  Recently NAano-Plasmonic, Laser Inertial Fusion Experiments (NAPLIFE) were
proposed, as an improved way to achieve laser driven fusion. The improvement is
the combination of two basic research discoveries: (i) The possibility of
detonations on space-time hyper-surfaces with time-like normal (i.e.
simultaneous detonation in a whole volume) and (ii) to increase this volume to
the whole target, by regulating the laser light absorption using nano-shells or
nano-rods as antennas. These principles can be realized in an in-line, one
dimensional configuration, in the simplest way with two opposing laser beams as
in particle colliders. Such, opposing laser beam experiments were also
performed recently. Here we study the consequences of the Laser Wake Field
Acceleration (LWFA) if we experience it in a colliding laser beam set up. These
studies can be applied to laser driven fusion, but also to other rapid phase
transition, combustion, or ignition studies in other materials.


  The polarization properties of the elastic electron scattering on H-like ions
are investigated within the framework of the relativistic QED theory. The
polarization properties are determined by a combination of relativistic effects
and spin exchange between the incident and bound electrons. The scattering of a
polarized electron on an initially unpolarized ion is fully described by five
parameters. We study these parameters for non-resonant scattering, as well as
in the vicinity of LL resonances, where scattering occurs through the formation
and subsequent decay of intermediate autoionizing states. The study was carried
out for ions from $\txt{B}^{4+}$ to $\txt{Xe}^{53+}$. Special attention was
paid to the study of asymmetry in electron scattering.


  This paper describes a method for the explicit reconstruction and
visualization of various polarization ellipses from the intensities measured
after a rotating polarizer. The state of polarization of a light beam is
represented by the variation of the electric field amplitude vector with polar
angle in the laboratory coordinate system defined by the experimental set-up.
The intensity of the light beam measured after passing through a rotating
polarizer gives the estimate of the wave electric field component parallel to
the polarizer pass axis averaged over a time period. The formulae for the
estimation of orthogonal field component and polar angle, in the polarizer
coordinate system have been derived. Both the orthogonally polarized components
and the polar angle are functions of ellipse parameters that have been deduced
from the intensity measurements. Finally, the resultant electric field and the
polar angle are mapped to the laboratory coordinate system and compared with
the representation of the polarization ellipse obtained from the Stokes
vectors.


  The problem of natural language processing over structured data has become a
growing research field, both within the relational database and the Semantic
Web community, with significant efforts involved in question answering over
knowledge graphs (KGQA). However, many of these approaches are either
specifically targeted at open-domain question answering using DBpedia, or
require large training datasets to translate a natural language question to
SPARQL in order to query the knowledge graph. Hence, these approaches often
cannot be applied directly to complex scientific datasets where no prior
training data is available.
  In this paper, we focus on the challenges of natural language processing over
knowledge graphs of scientific datasets. In particular, we introduce Bio-SODA,
a natural language processing engine that does not require training data in the
form of question-answer pairs for generating SPARQL queries. Bio-SODA uses a
generic graph-based approach for translating user questions to a ranked list of
SPARQL candidate queries. Furthermore, Bio-SODA uses a novel ranking algorithm
that includes node centrality as a measure of relevance for selecting the best
SPARQL candidate query. Our experiments with real-world datasets across several
scientific domains, including the official bioinformatics Question Answering
over Linked Data (QALD) challenge, show that Bio-SODA outperforms publicly
available KGQA systems by an F1-score of least 20% and by an even higher factor
on more complex bioinformatics datasets.


  One of the important problems in multiprocessor systems is Task Graph
Scheduling. Task Graph Scheduling is an NP-Hard problem. Both learning automata
and genetic algorithms are search tools which are used for solving many NP-Hard
problems. In this paper a new hybrid method based on Genetic Algorithm and
Learning Automata is proposed. The proposed algorithm begins with an initial
population of randomly generated chromosomes and after some stages, each
chromosome maps to an automaton. Experimental results show that superiority of
the proposed algorithm over the current approaches.


  The X-ray source eRASSU J131716.9-402647 was recently identified from
observations with Spectrum Roentgen Gamma (SRG)/eROSITA as a promising X-ray
dim isolated neutron star (XDINS) candidate on the premise of a soft energy
distribution, absence of catalogued counterparts, and a high X-ray-to-optical
flux ratio. Here, we report the results of a multi-wavelength observational
campaign with XMM-Newton, NICER and the FORS2 instrument at the ESO-VLT. We
found in both the XMM-Newton and NICER data that the X-ray emission is strongly
pulsed at a period of $12.757$ s (pulsed fraction $p_\mathrm{f} = (29.1 \pm
2.6)$% in the 0.2-2 keV band). The pulse profile is double-humped, and the
pulsed fraction increases with energy. The XMM-Newton and NICER epochs allow us
to derive a 3$\sigma$ upper limit of $\dot{P}\leq 8\times 10^{-11}$ s s$^{-1}$
on the spin-down rate of the neutron star. The source spectrum is well
described by a purely thermal continuum, either a blackbody with $kT\sim95$ eV
or a magnetised neutron star atmosphere model with $kT \sim 35$ eV. Similarly
to other thermally emitting isolated neutron stars, we found in either case
strong deviations from the continuum, a broad absorption feature at energy
$\sim260$ eV and a narrow one around $590$ eV. The FORS2 instrument at ESO-VLT
has not detected the optical counterpart ($m_\mathrm{R}>27.5$ mag, $5\sigma$
detection limit), implying an X-ray-to-optical flux ratio of $10^4$ at least.
The properties of eRASSU J131716.9-402647 strongly resemble those of a highly
magnetised isolated neutron star and favour an XDINS or high-B pulsar nature.


