  We look for possible evidence of a non-minimal coupling (NMC) between dark
matter (DM) and gravity using data from the X-COP compilation of galaxy
clusters. We consider a theoretically motivated NMC that may dynamically arise
from the collective behavior of the coarse-grained DM field (e.g., via
Bose-Einstein condensation) with averaging/coherence length
$\L_{\mathrm{nmc}}$. In the Newtonian limit, the NMC modifies the Poisson
equation by a term $\L_{\mathrm{nmc}}^2 \nabla^2 \rho$ proportional to the
Laplacian of the DM density itself. We show that this term when acting as a
perturbation over the standard Navarro-Frenk-White (NFW) profile of cold DM
particles, can yield DM halo density profiles capable of correctly fitting
galaxy clusters' pressure profiles with an accuracy comparable and in some
cases even better than the standard cold DM NFW profile. We also show that the
observed relation between the non-minimal coupling length scale and the virial
mass found in Gandolfi et al., 2022 for Late Type Galaxies is consistent with
the relation we find in the current work, suggesting that the previously
determined power-law scaling law holds up to galaxy cluster mass scales.


  A Monte Carlo method is used to calculate the profiles and the polarization
of the Raman scattered O VI lines(lambda lambda 6827,7088) in symbiotic stars.
A point-like isotropic UV radiation source is assumed and a simple spherical
wind model is adopted for the kinematics of the scattering material from the
cool giant. We first investigate the case where the incident line photons are
described by a Gaussian profile having a width of 10^4 K. We subsequently
investigate the effects of the extended ionized region and non-spherical wind
models including a disk-type wind and a bipolar wind. The cases where the
emissin source is described by non-Gaussian profiles are briefly studied.
Finally, as an additional component for the kinematics of symbiotic stars the
orbital motion of the hot component around the cool giant is included and the
effect on the spectropolarimetry is investigated. In this case the polarization
direction changes around the red part of the Raman-scattered emission lines,
when the observer's line of sight is perpendicular to the orbital plane, and no
such effect is seen when the line of sight lies in the orbital plane.
Furthermore, complex peak structures are seen in the degree of polarization and
polarized flux, which have often been observed in several symbiotic systems
including RR Tel. Brief observational consequences and preditions are discussed
in relation to the present and future spectropolarimetry for symbiotic stars.
It is concluded that spectropolarimetry may provide a powerful diagnostic of
the physical conditions of symbiotic stars.


  We study generalized Horn-Kapranov rational parametrizations of inhomogeneous
sparse discriminants from both a theoretical and an algorithmic perspective. We
show that all these parametrizations are birational and prove some results on
the corresponding implicit equations. We also propose a combinatorial algorithm
to compute the degree of inhomogeneous discriminantal surfaces associated to
uniform matrices.


  A Supersymmetric Grand unified model is constructed based on SO(10)xSO(10)
symmetry in which new types of Yukawa matrices couple standard and exotic
fermions. Evolution of these couplings from the Grand Unified scale to the
electroweak scale causes some of them to be driven to their fixed points. This
solves the supersymmetric alignment problem and ensures that there are no
observable flavor changing neutral currents mediated by supersymmetric
particles. Fermion hierarchy and neutrino mixing constraints are automatically
satisfied in this formalism.


  The human brain is constantly processing and integrating information in order
to make decisions and interact with the world, for tasks from recognizing a
familiar face to playing a game of tennis. These complex cognitive processes
require communication between large populations of neurons. The non-invasive
neuroimaging methods of electroencephalography (EEG) and magnetoencephalography
(MEG) provide population measures of neural activity with millisecond precision
that allow us to study the temporal dynamics of cognitive processes. However,
multi-sensor M/EEG data is inherently high dimensional, making it difficult to
parse important signal from noise. Multivariate pattern analysis (MVPA) or
"decoding" methods offer vast potential for understanding high-dimensional
M/EEG neural data. MVPA can be used to distinguish between different conditions
and map the time courses of various neural processes, from basic sensory
processing to high-level cognitive processes. In this chapter, we discuss the
practical aspects of performing decoding analyses on M/EEG data as well as the
limitations of the method, and then we discuss some applications for
understanding representational dynamics in the human brain.


  In this paper we present the first version of ganX -- generate artificially
new XRF, a Python library to generate X-ray fluorescence Macro maps (MA-XRF)
from a coloured RGB image. To do that, a Monte Carlo method is used, where each
MA-XRF pixel signal is sampled out of an XRF signal probability function. Such
probability function is computed using a database of couples (pigment
characteristic XRF signal, RGB), by a weighted sum of such pigment XRF signal
by proximity of the image RGB to the pigment characteristic RGB. The library is
released to PyPi and the code is available open source on GitHub.


  We apply a novel adaptive mesh refinement code, AMRVAC, to numerically
investigate the various evolutionary phases in the interaction of a
relativistic shell with its surrounding cold Interstellar Medium (ISM). We do
this for both 1D isotropic as well as full 2D jetlike fireball models. This is
relevant for Gamma Ray Bursts, and we demonstrate that, thanks to the AMR
strategy, we resolve the internal structure of the shocked shell-ISM matter,
which will leave its imprint on the GRB afterglow. We determine the
deceleration from an initial Lorentz factor $\gamma=100$ up to the almost
Newtonian $\gamma\sim{\cal O}(2)$ phase of the flow. We present axisymmetric 2D
shell evolutions, with the 2D extent characterized by their initial opening
angle. In such jetlike GRB models, we discuss the differences with the 1D
isotropic GRB equivalents. These are mainly due to thermally induced sideways
expansions of both the shocked shell and shocked ISM regions. We found that the
propagating 2D ultrarelativistic shell does not accrete all the surrounding
medium located within its initial opening angle. Part of this ISM matter gets
pushed away laterally and forms a wide bow-shock configuration with swirling
flow patterns trailing the thin shell. The resulting shell deceleration is
quite different from that found in isotropic GRB models. As long as the lateral
shell expansion is merely due to ballistic spreading of the shell, isotropic
and 2D models agree perfectly. As thermally induced expansions eventually lead
to significantly higher lateral speeds, the 2D shell interacts with comparably
more ISM matter and decelerates earlier than its isotropic counterpart.


  We calculate the cross section for hadroproduction of a pair of heavy quarks
in a 3S1 color-singlet state at next-to-leading order in QCD. This corresponds
to the leading contribution in the NRQCD expansion for J/psi and Upsilon
production. The higher-order corrections have a large impact on the p_T
distributions, enhancing the production at high p_T both at the Tevatron and at
the LHC. The total decay rate of a 3S1 into hadrons at NLO is also computed,
confirming for the first time the result obtained by Mackenzie and Lepage in
1981.


  In this letter we present a new analysis of the torques acting on the
accreting millisecond X-ray pulsars SAX J1808.4-365 and XTE J1814-338, and show
how our results can be used to constrain theoretical models of the spin
evolution. In particular we find upper limits on any spin-up/down phase of XTE
J1814-338 of $|\dot{\nu}|\lesssim\,1.5\times\,10^{-14}$ Hz/s at 95% confidence
level. We examine the possibility that a gravitational wave torque may be
acting in these systems and suggest that a more likely scenario is that both
systems are close to spin equilibrium, as set by the disc/magnetosphere
interaction.


  A measurement of the electroweak (EW) production of two jets in association
with a Z boson in proton-proton collisions at $\sqrt{s} = $ 13 TeV is
presented, based on data recorded in 2016 by the CMS experiment at the LHC
corresponding to an integrated luminosity of 35.9 fb$^{-1}$. The measurement is
performed in the $\ell\ell\mathrm{jj}$ final state with $\ell$ including
electrons and muons, and the jets j corresponding to the quarks produced in the
hard interaction. The measured cross section in a kinematic region defined by
invariant masses $m_{\ell\ell} > $ 50 GeV, $m_{\mathrm{jj}} > $ 120 GeV, and
transverse momenta $p_{\mathrm{T j}} > $ 25 GeV is
$\sigma_\mathrm{EW}(\ell\ell\mathrm{jj})= $ 534 $\pm$ 20 (stat) $\pm$ 57 (syst)
fb, in agreement with leading-order standard model predictions. The final state
is also used to perform a search for anomalous trilinear gauge couplings. No
evidence is found and limits on anomalous trilinear gauge couplings associated
with dimension-six operators are given in the framework of an effective field
theory. The corresponding 95% confidence level intervals are $-2.6 <
c_{WWW}/\Lambda^2 < 2.6 $ TeV$^{-2}$ and $-8.4 < c_{W}/\Lambda^2 < 10.1 $
TeV$^{-2}$. The additional jet activity of events in a signal-enriched region
is also studied, and the measurements are in agreement with predictions.


  We study the nonmesonic weak decay (NMWD) $\Lambda N \to nN$ of the
${}^{12}_{\Lambda} C$ hypernucleus induced by the nucleon $N=n,p$ with
transition rate $\Gamma_N$. The nuclear process is described by the interplay
of two models; one describing the NMWD of hyperon $\Lambda$ in the nuclear
environment, and the other taking into account the Final State Interaction
(FSI) of the two outgoing nucleons with the residual nucleus. The first one is
done in the framework of the Independent-Particle Shell-Model (IPSM), with the
decay dynamics represented by the exchange of $\pi+ \eta+ K+\rho+\omega+K^*$
mesons with usual parametrization. For the second one is used a time dependent
multicollisional intranuclear cascade schema (implemented in the CRISP code -
Collaboration Rio-S\~ao Paulo). The results obtained for inclusive and
exclusive kinetic energy spectra, and the angular correlation are compared with
recent data from KEK and FINUDA experiments. The calculated ratio $(\Gamma_n /
\Gamma_p)^{\partial{\tiny FSI}}$, between the numbers of emitted back-to-back
$nn$ and $np$ pairs, is in good agreement with the experimental data.


  The classification of simple biset functors is known, but the evaluation of a
simple biset functor at a finite group G may be zero. We investigate various
situations where this happens, as well as cases where this does not occur. We
also prove a closed formula for such an evaluation under some restrictive
conditions on G.


