  Self-attention is a useful mechanism to build generative models for language
and images. It determines the importance of context elements by comparing each
element to the current time step. In this paper, we show that a very
lightweight convolution can perform competitively to the best reported
self-attention results. Next, we introduce dynamic convolutions which are
simpler and more efficient than self-attention. We predict separate convolution
kernels based solely on the current time-step in order to determine the
importance of context elements. The number of operations required by this
approach scales linearly in the input length, whereas self-attention is
quadratic. Experiments on large-scale machine translation, language modeling
and abstractive summarization show that dynamic convolutions improve over
strong self-attention models. On the WMT'14 English-German test set dynamic
convolutions achieve a new state of the art of 29.7 BLEU.


  The pseudorapidity distributions of charged hadrons in proton-lead collisions
at nucleon-nucleon center-of-mass energies $\sqrt{s_{_\mathrm{NN}}} =$ 5.02 and
8.16 TeV are presented. The measurements are based on data samples collected by
the CMS experiment at the LHC. The number of primary charged hadrons produced
in non-single-diffractive proton-lead collisions is determined in the
pseudorapidity range $|\eta_\mathrm{lab}| <$ 2.4. The charged-hadron
multiplicity distributions are compared to the predictions from theoretical
calculations and Monte Carlo event generators. In the center-of-mass
pseudorapidity range $|\eta_\mathrm{cm}| < 0.5$, the average charged-hadron
multiplicity densities
$<\mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta_{\mathrm{cm}}>$$\vert_{|\eta_{\mathrm{cm}}|
< 0.5}$ are 17.31 $\pm$ 0.01 (stat) $\pm$ 0.59 (syst) and 20.10 $\pm$ 0.01
(stat) $\pm$ 0.85 (syst) at $\sqrt{s_{_\mathrm{NN}}} =$ 5.02 and 8.16 TeV,
respectively. The particle densities per participant nucleon are compared to
similar measurements in proton-proton, proton-nucleus, and nucleus-nucleus
collisions.


  In Kaszlikowski [Phys. Rev. Lett. {\bf 85}, 4418 (2000)], it has been shown
numerically that the violation of local realism for two maximally entangled
$N$-dimensional ($3 \leq N$) quantum objects is stronger than for two maximally
entangled qubits and grows with $N$. In this paper we present the analytical
proof of this fact for N=3.


  We investigate the propagating profiles of a degenerate chemotaxis model
describing the bacteria chemotaxis and consumption of oxygen by aerobic
bacteria, in particular, the effect of the initial attractant distribution on
bacterial clustering. We prove that the compact support of solutions may shrink
if the signal concentration satisfies a special structure, and show the finite
speed propagating property without assuming the special structure on attractant
concentration, and obtain an explicit formula of the population spreading speed
in terms of model parameters. The presented results suggest that bacterial
cluster formation can be affected by chemotactic attractants and
density-dependent dispersal.


  Photocatalytic materials are pivotal for the implementation of disruptive
clean energy applications such as conversion of H$_{2}$O and CO$_{2}$ into
fuels and chemicals driven by solar energy. However, efficient and
cost-effective materials able to catalyze the chemical reactions of interest
when exposed to visible light are scarce due to the stringent electronic
conditions that they must satisfy. Chemical and nanostructuring approaches are
capable of improving the catalytic performance of known photoactive compounds
however the complexity of the synthesized nanomaterials and sophistication of
the employed methods make systematic design of photocatalysts difficult. Here,
we show by means of first-principles simulation methods that application of
biaxial stress, $\eta$, on semiconductor oxide thin films can modify their
optoelectronic and catalytic properties in a significant and predictable
manner. In particular, we show that upon moderate tensile strains CeO$_{2}$ and
TiO$_{2}$ thin films become suitable materials for photocatalytic conversion of
H$_{2}$O into H$_{2}$ and CO$_{2}$ into CH$_{4}$ under sunlight. The band gap
shifts induced by $\eta$ are reproduced qualitatively by a simple analytical
model that depends only on structural and dielectric susceptibility changes.
Thus, epitaxial strain represents a promising route for methodical screening
and rational design of photocatalytic materials.


  Understanding star formation rates (SFR) is a central goal of modern
star-formation models, which mainly involve gravity, turbulence and, in some
cases, magnetic fields (B-fields). However, a connection between B-fields and
SFR has never been observed. Here, a comparison between the surveys of SFR and
a study of cloud-field alignment - which revealed a bimodal (parallel or
perpendicular) alignment - shows consistently lower SFR per solar mass for
clouds almost perpendicular to the B-fields. This is evidence of B-fields being
a primary regulator of SFR. The perpendicular alignment possesses a
significantly higher magnetic flux than the parallel alignment and thus a
stronger support of the gas against self-gravity. This results in overall lower
masses of the fragmented components, which are in agreement with the lower SFR.


  One-pixel attack is a curious way of deceiving neural network classifier by
changing only one pixel in the input image. The full potential and boundaries
of this attack method are not yet fully understood. In this research, the
successful and unsuccessful attacks are studied in more detail to illustrate
the working mechanisms of a one-pixel attack created using differential
evolution. The data comes from our earlier studies where we applied the attack
against medical imaging. We used a real breast cancer tissue dataset and a real
classifier as the attack target. This research presents ways to analyze
chromatic and spatial distributions of one-pixel attacks. In addition, we
present one-pixel attack confidence maps to illustrate the behavior of the
target classifier. We show that the more effective attacks change the color of
the pixel more, and that the successful attacks are situated at the center of
the images. This kind of analysis is not only useful for understanding the
behavior of the attack but also the qualities of the classifying neural
network.


  In this paper we prove that under certain conditions in a quasi Einstein semi
Riemannian warped product the fiber is necessarily a Einstein manifold. We
provide all the quasi Einstein manifolds when r Bakry Emery tensor is null, the
base is conformal to an n-dimensional pseudo-Euclidean space invariant under
the action of an n - 1 dimensional translation group and the fiber is Ricci
flat. As an application, we have built a family of Ricci flat Einstein warped
product whose base is not locally conformally flat.


  Non-magnetic impurities in an S=1/2 Heisenberg antiferromagnetic chain are
studied using boundary conformal field theory techniques and finite-temperature
quantum Monte Carlo simulations. We calculate the static structure function,
S_imp(k), measured in neutron scattering and the local susceptibility, chi_i
measured in Knight shift experiments. S_imp(k) becomes quite large near the
antiferromagnetic wave-vector, and exhibits much stronger temperature
dependence than the bulk structure function. \chi_i has a large component which
alternates and increases as a function of distance from the impurity.


  We propose a time-space discretization of a general notion of quasistatic
growth of brittle fractures in elastic bodies proposed in [13] by G. Dal Maso,
G.A. Francfort, and R. Toader, which takes into account body forces and surface
loads. We employ adaptive triangulations and prove convergence results for the
total, elastic and surface energies. In the case in which the elastic energy is
strictly convex, we prove also a convergence result for the deformations.


  Neuro-symbolic approaches to artificial intelligence, which combine neural
networks with classical symbolic techniques, are growing in prominence,
necessitating formal approaches to reason about their correctness. We propose a
novel modelling formalism called neuro-symbolic concurrent stochastic games
(NS-CSGs), which comprise two probabilistic finite-state agents interacting in
a shared continuous-state environment. Each agent observes the environment
using a neural perception mechanism, which converts inputs such as images into
symbolic percepts, and makes decisions symbolically. We focus on the class of
NS-CSGs with Borel state spaces and prove the existence and measurability of
the value function for zero-sum discounted cumulative rewards under
piecewise-constant restrictions on the components of this class of models. To
compute values and synthesise strategies, we present, for the first time,
practical value iteration (VI) and policy iteration (PI) algorithms to solve
this new subclass of continuous-state CSGs. These require a finite
decomposition of the environment induced by the neural perception mechanisms of
the agents and rely on finite abstract representations of value functions and
strategies closed under VI or PI. First, we introduce a Borel measurable
piecewise-constant (B-PWC) representation of value functions, extend minimax
backups to this representation and propose a value iteration algorithm called
B-PWC VI. Second, we introduce two novel representations for the value
functions and strategies, constant-piecewise-linear (CON-PWL) and
constant-piecewise-constant (CON-PWC) respectively, and propose
Minimax-action-free PI by extending a recent PI method based on alternating
player choices for finite state spaces to Borel state spaces, which does not
require normal-form games to be solved.


  Specific Language Impairment (SLI) is a disorder that affects communication
and can affect both comprehension and expression. This study focuses on
effectively detecting SLI in children using transcripts of spontaneous
narratives from 1063 interviews. A three-stage cascading pipeline was proposed
f. In the first stage, feature extraction and dimensionality reduction of the
data are performed using the Random Forest (RF) and Spearman correlation
methods. In the second stage, the most predictive variables from the first
stage are estimated using logistic regression, which is used in the last stage
to detect SLI in children from transcripts of spontaneous narratives using a
nearest neighbor classifier. The results revealed an accuracy of 97.13% in
identifying SLI, highlighting aspects such as the length of the responses, the
quality of their utterances, and the complexity of the language. This new
approach, framed in natural language processing, offers significant benefits to
the field of SLI detection by avoiding complex subjective variables and
focusing on quantitative metrics directly related to the child's performance.


  Speaker recognition systems are widely used in various applications to
identify a person by their voice; however, the high degree of variability in
speech signals makes this a challenging task. Dealing with emotional variations
is very difficult because emotions alter the voice characteristics of a person;
thus, the acoustic features differ from those used to train models in a neutral
environment. Therefore, speaker recognition models trained on neutral speech
fail to correctly identify speakers under emotional stress. Although
considerable advancements in speaker identification have been made using
convolutional neural networks (CNN), CNNs cannot exploit the spatial
association between low-level features. Inspired by the recent introduction of
capsule networks (CapsNets), which are based on deep learning to overcome the
inadequacy of CNNs in preserving the pose relationship between low-level
features with their pooling technique, this study investigates the performance
of using CapsNets in identifying speakers from emotional speech recordings. A
CapsNet-based speaker identification model is proposed and evaluated using
three distinct speech databases, i.e., the Emirati Speech Database, SUSAS
Dataset, and RAVDESS (open-access). The proposed model is also compared to
baseline systems. Experimental results demonstrate that the novel proposed
CapsNet model trains faster and provides better results over current
state-of-the-art schemes. The effect of the routing algorithm on speaker
identification performance was also studied by varying the number of
iterations, both with and without a decoder network.


