  In the theory of quantum communications, a deeper structure has been recently
unveiled, showing that the capacity does not completely characterize the
channel ability to transmit information due to phenomena -- namely,
superadditivity, superactivation and causal activation -- with no counterpart
in the classical world. Although how deep goes this structure is yet to be
fully uncovered, it is crucial for the communication engineering community to
own the implications of these phenomena for understanding and deriving the
fundamental limits of communications. Hence, the aim of this treatise is to
shed light on these phenomena by providing the reader with an easy access and
guide towards the relevant literature and the prominent results from a
communication engineering perspective.


  We present the results of exclusive electroproduction of vector mesons on the
proton at CLAS. We discuss the interpretation of these cross sections in terms
of t-channel Reggeon exchanges and in terms of Generalized Parton Distributions
(GPDs) formalism.


  We introduce a novel systematic construction for integrable (3+1)-dimensional
dispersionless systems using nonisospectral Lax pairs that involve contact
vector fields.
  In particular, we present new large classes of (3+1)-dimensional integrable
dispersionless systems associated to the Lax pairs which are polynomial and
rational in the spectral parameter.


  Staged trees are a relatively recent class of probabilistic graphical models
that extend Bayesian networks to formally and graphically account for
non-symmetric patterns of dependence. Machine learning algorithms to learn them
from data have been implemented in various pieces of software. However, to
date, methods to assess the robustness and validity of the learned,
non-symmetric relationships are not available. Here, we introduce validation
techniques tailored to staged tree models based on non-parametric bootstrap
resampling methods and investigate their use in practical applications. In
particular, we focus on the evaluation of transport services using large-scale
survey data. In these types of applications, data from heterogeneous sources
must be collated together. Staged trees provide a natural framework for this
integration of data and its analysis. For the thorough evaluation of transport
services, we further implement novel what-if sensitivity analyses for staged
trees and their visualization using software.


  In this letter, we investigate the transient rheological behavior of immersed
granular flows using both experiments of submerged granular column collapses
and corresponding numerical simulations. The simulations are performed with the
lattice-Boltzmann method (LBM) coupled with the discrete element method (DEM)
and provide a significant amount of data of the stress and deformation
conditions at different positions and times during the granular collapse. We
derive a new dimensionless number $\mathcal{G}$ that can unify the rheology of
transient granular flows in different regimes for all the simulation data
points. $\mathcal{G}$ smoothly transforms from an inertial number into a
viscous number, unifying both extremes of the rheology law. We also show the
need to introduce the kinetic stresses to achieve a universal relation. The
findings establish a transient constitutive framework for visco-inertial
granular flows and are important for a better understanding of granular-fluid
mixtures in both natural and engineering situations.


  We discuss the minimum of Willmore functional of torus in a Riemannian
manifold $N$, especially for the case that $N$ is a product manifold. We show
that when $N=S^2\times S^1$, the minimum of $W(T^2)$ is 0, and when
$N=R^2\times S^1$, there exists no torus having least Willmore functional. When
$N=H^2(-c)\times S^1$, and $x=\gamma\times S^1$, the minimum of $W(x)$ is
$2\pi^2\sqrt{c}$.


  We show that an $n$-dimensional compact K\"ahler manifold $X$ admitting a
non-degenerate meromorphic map $f:{\bf C}^n\to X$ of order $\rho_f<2$ is
rationally connected.


  We present Arecibo, GBT, VLA and WIYN/pODI observations of the ALFALFA source
AGC 226067. Originally identified as an ultra-compact high velocity cloud and
candidate Local Group galaxy, AGC 226067 is spatially and kinematically
coincident with the Virgo cluster, and the identification by multiple groups of
an optical counterpart with no resolved stars supports the interpretation that
this systems lies at the Virgo distance (D=17 Mpc). The combined observations
reveal that the system consists of multiple components: a central HI source
associated with the optical counterpart (AGC 226067), a smaller HI-only
component (AGC 229490), a second optical component (AGC 229491), and extended
low surface brightness HI. Only ~1/4 of the single-dish HI emission is
associated with AGC 226067; as a result, we find M_HI/L_g ~ 6 Msun/Lsun, which
is lower than previous work. At D=17 Mpc, AGC 226067 has an HI mass of 1.5 x
10^7 Msun and L_g = 2.4 x 10^6 Lsun, AGC 229490 (the HI-only component) has
M_HI = 3.6 x 10^6 Msun, and AGC 229491 (the second optical component) has L_g =
3.6 x 10^5 Lsun. The nature of this system of three sources is uncertain: AGC
226067 and AGC 229490 may be connected by an HI bridge, and AGC 229490 and AGC
229491 are separated by only 0.5'. The current data do not resolve the HI in
AGC 229490 and its origin is unclear. We discuss possible scenarios for this
system of objects: an interacting system of dwarf galaxies, accretion of
material onto AGC 226067, or stripping of material from AGC 226067.


  We establish a few properties of eigenvalues and eigenvectors of the
quaternionic Ginibre ensemble (QGE), analogous to what is known in the complex
Ginibre case. We first recover a version of Kostlan's theorem that was already
noticed by Rider: the set of the squared radii of the eigenvalues is
distributed as a set of independent gamma variables. Our proof technique uses
the De Bruijn identity and properties of Pfaffians; it also allows to prove
that the high powers of these eigenvalues are independent. These results extend
to any potential beyond the Gaussian case, as long as radial symmetry holds;
this includes for instance truncations of quaternionic unitary matrices,
products of quaternionic Ginibre matrices, and the quaternionic spherical
ensemble.
  We then study the eigenvectors of quaternionic Ginibre matrices. The angle
between eigenvectors and the matrix of overlaps both exhibit some specific
features that can be compared to the complex case. In particular, we compute
the distribution and the limit of the diagonal overlap associated to an
eigenvalue that is conditioned to be zero.


  We investigate finite energy solutions of the
Einstein--Yang-Mills--Chern-Simons system in odd spacetime dimensions, D=2n+1,
with n>1. Our configurations are static and spherically symmetric, approaching
at infinity a Minkowski spacetime background. In contrast with the Abelian
case, the contribution of the Chern-Simons term is nontrivial already in the
static, spherically symmetric limit. Both globally regular, particle-like
solutions and black holes are constructed numerically for several values of D.
These solutions carry a nonzero electric charge and have finite mass. For
globally regular solutions, the value of the electric charge is fixed by the
Chern-Simons coupling constant. The black holes can be thought as non-linear
superpositions of Reissner-Nordstrom and non-Abelian configurations. A
systematic discussion of the solutions is given for D=5, in which case the
Reissner-Nordstrom black hole becomes unstable and develops non-Abelian hair.
We show that some of these non-Abelian configurations are stable under linear,
spherically symmetric perturbations. A detailed discussion of an exact D=5
solution describing extremal black holes and solitons is also provided.


  The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020 posed
diverse challenges for unmanned aerial vehicles (UAVs). We present our four
tailored UAVs, specifically developed for individual aerial-robot tasks of
MBZIRC, including custom hardware- and software components.
  In Challenge 1, a target UAV is pursued using a high-efficiency, onboard
object detection pipeline to capture a ball from the target UAV. A second UAV
uses a similar detection method to find and pop balloons scattered throughout
the arena.
  For Challenge 2, we demonstrate a larger UAV capable of autonomous aerial
manipulation: Bricks are found and tracked from camera images. Subsequently,
they are approached, picked, transported, and placed on a wall.
  Finally, in Challenge 3, our UAV autonomously finds fires using LiDAR and
thermal cameras. It extinguishes the fires with an onboard fire extinguisher.
  While every robot features task-specific subsystems, all UAVs rely on a
standard software stack developed for this particular and future competitions.
We present our mostly open-source software solutions, including tools for
system configuration, monitoring, robust wireless communication, high-level
control, and agile trajectory generation. For solving the MBZIRC 2020 tasks, we
advanced the state of the art in multiple research areas like machine vision
and trajectory generation.
  We present our scientific contributions that constitute the foundation for
our algorithms and systems and analyze the results from the MBZIRC competition
2020 in Abu Dhabi, where our systems reached second place in the Grand
Challenge. Furthermore, we discuss lessons learned from our participation in
this complex robotic challenge.


  Propagation of surface waves on a background shear flow with constant
vorticity is studied and compared against the case when the background flow is
uniform in depth. For a shear flow with the linear vertical profile, the
dispersion relation of surface gravity waves is minutely analyzed for a fluid
of finite depth. Under the assumption that the background flow gradually varies
in the horizontal direction, the primary attention is paid to the wave blocking
phenomenon; the effect of vorticity on this phenomenon is studied in details.
The conditions of wave blocking are obtained and categorized for different
values of the governing dimensionless parameters.


  In this work, we study two-body bound states in two-component Bose gas with a
one-dimensional (1D) spin-orbit coupling (SOC) induced by Raman lasers. The
finite Raman coupling strength generates coupling among three spin channels,
resulting in the reconstruction of three bound-state bands. In addition,
multiple resonances can be induced at finite scattering lengths. By tuning the
interaction in one intra-species channel, one bound-state band can be lifted
and three resonances can be achieved at different center-of-mass momenta, which
can be observable under current experimental conditions in ${}^{87}$Rb atoms.


  Demand for health care is constantly increasing due to the ongoing
demographic change, while at the same time health service providers face
difficulties in finding skilled personnel. This creates pressure on health care
systems around the world, such that the efficient, nationwide provision of
primary health care has become one of society's greatest challenges. Due to the
complexity of health care systems, unforeseen future events, and a frequent
lack of data, analyzing and optimizing the performance of health care systems
means tackling a wicked problem. To support this task for primary care, this
paper introduces the hybrid agent-based simulation model SiM-Care. SiM-Care
models the interactions of patients and primary care physicians on an
individual level. By tracking agent interactions, it enables modelers to assess
multiple key indicators such as patient waiting times and physician
utilization. Based on these indicators, primary care systems can be assessed
and compared. Moreover, changes in the infrastructure, patient behavior, and
service design can be directly evaluated. To showcase the opportunities offered
by SiM-Care and aid model validation, we present a case study for a primary
care system in Germany. Specifically, we investigate the effects of an aging
population, a decrease in the number of physicians, as well as the combined
effects.