  The dynamics of a stabilized radion in the Randall-Sundrum model (RS) with
two branes is investigated, and the effects of the radion on electroweak
precision observables are evaluated. The radius is assumed to be stabilized
using a bulk scalar field as suggested by Goldberger and Wise. First the mass
and the wavefunction of the radion is determined including the backreaction of
the bulk stabilization field on the metric, giving a typical radion mass of
order the weak scale. This is demonstrated by a perturbative computation of the
radion wavefunction. A consequence of the background configuration for the
scalar field is that after including the backreaction the Kaluza-Klein (KK)
states of the bulk scalars couple directly to the Standard Model fields on the
TeV brane. Some cosmological implications are discussed, and in particular it
is found that the shift in the radion at late times is in agreement with the
four-dimensional effective theory result. The effect of the radion on the
oblique parameters is evaluated using an effective theory approach. In the
absence of a curvature-scalar Higgs mixing operator, these corrections are
small and give a negative contribution to S. In the presence of such a mixing
operator, however, the corrections can be sizable due to the modified Higgs and
radion couplings.


  We investigated domain nucleation process in epitaxial Pb(Zr,Ti)O3 capacitors
under a modified piezoresponse force microscope. We obtained domain evolution
images during polarization switching process and observed that domain
nucleation occurs at particular sites. This inhomogeneous nucleation process
should play an important role in an early stage of switching and under a high
electric field. We found that the number of nuclei is linearly proportional to
log(switching time), suggesting a broad distribution of activation energies for
nucleation. The nucleation sites for a positive bias differ from those for a
negative bias, indicating that most nucleation sites are located at
ferroelectric/electrode interfaces.


  Let $X$ be a normal projective variety admitting a polarized endomorphism
$f$, i.e., $f^*H\sim qH$ for some ample divisor $H$ and integer $q>1$. It was
conjectured by Broustet and Gongyo that $X$ is of Calabi-Yau type, i.e.,
$(X,\Delta)$ is lc for some effective $\mathbb{Q}$-divisor such that
$K_X+\Delta\sim_{\mathbb{Q}} 0$. In this paper, we establish a general
guideline based on the equivariant minimal model program and the canonical
bundle formula. In this way, we prove the conjecture when $X$ is a smooth
projective threefold.


  Adopting thin Si wafers for PV reduces capital expenditure (capex) and
manufacturing cost, and accelerates the growth of PV manufacturing. There are
two key questions about thin Si today: (a) how much can we still benefit
economically from thinning wafers? (b) what are the technological challenges to
transition to thin wafers? In this work, we re-evaluate the benefits and
challenges of thin Si for current and future PV modules using a comprehensive
techno-economic framework that couples device simulation, bottom-up cost
modeling, and a cash-flow growth model. When adopting an advanced technology
concept that features sufficiently good surface passivation, similarly high
efficiencies are achievable for 50-um wafers as for 160-um ones. We then
quantify the economic benefits for thin Si wafers in terms of poly-Si-to-module
manufacturing capex, module cost, and levelized cost of electricity (LCOE) for
utility PV systems. Particularly, LCOE favors thinner wafers for all
investigated device architectures, and can potentially be reduced by more than
5% from the value of 160-um wafers. With further improvements in module
efficiency, an advanced thin-wafer device concept with 50-um wafers could
reduce manufacturing capex by 48%, module cost by 28%, and LCOE by 24%.
Furthermore, we apply a sustainable growth model to investigate PV deployment
scenarios in 2030. It is found that the state-of-the-art industry concept could
not achieve the climate targets even with very aggressive financial scenarios,
therefore the capex reduction benefit of thin wafers is needed to facilitate
more rapid PV growth. Lastly, we discuss the remaining technological challenges
and areas for innovation to enable high-yield manufacturing of high-efficiency
PV modules with thin Si wafers.


  Autodock is a widely used molecular modeling tool which predicts how small
molecules bind to a receptor of known 3D structure. The current version of
AutoDock uses meta-heuristic algorithms in combination with local search
methods for doing the conformation search. Appropriate settings of
hyperparameters in these algorithms are important, particularly for novice
users who often find it hard to identify the best configuration. In this work,
we design a surrogate based multi-objective algorithm to help such users by
automatically tuning hyperparameter settings. The proposed method iteratively
uses a radial basis function model and non-dominated sorting to evaluate the
sampled configurations during the search phase. Our experimental results using
Autodock show that the introduced component is practical and effective.


  The equations governing weakly nonlinear modulations of $N$-dimensional
lattices are considered using a quasi-discrete multiple-scale approach. It is
found that the evolution of a short wave packet for a lattice system with cubic
and quartic interatomic potentials is governed by generalized Davey-Stewartson
(GDS) equations, which include mean motion induced by the oscillatory wave
packet through cubic interatomic interaction. The GDS equations derived here
are more general than those known in the theory of water waves because of the
anisotropy inherent in lattices. Generalized Kadomtsev-Petviashvili equations
describing the evolution of long wavelength acoustic modes in two and three
dimensional lattices are also presented. Then the modulational instability of a
$N$-dimensional Stokes lattice wave is discussed based on the $N$-dimensional
GDS equations obtained. Finally, the one- and two-soliton solutions of
two-dimensional GDS equations are provided by means of Hirota's bilinear
transformation method.


  The Hamiltonian for a particle constrained to motion near a toroidal helix
with loops of arbitrary eccentricity is developed. The resulting three
dimensional Schr\"odinger equation is reduced to a one dimensional effective
equation inclusive of curvature effects. A basis set is employed to find
low-lying eigenfunctions of the helix. Toroidal moments corresponding to the
individual eigenfunctions are calculated. The dependence of the toroidal
moments on the eccentricity of the loops is reported. Unlike the classical
case, the moments strongly depend on the details of loop eccentricity.


  Understanding how the adult human brain learns novel categories is an
important problem in neuroscience. Drift-diffusion models are popular in such
contexts for their ability to mimic the underlying neural mechanisms. One such
model for gradual longitudinal learning was recently developed by Paulon et al.
(2021). Fitting conventional drift-diffusion models, however, requires data on
both category responses and associated response times. In practice, category
response accuracies are often the only reliable measure recorded by behavioral
scientists to describe human learning. However, To our knowledge,
drift-diffusion models for such scenarios have never been considered in the
literature. To address this gap, in this article, we build carefully on Paulon
et al. (2021), but now with latent response times integrated out, to derive a
novel biologically interpretable class of `inverse-probit' categorical
probability models for observed categories alone. However, this new marginal
model presents significant identifiability and inferential challenges not
encountered originally for the joint model by Paulon et al. (2021). We address
these new challenges using a novel projection-based approach with a
symmetry-preserving identifiability constraint that allows us to work with
conjugate priors in an unconstrained space. We adapt the model for group and
individual-level inference in longitudinal settings. Building again on the
model's latent variable representation, we design an efficient Markov chain
Monte Carlo algorithm for posterior computation. We evaluate the empirical
performance of the method through simulation experiments. The practical
efficacy of the method is illustrated in applications to longitudinal tone
learning studies.


  We establish a generalisation of the fundamental state convertibility theorem
in quantum information to the context of bipartite quantum systems modelled by
commuting semi-finite von Neumann algebras. Namely, we establish a
generalisation to this setting of Nielsen's theorem on the convertibility of
quantum states under local operations and classical communication (LOCC)
schemes. Along the way, we introduce an appropriate generalisation of LOCC
operations and connect the resulting notion of approximate convertibility to
the theory of singular numbers and majorisation in von Neumann algebras. As an
application of our result in the setting of $II_1$-factors, we show that the
entropy of the singular value distribution relative to the unique tracial state
is an entanglement monotone in the sense of Vidal, thus yielding a new way to
quantify entanglement in that context. Building on previous work in the
infinite-dimensional setting, we show that trace vectors play the role of
maximally entangled states for general $II_1$-factors. Examples are drawn from
infinite spin chains, quasi-free representations of the CAR, and discretised
versions of the CCR.


  We have studied the effects of Lorentz-violation in the Bose-Einstein
condensation (BEC) of an ideal boson gas, by assessing both the nonrelativistic
and ultrarelativistic limits. Our model describes a massive complex scalar
field coupled to a CPT-even and Lorentz-violating background. We irst analyze
the nonrelativistic case, at this level by using experimental data, we obtain
upper-bounds for some LIV parameters. In the sequel, we have constructed the
partition function for the relativistic ideal boson gas which to be able of a
consistent description requires the imposition of severe restrictions on some
LIV coefficients. In both cases, we have demonstrated that the LIV
contributions are contained in an overall factor, which multiplies almost all
thermodynamical properties. An exception is the fraction of the condensed
particles.


  Netsukuku is a P2P network system designed to handle a large number of nodes
with minimal CPU and memory resources. It can be easily used to build a
worldwide distributed, anonymous and not controlled network, separated from the
Internet, without the support of any servers, ISPs or authority controls. In
this document, we give a generic and non technical description of the Netsukuku
network, emphasizing its main ideas and features.


  3D object detection has achieved significant performance in many fields,
e.g., robotics system, autonomous driving, and augmented reality. However, most
existing methods could cause catastrophic forgetting of old classes when
performing on the class-incremental scenarios. Meanwhile, the current
class-incremental 3D object detection methods neglect the relationships between
the object localization information and category semantic information and
assume all the knowledge of old model is reliable. To address the above
challenge, we present a novel Incremental 3D Object Detection framework with
the guidance of prompting, i.e., I3DOD. Specifically, we propose a task-shared
prompts mechanism to learn the matching relationships between the object
localization information and category semantic information. After training on
the current task, these prompts will be stored in our prompt pool, and perform
the relationship of old classes in the next task. Moreover, we design a
reliable distillation strategy to transfer knowledge from two aspects: a
reliable dynamic distillation is developed to filter out the negative knowledge
and transfer the reliable 3D knowledge to new detection model; the relation
feature is proposed to capture the responses relation in feature space and
protect plasticity of the model when learning novel 3D classes. To the end, we
conduct comprehensive experiments on two benchmark datasets and our method
outperforms the state-of-the-art object detection methods by 0.6% - 2.7% in
terms of<EMAIL_ADDRESS>


  We provide an alternative interpretation of recently published experimental
results that were represented as demonstrating entanglement between two
macroscopic quantum Josephson oscillators. We model the experimental system
using the well-established classical equivalent circuit of a resistively and
capacitively shunted junction. Simulation results are used to generate the
corresponding density matrix, which is strikingly similar to the previously
published matrix that has been declared to be an unambiguous demonstration of
quantum entanglement. Since our data are generated by a classical model, we
therefore submit that state tomography cannot be used to determine absolutely
whether or not quantum entanglement has taken place. Analytical arguments are
given for why the classical analysis provides an adequate explanation of the
experimental results.


