computation of the
upper bound of the tuning probability is achieved by first assuming that the
prior is chosen by the principle of maximum entropy (MaxEnt). The unknown
parameters of this MaxEnt distribution are then handled in such a way that the
weak anthropic principle is not violated. The MaxEnt assumption is "maximally
noncommittal with regard to missing information." This approach is sufficiently
general to be applied to constants of current cosmological models, or to other
constants possibly under different models. Application of the MaxEnt model
reveals, for example, that the ratio of the universal gravitational constant to
the square of the Hubble constant is finely tuned in some cases, whereas the
amplitude of primordial fluctuations is not.


Abstract:
  It will be presented in this chapter a historical account of the consistent
histories interpretation of quantum mechanics based on primary and secondary
literature. Firstly, the formalism of the consistent histories approach will be
outlined.
  Secondly, the works by Robert Griffiths and Roland Omn\`es will be discussed.
Griffiths' seminal 1984 paper, the first physicist to have proposed a
consistent-histories interpretation of quantum mechanics, followed by Omn\`es'
1990 paper, were instrumental to the consistent-histories model based on
Boolean logic.
  Thirdly, Murray Gell-Mann and James Hartle's steps to their own version of
consistent-histories approach, motivated by a cosmological perspective, will
then be described and evaluated. Gell-Mann and Hartle understood that
spontaneous decoherence could path the way to a concrete physical model to
Griffiths' consistent histories.
  Moreover, the collective biography of these figures will be put in the
context of the role played by the Santa Fe Institute, co-founded by Gell-Mann
in 1984 in Santa Fe, New Mexico, where Hartle is also a member of the external
faculty.


Abstract:
  In this paper, we prove a limiting absorption principle for high-order
Schr\"odinger operators with a large class of potentials which generalize some
results by A. Ionescu and W. Schlag. Our main idea is to handle the boundary
operators by the restriction theorem of Fourier transform. Two key tools we use
in this paper are the Stein--Tomas theorem in Lorentz spaces and a sharp trace
lemma given by S. Agmon and L. H\"ormander


Abstract:
  Kernel herding is a method used to construct quadrature formulas in a
reproducing kernel Hilbert space. Although there are some advantages of kernel
herding, such as numerical stability of quadrature and effective outputs of
nodes and weights, the convergence speed of worst-case integration error is
slow in comparison to other quadrature methods. To address this problem, we
propose two improved versions of the kernel herding algorithm. The fundamental
concept of both algorithms involves approximating negative gradients with a
positive linear combination of vertex directions. We analyzed the convergence
and validity of both algorithms theoretically; in particular, we showed that
the approximation of negative gradients directly influences the convergence
speed. In addition, we confirmed the accelerated convergence of the worst-case
integration error with respect to the number of nodes and computational time
through numerical experiments.


Abstract:
  Nucleus segmentation is a challenging task due to the crowded distribution
and blurry boundaries of nuclei. Recent approaches represent nuclei by means of
polygons to differentiate between touching and overlapping nuclei and have
accordingly achieved promising performance. Each polygon is represented by a
set of centroid-to-boundary distances, which are in turn predicted by features
of the centroid pixel for a single nucleus. However, using the centroid pixel
alone does not provide sufficient contextual information for robust prediction.
To handle this problem, we propose a Context-aware Polygon Proposal Network
(CPP-Net) for nucleus segmentation. First, we sample a point set rather than
one single pixel within each cell for distance prediction. This strategy
substantially enhances contextual information and thereby improves the
robustness of the prediction. Second, we propose a Confidence-based Weighting
Module, which adaptively fuses the predictions from the sampled point set.
Third, we introduce a novel Shape-Aware Perceptual (SAP) loss that constrains
the shape of the predicted polygons. Here, the SAP loss is based on an
additional network that is pre-trained by means of mapping the centroid
probability map and the pixel-to-boundary distance maps to a different nucleus
representation. Extensive experiments justify the effectiveness of each
component in the proposed CPP-Net. Finally, CPP-Net is found to achieve
state-of-the-art performance on three publicly available databases, namely
DSB2018, BBBC06, and PanNuke. Code of this paper will be released.


Abstract:
  Signs of new physics are probed in the context of an Effective Field Theory
using events containing one or more top quarks in association with additional
leptons. Data consisting of proton-proton collisions at a center-of-mass energy
of $\sqrt{s}=$13 TeV was collected at the LHC by the CMS experiment in 2017. We
apply a novel technique to parameterize 16 dimension-six EFT operators in terms
of the respective Wilson coefficients (WCs). A simultaneous fit is performed to
the data in order to extract the two standard deviation confidence intervals
(CIs) of the 16 WCs. The Standard Model value of zero is completely contained
in most CIs, and is not excluded by a statistically significant amount in any
interval.


Abstract:
  In the novel superfluid polar phase realized in liquid 3He in highly
anisotropic aerogels, a quantum transition to the polar-distorted A (PdA) phase
may occur at a low but finite pressure Pc(0). It is shown that a nontrivial
quantum dynamics of the critical fluctuation of the PdA order is induced by the
presence of both the columnar-like impurity scattering leading to the
Anderson's Theorem for the polar phase and the line node of the quasiparticle
gap in the state, and that, in contrast to the situation of the normal to the B
phase transition in isotropic aerogels, a weakly divergent behavior of the
compressibility appears in the quantum critical region close to Pc(0).


Abstract:
  Sanitizers are a relatively recent trend in software engineering. They aim at
automatically finding bugs in programs, and they are now commonly available to
programmers as part of compiler toolchains. For example, the LLVM project
includes out-of-the-box sanitizers to detect thread safety (tsan), memory
(asan,msan,lsan), or undefined behaviour (ubsan) bugs.
  In this article, we present nsan, a new sanitizer for locating and debugging
floating-point numerical issues, implemented inside the LLVM sanitizer
framework. nsan puts emphasis on practicality. It aims at providing precise,
and actionable feedback, in a timely manner.
  nsan uses compile-time instrumentation to augment each floating-point
computation in the program with a higher-precision shadow which is checked for
consistency during program execution. This makes nsan between 1 and 4 orders of
magnitude faster than existing approaches, which allows running it routinely as
part of unit tests, or detecting issues in large production applications.


Abstract:
  Rutherford scattering formula plays an important role in plasma classical
transport. It is urgent to need a magnetized Rutherford scattering formula
since the magnetic field increases significantly in different fusion areas
(e.g. tokamak magnetic field, self-generated magnetic field, and compressed
magnetic field). The electron-ion Coulomb collisions perpendicular to the
external magnetic field are studied in this paper. The scattering angle is
defined according to the electron trajectory and asymptotic line (without
magnetic field). A magnetized Rutherford scattering formula is obtained
analytically under the weak magnetic field approximation. It is found that the
scattering angle decreases as external magnetic field increases. It is easy to
find the scattering angle decreasing significantly as incident distance, and
incident velocity increasing. It is shown that the theoretical results agree
well with numerical calculation by checking the dependence of scattering angle
on external magnetic field.


Abstract:
  We adapt the arguments in the recent work of Duyckaerts, Landoulsi, and
Roudenko to establish a scattering result at the sharp threshold for the $3d$
focusing cubic NLS with a repulsive potential. We treat both the case of
short-range potentials as previously considered in the work of Hong, as well as
the inverse-square potential, previously considered in the work of the authors.


Abstract:
  Password managers help users more effectively manage their passwords,
encouraging them to adopt stronger passwords across their many accounts. In
contrast to desktop systems where password managers receive no system-level
support, mobile operating systems provide autofill frameworks designed to
integrate with password managers to provide secure and usable autofill for
browsers and other apps installed on mobile devices. In this paper, we evaluate
mobile autofill frameworks on iOS and Android, examining whether they achieve
substantive benefits over the ad-hoc desktop environment or become a
problematic single point of failure. Our results find that while the frameworks
address several common issues, they also enforce insecure behavior and fail to
provide password managers sufficient information to override the frameworks'
insecure behavior, resulting in mobile managers being less secure than their
desktop counterparts overall. We also demonstrate how these frameworks act as a
confused deputy in manager-assisted credential phishing attacks. Our results
demonstrate the need for significant improvements to mobile autofill
frameworks. We conclude the paper with recommendations for the design and
implementation of secure autofill frameworks.


Abstract:
  Purity and coherence of a quantum state are recognized as useful resources
for various information processing tasks. In this article, we propose a
fidelity based valid measure of purity and coherence monotone and establish a
relationship between them. This formulation of coherence is extended to quantum
correlation relative to measurement. We have also studied the role of weak
measurement on purity.


Abstract:
  The main objective of this paper is to outline a theoretical framework to
analyse how humans' decision-making strategies under uncertainty manage the
trade-off between information gathering (exploration) and reward seeking
(exploitation). A key observation, motivating this line of research, is the
awareness that human learners are amazingly fast and effective at adapting to
unfamiliar environments and incorporating upcoming knowledge: this is an
intriguing behaviour for cognitive sciences as well as an important challenge
for Machine Learning. The target problem considered is active learning in a
black-box optimization task and more specifically how the
exploration/exploitation dilemma can be modelled within Gaussian Process based
Bayesian Optimization framework, which is in turn based on uncertainty
quantification. The main contribution is to analyse humans' decisions with
respect to Pareto rationality where the two objectives are improvement expected
and uncertainty quantification. According to this Pareto rationality model, if
a decision set contains a Pareto efficient (dominant) strategy, a rational
decision maker should always select the dominant strategy over its dominated
alternatives. The distance from the Pareto frontier determines whether a choice
is (Pareto) rational (i.e., lays on the frontier) or is associated to
"exasperate" exploration. However, since the uncertainty is one of the two
objectives defining the Pareto frontier, we have investigated three different
uncertainty quantification measures and selected the one resulting more
compliant with the Pareto rationality model proposed. The key result is an
analytical framework to characterize how deviations from "rationality" depend
on uncertainty quantifications and the evolution of the reward seeking process.