  Stock market forecasting is very important in the planning of business
activities. Stock price prediction has attracted many researchers in multiple
disciplines including computer science, statistics, economics, finance, and
operations research. Recent studies have shown that the vast amount of online
information in the public domain such as Wikipedia usage pattern, news stories
from the mainstream media, and social media discussions can have an observable
effect on investors opinions towards financial markets. The reliability of the
computational models on stock market prediction is important as it is very
sensitive to the economy and can directly lead to financial loss. In this
paper, we retrieved, extracted, and analyzed the effects of news sentiments on
the stock market. Our main contributions include the development of a sentiment
analysis dictionary for the financial sector, the development of a
dictionary-based sentiment analysis model, and the evaluation of the model for
gauging the effects of news sentiments on stocks for the pharmaceutical market.
Using only news sentiments, we achieved a directional accuracy of 70.59% in
predicting the trends in short-term stock price movement.


  We introduce the concept of locally homogeneous space, and prove in this
context L^p and Holder estimates for singular and fractional integrals, as well
as L^p estimates on the commutator of a singular or fractional integral with a
BMO or VMO function. These results are motivated by local a-priori estimates
for subelliptic equations.


  I study a population model in which the reproduction rate lambda is inherited
with mutation, favoring fast reproducers in the short term, but conflicting
with a process that eliminates agglomerations of individuals. The model is a
variant of the triplet annihilation model introduced several decades ago [R.
Dickman, Phys. Rev. B~{\bf 40}, 7005 (1989)] in which organisms ("particles")
reproduce and diffuse on a lattice, subject to annihilation when (and only
when) occupying three consecutive sites. For diffusion rates below a certain
value, the population possesses two "survival strategies": (i) rare
reproduction (0 < lambda < lambda_{c,1}), in which a low density of diffusing
particles renders triplets exceedingly rare, and (ii) frequent reproduction
(lambda > lambda_{c,2}). For lambda between lambda_{c,1} and lambda_{c,2} there
is no active steady state. In the rare-reproduction regime, a mutating
$\lambda$ leads to stochastic boom-and-bust cycles in which the reproduction
rate fluctuates upward in certain regions, only to lead to extinction as the
local value of lambda becomes excessive. The global population can nevertheless
survive due to the presence of other regions, with reproduction rates that have
yet to drift upward.


  We present a detailed study of the non-linear thermoelectric properties of a
molecular junction, represented by a dissipative Anderson-Holstein model. A
single orbital level with strong Coulomb interaction is coupled to a localized
vibrational mode and we account for both electron and phonon exchange with both
electrodes, investigating how these contribute to the heat and charge
transport. We calculate the efficiency and power output of the device operated
as a heat to electric power converter and identify the optimal operating
conditions, which are found to be qualitatively changed by the presence of the
vibrational mode. Based on this study of a generic model system, we discuss the
desirable properties of molecular junctions for thermoelectric applications.


  We review some facts about the representation theory of the Hecke algebra. We
adapt for the Hecke algebra case the approach of Okounkov and Vershik which was
developed for the representation theory of symmetric groups. We justify an
explicit construction of the idempotents in the Hecke algebra in terms of
Jucys-Murphy elements. Ocneanu's traces for these idempotents (which can be
interpreted as q-dimensions of corresponding irreducible representations of
quantum linear groups) are presented.


  Proofs of Tsygan's formality conjectures for chains would unlock important
algebraic tools which might lead to new generalizations of the
Atiyah-Patodi-Singer index theorem and the Riemann-Roch-Hirzebruch theorem.
Despite this pivotal role in the traditional investigations and the efforts of
various people the most general version of Tsygan's formality conjecture has
not yet been proven. In my thesis I propose Fedosov resolutions for the
Hochschild cohomological and homological complexes of the algebra of functions
on an arbitrary smooth manifold. Using these resolutions together with
Kontsevich's formality quasi-isomorphism for Hochschild cochains of R[[y_1,
>..., y_d]] and Shoikhet's formality quasi-isomorphism for Hochschild chains of
R[[y_1,..., y_d]] I prove Tsygan's formality conjecture for Hochschild chains
of the algebra of functions on an arbitrary smooth manifold. The construction
of the formality quasi-isomorphism for Hochschild chains is manifestly
functorial for isomorphisms of the pairs (M,\nabla), where M is the manifold
and \nabla is an affine connection on the tangent bundle. In my thesis I apply
these results to equivariant quantization, computation of Hochschild homology
of quantum algebras and description of traces in deformation quantization.


  Bismuth ferrite, BiFeO3, is the only known room-temperature 'multiferroic'
material. We demonstrate here, using neutron scattering measurements in high
quality single crystals, that the antiferromagnetic and ferroelectric orders
are intimately coupled. Initially in a single ferroelectric state, our crystals
have a canted antiferromagnetic structure describing a unique cycloid. Under
electrical poling, polarisation re-orientation induces a spin flop. We argue
here that the coupling between the two orders may be stronger in the bulk than
that observed in thin films where the cycloid is absent.