  A stable, reliable, and controllable orbit lock system is crucial to an
electron (or ion) accelerator because the beam orbit and beam energy
instability strongly affect the quality of the beam delivered to experimental
halls. Currently, when the orbit lock system fails operators must manually
intervene. This paper develops a Machine Learning based fault detection
methodology to identify orbit lock anomalies and notify accelerator operations
staff of the off-normal behavior. Our method is unsupervised, so it does not
require labeled data. It uses Long-Short Memory Networks (LSTM) Auto Encoder to
capture normal patterns and predict future values of monitoring sensors in the
orbit lock system. Anomalies are detected when the prediction error exceeds a
threshold. We conducted experiments using monitoring data from Jefferson Lab's
Continuous Electron Beam Accelerator Facility (CEBAF). The results are
promising: the percentage of real anomalies identified by our solution is
68.6%-89.3% using monitoring data of a single component in the orbit lock
control system. The accuracy can be as high as 82%.


  We investigate the magnetic field dependence of the Hall and the bend
resistances for a ballistic Hall bar structure containing a pn-junction
sculptured from a bilayer of graphene. The electric response is obtained using
the billiard model and we investigate the cases of bilayer graphene with and
without a band gap. Two different conduction regimes are possible: $i$) both
sides of the junction have the same carrier type, and $ii$) one side of the
junction is n-type while the other one is p-type. The first case shows Hall
plateau-like features in the Hall resistance that fade away as the band gap
opens. The second case exhibits a bend resistance that is asymmetric in
magnetic field as a consequence of snake states along the pn-interface, where
the maximum is shifted away from zero magnetic field.


  Let R be a commutative local noetherian ring, and let L and L' be R-modules.
We investigate the properties of the functors Tor_i^R(L,-) and Ext^i_R(L,-).
For instance, we show the following:
  (a) if L is artinian and L' is noetherian, then Hom_R(L,L') has finite
length;
  (b) if L and L' are artinian, then the tensor product L \otimes_R L' has
finite length;
  (c) if L and L' are artinian, then Tor_i^R(L,L') is artinian, and
Ext^i_R(L,L') is noetherian over the completion \hat R; and
  (d) if L is artinian and L' is Matlis reflexive, then Ext^i_R(L,L'),
Ext^i_R(L',L), and Tor_i^R(L,L') are Matlis reflexive.
  Also, we study the vanishing behavior of these functors, and we include
computations demonstrating the sharpness of our results.


  Even if at the level of the non-relativistic limit of full QED, C is not a
symmetry, the limit of this operation does exist for the particular case when
the electromagnetic field is considered a classical external object coupled to
the Dirac field. This result extends the one obtained when fermions are
described by the Schroedinger-Pauli equation. We give the expressions for both
the C matrix and the $\hat{C}$ operator for galilean electrons and positrons
interacting with the external electromagnetic field. The result is relevant in
relation to recent experiments with antihydrogen.


  We generalize the Power-Zineau-Woolley transformation to obtain a canonical
Hamiltonian of cavity quantum electrodynamics for arbitrary geometry of
boundaries. This Hamiltonian is free from the A-square term and the
instantaneous Coulomb interaction between distinct atoms. The single-mode
models of cavity QED (Dicke, Tavis-Cummings, Jaynes-Cummings) are justified by
a term by term mapping to the proposed microscopic Hamiltonian. As one
straightforward consequence, the basis of no-go argumentations concerning the
Dicke phase transition with atoms in electromagnetic fields dissolves.


  Infectious diseases are a threat for human health with tremendous impact on
our society at large. The recent COVID-19 pandemic, caused by the SARS-CoV-2,
is the latest example of a highly infectious disease ravaging the world, since
late 2019. It is therefore imperative to develop efficient mathematical models,
able to substantially curb the damages of a pandemic by unveiling disease
spreading dynamics and symmetries. This will help inform (non)-pharmaceutical
prevention strategies. For the reasons above we wrote this report that goes at
the heart of mathematical modelling of infectious disease diffusion by
simultaneously investigating the underlying microscopic dynamics in terms of
percolation models, effective description via compartmental models and the
employment of temporal symmetries naturally encoded in the mathematical
language of critical phenomena. Our report reviews these approaches and
determines their common denominators, relevant for theoretical epidemiology and
its link to important concepts in theoretical physics. We show that the
different frameworks exhibit common features such as criticality and
self-similarity under time rescaling. These features are naturally encoded
within the unifying field theoretical approach. The latter leads to an
efficient description of the time evolution of the disease via a framework in
which (near) time-dilation invariance is explicitly realised. As important test
of the relevance of symmetries we show how to mathematically account for
observed phenomena such as multi-wave dynamics. The models presented here are
of immediate relevance for different realms of scientific enquiry from medical
applications to the understanding of human behaviour. Our review offers novel
perspectives on how to model, capture, organise and understand epidemiological
data and disease dynamics for modelling real-world phenomena.


  We study the effective versions of several notions related to incompleteness,
undecidability and inseparability along the lines of Pour-El's insights.
Firstly, we strengthen Pour-El's theorem on the equivalence between effective
essential incompleteness and effective inseparability. Secondly, we compare the
notions obtained by restricting that of effective essential incompleteness to
intensional finite extensions and extensional finite extensions. Finally, we
study the combination of effectiveness and hereditariness, and prove an adapted
version of Pour-El's result for this combination.


  Over the years the Artificial Intelligence (AI) community has produced
several datasets which have given the machine learning algorithms the
opportunity to learn various skills across various domains. However, a subclass
of these machine learning algorithms that aimed at learning logic programs,
namely the Inductive Logic Programming algorithms, have often failed at the
task due to the vastness of these datasets. This has impacted the usability of
knowledge representation and reasoning techniques in the development of AI
systems. In this research, we try to address this scalability issue for the
algorithms that learn answer set programs. We present a sound and complete
algorithm which takes the input in a slightly different manner and performs an
efficient and more user controlled search for a solution. We show via
experiments that our algorithm can learn from two popular datasets from machine
learning community, namely bAbl (a question answering dataset) and MNIST (a
dataset for handwritten digit recognition), which to the best of our knowledge
was not previously possible. The system is publicly available at
https://goo.gl/KdWAcV. This paper is under consideration for acceptance in
TPLP.


  Habitat selection models are used in ecology to link the distribution of
animals to environmental covariates, and identify habitats that are important
for conservation. The most widely used models of this type, resource selection
functions, assume independence between the observed locations of an animal.
This is unrealistic when location data display spatio-temporal autocorrelation.
Alternatively, step selection functions embed habitat selection in a model of
animal movement, to account for the autocorrelation. However, inferences from
step selection functions depend on the movement model, and they cannot readily
be used to predict long-term space use. We recently suggested that a Markov
chain Monte Carlo (MCMC) algorithm could define a step selection model with an
explicit stationary distribution: the target distribution. Here, we explain how
the likelihood of a MCMC step selection model is derived, and how maximum
likelihood estimation can be used for inference about parameters of movement
and habitat selection. We describe the local Gibbs sampler, a rejection-free
MCMC scheme designed to capture important features of real animal movement. The
sampler can be used as the basis for a flexible class of movement models, and
we derive the likelihood function for several important special cases. In a
simulation study, we verify that maximum likelihood estimation can be used to
recover all model parameters. We illustrate the application of the method with
data from a plains zebra.


  The Weyl semimetal exhibits various interesting physical phenomena because of
the Weyl points, i.e., linear band-crossings. We show by Floquet theory that a
linearly polarized light applied to a band insulator can induce controllable
Weyl points. In a tight-binding model, we classify different types of
photoinduced Weyl points that lead to a rich phase diagram characterized by the
Chern number defined on each momentum slices of the bulk states. Taking account
of the nonequilibrium electron distribution, we calculate and explain the
nonmonotonous anomalous Hall conductivity in terms of the light frequency
controlled shift of Weyl points' position, which also allows us to examine the
conductivity's dependence on the driving electric field.


  The influence of Deep Learning on image identification and natural language
processing has attracted enormous attention globally. The convolution neural
network that can learn without prior extraction of features fits well in
response to the rapid iteration of Android malware. The traditional solution
for detecting Android malware requires continuous learning through
pre-extracted features to maintain high performance of identifying the malware.
In order to reduce the manpower of feature engineering prior to the condition
of not to extract pre-selected features, we have developed a coloR-inspired
convolutional neuRal networks (CNN)-based AndroiD malware Detection (R2-D2)
system. The system can convert the bytecode of classes.dex from Android archive
file to rgb color code and store it as a color image with fixed size. The color
image is input to the convolutional neural network for automatic feature
extraction and training. The data was collected from Jan. 2017 to Aug 2017.
During the period of time, we have collected approximately 2 million of benign
and malicious Android apps for our experiments with the help from our research
partner Leopard Mobile Inc. Our experiment results demonstrate that the
proposed system has accurate security analysis on contracts. Furthermore, we
keep our research results and experiment materials on http://R2D2.TWMAN.ORG.


  We report the detection of 15 GHz radio continuum emission associated with
the classical Cepheid variable star delta Cephei based on observations with the
Karl G. Jansky Very Large Array. Our results constitute the first probable
detection of radio continuum emission from a classical Cepheid. We observed the
star at pulsation phase phi~0.43 (corresponding to the phase of maximum radius
and minimum temperature) during three pulsation cycles in late 2018 and
detected statistically significant emission (>5 sigma) during one of the three
epochs. The observed radio emission appears to be variable at a >~10% level on
timescales of days to weeks. We also present an upper limit on the 10 GHz flux
density at pulsation phase phi=0.31 from an observation in 2014. We discuss
possible mechanisms that may produce the observed 15 GHz emission, but cannot
make a conclusive identification from the present data. The emission does not
appear to be consistent with originating from a close-in, late-type dwarf
companion, although this scenario cannot yet be strictly excluded. Previous
X-ray observations have shown that delta Cephei undergoes periodic increases in
X-ray flux during pulsation phase phi~0.43. The lack of radio detection in two
out of three observing epochs at phi~0.43 suggests that either the radio
emission is not linked with a particular pulsation phase, or else that the
strength of the generated radio emission in each pulsation cycle is variable.