Abstract:
  The finite temperature phase diagram of QCD with two massless quark flavors
is not yet understood because of the subtle effects of anomalous $U_A(1)$
symmetry. In this work we address this issue by studying the fate of the
anomalous $U_A(1)$ symmetry in $2+1$ flavor QCD just above the chiral crossover
transition temperature $T_c$, lowering the light quark mass towards the chiral
limit along line of constant physical strange quark mass. We use the gauge
configurations generated using the Highly Improved Staggered Quark (HISQ)
discretization on lattice volumes $32^3\times8$ and $56^3\times 8$ to study the
renormalized eigenvalue spectrum of QCD with valence overlap Dirac operator. We
have implemented new numerical techniques that have allowed us to measure about
$100$-$200$ eigenvalues of the gauge ensembles with light quark masses $\gtrsim
0.6$ MeV. From a detailed analysis of the dependence of the renormalized
eigenvalue spectrum and $U_A(1)$ breaking observables on the light quark mass,
our study suggests $U_A(1)$ is broken at $T\gtrsim T_c$ even when the chiral
limit is approached.


Abstract:
  We consider the 3D damped driven Maxwell--Schr\"odinger equations in a
bounded region under suitable boundary conditions. We establish new a priori
estimates, which provide the existence of global finite energy weak solutions
and bounded absorbing set. The proofs rely on the Sobolev type estimates for
magnetic Schr\"odinger operator.


Abstract:
  We study the minimal number of existential quantifiers needed to define a
diophantine set over a field and relate this number to the essential dimension
of the functor of points associated to such a definition.


Abstract:
  We propose a new method to estimate causal effects from nonexperimental data.
Each pair of sample units is first associated with a stochastic 'treatment' -
differences in factors between units - and an effect - a resultant outcome
difference. It is then proposed that all such pairs can be combined to provide
more accurate estimates of causal effects in observational data, provided a
statistical model connecting combinatorial properties of treatments to the
accuracy and unbiasedness of their effects. The article introduces one such
model and a Bayesian approach to combine the $O(n^2)$ pairwise observations
typically available in nonexperimnetal data. This also leads to an
interpretation of nonexperimental datasets as incomplete, or noisy, versions of
ideal factorial experimental designs.
  This approach to causal effect estimation has several advantages: (1) it
expands the number of observations, converting thousands of individuals into
millions of observational treatments; (2) starting with treatments closest to
the experimental ideal, it identifies noncausal variables that can be ignored
in the future, making estimation easier in each subsequent iteration while
departing minimally from experiment-like conditions; (3) it recovers individual
causal effects in heterogeneous populations. We evaluate the method in
simulations and the National Supported Work (NSW) program, an intensively
studied program whose effects are known from randomized field experiments. We
demonstrate that the proposed approach recovers causal effects in common NSW
samples, as well as in arbitrary subpopulations and an order-of-magnitude
larger supersample with the entire national program data, outperforming
Statistical, Econometrics and Machine Learning estimators in all cases...


Abstract:
  A general method is proposed for identifying the gauge-invariant part of the
metric perturbation within linearized gravity, and the six independent gauge
invariants per se, for an arbitrary background metric. For the Minkowski
background, the operator that projects the metric perturbation on the invariant
subspace is proportional to the well-known dispersion operator of linear
gravitational waves in vacuum.


Abstract:
  This ongoing work attempts to understand and address the requirements of
UNICEF, a leading organization working in children's welfare, where they aim to
tackle the problem of air quality for children at a global level. We are
motivated by the lack of a proper model to account for heavily fluctuating air
quality levels across the world in the wake of the COVID-19 pandemic, leading
to uncertainty among public health professionals on the exact levels of
children's exposure to air pollutants. We create an initial model as per the
agency's requirement to generate insights through a combination of virtual
meetups and online presentations. Our research team comprised of UNICEF's
researchers and a group of volunteer data scientists. The presentations were
delivered to a number of scientists and domain experts from UNICEF and
community champions working with open data. We highlight their feedback and
possible avenues to develop this research further.


Abstract:
  The number of reviews on Amazon has grown significantly over the years.
Customers who made purchases on Amazon provide reviews by rating the product
from 1 to 5 stars and sharing a text summary of their experience and opinion of
the product. The ratings of a product are averaged to provide an overall
product rating. We analyzed what ratings score customers give to a specific
product (a music track) in order to build a recommender model for digital music
tracks on Amazon. We test various traditional models along with our proposed
deep neural network (DNN) architecture to predict the reviews rating score. The
Amazon review dataset contains 200,000 data samples; we train the models on 70%
of the dataset and test the performance of the models on the remaining 30% of
the dataset.


Abstract:
  Dynamical properties of ultradiscrete Hopf bifurcation, similar to those of
the standard Hopf bifurcation, are discussed by proposing a simple model of
ultradiscrete equations with max-plus algebra. In ultradiscrete Hopf
bifurcation, limit cycles emerge depending on the value of a bifurcation
parameter in the model. The limit cycles are composed of a finite number of
discrete states. Furthermore, the model exhibits excitability. The model is
derived from two different dynamical models with Hopf bifurcation by means of
ultradiscretization; it is a candidate for a normal form for ultradiscrete Hopf
bifurcation.


Abstract:
  If A is a finite-dimensional symmetric algebra, then it is well-known that
the only silting complexes in $\mathrm{K^b}(\mathrm{proj}A)$ are the tilting
complexes. In this note we investigate to what extent the same can be said for
weakly symmetric algebras. On one hand, we show that this holds for all
tilting-discrete weakly symmetric algebras. In particular, a tilting-discrete
weakly symmetric algebra is also silting-discrete. On the other hand, we also
construct an example of a weakly symmetric algebra with silting complexes that
are not tilting.


Abstract:
  Are critical points important in the Solar Probe Mission? This is a brief
discussion of the nature of critical points in solar wind models, what this
means physically in the 'real' solar wind, and what can be expected along a
nominal Solar Probe Orbit. The conclusion is that the regions where the wind
becomes transonic and trans-Alfvenic, which may be irregular and varying, may
reveal interesting physics, but the mathematically defined critical points
themselves are of less importance.


Abstract:
  Ferromagnet/heavy metal (FM/HM) multilayer thin films with $C_{2v}$ symmetry
have the potential to host antiskyrmions and other chiral spin textures via an
anisotropic Dzyaloshinkii-Moriya interaction (DMI). Here, we present a
candidate material system that also has a strong uniaxial magnetocrystalline
anisotropy aligned in the plane of the film. This system is based on a new
Co/Pt epitaxial relationship, which is the central focus of this work:
hexagonal closed-packed Co$(10\bar{1}0)[0001]$ $\parallel$ face-centered cubic
Pt$(110)[001]$. We characterized the crystal structure and magnetic properties
of our films using X-ray diffraction techniques and magnetometry respectively,
including q-scans to determine stacking fault densities and their correlation
with the measured magnetocrystalline anisotropy constant and thickness of Co.
In future ultrathin multilayer films, we expect this epitaxial relationship to
further enable an anisotropic DMI while supporting interfacial perpendicular
magnetic anisotropy. The anticipated confluence of these properties, along with
the tunability of multilayer films, make this material system a promising
testbed for unveiling new spin configurations in FM/HM films.


Abstract: We present the envelope of holomorphy of a classical truncated tube domain.


Abstract:
  Recent years have witnessed an upsurge of interest in employing flexible
machine learning models for instrumental variable (IV) regression, but the
development of uncertainty quantification methodology is still lacking. In this
work we present a novel quasi-Bayesian procedure for IV regression, building
upon the recently developed kernelized IV models and the dual/minimax
formulation of IV regression. We analyze the frequentist behavior of the
proposed method, by establishing minimax optimal contraction rates in $L_2$ and
Sobolev norms, and discussing the frequentist validity of credible balls. We
further derive a scalable inference algorithm which can be extended to work
with wide neural network models. Empirical evaluation shows that our method
produces informative uncertainty estimates on complex high-dimensional
problems.


Abstract:
  Thermotropic biaxial nematic phases seem to be rare, but biaxial smectic A
phases less so. Here we use molecular field theory to study a simple
two-parameter model, with one parameter promoting a biaxial phase and the
second promoting smecticity. The theory combines the biaxial Maier-Saupe and
McMillan models. We use alternatively the Sonnet-Virga-Durand (SVD) and
geometric mean approximations (GMA) to characterize molecular biaxiality by a
single parameter. For non-zero smecticity and biaxiality, the model always
predicts a ground state biaxial smectic A phase. For a low degree of smectic
order, the phase diagram is very rich, predicting uniaxial and biaxial nematic
and smectic phases, with in addition a variety of tricritical and tetracritical
points. For higher degrees of smecticity, the region of stability of the
biaxial nematic phase is restricted and eventually disappears, yielding to the
biaxial smectic phase. Phase diagrams from the two alternative approximations
for molecular biaxiality are similar, except inasmuch that SVD allows for a
first order isotropic-nematic biaxial transition, whereas GMA predicts a Landau
point separating isotropic and biaxial nematic phases. We speculate that the
rarity of thermotropic biaxial nematic phases is partly a consequence of the
presence of stabler analogous smectic phases.


Abstract:
  Recently, FGSM adversarial training is found to be able to train a robust
model which is comparable to the one trained by PGD but an order of magnitude
faster. However, there is a failure mode called catastrophic overfitting (CO)
that the classifier loses its robustness suddenly during the training and
hardly recovers by itself. In this paper, we find CO is not only limited to
FGSM, but also happens in $\mbox{DF}^{\infty}$-1 adversarial training. Then, we
analyze the geometric properties for both FGSM and $\mbox{DF}^{\infty}$-1 and
find they have totally different decision boundaries after CO. For FGSM, a new
decision boundary is generated along the direction of perturbation and makes
the small perturbation more effective than the large one. While for
$\mbox{DF}^{\infty}$-1, there is no new decision boundary generated along the
direction of perturbation, instead the perturbation generated by
$\mbox{DF}^{\infty}$-1 becomes smaller after CO and thus loses its
effectiveness. We also experimentally analyze three hypotheses on potential
factors causing CO. And then based on the empirical analysis, we modify the
RS-FGSM by not projecting perturbation back to the $l_\infty$ ball. By this
small modification, we could achieve $47.56 \pm 0.37\% $ PGD-50-10 accuracy on
CIFAR10 with $\epsilon=8/255$ in contrast to $43.57 \pm 0.30\% $ by RS-FGSM and
also further extend the working range of $\epsilon$ from 8/255 to 11/255 on
CIFAR10 without CO occurring.