  How do we know how much we know? Quantifying uncertainty associated with our
modelling work is the only way we can answer how much we know about any
phenomenon. With quantitative science now highly influential in the public
sphere and the results from models translating into action, we must support our
conclusions with sufficient rigour to produce useful, reproducible results.
Incomplete consideration of model-based uncertainties can lead to false
conclusions with real world impacts. Despite these potentially damaging
consequences, uncertainty consideration is incomplete both within and across
scientific fields. We take a unique interdisciplinary approach and conduct a
systematic audit of model-related uncertainty quantification from seven
scientific fields, spanning the biological, physical, and social sciences. Our
results show no single field is achieving complete consideration of model
uncertainties, but together we can fill the gaps. We propose opportunities to
improve the quantification of uncertainty through use of a source framework for
uncertainty consideration, model type specific guidelines, improved
presentation, and shared best practice. We also identify shared outstanding
challenges (uncertainty in input data, balancing trade-offs, error propagation,
and defining how much uncertainty is required). Finally, we make nine concrete
recommendations for current practice (following good practice guidelines and an
uncertainty checklist, presenting uncertainty numerically, and propagating
model-related uncertainty into conclusions), future research priorities
(uncertainty in input data, quantifying uncertainty in complex models, and the
importance of missing uncertainty in different contexts), and general research
standards across the sciences (transparency about study limitations and
dedicated uncertainty sections of manuscripts).


  The quantized form of the soft N=8 superconformal algebra is investigated.
Its operator product expansions are shown to exhibit a one-parameter-class of
(soft) anomalies, which may be arbitrarily shifted by certain suitable quantum
corrections of the generators. In particular, the BRST operator can be
constructed and made nilpotent in the quantum version of all known realizations
of the algebra. This generalizes the results of Cederwall and Preitschopf, who
studied the $\widehat{S^7}$-algebra, that is contained as a soft Kac-Moody part
in the superconformal algebra. A Fock-space representation is given, that has
to be somewhat unusual in certain modes.


  We study symplectic resolutions of the Hilbert scheme of two points on a
surface with one ADE-singularity. We also characterize such singularities by
central fibers of their symplectic resolutions. As an application, we show that
these singularities are isomorphic to the Slodowy slices which are transversal
to the `sub-subregular' orbits in the nilpotent cones of ADE-types.


  We provide an algorithm, running in polynomial time in the number of
vertices, computing the unique solution to the biased infinity Laplacian
Boundary Problem on finite graphs. The algorithm is based on the general
outline and approach taken in the corresponding algorithm for the unbiased case
provided by Lazarus et al. The new ingredient is an adjusted (biased) notion of
a slope of a function on a path in a graph. The algorithm can be used to
determine efficiently numerical approximations to the viscosity solutions of
biased infinity Laplacian PDEs.


  A long-standing issue in the parallel-in-time community is the poor
convergence of standard iterative parallel-in-time methods for hyperbolic
partial differential equations (PDEs), and for advection-dominated PDEs more
broadly. Here, a local Fourier analysis (LFA) convergence theory is derived for
the two-level variant of the iterative parallel-in-time method of multigrid
reduction-in-time (MGRIT). This closed-form theory allows for new insights into
the poor convergence of MGRIT for advection-dominated PDEs when using the
standard approach of rediscretizing the fine-grid problem on the coarse grid.
Specifically, we show that this poor convergence arises, at least in part, from
inadequate coarse-grid correction of certain smooth Fourier modes known as
characteristic components, which was previously identified as causing poor
convergence of classical spatial multigrid on steady-state advection-dominated
PDEs. We apply this convergence theory to show that, for certain
semi-Lagrangian discretizations of advection problems, MGRIT convergence using
rediscretized coarse-grid operators cannot be robust with respect to CFL number
or coarsening factor. A consequence of this analysis is that techniques
developed for improving convergence in the spatial multigrid context can be
re-purposed in the MGRIT context to develop more robust parallel-in-time
solvers. This strategy has been used in recent work to great effect; here, we
provide further theoretical evidence supporting the effectiveness of this
approach.


  In the mobile Internet era, the recommender system has become an
irreplaceable tool to help users discover useful items, and thus alleviating
the information overload problem. Recent deep neural network (DNN)-based
recommender system research have made significant progress in improving
prediction accuracy, which is largely attributed to the access to a large
amount of users' personal data collected from users' devices and then centrally
stored in the cloud server. However, as there are rising concerns around the
globe on user privacy leakage in the online platform, the public is becoming
anxious by such abuse of user privacy. Therefore, it is urgent and beneficial
to develop a recommender system that can achieve both high prediction accuracy
and high degree of user privacy protection.
  To this end, we propose a DNN-based recommendation model called PrivRec
running on the decentralized federated learning (FL) environment, which ensures
that a user's data never leaves his/her during the course of model training. On
the other hand, to better embrace the data heterogeneity commonly existing in
FL, we innovatively introduce a first-order meta-learning method that enables
fast in-device personalization with only few data points. Furthermore, to
defense from potential malicious participant that poses serious security threat
to other users, we develop a user-level differentially private DP-PrivRec model
so that it is unable to determine whether a particular user is present or not
solely based on the trained model. Finally, we conduct extensive experiments on
two large-scale datasets in a simulated FL environment, and the results
validate the superiority of our proposed PrivRec and DP-PrivRec.


  In the first part of this article we introduce the notion of a
backward-forward conditioning (BFC) system that generalises the notion of
zero-class admissibiliy introduced in [Xu,Liu,Yung]. We can show that unless
the spectum contains a halfplane, the BFC property occurs only in siutations
where the underlying semigroup extends to a group. In a second part we present
a sufficient condition for exact observability in Banach spaces that is
designed for infinite-dimensional output spaces and general strongly continuous
semigroups. To obtain this we make use of certain weighted square function
estimates. Specialising to the Hilbert space situation we obtain a result for
contraction semigroups without an analyticity condition on the semigroup.


  A superconducting magnetometers based on the magnetic field dependence of the
Eck step voltage in long Josephson tunnel junctions (LJTJs) is demonstrated.
The field to be measured is applied perpendicular to a continuous
superconducting pickup loop. Wherever the loop has a narrow constriction, the
density of the flux-restoring circulating currents will become relatively high
and will locally create a magnetic field large enough to bring a biased LJTJ in
the flux-flow state, i.e., at a finite voltage proportional to the field
strength. This method allows the realization of a novel family of robust and
general-purpose superconducting devices which, despite their simplicity,
function as ultra-low-noise, wide-band and high-dynamics magnetometers. The
performances of low-T$_c$ sensor prototypes, among which a highly linear
voltage responsivity and a magnetic spectral density $S_B^{1/2}<
3\,fT/Hz^{1/2}$, promise to be competitive with those of the best
superconducting quantum interference devices.


  Electronic states of single-component molecular metals M(tmdt)2 (M = Ni, Au)
are studied theoretically. We construct an effective three-band Hubbard model
for each material by numerical fitting to first-principles band calculations,
while referring to molecular orbital calculations for the isolated molecules.
The model consists of two kinds of base orbital for each molecule with
hybridization between them, i.e., a \pi-character orbital for each of the two
tmdt ligands, and, a pd\pi-orbital for M = Ni or a pd\sigma-orbital for M = Au
centered on the metal site; this indicates that these materials can be
considered as novel multiband \pi-d systems. We find that both orbitals
contribute to realize the metallic character in Ni(tmdt)2. The origin of the
antiferromagnetic transition observed in Au(tmdt)2 is also discussed based on
this model.


  A quantum theory is developed for the scattering of a nonrelativistic
particle in the field of a cosmic string regarded as a combination of a
magnetic and gravitational strings. Allowance is made for the effects due to
the finite transverse dimensions of the string under fairly general assumptions
about the distribution of the magnetic field and spatial curvature in the
string. It is shown that in a definite range of angles the differential cross
section at all absolute values of the wave vector of the incident particle
depends strongly on the magnetic flux of the string.


  The use of composite materials for structures subjected to impacts requires a
deep understanding about the dynamic behaviour of the material. To this end, a
physically-based computational micromechanics simulation tool has been
developed to predict failure initiation in a composite ply over a wide range of
strain rates. The computational micromechanics framework incorporates
constitutive models for the fibre, matrix and fibre-matrix interface, which are
partly calibrated with novel micromechanical testing techniques. The simulation
tool was applied to two matrix-dominated deformation modes of the ply:
transverse compression and in-plane shear. The comparison of simulation and
experimental results at coupon level have revealed a change in failure
initiation mechanism of the composite ply with strain rate, which was then
corroborated through observation of the fracture surfaces on the samples.


  Self-gravitating systems such as elliptical galaxies appear to have a
constant specific entropy and obey a scaling law relating their potential
energy to their mass. These properties can be interpreted as due to the
physical processes involved in the formation of these structures. Dark matter
haloes obtained through numerical simulations have also been found to obey a
scaling law relating their potential energy to their mass with the same slope
as ellipticals. Since the X-ray gas in clusters is weakly dissipative, we have
checked the hypothesis that it might verify similar properties.
  We have analyzed ROSAT-PSPC images of 24 clusters, and also found that: 1)
the S\'ersic law parameters (intensity, shape and scale) describing the X-ray
gas emission are correlated two by two; 2) the hot gas in all these clusters
roughly has the same specific entropy; 3) a scaling law linking the cluster
potential energy to the mass of the X-ray gas is observed, with the same slope
as for elliptical galaxies and dark matter haloes.