  We address the issue of safe optimal path planning under parametric
uncertainties using a novel regularizer that allows trading off optimality with
safety. The proposed regularizer leverages the notion that collisions may be
modeled as constraint violations in an optimal control setting in order to
produce open-loop trajectories with reduced risk of collisions. The risk of
constraint violation is evaluated using a state-dependent relevance function
and first-order variations in the constraint function with respect to
parametric variations. The approach is generic and can be adapted to any
optimal control formulation that deals with constraints under parametric
uncertainty. Simulations using a holonomic robot avoiding multiple dynamic
obstacles with uncertain velocities are used to demonstrate the effectiveness
of the proposed approach. Finally, we introduce the car vs. train problem to
emphasize the dependence of the resultant risk aversion behavior on the form of
the constraint function used to derive the regularizer.


  We report on CTIO/NEWFIRM and CTIO/OSIRIS photometric and spectroscopic
observations of 20 new X-ray (0.5-10 keV) emitters discovered in the Norma Arm
Region Chandra Survey (NARCS). NEWFIRM photometry was obtained to pinpoint the
near-infrared counterparts of NARCS sources, while OSIRIS spectroscopy was used
to help identify 20 sources with possible high mass X-ray binary properties. We
find that (1) two sources are WN8 Wolf-Rayet stars, maybe in colliding wind
binaries, part of the massive star cluster Mercer 81; (2) two are emission-line
stars, possibly in X-ray binaries, that exhibit near- and mid-infrared excesses
either due to free-free emission from the decretion discs of Be stars or warm
dust in the stellar winds of peculiar massive stars such as B[e] supergiants or
luminous blue variables; (3) one is a B8-A3 IV-V star that could be in a
quiescent high mass X-ray binary system; (4) two are cataclysmic variables
including one intermediate polar; (5) three may be neutron star symbiotic
binaries; (6) five are most likely white dwarf symbiotic binaries; and (7) five
exhibit properties more consistent with isolated giant/dwarf stars. The
possible detection of one to three high mass X-ray binaries is in good
agreement with our predictions. However, our study illustrates the difficulty
of clearly differentiating quiescent or intermediate X-ray luminosity systems
from isolated massive stars, which may lead to an underestimation of the number
of known high mass X-ray binaries.


  We present an overview of recent STAR results on strangeness production in
p+p and heavy-ion collisions at RHIC. In both Cu+Cu and Au+Au collisions we
show the centrality dependencies of bulk yield and mid-$p_{T}$ spectrum
measurements with new comparisons to theory. The latest $v_{2}$ results for
strange particles are presented and prospects for strangeness production in the
low energy scan program will be outlined. Finally, we report new measurements
of strangeness fragmentation functions for jets in p+p collisions.


  This paper presents a new and efficient nodal scheme for cell-centered
compressible flows in Lagrangian formulation. A single pressure and the
velocity at cell vertex are computed by the nodal solver in a least-squares
sense, and both variables are used to evaluate the numerical flux across the
cell interface. The resulting nodal velocity is also responsible for moving the
mesh. The accuracy and robustness of the proposed method is studied by several
numerical examples in the finite volume discretization, and compared with two
other nodal Riemann solvers. It is shown that its performance is comparable to
the latter two. Although the current paper mainly focuses on the first-order
finite volume (FV), its extension to higher order methods such as high-order
FV, discontinuous Galerkin (DG) or reconstructed discontinous Galerkin (rDG),
is quite straightforward. And this method has the capability of easily
extending to three dimensions.


  The JYFLTRAP double Penning trap at the Ion Guide Isotope Separator On-Line
(IGISOL) facility has been used to measure the atomic masses of 13 neutron-rich
rare-earth isotopes. Eight of the nuclides, $^{161}$Pm, $^{163}$Sm,
$^{164,165}$Eu, $^{167}$Gd, and $^{165,167,168}$Tb, were measured for the first
time. The systematics of the mass surface has been studied via one- and
two-neutron separation energies as well as neutron pairing-gap and shell-gap
energies. The proton-neutron pairing strength has also been investigated. The
impact of the new mass values on the astrophysical rapid neutron capture
process has been studied. The calculated abundance distribution results in a
better agreement with the solar abundance pattern near the top of the
rare-earth abundance peak at around $A\approx165$.


  The rapid growth in biological sequence data is revolutionizing our
understanding of genotypic diversity and challenging conventional approaches to
informatics. With the increasing availability of genomic data, traditional
bioinformatic tools require substantial computational time and the creation of
ever-larger indices each time a researcher seeks to gain insight from the data.
To address these challenges, we pre-computed important relationships between
biological entities spanning the Central Dogma of Molecular Biology and
captured this information in a relational database. The database can be queried
across hundreds of millions of entities and returns results in a fraction of
the time required by traditional methods. In this paper, we describe
\textit{IBM Functional Genomics Platform} (formerly known as OMXWare), a
comprehensive database relating genotype to phenotype for bacterial life.
Continually updated, IBM Functional Genomics Platform today contains data
derived from 200,000 curated, self-consistently assembled genomes. The database
stores functional data for over 68 million genes, 52 million proteins, and 239
million domains with associated biological activity annotations from Gene
Ontology, KEGG, MetaCyc, and Reactome. IBM Functional Genomics Platform maps
all of the many-to-many connections between each biological entity including
the originating genome, gene, protein, and protein domain. Various microbial
studies, from infectious disease to environmental health, can benefit from the
rich data and connections. We describe the data selection, the pipeline to
create and update the IBM Functional Genomics Platform, and the developer tools
(Python SDK and REST APIs) which allow researchers to efficiently study
microbial life at scale.


  We present a study of longitudinal thermal transport in the Kitaev spin model
on the honeycomb lattice, focusing on the role of anisotropic exchange to cover
both, gapless and gapped phases. Employing a complementary combination of exact
diagonalization on small systems and an average gauge configuration approach
for up to $\sim O(10^4)$ spinful sites, we report results for the dynamical
energy current auto-correlation function as well as the dc thermal conductivity
over a wide range of temperatures and exchange anisotropies. Despite a
pseudogap in the current correlation spectra, induced by emergent thermal gauge
disorder on any finite system, we find that in the thermodynamic limit, gapless
and gapful phases both feature normal dissipative transport, with a temperature
dependence crossing over from power law to exponentially activated behavior
upon gap opening.


  We present a novel and quite general analysis of the interaction of a
high-field chirped laser pulse and a relativistic electron, in which exquisite
control of the spectral brilliance of the upshifted Thomson-scattered photon is
shown to be possible. Normally, when Thomson scattering occurs at high field
strengths, there is ponderomotive line broadening in the scattered radiation.
This effect makes the bandwidth too large for some applications and reduces the
spectral brilliance. We show that such broadening can be corrected and
eliminated by suitable frequency modulation of the incident laser pulse.
Further, we suggest a practical realization of this compensation idea in terms
of a chirped-beam driven free electron laser oscillator configuration and show
that significant compensation can occur, even with the imperfect matching to be
expected in these conditions.


  In this work, a question is tackled concerning the formation of a
superconducting condensate in an earlier proposed model of "elastic jelly", in
which phonons of the ion system play the part of initiating ones. It was shown
that in distinction from the BCS theory, the momenta of forming electron pairs
are different from zero. This fact changes the pattern of the description of
the superconductivity phenomenon in the proposed model. First, the gap in the
one-electron spectrum appears due to the effect of a "mean field" on the energy
of electron state from the side of occupied states, the nearest neighbors over
the momenta grid. Second, the condensate is formed by electron states with
energies below that of the gap edge, this is why the Fermi-condensation arises
in the system. Third, anomalous expectation values are strictly equal to zero
in the proposed model.


  Computerized adaptive testing (CAT) is an interesting and promising approach
to testing human abilities. In our research we use Bayesian networks to create
a model of tested humans. We collected data from paper tests performed with
grammar school students. In this article we first provide the summary of data
used for our experiments. We propose several different Bayesian networks, which
we tested and compared by cross-validation. Interesting results were obtained
and are discussed in the paper. The analysis has brought a clearer view on the
model selection problem. Future research is outlined in the concluding part of
the paper.


  In this paper, we propose a generalization of Bakry-\'Emery's calculus which
allows us to formulate both Bakry-\'Emery and entropic curvature
simultaneously. This formulation represents both curvatures as an integral of
the Bochner formula against some measure. This leads to a natural optimality
criterion of measures, which we investigate in the Bakry-\'Emery setting.
Moreover, our approach leads also to a dimension parameter in the framework of
entropic curvature.
  We also present gradient estimate applications, that is, diameter estimates
for Markov chains with strictly positive entropic curvature and a spectral gap
estimate. The latter implies non-existence of non-negatively curved expanders.


  The recently proposed Russian doll BCS model provides a simple example of a
many body system whose renormalization group analysis reveals the existence of
limit cycles in the running coupling constants of the model. The model was
first studied using RG, mean field and numerical methods showing the Russian
doll scaling of the spectrum, E(n) ~ E0 exp(-l n}, where l is the RG period. In
this paper we use the recently discovered exact solution of this model to study
the low energy spectrum. We find that, in addition to the standard
quasiparticles, the electrons can bind into Cooper pairs that are different
from those forming the condensate and with higher energy. These excited Cooper
pairs can be described by a quantum number Q which appears in the Bethe ansatz
equation and has a RG interpretation.


  The potential energy surface (PES) of molecules with respect to their nuclear
positions is a primary tool in understanding chemical reactions from first
principles. However, obtaining this information is complicated by the fact that
sampling a large number of ground states over a high-dimensional PES can
require a vast number of state preparations. In this work, we propose using a
generative quantum machine learning model to prepare quantum states at
arbitrary points on the PES. The model is trained using quantum data consisting
of ground-state wavefunctions associated with different classical nuclear
coordinates. Our approach uses a classical neural network to convert the
nuclear coordinates of a molecule into quantum parameters of a variational
quantum circuit. The model is trained using a fidelity loss function to
optimize the neural network parameters. We show that gradient evaluation is
efficient and numerically demonstrate our method's ability to prepare
wavefunctions on the PES of hydrogen chains, water, and beryllium hydride. In
all cases, we find that a small number of training points are needed to achieve
very high overlap with the groundstates in practice. From a theoretical
perspective, we further prove limitations on these protocols by showing that if
we were able to learn across an avoided crossing using a small number of
samples, then we would be able to violate Grover's lower bound. Additionally,
we prove lower bounds on the amount of quantum data needed to learn a locally
optimal neural network function using arguments from quantum Fisher
information. This work further identifies that quantum chemistry can be an
important use case for quantum machine learning.