  This article builds on recent work (A. Akhmeteli, Int'l Journ. of Quantum
Information, vol. 9, Suppl. (2011) p. 17, and A. Akhmeteli, Journ. Math. Phys.,
vol. 52 (2011) p. 082303), providing a theory that is based on spinor
electrodynamics, is described by a system of partial differential equations in
3+1 dimensions, but reproduces unitary evolution of a quantum field theory in
the Fock space. To this end, after introduction of a complex four-potential of
electromagnetic field, which generates the same electromagnetic fields as the
initial real four-potential, spinor field is algebraically eliminated from the
equations of spinor electrodynamics. It is proven that the resulting equations
for electromagnetic field describe independent evolution of the latter and can
be embedded into a quantum field theory using a generalized Carleman
linearization procedure. The theory provides a simple and at least reasonably
realistic model, valuable for interpretation of quantum theory. The issues
related to the Bell theorem are discussed.


  Magic states are the resource that allows quantum computers to attain an
advantage over classical computers. This resource consists in the deviation
from a property called stabilizerness which in turn implies that stabilizer
circuits can be efficiently simulated on a classical computer. Without magic,
no quantum computer can do anything that a classical computer cannot do. Given
the importance of magic for quantum computation, it would be useful to have a
method for measuring the amount of magic in a quantum state. In this work, we
propose and experimentally demonstrate a protocol for measuring magic based on
randomized measurements. Our experiments are carried out on two IBM Quantum
Falcon processors. This protocol can provide a characterization of the
effectiveness of a quantum hardware in producing states that cannot be
effectively simulated on a classical computer. We show how from these
measurements one can construct realistic noise models affecting the hardware.


  In this note we investigate connections between zero density estimates for
the Riemann zeta function and large value estimates for Dirichlet polynomials.
It is well known that estimates of the latter type imply estimates of the
former type. Our goal is to show that there is an implication to the other
direction as well, i.e. zero density estimates for the Riemann zeta function
imply large value estimates for Dirichlet polynomials.


  We consider a Gaussian two-hop network where the source and the destination
can communicate only via a relay node who is both an eavesdropper and a
Byzantine adversary. Both the source and the destination nodes are allowed to
transmit, and the relay receives a superposition of their transmitted signals.
We propose a new coding scheme that satisfies two requirements simultaneously:
the transmitted message must be kept secret from the relay node, and the
destination must be able to detect any Byzantine attack that the relay node
might launch reliably and fast. The three main components of the scheme are the
nested lattice code, the privacy amplification and the algebraic manipulation
detection (AMD)code. Specifically, for the Gaussian two-hop network, we show
that lattice coding can successfully pair with AMD codes enabling its first
application to a noisy channel model. We prove, using this new coding scheme,
that the probability that the Byzantine attack goes undetected decreases
exponentially fast with respect to the number of channel uses, while the loss
in the secrecy rate, compared to the rate achievable when the relay is honest,
can be made arbitrarily small. In addition, in contrast with prior work in
Gaussian channels, the notion of secrecy provided here is strong secrecy.


  We present a highly-parallel multi-frequency hybrid radiation hydrodynamics
algorithm that combines a spatially-adaptive long characteristics method for
the radiation field from point sources with a moment method that handles the
diffuse radiation field produced by a volume-filling fluid. Our Hybrid Adaptive
Ray-Moment Method (HARM$^2$) operates on patch-based adaptive grids, is
compatible with asynchronous time stepping, and works with any moment method.
In comparison to previous long characteristics methods, we have greatly
improved the parallel performance of the adaptive long-characteristics method
by developing a new completely asynchronous and non-blocking communication
algorithm. As a result of this improvement, our implementation achieves
near-perfect scaling up to $\mathcal{O}(10^3)$ processors on distributed memory
machines. We present a series of tests to demonstrate the accuracy and
performance of the method.


  Survivors of central nervous system injury commonly present with spastic
hypertonia. The affected muscles are hyperexcitable and can display involuntary
static muscle tone and an exaggerated stretch reflex. These symptoms affect
posture and disrupt activities of daily living. Symptoms are typically measured
using subjective manual tests such as the Modified Ashworth Scale; however,
more quantitative measures are necessary to evaluate potential treatments. The
hands are one of the most common targets for intervention, but few
investigators attempt to quantify symptoms of spastic hypertonia affecting the
fingers. We present the isometric force pillow (IFP) to quantify involuntary
grip force. This lightweight, computerized tool provides a holistic measure of
finger flexion force and can be used in various orientations for clinical
testing and to measure the impact of assistive devices.