  Off-policy learning algorithms have been known to be sensitive to the choice
of hyper-parameters. However, unlike near on-policy algorithms for which
hyper-parameters could be optimized via e.g. meta-gradients, similar techniques
could not be straightforwardly applied to off-policy learning. In this work, we
propose a framework which entails the application of Evolutionary Strategies to
online hyper-parameter tuning in off-policy learning. Our formulation draws
close connections to meta-gradients and leverages the strengths of black-box
optimization with relatively low-dimensional search spaces. We show that our
method outperforms state-of-the-art off-policy learning baselines with static
hyper-parameters and recent prior work over a wide range of continuous control
benchmarks.


  The $R$-parity-violating Minimal Supersymmetric Standard Model (RPV-MSSM) can
naturally accommodate massive neutrinos as required by the oscillation data.
However, studying the phenomenology is complicated due to the large number of
undetermined parameters involved. Thus, studies are usually restricted to
specific submodels. In this work, we develop an approach that allows us to be
less restrictive. Working in (almost) the completely general RPV-MSSM setting,
we analyze the structure of the neutrino mass matrix, and identify -- for the
case of two massive neutrinos -- only four minimal classes of structures that
can solve the neutrino data; we call these Minimal Oscillation Models (MOMs).
We study the general features of each MOM class, and present numerical fits to
the oscillation data. Our approach allows us to study all RPV models satisfying
the neutrino data in a unified manner, as long as they satisfy the MOM
criteria. Through several examples, we show that this indeed holds for many
interesting scenarios.


  We establish connections between optimal transport theory and the dynamic
version of the Kyle model, including new characterizations of informed trading
profits via conjugate duality and Monge-Kantorovich duality. We use these
connections to extend the model to multiple assets, general distributions, and
risk-averse market makers. With risk-averse market makers, liquidity is lower,
assets exhibit short-term reversals, and risk premia depend on market maker
inventories, which are mean reverting. We illustrate the model by showing that
implied volatilities predict stock returns when there is informed trading in
stocks and options and market makers are risk averse.


  Cold dark matter model predicts that the large-scale structure grows
hierarchically. Small dark matter halos form first. Then, they grow gradually
via continuous merger and accretion. These halos host the majority of baryonic
matter in the Universe in the form of hot gas and cold stellar phase.
Determining how baryons are partitioned into these phases requires detailed
modeling of galaxy formation and their assembly history. It is speculated that
formation time of the same mass halos might be correlated with their baryonic
content. To evaluate this hypothesis, we employ halos of mass above
$10^{14}\,M_{\odot}$ realized by TNG300 solution of the IllustrisTNG project.
Formation time is not directly observable. Hence, we rely on the magnitude gap
between the brightest and the fourth brightest halo galaxy member, which is
shown that traces formation time of the host halo. We compute the conditional
statistics of the stellar and gas content of halos conditioned on their total
mass and magnitude gap. We find a strong correlation between magnitude gap and
gas mass, BCG stellar mass, and satellite galaxies stellar mass, but not the
total stellar mass of halo. Conditioning on the magnitude gap can reduce the
scatter about halo property--halo mass relation and has a significant impact on
the conditional covariance. Reduction in the scatter can be as significant as
30%, which implies more accurate halo mass prediction. Incorporating the
magnitude gap has a potential to improve cosmological constraints using halo
abundance and allows us to gain insight into the baryon evolution within these
systems.


  We present the results of a 6.1 square degree survey for clusters of galaxies
via their Sunyaev- Zel'dovich (SZ) effect at 31 GHz. From late 2005 to mid 2007
the Sunyaev-Zel'dovich Array (SZA) observed four fields of roughly 1.5 square
degrees each. One of the fields shows evidence for significant diffuse Galactic
emission, and we therefore restrict our analysis to the remaining 4.4 square
degrees. We estimate the cluster detectability for the survey using mock
observations of simulations of clusters of galaxies; and determine that, at
intermediate redshifts (z ~ 0.8), the survey is 50% complete to a limiting mass
(M200 rho mean) of ~ 6.0 x 10^14M_{solar}, with the mass limit decreasing at
higher redshifts. We detect no clusters at a significance greater than 5 times
the RMS noise level in the maps, and place an upper limit on \sigma_8, the
amplitude of mass density fluctuations on a scale of 8h^-1 Mpc, of 0.84 + 0.07
at 95% confidence, where the uncertainty reflects calibration and systematic
effects. This result is consistent with estimates from other cluster surveys
and CMB anisotropy experiments.


  We introduce an algorithmic model of heat conduction, the thermodynamic
graph. The thermodynamic graph is analogous to meshes in the finite difference
method in the sense that the calculation of temperature is carried out at the
vertices of the graph, and the edges indicate the direct heat exchange between
the vertices. Recurrence relations of heat conduction in graph are derived
without using of differential equations and based on the coefficients of
thermal conductivity and heat capacity. This approach seems to be more direct
and flexible from the point of view of algorithmic modeling of thermodynamic
process than the derivation of difference schemes from differential equations.
We introduce also the notion of entropy of thermodynamic graph. We find the
maximum length of the time step at which the entropy does not decrease in the
general case. As a result, this give us the accurate boundary of the model
stability.


  A slice regular analogue of the Malmquist-Takenaka system is investigated. It
is proved that they form a complete orthonormal system in the quaternionic
Hardy spaces of the unit ball. The properties of associated projection operator
are studied.
  Key Words and Phrases: Functions of hypercomplex variables, quaternionic
Hardy spaces, series expansions, interpolation, approximation by rational
functions, quaternionic Malmquist-Takenaka system


  We investigated the transport properties of BaFe$_2$As$_2$ single crystals
before and after annealing with BaAs powder. The annealing remarkably improves
transport properties, in particular the magnitude of residual resistivity which
decreases by a factor of more than 10. From the resistivity measurement on
detwinned crystals we found that the anisotropy of the in-plane resistivity is
remarkably diminished after annealing, indicative of dominant contributions to
the charge transport from the carriers with isotropic and high mobility below
magnetostructural transition temperature $T_{\rm s}$ and the absence of nematic
state above $T_{\rm s}$. We found that the Hall resistivity shows strong
non-linearity against magnetic field and the magnetoresistance becomes very
large at low temperatures. These results give evidence for the manifestation of
multiple carriers with distinct characters in the ordered phase below $T_{\rm
s}$. By analyzing the magnetic field dependences, we found that at least three
carriers equally contribute to the charge transport in the ordered phase, which
is in good agreement with the results of recent quantum oscillation
measurements.


  Traditional ad-hoc network packet scheduling schemes cannot fulfill the
requirements of proximity-based ad-hoc social networks (ASNETs) and they do not
behave properly in congested environments. To address this issue, we propose a
user popularity-based packet scheduling scheme for congestion control in ASNETs
called Pop-aware. The proposed algorithm exploits social popularity of sender
nodes to prioritize all incoming flows. Pop-aware also provides fairness of
service received by each flow. We evaluate the performance of Pop-aware through
a series of simulations. In comparison with some existing scheduling
algorithms, Pop-aware performs better in terms of control overhead, total
overhead, average throughput, packet loss rate, packet delivery rate and
average delay.


  We consider a $Q$-polynomial distance-regular graph $\Gamma$ with vertex set
$X$ and diameter $D \geq 3$. For $\mu, \nu \in \lbrace \downarrow, \uparrow
\rbrace$ we define a direct sum decomposition of the standard module $V=\C X$,
called the $(\mu,\nu)$--split decomposition. For this decomposition we compute
the complex conjugate and transpose of the associated primitive idempotents.
Now fix $b,\beta \in \mathbb C$ such that $b \neq 1$ and assume $\Gamma$ has
classical parameters $(D,b,\alpha,\beta)$ with $\alpha = b-1$. Under this
assumption Ito and Terwilliger displayed an action of the $q$-tetrahedron
algebra $\boxtimes_q$ on the standard module of $\Gamma$. To describe this
action they defined eight matrices in
  $\hbox{Mat}_X(\mathbb C)$, called \begin{eqnarray*} \label{eq:list} A,\quad
A^*,\quad B,\quad B^*, \quad K,\quad K^*,\quad \Phi,\quad \Psi. \end{eqnarray*}
  For each matrix in the above list we compute the transpose and complex
conjugate. Using this information we compute the transpose and complex
conjugate for each generator of $\boxtimes_q$ on $V$.


  A study of the order $\alpha_s^2$ corrections to the Drell Yan (non-singlet)
cross section for incoming states of arbitrary longitudinal helicities is
presented. The study is performed in the case of the transverse momentum
distributions, $(q_T)$, of the lepton pair and extends the calculation of Ellis
Martinelli and Petronzio (EMP) to the polarized case. We use the $\bar{MS}$
scheme and the t'Hooft-Veltman regularization for the helicity projectors. From
our results one can obtain the bulk of the totally inclusive NNLO cross section
for the production of a Drell Yan pair in the non-singlet sector by a simple
integration over the heavy photon momentum. We show that in the $\bar{MS}$
helicity is not conserved, unless a finite renormalization is done to reenter
into a physical scheme ($\bar{MS}_p$). This aspect of the calculation follows
the same trend as in polarized production of single and double photon to
$O(\alpha_s^2)$. In the unpolarized limit we reproduce all the results of EMP.


  We consider two-stage hybrid protocols that combine quantum resource and
classical resource to generate classical correlations shared by two separated
players. Our motivation is twofold. First, in the near future the scale of
quantum information processing is quite limited, and when quantum resource
available is not sufficient for certain tasks, a possible way to strengthen the
capability of quantum schemes is introducing extra classical resource. We
analyze the mathematical structures of these hybrid protocols, and characterize
the relation between the amount of quantum resource and classical resource
needed. Second, a fundamental open problem in communication complexity theory
is to describe the advantages of sharing prior quantum entanglement over
sharing prior randomness, which is still widely open. It turns out that our
quantum and classical hybrid protocols provide new insight into this important
problem.