Abstract:
  Traditional automated theorem provers have relied on manually tuned
heuristics to guide how they perform proof search. Recently, however, there has
been a surge of interest in the design of learning mechanisms that can be
integrated into theorem provers to improve their performance automatically. In
this work, we introduce TRAIL, a deep learning-based approach to theorem
proving that characterizes core elements of saturation-based theorem proving
within a neural framework. TRAIL leverages (a) an effective graph neural
network for representing logical formulas, (b) a novel neural representation of
the state of a saturation-based theorem prover in terms of processed clauses
and available actions, and (c) a novel representation of the inference
selection process as an attention-based action policy. We show through a
systematic analysis that these components allow TRAIL to significantly
outperform previous reinforcement learning-based theorem provers on two
standard benchmark datasets (up to 36% more theorems proved). In addition, to
the best of our knowledge, TRAIL is the first reinforcement learning-based
approach to exceed the performance of a state-of-the-art traditional theorem
prover on a standard theorem proving benchmark (solving up to 17% more
problems).


Abstract:
  Families of coupled solitons of $\mathcal{PT}$-symmetric physical models with
gain and loss in fractional dimension and in settings with and without
cross-interactions modulation (CIM), are reported. Profiles, powers, stability
areas, and propagation dynamics of the obtained $\mathcal{PT}$-symmetric
coupled solitons are investigated. By comparing the results of the models with
and without CIM, we find that the stability area of the model with CIM is much
broader than the one without CIM. Remarkably, oscillating
$\mathcal{PT}$-symmetric coupled solitons can also exist in the model of CIM
with the same coefficients of the self- and cross-interactions modulations. In
addition, the period of these oscillating coupled solitons can be controlled by
the linear coupling coefficient.


Abstract:
  An additional scalar degree of freedom for a gravitational wave is often
predicted in theories of gravity beyond general relativity and can be used for
a model-agnostic test of gravity. In this letter, we report the direct search
for the scalar-tensor mixed polarization modes of gravitational waves from
compact binaries in a strong regime of gravity by analyzing the data of
GW170814 and GW170817, which are the merger events of binary black holes and
binary neutron stars, respectively. Consequently, we obtain the constraints on
the ratio of scalar-mode amplitude to tensor-mode amplitude: $\lesssim 0.20$
for GW170814 and $\lesssim 0.068$ for GW170817, which are the tightest
constraints on the scalar amplitude in a strong regime of gravity before
merger.


Abstract:
  Cherenkov radiation generated by a charge moving along one of the faces of a
dielectric prism is analyzed. Unlike our previous works, here we suppose that
the charge moves from the top of the prism to its base. We use the technique
which was called by us "the aperture method". However here we develop the new
version of this technique which is suitable for objects with plane faces: we
use inside the object only the expansion over plane waves. This approach is
convenient for objects having plane borders especially in the case of two or
more borders on which the waves are reflected and/or refracted. Using this
technique we obtain the field on the aperture and then apply Stretton-Chu
formulas (aperture integrals). Further, the main attention is paid to the
calculation of the radiation field in the Fraunhofer (far-field) area. We
obtain the expressions for the Fourier transforms of the field components in
form of the single integrals. Using them, the series of typical angular
diagrams are computed and physical conclusions are made.


Abstract:
  In this paper, we examine the potential for a reconfigurable intelligent
surface (RIS) to be powered by energy harvested from information signals. This
feature might be key to reap the benefits of RIS technology's lower power
consumption compared to active relays. We first identify the main RIS
power-consuming components and then propose an energy harvesting and power
consumption model. Furthermore, we formulate and solve the problem of the
optimal RIS placement together with the amplitude and phase response adjustment
of its elements in order to maximize the signal-to-noise ratio (SNR) while
harvesting sufficient energy for its operation. Finally, numerical results
validate the autonomous operation potential and reveal the range of power
consumption values that enables it.


Abstract:
  Hierarchical and k-medoids clustering are deterministic clustering algorithms
based on pairwise distances. Using these same pairwise distances, we propose a
novel stochastic clustering method based on random partition distributions. We
call our method CaviarPD, for cluster analysis via random partition
distributions. CaviarPD first samples clusterings from a random partition
distribution and then finds the best cluster estimate based on these samples
using algorithms to minimize an expected loss. We compare CaviarPD with
hierarchical and k-medoids clustering through eight case studies. Cluster
estimates based on our method are competitive with those of hierarchical and
k-medoids clustering. They also do not require the subjective choice of the
linkage method necessary for hierarchical clustering. Furthermore, our
distribution-based procedure provides an intuitive graphical representation to
assess clustering uncertainty.


Abstract:
  We present the full magnetic g tensors of the $^{6}$H$_{5/2}$Z$_{1}$ and
$^{4}$G$_{5/2}$A$_{1}$ electronic states for both crystallographic sites in
Sm$^{3+}$:Y$_{2}$SiO$_{5}$, deduced through the use of Raman heterodyne
spectroscopy performed along 9 different crystallographic directions. The
maximum principle g values were determined to be 0.447 (site 1) and 0.523 (site
2) for the ground state and 2.490 (site 1) and 3.319 (site 2) for the excited
state. The determination of these g tensors provide essential spin Hamiltonian
parameters that can be utilized in future magnetic and hyperfine studies of
Sm$^{3+}$:Y$_{2}$SiO$_{5}$, with applications in quantum information storage
and communication devices.


Abstract:
  In this paper, we derive second order hydrodynamic traffic models from
kinetic-controlled equations for driver-assist vehicles. At the vehicle level
we take into account two main control strategies synthesising the action of
adaptive cruise controls and cooperative adaptive cruise controls. The
resulting macroscopic dynamics fulfil the anisotropy condition introduced in
the celebrated Aw-Rascle-Zhang model. Unlike other models based on heuristic
arguments, our approach unveils the main physical aspects behind frequently
used hydrodynamic traffic models and justifies the structure of the resulting
macroscopic equations incorporating driver-assist vehicles. Numerical insights
show that the presence of driver-assist vehicles produces an aggregate
homogenisation of the mean flow speed, which may also be steered towards a
suitable desired speed in such a way that optimal flows and traffic
stabilisation are reached.


Abstract:
  We study the quantum quench in two coupled Tomonaga-Luttinger Liquids (TLLs),
from the off-critical to the critical regime, relying on the conformal field
theory approach and the known solutions for single TLLs. We consider a squeezed
form of the initial state, whose low energy limit is fixed in a way to describe
a massive and a massless mode, and we encode the non-equilibrium dynamics in a
proper rescaling of the time. In this way, we compute several correlation
functions, which at leading order factorize into multipoint functions evaluated
at different times for the two modes. Depending on the observable, the
contribution from the massive or from the massless mode can be the dominant
one, giving rise to exponential or power-law decay in time, respectively. Our
results find a direct application in all the quench problems where, in the
scaling limit, there are two independent massless fields: these include the
Hubbard model, the Gaudin-Yang gas, and tunnel-coupled tubes in cold atoms
experiments.


Abstract:
  We developed a noncontact measurement system for monitoring the respiration
of multiple people using millimeter-wave array radar. To separate the radar
echoes of multiple people, conventional techniques cluster the radar echoes in
the time, frequency, or spatial domain. Focusing on the measurement of the
respiratory signals of multiple people, we propose a method called
respiratory-space clustering, in which individual differences in the
respiratory rate are effectively exploited to accurately resolve the echoes
from human bodies. The proposed respiratory-space clustering can separate
echoes, even when people are located close to each other. In addition, the
proposed method can be applied when the number of targets is unknown and can
accurately estimate the number and positions of people. We perform multiple
experiments involving five or seven participants to verify the performance of
the proposed method, and quantitatively evaluate the estimation accuracy for
the number of people and the respiratory intervals. The experimental results
show that the average root-mean-square error in estimating the respiratory
interval is 196 ms using the proposed method. The use of the proposed method,
rather the conventional method, improves the accuracy of the estimation of the
number of people by 85.0%, which indicates the effectiveness of the proposed
method for the measurement of the respiration of multiple people.


Abstract:
  The band structure, density of states, and the Fermi surface of a tungsten
oxide WO$_{2.9}$ with idealized crystal structure (ideal octahedra WO$_6$
creating a "square lattice") is obtained within the density functional theory
in the generalized gradient approximation. Because of the oxygen vacancies
ordering this system is equivalent to the compound W$_{20}$O$_{58}$
(Magn\'{e}li phase), which has 78 atoms in unit cell. We show that
5$d$-orbitals of tungsten atoms located immediately around the voids in the
zigzag chains of edge-sharing octahedra give the dominant contribution near the
Fermi level. These particular tungsten atoms are responsible of a low-energy
properties of the system.


Abstract:
  Process mining studies ways to derive value from process executions recorded
in event logs of IT-systems, with process discovery the task of inferring a
process model for an event log emitted by some unknown system. One quality
criterion for discovered process models is generalization. Generalization seeks
to quantify how well the discovered model describes future executions of the
system, and is perhaps the least understood quality criterion in process
mining. The lack of understanding is primarily a consequence of generalization
seeking to measure properties over the entire future behavior of the system,
when the only available sample of behavior is that provided by the event log
itself. In this paper, we draw inspiration from computational statistics, and
employ a bootstrap approach to estimate properties of a population based on a
sample. Specifically, we define an estimator of the model's generalization
based on the event log it was discovered from, and then use bootstrapping to
measure the generalization of the model with respect to the system, and its
statistical significance. Experiments demonstrate the feasibility of the
approach in industrial settings.