  We introduce a sequential estimator for continuous time dynamic discrete
choice models (single-agent models and games) by adapting the nested pseudo
likelihood (NPL) estimator of Aguirregabiria and Mira (2002, 2007), developed
for discrete time models with discrete time data, to the continuous time case
with data sampled either discretely (i.e., uniformly-spaced snapshot data) or
continuously. We establish conditions for consistency and asymptotic normality
of the estimator, a local convergence condition, and, for single agent models,
a zero Jacobian property assuring local convergence. We carry out a series of
Monte Carlo experiments using an entry-exit game with five heterogeneous firms
to confirm the large-sample properties and demonstrate finite-sample bias
reduction via iteration. In our simulations we show that the convergence issues
documented for the NPL estimator in discrete time models are less likely to
affect comparable continuous-time models. We also show that there can be large
bias in economically-relevant parameters, such as the competitive effect and
entry cost, from estimating a misspecified discrete time model when in fact the
data generating process is a continuous time model.


  This is part of an ongoing program to classify maximal orders on surfaces via
their ramification data. Del Pezzo and ruled orders have already been
classified. In this paper, we classify numerically Calabi-Yau orders which are
the noncommutative analogues of surfaces of Kodaira dimension zero.


  The present status of our ongoing systematic study of the discovery potential
of QCD-instanton induced events in deep-inelastic scattering at HERA is briefly
reviewed. We emphasize our recent progress in predicting the cross-sections of
instanton-induced processes. Our finalized predictions include a dramatic
improvement of the residual renormalization-scale dependencies and the
specification of a ``fiducial'' kinematical region in the relevant Bjorken
variables extracted from recent lattice simulations. Published upper limits on
instanton-induced cross-sections based on single observables in the final state
such as the flow of strange particles and the multiplicity distribution of
charged particles are already of the order of our estimate. Thus, a decisive
search for instanton-induced events in deep-inelastic scattering at HERA, based
on a multi-observable analysis, seems feasible.


  Remote sensing (RS) applications in the space domain demand machine learning
(ML) models that are reliable, robust, and quality-assured, making red teaming
a vital approach for identifying and exposing potential flaws and biases. Since
both fields advance independently, there is a notable gap in integrating red
teaming strategies into RS. This paper introduces a methodology for examining
ML models operating on hyperspectral images within the HYPERVIEW challenge,
focusing on soil parameters' estimation. We use post-hoc explanation methods
from the Explainable AI (XAI) domain to critically assess the best performing
model that won the HYPERVIEW challenge and served as an inspiration for the
model deployed on board the INTUITION-1 hyperspectral mission. Our approach
effectively red teams the model by pinpointing and validating key shortcomings,
constructing a model that achieves comparable performance using just 1% of the
input features and a mere up to 5% performance loss. Additionally, we propose a
novel way of visualizing explanations that integrate domain-specific
information about hyperspectral bands (wavelengths) and data transformations to
better suit interpreting models for hyperspectral image analysis.


  The first determination of the age of the Galactic thin disk from Th/Eu
nucleocosmochronology was accomplished by us in Papers I and II. The present
work aimed at reducing the age uncertainty by expanding the stellar sample with
the inclusion of seven new objects - an increase by 37%. A set of [Th/Eu]
abundance ratios was determined from spectral synthesis and merged with the
results from Paper I. Abundances for the new, extended sample were analyzed
with the aid of a Galactic disk chemical evolution (GDCE) model developed by us
is Paper II. The result was averaged with an estimate obtained in Paper II from
a conjunction of literature data and our GDCE model, providing our final,
adopted disk age T_G = (8.8 +/- 1.7) Gyr with a reduction of 0.1 Gyr (6%) in
the uncertainty. This value is compatible with the most up-to-date white dwarf
age determinations (<~ 10 Gyr). Considering that the halo is currently presumed
to be (13.5 +/- 0.7) Gyr old, our result prompts groups developing Galactic
formation models to include an hiatus of (4.7 +/- 1.8) Gyr between the
formation of halo and disk.


  This paper considers the transmission of an infinite sequence of messages (a
streaming source) over a packet erasure channel, where every source message
must be recovered perfectly at the destination subject to a fixed decoding
delay. While the capacity of a channel that introduces only bursts of erasures
is well known, only recently, the capacity of a channel with either one burst
of erasures or multiple arbitrary erasures in any fixed-sized sliding window
has been established. However, the codes shown to achieve this capacity are
either non-explicit constructions (proven to exist) or explicit constructions
that require large field size that scales exponentially with the delay. This
work describes an explicit rate-optimal construction for admissible channel and
delay parameters over a field size that scales only quadratically with the
delay.


  We consider a version of dimensional regularization (reduction) in which the
dimensionful regularization parameter $\Lambda$ is in general different from
the renormalization scale $\mu$. Then in the scheme analogous to the minimal
subtraction the renormalization constants contain $\varepsilon$-poles, powers
of $\ln\Lambda/\mu$, and mixed terms of the structure
$\varepsilon^{-q}\ln^{p}\Lambda/\mu$. For the MS-like schemes we present
explicit expressions for the coefficients at all these structures which relate
them to the coefficients in the renormalization group functions, namely in the
$\beta$-function and in the anomalous dimension. In particular, for the pure
$\varepsilon$-poles we present explicit solutions of the 't~Hooft pole
equations. Also we construct simple all-loop expressions for the
renormalization constants (also written in terms of the renormalization group
functions) which produce all $\varepsilon$-poles and logarithms and establish a
number of relations between various coefficients at $\varepsilon$-poles and
logarithms. The results are illustrated by some examples.