  Ultrasound (US) imaging is one of the most commonly used non-invasive imaging
techniques. However, US image acquisition requires simultaneous guidance of the
transducer and interpretation of images, which is a highly challenging task
that requires years of training. Despite many recent developments in
intra-examination US image analysis, the results are not easy to translate to a
clinical setting. We propose a generic framework to extract the US images and
superimpose the results of an analysis task, without any need for physical
connection or alteration to the US system. The proposed method captures the US
image by tracking the screen with a camera fixed at the sonographer's view
point and reformats the captured image to the right aspect ratio, in 87.66 +-
3.73ms on average.
  It is hypothesized that this would enable to input such retrieved image into
an image processing pipeline to extract information that can help improve the
examination. This information could eventually be projected back to the
sonographer's field of view in real time using, for example, an augmented
reality (AR) headset.


  We estimate the density laws of the Galactic stellar populations as a
function of absolute magnitude in a near-polar Galactic field. The density laws
are determined by the direct fit to photometric parallaxes from Vega photometry
in the ELAIS (l=84.27, b=+44.90; 6.571 deg^2) field both independently for each
population and simultaneously for all stellar populations. Stars have been
separated into different populations based on their spatial location. The thick
disc and halo best fit by an exponential. However, the thin disc best fits by
using a sech^2 law for stars at faint absolute magnitudes, (10,11], (11,12] and
(12,13], whereas an exponential law for stars at relatively bright absolute
magnitudes, (5,6], (6,7], (7,8], (8,9] and (9,10]. The scaleheights for the
sech^2 density laws are the equivalent exponential scaleheights. Galactic model
parameters are absolute magnitude dependent: The scaleheight for thin disc
decreases monotonically from stars at bright absolute magnitudes to stars at
faint absolute magnitudes in the range 363-163 pc, except the minimum H=211 pc
at (9,10] where sech density law fits better. Its local density is flat at
bright absolute magnitudes but it increases at faint absolute magnitudes. For
thick disc, the scaleheight is flat within the uncertainties. The local space
density of thick disc relative to the local space density for the thin disc is
almost flat at absolute magnitude intervals (5,6] and (6,7], 7.59 and 7.41%
respectively, whereas it decreases down to 3.31% at absolute magnitude interval
(7,8]. The axial ratio for the halo is e=0.60, 0.73 and 0.78 for the absolute
magnitude intervals (4,5], (5,6] and (6,7] respectively, and its local space
density relative to the local space density for the thin disc is 0.06 and 0.04%
for the intervals (5,6], and (6,7] respectively.


  Spectrum of boson fields and two-point correlators are analyzed in quantum
crossbars (QCB, a superlattice formed by two crossed interacting arrays of
quantum wires), with short range inter-wire interaction. It is shown that the
standard bosonization procedure is valid, and the system behaves as a sliding
Luttinger liquid in the infrared limit, but the high frequency spectral and
correlation characteristics have either 1D or 2D nature depending on the
direction of the wave vector in the 2D Brillouin zone. As a result, the
crossover from 1D to 2D regime may be experimentally observed. Plasmon
propagation in arbitrary direction is possible. Periodic energy transfer
between arrays ("Rabi oscillations") is predicted.


  Migration of planetesimals from the feeding zone of the terrestrial planets,
which was divided into seven regions depending on the distance to the Sun, was
simulated. The influence of gravity of all planets was taken into account. In
some cases, the embryos of the terrestrial planets rather than the planets
themselves were considered; their masses were assumed to be 0.1 or 0.3 of the
current masses of the planets. The arrays of orbital elements of migrated
planetesimals were used to calculate the probabilities of their collisions with
the planets, the Moon, or their embryos. Based on our calculations, we drew
conclusions on the process of accumulation of the terrestrial planets. The
embryos of the terrestrial planets, the masses of which did not exceed a tenth
of the current planetary masses, accumulated planetesimals mainly from the
vicinity of their orbits. When planetesimals fell onto the embryos of the
terrestrial planets from the feeding zone of Jupiter and Saturn, these embryos
had not yet acquired the current masses of the planets, and the material of
this zone (including water and volatiles) could be accumulated in the inner
layers of the terrestrial planets. The inner layers of each of the terrestrial
planets were mainly formed from the material located in the vicinity of the
orbit of a certain planet. The outer layers of the Earth and Venus could
accumulate the same material for these two planets from different parts of the
feeding zone of the terrestrial planets. The Earth and Venus could acquire more
than half of their masses in 5 Myr. A relatively rapid growth of the bulk of
the Martian mass can be explained by the formation of Mars' embryo (the mass of
which is several times less than that of Mars) due to contraction of a rarefied
condensation.


  In this work, we review several results on development and application of
incoherent version of GRAPE (Gradient Ascent Pulse Engineering) approach,
inGRAPE, to optimization for open quantum systems driven by both coherent and
incoherent controls. In the incoherent control approach, the environment serves
as a control together with coherent field, and decoherence rates become
generally time-dependent. For a qubit, explicit analytic expressions for
evolution of the density matrix were obtained by solving a cubic equation via
Cardano method. We discuss applications of incoherent GRAPE method to high
fidelity gate generation for open one- and two-qubit systems and surprising
properties of the underlying control landscapes, forming two groups - smooth
single peak landscapes for Hadamard, C-NOT and C-Z gates, and more complicated
with two peaks for T (or $\pi/8$) gate. For a qutrit, a formulation of the
environment-assisted incoherent control with time-dependent decoherence rates
is provided.


  We examine recent claims that evidence for an overtone in the ringdown of the
GW150914 binary black hole merger was a result of noise anomalies. We cannot
reproduce these claims, finding that our previous analysis of this event is
robust to data analysis choices and consistent with the expectation that strain
after the peak is well described as a superposition of quasinormal modes of the
remnant black hole. We discuss the meaning and implications of establishing
that any specific ringdown mode was detected, and argue that it is misguided to
expect actual LIGO-Virgo data to inform the discussion of whether or why the
merger looks linear.


  It is common in the literature on classical electrodynamics and relativity
theory that the transformation rules for the basic electrodynamic quantities
are derived from the pre-assumption that the equations of electrodynamics are
covariant against these---unknown---transformation rules. There are several
problems to be raised concerning these derivations. This is, however, not our
main concern in this paper. Even if these derivations are regarded as
unquestionable, they leave open the following fundamental question: Are the
so-obtained transformation rules indeed identical with the true transformation
laws of the empirically ascertained electrodynamic quantities? This is of
course an empirical question. In this paper, we will answer this question in a
purely theoretical framework by applying what J. S. Bell calls "Lorentzian
pedagogy"---according to which the laws of physics in any one reference frame
account for all physical phenomena, including what a moving observer must see
when performs measurement operations with moving measuring devices. We will
show that the real transformation laws are indeed identical with the ones
obtained by presuming the covariance of the equations of electrodynamics, and
that the covariance is indeed satisfied. Beforehand, however, we need to
clarify the operational definitions of the fundamental electrodynamic
quantities. As we will see, these semantic issues are not as trivial as one
might think.


  We present a study of wave processes in the sunspot region NOAA 12670 on 2017
August 10 observed by the Goode Solar Telescope in the TiO 7057 A and Ha 6563 A
spectral lines. To study the distribution of power oscillations and their
dynamics, we applied the pixelized wavelet filtering (PWF) technique. For the
first time, the spatial structure of oscillation sources as a footpoints of
fine magnetic tubes, anchored in the sunspot umbra was obtained. We found that
at the chromosphere level, emission variation is a combination of numerous
independent oscillations located in the sources with small angular size. Their
spatial shape varies from dots and cellular in the umbra to filaments in the
penumbra. Each narrow spectrum harmonic corresponds to its source, without
global correlation among themselves. There is weak background as low-frequency
oscillation sources are distributed on whole umbra. At the photosphere level we
found regions with co-phased broadband oscillations of the whole umbra. Their
spectrum includes the ~3-min harmonic, whose maximal value is localized in
umbral dots (UDs), and the low frequency part near the ~5-min period. It is
shown that the oscillation sources are displaced at different heights with
increasing angular size. We assume that the observed spatial distribution of
wave sources indicates the existence a slow subphotosphere resonator with a
vertical magnetic field in the umbra and wave cutoff frequency due to
inclination of the magnetic field line in the penumbra.


  Kronecker coefficients encode the tensor products of complex irreducible
representations of symmetric groups. Their stability properties have been
considered recently by several authors (Vallejo, Pak and Panova, Stembridge).
In previous works we described a geometric method, based on Schur-Weyl duality,
that allows to produce huge series of instances of this phenomenon. In this
note we show how to go beyond these so-called additive triples. We show that
the set of stable triples defines a union of faces of the moment polytope.
Moreover these faces may have different dimensions, and many of them have
codimension one.


  We consider the computational task of sampling a bit string $x$ from a
distribution $\pi(x)=|\langle x|\psi\rangle|^2$, where $\psi$ is the unique
ground state of a local Hamiltonian $H$. Our main result describes a direct
link between the inverse spectral gap of $H$ and the mixing time of an
associated continuous-time Markov Chain with steady state $\pi$. The Markov
Chain can be implemented efficiently whenever ratios of ground state amplitudes
$\langle y|\psi\rangle/\langle x|\psi\rangle$ are efficiently computable, the
spectral gap of $H$ is at least inverse polynomial in the system size, and the
starting state of the chain satisfies a mild technical condition that can be
efficiently checked. This extends a previously known relationship between
sign-problem free Hamiltonians and Markov chains. The tool which enables this
generalization is the so-called fixed-node Hamiltonian construction, previously
used in Quantum Monte Carlo simulations to address the fermionic sign problem.
We implement the proposed sampling algorithm numerically and use it to sample
from the ground state of Haldane-Shastry Hamiltonian with up to 56 qubits. We
observe empirically that our Markov chain based on the fixed-node Hamiltonian
mixes more rapidly than the standard Metropolis-Hastings Markov chain.