  Magnetotransport theory of layered superconductors in the flux flow steady
state is revisited. Longstanding controversies concerning observed Hall sign
reversals are resolved. The conductivity separates into a Bardeen-Stephen
vortex core contribution, and a Hall conductivity due to moving vortex charge.
This charge, which is responsible for Hall anomaly, diverges logarithmically at
weak magnetic field. Its values can be extracted from magetoresistivity data by
extrapolation of vortex core Hall angle from the normal phase. Hall anomalies
in YBCO, BSCCO, and NCCO data are consistent with theoretical estimates based
on doping dependence of London penetration depths. In the appendices, we derive
the Streda formula for the hydrodynamical Hall conductivity, and refute
previously assumed relevance of Galilean symmetry to Hall anomalies.


  We construct the general spherically symmetric and self-similar solution of
the Einstein-Vlasov system (collisionless matter coupled to general relativity)
with massless particles, under certain regularity conditions. Such solutions
have a curvature singularity by construction, and their initial data on a
Cauchy surface to the past of the singularity can be chosen to have compact
support in momentum space. They can also be truncated at large radius so that
they have compact support in space, while retaining self-similarity in a
central region that includes the singularity. However, the Vlasov distribution
function can not be bounded. As a simpler illustration of our techniques and
notation we also construct the general spherically symmetric and static
solution, for both massive and massless particles.


  We explore the impact of asymptotically safe quantum gravity on the Abelian
gauge coupling in a model including a charged scalar, confirming indications
that asymptotically safe quantum fluctuations of gravity could trigger a
power-law running towards a free fixed point for the gauge coupling above the
Planck scale. Simultaneously, quantum gravity fluctuations balance against
matter fluctuations to generate an interacting fixed point, which acts as a
boundary of the basin of attraction of the free fixed point. This enforces an
upper bound on the infrared value of the Abelian gauge coupling. In the regime
of gravity couplings which in our approximation also allows for a prediction of
the top quark and Higgs mass close to the experimental value [1], we obtain an
upper bound approximately 35% above the infrared value of the hypercharge
coupling in the Standard Model.


  The properties of non-equilibrium magnetized plasmas confined in planar
geometry are studied on the basis of the first principle microscopic Langevin
dynamics computer simulations. The non-equilibrium state of plasmas is
maintained due to the recombination and generation of charges.The intrinsic
microscopic structure of non-equilibrium steady-state magnetized plasmas, in
particular, the inter-particle correlations and self-organization of vortex
structures are examined. The simulations have been performed for a wide range
of parameters including strong plasma coupling, high charge recombination and
generation rates, and intense magnetic field. As is shown in simulations, the
non-equilibrium recombination and generation processes trigger the formation of
ordered dissipative or coherent drift vortex states in 2D plasmas with
distinctly spatially separated components, which are far from thermal
equilibrium. This is evident from the unusual properties of binary
distributions and behavior of the Coulomb energy of the system, which turn out
to be quite different from the ones typical for the equilibrium state of
plasmas under the same conditions.


  We derive the free energy of the chiral Potts model by the infinite lattice
``inversion relation'' method. This method is non-rigorous in that it always
needs appropriate analyticity assumptions. Guided by previous calculations
based on exact finite-lattice functional relations, we find that in addition to
the usual assumption that the free energy be analytic and bounded in some
principal domain of the rapidity parameter space that includes the physical
regime, we also need a much less obvious symmetry. We can then obtain the free
energy by Wiener-Hopf factorization in the complex planes of appropriate
variables. Together with the inversion relation, this symmetry relates the
values of the free energy in all neighbouring domains to those in the principal
domain.


  We examine the entanglement properties of the Yang-Mills theory by
calculating $\alpha$ entanglement entropy with $\alpha=2$ using a SU(3)
quenched lattice gauge simulation both in the confinement and the deconfinement
phases. In the confinement phase, the derivative of the $\alpha$ entropy with
respect to the size $l$ of the subregion, whose entanglement properties are
interested in, scales as $1/l^3$, and a clear discontinuity cannot be found
within our statistical errors. The $\alpha$ entropy in the deconfinement phase
saturates at large $l$. The saturation value is comparable with the thermal
entropy of the pure Yang-Mills theory, indicating that the $\alpha$ entropy
obeys the volume law at large $l$ in the deconfinement phase.


  We study a variant of the ferromagnetic Potts model, recently introduced by
Tamura, Tanaka and Kawashima, consisting of a ferromagnetic interaction among
$q$ "visible" colours along with the presence of $r$ non-interacting
"invisible" colours. We introduce a random-cluster representation for the
model, for which we prove the existence of a first-order transition for any
$q>0$, as long as $r$ is large enough. When $q>1$, the low-temperature regime
displays a $q$-fold symmetry breaking. The proof involves a Pirogov-Sinai
analysis applied to this random-cluster representation of the model.


  In this paper we study some relationships between polyhedral convex sets
(PCS) and generalized polyhedral convex sets (GPCS). In particular, we clarify
by a counterexample that the necessary and sufficient conditions for the
separation of a convex set and a PCS obtained by Kung Fu Ng and Wen Song in
[Fenchel duality in finite-dimensional setting and its applications, Nonlinear
Anal. 55(2003), 845--858; Theorem~3.1] are no longer valid when considering
GPCS instead of PCS. We also introduce and study the notions of generalized
polyhedral set-valued mappings and optimal value functions generated by
generalized polyhedral convex set-valued mappings along with their generalized
differentiation calculus rules.


  We give predictions for the partial wave amplitudes of pion photoproduction
near threshold by means of dispersion relations at fixed t. The free parameters
of this approach are determined by a fit to experimental data in the energy
range 160 MeV $\le E_{\gamma} \le$ 420 MeV. The observables near threshold are
found to be rather sensitive to the amplitudes in the resonance region, in
particular to the $\Delta$ (1232) and $N^*$ (1440). We obtain a good agreement
with the existing threshold data for both charged and neutral pion production.
Our predictions also agree well with the results of chiral perturbation theory,
except for neutral pion production off the neutron.


  A review of recent experimental measurements of the lifetime and mixing
parameters of neutral D and B hadrons is presented. In particular, focus is
given to measurements of D mixing and the Lambda_B lifetime.


  The identification of an emission line is unambiguous when multiple spectral
features are clearly visible in the same spectrum. However, in many cases, only
one line is detected, making it difficult to correctly determine the redshift.
We developed a freely available unsupervised machine-learning algorithm based
on unbiased topology (UMLAUT) that can be used in a very wide variety of
contexts, including the identification of single emission lines. To this
purpose, the algorithm combines different sources of information, such as the
apparent magnitude, size and color of the emitting source, and the equivalent
width and wavelength of the detected line. In each specific case, the algorithm
automatically identifies the most relevant ones (i.e., those able to minimize
the dispersion associated with the output parameter). The outputs can be easily
integrated into different algorithms, allowing us to combine supervised and
unsupervised techniques and increasing the overall accuracy. We tested our
software on WISP (WFC3 IR Spectroscopic Parallel) survey data. WISP represents
one of the closest existing analogs to the near-IR spectroscopic surveys that
are going to be performed by the future Euclid and Roman missions. These
missions will investigate the large-scale structure of the universe by
surveying a large portion of the extragalactic sky in near-IR slitless
spectroscopy, detecting a relevant fraction of single emission lines. In our
tests, UMLAUT correctly identifies real lines in 83.2% of the cases. The
accuracy is slightly higher (84.4%) when combining our unsupervised approach
with a supervised approach we previously developed.


  I present the two-loop QCD corrections to the production of a massive
quark-anti-quark pair in the massless quark-anti-quark annihilation channel.
The result is obtained as a combination of a deep expansion in the mass around
the high energy limit and of a numerical integration of a system of
differential equations. The primary application of the outcome and developed
methods is top quark pair production at the Large Hadron Collider.


  Knowledge graphs (KGs) are an important source repository for a wide range of
applications and rule mining from KGs recently attracts wide research interest
in the KG-related research community. Many solutions have been proposed for the
rule mining from large-scale KGs, which however are limited in the inefficiency
of rule generation and ineffectiveness of rule evaluation. To solve these
problems, in this paper we propose a generation-then-evaluation rule mining
approach guided by reinforcement learning. Specifically, a two-phased framework
is designed. The first phase aims to train a reinforcement learning agent for
rule generation from KGs, and the second is to utilize the value function of
the agent to guide the step-by-step rule generation. We conduct extensive
experiments on several datasets and the results prove that our rule mining
solution achieves state-of-the-art performance in terms of efficiency and
effectiveness.


  Let $\Omega$ be a bounded domain of $\mathbb{R}^{N}$, and $Q=\Omega
\times(0,T).$ We first study the problem \[ \left\{ \begin{array} [c]{l}%
{u_{t}}-{\Delta_{p}}u=\mu\qquad\text{in }Q,\\ {u}=0\qquad\text{on
}\partial\Omega\times(0,T),\\ u(0)=u_{0}\qquad\text{in }\Omega, \end{array}
\right. \] where $p>1$, $\mu\in\mathcal{M}_{b}(\Omega)$ and $u_{0}\in
L^{1}(\Omega).$ Our main result is a \textit{stability theorem }extending the
results of Dal Maso, Murat, Orsina, Prignet, for the elliptic case\textit{. }
As an application, we consider the perturbed problem\textit{ } \[ \left\{
\begin{array} [c]{l}% {u_{t}}-{\Delta_{p}}u+\mathcal{G}(u)=\mu\qquad\text{in
}Q,\\ {u}=0\qquad\text{on }\partial\Omega\times(0,T),\\
u(0)=u_{0}\qquad\text{in }\Omega, \end{array} \right. \] where $\mathcal{G}(u)$
may be an absorption or a source term$.$ In the model case
$\mathcal{G}(u)=\pm\left\vert u\right\vert ^{q-1}u$ $(q>p-1),$ or $\mathcal{G}$
has an exponential type. We give existence results when $q$ is subcritical, or
when the measure $\mu$ is good in time and satisfies suitable capacity
conditions.


  The training of deep neural nets is expensive. We present a predictor-
corrector method for the training of deep neural nets. It alternates a
predictor pass with a corrector pass using stochastic gradient descent with
backpropagation such that there is no loss in validation accuracy. No special
modifications to SGD with backpropagation is required by this methodology. Our
experiments showed a time improvement of 9% on the CIFAR-10 dataset.