  Object Proposals is a recent computer vision technique receiving increasing
interest from the research community. Its main objective is to generate a
relatively small set of bounding box proposals that are most likely to contain
objects of interest. The use of Object Proposals techniques in the scene text
understanding field is innovative. Motivated by the success of powerful while
expensive techniques to recognize words in a holistic way, Object Proposals
techniques emerge as an alternative to the traditional text detectors.
  In this paper we study to what extent the existing generic Object Proposals
methods may be useful for scene text understanding. Also, we propose a new
Object Proposals algorithm that is specifically designed for text and compare
it with other generic methods in the state of the art. Experiments show that
our proposal is superior in its ability of producing good quality word
proposals in an efficient way. The source code of our method is made publicly
available.


  We study the problem of finding a triangulation T of a planar point set S
such as to minimize the expected distance between two points x and y chosen
uniformly at random from S. By distance we mean the length of the shortest path
between x and y along edges of T. The length of a path is the sum of the
weights of its edges. Edge weights are assumed to be given as part of the
problem for every pair of distinct points (x,y) in S^2.
  In a different variant of the problem, the points are vertices of a simple
polygon and we look for a triangulation of the interior of the polygon that is
optimal in the same sense.
  We prove that a general formulation of the problem in which the weights are
arbitrary positive numbers is strongly NP-complete. For the case when all the
weights are equal we give polynomial-time algorithms. In the end we mention
several open problems.


  Let $R\subset F$ be an extension of real closed fields and ${\mathcal
S}(M,R)$ the ring of (continuous) semialgebraic functions on a semialgebraic
set $M\subset R^n$. We prove that every $R$-homomorphism $\varphi:{\mathcal
S}(M,R)\to F$ is essentially the evaluation homomorphism at a certain point
$p\in F^n$ \em adjacent \em to the extended semialgebraic set $M_F$. This type
of result is commonly known in Real Algebra as Substitution Theorem. In case
$M$ is locally closed, the results are neat while the non locally closed case
requires a more subtle approach and some constructions (weak continuous
extension theorem, \em appropriate immersion \em of semialgebraic sets) that
have interest on their own. We afford the same problem for the ring of bounded
(continuous) semialgebraic functions getting results of a different nature.


  Natural language numbers are an example of compositional structures, where
larger numbers are composed of operations on smaller numbers. Given that
compositional reasoning is a key to natural language understanding, we propose
novel multilingual probing tasks tested on DistilBERT, XLM, and BERT to
investigate for evidence of compositional reasoning over numerical data in
various natural language number systems. By using both grammaticality judgment
and value comparison classification tasks in English, Japanese, Danish, and
French, we find evidence that the information encoded in these pretrained
models' embeddings is sufficient for grammaticality judgments but generally not
for value comparisons. We analyze possible reasons for this and discuss how our
tasks could be extended in further studies.


  If the alpha effect plays a role in the generation of the Sun's magnetic
field, the field should show evidence of magnetic helicity of opposite signs at
large and small length scales. Measuring this faces two challenges: (i) in
weak-field regions, horizontal field measurements are unreliable because of the
pi ambiguity, and (ii) one needs a truly global approach to computing helicity
spectra in the case where one expects a sign reversal across the equator at all
wavenumbers. Here we develop such a method using spin-2 spherical harmonics to
decompose the linear polarization in terms of the parity-even and parity-odd E
and B polarizations, respectively. Using simple one- and two-dimensional
models, we show that the product of the spectral decompositions of E and B,
taken at spherical harmonic degrees that are shifted by one, can act as a proxy
of the global magnetic helicity with a sign that represents that in the
northern hemisphere. We then apply this method to the analysis of solar
synoptic vector magnetograms, from which we extract a pseudo-polarization
corresponding to a "pi-ambiguated" magnetic field, i.e., a magnetic field
vector that has no arrow. We find a negative sign of the global EB helicity
proxy at spherical harmonic degrees of around 6. This could indicate a positive
magnetic helicity at large length scales, but the spectrum fails to capture
clear evidence of the well-known negative magnetic helicity at smaller scales.
This method might also be applicable to stellar and Galactic polarization data.


  Improved lattice actions for Kogut-Susskind quarks have been shown to improve
rotational symmetry and flavor symmetry. In this work we find improved scaling
behavior of the rho and nucleon masses expressed in units of a length scale
obtained from the static quark potential, and better behavior of the Dirac
operator in instanton backgrounds.


  A technique based on speckle contrast measurement is proposed and
demonstrated to track the polarization rotation of an optical beam behind a
scattering layer. It is shown that with the help of a reference speckle pattern
with a known linear polarization, it is possible to track the polarization
rotation behind a scattering layer in a non-invasive manner by measuring the
contrast of the resultant speckle pattern. Working principle and experimental
demonstration are presented, and results are compared with the theoretically
known results in certain cases. Performance of the proposed technique is
quantitatively evaluated by determining the polarization vector rotation behind
the scattering layer for two different cases, namely polarization rotation by a
half wave plate and sugar solution of different concentrations, and good match
are found between the experimental results and theoretical prediction. Issues
like resolution of determination of the polarization vector rotation and sugar
solution concentration are also discussed in the context of the recovered
results. The proposed technique can have potential application in non-invasive
sensing and biomedical imaging.


  Fake news emerged as an apparent global problem during the 2016 U.S.
Presidential election. Addressing it requires a multidisciplinary effort to
define the nature and extent of the problem, detect fake news in real time, and
mitigate its potentially harmful effects. This will require a better
understanding of how the Internet spreads content, how people process news, and
how the two interact. We review the state of knowledge in these areas and
discuss two broad potential mitigation strategies: better enabling individuals
to identify fake news, and intervention within the platforms to reduce the
attention given to fake news. The cooperation of Internet platforms (especially
Facebook, Google, and Twitter) with researchers will be critical to
understanding the scale of the issue and the effectiveness of possible
interventions.


  We discuss the elastic ed scattering beyond Born approximation. It is shown
that the reaction amplitude contains six generalized form factors, but only
three linearly independent combinations of them (we call them generalized
charge, quadrupole and magnetic form factors) contribute to the reaction cross
section in the second order perturbation theory. We examine two-photon exchange
and find that it includes two types of diagrams, when two virtual photons
interact with the same nucleon and when the photons interact with different
nucleons. Estimations based on nonrelativistic calculations with the deuteron
wave function for realistic NN potential show that the main contribution to the
generalized charge, quadrupole and magnetic form factors comes from diagrams of
the first type.


  We derive semi-analytic solutions for optically thick, super-Eddington
stellar winds, induced by an assumed steady energy addition $\Delta {\dot E}$
concentrated around a near-surface heating radius $R$ in a massive star of
central luminosity $L_\ast$. We show that obtaining steady wind solutions
requires both that the resulting total luminosity $L_o = L_\ast + \Delta {\dot
E}$ exceed the Eddington luminosity, $\Gamma_o \equiv L_o/L_{Edd} > 1$, and
that the induced mass loss rate be such that the "photon-tiring" parameter $m
\equiv {\dot M} GM/R L_o \le 1-1/\Gamma_o$, ensuring the luminosity is
sufficient to overcome the gravitational potential $GM/R$. Our analysis unifies
previous super-Eddington wind models that either: (1) assumed a direct
radiative flux-driving without accounting for the advection of radiative
enthalpy that can become important in such an optically thick flow; or (2)
assumed that such super-Eddington outflows are adiabatic, neglecting the
effects of the diffusive radiative flux. We show that these distinct models
become applicable in the asymptotic limits of small vs. large values of $m
\Gamma_o $, respectively. By solving the coupled differential equations for
radiative diffusion and wind momentum, we obtain general solutions that
effectively bridge the behaviours of these limiting models. Two key scaling
results are for the terminal wind speed to escape speed, which is found to vary
as $v_\infty^2/v_{esc}^2 = \Gamma_o/(1+m \Gamma_o) -1$, and for the final
observed luminosity $L_{ obs}$, which for all allowed steady-solutions with $m
< 1 - 1/\Gamma_o$, exceeds the Eddington luminosity, $L_{obs} > L_{Edd}$. Our
super-Eddington wind solutions have potential applicability for modeling phases
of eruptive mass loss from massive stars, classical novae, and the remnants of
stellar mergers.


  In order to ensure traffic safety through a reduction in fatalities and
accidents, vehicle speed detection is essential. Relentless driving practices
are discouraged by the enforcement of speed restrictions, which are made
possible by accurate monitoring of vehicle speeds. Road accidents remain one of
the leading causes of death in Bangladesh. The Bangladesh Passenger Welfare
Association stated in 2023 that 7,902 individuals lost their lives in traffic
accidents during the course of the year. Efficient vehicle speed detection is
essential to maintaining traffic safety. Reliable speed detection can also help
gather important traffic data, which makes it easier to optimize traffic flow
and provide safer road infrastructure. The YOLOv8 model can recognize and track
cars in videos with greater speed and accuracy when trained under close
supervision. By providing insights into the application of supervised learning
in object identification for vehicle speed estimation and concentrating on the
particular traffic conditions and safety concerns in Bangladesh, this work
represents a noteworthy contribution to the area. The MAE was 3.5 and RMSE was
4.22 between the predicted speed of our model and the actual speed or the
ground truth measured by the speedometer Promising increased efficiency and
wider applicability in a variety of traffic conditions, the suggested solution
offers a financially viable substitute for conventional approaches.


  Transition metal oxide memristors, or resistive random-access memory (RRAM)
switches, are under intense development for storage-class memory because of
their favorable operating power, endurance, speed, and density. Their
commercial deployment critically depends on predictive compact models based on
understanding nanoscale physico-chemical forces, which remains elusive and
controversial owing to the difficulties in directly observing atomic motions
during resistive switching, Here, using scanning transmission synchrotron x-ray
spectromicroscopy to study in-situ switching of hafnium oxide memristors, we
directly observed the formation of a localized oxygen-deficiency-derived
conductive channel surrounded by a low-conductivity ring of excess oxygen.
Subsequent thermal annealing homogenized the segregated oxygen, resetting the
cells towards their as-grown resistance state. We show that the formation and
dissolution of the conduction channel are successfully modeled by radial
thermophoresis and Fick diffusion of oxygen atoms driven by Joule heating. This
confirmation and quantification of two opposing nanoscale radial forces that
affect bipolar memristor switching are important components for any future
physics-based compact model for the electronic switching of these devices.