  Let $n$ be a positive integer $\geqslant 2$ and $\mathbb{N}^n_{\leqslant}$ be
the $n$-th power of positive integers with the product order of the usual order
on $\mathbb{N}$. In the paper we study the semigroup of injective partial
monotone selfmaps of $\mathbb{N}^n_{\leqslant}$ with cofinite domains and
images. We show that the group of units $H(\mathbb{I})$ of the semigroup
$\mathscr{P\!O}\!_{\infty}(\mathbb{N}^n_{\leqslant})$ is isomorphic to the
group $\mathscr{S}_n$ of permutations of an $n$-element set, and describe the
subsemigroup of idempotents of
$\mathscr{P\!O}\!_{\infty}(\mathbb{N}^n_{\leqslant})$. Also in the case $n=3$
we describe the property of elements of the semigroup
$\mathscr{P\!O}\!_{\infty}(\mathbb{N}^3_{\leqslant})$ as partial bijections of
the poset $\mathbb{N}^3_{\leqslant}$ and Green's relations on the semigroup
$\mathscr{P\!O}\!_{\infty}(\mathbb{N}^3_{\leqslant})$. In particular we show
that $\mathscr{D}=\mathscr{J}$ in
$\mathscr{P\!O}\!_{\infty}(\mathbb{N}^3_{\leqslant})$.


  An analogue of the Riemannian Geometry for an ultrametric Cantor set (C, d)
is described using the tools of Noncommutative Geometry. Associated with (C, d)
is a weighted rooted tree, its Michon tree. This tree allows to define a family
of spectral triples giving the Cantor set the structure of a noncommutative
Riemannian manifold. The family of spectral triples is indexed by the space of
choice functions which is shown to be the analogue of the sphere bundle of a
Riemannian manifold. The Connes metric coming from the Dirac operator D then
allows to recover the metric on C. The corresponding zeta function is shown to
have abscissa of convergence equal to the upper box dimension of (C, d). Taking
the residue at this singularity leads to the definition of a canonical
probability measure on C which in certain cases coincides with the Hausdorff
measure. This measure in turns induces a measure on the space of choices. Given
a choice, the commutator of D with a Lipschitz continuous function can be
intepreted as a directional derivative. By integrating over all choices, this
leads to the definition of an analogue of the Laplace-Beltrami operator. This
operator has compact resolvent and generates a Markov semigroup which plays the
role of a Brownian motion on C. This construction is applied to the simplest
case, the triadic Cantor set.


  We consider the problem of allocating radio channels to links in a wireless
network. Links interact through interference, modelled as a conflict graph
(i.e., two interfering links cannot be simultaneously active on the same
channel). We aim at identifying the channel allocation maximizing the total
network throughput over a finite time horizon. Should we know the average radio
conditions on each channel and on each link, an optimal allocation would be
obtained by solving an Integer Linear Program (ILP). When radio conditions are
unknown a priori, we look for a sequential channel allocation policy that
converges to the optimal allocation while minimizing on the way the throughput
loss or {\it regret} due to the need for exploring sub-optimal allocations. We
formulate this problem as a generic linear bandit problem, and analyze it first
in a stochastic setting where radio conditions are driven by a stationary
stochastic process, and then in an adversarial setting where radio conditions
can evolve arbitrarily. We provide new algorithms in both settings and derive
upper bounds on their regrets.


  Interesting emerging observational properties of the period-mass distribution
of extra-solar planets are discussed. New recent detections confirm the already
emphasized lack of massive planets (m_2sini>=2M_Jup) on short-period orbits
(P<=100 days). Furthermore, we point out i) a shortage of planets in the
10--100 day period range as well as ii) a lack of light planets
(m_2sini<=0.75M_Jup) on orbits with periods larger than ~100 days. The latter
feature is shown not to be due to small-number statistics with Monte-Carlo
simulations. These observational period-related characteristics are discussed
in the context of the migration process of exoplanets. They are found to be in
agreement with recent simulations of planet interactions with viscous disks.
The observed valley at a few tens of days in the period distribution is
interpreted as a transition region between two categories of planets that
suffered different migration scenarios. The lack of light planets on
longer-period orbits and the corresponding intriguing sharp limit in mass is
tentatively explained by the runaway migration process recently studied by
Masset & Papaloizou (2003). The observed properties also have implications for
the observation strategies of the on-going surveys and of future
higher-precision searches.


  We prove that there exists an exponent beyond which all continuous
conventional powers of n-by-n doubly nonnegative matrices are doubly
nonnegative. We show that this critical exponent cannot be less than $n-2$ and
we conjecture that it is always $n-2$ (as it is with Hadamard powering). We
prove this conjecture when $n<6$ and in certain other special cases. We
establish a quadratic bound for the critical exponent in general.


  A clear gamma polarization in the gamma signals from GRB021206 probes the
presence of a very thin collimated jet (opening angle Delta\theta < 0.6^o,
(Delta\Omega)\Omega< 2.5 10^-5) in Gamma Ray Burst, GRBs. The last and well
proved GRB030329/SN2003dh time and space coincidence confirms definitively the
earliest GRB980425/SN1998bw connection among GRB and Supernova. The apparent
extreme GRBs luminosity is just the extreme beamed blazing gamma Jet observed
in axis during a Supernova birth. The late relic GRBs sources may be found in
compact SNRs core, as NS or BH jets; at much later epoch, their lower power
gamma jets may be within detectability only from nearby galactic distances, as
Soft Gamma Repeaters (SGRs) or anomalous X-ray Pulsars, AXPs. This common Jet
nature explain some connection between GRBs and SGRs spectra. The geometrical
multi-precessing beaming and their reappearance to the observer may explain the
puzzling presence of X-Ray precursors in GRBs as well as in SGRs. A mysterious
re-brightening afterglows observed in early and late GRB 030329 optical
transient, like in the 27 August 1998 and 18 April 2001 SGR 1900+14 events,
might be understood as the damped oscillatory imprint left by such a
multi-precessing gamma-X-Optical and Radio Jet.


  In the study of subsurface seismic imaging, solving the acoustic wave
equation is a pivotal component in existing models. The advancement of deep
learning enables solving partial differential equations, including wave
equations, by applying neural networks to identify the mapping between the
inputs and the solution. This approach can be faster than traditional numerical
methods when numerous instances are to be solved. Previous works that
concentrate on solving the wave equation by neural networks consider either a
single velocity model or multiple simple velocity models, which is restricted
in practice. Instead, inspired by the idea of operator learning, this work
leverages the Fourier neural operator (FNO) to effectively learn the frequency
domain seismic wavefields under the context of variable velocity models. We
also propose a new framework paralleled Fourier neural operator (PFNO) for
efficiently training the FNO-based solver given multiple source locations and
frequencies. Numerical experiments demonstrate the high accuracy of both FNO
and PFNO with complicated velocity models in the OpenFWI datasets. Furthermore,
the cross-dataset generalization test verifies that PFNO adapts to
out-of-distribution velocity models. Moreover, PFNO has robust performance in
the presence of random noise in the labels. Finally, PFNO admits higher
computational efficiency on large-scale testing datasets than the traditional
finite-difference method. The aforementioned advantages endow the FNO-based
solver with the potential to build powerful models for research on seismic
waves.


  A recent study by Bojowald and Paily provided a path toward the
identification of an effective quantum-spacetime picture of Loop Quantum
Gravity, applicable in the "Minkowski regime", the regime where the large-scale
(coarse-grained) spacetime metric is flat. A pivotal role in the analysis is
played by Loop-Quantum-Gravity-based modifications to the hypersurface
deformation algebra, which leave a trace in the Minkowski regime. We here show
that the symmetry-algebra results reported by Bojowald and Paily are consistent
with a description of spacetime in the Minkowski regime given in terms of the
$\kappa$-Minkowski noncommutative spacetime, whose relevance for the study of
the quantum-gravity problem had already been proposed for independent reasons.


  We present a new type of Fayet-Iliopoulos (FI) terms in ${\mathcal N}=2$
supergravity that do not require the gauging of the $R$-symmetry. We elaborate
on the impact of such terms on the vacuum structure of the ${\mathcal N}=2$
theory and compare their properties with the standard Fayet-Iliopoulos terms
that arise from gaugings. In particular, we show that, with the use of the new
FI terms, models with a single physical ${\mathcal N}=2$ vector multiplet can
be constructed that give stable de Sitter vacua.


  This paper describes the Spot the Difference Corpus which contains 54
interactions between pairs of subjects interacting to find differences in two
very similar scenes. The setup used, the participants' metadata and details
about collection are described. We are releasing this corpus of task-oriented
spontaneous dialogues. This release includes rich transcriptions, annotations,
audio and video. We believe that this dataset constitutes a valuable resource
to study several dimensions of human communication that go from turn-taking to
the study of referring expressions. In our preliminary analyses we have looked
at task success (how many differences were found out of the total number of
differences) and how it evolves over time. In addition we have looked at scene
complexity provided by the RGB components' entropy and how it could relate to
speech overlaps, interruptions and the expression of uncertainty. We found
there is a tendency that more complex scenes have more competitive
interruptions.


  Let $K$ be a complete discrete valued field of characteristic zero with
residue field $k_K$ of characteristic $p > 0$. Let $L/K$ be a finite Galois
extension with the Galois group $G$ and suppose that the induced extension of
residue fields $k_L/k_K$ is separable. In his paper, Hesselholt conjectured
that $H^1(G,W(\sO_L))$ is zero, where $\sO_L$ is the ring of integers of $L$
and $W(\sO_L)$ is the Witt ring of $\sO_L$ w.r.t. the prime $p$. He partially
proved this conjecture for a large class of extensions. In this paper, we prove
Hesselholt's conjecture for all Galois extensions.


  The data produced by the future space-based millihertz gravitational-wave
detector LISA will require nontrivial pre-processing, which might affect the
science results. It is crucial to demonstrate the feasibility of such
processing algorithms and assess their performance and impact on the science.
We are building an end-to-end pipeline that includes state-of-the-art
simulations and noise reduction algorithms. The simulations must include a
detailed model of the full measurement chain, capturing the main features that
affect the instrument performance and processing algorithms. In particular, we
include in these simulations, for the first time, proper relativistic treatment
of reference frames with realistic numerically-optimized orbits; a model for
onboard clocks and clock synchronization measurements; proper modeling of total
laser frequencies, including laser locking, frequency planning and Doppler
shifts; and a better treatment of onboard processing. Using these simulated
data, we show that our pipeline is able to reduce the most critical noises and
form synchronized observables. By injecting signals from a verification binary,
we demonstrate that good parameter estimation can be obtained on this more
realistic setup, extending existing results from previous LISA Data Challenges.


  We generalize the notion of monetary value measures developed with category
theory in [Adachi, 2014] by extending their base category from the category
\c{hi} to the category of probability spaces Prob introduced in [Adachi and
Ryu, 2016].