  Most TLS clients such as modern web browsers enforce coarse-grained TLS
security configurations. They support legacy versions of the protocol that have
known design weaknesses, and weak ciphersuites that provide fewer security
guarantees (e.g. non Forward-Secrecy), mainly to provide backward
compatibility. This opens doors to downgrade attacks, as is the case of the
POODLE attack [18], which exploits the client's silent fallback to downgrade
the protocol version to exploit the legacy version's flaws. To achieve a better
balance between security and backward compatibility, we propose a DNS-based
mechanism that enables TLS servers to advertise their support for the latest
version of the protocol and strong ciphersuites (that provide Forward-Secrecy
and Authenticated-Encryption simultaneously). This enables clients to consider
prior knowledge about the servers' TLS configurations to enforce a fine-grained
TLS configurations policy. That is, the client enforces strict TLS
configurations for connections going to the advertising servers, while
enforcing default configurations for the rest of the connections. We implement
and evaluate the proposed mechanism and show that it is feasible, and incurs
minimal overhead. Furthermore, we conduct a TLS scan for the top 10,000 most
visited websites globally, and show that most of the websites can benefit from
our mechanism.


  The discrepancy between the FOPT and CIPT approaches for hadronic $\tau$
spectral function moments constitutes the major theoretical uncertainty for
strong coupling determinations from tau decay data. We show the discrepancy can
be analytically understood since the Borel representations -- which have been
assumed to be identical for both approaches previously -- differ in the
presence of IR renormalons. This implies that the OPE condensate corrections
are different for both approaches and that the discrepancy may eventually be
reconciled. In the talk we explain the difference and some mathematical aspects
of of the FOPT and CIPT Borel representations and show numerical results.


  NPN classification is an essential problem in the design and verification of
digital circuits. Most existing works explored variable symmetries and cofactor
signatures to develop their classification methods. However, cofactor
signatures only consider the face characteristics of Boolean functions. In this
paper, we propose a new NPN classifier using both face and point
characteristics of Boolean functions, including cofactor, influence, and
sensitivity. The new method brings a new perspective to the classification of
Boolean functions. The classifier only needs to compute some signatures, and
the equality of corresponding signatures is a prerequisite for NPN equivalence.
Therefore, these signatures can be directly used for NPN classification, thus
avoiding the exhaustive transformation enumeration. The experiments show that
the proposed NPN classifier gains better NPN classification accuracy with
comparable speed.


  Liquids near the glass transition exhibit dynamical heterogeneity, i.e. local
relaxation rates fluctuate strongly over space and time. Here we introduce a
simple continuum model that allows for quantitative predictions for the
correlators describing these fluctuations. We find remarkable agreement of the
model predictions for the dynamic susceptibility $\chi_4(t)$ with numerical
results for a binary hard-sphere liquid and for a Kob-Andersen Lennard-Jones
mixture. Under this model, the lifetime $\tau_{\rm ex}$ of the heterogeneities
has little effect on the position $t = t_4 \sim \tau_{\alpha}$ of the peak of
$\chi_4(t)$, but it controls the decay of $\chi_4(t)$ after the peak, and we
show how to estimate it from this decay.


  We selected a sample of 33 Gamma Ray Bursts (GRBs) detected by Swift, with
known redshift and optical extinction at the host frame. For these, we
constructed the de-absorbed and K-corrected X-ray and optical rest frame light
curves. These are modelled as the sum of two components: emission from the
forward shock due to the interaction of a fireball with the circum-burst medium
and an additional component, treated in a completely phenomenological way. The
latter can be identified, among other possibilities, as "late prompt" emission
produced by a long lived central engine with mechanisms similar to those
responsible for the production of the "standard" early prompt radiation. Apart
from flares or re-brightenings, that we do not model, we find a good agreement
with the data, despite of their complexity and diversity. Although based in
part on a phenomenological model with a relatively large number of free
parameters, we believe that our findings are a first step towards the
construction of a more physical scenario. Our approach allows us to interpret
the behaviour of the optical and X-ray afterglows in a coherent way, by a
relatively simple scenario. Within this context it is possible to explain why
sometimes no jet break is observed; why, even if a jet break is observed, it is
often chromatic; why the steepening after the jet break time is often shallower
than predicted. Finally, the decay slope of the late prompt emission after the
shallow phase is found to be remarkably similar to the time profile expected by
the accretion rate of fall-back material (i.e. proportional to t^{-5/3}),
suggesting that this can be the reason why the central engine can be active for
a long time.


  In learning-based approaches to image compression, codecs are developed by
optimizing a computational model to minimize a rate-distortion objective.
Currently, the most effective learned image codecs take the form of an
entropy-constrained autoencoder with an entropy model that uses both forward
and backward adaptation. Forward adaptation makes use of side information and
can be efficiently integrated into a deep neural network. In contrast, backward
adaptation typically makes predictions based on the causal context of each
symbol, which requires serial processing that prevents efficient GPU / TPU
utilization. We introduce two enhancements, channel-conditioning and latent
residual prediction, that lead to network architectures with better
rate-distortion performance than existing context-adaptive models while
minimizing serial processing. Empirically, we see an average rate savings of
6.7% on the Kodak image set and 11.4% on the Tecnick image set compared to a
context-adaptive baseline model. At low bit rates, where the improvements are
most effective, our model saves up to 18% over the baseline and outperforms
hand-engineered codecs like BPG by up to 25%.


  Motivated by the extensive investigations of integro-differential equations
on $\mathbb{R}^n$, we consider nonlocal filtration type equations with rough
kernels on the Heisenberg group $\mathbb{H}^n$. We prove the existence and
uniqueness of weak solutions corresponding to suitable initial data.
Furthermore, we obtain the large time behavior of solutions and the uniform
H\"older regularity of sign-changing solutions for the porous medium type
equations ($m\geq 1$). Notice that both conformal fractional operators
$\mathscr{L}_{\alpha/2}$ and pure power fractional operators
$\mathscr{L}^{\alpha/2}$ on the Heisenberg group $\mathbb{H}^n$ have their
integral representations with suitable kernels. Therefore, all the results in
this paper will hold for these equations with operators
$\mathscr{L}_{\alpha/2}$ or $\mathscr{L}^{\alpha/2}$.


  We consider spin and electronic properties of itinerant electron systems,
described by the spin-fermion model, near the antiferromagnetic critical point.
We expand in the inverse number of hot spots in the Brillouin zone, N and
present the results beyond previously studied $N = \infty $ limit. We found two
new effects: (i) Fermi surface becomes nested at hot spots, and (ii) vertex
corrections give rise to anomalous spin dynamics and change the dynamical
critical exponent from z=2 to z>2.
  To first order in 1/N we found $z = 2N/(N-2)$ which for a physical N=8 yields
$z\approx 2.67$.


  Grammar convergence is a method that helps discovering relationships between
different grammars of the same language or different language versions. The key
element of the method is the operational, transformation-based representation
of those relationships. Given input grammars for convergence, they are
transformed until they are structurally equal. The transformations are composed
from primitive operators; properties of these operators and the composed chains
provide quantitative and qualitative insight into the relationships between the
grammars at hand. We describe a refined method for grammar convergence, and we
use it in a major study, where we recover the relationships between all the
grammars that occur in the different versions of the Java Language
Specification (JLS). The relationships are represented as grammar
transformation chains that capture all accidental or intended differences
between the JLS grammars. This method is mechanized and driven by nominal and
structural differences between pairs of grammars that are subject to
asymmetric, binary convergence steps. We present the underlying operator suite
for grammar transformation in detail, and we illustrate the suite with many
examples of transformations on the JLS grammars. We also describe the
extraction effort, which was needed to make the JLS grammars amenable to
automated processing. We include substantial metadata about the convergence
process for the JLS so that the effort becomes reproducible and transparent.


  Among topics of opinion formation it is of interest to observe the
characteristics of networks with a priori distinct communities. As an
illustration, we report on the citation network(s) unfolded in the recent
decades through web available works belonging to selected members of the
Neocreationist and Intelligent Design Proponents (IDP) and the Darwinian
Evolution Defenders (DED) communities. An adjacency matrix of tagged nodes is
first constructed; it is not symmetric. A generalization of considerations
pertaining to the case of networks with biased links, directed or undirected,
is thus presented. The main characteristic coefficients describing the
structure of such partially directed networks with tagged nodes are outlined.
The structural features are discussed searching for statistical aspects,
equivalence or not of subnetworks through the degree distributions, each
network assortativity, the global and local clustering coefficients and the
Average Overlap Indices. The various closed and open triangles made from nodes,
moreover distinguishing the community, are especially listed to calculate the
clustering characteristics. The distribution of elements in the rectangular
submatrices are specially examined since they represent inter-community
connexions. The emphasis being on distinguishing the number of vertices
belonging to a given community. Using such informations one can distinguish
between opinion leaders, followers and main rivals and briefly interpret their
relationships through psychological-like conditions intrinsic to behavior rules
in either community. Considerations on other controversy cases with similar
social constraints are outlined, as well as suggestions on further, more
general, work deduced from our observations on such networks.


  The search for binary sequences with low peak sidelobe level value represents
a formidable computational problem. To locate better sequences for this
problem, we designed a stochastic algorithm that uses two fitness functions. In
these fitness functions, the value of the autocorrelation function has a
different impact on the final fitness value. It is defined with the value of
the exponent over the autocorrelation function values. Each function is used in
the corresponding optimization phase, and the optimization process switches
between these two phases until the stopping condition is satisfied. The
proposed algorithm was implemented using the compute unified device
architecture and therefore allowed us to exploit the computational power of
graphics processing units. This algorithm was tested on sequences with lengths
$L = 2^m - 1$, for $14 \le m \le 20$. From the obtained results it is evident
that the usage of two fitness functions improved the efficiency of the
algorithm significantly, new-best known solutions were achieved, and the
achieved PSL values were significantly less than $\sqrt{L}$.