  The necessity for accurate and computationally efficient representations of
water in atomistic simulations that can span biologically relevant timescales
has born the necessity of coarse-grained (CG) modeling. Despite numerous
advances, CG water models rely mostly on a-priori specified assumptions. How
these assumptions affect the model accuracy, efficiency, and in particular
transferability, has not been systematically investigated. Here we propose a
data driven, comparison and selection for CG water models through a
Hierarchical Bayesian framework. We examine CG water models that differ in
their level of coarse-graining, structure, and number of interaction sites. We
find that the importance of electrostatic interactions for the physical system
under consideration is a dominant criterion for the model selection. Multi-site
models are favored, unless the effects of water in electrostatic screening are
not relevant, in which case the single site model is preferred due to its
computational savings. The charge distribution is found to play an important
role in the multi-site model's accuracy while the flexibility of the
bonds/angles may only slightly improve the models. Furthermore, we find
significant variations in the computational cost of these models. We present a
data informed rationale for the selection of CG water models and provide
guidance for future water model designs.


  The energy loss parameter $\hat q$ is one of the fundamental transport
parameters of hadronic matter. Using the twist-4 collinear approach, we show
that the $\cos \phi$ azimuthal asymmetry in unpolarized semi-inclusive deeply
inelastic scattering (SIDIS) off a large nucleus at intermediate transverse
momentum is a sensitive observable for its determination. The effect is due to
the suppression of the azimuthal asymmetry by final-state multiple scattering.


  Our goal is to develop a principled and general algorithmic framework for
task-driven estimation and control for robotic systems. State-of-the-art
approaches for controlling robotic systems typically rely heavily on accurately
estimating the full state of the robot (e.g., a running robot might estimate
joint angles and velocities, torso state, and position relative to a goal).
However, full state representations are often excessively rich for the specific
task at hand and can lead to significant computational inefficiency and
brittleness to errors in state estimation. In contrast, we present an approach
that eschews such rich representations and seeks to create task-driven
representations. The key technical insight is to leverage the theory of
information bottlenecks}to formalize the notion of a "task-driven
representation" in terms of information theoretic quantities that measure the
minimality of a representation. We propose novel iterative algorithms for
automatically synthesizing (offline) a task-driven representation (given in
terms of a set of task-relevant variables (TRVs)) and a performant control
policy that is a function of the TRVs. We present online algorithms for
estimating the TRVs in order to apply the control policy. We demonstrate that
our approach results in significant robustness to unmodeled measurement
uncertainty both theoretically and via thorough simulation experiments
including a spring-loaded inverted pendulum running to a goal location.


  A model for the Universe is proposed where it is considered as a mixture of
scalar and matter fields. The particle production is due to an irreversible
transfer of energy from the gravitational field to the matter field and
represented by a non-equilibrium pressure. This model can simulate three
distinct periods of the Universe: (a) an accelerated epoch where the energy
density of the scalar field prevails over the matter field, (b) a past
decelerated period where the energy density of the matter field becomes more
predominant than the scalar energy density, and (c) a present acceleration
phase where the scalar energy density overcomes the energy density of the
matter field.


  Contact inhibition plays a crucial role in the motility of cells, the process
of wound healing, and the formation of tumors. By mimicking the mechanical
motion of calls crawling on a substrate using a pseudopod, we constructed a
minimal model for migrating cells which gives rise to contact inhibition of
locomotion (CIL) naturally. The model cell consists of two disks, one in the
front (a pseudopod) and the other one in the back (cell body), connected by a
finitely extensible spring. Despite the simplicity of the model, the cells'
collective behavior is highly nontrivial, depending on the shape of cells and
whether CIL is enabled or not. Cells with a small front circle (i.e. a narrow
pseudopod) form immobile colonies. In contrast, cells with a large front circle
(i.e. such as a lamellipodium) exhibit coherent migration without any explicit
alignment mechanism being present in the model. This suggests that crawling
cells often exhibit broad fronts because it helps them avoid clustering. Upon
increasing the density, the cells develop density waves which propagate against
the direction of cell migration and finally arrest at higher densities.


  We consider the transition from quark to hadronic matter which may result
during the cooling/expansion of the quark-gluon plasma formed in energetic
collisions of weakly isospin-asymmetric ions. This transition involves the
energy density of u and d quark matter and the one of nearly isospin-symmetric
nuclear matter. Within bag models, the former entails knowledge of the bag
pressure, a poorly constrained quantity. The bag pressure at high density can
be fixed imposing equality of quark and nucleonic energy densities at the
(assumed known) transition point. We find this value to be very model
dependent.


  Blockchains have become immensely popular and are high on the list of
national and international research and innovation agenda's. This is partly
caused by the numerous interesting applications, combined with the promise of
full decentralization and high scalability (among others). Keeping that
promise, however, is technically extremely demanding and has already required
substantial scientific efforts, which so far have not been overwhelmingly
successful. In this paper, we provide a laymen's description of what may turn
out to be a fundamental hurdle in attaining blockchains that combine
scalability, high transaction processing capabilities, and are indeed fully
decentralized.


  In this note, we present a formula for the action of the primitive Milnor
operations on generators of algebra of invariants of the general linear group
$GL_n=GL(n, \mathbb F_p)$ in the polynomial algebra $P_n= \mathbb
F_p[x_1,x_2,\ldots,x_n]$ with $p$ an odd prime number.