Abstract:
  Multiple-Intent Inverse Reinforcement Learning (MI-IRL) seeks to find a
reward function ensemble to rationalize demonstrations of different but
unlabelled intents. Within the popular expectation maximization (EM) framework
for learning probabilistic MI-IRL models, we present a warm-start strategy
based on up-front clustering of the demonstrations in feature space. Our
theoretical analysis shows that this warm-start solution produces a
near-optimal reward ensemble, provided the behavior modes satisfy mild
separation conditions. We also propose a MI-IRL performance metric that
generalizes the popular Expected Value Difference measure to directly assesses
learned rewards against the ground-truth reward ensemble. Our metric elegantly
addresses the difficulty of pairing up learned and ground truth rewards via a
min-cost flow formulation, and is efficiently computable. We also develop a
MI-IRL benchmark problem that allows for more comprehensive algorithmic
evaluations. On this problem, we find our MI-IRL warm-start strategy helps
avoid poor quality local minima reward ensembles, resulting in a significant
improvement in behavior clustering. Our extensive sensitivity analysis
demonstrates that the quality of the learned reward ensembles is improved under
various settings, including cases where our theoretical assumptions do not
necessarily hold. Finally, we demonstrate the effectiveness of our methods by
discovering distinct driving styles in a large real-world dataset of driver GPS
trajectories.


Abstract:
  We give here a proof of the convergence of the Stochastic Gradient Descent
(SGD) in a self-contained manner.


Abstract:
  Our aim was to determine the initial Li content of two clusters of similar
metallicity but very different ages, the old open cluster NGC 2243 and the
metal-rich globular cluster NGC 104. We compared the lithium abundances derived
for a large sample of stars (from the turn-off to the red giant branch) in each
cluster. For NGC 2243 the Li abundances are from the catalogues released by the
Gaia-ESO Public Spectroscopic Survey, while for NGC 104 we measured the Li
abundance using FLAMES/GIRAFFE spectra, which include archival data and new
observations. We took the initial Li of NGC 2243 to be the lithium measured in
stars on the hot side of the Li dip. We used the difference between the initial
abundances and the post first dredge-up Li values of NGC 2243, and by adding
this amount to the post first dredge-up stars of NGC~104 we were able to infer
the initial Li of this cluster. Moreover, we compared our observational results
to the predictions of theoretical stellar models for the difference between the
initial Li abundance and that after the first dredge-up. The initial lithium
content of NGC 2243 was found to be A(Li)_i = 2.85dex by taking the average Li
abundance measured from the five hottest stars with the highest lithium
abundance. This value is 1.69 dex higher than the lithium abundance derived in
post first dredge-up stars. By adding this number to the lithium abundance
derived in the post first dredge-up stars in NGC~104, we infer a lower limit of
its initial lithium content of A(Li)_i= 2.30dex. Stellar models predict similar
values. Therefore, our result offers important insights for further theoretical
developments.


Abstract:
  In this paper, we study the Orienteering Aisle-graphs Single-access Problem
(OASP), a variant of the orienteering problem for a robot moving in a so-called
single-access aisle-graph, i.e., a graph consisting of a set of rows that can
be accessed from one side only. Aisle-graphs model, among others, vineyards or
warehouses. Each aisle-graph vertex is associated with a reward that a robot
obtains when visits the vertex itself. As the robot's energy is limited, only a
subset of vertices can be visited with a fully charged battery. The objective
is to maximize the total reward collected by the robot with a battery charge.
We first propose an optimal algorithm that solves OASP in O(m^2 n^2) time for
aisle-graphs with a single access consisting of m rows, each with n vertices.
With the goal of designing faster solutions, we propose four greedy sub-optimal
algorithms that run in at most O(mn (m+n)) time. For two of them, we guarantee
an approximation ratio of 1/2(1-1/e), where e is the base of the natural
logarithm, on the total reward by exploiting the well-known submodularity
property. Experimentally, we show that these algorithms collect more than 80%
of the optimal reward.


Abstract:
  In this paper, for a locally compact commutative hypergroup $K$ and for a
pair $(\Phi_1, \Phi_2)$ of Young functions satisfying sequence condition, we
give a necessary condition in terms of aperiodic elements of the center of $K,$
for the convolution $f\ast g$ to exist a.e., where $f$ and $g$ are arbitrary
elements of Orlicz spaces $L^{\Phi_1}(K)$ and $L^{\Phi_2}(K)$, respectively. As
an application, we present some equivalent conditions for compactness of a
compactly generated locally compact abelian group. Moreover, we also
characterize compact convolution operators from $L^1_w(K)$ into $L^\Phi_w(K)$
for a weight $w$ on a locally compact hypergroup $K$.


Abstract:
  Elasticities in depth, width, kernel size and resolution have been explored
in compressing deep neural networks (DNNs). Recognizing that the kernels in a
convolutional neural network (CNN) are 4-way tensors, we further exploit a new
elasticity dimension along the input-output channels. Specifically, a novel
nuclear-norm rank minimization factorization (NRMF) approach is proposed to
dynamically and globally search for the reduced tensor ranks during training.
Correlation between tensor ranks across multiple layers is revealed, and a
graceful tradeoff between model size and accuracy is obtained. Experiments then
show the superiority of NRMF over the previous non-elastic variational Bayesian
matrix factorization (VBMF) scheme.


Abstract:
  Electronic topology in metallic kagome compounds is under intense scrutiny.
We present transport experiments in Na2/3CoO2 in which the Na order
differentiates a Co kagome sub-lattice in the triangular CoO2 layers. Hall and
magnetoresistance (MR) data under high fields give evidence for the coexistence
of light and heavy carriers. At low temperatures, the dominant light carrier
conductivity at zero field is suppressed by a B-linear MR suggesting Dirac like
quasiparticles. Lifshitz transitions induced at large B and T unveil the lower
mobility carriers. They display a negative B^2 MR due to scattering from
magnetic moments likely pertaining to a flat band. We underline an analogy with
heavy Fermion physics.


Abstract:
  The work attempts to unify the conceptual model of the user's virtual
computer environment, with the aim of combining the local environments of
operating systems and the global Internet environment into a single virtual
environment built on general principles. To solve this problem, it is proposed
to unify the conceptual basis of these environments. The existing conceptual
basis of operating systems, built on the "desktop" metaphor, contains redundant
concepts associated with computer architecture. The use of the spatial
conceptual basis "object - place" with the concepts of "domain", "site", and
"data object" allows to completely virtualize the user environment, separating
it from the hardware concepts. The virtual concept "domain" is becoming a
universal way of structuring the user's space. The introduction of this concept
to describe the environments of operating systems provides at the mental level
the integration of the structures of the local and global space. The use of the
concept of "personal domain" will allow replacing the concept of "personal
computer" in the mind of the user. The virtual concept of "site" as an
environment for activities and data storage will allow abandoning such concepts
as "application" (program), or "memory device". The site in the mind of the
user is a virtual environment that includes both places for storing data
objects and places for working with them. The introduction of the concept
"site" into the structure of operating systems environments and the concept of
"data site" into the structure of the global network integrates the structure
of the global and local space in the user's mind. The introduction of the
concept of "portal" as a means of integrating information necessary for
interaction, allows ensuring the methodological homogeneity of the user's work
in a single virtual environment.


Abstract:
  Existing gradient-based meta-learning approaches to few-shot learning assume
that all tasks have the same input feature space. However, in the real world
scenarios, there are many cases that the input structures of tasks can be
different, that is, different tasks may vary in the number of input modalities
or data types. Existing meta-learners cannot handle the heterogeneous task
distribution (HTD) as there is not only global meta-knowledge shared across
tasks but also type-specific knowledge that distinguishes each type of tasks.
To deal with task heterogeneity and promote fast within-task adaptions for each
type of tasks, in this paper, we propose HetMAML, a task-heterogeneous
model-agnostic meta-learning framework, which can capture both the
type-specific and globally shared knowledge and can achieve the balance between
knowledge customization and generalization. Specifically, we design a
multi-channel backbone module that encodes the input of each type of tasks into
the same length sequence of modality-specific embeddings. Then, we propose a
task-aware iterative feature aggregation network which can automatically take
into account the context of task-specific input structures and adaptively
project the heterogeneous input spaces to the same lower-dimensional embedding
space of concepts. Our experiments on six task-heterogeneous datasets
demonstrate that HetMAML successfully leverages type-specific and globally
shared meta-parameters for heterogeneous tasks and achieves fast within-task
adaptions for each type of tasks.


Abstract:
  Video anomaly detection is a challenging task because of diverse abnormal
events. To this task, methods based on reconstruction and prediction are wildly
used in recent works, which are built on the assumption that learning on normal
data, anomalies cannot be reconstructed or predicated as good as normal
patterns, namely the anomaly result with more errors. In this paper, we propose
to discriminate anomalies from normal ones by the duality of normality-granted
optical flow, which is conducive to predict normal frames but adverse to
abnormal frames. The normality-granted optical flow is predicted from a single
frame, to keep the motion knowledge focused on normal patterns. Meanwhile, We
extend the appearance-motion correspondence scheme from frame reconstruction to
prediction, which not only helps to learn the knowledge about object
appearances and correlated motion, but also meets the fact that motion is the
transformation between appearances. We also introduce a margin loss to enhance
the learning of frame prediction. Experiments on standard benchmark datasets
demonstrate the impressive performance of our approach.


Abstract:
  Latest study reports that plasma emission can be generated by energetic
electrons of DGH distribution via the electron cyclotron maser instability
(ECMI) in plasmas characterized by a large ratio of plasma oscillation
frequency to electron gyro-frequency ($\omega_{pe}/\Omega_{ce}$). In this
study, on the basis of the ECMI-plasma emission mechanism, we examine the
double plasma resonance (DPR) effect and the corresponding plasma emission at
both harmonic (H) and fundamental (F) bands using PIC simulations with various
$\omega_{pe}/\Omega_{ce}$. This allows us to directly simulate the feature of
zebra pattern (ZP) observed in solar radio bursts for the first time. We find
that (1) the simulations reproduce the DPR effect nicely for the upper hybrid
(UH) and Z modes, as seen from their variation of intensity and linear growth
rate with $\omega_{pe}/\Omega_{ce}$, (2) the intensity of the H emission is
stronger than that of the F emission by $\sim$ 2 orders of magnitude and vary
periodically with increasing $\omega_{pe}/\Omega_{ce}$, while the F emission is
too weak to be significant, therefore we suggest that it is the H emission
accounting for solar ZPs, (3) the peak-valley contrast of the total intensity
of H is $\sim 4$, and the peak lies around integer values of
$\omega_{pe}/\Omega_{ce}$ (= 10 and 11) for the present parameter setup. We
also evaluate the effect of energy of energetic electrons on the
characteristics of ECMI-excited waves and plasma radiation. The study provides
novel insight on the physical origin of ZPs of solar radio bursts.