  An artificial stock market is established based on multi-agent . Each agent
has a limit memory of the history of stock price, and will choose an action
according to his memory and trading strategy. The trading strategy of each
agent evolves ceaselessly as a result of self-teaching mechanism. Simulation
results exhibit that large events are frequent in the fluctuation of the stock
price generated by the present model when compared with a normal process, and
the price returns distribution is L\'{e}vy distribution in the central part
followed by an approximately exponential truncation. In addition, by defining a
variable to gauge the "evolvement complexity" of this system, we have found a
phase cross-over from simple-phase to complex-phase along with the increase of
the number of individuals, which may be a ubiquitous phenomenon in multifarious
real-life systems.


  Active surveillance (AS) using repeated biopsies to monitor disease
progression has been a popular alternative to immediate surgical intervention
in cancer care. However, a biopsy procedure is invasive and sometimes leads to
severe side effects of infection and bleeding. To reduce the burden of repeated
surveillance biopsies, biomarker-assistant decision rules are sought to replace
the fix-for-all regimen with tailored biopsy intensity for individual patients.
Constructing or evaluating such decision rules is challenging. The key AS
outcome is often ascertained subject to interval censoring. Furthermore,
patients will discontinue their participation in the AS study once they receive
a positive surveillance biopsy. Thus, patient dropout is affected by the
outcomes of these biopsies. In this work, we propose a nonparametric
kernel-based method to estimate the true positive rates (TPRs) and true
negative rates (TNRs) of a tailored AS strategy, accounting for interval
censoring and immediate dropouts. Based on these estimates, we develop a
weighted classification framework to estimate the optimal tailored AS strategy
and further incorporate the cost-benefit ratio for cost-effectiveness in
medical decision-making. Theoretically, we provide a uniform generalization
error bound of the derived AS strategy accommodating all possible trade-offs
between TPRs and TNRs. Simulation and application to a prostate cancer
surveillance study show the superiority of the proposed method.


  Molecularly targeted contrast enhanced ultrasound (mCEUS) is a clinically
promising approach for early cancer detection through targeted imaging of
VEGFR2 (KDR) receptors. We have developed computational enhancement techniques
for mCEUS tailored to address the unique challenges of imaging contrast
accumulation in humans. These techniques utilize dynamic analysis to
distinguish molecularly bound contrast agent from other contrast-mode signal
sources, enabling analysis of contrast agent accumulation to be performed
during contrast bolus arrival when the signal due to molecular binding is
strongest.
  Applied to the 18 human patient examinations of the first-in-human molecular
ultrasound breast lesion study, computational enhancement improved the ability
to differentiate between pathology-proven lesion and pathology-proven normal
tissue in real-world human examination conditions that involved both patient
and probe motion, with improvements in contrast ratio between lesion and normal
tissue that in most cases exceed an order of magnitude (10x). Notably,
computational enhancement eliminated a false positive result in which tissue
leakage signal was misinterpreted by radiologists to be contrast agent
accumulation.


  We study the excitonic coupling and homogeneous spectral line width of brick
layer J-aggregate films. We begin by analysing the structural information
revealed by the two-exciton states probed in two-dimensional spectra. Our first
main result is that the relation between the excitonic couplings and the
spectral shift in a two-dimensional structure is different (larger shift for
the same nearest neighbour coupling) from that in a one-dimensional structure,
which leads to an estimation of dipolar coupling in two-dimensional lattices.
We next investigate the mechanisms of homogeneous broadening - population
relaxation and pure dephasing - and evaluate their relative importance in
linear and two-dimensional aggregates. Our second main result is that pure
dephasing dominates the line width in two-dimensional systems up to a crossover
temperature, which explains the linear temperature dependence of the
homogeneous line width. This is directly related to the decreased density of
states at the band edge when compared with linear aggregates, thus reducing the
contribution of population relaxation to dephasing. Pump-probe experiments are
suggested to directly measure the lifetime of the bright state and can
therefore support the proposed model.


  We present a preliminary investigation of the spectral energy distributions
(SEDs), and star-formation properties of a sample of Mid-IR selected Quasars.
The mid-infrared SEDs of our objects are consistent with that expected from
clumpy torus models. At longer wavelengths, the radio to infrared ratios of
several objects are consistent with those of star-forming galaxies.


  We investigate models in minimal supergravity parameter space which contain
metastable sleptons. We find the luminosity required to determine the slepton
spin in these scenarios, and apply our analysis to two benchmark models. We
show that the spin of the slepton in one of the benchmark models can be
determined with less than 30 fb$^{-1}$ of data, while the slepton spin in the
other model can be determined with roughly 40 fb$^{-1}$ of data. We show how
our analysis can be applied to other models, and give an estimate of the
luminosity needed for a spin determination as a function of slepton mass. This
analysis can be used to distinguish supersymmetry and extra dimensions.


  Dynamic aperture (DA) is an important nonlinear property of a storage ring
lattice, which has a dominant effect on beam injection efficiency and beam
lifetime. Generally, minimizing both resonance driving terms (RDTs) and
amplitude dependent tune shifts is an essential condition for enlarging the DA.
In this paper, we study the correlation between the fluctuation of RDTs along
the ring and the DA area with double- and multi-bend achromat lattices. It is
found that minimizing the RDT fluctuations is more effective than minimizing
RDTs themselves in enlarging the DA, and thus can serve as a very powerful
indicator in the DA optimization. Besides, it is found that minimizing
lower-order RDT fluctuations can also reduce higher-order RDTs, which are not
only more computationally complicated but also more numerous. The effectiveness
of controlling the RDT fluctuations in enlarging the DA confirms that the local
cancellation of nonlinear effects used in some diffraction-limited storage ring
lattices is more effective than the global cancellation.


  We explore the hypothesis that the unexplained data from LSND and MiniBooNE
are evidence for a new, heavy neutrino mass-eigenstate that mixes with the
muon-type neutrino and decays into an electron-type neutrino and a new, very
light scalar particle. We consider two different decay scenarios, one with
Majorana neutrinos, one with Dirac neutrinos; both fit the data equally well.
We find a reasonable, albeit not excellent, fit to the data of MiniBooNE and
LSND. The decaying-sterile-neutrino hypothesis, however, cleanly evades
constraints from disappearance searches and precision measurements of leptonic
meson decays, as long as $1~{\rm MeV}\gtrsim m_4\gtrsim 10$~keV. The SBN
program at Fermilab should be able to definitively test the
decaying-sterile-neutrino hypothesis.


  We demonstrate `deterministic' launching of propagative quantum
surface-plasmon polaritons at freely chosen positions on gold plasmonic
receptacles. This is achieved by using as plasmon launcher a near-field
scanning optical source made of a diamond nanocrystal with two Nitrogen-Vacancy
color-center occupancy. Our demonstration relies on leakage-radiation
microscopy of a thin homogeneous gold film and on near-field optical microscopy
of a nanostructured thick gold film. Our work paves the way to future
fundamental studies and applications in quantum plasmonics that require an
accurate positioning of single-plasmon sources and may open a new branch in
plasmonics and nanophotonics, namely scanning quantum plasmonics.


  In this paper, we define the Horadam hybrid quaternions and give some of
their properties. Moreover, we investigate the relations between the Fibonacci
hybrid quaternions and the Lucas hybrid quaternions which connected the
Fibonacci quternions and Lucas quaternions. Furthermore, we give the Binet
formulas and Cassini identities for these quaternions.


  In this paper, we consider a molecular descriptor called the Wiener polarity
index, which is defined as the number of unordered pairs of vertices at
distance three in a graph. Molecular descriptors play a fundamental role in
chemistry, materials engineering, and in drug design since they can be
correlated with a large number of physico-chemical properties of molecules. As
the main result, we develop a method for computing the Wiener polarity index
for two basic and most commonly studied families of molecular graphs, benzenoid
systems and carbon nanotubes. The obtained method is then used to find a closed
formula for the Wiener polarity index of any benzenoid system. Moreover, we
also compute this index for zig-zag and armchair nanotubes.


  Let $L$ denote the Laplacian matrix of a graph $G$. We study continuous
quantum walks on $G$ defined by the transition matrix
$U(t)=\exp\left(itL\right)$. The initial state is of the pair state form,
$e_a-e_b$ with $a,b$ being any two vertices of $G$. We provide two ways to
construct infinite families of graphs that have perfect pair transfer. We study
a "transitivity" phenomenon which cannot occur in vertex state transfer. We
characterize perfect pair state transfer on paths and cycles. We also study the
case when quantum walks are generated by the unsigned Laplacians of underlying
graphs and the initial states are of the plus state form, $e_a+e_b$. When the
underlying graphs are bipartite, plus state transfer is equivalent to pair
state transfer.


  Successful realization of polarized Drell-Yan physics program is one of the
main goals of the second stage of the COMPASS experiment. Drell-Yan
measurements with high energy (190 GeV/c) pion beam and transversely polarized
NH3 target have been initiated by a pilot-run in the October 2014 and will be
followed by 140 days of data taking in 2015. In the past twelve years COMPASS
experiment performed series of SIDIS measurements with high energy muon beam
and transversely polarized deuteron and proton targets. Results obtained for
Sivers effect and other target transverse spin dependent and unpolarized
azimuthal asymmetries in SIDIS serve as an important input for general
understanding of spin-structure of the nucleon and are being used in numerous
theoretical and phenomenological studies being carried out in the field of
transvers-spin physics. Measurement of the Sivers and all other azimuthal
effects in polarized Drell-Yan at COMPASS will reveal another side of the
spin-puzzle providing a link between SIDIS and Drell-Yan branches. This will be
a unique possibility to test universality and key-features of transverse
momentum dependent distribution functions (TMD PDFs) using essentially same
experimental setup and exploring same kinematic domain. In this review man
physics aspects of future COMPASS polarized Drell-Yan measurement of azimuthal
transverse spin asymmetries will be presented, giving a particular emphasis on
the link with very recent COMPASS results obtained for SIDIS transverse spin
asymmetries from four "Drell-Yan" $Q^2$-ranges.