  In learning action recognition, models are typically pre-trained on object
recognition with images, such as ImageNet, and later fine-tuned on target
action recognition with videos. This approach has achieved good empirical
performance especially with recent transformer-based video architectures. While
recently many works aim to design more advanced transformer architectures for
action recognition, less effort has been made on how to train video
transformers. In this work, we explore several training paradigms and present
two findings. First, video transformers benefit from joint training on diverse
video datasets and label spaces (e.g., Kinetics is appearance-focused while
SomethingSomething is motion-focused). Second, by further co-training with
images (as single-frame videos), the video transformers learn even better video
representations. We term this approach as Co-training Videos and Images for
Action Recognition (CoVeR). In particular, when pretrained on ImageNet-21K
based on the TimeSFormer architecture, CoVeR improves Kinetics-400 Top-1
Accuracy by 2.4%, Kinetics-600 by 2.3%, and SomethingSomething-v2 by 2.3%. When
pretrained on larger-scale image datasets following previous state-of-the-art,
CoVeR achieves best results on Kinetics-400 (87.2%), Kinetics-600 (87.9%),
Kinetics-700 (79.8%), SomethingSomething-v2 (70.9%), and Moments-in-Time
(46.1%), with a simple spatio-temporal video transformer.


  Collaboration requires agents to coordinate their behavior on the fly,
sometimes cooperating to solve a single task together and other times dividing
it up into sub-tasks to work on in parallel. Underlying the human ability to
collaborate is theory-of-mind, the ability to infer the hidden mental states
that drive others to act. Here, we develop Bayesian Delegation, a decentralized
multi-agent learning mechanism with these abilities. Bayesian Delegation
enables agents to rapidly infer the hidden intentions of others by inverse
planning. We test Bayesian Delegation in a suite of multi-agent Markov decision
processes inspired by cooking problems. On these tasks, agents with Bayesian
Delegation coordinate both their high-level plans (e.g. what sub-task they
should work on) and their low-level actions (e.g. avoiding getting in each
other's way). In a self-play evaluation, Bayesian Delegation outperforms
alternative algorithms. Bayesian Delegation is also a capable ad-hoc
collaborator and successfully coordinates with other agent types even in the
absence of prior experience. Finally, in a behavioral experiment, we show that
Bayesian Delegation makes inferences similar to human observers about the
intent of others. Together, these results demonstrate the power of Bayesian
Delegation for decentralized multi-agent collaboration.


  We explore the quantum version of Brayton cycle with a composite system as
the working substance. The actual Brayton cycle consists of two adiabatic and
two isobaric processes. Two pressures can be defined in our isobaric process,
one corresponds to the external magnetic field (characterized by $F_x$) exerted
on the system, while the other corresponds to the coupling constant between the
subsystems (characterized by $F_y$). As a consequence, we can define two types
of quantum Brayton cycle for the composite system. We find that the subsystem
experiences a quantum Brayton cycle in one quantum Brayton cycle (characterized
by $F_x$), whereas the subsystem's cycle is of quantum Otto in another Brayton
cycle (characterized by $F_y$). The efficiency for the composite system equals
to that for the subsystem in both cases, but the work done by the total system
are usually larger than the sum of work done by the two subsystems. The other
interesting finding is that for the cycle characterized by $F_y$, the subsystem
can be a refrigerator while the total system is a heat engine. The result in
the paper can be generalized to a quantum Brayton cycle with a general coupled
system as the working substance.


  In this paper, we make the first attempt to apply the boundary integrated
neural networks (BINNs) for the numerical solution of two-dimensional (2D)
elastostatic and piezoelectric problems. BINNs combine artificial neural
networks with the well-established boundary integral equations (BIEs) to
effectively solve partial differential equations (PDEs). The BIEs are utilized
to map all the unknowns onto the boundary, after which these unknowns are
approximated using artificial neural networks and resolved via a training
process. In contrast to traditional neural network-based methods, the current
BINNs offer several distinct advantages. First, by embedding BIEs into the
learning procedure, BINNs only need to discretize the boundary of the solution
domain, which can lead to a faster and more stable learning process (only the
boundary conditions need to be fitted during the training). Second, the
differential operator with respect to the PDEs is substituted by an integral
operator, which effectively eliminates the need for additional differentiation
of the neural networks (high-order derivatives of neural networks may lead to
instability in learning). Third, the loss function of the BINNs only contains
the residuals of the BIEs, as all the boundary conditions have been inherently
incorporated within the formulation. Therefore, there is no necessity for
employing any weighing functions, which are commonly used in traditional
methods to balance the gradients among different objective functions. Moreover,
BINNs possess the ability to tackle PDEs in unbounded domains since the
integral representation remains valid for both bounded and unbounded domains.
Extensive numerical experiments show that BINNs are much easier to train and
usually give more accurate learning solutions as compared to traditional neural
network-based methods.


  End-to-end semantic role labeling (SRL) has been received increasing
interest. It performs the two subtasks of SRL: predicate identification and
argument role labeling, jointly. Recent work is mostly focused on graph-based
neural models, while the transition-based framework with neural networks which
has been widely used in a number of closely-related tasks, has not been studied
for the joint task yet. In this paper, we present the first work of
transition-based neural models for end-to-end SRL. Our transition model
incrementally discovers all sentential predicates as well as their arguments by
a set of transition actions. The actions of the two subtasks are executed
mutually for full interactions. Besides, we suggest high-order compositions to
extract non-local features, which can enhance the proposed transition model
further. Experimental results on CoNLL09 and Universal Proposition Bank show
that our final model can produce state-of-the-art performance, and meanwhile
keeps highly efficient in decoding. We also conduct detailed experimental
analysis for a deep understanding of our proposed model.


  Filament-based coil optimizations are performed for several quasihelical
stellarator configurations, notably the one from [M. Landreman, E. Paul, PRL
128, 035001, 2022], demonstrating that precise quasihelical symmetry can be
achieved with realistic coils. Several constraints are placed on the shape and
spacing of the coils, such as low curvature and sufficient plasma-coil distance
for neutron shielding. The coils resulting from this optimization have a
maximum curvature 0.8 times that of the coils of the Helically Symmetric
eXperiment (HSX) and a mean squared curvature 0.4 times that of the HSX coils
when scaled to the same plasma minor radius. When scaled up to reactor size and
magnetic field strength, no fast particle losses were found in the
free-boundary configuration when simulating 5000 alpha particles launched at
3.5 MeV on the flux surface with normalized toroidal flux of s=0.5. An analysis
of the tolerance of the coils to manufacturing errors is performed using a
Gaussian process model, and the coils are found to maintain low particle losses
for smooth, large-scale errors up to amplitudes of about 0.15 m. Another coil
optimization is performed for the Landreman-Paul configuration with the
additional constraint that the coils are purely planar. Visual inspection of
the Poincar\'e plot of the resulting magnetic field-lines reveal that the
planar modular coils alone do a poor job of reproducing the target equilibrium.
Additional non-planar coil optimizations are performed for the quasihelical
configuration with 5% volume-averaged plasma beta from [M. Landreman, S.
Buller, M. Drevlak, PoP 29, 082501, 2022], and a similar configuration also
optimized to satisfy the Mercier criterion. The finite beta configurations had
larger fast-particle losses, with the free-boundary Mercier-optimized
configuration performing the worst, losing about 5.5% of alpha particles
launched at s=0.5.


  A number of modern experiments in high-energy astrophysics produce images of
supernova remnants (SNRs) in the TeV and GeV gamma-rays. Either relativistic
electrons (due to the inverse-Compton scattering) or protons (due to the pion
decays) may be responsible for this emission. In particular, the broad-band
spectra of SNRs may be explained in both leptonic and hadronic scenarios.
Another kind of observational data, namely, images of SNRs, is an important
part of experimental information. We present a method to model gamma-ray images
of Sedov SNRs in uniform media and magnetic field due to hadronic emission.
These gamma-rays are assumed to appear as a consequence of meson decays
produced in inelastic collisions of accelerated protons with thermal protons
downstream of the shock - a model would be relevant for SNRs without firm
confirmations of the shock-cloud interaction, as e.g. SN1006. Distribution of
surface brightness of the shell-like SNR is synthesized numerically for a
number of configurations. An approximate analytical formula for azimuthal and
radial variation of hadronic gamma-ray brightness close to the shock is
derived. The properties of images as well as the main factors determining the
surface brightness distribution are determined. Some conclusions which would be
relevant to SN1006 are discussed.


  Inversive distance circle packing metric was introduced by P Bowers and K
Stephenson \cite{BS} as a generalization of Thurston's circle packing metric
\cite{T1}. They conjectured that the inversive distance circle packings are
rigid. For nonnegative inversive distance, Guo \cite{Guo} proved the
infinitesimal rigidity and then Luo \cite{L3} proved the global rigidity. In
this paper, based on an observation of Zhou \cite{Z}, we prove this conjecture
for inversive distance in $(-1, +\infty)$ by variational principles. We also
study the global rigidity of a combinatorial curvature introduced in
\cite{GJ4,GX4,GX6} with respect to the inversive distance circle packing
metrics where the inversive distance is in $(-1, +\infty)$.


  The degree-Kirchhoff index of a graph is given by the sum of inverses of
non-zero eigenvalues of the normalized Laplacian matrix of the graph multiplied
with the total degree of the graph. Explicit formulas for the degree-Kirchhoff
index of various types of cylinder chain and M\"{o}bius chain have been
obtained by many researchers in the recent past. In the present paper, we
obtain closed-form formulas for the degree-Kirchhoff index of pentagonal
cylinder/ M\"{o}bius chain. Also we find here the Gutman index and the Schultz
index for those graphs.


  Complex computer codes are widely used in science and engineering to model
physical phenomena. Furthermore, it is common that they have a large number of
input parameters. Global sensitivity analysis aims to identify those which have
the most important impact on the output. Sobol indices are a popular tool to
perform such analysis. However, their estimations require an important number
of simulations and often cannot be processed under reasonable time constraint.
To handle this problem, a Gaussian process regression model is built to
surrogate the computer code and the Sobol indices are estimated through it. The
aim of this paper is to provide a methodology to estimate the Sobol indices
through a surrogate model taking into account both the estimation errors and
the surrogate model errors. In particular, it allows us to derive
non-asymptotic confidence intervals for the Sobol index estimations.
Furthermore, we extend the suggested strategy to the case of multi-fidelity
computer codes which can be run at different levels of accuracy. For such
simulators, we use an extension of Gaussian process regression models for
multivariate outputs.