Abstract:
  In order to solve the critical issues in Wireless Sensor Networks (WSNs),
with concern for limited sensor lifetime, nature-inspired algorithms are
emerging as a suitable method. Getting optimal network coverage is one of those
challenging issues that need to be examined critically before any network
setup. Optimal network coverage not only minimizes the consumption of limited
energy of battery-driven sensors but also reduce the sensing of redundant
information. In this paper, we focus on nature-inspired optimization algorithms
concerning the optimal coverage in WSNs. In the first half of the paper, we
have briefly discussed the taxonomy of the optimization algorithms along with
the problem domains in WSNs. In the second half of the paper, we have compared
the performance of two nature-inspired algorithms for getting optimal coverage
in WSNs. The first one is a combined Improved Genetic Algorithm and Binary Ant
Colony Algorithm (IGABACA), and the second one is Lion Optimization (LO). The
simulation results confirm that LO gives better network coverage, and the
convergence rate of LO is faster than that of IGA-BACA. Further, we observed
that the optimal coverage is achieved at a lesser number of generations in LO
as compared to IGA-BACA. This review will help researchers to explore the
applications in this field as well as beyond this area. Keywords: Optimal
Coverage, Bio-inspired Algorithm, Lion Optimization, WSNs.


Abstract:
  This paper proposes a novel scheme for mitigating strong interferences, which
is applicable to various wireless scenarios, including full-duplex wireless
communications and uncoordinated heterogenous networks. As strong interferences
can saturate the receiver's analog-to-digital converters (ADC), they need to be
mitigated both before and after the ADCs, i.e., via hybrid processing. The key
idea of the proposed scheme, namely the Hybrid Interference Mitigation using
Analog Prewhitening (HIMAP), is to insert an M-input M-output analog phase
shifter network (PSN) between the receive antennas and the ADCs to spatially
prewhiten the interferences, which requires no signal information but only an
estimate of the covariance matrix. After interference mitigation by the PSN
prewhitener, the preamble can be synchronized, the signal channel response can
be estimated, and thus a minimum mean squared error (MMSE) beamformer can be
applied in the digital domain to further mitigate the residual interferences.
The simulation results verify that the HIMAP scheme can suppress interferences
80dB stronger than the signal by using off-the-shelf phase shifters (PS) of
6-bit resolution.


Abstract:
  In the standard (classic) approach, galaxy clustering measurements from
spectroscopic surveys are compressed into baryon acoustic oscillations and
redshift space distortions measurements, which in turn can be compared to
cosmological models. Recent works have shown that avoiding this intermediate
step and fitting directly the full power spectrum signal (full modelling) leads
to much tighter constraints on cosmological parameters. Here we show where this
extra information is coming from and extend the classic approach with one
additional effective parameter, such that it captures, effectively, the same
amount of information as the full modelling approach, but in a
model-independent way. We validate this new method (ShapeFit) on mock catalogs,
and compare its performance to the full modelling approach finding both to
deliver equivalent results. The ShapeFit extension of the classic approach
promotes the standard analyses at the level of full modelling ones in terms of
information content, with the advantages of i) being more model independent;
ii) offering an understanding of the origin of the extra cosmological
information; iii) allowing a robust control on the impact of observational
systematics.


Abstract:
  INTRODUCTION: Wald's, the likelihood ratio (LR) and Rao's score tests and
their corresponding confidence intervals (CIs), are the three most common
estimators of parameters of Generalized Linear Models. On finite samples, these
estimators are biased. The objective of this work is to analyze the coverage
errors of the CI estimators in small samples for the log-Poisson model (i.e.
estimation of incidence rate ratio) with innovative evaluation criteria, taking
in account the overestimation/underestimation unbalance of coverage errors and
the variable inclusion rate and follow-up in epidemiological studies.
  METHODS: Exact calculations equivalent to Monte Carlo simulations with an
infinite number of simulations have been used. Underestimation errors (due to
the upper bound of the CI) and overestimation coverage errors (due to the lower
bound of the CI) have been split. The level of confidence has been analyzed
from $0.95$ to $1-10^{-6}$, allowing the interpretation of P-values below
$10^{-6}$ for hypothesis tests.
  RESULTS: The LR bias was small (actual coverage errors less than 1.5 times
the nominal errors) when the expected number of events in both groups was above
1, even when unbalanced (e.g. 10 events in one group vs 1 in the other). For
95% CI, Wald's and the Score estimators showed high bias even when the number
of events was large ($\geq 20$ in both groups) when groups were unbalanced. For
small P-values ($<10^{-6}$), the LR kept acceptable bias while Wald's and the
score P-values had severely inflated errors ($\times 100$).
  CONCLUSION: The LR test and LR CI should be used.


Abstract:
  Recovering the wavelength from disordered speckle patterns has become an
exciting prospect as a wavelength measurement method due to its high resolution
and simple design. In previous studies, panel cameras have been used to detect
the subtle differences between speckle patterns. However, the volume,
bandwidth, sensitivity, and cost (in non-visible bands) associated with panel
cameras have hindered their utility in broader applications, especially in high
speed and low-cost measurements. In this work, we broke the limitations imposed
by panel cameras by using a quadrant detector (QD) to capture the speckle
images. In the scheme of QD detection, speckle images are directly filtered by
convolution, where the kernel is equal to one quarter of a speckle pattern.
First, we proposed an up-sampling algorithm to pre-process the QD data. Then a
new convolution neural network (CNN) based algorithm, shallow residual network
(SRN), was proposed to train the up-sampled images. The experimental results
show that a resolution of 4 fm (~ 0.5 MHz) was achieved at 1550nm with an
updating speed of ~ 1 kHz. More importantly, the SRN shows excellent
robustness. The wavelength can be precisely reconstructed from raw QD data
without any averaging, even where there exists apparent noise. The low-cost,
simple structure, high speed and robustness of this design promote the
speckle-based wavemeter to the industrial grade. In addition, without the
restriction of panel cameras, it is believed that this wavemeter opens new
routes in many other fields, such as distributed optical fiber sensors, optical
communications, and laser frequency stabilization.


Abstract:
  The hidden ancestor graph is a new stochastic model for a vertex-labelled
multigraph $G$ in which the observable vertices are the leaves $L$ of a random
rooted tree $T$, whose edges and non-leaf nodes are hidden. The likelihood of
an edge in $G$ between two vertices in $L$ depends on the height of their
lowest common ancestor in $T$. The label of a vertex $v \in L$ depends on a
randomized label inheritance mechanism within $T$ such that vertices with the
same parent often have the same label. High label assortativity, high average
local clustering, heavy tailed vertex degree distribution, and sparsity, can
all coexist in this model. The agreement edges (end point labels agree) and the
conflict edges (end point labels differ) constitute complementary subgraphs,
useful for testing anomaly correction algorithms. Instances with a hundred
million edges can easily be built on a workstation in minutes.


Abstract:
  We give a simple proof for the global convergence of gradient descent in
training deep ReLU networks with the standard square loss, and show some of its
improvements over the state-of-the-art. In particular, while prior works
require all the hidden layers to be wide with width at least $\Omega(N^8)$ ($N$
being the number of training samples), we require a single wide layer of
linear, quadratic or cubic width depending on the type of initialization.
Unlike many recent proofs based on the Neural Tangent Kernel (NTK), our proof
need not track the evolution of the entire NTK matrix, or more generally, any
quantities related to the changes of activation patterns during training.
Instead, we only need to track the evolution of the output at the last hidden
layer, which can be done much more easily thanks to the Lipschitz property of
ReLU. Some highlights of our setting: (i) all the layers are trained with
standard gradient descent, (ii) the network has standard parameterization as
opposed to the NTK one, and (iii) the network has a single wide layer as
opposed to having all wide hidden layers as in most of NTK-related results.


Abstract:
  Vision models trained on multimodal datasets can benefit from the wide
availability of large image-caption datasets. A recent model (CLIP) was found
to generalize well in zero-shot and transfer learning settings. This could
imply that linguistic or "semantic grounding" confers additional generalization
abilities to the visual feature space. Here, we systematically evaluate various
multimodal architectures and vision-only models in terms of unsupervised
clustering, few-shot learning, transfer learning and adversarial robustness. In
each setting, multimodal training produced no additional generalization
capability compared to standard supervised visual training. We conclude that
work is still required for semantic grounding to help improve vision models.


Abstract:
  Detecting cyber-anomalies and attacks are becoming a rising concern these
days in the domain of cybersecurity. The knowledge of artificial intelligence,
particularly, the machine learning techniques can be used to tackle these
issues. However, the effectiveness of a learning-based security model may vary
depending on the security features and the data characteristics. In this paper,
we present "CyberLearning", a machine learning-based cybersecurity modeling
with correlated-feature selection, and a comprehensive empirical analysis on
the effectiveness of various machine learning based security models. In our
CyberLearning modeling, we take into account a binary classification model for
detecting anomalies, and multi-class classification model for various types of
cyber-attacks. To build the security model, we first employ the popular ten
machine learning classification techniques, such as naive Bayes, Logistic
regression, Stochastic gradient descent, K-nearest neighbors, Support vector
machine, Decision Tree, Random Forest, Adaptive Boosting, eXtreme Gradient
Boosting, as well as Linear discriminant analysis. We then present the
artificial neural network-based security model considering multiple hidden
layers. The effectiveness of these learning-based security models is examined
by conducting a range of experiments utilizing the two most popular security
datasets, UNSW-NB15 and NSL-KDD. Overall, this paper aims to serve as a
reference point for data-driven security modeling through our experimental
analysis and findings in the context of cybersecurity.