  A fundamental assumption in software security is that memory contents do not
change unless there is a legitimate deliberate modification. Classical fault
attacks show that this assumption does not hold if the attacker has physical
access. Rowhammer attacks showed that local code execution is already
sufficient to break this assumption. Rowhammer exploits parasitic effects in
DRAM to modify the content of a memory cell without accessing it. Instead,
other memory locations are accessed at a high frequency. All Rowhammer attacks
so far were local attacks, running either in a scripted language or native
code. In this paper, we present Nethammer. Nethammer is the first truly remote
Rowhammer attack, without a single attacker-controlled line of code on the
targeted system. Systems that use uncached memory or flush instructions while
handling network requests, e.g., for interaction with the network device, can
be attacked using Nethammer. Other systems can still be attacked if they are
protected with quality-of-service techniques like Intel CAT. We demonstrate
that the frequency of the cache misses is in all three cases high enough to
induce bit flips. We evaluated different bit flip scenarios. Depending on the
location, the bit flip compromises either the security and integrity of the
system and the data of its users, or it can leave persistent damage on the
system, i.e., persistent denial of service. We investigated Nethammer on
personal computers, servers, and mobile phones. Nethammer is a security
landslide, making the formerly local attack a remote attack.


  In this paper we consider a fully third order nonlinear boundary value
problem which is of great interest of many researchers. First we establish the
existence, uniqueness of solution. Next, we propose simple iterative methods on
both continuous and discrete levels. We prove that the discrete methods are of
second order and third accuracy due to the use of appropriate formulas for
numerical integration and obtain estimate for total error. Some examples
demonstrate the validity of the obtained theoretical results and the efficiency
of the iterative method.


  The analysis of branching problems for restriction of representations brings
the concept of symmetry breaking transform and holographic transform. Symmetry
breaking operators decrease the number of variables in geometric models,
whereas holographic operators increase it. Various expansions in classical
analysis can be interpreted as particular occurrences of these transforms. From
this perspective, we investigate two remarkable families of differential
operators: the Rankin-Cohen operators and the holomorphic Juhl conformally
covariant operators. Then we establish for the corresponding symmetry breaking
transforms the Parseval-Plancherel type theorems and find explicit inversion
formul{\ae} with integral expression of holographic operators.
  The proof uses the F-method which provides a duality between symmetry
breaking operators in the holomorphic model and holographic operators in the
$L^2$-model, leading us to deep links between special orthogonal polynomials
and branching laws for infinite-dimensional representations of real reductive
Lie groups.


  Many Galactic sources of gamma rays, such as supernova remnants, are expected
to produce neutrinos with a typical energy cutoff well below 100 TeV. For the
IceCube Neutrino Observatory located at the South Pole, the southern sky,
containing the inner part of the Galactic plane and the Galactic Center, is a
particularly challenging region at these energies, because of the large
background of atmospheric muons. In this paper, we present recent advancements
in data selection strategies for track-like muon neutrino events with energies
below 100 TeV from the southern sky. The strategies utilize the outer detector
regions as veto and features of the signal pattern to reduce the background of
atmospheric muons to a level which, for the first time, allows IceCube
searching for point-like sources of neutrinos in the southern sky at energies
between 100 GeV and several TeV in the muon neutrino charged current channel.
No significant clustering of neutrinos above background expectation was
observed in four years of data recorded with the completed IceCube detector.
Upper limits on the neutrino flux for a number of spectral hypotheses are
reported for a list of astrophysical objects in the southern hemisphere.


  Predicting the trajectory of an ego vehicle is a critical component of
autonomous driving systems. Current state-of-the-art methods typically rely on
Deep Neural Networks (DNNs) and sequential models to process front-view images
for future trajectory prediction. However, these approaches often struggle with
perspective issues affecting object features in the scene. To address this, we
advocate for the use of Bird's Eye View (BEV) perspectives, which offer unique
advantages in capturing spatial relationships and object homogeneity. In our
work, we leverage Graph Neural Networks (GNNs) and positional encoding to
represent objects in a BEV, achieving competitive performance compared to
traditional DNN-based methods. While the BEV-based approach loses some detailed
information inherent to front-view images, we balance this by enriching the BEV
data by representing it as a graph where relationships between the objects in a
scene are captured effectively.


  We give a leisurely proof of a result of Ferguson--Lacey (math.CA/0104144)
and Lacey--Terwelleger (math.CA/0601192) on a Nehari theorem for "little"
Hankel operators on a polydisk. If H_b is a little Hankel operator with symbol
b on product Hardy space we have || H_b || \simeq || b ||_{BMO} where BMO is
the product BMO space identified by Chang and Fefferman. This article begins
with the classical Nehari theorem, and presents the necessary background for
the proof of the extension above. The proof of the extension is an induction on
parameters, with a bootstrapping argument. Some of the more technical details
of the earlier proofs are now seen as consequences of a paraproduct theory.


  We study the possibility of generating tiny neutrino mass through a
combination of type I and type II seesaw mechanism within the framework of an
abelian extension of standard model. The model also provides a naturally stable
dark matter candidate in terms of the lightest neutral component of a scalar
doublet. We compute the relic abundance of such a dark matter candidate and
also point out how the strength of type II seesaw term can affect the relic
abundance of dark matter. Such a model which connects neutrino mass and dark
matter abundance has the potential of being verified or ruled out in the
ongoing neutrino, dark matter as well as accelerator experiments.


  The time-reparametrization-invariant dynamics of a relativistic string is
studied in the Dirac generalized Hamiltonian theory by resolving the first
class constraints. The reparametrization-invariant evolution parameter is
identified with the time-like coordinate of the "center of mass" of a string
which is separated from local degrees of freedom by transformations conserving
the group of diffeomorphisms of the generalized Hamiltonian formulation and the
Poincare covariance of local constraints. To identify the "center of mass"
time-like coordinate with the invariant proper time (measured by an observer in
the comoving frame of reference), we apply the Levi-Civita - Shanmugadhasan
canonical transformations which convert the global (mass-shell) constraint into
a new momentum, so that the corresponding gauge is not needed for the
Hamiltonian reduction. The resolving of local constraints leads to an
"equivalent unconstrained system" in the reduced phase space with the
Roehrlich-type Hamiltonian of evolution with respect to the proper time.


  We propose that local quantum systems defined on expander graphs provide a
simple microscopic model for thermalization on quantum horizons. Such systems
are automatically fast scramblers and are motivated from the membrane paradigm
by a conformal transformation to the so-called optical metric.


  The study of anisotropies in the Cosmic Microwave Background radiation is
progressing at a phenomenal rate, both experimentally and theoretically. These
anisotropies can teach us an enormous amount about the way that fluctuations
were generated and the way they subsequently evolved into the clustered
galaxies which are observed today. In particular, on sub-degree scales the rich
structure in the anisotropy spectrum is the consequence of gravity-driven
acoustic oscillations occurring before the matter in the universe became
neutral. The frozen-in phases of these sound waves imprint a dependence on many
cosmological parameters, that we may be on the verge of extracting.


  Recently proposed relation between conformal field theories in two dimensions
and supersymmetric gauge theories in four dimensions predicts the existence of
the distinguished basis in the space of local fields in CFT. This basis has a
number of remarkable properties, one of them is the complete factorization of
the coefficients of the operator product expansion. We consider a particular
case of the U(r) gauge theory on C^2/Z_p which corresponds to a certain coset
conformal field theory and describe the properties of this basis. We argue that
in the case p=2, r=2 there exist different bases. We give an explicit
construction of one of them. For another basis we propose the formula for
matrix elements.


  To a proper inclusion N\subset M of II_1 factors of finite Jones index [M:N],
we associate an ergodic C*-action of the quantum group S_\mu U(2). The
deformation parameter is determined by -1<\mu<0 and [M:N]=|\mu+\mu^{-1}|. The
higher relative commutants can be identified with the spectral spaces of the
tensor powers of the defining representation of the quantum group.
  This ergodic action may be thought of as a virtual subgroup of S_\mu U(2) in
the sense of Mackey arising from the tensor category generated by M regarded as
a bimodule over N. \mu is negative as M is a real bimodule.


  Persistence is defined as the probability that the local value of a
fluctuating field remains at a particular state for a certain amount of time,
before being switched to another state. The concept of persistence has been
found to have many diverse practical applications, ranging from non-equilibrium
statistical mechanics to financial dynamics to distribution of time scales in
turbulent flows and many more. In this study, we carry out a detailed analysis
of the statistical characteristics of the persistence probability density
functions (PDFs) of velocity and temperature fluctuations in the surface layer
of a convective boundary layer, using a field-experimental dataset. Our results
demonstrate that for the time scales smaller than the integral scales, the
persistence PDFs of turbulent velocity and temperature fluctuations display a
clear power-law behaviour, associated with self-similar eddy cascading
mechanism. Moreover, we also show that the effects of non-Gaussian temperature
fluctuations act only at those scales which are larger than the integral
scales, where the persistence PDFs deviate from the power-law and drop
exponentially. Furthermore, the mean time scales of the negative temperature
fluctuation events persisting longer than the integral scales are found to be
approximately equal to twice the integral scale in highly convective
conditions. However, with stability this mean time scale gradually decreases to
almost being equal to the integral scale in the near neutral conditions.
Contrarily, for the long positive temperature fluctuation events, the mean time
scales remain roughly equal to the integral scales, irrespective of stability.


  We argue that the paradoxes associated with infinitely degenerate states,
which plague relic particle scenarios for the endpoint of black hole
evaporation, may be absent when the relics are horned particles. Most of our
arguments are based on simple observations about the classical geometry of
extremal dilaton black holes, but at a crucial point we are forced to speculate
about classical solutions to string theory in which the infinite coupling
singularity of the extremal dilaton solution is shielded by a condensate of
massless modes propagating in its infinite horn. We use the nonsingular $c=1$
solution of (1+1) dimensional string theory as a crude model for the properties
of the condensate. We also present a brief discussion of more general relic
scenarios based on large relics of low mass.