  A galactic disk in a spiral galaxy is generally believed to be embedded in an
extended dark matter halo, which dominates its dynamics in the outer parts.
However, the shape of the halo is not clearly understood. Here we show that the
dark matter halo in the Milky Way Galaxy is prolate in shape. Further, it is
increasingly more prolate at larger radii, with the vertical-to-planar axis
ratio monotonically increasing to 2.0 at 24 kpc. This is obtained by modeling
the observed steeply flaring atomic hydrogen gas layer in the outer Galactic
disk, where the gas is supported by pressure against the net gravitational
field of the disk and the halo. The resulting prolate-shaped halo can explain
several long-standing puzzles in galactic dynamics, for example, it permits
long-lived warps thus explaining their ubiquitous nature.


  We calculate the key sharing rate of Lu et al.'s Quantum Key Recycling (QKR)
protocol. The key sharing rate is another version of the key rate, but it can
be calculated for both the Quantum Key Distribution (QKD) protocols and the QKR
protocols. We define the key sharing rate in this study and compare the key
sharing rate of the QKR protocol to the rate of the QKD protocols. We found Lu
et al.'s QKR protocol can be used to share keys more efficiently than BB84 in
some situations. We also compare the six-state version of Lu et al.'s QKR
protocol to the six-state QKD protocol. The results of this study show the
potential advantages of using pre-shared keys to replace the public discussion
in quantum protocols.


  In this paper, we investigate the existence, uniqueness, and spectral
stability of traveling waves arising from a single threshold neural field model
with one spatial dimension, a Heaviside firing rate function, axonal
propagation delay, and biologically motivated oscillatory coupling types.
Neuronal tracing studies show that long-ranged excitatory connections form
stripe-like patterns throughout the mammalian cortex; thus, we aim to
generalize the notions of pure excitation, lateral inhibition, and lateral
excitation by allowing coupling types to spatially oscillate between excitation
and inhibition. In turn, we hope to analyze traveling fronts and pulses with
novel shapes. With fronts as our main focus, we exploit Heaviside firing rate
functions in order to establish existence and utilize speed index functions
with at most one critical point as a tool for showing uniqueness of wave speed.
We are able to construct Evans functions, the so-called stability index
functions, in order to provide positive spectral stability results. Finally, we
show that by incorporating slow linear feedback, we can compute fast pulses
numerically with phase space dynamics that are similar to their corresponding
singular homoclinical orbits; hence, our work answers open problems and
provides insight into new ones.


  We study in which conditions the Hyperextended Scalar Tensor theory in an
FLRW background admits a Noether symmetry and derive the vectors field
generating it.


  We present FlipDial, a generative model for visual dialogue that
simultaneously plays the role of both participants in a visually-grounded
dialogue. Given context in the form of an image and an associated caption
summarising the contents of the image, FlipDial learns both to answer questions
and put forward questions, capable of generating entire sequences of dialogue
(question-answer pairs) which are diverse and relevant to the image. To do
this, FlipDial relies on a simple but surprisingly powerful idea: it uses
convolutional neural networks (CNNs) to encode entire dialogues directly,
implicitly capturing dialogue context, and conditional VAEs to learn the
generative model. FlipDial outperforms the state-of-the-art model in the
sequential answering task (one-way visual dialogue) on the VisDial dataset by 5
points in Mean Rank using the generated answers. We are the first to extend
this paradigm to full two-way visual dialogue, where our model is capable of
generating both questions and answers in sequence based on a visual input, for
which we propose a set of novel evaluation measures and metrics.


  Training generalist agents is difficult across several axes, requiring us to
deal with high-dimensional inputs (space), long horizons (time), and
generalization to novel tasks. Recent advances with architectures have allowed
for improved scaling along one or two of these axes, but are still
computationally prohibitive to use. In this paper, we propose to address all
three axes by leveraging \textbf{L}anguage to \textbf{C}ontrol
\textbf{D}iffusion models as a hierarchical planner conditioned on language
(LCD). We effectively and efficiently scale diffusion models for planning in
extended temporal, state, and task dimensions to tackle long horizon control
problems conditioned on natural language instructions, as a step towards
generalist agents. Comparing LCD with other state-of-the-art models on the
CALVIN language robotics benchmark finds that LCD outperforms other SOTA
methods in multi-task success rates, whilst improving inference speed over
other comparable diffusion models by 3.3x~15x. We show that LCD can
successfully leverage the unique strength of diffusion models to produce
coherent long range plans while addressing their weakness in generating
low-level details and control.


  We perform dynamical simulations of a two-dimensional active nematic fluid in
coexistence with an isotropic fluid. Drops of active nematic become elongated,
and an effective anchoring develops at the nematic-isotropic interface. The
activity also causes an undulatory instability of the interface. This results
in defects of positive topological charge being ejected into the nematic,
leaving the interface with a diffuse negative charge. Quenching the active
lyotropic fluid results in a steady state in which phase-separating domains are
elongated and then torn apart by active stirring.


  Muon colliders and neutrino factories are attractive options for future
facilities aimed at achieving the highest lepton-antilepton collision energies
and precision measurements of parameters of the neutrino mixing matrix. The
performance and cost of these depend sensitively on how well a beam of muons
can be cooled. Recent progress in muon cooling design studies and prototype
tests nourishes the hope that such facilities can be built in the decade to
come.