Abstract:
  Concept of p-frame with the help of b-linear functional in the case of
n-Banach space is being presented and its few properties, one of them,
Cartesian product of two p-frames again becomes a p-frame, have been discussed.
Finally, the perturbation results and the stability of p-frame in n-Banach
space with respect to b-linear functional are being studied.


Abstract:
  Cannabis legalization has been welcomed by many U.S. states but its role in
escalation from tobacco e-cigarette use to cannabis vaping is unclear.
Meanwhile, cannabis vaping has been associated with new lung diseases and
rising adolescent use. To understand the impact of cannabis legalization on
escalation, we design an observational study to estimate the causal effect of
recreational cannabis legalization on the development of pro-cannabis attitude
for e-cigarette users. We collect and analyze Twitter data which contains
opinions about cannabis and JUUL, a very popular e-cigarette brand. We use
weakly supervised learning for personal tweet filtering and classification for
stance detection. We discover that recreational cannabis legalization policy
has an effect on increased development of pro-cannabis attitudes for users
already in favor of e-cigarettes.


Abstract:
  In finite element calculations, the integral forms are usually evaluated
using nested loops over elements, and over quadrature points. Many such forms
(e.g. linear or multi-linear) can be expressed in a compact way, without the
explicit loops, using a single tensor contraction expression by employing the
Einstein summation convention. To automate this process and leverage existing
high performance codes, we first introduce a notation allowing trivial
differentiation of multi-linear finite element forms. Based on that we propose
and describe a new transpiler from Einstein summation based expressions,
augmented to allow defining multi-linear finite element weak forms, to regular
tensor contraction expressions. The resulting expressions are compatible with a
number of Python scientific computing packages, that implement, optimize and in
some cases parallelize the general tensor contractions. We assess the
performance of those packages, as well as the influence of operand memory
layouts and tensor contraction paths optimizations on the elapsed time and
memory requirements of the finite element form evaluations. We also compare the
efficiency of the transpiled weak form implementations to the C-based functions
available in the finite element package SfePy.


Abstract:
  Efficient methods for loading given classical data into quantum circuits are
essential for various quantum algorithms. In this paper, we propose an
algorithm called that can effectively load all the components of a given
real-valued data vector into the amplitude of quantum state, while the previous
proposal can only load the absolute values of those components. The key of our
algorithm is to variationally train a shallow parameterized quantum circuit,
using the results of two types of measurement; the standard computational-basis
measurement plus the measurement in the Hadamard-transformed basis, introduced
in order to handle the sign of the data components. The variational algorithm
changes the circuit parameters so as to minimize the sum of two costs
corresponding to those two measurement basis, both of which are given by the
efficiently-computable maximum mean discrepancy. We also consider the problem
of constructing the singular value decomposition entropy via the stock market
dataset to give a financial market indicator; a quantum algorithm (the
variational singular value decomposition algorithm) is known to produce a
solution faster than classical, which yet requires the sign-dependent amplitude
encoding. We demonstrate, with an in-depth numerical analysis, that our
algorithm realizes loading of time-series of real stock prices on quantum state
with small approximation error, and thereby it enables constructing an
indicator of the financial market based on the stock prices.


Abstract:
  Helical edge states of two-dimensional topological insulators show a gap in
the density of states (DOS) and suppressed conductance in the presence of
ordered magnetic impurities. Here we will consider the dynamical effects on the
DOS and transmission when the magnetic impurities are driven periodically.
Using the Floquet formalism and Green's functions, the system properties are
studied as a function of the driving frequency and the potential energy
contribution of the impurities. We see that increasing the potential part
closes the DOS gap for all driving regimes. The transmission gap is also
closed, showing a pronounced asymmetry as a function of energy. These features
indicate that the dynamical transport properties could yield valuable
information about the magnetic impurities.


Abstract:
  Subglacial lakes are isolated, cold-temperature and high-pressure water
environments hidden under ice sheets, which might host extreme microorganisms.
Here, we use two-dimensional direct numerical simulations in order to
investigate the characteristic temperature fluctuations and velocities in
freshwater subglacial lakes as functions of the ice overburden pressure, $p_i$,
the water depth, $h$, and the geothermal flux, $F$. Geothermal heating is the
unique forcing mechanism as we consider a flat ice-water interface. Subglacial
lakes are fully convective when $p_i$ is larger than the critical pressure
$p_*\approx 2848$ dbar, but self organize into a lower convective bulk and an
upper stably-stratified layer when $p_i < p_*$, because of the existence at low
pressure of a density maximum at temperature $T_d$ greater than the freezing
temperature $T_f$. For both high and low $p_i$, we demonstrate that the Nusselt
number $Nu$ and Reynolds number $Re$ satisfy classical scaling laws provided
that an effective Rayleigh number $Ra_{eff}$ is considered. We show that the
convective and stably-stratified layers at low pressure are dynamically
decoupled at leading order because plume penetration is weak and induces
limited entrainment of the stable fluid. From the empirical power law equation
for $Nu$ with $Ra_{eff}$, we derive two sets of closed-form expressions for the
variables of interest, including the unknown bottom temperature, in terms of
the problem parameters $p_i$, $h$ and $F$. The two predictions correspond to
two limiting regimes obtained when the effective thermal expansion coefficient
is either approximately constant or linearly proportional to the temperature
difference driving the convection.


Abstract:
  We consider the canonical periodic review lost sales inventory system with
positive lead-times and stochastic i.i.d. demand under the average cost
criterion. We introduce a new policy that places orders such that the expected
inventory level at the time of arrival of an order is at a fixed level and call
it the Projected Inventory Level (PIL) policy. We prove that this policy has a
cost-rate superior to the equivalent system where excess demand is back-ordered
instead of lost and is therefore asymptotically optimal as the cost of losing a
sale approaches infinity under mild distributional assumptions. We further show
that this policy dominates the constant order policy for any finite lead-time
and is therefore asymptotically optimal as the lead-time approaches infinity
for the case of exponentially distributed demand per period. Numerical results
show this policy also performs superior relative to other policies.


Abstract:
  This paper describes a novel approach to emulate a universal quantum computer
with a wholly classical system, one that uses a signal of bounded duration and
amplitude to represent an arbitrary quantum state. The signal may be of any
modality (e.g. acoustic, electromagnetic, etc.) but this paper will focus on
electronic signals. Individual qubits are represented by in-phase and
quadrature sinusoidal signals, while unitary gate operations are performed
using simple analog electronic circuit devices. In this manner, the Hilbert
space structure of a multi-qubit quantum state, as well as a universal set of
gate operations, may be fully emulated classically. Results from a programmable
prototype system are presented and discussed.


Abstract:
  Generative adversarial networks (GANs), e.g., StyleGAN2, play a vital role in
various image generation and synthesis tasks, yet their notoriously high
computational cost hinders their efficient deployment on edge devices. Directly
applying generic compression approaches yields poor results on GANs, which
motivates a number of recent GAN compression works. While prior works mainly
accelerate conditional GANs, e.g., pix2pix and CycleGAN, compressing
state-of-the-art unconditional GANs has rarely been explored and is more
challenging. In this paper, we propose novel approaches for unconditional GAN
compression. We first introduce effective channel pruning and knowledge
distillation schemes specialized for unconditional GANs. We then propose a
novel content-aware method to guide the processes of both pruning and
distillation. With content-awareness, we can effectively prune channels that
are unimportant to the contents of interest, e.g., human faces, and focus our
distillation on these regions, which significantly enhances the distillation
quality. On StyleGAN2 and SN-GAN, we achieve a substantial improvement over the
state-of-the-art compression method. Notably, we reduce the FLOPs of StyleGAN2
by 11x with visually negligible image quality loss compared to the full-size
model. More interestingly, when applied to various image manipulation tasks,
our compressed model forms a smoother and better disentangled latent manifold,
making it more effective for image editing.


Abstract:
  Property-preserving hash functions allow for compressing long inputs $x_0$
and $x_1$ into short hashes $h(x_0)$ and $h(x_1)$ in a manner that allows for
computing a predicate $P(x_0, x_1)$ given only the two hash values without
having access to the original data. Such hash functions are said to be
adversarially robust if an adversary that gets to pick $x_0$ and $x_1$ after
the hash function has been sampled, cannot find inputs for which the predicate
evaluated on the hash values outputs the incorrect result.
  In this work we construct robust property-preserving hash functions for the
hamming-distance predicate which distinguishes inputs with a hamming distance
at least some threshold $t$ from those with distance less than $t$. The
security of the construction is based on standard lattice hardness assumptions.
  Our construction has several advantages over the best known previous
construction by Fleischhacker and Simkin. Our construction relies on a single
well-studied hardness assumption from lattice cryptography whereas the previous
work relied on a newly introduced family of computational hardness assumptions.
In terms of computational effort, our construction only requires a small number
of modular additions per input bit, whereas previously several exponentiations
per bit as well as the interpolation and evaluation of high-degree polynomials
over large fields were required. An additional benefit of our construction is
that the description of the hash function can be compressed to $\lambda$ bits
assuming a random oracle. Previous work has descriptions of length
$\mathcal{O}(\ell \lambda)$ bits for input bit-length $\ell$, which has a
secret structure and thus cannot be compressed.
  We prove a lower bound on the output size of any property-preserving hash
function for the hamming distance predicate. The bound shows that the size of
our hash value is not far from optimal.


Abstract:
  Quantum State Sharing (QSS) is a protocol by which a (secret) quantum state
may be securely split, shared between multiple potentially dishonest players,
and reconstructed. Crucially the players are each assumed to be dishonest, and
so QSS requires that only a collaborating authorised subset of players can
access the original secret state; any dishonest unauthorised conspiracy cannot
reconstruct it. We analyse a QSS protocol involving three untrusted players and
demonstrate that quantum steering is the required resource which enables the
protocol to proceed securely. We analyse the level of steering required to
share any single-mode Gaussian secret which enables the states to be shared
with the optimal use of resources.