  We propose a new approach to superrigidity phenomena and implement it for
lattice representations and measurable cocycles with homeomorphisms of the
circle as the target group. We are motivated by Ghys' theorem stating that any
representation $\ro:\Gamma\to\Homeo_+(S^1)$ of an irreducible lattice $\Gamma$
in a semi-simple real Lie group $G$ of higher rank, either has a finite orbit
or, up to a semi-conjugacy, extends to $G$ which acts through an epimorphism
$G\to\PSL_{2}(\RR)$.
  Our approach, based on the study of abstract boundary theory and,
specifically, on the notion of a generalized Weyl group, allows: (A) to prove a
similar superrigidity result for irreducible lattices in products
$G=G_{1}\times... G_{n}$ of $n\ge 2$ general locally compact groups, (B) to
give a new (shorter) proof of Ghys' theorem, (C) to establish a commensurator
superrigidity for general locally compact groups, (D) to prove first
superrigidity theorems for $\tilde{A}_{2}$ groups.
  This approach generalizes to the setting of measurable circle bundles; in
this context we prove cocycle versions of (A), (B) and (D). This is the first
part of a broader project of studying superrigidity via generalized Weyl
groups.


  A common approach for defining a reward function for Multi-objective
Reinforcement Learning (MORL) problems is the weighted sum of the multiple
objectives. The weights are then treated as design parameters dependent on the
expertise (and preference) of the person performing the learning, with the
typical result that a new solution is required for any change in these
settings. This paper investigates the relationship between the reward function
and the optimal value function for MORL; specifically addressing the question
of how to approximate the optimal value function well beyond the set of weights
for which the optimization problem was actually solved, thereby avoiding the
need to recompute for any particular choice. We prove that the value function
transforms smoothly given a transformation of weights of the reward function
(and thus a smooth interpolation in the policy space). A Gaussian process is
used to obtain a smooth interpolation over the reward function weights of the
optimal value function for three well-known examples: GridWorld, Objectworld
and Pendulum. The results show that the interpolation can provide very robust
values for sample states and action space in discrete and continuous domain
problems. Significant advantages arise from utilizing this interpolation
technique in the domain of autonomous vehicles: easy, instant adaptation of
user preferences while driving and true randomization of obstacle vehicle
behavior preferences during training.


  Generalised indiscernibles highlight a strong link between model theory and
structural Ramsey theory. In this paper, we use generalised indiscernibles as
tools to prove results in both these areas. More precisely, we first show that
a reduct of an ultrahomogenous $\aleph_0$-categorical structure which has
higher arity than the original structure cannot be Ramsey. In particular, the
only nontrivial Ramsey reduct of the generically ordered random $k$-hypergraph
is the linear order. We then turn our attention to model-theoretic dividing
lines that are characterised by collapsing generalised indiscernibles, and
prove, for these dividing lines, several transfer principles in (full and
lexicographic) products of structures. As an application, we construct new
algorithmically tame classes of graphs.


  Metals composed of weakly-coupled, stacked layers possess a Fermi surface
that slightly varies in size along the stacking direction. This appears in de
Haas-van Alphen (dHvA) oscillations of the magnetisation with magnetic field as
two close frequencies, corresponding to the two extremal Fermi surface
cross-sectional areas. We show that, for layered materials of sufficiently high
mobility, Coulomb interactions can have a dramatic effect on the form of the
dHvA oscillations: there is also generically an oscillation at the small
difference of the two large frequencies. We determine the size and form of this
effect, and show that it probes the short-range part of the Coulomb
interactions within the layered material. We argue that this interaction effect
may explain recent experimental observations of anomalous low-frequency dHvA
oscillations in the ultrapure delafossites.


  Let $R$ be an algebraic curvature tensor on a vector space of signature
$(p,q)$ defining a spacelike Jordan Osserman Jacobi operator $\JJ_R$. We show
that the eigenvalues of $\JJ_R$ are real and that $\JJ_R$ is diagonalizable if
$p<q$.


  We develop inference procedures robust to general forms of weak dependence.
The procedures utilize test statistics constructed by resampling in a manner
that does not depend on the unknown correlation structure of the data. We prove
that the statistics are asymptotically normal under the weak requirement that
the target parameter can be consistently estimated at the parametric rate. This
holds for regular estimators under many well-known forms of weak dependence and
justifies the claim of dependence-robustness. We consider applications to
settings with unknown or complicated forms of dependence, with various forms of
network dependence as leading examples. We develop tests for both moment
equalities and inequalities.


  In recent years it became clear that a population of galaxies radiating most
of their power in the far-infrared contribute an important part of the whole
star formation activity in the universe. These galaxies emit up to 99% of their
energy output in the infrared by dust. The optical properties of dust in
galaxies are thus very important to understand this population. Mayo Greenberg
who has brought many of the ideas underlying our understanding of the physics
of interstellar dust has been a pioneer in predicting the important role of
organic solid material formed on dust grains in molecular clouds and of
potential importance of transient heating of very small particles. It appeared
with the ISO data on galaxies and even more today with the first Spitzer data
that these mechanisms are important globally for the observations of infrared
galaxies at significant redshifts. The understanding of their evolution is one
of the keys to the understanding of galaxy built up and evolution.


  We demonstrate that an $SU(2)_L\times U(1)_Y$ model with extended Higgs
sector gives rise to bi-maximal neutrino mixing through the incorporation of
SO(3) flavour symmetry and discrete symmetry. The neutrino and the charged
lepton masses are generated due to higher dimensional terms. The hierarchical
structures of neutrinos and charged leptons are obtained due to inclusion of
SO(3) flavour symmetry and discrete symmetry.The model can accommodate the
vacuum oscillation solution of solar neutrino problem, through reasonable
choice of model parameters along with the atmospheric neutrino experimental
result.


  Recently, gate-defined Josephson junctions based on magic-angle twisted
bilayer graphene (MATBG) have been fabricated. In such a junction, local
electrostatic gating can create two superconducting regions connected by an
interaction-driven valley-polarized state as the weak link. Due to the
spontaneous time-reversal and inversion symmetry breaking of the
valley-polarized state, novel phenomena such as the Josephson diode effect have
been observed without applying external fields. Importantly, when the so-called
nonreciprocity efficiency (which measures the sign and strength of the
Josephson effect) changes sign, the energy-phase relation of the junction is
approximate $F(\phi) \approx \cos(2\phi)$ where $F$ is the free energy and
$\phi$ is the phase difference of the two superconductors. In this work, we
show that such a MATBG-based Josephson junction, when shunted by a capacitor,
can be used to realize the long-sought-after $0$-$\pi$ qubits which are
protected from local perturbation-induced decoherence. Interestingly, by
changing the junction parameters, transmon-like qubits with large anharmonicity
can also be realized. In short, by utilizing the novel interaction-driven
valley-polarized state in MATBG, a single gate-defined Josephson junction can
be used to replace complicated superconducting circuits for realizing qubits
that are protected from local perturbations.


  The time-fractional porous medium equation is an important model of many
hydrological, physical, and chemical flows. We study its self-similar
solutions, which make up the profiles of many important experimentally measured
situations. We prove that there is a unique solution to the general
initial-boundary value problem in the one-dimensional setting. When
supplemented with boundary conditions from the physical models, the problem
exhibits a self-similar solution described with the use of the Erd\'elyi-Kober
fractional operator. Using a backward shooting method, we show that there
exists a unique solution to our problem.
  The shooting method is not only useful in deriving the theoretical results.
We utilize it to devise an efficient numerical scheme to solve the governing
problem along with two ways of discretizing the Erd\'elyi-Kober fractional
derivative. Since the latter is a nonlocal operator, its numerical realization
has to include some truncation. We find the correct truncation regime and prove
several error estimates. Furthermore, the backward shooting method can be used
to solve the main problem, and we provide a convergence proof. The main
difficulty lies in the degeneracy of the diffusivity. We overcome it with some
regularization. Our findings are supplemented with numerical simulations that
verify the theoretical findings.


  Phonon heat conduction over length scales comparable to their mean free paths
is a topic of considerable interest for basic science and thermal management
technologies. Although the failure of Fourier's law beyond the diffusive regime
is well understood, debate exists over the proper physical description of
thermal transport in the ballistic to diffusive crossover. Here, we derive a
generalized Fourier's law that links the heat flux and temperature fields,
valid from ballistic to diffusive regimes and for general geometries, using the
Peierls-Boltzmann transport equation within the relaxation time approximation.
This generalized Fourier's law predicts that thermal conductivity not only
becomes nonlocal at length scales smaller than phonon mean free paths, but also
requires the inclusion of an inhomogeneous nonlocal source term that has been
previously neglected. We demonstrate the validity of this generalized Fourier's
law through direct comparison with time-domain thermoreflectance (TDTR)
measurements in the nondiffusive regime without adjustable parameters.
Furthermore, we show that interpreting experimental data without this
generalized Fourier's law leads to inaccurate measurement of thermal transport
properties.


  An extension of the H-theorem for dissipative particle dynamics (DPD) to the
case of a multi-component fluid is made. Detailed balance and an additional
H-theorem are proved for an energy-conserving version of the DPD algorithm. The
implications of these results for the statistical mechanics of the method are
discussed.


  Many computer vision applications require robust and efficient estimation of
camera geometry from a minimal number of input data measurements, i.e., solving
minimal problems in a RANSAC framework. Minimal problems are usually formulated
as complex systems of sparse polynomials. The systems usually are
overdetermined and consist of polynomials with algebraically constrained
coefficients. Most state-of-the-art efficient polynomial solvers are based on
the action matrix method that has been automated and highly optimized in recent
years. On the other hand, the alternative theory of sparse resultants and
Newton polytopes has been less successful for generating efficient solvers,
primarily because the polytopes do not respect the constraints on the
coefficients. Therefore, in this paper, we propose a simple iterative scheme to
test various subsets of the Newton polytopes and search for the most efficient
solver. Moreover, we propose to use an extra polynomial with a special form to
further improve the solver efficiency via a Schur complement computation. We
show that for some camera geometry problems our extra polynomial-based method
leads to smaller and more stable solvers than the state-of-the-art Grobner
basis-based solvers. The proposed method can be fully automated and
incorporated into existing tools for automatic generation of efficient
polynomial solvers. It provides a competitive alternative to popular Grobner
basis-based methods for minimal problems in computer vision. We also study the
conditions under which the minimal solvers generated by the state-of-the-art
action matrix-based methods and the proposed extra polynomial resultant-based
method, are equivalent. Specifically we consider a step-by-step comparison
between the approaches based on the action matrix and the sparse resultant,
followed by a set of substitutions, which would lead to equivalent minimal
solvers.