  According to recent works [Tsui PSS \textbf{55}, 237-242 (2007), \textbf{55},
2042-2044 (2007)], the Neptune Adams ring main arc Fraternite is regarded as
captured by the corotation elliptic resonance (CER) potential of Galatea. The
minor arcs Egalite(2,1), Liberte, and Courage are located at positions where
the time averaged forces, due to the 42-43 corotation-Lindblad resonances under
the central field of Neptune, vanish. With adequately chosen Fraternite mass
and Galatea eccentricity, this model gives minor arc locations compatible to
observed positions, and allows a dynamic transport of materials among arcs. To
complement this model, the effect of self-gravity of Fraternite, with a
distributed mass, is evaluated together with the CER potential to account for
its $10^{0}$ longitudinal span. Although self-gravity is the collective action
of all the particles in the arc, each individual particle will see the
self-potential with a central maximum as an external potential generated by
other particles.


  The 43m telescope at the NRAO site in Green Bank, WV has recently been
outfitted with a clone of the Green Bank Ultimate Pulsar Processing Instrument
(GUPPI \cite{Ransom:2009}) backend, making it very useful for a number of
pulsar related studies in frequency ranges 800-1600 MHz and 220-440 MHz. Some
of the recent science being done with it include: monitoring of the Crab
pulsar, a blind search for transient sources, pulsar searches of targets of
opportunity, and an all-sky mapping project. For the Crab monitoring project,
regular observations are searched for giant pulses (GPs), which are then
correlated with $\gamma$-ray photons from the \emph{Fermi} spacecraft. Data
from the all-sky mapping project are first run through a pipeline that does a
blind transient search, looking for single pulses over a DM range of 0-500
pc~cm$^{-3}$. These projects are made possible by MIT Lincoln Labs.


  The 18F(p,a)15O reaction is recognized as one of the most important reaction
for nova gamma-ray astronomy as it governs the early <= 511 keV emission.
However, its rate remains largely uncertain at nova temperatures due to unknown
low-energy resonance strengths. We report here on our last results concerning
the study of the D(18F,pa)15N reaction, as well as on the determination of the
18F(p,a)15O reaction rate using the R-matrix theory. Remaining uncertainties
are discussed.


  Understanding battery degradation in electric vehicles (EVs) under real-world
conditions remains a critical yet under-explored area of research. Central to
this investigation is the challenge of estimating the specific degradation
modes in aged cells with no available information on usage history, bypassing
the conventional yet invasive method of tear-down tests. Using an
electrochemical model, this study pioneers a methodology to decouple and
isolate the aging mechanisms in batteries sourced from EVs with varying
mileages. A robust correlation is established between the model parameters and
distinct degradation processes, enabling the diagnosis and estimation of each
mechanism's impact on the battery's parameters. This paper sheds light on
battery degradation in real-world scenarios and demonstrates the feasibility of
their identification, isolation, and approximate quantification of their
effects.


  We propose and test an algorithm to simulate a lattice system of interacting
fermions in two spatial dimensions. The approach is an extension of the
entanglement renormalization technique [Phys. Rev. Lett. 99, 220405 (2007)] and
the related multi-scale entanglement renormalization ansatz. Benchmark
calculations for free and interacting fermions on a lattice of $6\times 6$
sites and periodic boundary conditions confirm the validity of this proposal.


  Starting from the Helmholtz free energy we calculate analytically first- and
second-order derivatives, as internal energy and specific heats, for the ideal
system and the exchange and correlation interactions covering a broad range of
degeneracy and relativity. The complex physics of Coulomb interactions is
expressed by Pade Approximants, which reflect the actual state of our knowledge
with high accuracy. We assume complete ionization and provide a base system of
thermodynamical functions from which any other thermodynamical quantities can
be calculated. We chose for the base system the free energy, the pressure, the
internal energy, the isothermal compressibility (or density exponent), the
coefficient of strain (or temperature exponent), and the isochoric specific
heat. By means of the latter potentials entropy, isobaric specific heat and
adiabatic temperature gradient can be determined. We give comparisons with
quantities which are composed by numerical second-order derivatives of the free
energy and show that numerical derivatives of the free energy as calculated,
for instance, from EOS tables, may produce discontinuities for astrophysically
relevant quantities as, e.g., the adiabatic temperature gradient. Adiabatic
temperature gradients are shown for different chemical compositions (hydrogen,
helium, carbon). Finally the used formalism of Pade Approximants allows
immediate incorporation of recent results from many particle statistics.


  We give an intrinsic definition of (affine very) special real manifolds and
realise any such manifold $M$ as a domain in affine space equipped with a
metric which is the Hessian of a cubic polynomial. We prove that the tangent
bundle $N=TM$ carries a canonical structure of (affine) special K\"ahler
manifold. This gives an intrinsic description of the $r$-map as the map
$M\mapsto N=TM$. On the physics side, this map corresponds to the dimensional
reduction of rigid vector multiplets from 5 to 4 space-time dimensions. We
generalise this construction to the case when $M$ is any Hessian manifold.


  The discovery process of building new theoretical physics models involves the
dual aspect of both fitting to the existing experimental data and satisfying
abstract theorists' criteria like beauty, naturalness, etc. We design loss
functions for performing both of those tasks with machine learning techniques.
We use the Yukawa quark sector as a toy example to demonstrate that the
optimization of these loss functions results in true and beautiful models.


  Classical person re-identification approaches assume that a person of
interest has appeared across different cameras and can be queried by one of the
existing images. However, in real-world surveillance scenarios, frequently no
visual information will be available about the queried person. In such
scenarios, a natural language description of the person by a witness will
provide the only source of information for retrieval. In this work, person
re-identification using both vision and language information is addressed under
all possible gallery and query scenarios. A two stream deep convolutional
neural network framework supervised by cross entropy loss is presented. The
weights connecting the second last layer to the last layer with class
probabilities, i.e., logits of softmax layer are shared in both networks.
Canonical Correlation Analysis is performed to enhance the correlation between
the two modalities in a joint latent embedding space. To investigate the
benefits of the proposed approach, a new testing protocol under a multi modal
ReID setting is proposed for the test split of the CUHK-PEDES and CUHK-SYSU
benchmarks. The experimental results verify the merits of the proposed system.
The learnt visual representations are more robust and perform 22\% better
during retrieval as compared to a single modality system. The retrieval with a
multi modal query greatly enhances the re-identification capability of the
system quantitatively as well as qualitatively.


  Stochastic differential equations are widely used in various fields; in
particular, the usefulness of duality relations has been demonstrated in some
models such as population models and Brownian momentum processes. In this
study, a discussion based on combinatorics is made and applied to calculate the
expectation values of functions in systems in which evolution is governed by
stochastic differential equations. Starting with the duality theory of
stochastic processes, some modifications to the interpretation and usage of
time-ordering operators naturally lead to discussions on combinatorics. For
demonstration, the first and second moments of the Ornstein-Uhlenbeck process
are re-derived from the discussion on combinatorics. Furthermore, two numerical
methods for practical applications are proposed. One method is based on a
conventional exponential expansion and the Pade approximation. The other uses a
resolvent of a time-evolution operator, along with the application of the
Aitken series acceleration method. Both methods yield reasonable
approximations. Particularly, the resolvent and Aitken acceleration show
satisfactory results. These findings will provide a new way of calculating
expectations numerically and directly without using time-discretization.


  Quantization and Knowledge distillation (KD) methods are widely used to
reduce memory and power consumption of deep neural networks (DNNs), especially
for resource-constrained edge devices. Although their combination is quite
promising to meet these requirements, it may not work as desired. It is mainly
because the regularization effect of KD further diminishes the already reduced
representation power of a quantized model. To address this short-coming, we
propose Quantization-aware Knowledge Distillation (QKD) wherein quantization
and KD are care-fully coordinated in three phases. First, Self-studying (SS)
phase fine-tunes a quantized low-precision student network without KD to obtain
a good initialization. Second, Co-studying (CS) phase tries to train a teacher
to make it more quantizaion-friendly and powerful than a fixed teacher.
Finally, Tutoring (TU) phase transfers knowledge from the trained teacher to
the student. We extensively evaluate our method on ImageNet and CIFAR-10/100
datasets and show an ablation study on networks with both standard and
depthwise-separable convolutions. The proposed QKD outperformed existing
state-of-the-art methods (e.g., 1.3% improvement on ResNet-18 with W4A4, 2.6%
on MobileNetV2 with W4A4). Additionally, QKD could recover the full-precision
accuracy at as low as W3A3 quantization on ResNet and W6A6 quantization on
MobilenetV2.


  Robot sequential decision-making in the real world is a challenge because it
requires the robots to simultaneously reason about the current world state and
dynamics, while planning actions to accomplish complex tasks. On the one hand,
declarative languages and reasoning algorithms well support representing and
reasoning with commonsense knowledge. But these algorithms are not good at
planning actions toward maximizing cumulative reward over a long, unspecified
horizon. On the other hand, probabilistic planning frameworks, such as Markov
decision processes (MDPs) and partially observable MDPs (POMDPs), well support
planning to achieve long-term goals under uncertainty. But they are
ill-equipped to represent or reason about knowledge that is not directly
related to actions.
  In this article, we present a novel algorithm, called iCORPP, to
simultaneously estimate the current world state, reason about world dynamics,
and construct task-oriented controllers. In this process, robot decision-making
problems are decomposed into two interdependent (smaller) subproblems that
focus on reasoning to "understand the world" and planning to "achieve the goal"
respectively. Contextual knowledge is represented in the reasoning component,
which makes the planning component epistemic and enables active information
gathering. The developed algorithm has been implemented and evaluated both in
simulation and on real robots using everyday service tasks, such as indoor
navigation, dialog management, and object delivery. Results show significant
improvements in scalability, efficiency, and adaptiveness, compared to
competitive baselines including handcrafted action policies.