Abstract:
  Consider the universal gate set for quantum computing consisting of the gates
X, CX, CCX, omega^dagger H, and S. All of these gates have matrix entries in
the ring Z[1/2,i], the smallest subring of the complex numbers containing 1/2
and i. Amy, Glaudell, and Ross proved the converse, i.e., any unitary matrix
with entries in Z[1/2,i] can be realized by a quantum circuit over the above
gate set using at most one ancilla. In this paper, we give a finite
presentation by generators and relations of U_n(Z[1/2,i]), the group of unitary
nxn-matrices with entries in Z[1/2,i].


Abstract:
  In this work, we explore the recently proposed new Tsallis agegraphic dark
energy model in a flat FLRW Universe by taking the conformal time as IR cutoff
with interaction. The deceleration parameter of the interacting new Tsallis
agegraphic dark energy model provides the phase transition of the Universe from
decelerated to accelerated phase. The EoS parameter of the model shows a rich
behaviour as it can be quintessence-like or phantom-like depending on the
interaction ($b^2$) and parameter $B$. The evolutionary trajectories of the
statefinder parameters and $(\omega_D, \omega_D^{'})$ planes are plotted by
considering the initial condition $\Omega_{D}^{0} =0.73$, $H_{0}= 67$ according
to $\Lambda$CDM observational Planck 2018 data for different $b^2$ and $B$. The
model shows both quintessence and Chaplygin gas behaviour in the statefinder
$(r, s)$ and $(r, q)$ pair planes for different $b^2$ and $B$.


Abstract:
  These lectures present some basic ideas and techniques in the spectral
analysis of lattice Schrodinger operators with disordered potentials. In
contrast to the classical Anderson tight binding model, the randomness is also
allowed to possess only finitely many degrees of freedom. This refers to
dynamically defined potentials, i.e., those given by evaluating a function
along an orbit of some ergodic transformation (or of several commuting such
transformations on higher-dimensional lattices). Classical localization
theorems by Frohlich--Spencer for large disorders are presented, both for
random potentials in all dimensions, as well as even quasi-periodic ones on the
line. After providing the needed background on subharmonic functions, we then
discuss the Bourgain-Goldstein theorem on localization for quasiperiodic
Schrodinger cocycles assuming positive Lyapunov exponents.


Abstract:
  We study at the single-photon level the nonreciprocal excitation transfer
between emitters coupled with a common waveguide. Non-Markovian retarded
effects are taken into account due to the large separation distance between
different emitter-waveguide coupling ports. It is shown that the excitation
transfer between the emitters of a small-atom dimer can be obviously
nonreciprocal by introducing between them a coherent coupling channel with
nontrivial coupling phase. We prove that for dimer models the nonreciprocity
cannot coexist with the decoherence-free giant-atom structure although the
latter markedly lengthens the lifetime of the emitters. In view of this, we
further propose a giant-atom trimer which supports both nonreciprocal transfer
(directional circulation) of the excitation and greatly lengthened lifetime.
Such a trimer model also exhibits incommensurate emitter-waveguide entanglement
for different initial states in which case the excitation transfer is however
reciprocal. We believe that the proposals in this paper are of potential
applications in large-scale quantum networks and quantum information
processing.


Abstract:
  We prove the non-linear asymptotic stability of the Schwarzschild family as
solutions to the Einstein vacuum equations in the exterior of the black hole
region: general vacuum initial data, with no symmetry assumed, sufficiently
close to Schwarzschild data evolve to a vacuum spacetime which (i) possesses a
complete future null infinity $\mathcal{I}^+$ (whose past $J^-(\mathcal{I}^+)$
is moreover bounded by a regular future complete event horizon
$\mathcal{H}^+$), (ii) remains close to Schwarzschild in its exterior, and
(iii) asymptotes back to a member of the Schwarzschild family as an appropriate
notion of time goes to infinity, provided that the data are themselves
constrained to lie on a teleologically constructed codimension-$3$
"submanifold" of moduli space. This is the full nonlinear asymptotic stability
of Schwarzschild since solutions not arising from data lying on this
submanifold should by dimensional considerations approach a Kerr spacetime with
rotation parameter $a\neq 0$, i.e. such solutions cannot satisfy (iii). The
proof employs teleologically normalised double null gauges, is expressed
entirely in physical space and makes essential use of the analysis in our
previous study of the linear stability of the Kerr family around Schwarzschild
[DHR], as well as techniques developed over the years to control the
non-linearities of the Einstein equations. The present work, however, is
entirely self-contained. In view of the recent [DHR19, TdCSR20] our approach
can be applied to the full non-linear asymptotic stability of the subextremal
Kerr family.


Abstract:
  Automatic extraction of forum posts and metadata is a crucial but challenging
task since forums do not expose their content in a standardized structure.
Content extraction methods, therefore, often need customizations such as
adaptations to page templates and improvements of their extraction code before
they can be deployed to new forums. Most of the current solutions are also
built for the more general case of content extraction from web pages and lack
key features important for understanding forum content such as the
identification of author metadata and information on the thread structure.
  This paper, therefore, presents a method that determines the XPath of forum
posts, eliminating incorrect mergers and splits of the extracted posts that
were common in systems from the previous generation. Based on the individual
posts further metadata such as authors, forum URL and structure are extracted.
We also introduce Harvest, a new open source toolkit that implements the
presented methods and create a gold standard extracted from 52 different Web
forums for evaluating our approach. A comprehensive evaluation reveals that
Harvest clearly outperforms competing systems.


Abstract:
  We provide a number of new conjectures and questions concerning the syzygies
of $\mathbb{P}^1\times \mathbb{P}^1$. The conjectures are based on computing
the graded Betti tables and related data for large number of different
embeddings of $\mathbb{P}^1\times \mathbb{P}^1$. These computations utilize
linear algebra over finite fields and high-performance computing.


Abstract:
  This study examines the influence of learning in a female teacher homeroom
class in elementary school on pupils' voting behavior later in life, using
independently collected individual-level data. Further, we evaluate its effect
on preference for women's participation in the workplace in adulthood. Our
study found that having a female teacher in the first year of school makes
individuals more likely to vote for female candidates, and to prefer policy for
female labor participation in adulthood. However, the effect is only observed
among males, and not female pupils. These findings offer new evidence for the
female socialization hypothesis.


Abstract:
  Main results are, firstly, a generalization of the Conley-Zehnder index from
ODEs to the delay equation at hand and, secondly, the equality of the Morse
index and the clockwise normalized Conley-Zehnder index.


Abstract:
  We revisit to investigate shadows cast by Kerr-like wormholes. The boundary
of the shadow is determined by unstable circular photon orbits. We find that,
in certain parameter regions, the orbit is located at the throat of the
Kerr-like wormhole, which was not considered in the literature. In these cases,
the existence of the throat alters the shape of the shadow significantly, and
makes it possible for us to differentiate it from that of a Kerr black hole.


Abstract:
  Low-electron-dose observation is indispensable for observing various samples
using a transmission electron microscope; consequently, image processing has
been used to improve transmission electron microscopy (TEM) images. To apply
such image processing to in situ observations, we here apply a convolutional
neural network to TEM imaging. Using a dataset that includes short-exposure
images and long-exposure images, we develop a pipeline for processed
short-exposure images, based on end-to-end training. The quality of images
acquired with a total dose of approximately 5 e- per pixel becomes comparable
to that of images acquired with a total dose of approximately 1000 e- per
pixel. Because the conversion time is approximately 8 ms, in situ observation
at 125 fps is possible. This imaging technique enables in situ observation of
electron-beam-sensitive specimens.


Abstract:
  A strong connection between cluster algebras and representation theory was
established by the cluster category. Cluster characters, like the original
Caldero-Chapoton (CC) map, are maps from certain triangulated categories to
cluster algebras and they have generated much interest. Holm and J{\o}rgensen
constructed a modified CC map from a sufficiently nice triangulated category to
a commutative ring, which is a generalised frieze under some conditions. In
their construction, a quotient $K_{0}^{sp}(\mathcal{T})/M$ of a Grothendieck
group of a cluster tilting subcategory $\mathcal{T}$ is used. In this article,
we show that this quotient is the Grothendieck group of a certain
extriangulated category, thereby exposing the significance of it and the
relevance of extriangulated structures. We use this to define another modified
CC map that recovers the one of Holm--J{\o}rgensen.
  We prove our results in a higher homological context. Suppose $\mathcal{S}$
is a $(d+2)$-angulated category with subcategories
$\mathcal{X}\subseteq\mathcal{T}\subseteq\mathcal{S}$, where $\mathcal{X}$ is
functorially finite and $\mathcal{T}$ is $2d$-cluster tilting, satisfying some
mild conditions. We show there is an isomorphism between the Grothendieck group
$K_{0}(\mathcal{S},\mathbb{E}_{\mathcal{X}},\mathfrak{s}_{\mathcal{X}})$ of the
category $\mathcal{S}$, equipped with the $d$-exangulated structure induced by
$\mathcal{X}$, and the quotient $K_{0}^{sp}(\mathcal{T})/N$, where $N$ is the
higher analogue of $M$ above. When $\mathcal{X}=\mathcal{T}$ the isomorphism is
induced by the higher index with respect to $\mathcal{T}$ introduced recently
by J{\o}rgensen. Thus, in the general case, we can understand the map taking an
object in $\mathcal{S}$ to its $K_{0}$-class in
$K_{0}(\mathcal{S},\mathbb{E}_{\mathcal{X}},\mathfrak{s}_{\mathcal{X}})$ as a
higher index with respect to the rigid subcategory $\mathcal{X}$.


Abstract:
  In this paper, a three-dimensional light detection and ranging simultaneous
localization and mapping (SLAM) method is proposed that is available for
tracking and mapping with 500--1000 Hz processing. The proposed method
significantly reduces the number of points used for point cloud registration
using a novel ICP metric to speed up the registration process while maintaining
accuracy. Point cloud registration with ICP is less accurate when the number of
points is reduced because ICP basically minimizes the distance between points.
To avoid this problem, symmetric KL-divergence is introduced to the ICP cost
that reflects the difference between two probabilistic distributions. The cost
includes not only the distance between points but also differences between
distribution shapes. The experimental results on the KITTI dataset indicate
that the proposed method has high computational efficiency, strongly
outperforms other methods, and has similar accuracy to the state-of-the-art
SLAM method.