  We describe types of double magma associated with Ward quasigroups, double
Ward quasigroups, their duals and the groups they generate. Ward quasigroup
double magma and unipotent, right modular, left unital double magma are proved
to be improper. Necessary and sufficient conditions are found on a pair of
right modular, left unital magma (and right-left unital magma) for them to form
a double magma. We give further insight into the intimate connection between
mediality and the interchange law by proving that a quasigroup is medial if and
only if any pair of its parastrophic binary operations satisfy the interchange
law.


  We study the heating of the cool cores in galaxy clusters by cosmic-rays
(CRs) accelerated by the central active galactic nuclei (AGNs). We especially
focus on the stability of the heating. The CRs stream with Alfv\'en waves in
the intracluster medium (ICM) and heat the ICM. First, assuming that the
heating and radiative cooling is balanced, we search steady state solutions for
the ICM and CR profiles of clusters by solving a boundary value problem. The
boundary conditions are set so that the solutions are consistent with
observations of clusters. We find steady state solutions if the magnetic fields
are strong enough and the association between the magnetic fields and the ICM
is relatively weak. Then, we analyse the stability of the solutions via a
Lagrangian perturbation analysis and find that the solutions are globally
stable. We confirm the results by numerical simulations. Using the steady state
solutions as the initial conditions, we follow the evolution of the profiles
for 100 Gyr. We find that the profiles do not evolve on time scales much larger
than cluster lifetimes. These results, as well as consistency with observations
of radio mini-halos, suggest that the CR heating is a promising mechanism to
solve the so-called "cooling flow problem".


  The productions of data with internationally agreed standards are the common
pursuits for all astronomical observation instruments both ground and
spacebased undoubtedly, Since standard data to facilitate the use of scientific
research. The routine solar observations are available at Huairou Solar
Observing Station (HSOS) since 1987, which should be regarded as one main solar
observations in the world. The data storage medium and format at HSOS
experienced several changes, so there exist some inconveniences for solar
physicist. This paper shows that the observations data of HSOS are further
processed both for storage medium and format toward international standards, in
order to explore HSOS observations data for scientific research.


  Motivated by the discovery of the non-thermal Fermi bubble features both
below and above the Galactic plane, we investigate a scenario in which these
bubbles are formed through Galacto-centric outflow. Cosmic rays (CR) both
diffusing and advecting within a Galactic breeze outflow, interacting with the
ambient gas present, give rise to gamma-ray emission, providing an
approximately flat surface brightness profile of this emission, as observed.
Applying the same outflow profile further out within the disk, the resultant
effects on the observable CR spectral properties are determined. A hardening in
the spectra due to the competition of advective and diffusive propagation
within a particular energy range is noted, even in the limiting case of equal
CR diffusion coefficients in the disk and halo. It is postulated that this
hardening effect may relate to the observed hardening feature in the CR
spectrum at a rigidity of $\approx 200$ GV.


  In this talk, we studied the implication of the constraint on the reheating
temperature coming from the gravitino problem on models of leptogenesis. We
point out that in supersymmetric extensions of the standard model, all existing
models of neutrino masses and leptogenesis, except the one with right-handed
singlet neutrinos are ruled out for a large range of the gravitino mass.


  For the class of solvable groups of homeomorphisms of the line preserving
orientation and containing a freely acting element, we establish the
metabelianity of the quotient group $G/H_G$, where the elements of the normal
subgroup $H_G$ are stabilizers of the minimal set. This fact is an important
element in the classification theorem, used, in particular, in the study of the
Thompson's group $F$.


  We first propose the concept of Stepanov-like weighted pseudo-almost
automorphic on time-space scales and we apply this type of oscillation to
high-order BAM neural networks with mixed delays. Then, we study the existence
and exponential stability of Sp-weighted pseudo almost automorphic on
time-space scales solutions for the suggested system. Some criteria assuring
the convergence are proposed. Our method is mainly based on the Banachs fixed
point theorem, the theory of calculus on time scales and the
Lyapunov-Krasovskii functional method. Moreover, a numerical example is given
to show the effectiveness of the main results.


  Comparative settings (e.g. pairwise choice, listwise ranking) have been
adopted by a wide range of subjective studies for image quality assessment
(IQA), as it inherently standardizes the evaluation criteria across different
observers and offer more clear-cut responses. In this work, we extend the edge
of emerging large multi-modality models (LMMs) to further advance visual
quality comparison into open-ended settings, that 1) can respond to open-range
questions on quality comparison; 2) can provide detailed reasonings beyond
direct answers. To this end, we propose the Co-Instruct. To train this
first-of-its-kind open-source open-ended visual quality comparer, we collect
the Co-Instruct-562K dataset, from two sources: (a) LLM-merged single image
quality description, (b) GPT-4V "teacher" responses on unlabeled data.
Furthermore, to better evaluate this setting, we propose the MICBench, the
first benchmark on multi-image comparison for LMMs. We demonstrate that
Co-Instruct not only achieves in average 30% higher accuracy than
state-of-the-art open-source LMMs, but also outperforms GPT-4V (its teacher),
on both existing related benchmarks and the proposed MICBench. Our model is
published at https://huggingface.co/q-future/co-instruct.


  The distributed transaction commit problem requires reaching agreement on
whether a transaction is committed or aborted. The classic Two-Phase Commit
protocol blocks if the coordinator fails. Fault-tolerant consensus algorithms
also reach agreement, but do not block whenever any majority of the processes
are working. Running a Paxos consensus algorithm on the commit/abort decision
of each participant yields a transaction commit protocol that uses 2F +1
coordinators and makes progress if at least F +1 of them are working. In the
fault-free case, this algorithm requires one extra message delay but has the
same stable-storage write delay as Two-Phase Commit. The classic Two-Phase
Commit algorithm is obtained as the special F = 0 case of the general Paxos
Commit algorithm.


  Recent years have witnessed the great success of deep learning algorithms in
the geoscience and remote sensing realm. Nevertheless, the security and
robustness of deep learning models deserve special attention when addressing
safety-critical remote sensing tasks. In this paper, we provide a systematic
analysis of backdoor attacks for remote sensing data, where both scene
classification and semantic segmentation tasks are considered. While most of
the existing backdoor attack algorithms rely on visible triggers like squared
patches with well-designed patterns, we propose a novel wavelet transform-based
attack (WABA) method, which can achieve invisible attacks by injecting the
trigger image into the poisoned image in the low-frequency domain. In this way,
the high-frequency information in the trigger image can be filtered out in the
attack, resulting in stealthy data poisoning. Despite its simplicity, the
proposed method can significantly cheat the current state-of-the-art deep
learning models with a high attack success rate. We further analyze how
different trigger images and the hyper-parameters in the wavelet transform
would influence the performance of the proposed method. Extensive experiments
on four benchmark remote sensing datasets demonstrate the effectiveness of the
proposed method for both scene classification and semantic segmentation tasks
and thus highlight the importance of designing advanced backdoor defense
algorithms to address this threat in remote sensing scenarios. The code will be
available online at \url{https://github.com/ndraeger/waba}.


  We derive the moments of the first passage time for Brownian motion
conditioned by either the maximum value or the area swept out by the motion.
These quantities are the natural counterparts to the moments of the maximum
value and area of Brownian excursions of fixed duration, which we also derive
for completeness within the same mathematical framework. Various applications
are indicated.


  The evolution of generative artificial intelligence (GenAI) constitutes a
turning point in reshaping the future of technology in different aspects.
Wireless networks in particular, with the blooming of self-evolving networks,
represent a rich field for exploiting GenAI and reaping several benefits that
can fundamentally change the way how wireless networks are designed and
operated nowadays. To be specific, large GenAI models are envisioned to open up
a new era of autonomous wireless networks, in which multi-modal GenAI models
trained over various Telecom data, can be fine-tuned to perform several
downstream tasks, eliminating the need for building and training dedicated AI
models for each specific task and paving the way for the realization of
artificial general intelligence (AGI)-empowered wireless networks. In this
article, we aim to unfold the opportunities that can be reaped from integrating
large GenAI models into the Telecom domain. In particular, we first highlight
the applications of large GenAI models in future wireless networks, defining
potential use-cases and revealing insights on the associated theoretical and
practical challenges. Furthermore, we unveil how 6G can open up new
opportunities through connecting multiple on-device large GenAI models, and
hence, paves the way to the collective intelligence paradigm. Finally, we put a
forward-looking vision on how large GenAI models will be the key to realize
self-evolving networks.


  A nonlinear stochastic differential equation with the order of nonlinearity
higher than one, with several discrete and distributed delays and time varying
coefficients is considered. It is shown that the sufficient conditions for
exponential mean square stability of the linear part of the considered
nonlinear equation also are sufficient conditions for stability in probability
of the initial nonlinear equation. Some new sufficient condition of stability
in probability for the zero solution of the considered nonlinear non-autonomous
stochastic differential equation is obtained which can be considered as a
multi-condition of stability because it allows to get for one considered
equation at once several different complementary of each other sufficient
stability conditions. The obtained results are illustrated with numerical
simulations and figures.


  We consider the problem of automatically generating longer stories of over
two thousand words. Compared to prior work on shorter stories, long-range plot
coherence and relevance are more central challenges here. We propose the
Recursive Reprompting and Revision framework (Re3) to address these challenges
by (a) prompting a general-purpose language model to construct a structured
overarching plan, and (b) generating story passages by repeatedly injecting
contextual information from both the plan and current story state into a
language model prompt. We then revise by (c) reranking different continuations
for plot coherence and premise relevance, and finally (d) editing the best
continuation for factual consistency. Compared to similar-length stories
generated directly from the same base model, human evaluators judged
substantially more of Re3's stories as having a coherent overarching plot (by
14% absolute increase), and relevant to the given initial premise (by 20%).


  In this paper, we consider homogeneous cosmological solutions in the context
of the Weyl geometrical scalar-tensor theory. Firstly, we exhibit an
anisotropic Kasner type solution taking advantage of some similarities between
this theory and the Brans-Dicke theory. Next, we consider an isotropic model
with a flat spatial section sourced by matter configurations described by a
perfect fluid. In this model, we obtain an analytical solution for the stiff
matter case. For other cases, we carry out a complete qualitative analysis
theory to investigate the general behaviour of the solutions, presenting some
possible scenarios. In this work, we do not consider the presence of the
cosmological constant nor do we take any potential of the scalar field into
account. Because of this, we do not find any solution describing the
acceleration of the universe.