  Let $\CRF_S$ denote the category of $S$-colored rooted forests, and
$\H_{\CRF_S}$ denote its Ringel-Hall algebra as introduced in \cite{KS}. We
construct a homomorphism from a $K^+_0 (\CRF_S)$--graded version of the Hopf
algebra of noncommutative symmetric functions to $\H_{\CRF_S}$. Dualizing, we
obtain a homomorphism from the Connes-Kreimer Hopf algebra to a $K^+_0
(\CRF_S)$--graded version of the algebra of quasisymmetric functions. This
homomorphism is a refinement of one considered by W. Zhao in \cite{Z}.


  In this note, we propose an original extended formulation of Euler-Korteweg
systems based on a generalization of the quantum Bohm potential identity. This
new formulation allows to propose a useful construction of a numerical scheme
with entropy stability property under a hyperbolic CFL condition. We also
comment the use of the identity for compressible Navier-Stokes equations with
degenerate viscosities.


  Quantum algorithms and protocols are often presented as quantum circuits for
a better understanding. We give a list of equivalence rules which can help in
the analysis and design of quantum circuits. As example applications we study
quantum teleportation and dense coding protocols in terms of a simple XOR
swapping circuit and give an intuitive picture of a basic gate teleportation
circuit.


  Predicting the flow of information in dynamic social environments is relevant
to many areas of the contemporary society, from disseminating health care
messages to meme tracking. While predicting the growth of information cascades
has been successfully addressed in diverse social platforms, predicting the
temporal and topological structure of information cascades has seen limited
exploration. However, accurately predicting how many users will transmit the
message of a particular user and at what time is paramount for designing
practical intervention techniques.
  This paper leverages Long-Short Term Memory (LSTM) neural network techniques
to predict two spatio-temporal properties of information cascades, namely the
size and speed of individual-level information transmissions. We combine these
prediction algorithms with probabilistic generation of cascade trees into a
generative test model that is able to accurately generate cascade trees in two
different platforms, Reddit and Github. Our approach leads to a classification
accuracy of over 73% for information transmitters and 83% for early
transmitters in a variety of social platforms.


  We introduce and demonstrate two linear inverse modelling methods for systems
of stochastic ODE's with accuracy that is independent of the dimensionality
(number of elements) of the state vector representing the system in question.
Truncation of the state space is not required. Instead we rely on the principle
that perturbations decay with distance or the fact that for many systems, the
state of each data point is only determined at an instant by itself and its
neighbours. We further show that all necessary calculations, as well as
numerical integration of the resulting linear stochastic system, require
computational time and memory proportional to the dimensionality of the state
vector.


  In this paper we investigate the complexity of model selection and model
testing for dynamical systems with toric steady states. Such systems frequently
arise in the study of chemical reaction networks. We do this by formulating
these tasks as a constrained optimization problem in Euclidean space. This
optimization problem is known as a Euclidean distance problem; the complexity
of solving this problem is measured by an invariant called the Euclidean
distance (ED) degree. We determine closed-form expressions for the ED degree of
the steady states of several families of chemical reaction networks with toric
steady states and arbitrarily many reactions. To illustrate the utility of this
work we show how the ED degree can be used as a tool for estimating the
computational cost of solving the model testing and model selection problems.


  Pulsars are amongst the most stable rotators known in the Universe. Over many
years some millisecond pulsars rival the stability of atomic clocks. Comparing
observations of many such stable pulsars may allow the first direct detection
of gravitational waves, improve the Solar System planetary ephemeris and
provide a means to study irregularities in terrestrial time scales. Here we
review the goals and status of current and future pulsar timing array projects.


  In 2012 Monod introduced examples of groups of piecewise projective
homeomorphisms which are not amenable and which do not contain free subgroups,
and later Lodha and Moore introduced examples of finitely presented groups with
the same property. In this article we examine the normal subgroup structure of
these groups. Two important cases of our results are the groups $H$ and $G_0$.
We show that the group $H$ of piecewise projective homeomorphisms of
$\mathbb{R}$ has the property that $H"$ is simple and that every proper
quotient of $H$ is metabelian. We establish simplicity of the commutator
subgroup of the group $G_0$, which admits a presentation with $3$ generators
and $9$ relations. Further we show that every proper quotient of $G_0$ is
abelian. It follows that the normal subgroups of these groups are in bijective
correspondence with those of the abelian (or metabelian) quotient.


  At SRON we are developing the Frequency Domain Multiplexing (FDM) for the
read-out of the TES-based detector array for the future infrared and X-ray
space mission. We describe the performances of a multiplexer designed to
increase the experimental throughput in the characterisation of ultra-low noise
equivalent power (NEP) TES bolometers and high energy resolving power X-ray
microcalorimeters arrays under ac and dc bias. We discuss the results obtained
using the TiAu TES bolometers array fabricated at SRON with measured dark NEP
below $5\cdot 10^{-19}W/Hz^{1/2}$ and saturation power of several fW


  RF fingerprinting is emerging as a physical layer security scheme to identify
illegitimate and/or unauthorized emitters sharing the RF spectrum. However, due
to the lack of publicly accessible real-world datasets, most research focuses
on generating synthetic waveforms with software-defined radios (SDRs) which are
not suited for practical deployment settings. On other hand, the limited
datasets that are available focus only on chipsets that generate only one kind
of waveform. Commercial off-the-shelf (COTS) combo chipsets that support two
wireless standards (for example WiFi and Bluetooth) over a shared dual-band
antenna such as those found in laptops, adapters, wireless chargers, Raspberry
Pis, among others are becoming ubiquitous in the IoT realm. Hence, to keep up
with the modern IoT environment, there is a pressing need for real-world open
datasets capturing emissions from these combo chipsets transmitting
heterogeneous communication protocols. To this end, we capture the first known
emissions from the COTS IoT chipsets transmitting WiFi and Bluetooth under two
different time frames. The different time frames are essential to rigorously
evaluate the generalization capability of the models. To ensure widespread use,
each capture within the comprehensive 72 GB dataset is long enough (40
MSamples) to support diverse input tensor lengths and formats. Finally, the
dataset also comprises emissions at varying signal powers to account for the
feeble to high signal strength emissions as encountered in a real-world
setting.


  A strong certification process is required to insure the safety of airplanes,
and more specifically the robustness of avionics applications. To implement
this process, the development of avionics software must follow long and costly
procedures. Most of these procedures have to be reexecuted each time the
software is modified. In this paper, we propose a framework to reduce the cost
and time impact of a software modification. With this new approach, the piece
of software likely to change is isolated from the rest of the application, so
it can be certified independently. This helps the system integrator to adapt an
avionics application to the specificities of the target airplane, without the
need for a new certification of the application.


  This paper deals with generating of an optimized route for multiple Vehicle
routing Problems (mVRP). We used a methodology of clustering the given cities
depending upon the number of vehicles and each cluster is allotted to a
vehicle. k- Means clustering algorithm has been used for easy clustering of the
cities. In this way the mVRP has been converted into VRP which is simple in
computation compared to mVRP. After clustering, an optimized route is generated
for each vehicle in its allotted cluster. Once the clustering had been done and
after the cities were allocated to the various vehicles, each cluster/tour was
taken as an individual Vehicle Routing problem and the steps of Genetic
Algorithm were applied to the cluster and iterated to obtain the most optimal
value of the distance after convergence takes place. After the application of
the various heuristic techniques, it was found that the Genetic algorithm gave
a better result and a more optimal tour for mVRPs in short computational time
than other Algorithms due to the extensive search and constructive nature of
the algorithm.


  Enhancements in the invariant mass distribution or scattering cross-section
are usually associated with resonances. However, the nature of exotic signals
found near hadron-hadron thresholds remain a puzzle today. In fact, a purely
kinematic triangle mechanism is also capable of producing similar structures,
but do not correspond to any unstable quantum state. In this paper, we report
for the first time, that a deep neural network can be trained to distinguish
triangle singularity from pole-based enhancements. We also identify the type of
dynamic pole structure that can be misidentified as triangle enhancement. We
apply our method to confirm that the $P_\psi^N(4312)^+$ state is not due to a
single triangle singularity, and is more favored towards a pole-based
interpretation based solely on pure line-shape analysis. Lastly, we argue how
our method can be used as a model-selection framework to help in classifying
other exotic hadron candidates.


  Gamma-ray bursts (GRBs) are powered by ultra-relativistic jets. The launching
sites of these jets are surrounded by dense media, which the jets must cross
before they can accelerate and release the high energy emission. Interaction
with the medium leads to the formation of a mildly relativistic sheath around
the jet resulting in an angular structures in the jet's asymptotic Lorentz
factor and energy per solid angle, which modifies the afterglow emission. We
build a semi-analytical tool to analyze the afterglow light curve and
polarization signatures of jets observed from a wide range of viewing angles,
and focus on ones with slowly declining energy profiles known as shallow jets.
We find overall lower polarization compared to the classical top-hat jet model.
We provide an analytical expression for the peak polarization degree as a
function of the energy profile power-law index, magnetic field configuration
and viewing angle, and show that it occurs near the light curve break time for
all viewers. When applying our tool to GRB 221009A, suspected to originate from
a shallow jet, we find that the suggested jet structures for this event agree
with the upper limits placed on the afterglow polarization in the optical and
X-ray bands. We also find that at early times the polarization levels may be
significantly higher, allowing for a potential distinction between different
jet structure models and possibly constraining the magnetization in both
forward and reverse shocks at that stage.


  We study a special class of weakly associative algebras: the symmetric
Leibniz algebras. We describe the structure of the commutative and skew
symmetric algebras associated with the polarization-depolarization principle.
We also give a structure theorem for the symmetric Leibniz algebras and we
study formal deformations in the context of deformation quantization.