Abstract:
  A new metric for quantifying pairwise vertex connectivity in graphs is
defined and an implementation presented. While general in nature, it features a
combination of input features well-suited for social networks, including
applicability to directed or undirected graphs, weighted edges, and computes
using the impact from all-paths between the vertices. Moreover, the $O(V+E)$
method is applicable to large graphs. Comparisons with other techniques are
included.


Abstract:
  Topological orders are a prominent paradigm for describing quantum many-body
systems without symmetry-breaking orders. We present a topological quantum
field theoretical (TQFT) study on topological orders in five-dimensional
spacetime ($5$D) in which \textit{topological excitations} include not only
point-like \textit{particles}, but also two types of spatially extended
objects: closed string-like \textit{loops} and two-dimensional closed
\textit{membranes}. Especially, membranes have been rarely explored in the
literature of topological orders. By introducing higher-form gauge fields, we
construct exotic TQFT actions that include mixture of two distinct types of
$BF$ topological terms and many twisted topological terms. The gauge
transformations are properly defined and utilized to compute level quantization
and classification of TQFTs. Among all TQFTs, some are not in Dijkgraaf-Witten
cohomological classification. To characterize topological orders, we concretely
construct all braiding processes among topological excitations, which leads to
very exotic links formed by closed spacetime trajectories of particles, loops,
and membranes. For each braiding process, we construct gauge-invariant Wilson
operators and calculate the associated braiding statistical phases. As a
result, we obtain expressions of link invariants all of which have manifest
geometric interpretation. Following Wen's definition, the boundary theory of a
topological order exhibits gravitational anomaly. We expect that the
characterization and classification of 5D topological orders in this paper
encode information of 4D gravitational anomaly. Further consideration, e.g.,
putting TQFTs on 5D manifolds with boundaries, is left to future work.


Abstract:
  This article is the first of two in which we develop a geometric framework
for analysing silent and anisotropic big bang singularities. The results of the
present article concern the asymptotic behaviour of solutions to linear systems
of wave equations on the corresponding backgrounds. The main features are the
following: The assumptions do not involve any symmetry requirements and are
weak enough to be consistent with most big bang singularities for which the
asymptotic geometry is understood. The asymptotic rate of growth/decay of
solutions to linear systems of wave equations along causal curves going into
the singularity is determined by model systems of ODE's (depending on the
causal curve). Moreover, the model systems are essentially obtained by dropping
spatial derivatives and localising the coefficients along the causal curve.
This is in accordance with the BKL proposal. Note, however, that we here prove
this statement, we do not assume it. If the coefficients of the unknown and its
expansion normalised normal derivatives converge sufficiently quickly along a
causal curve, we obtain leading order asymptotics (along the causal curve) and
prove that the localised energy estimate (along the causal curve) is optimal.
In this setting, it is also possible to specify the leading order asymptotics
of solutions along the causal curve. On the other hand, the localised energy
estimate typically entails a substantial loss of derivatives.
  In the companion article, we deduce geometric conclusions by combining the
framework with Einstein's equations. In particular, the combination reproduces
the Kasner map and yields partial bootstrap arguments.


Abstract:
  Synthetic data generation has become essential in last years for feeding
data-driven algorithms, which surpassed traditional techniques performance in
almost every computer vision problem. Gathering and labelling the amount of
data needed for these data-hungry models in the real world may become
unfeasible and error-prone, while synthetic data give us the possibility of
generating huge amounts of data with pixel-perfect annotations. However, most
synthetic datasets lack from enough realism in their rendered images. In that
context UnrealROX generation tool was presented in 2019, allowing to generate
highly realistic data, at high resolutions and framerates, with an efficient
pipeline based on Unreal Engine, a cutting-edge videogame engine. UnrealROX
enabled robotic vision researchers to generate realistic and visually plausible
data with full ground truth for a wide variety of problems such as class and
instance semantic segmentation, object detection, depth estimation, visual
grasping, and navigation. Nevertheless, its workflow was very tied to generate
image sequences from a robotic on-board camera, making hard to generate data
for other purposes. In this work, we present UnrealROX+, an improved version of
UnrealROX where its decoupled and easy-to-use data acquisition system allows to
quickly design and generate data in a much more flexible and customizable way.
Moreover, it is packaged as an Unreal plug-in, which makes it more comfortable
to use with already existing Unreal projects, and it also includes new features
such as generating albedo or a Python API for interacting with the virtual
environment from Deep Learning frameworks.


Abstract:
  In the context of tomographic cosmic shear surveys, there exists a nulling
transformation of weak lensing observations (also called BNT transform) that
allows us to simplify the correlation structure of tomographic cosmic shear
observations, as well as to build observables that depend only on a localised
range of redshifts and thus independent from the low-redshift/small-scale
modes. This procedure renders possible accurate, and from-first-principles,
predictions of the convergence and aperture mass one-point distributions (PDF).
We here explore other consequences of this transformation on the (reduced)
numerical complexity of the estimation of the joint PDF between nulled bins and
demonstrate how to use these results to make theoretical predictions.


Abstract:
  We investigate the spin Seebeck effect and spin pumping in a junction between
a ferromagnetic insulator and a magnetic impurity deposited on a normal metal.
By the numerical renormalization group calculation, we show that spin current
is enhanced by the Kondo effect. This spin current is suppressed by increase of
the temperature or the magnetic field which is comparable with the Kondo
temperature. Our results indicate that spin transport can be a direct probe of
spin excitation in strongly correlated systems.


Abstract:
  In this review, we summarize recent progress on the possible phases of
quantum chromodynamics (QCD) in the presence of a strong magnetic field, mainly
from the views of the chiral effective Nambu--Jona-Lasinio model. Four kinds of
phase transitions are explored in detail: chiral symmetry breaking and
restoration, neutral pseudoscalar superfluidity, charged pion superfluidity and
charged rho superconductivity. In particular, we revisit the unsolved problems
of inverse magnetic catalysis effect and competition between the chiral density
wave and solitonic modulation phases. It is shown that useful results can be
obtained by adopting self-consistent schemes.


Abstract:
  Quantum algorithms offer significant speedups over their classical
counterparts for a variety of problems. The strongest arguments for this
advantage are borne by algorithms for quantum search, quantum phase estimation,
and Hamiltonian simulation, which appear as subroutines for large families of
composite quantum algorithms. A number of these quantum algorithms were
recently tied together by a novel technique known as the quantum singular value
transformation (QSVT), which enables one to perform a polynomial transformation
of the singular values of a linear operator embedded in a unitary matrix. In
the seminal GSLW'19 paper on QSVT [Gily\'en, Su, Low, and Wiebe, ACM STOC
2019], many algorithms are encompassed, including amplitude amplification,
methods for the quantum linear systems problem, and quantum simulation. Here,
we provide a pedagogical tutorial through these developments, first
illustrating how quantum signal processing may be generalized to the quantum
eigenvalue transform, from which QSVT naturally emerges. Paralleling GSLW'19,
we then employ QSVT to construct intuitive quantum algorithms for search, phase
estimation, and Hamiltonian simulation, and also showcase algorithms for the
eigenvalue threshold problem and matrix inversion. This overview illustrates
how QSVT is a single framework comprising the three major quantum algorithms,
thus suggesting a grand unification of quantum algorithms.


Abstract:
  With music becoming an essential part of daily life, there is an urgent need
to develop recommendation systems to assist people targeting better songs with
fewer efforts. As the interactions between users and songs naturally construct
a complex network, community detection approaches can be applied to reveal
users' potential interests on songs by grouping relevant users & songs to the
same community. However, as the types of interaction could be heterogeneous, it
challenges conventional community detection methods designed originally for
homogeneous networks. Although there are existing works on heterogeneous
community detection, they are mostly task-driven approaches and not feasible
for specific music recommendation. In this paper, we propose a genetic based
approach to learn an edge-type usefulness distribution (ETUD) for all
edge-types in heterogeneous music networks. ETUD can be regarded as a linear
function to project all edges to the same latent space and make them
comparable. Therefore a heterogeneous network can be converted to a homogeneous
one where those conventional methods are eligible to use. We validate the
proposed model on a heterogeneous music network constructed from an online
music streaming service. Results show that for conventional methods, ETUD can
help to detect communities significantly improving music recommendation
accuracy while simultaneously reducing user searching cost.


Abstract:
  It is standard to assume that the Wigner distribution of a mixed quantum
state consisting of square-integrable functions is a quasi-probability
distribution, that is that its integral is one and that the marginal properties
are satisfied. However this is in general not true. We introduce a class of
quantum states for which this property is satisfied, these states are dubbed
"Feichtinger states" because they are defined in terms of a class of functional
spaces (modulation spaces) introduced in the 1980's by H. Feichtinger. The
properties of these states are studied, which gives us the opportunity to prove
an extension to the general case of a result of Jaynes on the non-uniqueness of
the statistical ensemble generating a density operator. As a bonus we obtain a
result for convex sums of Wigner transforms.


Abstract:
  Most recent approaches for online action detection tend to apply Recurrent
Neural Network (RNN) to capture long-range temporal structure. However, RNN
suffers from non-parallelism and gradient vanishing, hence it is hard to be
optimized. In this paper, we propose a new encoder-decoder framework based on
Transformers, named OadTR, to tackle these problems. The encoder attached with
a task token aims to capture the relationships and global interactions between
historical observations. The decoder extracts auxiliary information by
aggregating anticipated future clip representations. Therefore, OadTR can
recognize current actions by encoding historical information and predicting
future context simultaneously. We extensively evaluate the proposed OadTR on
three challenging datasets: HDD, TVSeries, and THUMOS14. The experimental
results show that OadTR achieves higher training and inference speeds than
current RNN based approaches, and significantly outperforms the
state-of-the-art methods in terms of both mAP and mcAP. Code is available at
https://github.com/wangxiang1230/OadTR.