  The purpose of this work is to draw attention of readers to a problem of
possible differences in generation of magnetic storms induced by various large
scale solar wind (SW) streams: CIR, Sheath and ICME (including MC and Ejecta).
Recently we showed that when using a modification of formula by Burton et al.
[1975] for connection of interplanetary conditions with Dst and Dst* indices
the efficiency of storm generation by Sheath and CIR is about 50 percent higher
than generation by ICME [Nikolaeva et al., 2013, 2015]. In the literature there
are many various functions coupling (FC) various interplanetary parameters with
magnetospheric state. In this work we study the efficiency of main phase storm
generation by different SW streams when using 12 another FCs on the basis of
OMNI data during 1976 ... 2000. Obtained results show that for most part of FCs
Sheaths have the highest efficiency and MCs have the lowest efficiency in
accordance with our previous results. The reliability of the obtained data and
possible reasons of divergences for various FCs and various SW types require
further researches.


  Unconventional superconductivity frequently emerges as the transition
temperature of a magnetic phase, typically antiferromagnetic, is suppressed
continuously toward zero temperature. Here, we report contrary behavior in
pressurized CeRhGe3, a non-centrosymmetric heavy fermion compound. We find that
its pressure-tuned antiferromagnetic transition temperature (TN) appears to
avoid a continuous decrease to zero temperature by terminating abruptly above a
dome of pressure-induced superconductivity. Near 21.5 GPa, evidence for TN
suddenly vanishes, the electrical resistance becomes linear in temperature and
the superconducting transition reaches a maximum. In light of X-ray absorption
spectroscopy measurements, these characteristics appear to be related to a
pressured-induced Ce valence instability, which reveals as a sharp increase in
the rate of change of Ce valence with applied pressure.


  For any complex parameters $x$ and $\nu$, we provide a new class of linear
inversion formulas $T = A(x,\nu) \cdot S \Leftrightarrow S = B(x,\nu) \cdot T$
between sequences $S = (S_n)_{n \in \mathbb{N}^*}$ and $T = (T_n)_{n \in
\mathbb{N}^*}$, where the infinite lower-triangular matrix $A(x,\nu)$ and its
inverse $B(x,\nu)$ involve Hypergeometric polynomials $F(\cdot)$, namely $$
  \left\{
  \begin{array}{ll}
  A_{n,k}(x,\nu) = \displaystyle (-1)^k\binom{n}{k}F(k-n,-n\nu;-n;x),
  \\
  B_{n,k}(x,\nu) = \displaystyle (-1)^k\binom{n}{k}F(k-n,k\nu;k;x)
  \end{array} \right. $$ for $1 \leqslant k \leqslant n$. Functional relations
between the ordinary (resp. exponential) generating functions of the related
sequences $S$ and $T$ are also given.
  These new inversion formulas have been initially motivated by the resolution
of an integral equation recently appeared in the field of Queuing Theory; we
apply them to the full resolution of this integral equation. Finally, matrices
involving generalized Laguerre polynomials polynomials are discussed as
specific cases of our general inversion scheme.


  The Information Bottleneck (IB) is a conceptual method for extracting the
most compact, yet informative, representation of a set of variables, with
respect to the target. It generalizes the notion of minimal sufficient
statistics from classical parametric statistics to a broader
information-theoretic sense. The IB curve defines the optimal trade-off between
representation complexity and its predictive power. Specifically, it is
achieved by minimizing the level of mutual information (MI) between the
representation and the original variables, subject to a minimal level of MI
between the representation and the target. This problem is shown to be in
general NP hard. One important exception is the multivariate Gaussian case, for
which the Gaussian IB (GIB) is known to obtain an analytical closed form
solution, similar to Canonical Correlation Analysis (CCA). In this work we
introduce a Gaussian lower bound to the IB curve; we find an embedding of the
data which maximizes its "Gaussian part", on which we apply the GIB. This
embedding provides an efficient (and practical) representation of any arbitrary
data-set (in the IB sense), which in addition holds the favorable properties of
a Gaussian distribution. Importantly, we show that the optimal Gaussian
embedding is bounded from above by non-linear CCA. This allows a fundamental
limit for our ability to Gaussianize arbitrary data-sets and solve complex
problems by linear methods.


  We introduce and develop the theory of metaparticles. At the classical level,
this is a world-line theory with the usual reparameterization invariance and
two additional features. The theory is motivated by string theory on compact
targets, and can be thought of, at least at the non-interacting level, as a
theory of particles at a given string level, or as a particle model for Born
geometries. The first additional feature of the model is the presence of an
additional local symmetry, which from the string point of view corresponds to
the completion of worldsheet diffeomorphism invariance. From the particle
world-line point of view, this symmetry is associated with an additional local
constraint. The second feature is the presence of a non-trivial symplectic form
on the metaparticle phase space, also motivated by string theory [1, 2].
Because of its interpretation as a particle model on Born geometry, the
space-time on which the metaparticle propagates is ambiguous, with different
choices related by what in string theory we would call T-duality. In this
paper, we define the model, and explore some of its principle classical and
quantum properties, including causality and unitarity.


  Inspired by the digital twinning systems, a novel real-time digital double
framework is developed to enhance robot perception of the terrain conditions.
Based on the very same physical model and motion control, this work exploits
the use of such simulated digital double synchronized with a real robot to
capture and extract discrepancy information between the two systems, which
provides high dimensional cues in multiple physical quantities to represent
differences between the modelled and the real world. Soft, non-rigid terrains
cause common failures in legged locomotion, whereby visual perception solely is
insufficient in estimating such physical properties of terrains. We used
digital double to develop the estimation of the collapsibility, which addressed
this issue through physical interactions during dynamic walking. The
discrepancy in sensory measurements between the real robot and its digital
double are used as input of a learning-based algorithm for terrain
collapsibility analysis. Although trained only in simulation, the learned model
can perform collapsibility estimation successfully in both simulation and real
world. Our evaluation of results showed the generalization to different
scenarios and the advantages of the digital double to reliably detect nuances
in ground conditions.


  By combining the Self-Interaction Correction (SIC) with pseudopotential
perturbation theory, the role of self-interaction errors inherent to the Local
Density Approximation (LDA) to Density Functional Theory is estimated in the
determination of ground state and low energy isomeric structures of small
metallic clusters. Its application to neutral sodium clusters with 8 and 20
atoms shows that the SIC provides sizeable effects in Na_8, leading to a
different ordering of the low lying isomeric states compared with ab-initio LDA
predictions, whereas for Na_20, the SIC effects are less pronounced, such that
a quantitative agreement is achieved between the present method and ab-initio
LDA calculations.


  Using a tight-binding approach we study theoretically the nature of surface
states in Pb0.4Sn0.6Te - the newly discovered
topological-crystalline-insulator. Apart from the studied before (001) surface
states, two other surface families, {011} and {111}, in which the mirror
symmetry of the crystal's rock-salt structure plays the same role in
topological protection, are considered. Our calculations show that while in
(111) surface states of (Pb,Sn)Te four single topologically protected
Dirac-cones should appear, for the (110) surface states the protection is
lifted for two L points. In this case, instead of the Dirac points energy gaps
occur in the surface states, due to the interaction between the two L valleys.
In all studied cases a chiral spin texture is obtained.


  Stochastic processes provide a mathematically elegant way model complex data.
In theory, they provide flexible priors over function classes that can encode a
wide range of interesting assumptions. In practice, however, efficient
inference by optimisation or marginalisation is difficult, a problem further
exacerbated with big data and high dimensional input spaces. We propose a novel
variational autoencoder (VAE) called the prior encoding variational autoencoder
($\pi$VAE). The $\pi$VAE is finitely exchangeable and Kolmogorov consistent,
and thus is a continuous stochastic process. We use $\pi$VAE to learn low
dimensional embeddings of function classes. We show that our framework can
accurately learn expressive function classes such as Gaussian processes, but
also properties of functions to enable statistical inference (such as the
integral of a log Gaussian process). For popular tasks, such as spatial
interpolation, $\pi$VAE achieves state-of-the-art performance both in terms of
accuracy and computational efficiency. Perhaps most usefully, we demonstrate
that the low dimensional independently distributed latent space representation
learnt provides an elegant and scalable means of performing Bayesian inference
for stochastic processes within probabilistic programming languages such as
Stan.


  In this paper, we present the detailed mathematical derivation of the
gradient and Hessian matrix for the Vora-Value based colorimetric filter
optimization. We make a full recapitulation of the steps involved in
differentiating the objective function and reveal the positive-definite Hessian
matrix when a positive regularizer is applied. This paper serves as a
supplementary material for our paper in the colorimetric filter design theory.


  In previous studies, it has been shown that the large line widths observed in
high surface brightness H-alpha emitters at low and high redshifts are likely
due to the mechanical energy injected by intense star formation. Here we
discuss the possibility that the high surface brightnesses observed are not due
to star formation, but due to cosmological gas accretion. We assume that all of
the accretion energy is dissipated as shocks from the accreting gas. We show
that in order to explain the high surface brightnesses both the mass accretion
rate and energy would have to be much higher than expected from simulations or
from equating the star formation with the accretion rate. We also investigate
scaling relations between the surface brightness expected from accretion and
for star formation through mechanical heating and photo-ionization, trying to
identify a regime where such accretion may become evident in galaxies.
Unfortunately, the surface brightness necessary to detect the gas in optical
line emission is about an order of magnitude lower than what has currently been
achieved with near-infrared observations of distant galaxies.


  This paper presents Mixed Formal Learning, a new architecture that learns
models based on formal mathematical representations of the domain of interest
and exposes latent variables. The second element in the architecture learns a
particular skill, typically by using traditional prediction or classification
mechanisms. Our key findings include that this architecture: (1) Facilitates
transparency by exposing key latent variables based on a learned mathematical
model; (2) Enables Low Shot and Zero Shot training of machine learning without
sacrificing accuracy or recall.