  For any formal group law, there is a formal affine Hecke algebra defined by
Hoffnung, Malag\'on-L\'opez, Savage, and Zainoulline. Coming from this formal
group law, there is also an oriented cohomology theory. We identify the formal
affine Hecke algebra with a convolution algebra coming from the oriented
cohomology theory applied to the Steinberg variety. As a consequence, this
algebra acts on the corresponding cohomology of the Springer fibers. This
generalizes the action of classical affine Hecke algebra on the $K$-theory of
the Springer fibers constructed by Lusztig. We also give a residue
interpretation of the formal affine Hecke algebra, which coincides with the
residue construction of Ginzburg, Kapranov, and Vasserot when the formal group
law comes from a 1-dimensional algebraic group.


  A cube is used as a fair die of 6 faces. However, there are many dice of
different shapes on the market. To make them fair, most of them usually have
some symmetric shapes. We here classify these variants of dice on the market
into two groups. We first consider that a sphere as a model of a fair die with
infinity faces. Based on this model, many symmetric shapes can be modeled as
dice obtained by caving spheres. We also have a familiar fair device; a coin.
That is, a fair coin can be seen as a fair die with 2 faces. However, a real
coin has a thickness, and hence it is, in fact, an unfair die with 3 faces.
From this viewpoint, we propose a way for designing a fair die with n faces for
arbitrary n.


  In this work, we revisit the classical Holling type II three species food
chain model with a different viewpoint. Two critical parameters $\lambda_1$ and
$\lambda_2$ dependent on other six parameters are defined. We show that local
stabilities and existence of all equilibria can be reformulated by $\lambda_1$
and $\lambda_2$, and the complete classifications of parameters and its
corresponding dynamics are given. Three global extinction results are showed by
comparison principle analytically. Moreover, some interesting numerical
simulations, including the chaotic phenomena, and the bi-stability phenomena,
including equilibrium-cycle bi-stability and cycle-cycle bi-stability, are
presented numerically. Finally, a brief discussion and biological implications
are given.


  To explore and react to their environment, living micro-swimmers have
developed sophisticated strategies for locomotion - in particular, motility
with multiple gaits. To understand the physical principles associated with such
a behavioural variability,synthetic model systems capable of mimicking it are
needed. Here, we demonstrate bimodal gait switching in autophoretic droplet
swimmers. This minimal experimental system is isotropic at rest, a symmetry
that can be spontaneously broken due to the nonlinear coupling between
hydrodynamic and chemical fields, inducing a variety of flow patterns that lead
to different propulsive modes. We report a dynamical transition from
quasi-ballistic to bimodal chaotic motion, controlled by the viscosity of the
swimming medium. By simultaneous visualisation of the chemical and hydrodynamic
fields, supported quantitatively by an advection-diffusion model, we show that
higher hydrodynamic modes become excitable with increasing viscosity, while the
recurrent mode-switching is driven by the droplet's interaction with
self-generated chemical gradients. We further demonstrate that this gradient
interaction results in anomalous diffusive swimming akin to self-avoiding
spatial exploration strategies observed in nature.


  The possibility to test quantum measurement theories is discussed in the more
phenomenological framework of the quantum nondemolition theory. A simple test
of the hypothesis of the state vector collapse is proposed by looking for
deviations from the Boltzmann distribution of the energy associated to only one
complex amplitude of a macroscopic harmonic oscillator.


  Faraday rotation induced B-modes can provide a distinctive signature of
primordial magnetic fields because of their characteristic frequency dependence
and because they are only weakly damped on small scales, allowing them to
dominate B-modes from other sources. By numerically solving the full CMB
radiative transport equations, we study the B-mode power spectrum induced by
stochastic magnetic fields that have significant power on scales smaller than
the thickness of the last scattering surface. Constraints on the magnetic field
energy density and inertial scale are derived from WMAP 7-year data, and are
stronger than the big bang nucleosynthesis (BBN) bound for a range of
parameters. Observations of the CMB polarization at smaller angular scales are
crucial to provide tighter constraints or a detection.


  We experimentally show the enhancement of the Faraday and transverse
magneto-optical Kerr effects in the two-dimensional arrays of nanocylinders
made of bismuth-substituted iron-garnet and supporting both localized and
lattice modes. Simultaneous excitation of these modes makes it possible to
increase the Faraday rotation by 3 times and TMOKE by an order of magnitude
compared to the smooth magnetic film of the equal effective thickness. Both
magneto-optical effects are enhanced in wide spectral and angular ranges making
the nanocylinder array magnetic dielectric structures promising for
applications with short and tightly-focused laser pulses.


  We show that any convex region which contains a unit segment, an equilateral
triangle of sides 1/2, and a square of side 1/3 always has area at least
0.227498. Using grid-search algorithm, we attempt to find a configuration of
these three objects with minimal convex hull area. Consequently, we improve a
lower bound for Moser's worm problem from 0.2194 to 0.227498.