  Semispecial quasi-Jordan algebras (also called Jordan dialgebras) are defined
by the polynomial identities $a(bc) = a(cb)$, $(ba)a^2 = (ba^2)a$, and
$(b,a^2,c) = 2(b,a,c)a$. These identities are satisfied by the product $ab = a
\dashv b + b \vdash a$ in an associative dialgebra. We use computer algebra to
show that every identity for this product in degree $\le 7$ is a consequence of
the three identities in degree $\le 4$, but that six new identities exist in
degree 8. Some but not all of these new identities are noncommutative preimages
of the Glennie identity.


  Precise low-frequency light scattering experiments on silica glass are
presented, covering a broad temperature and frequency range (9 GHz < \nu < 2
THz). For the first time the spectral shape of relaxations is observed over
more than one decade in frequency. The spectra show a power-law low-frequency
wing of the relaxational part of the spectrum with an exponent $\alpha $
proportional to temperature in the range 30 K < T < 200 K. A comparison of our
results with those from acoustic attenuation experiments performed at different
frequencies shows that this power-law behaviour rather well describes
relaxations in silica over 9 orders of magnitude in frequency. These findings
can be explained by a model of thermally activated transitions in double well
potentials.


  In this letter we describe a new method to use Baryon Acoustic Oscillations
(BAO) to derive a constraint on the possible variation of the speed of light.
The method relies on the fact that there is a simple relation between the
angular diameter distance $(D_{A})$ maximum and the Hubble function $(H)$
evaluated at the same maximum-condition redshift, which includes speed of light
$c$. We note the close analogy of the BAO probe with a laboratory experiment:
here we have $D_{A}$ which plays the role of a standard (cosmological) ruler,
and $H^{-1}$, with the dimension of time, as a (cosmological) clock. We
evaluate if current or future missions such as Euclid can be sensitive enough
to detect any variation of $c$.


  Dalitz plot distribution of the $\eta\to\pi^+\pi^-\pi^0$ decay is determined
using a data sample of $1.2\cdot 10^7$ $\eta$ mesons from $pd\to
^3\textrm{He}\eta$ reaction at 1 GeV collected by the WASA detector at COSY.


  This paper revisits recognition of natural image pleasantness by employing
deep convolutional neural networks and affordable eye trackers. There exist
several approaches to recognize image pleasantness: (1) computer vision, and
(2) psychophysical signals. For natural images, computer vision approaches have
not been as successful as for abstract paintings and is lagging behind the
psychophysical signals like eye movements. Despite better results, the
scalability of eye movements is adversely affected by the sensor cost. While
the introduction of affordable sensors have helped the scalability issue by
making the sensors more accessible, the application of such sensors in a
loosely controlled human-computer interaction setup is not yet studied for
affective image tagging. On the other hand, deep convolutional neural networks
have boosted the performance of vision-based techniques significantly in recent
years. To investigate the current status in regard to affective image tagging,
we (1) introduce a new eye movement dataset using an affordable eye tracker,
(2) study the use of deep neural networks for pleasantness recognition, (3)
investigate the gap between deep features and eye movements. To meet these
ends, we record eye movements in a less controlled setup, akin to daily
human-computer interaction. We assess features from eye movements, visual
features, and their combination. Our results show that (1) recognizing natural
image pleasantness from eye movement under less restricted setup is difficult
and previously used techniques are prone to fail, and (2) visual class
categories are strong cues for predicting pleasantness, due to their
correlation with emotions, necessitating careful study of this phenomenon. This
latter finding is alerting as some deep learning approaches may fit to the
class category bias.


  In this paper, we propose a method to partially mimic natural intelligence
for the problem of lifelong learning representations that are compatible. We
take the perspective of a learning agent that is interested in recognizing
object instances in an open dynamic universe in a way in which any update to
its internal feature representation does not render the features in the gallery
unusable for visual search. We refer to this learning problem as Compatible
Lifelong Learning Representations (CL2R) as it considers compatible
representation learning within the lifelong learning paradigm. We identify
stationarity as the property that the feature representation is required to
hold to achieve compatibility and propose a novel training procedure that
encourages local and global stationarity on the learned representation. Due to
stationarity, the statistical properties of the learned features do not change
over time, making them interoperable with previously learned features.
Extensive experiments on standard benchmark datasets show that our CL2R
training procedure outperforms alternative baselines and state-of-the-art
methods. We also provide novel metrics to specifically evaluate compatible
representation learning under catastrophic forgetting in various sequential
learning tasks. Code at
https://github.com/NiccoBiondi/CompatibleLifelongRepresentation.


  Inspired by recent successes of deep learning in computer vision, we propose
a novel framework for encoding time series as different types of images,
namely, Gramian Angular Summation/Difference Fields (GASF/GADF) and Markov
Transition Fields (MTF). This enables the use of techniques from computer
vision for time series classification and imputation. We used Tiled
Convolutional Neural Networks (tiled CNNs) on 20 standard datasets to learn
high-level features from the individual and compound GASF-GADF-MTF images. Our
approaches achieve highly competitive results when compared to nine of the
current best time series classification approaches. Inspired by the bijection
property of GASF on 0/1 rescaled data, we train Denoised Auto-encoders (DA) on
the GASF images of four standard and one synthesized compound dataset. The
imputation MSE on test data is reduced by 12.18%-48.02% when compared to using
the raw data. An analysis of the features and weights learned via tiled CNNs
and DAs explains why the approaches work.


  In this paper, we propose an automated computer platform for the purpose of
classifying Electroencephalography (EEG) signals associated with left and right
hand movements using a hybrid system that uses advanced feature extraction
techniques and machine learning algorithms. It is known that EEG represents the
brain activity by the electrical voltage fluctuations along the scalp, and
Brain-Computer Interface (BCI) is a device that enables the use of the brain
neural activity to communicate with others or to control machines, artificial
limbs, or robots without direct physical movements. In our research work, we
aspired to find the best feature extraction method that enables the
differentiation between left and right executed fist movements through various
classification algorithms. The EEG dataset used in this research was created
and contributed to PhysioNet by the developers of the BCI2000 instrumentation
system. Data was preprocessed using the EEGLAB MATLAB toolbox and artifacts
removal was done using AAR. Data was epoched on the basis of Event-Related (De)
Synchronization (ERD/ERS) and movement-related cortical potentials (MRCP)
features. Mu/beta rhythms were isolated for the ERD/ERS analysis and delta
rhythms were isolated for the MRCP analysis. The Independent Component Analysis
(ICA) spatial filter was applied on related channels for noise reduction and
isolation of both artifactually and neutrally generated EEG sources. The final
feature vector included the ERD, ERS, and MRCP features in addition to the
mean, power and energy of the activations of the resulting independent
components of the epoched feature datasets. The datasets were inputted into two
machine-learning algorithms: Neural Networks (NNs) and Support Vector Machines
(SVMs). Intensive experiments were carried out and optimum classification
performances of 89.8 and 97.1 were obtained using NN and SVM, respectively.


  We address the problem of reasoning on graph transformations featuring
actions such as \emph{addition} and \emph{deletion} of nodes and edges, node
\emph{merging} and \emph{cloning}, node or edge \emph{labelling} and edge
\emph{redirection}. First, we introduce the considered graph rewrite systems
which are parameterized by a given logic $\mathcal{L}$. Formulas of
$\mathcal{L}$ are used to label graph nodes and edges. In a second step, we
tackle the problem of formal verification of the considered rewrite systems by
using a Hoare-like weakest precondition calculus. It acts on triples of the
form $\{\texttt{Pre}\}(\texttt{R},\texttt{strategy}) \{\texttt{Post}\}$ where
\texttt{Pre} and \texttt{Post} are conditions specified in the given logic
$\mathcal{L}$, \texttt{R} is a graph rewrite system and \texttt{strategy} is an
expression stating how rules in \texttt{R} are to be performed. We prove that
the calculus we introduce is sound. Moreover, we show how the proposed
framework can be instantiated successfully with different logics. We
investigate first-order logic and several of its decidable fragments with a
particular focus on different dialects of description logic (DL). We also show,
by using bisimulation relations, that some DL fragments cannot be used due to
their lack of expressive power.


  In this paper we give a geometric construction of the quantum group Ut(G)
using Nakajima categories, which were developed in [29]. Our methods allow us
to establish a direct connection between the algebraic realization of the
quantum group as Hall algebra by Bridgeland [1] and its geometric counterpart
by Qin [24].


  We consider the relationship between hyperbolic cone-manifold structures on
surfaces, and algebraic representations of the fundamental group into a group
of isometries. A hyperbolic cone-manifold structure on a surface, with all
interior cone angles being integer multiples of $2\pi$, determines a holonomy
representation of the fundamental group. We ask, conversely, when a
representation of the fundamental group is the holonomy of a hyperbolic
cone-manifold structure. In this paper we build upon previous work with
punctured tori to prove results for higher genus surfaces. Our techniques
construct fundamental domains for hyperbolic cone-manifold structures, from the
geometry of a representation. Central to these techniques are the Euler class
of a representation, the group $\widetilde{PSL_2\R}$, the twist of hyperbolic
isometries, and character varieties. We consider the action of the outer
automorphism and related groups on the character variety, which is
measure-preserving with respect to a natural measure derived from its
symplectic structure, and ergodic in certain regions. Under various hypotheses,
we almost surely or surely obtain a hyperbolic cone-manifold structure with
prescribed holonomy.


  We calculate $K\to\pi\pi$ matrix elements using periodic boundary conditions
as an independent calculation from our previous study with G-parity boundary
conditions. We present our preliminary results for $K\to\pi\pi$ three-point
functions and matrix elements on a $24^3, a^{-1} = 1$~GeV, $2+1$-flavor
M\"obius DWF ensemble at physical pion and kaon masses generated by the RBC and
UKQCD collaborations and discuss the prospect for high-precision computation of
$\varepsilon'$ with periodic boundary conditions.