  This paper deals with a new class of random flights $\underline{\bf
X}_d(t),t>0,$ defined in the real space $\mathbb{R}^d, d\geq 2,$ characterized
by non-uniform probability distributions on the multidimensional sphere. These
random motions differ from similar models appeared in literature which take
directions according to the uniform law. The family of angular probability
distributions introduced in this paper depends on a parameter $\nu\geq 0$ which
gives the level of drift of the motion. Furthermore, we assume that the number
of changes of direction performed by the random flight is fixed. The time
lengths between two consecutive changes of orientation have joint probability
distribution given by a Dirichlet density function.
  The analysis of $\underline{\bf X}_d(t),t>0,$ is not an easy task, because it
involves the calculation of integrals which are not always solvable. Therefore,
we analyze the random flight $\underline{\bf X}_m^d(t),t>0,$ obtained as
projection onto the lower spaces $\mathbb{R}^m,m<d,$ of the original random
motion in $\mathbb{R}^d$. Then we get the probability distribution of
$\underline{\bf X}_m^d(t),t>0.$
  Although, in its general framework, the analysis of $\underline{\bf
X}_d(t),t>0,$ is very complicated, for some values of $\nu$, we can provide
some results on the process. Indeed, for $\nu=1$, we obtain the characteristic
function of the random flight moving in $\mathbb{R}^d$. Furthermore, by
inverting the characteristic function, we are able to give the analytic form
(up to some constants) of the probability distribution of $\underline{\bf
X}_d(t),t>0.$


  We consider the model $Z_i=X_i+\varepsilon_i$, for i.i.d. $X_i$'s and
$\varepsilon_i$'s and independent sequences $(X_i)_{i\in{\mathbb{N}}}$ and
$(\varepsilon_i)_{i\in{\mathbb{N}}}$. The density $f_{\varepsilon}$ of
$\varepsilon_1$ is assumed to be known, whereas the one of $X_1$, denoted by
$g$, is unknown. Our aim is to estimate linear functionals of $g$, $<\psi,g>$
for a known function $\psi$. We propose a general estimator of $<\psi,g>$ and
study the rate of convergence of its quadratic risk as a function of the
smoothness of $g$, $f_{\varepsilon}$ and $\psi$. Different contexts with
dependent data, such as stochastic volatility and AutoRegressive Conditionally
Heteroskedastic models, are also considered. An estimator which is adaptive to
the smoothness of unknown $g$ is then proposed, following a method studied by
Laurent et al. (Preprint (2006)) in the Gaussian white noise model. We give
upper bounds and asymptotic lower bounds of the quadratic risk of this
estimator. The results are applied to adaptive pointwise deconvolution, in
which context losses in the adaptive rates are shown to be optimal in the
minimax sense. They are also applied in the context of the stochastic
volatility model.


  In a millimeter wave (mmWave) network, user association, the process of
deciding as to which base station (BS) a given user should associate with, is a
crucial process which affects the throughput and delay performance seen by
users in the network and the amount of load at each BS. In the existing
research literature, the stability region of a user association policy, i.e.,
the set of user arrival rates for which the user association policy stabilizes
the network, has not been analytically characterized for any user association
policy for mmWave networks. In this paper, we study the user association
problem in mmWave networks and compare the performance of four user association
policies: Signal to Noise Ratio (SNR) based, Throughput based, Load based and
Mixed. All these policies are simple, easy to implement, distributed and
online. We use a Continuous Time Markov Chain (CTMC) model and Lyapunov
function techniques to analytically characterize the stability region of each
of the above four user association policies. We also evaluate the performance
of the above four user association policies in a large mmWave network, in which
link qualities fluctuate with time and users are mobile, via detailed
simulations. Our results show that the Throughput based policy outperforms the
other three user association policies in terms of stability region as well as
average throughput, average delay and fairness performance.


  Nowadays the app-in-app paradigm is becoming increasingly popular, and
sub-apps have become an important form of mobile applications. WeChat, the
leading app-in-app platform, provides millions of sub-apps that can be used for
online shopping, financing, social networking, etc. However, privacy issues in
this new ecosystem have not been well understood. This paper performs the first
systematic study of privacy over-collection in sub-apps (denoted as SPO), where
sub-apps actually collect more privacy data than they claim in their privacy
policies. We propose a taxonomy of privacy for this ecosystem and a framework
named SPOChecker to automatically detect SPO in real-world sub-apps. Based on
SPOChecker, we collect 5,521 popular and representative WeChat sub-apps and
conduct a measurement study to understand SPO from three aspects: its
landscape, accountability, and defense methods. The result is worrisome, that
more than half of all studied sub-apps do not provide users with privacy
policies. Among 2,511 sub-apps that provide privacy policies, 489 (19.47%) of
them contain SPO. We look into the detailed characteristics of SPO, figure out
possible reasons and the responsibilities of stakeholders in the ecosystem, and
rethink current defense methods. The measurement leads to several insightful
findings that can help the community to better understand SPO and protect
privacy in sub-apps.


  Consider a stochastic search model with resetting for an unknown stationary
target $a\in\mathbb{R}$ with known distribution $\mu$. The searcher begins at
the origin and performs Brownian motion with diffusion constant $D$. The
searcher is also armed with an exponential clock with spatially dependent rate
$r$, so that if it has failed to locate the target by the time the clock rings,
then its position is reset to the origin and it continues its search anew from
there. Denote the position of the searcher at time $t$ by $X(t)$. Let
$E_0^{(r)}$ denote expectations for the process $X(\cdot)$.
  The search ends at time $T_a=\inf\{t\ge0:X(t)=a\}$. The expected time of the
search is then $\int_{\mathbb{R}}(E_0^{(r)}T_a)\thinspace\mu(da)$. Ideally, one
would like to minimize this over all resetting rates $r$. We obtain
quantitative growth rates for $E_0^{(r)}T_a$ as a function of $a$ in terms of
the asymptotic behavior of the rate function $r$, and also a rather precise
dichotomy on the asymptotic behavior of the resetting function $r$ to determine
whether $E_0^{(r)}T_a$ is finite or infinite. We show generically that if
$r(x)$ is on the order $|x|^{2l}$, with $l>-1$, then $\log E_0^{(r)}T_a$ is on
the order $|a|^{l+1}$; in particular, the smaller the asymptotic size of $r$,
the smaller the asymptotic growth rate of $E_0^{(r)}T_a$. The asymptotic growth
rate of $E_0^{(r)}T_a$ continues to decrease when $r(x)\sim
\frac{D\lambda}{x^2}$ with $\lambda>1$; now the growth rate of $E_0^{(r)}T_a$
is more or less on the order $|a|^{\frac{1+\sqrt{1+8\lambda}}2}$. However, if
$\lambda=1$, then $E_0^{(r)}T_a=\infty$, for $a\neq0$.


  We study the parameterized complexity of two classic problems on directed
graphs: Hamiltonian Cycle and its generalization {\sc Longest Cycle}. Since
2008, it is known that Hamiltonian Cycle is W[1]-hard when parameterized by
directed treewidth [Lampis et al., ISSAC'08]. By now, the question of whether
it is FPT parameterized by the directed feedback vertex set (DFVS) number has
become a longstanding open problem. In particular, the DFVS number is the
largest natural directed width measure studied in the literature. In this
paper, we provide a negative answer to the question, showing that even for the
DFVS number, the problem remains W[1]-hard. As a consequence, we also obtain
that Longest Cycle is W[1]-hard on directed graphs when parameterized
multiplicatively above girth, in contrast to the undirected case. This resolves
an open question posed by Fomin et al. [ACM ToCT'21] and Gutin and Mnich
[arXiv:2207.12278]. Our hardness results apply to the path versions of the
problems as well. On the positive side, we show that Longest Path parameterized
multiplicatively above girth} belongs to the class XP.


  We set up and study the hydrodynamic theory for active fluid and tethered
membranes. We focus on those membranes which are inversion-symmetric. We show
that such membranes are either described by appropriate linear hydrodynamic
equations, which are exact in the asymptotic long wavelength limit, giving
stable flat phases with positional quasi long range orders, or be linearly
unstable, implying crumpling of the membranes. We argue that in a such an
active membrane thermal noises dominate over any active noises, and use it to
calculate the correlation functions of the undulation and in-plane
displacements of the membrane in the stable case, and the associated
correlation functions of the embedding bulk flow velocities.


  This article is intended to supplement our 2015 paper in Medical Physics
titled "Noise properties of CT images reconstructed by use of constrained
total-variation, data-discrepancy minimization", in which ordered subsets
methods were employed to perform total-variation constrained data-discrepancy
minimization for image reconstruction in X-ray computed tomography. Here we
provide details regarding implementation of the ordered subsets algorithms and
suggestions for selection of algorithm parameters. Detailed pseudo-code is
included for every algorithm implemented in the original manuscript.


  Multiple hypothesis testing is a central topic in statistics, but despite
abundant work on the false discovery rate (FDR) and the corresponding Type-II
error concept known as the false non-discovery rate (FNR), a fine-grained
understanding of the fundamental limits of multiple testing has not been
developed. Our main contribution is to derive a precise non-asymptotic tradeoff
between FNR and FDR for a variant of the generalized Gaussian sequence model.
Our analysis is flexible enough to permit analyses of settings where the
problem parameters vary with the number of hypotheses $n$, including various
sparse and dense regimes (with $o(n)$ and $\mathcal{O}(n)$ signals). Moreover,
we prove that the Benjamini-Hochberg algorithm as well as the Barber-Cand\`{e}s
algorithm are both rate-optimal up to constants across these regimes.


  The time evolution of both proton and anti-proton $v_2$ flows from Au+Au
collisions at $\sqrt{s_{NN}}$=7.7 GeV are examined by using both pure cascade
and mean-field potential versions of the UrQMD model. Due to a stronger
repulsion at the early stage introduced by the repulsive potentials and hence
much less annihilation probabilities, anti-protons are frozen out earlier with
smaller $v_2$ values. Therefore, the experimental data of anti-proton $v_2$ as
well as the flow difference between proton and anti-proton can be reasonably
described with the potential version of UrQMD.


  The agent program, called Samu, is an experiment to build a disembodied
DevRob (Developmental Robotics) chatter bot that can talk in a natural language
like humans do. One of the main design feature is that Samu can be interacted
with using only a character terminal. This is important not only for practical
aspects of Turing test or Loebner prize, but also for the study of basic
principles of Developmental Robotics. Our purpose is to create a rapid
prototype of Q-learning with neural network approximators for Samu. We sketch
out the early stages of the development process of this prototype, where Samu's
task is to predict the next sentence of tales or conversations. The basic
objective of this paper is to reach the same results using reinforcement
learning with general function approximators that can be achieved by using the
classical Q lookup table on small input samples. The paper is closed by an
experiment that shows a significant improvement in Samu's learning when using
LZW tree to narrow the number of possible Q-actions.