  Natural systems with emergent behaviors often organize along low-dimensional
subsets of high-dimensional spaces. For example, despite the tens of thousands
of genes in the human genome, the principled study of genomics is fruitful
because biological processes rely on coordinated organization that results in
lower dimensional phenotypes. To uncover this organization, many nonlinear
dimensionality reduction techniques have successfully embedded high-dimensional
data into low-dimensional spaces by preserving local similarities between data
points. However, the nonlinearities in these methods allow for too much
curvature to preserve general trends across multiple non-neighboring data
clusters, thereby limiting their interpretability and generalizability to
out-of-distribution data. Here, we address both of these limitations by
regularizing the curvature of manifolds generated by variational autoencoders,
a process we coin ``$\Gamma$-VAE''. We demonstrate its utility using two
example data sets: bulk RNA-seq from the The Cancer Genome Atlas (TCGA) and the
Genotype Tissue Expression (GTEx); and single cell RNA-seq from a lineage
tracing experiment in hematopoietic stem cell differentiation. We find that the
resulting regularized manifolds identify mesoscale structure associated with
different cancer cell types, and accurately re-embed tissues from completely
unseen, out-of distribution cancers as if they were originally trained on them.
Finally, we show that preserving long-range relationships to differentiated
cells separates undifferentiated cells -- which have not yet specialized --
according to their eventual fate. Broadly, we anticipate that regularizing the
curvature of generative models will enable more consistent, predictive, and
generalizable models in any high-dimensional system with emergent
low-dimensional behavior.


  While medical images such as computed tomography (CT) are stored in DICOM
format in hospital PACS, it is still quite routine in many countries to print a
film as a transferable medium for the purposes of self-storage and secondary
consultation. Also, with the ubiquitousness of mobile phone cameras, it is
quite common to take pictures of the CT films, which unfortunately suffer from
geometric deformation and illumination variation. In this work, we study the
problem of recovering a CT film, which marks the first attempt in the
literature, to the best of our knowledge. We start with building a large-scale
head CT film database CTFilm20K, consisting of approximately 20,000 pictures,
using the widely used computer graphics software Blender. We also record all
accompanying information related to the geometric deformation (such as 3D
coordinate, depth, normal, and UV maps) and illumination variation (such as
albedo map). Then we propose a deep framework to disentangle geometric
deformation and illumination variation using the multiple maps extracted from
the CT films to collaboratively guide the recovery process. Extensive
experiments on simulated and real images demonstrate the superiority of our
approach over the previous approaches. We plan to open source the simulated
images and deep models for promoting the research on CT film recovery
(https://anonymous.4open.science/r/e6b1f6e3-9b36-423f-a225-55b7d0b55523/).


  We obtain sufficient conditions of stochastic equivalence of Gaussian random
fields with special covariance function. These results generalize Doob's
transformation (condition of stochastic equivalence of a Gaussian and a Wiener
processes) to the case of random fields (condition of stochastic equivalence of
a Gaussian process and a Brownian sheet). We look at the problem of finding the
distribution of supremum of a Brownian sheet on a set with a dimension lower
than the dimension of the field. We consider the probability of a Brownian
sheet with a certain drift to attain zero level. The obtained results can
significantly simplify the problem of finding distributions of functionals of a
Brownian sheet by reducing it to the problem of finding distributions on
parallelepipeds with dimension lower than the dimension of the field. We
consider examples that verify validity of the obtained theorem by modeling
corresponding fields and comparing empirical probabilities with theoretical
ones.


  We investigate the relationship of resting-state fMRI functional connectivity
estimated over long periods of time with time-varying functional connectivity
estimated over shorter time intervals. We show that using Pearson's correlation
to estimate functional connectivity implies that the range of fluctuations of
functional connections over short time scales is subject to statistical
constraints imposed by their connectivity strength over longer scales. We
present a method for estimating time-varying functional connectivity that is
designed to mitigate this issue and allows us to identify episodes where
functional connections are unexpectedly strong or weak. We apply this method to
data recorded from $N=80$ participants, and show that the number of
unexpectedly strong/weak connections fluctuates over time, and that these
variations coincide with intermittent periods of high and low modularity in
time-varying functional connectivity. We also find that during periods of
relative quiescence regions associated with default mode network tend to join
communities with attentional, control, and primary sensory systems. In
contrast, during periods where many connections are unexpectedly strong/weak,
default mode regions dissociate and form distinct modules. Finally, we go on to
show that, while all functional connections can at times manifest stronger
(more positively correlated) or weaker (more negatively correlated) than
expected, a small number of connections, mostly within the visual and
somatomotor networks, do so a disproportional number of times. Our statistical
approach allows the detection of functional connections that fluctuate more or
less than expected based on their long-time averages and may be of use in
future studies characterizing the spatio-temporal patterns of time-varying
functional connectivity


  We consider spatially separated qubits coupled to a thermal bosonic field
that causes pure dephasing. Our focus is on the entanglement of two Bell states
which for vanishing separation are known as robust and fragile entangled
states. The reduced two-qubit dynamics is solved exactly and explicitly. Our
results allow us to gain information about the robustness of two-qubit
decoherence-free subspaces with respect to physical parameters such as
temperature, qubit-bath coupling strength and spatial separation of the qubits.
Moreover, we clarify the relation between single-qubit coherence and two-qubit
entanglement and identify parameter regimes in which the terms robust and
fragile are no longer appropriate.


  Having a far detector in Korea for the J-PARC neutrino beam in addition to
one at Kamioka has been shown to be a powerful way to lift neutrino parameter
($\Delta m^2$ and mixing angles) degeneracies. In this talk, I report the
sensitivity of the same experimental setup to nonstandard neutrino physics,
such as quantum decoherence, violation of Lorentz symmetry (with/without CPT
invariance), and nonstandard neutrino interactions with matter. In many cases,
two detector setup is better than one detector setup at SK. This observation
makes another support for the two detector setup.


  Macroeconomic data are crucial for monitoring countries' performance and
driving policy. However, traditional data acquisition processes are slow,
subject to delays, and performed at a low frequency. We address this
'ragged-edge' problem with a two-step framework. The first step is a supervised
learning model predicting observed low-frequency figures. We propose a
neural-network-based nowcasting model that exploits mixed-frequency,
high-dimensional data. The second step uses the elasticities derived from the
previous step to interpolate unobserved high-frequency figures. We apply our
method to nowcast countries' yearly research and development (R&D) expenditure
series. These series are collected through infrequent surveys, making them
ideal candidates for this task. We exploit a range of predictors, chiefly
Internet search volume data, and document the relevance of these data in
improving out-of-sample predictions. Furthermore, we leverage the high
frequency of our data to derive monthly estimates of R&D expenditures, which
are currently unobserved. We compare our results with those obtained from the
classical regression-based and the sparse temporal disaggregation methods.
Finally, we validate our results by reporting a strong correlation with monthly
R&D employment data.


  We propose a supersymmetric extra U(1) model, which can generate small
neutrino masses and necessary $\mu$ terms, simultaneously. Fields including
quarks and leptons are embedded in three ${\bf 27}$s of $E_6$ in a different
way among generations. The model has an extra U(1) gauge symmetry at TeV
regions, which has discriminating features from other models studied
previously. Since a neutrino mass matrix induced in the model has a constrained
texture with limited parameters, it can give a prediction. If we impose
neutrino oscillation data to fix those parameters, a value of $\sin\theta_{13}$
can be determined. We also discuss several phenomenological features which are
discriminated from the ones of the MSSM.


  In recent decades, advancements in motion learning have enabled robots to
acquire new skills and adapt to unseen conditions in both structured and
unstructured environments. In practice, motion learning methods capture
relevant patterns and adjust them to new conditions such as dynamic obstacle
avoidance or variable targets. In this paper, we investigate the robot motion
learning paradigm from a Riemannian manifold perspective. We argue that
Riemannian manifolds may be learned via human demonstrations in which geodesics
are natural motion skills. The geodesics are generated using a learned
Riemannian metric produced by our novel variational autoencoder (VAE), which is
especially intended to recover full-pose end-effector states and joint space
configurations. In addition, we propose a technique for facilitating on-the-fly
end-effector/multiple-limb obstacle avoidance by reshaping the learned manifold
using an obstacle-aware ambient metric. The motion generated using these
geodesics may naturally result in multiple-solution tasks that have not been
explicitly demonstrated previously. We extensively tested our approach in task
space and joint space scenarios using a 7-DoF robotic manipulator. We
demonstrate that our method is capable of learning and generating motion skills
based on complicated motion patterns demonstrated by a human operator.
Additionally, we assess several obstacle avoidance strategies and generate
trajectories in multiple-mode settings.


  Document ranking experiments should be repeatable. However, the interaction
between multi-threaded indexing and score ties during retrieval may yield
non-deterministic rankings, making repeatability not as trivial as one might
imagine. In the context of the open-source Lucene search engine, score ties are
broken by internal document ids, which are assigned at index time. Due to
multi-threaded indexing, which makes experimentation with large modern document
collections practical, internal document ids are not assigned consistently
between different index instances of the same collection, and thus score ties
are broken unpredictably. This short paper examines the effectiveness impact of
such score ties, quantifying the variability that can be attributed to this
phenomenon. The obvious solution to this non-determinism and to ensure
repeatable document ranking is to break score ties using external collection
document ids. This approach, however, comes with measurable efficiency costs
due to the necessity of consulting external identifiers during query
evaluation.


  The parameters of the quantum circuit in a variational quantum algorithm
induce a landscape that contains the relevant information regarding its
optimization hardness. In this work we investigate such landscapes through the
lens of information content, a measure of the variability between points in
parameter space. Our major contribution connects the information content to the
average norm of the gradient, for which we provide robust analytical bounds on
its estimators. This result holds for any (classical or quantum) variational
landscape. We validate the analytical understating by numerically studying the
scaling of the gradient in an instance of the barren plateau problem. In such
instance we are able to estimate the scaling pre-factors in the gradient. Our
work provides a new way to analyze variational quantum algorithms in a
data-driven fashion well-suited for near-term quantum computers.


  In this work, we propose a convenient framework for infinite-dimensional
analysis (including real and complex analysis), in which differentiation (in
some weak sense) and integration operations can be easily performed,
integration by parts can be conveniently established under rather weak
conditions, and especially some nice properties and consequences obtained by
convolution in Euclidean spaces can be extended to infinite-dimensional spaces
in some sense by taking the limit. Compared to the existing tools in
infinite-dimensional analysis, our framework enjoys more convenient and clearer
links with that of finite dimensions, and hence it is more suitable for
computation and studying some analysis problems in infinite-dimensional spaces.