  Humans and many animals exhibit a robust capability to manipulate diverse
objects, often directly with their bodies and sometimes indirectly with tools.
Such flexibility is likely enabled by the fundamental consistency in underlying
physics of object manipulation such as contacts and force closures. Inspired by
viewing tools as extensions of our bodies, we present Tool-As-Embodiment (TAE),
a parameterization for tool-based manipulation policies that treat hand-object
and tool-object interactions in the same representation space. The result is a
single policy that can be applied recursively on robots to use end effectors to
manipulate objects, and use objects as tools, i.e. new end-effectors, to
manipulate other objects. By sharing experiences across different embodiments
for grasping or pushing, our policy exhibits higher performance than if
separate policies were trained. Our framework could utilize all experiences
from different resolutions of tool-enabled embodiments to a single generic
policy for each manipulation skill. Videos at
https://sites.google.com/view/recursivemanipulation


  In the effort to obtain low cost routine space debris observations in low
Earth orbit, ESA plans to utilise the radar facilities of the European
Incoherent Scatter Scientific Association. First demonstration measurements
were performed from 11 to 23 February 2001. In total $16{\rm hours}$ of radar
signals were collected. Here we compare these initial measurements with the
predictions of the ESA MASTER/PROOF'99 model in order to assess the sensitivity
as well as the reliability of the data. We find that while the determination of
object size needs to be reviewed, the altitude distribution provides a good fit
to the model prediction. The absolute number of objects detected in the various
altitude bins indicates that the coherent integration method indeed increases
the detection sensitivity when compared to incoherent integration. In the data
presented here integration times from 0.1 to $0.3{\rm s}$ were used. As
expected, orbit information cannot be obtained from the measurements if they
are linked to ionospheric measurements as planned. In addition routine space
debris observations provide also useful information for the validation of
large-object catalogues.


  Component-based design paradigm is of paramount importance due to prolific
growth in the complexity of modern-day systems. Since the components are
developed primarily by multi-party vendors and often assembled to realize the
overall system, it is an onus of the designer to certify both the functional
and non-functional requirements of such systems. Several of the earlier works
concentrated on formally analyzing the behavioral correctness, safety,
security, reliability and robustness of such compositional systems. However,
the assurance for quality measures of such systems is also considered as an
important parameter for their acceptance. Formalization of quality measures is
still at an immature state and often dictated by the user satisfaction. This
paper presents a novel compositional framework for reliable quality analysis of
component-based systems from the formal quality specifications of its
constituent components. The proposed framework enables elegant and generic
computation methods for quality attributes of various component-based system
structures. In addition to this, we provide a formal query-driven quality
assessment and design exploration framework which enables the designer to
explore various component structures and operating setups and finally converge
into better acceptable systems. A detailed case-study is presented over a
component-based system structure to show the efficacy and practicality of our
proposed framework.


  Young's and shear moduli and Poisson's ratio of polycrystalline solids
consisting of 2D quadratic and 3D cubic randomly oriented grains of the same
size and shape is studied. Considered polycrystals are initially unstrained. It
is shown that for such polycrystals the division of the mechanical stability
regions into areas of various auxeticity properties is different than for
monocrystalline solids. In particular the regions of complete auxeticity
enlarge.


  We propose an ultrametric broken replica symmetry trial function for the
diluted mean field sping glass model in the framework of random multi-overlap
structure.


  The lightest mass eigenvalue of a six-dimensional theory compactified on a
torus is numerically evaluated in the presence of the brane-localized mass
term. The dependence on the cutoff scale $\Lambda$ is non-negligible even when
$\Lambda$ is two orders of magnitude above the compactification scale, which
indicates that the mass eigenvalue is sensitive to the size of the brane, in
contrast to five-dimensional theories. We obtain an approximate expression of
the lightest mass in the thin brane limit, which well fits the numerical
calculations, and clarifies its dependence on the torus moduli parameter
$\tau$. We find that the lightest mass is typically much lighter than the
compactification scale by an order of magnitude even in the limit of a large
brane mass.


  We predict from first-principles calculations a novel structure of stanene
with dumbbell units (DB), and show that it is a two-dimensional topological
insulator with inverted band gap which can be tuned by compressive strain.
Furthermore, we propose that the boron nitride sheet and reconstructed
($2\times2$) InSb(111) surfaces are ideal substrates for the experimental
realization of DB stanene, maintaining its non-trivial topology. Combined with
standard semiconductor technologies, such as magnetic doping and electrical
gating, the quantum anomalous Hall effect, Chern half metallicity and
topological superconductivity can be realized in DB stanene on those
substrates. These properties make the two-dimensional supported stanene a good
platform for the study of new quantum spin Hall insulator as well as other
exotic quantum states of matter.


  The recently discovered resonant drag instability (RDI) of dust streaming in
protoplanetary disc is considered as the mode coupling of subsonic gas-dust
mixture perturbations. This mode coupling is coalescence of two modes with
nearly equal phase velocities: inertial wave (IW) having positive energy and a
streaming dust wave (SDW) having negative energy as measured in the frame of
gas environment being at rest in vertical hydrostatic equilibrium. SDW is a
trivial mode produced by the bulk streaming of dust, which transports
perturbations of dust density. In this way, settling combined with radial drift
of the dust makes possible coupling of SDW with IW and the onset of the
instability. In accordance with the concept of the mode coupling, RDI growth
rate is proportional to the square root of the coupling term of the dispersion
equation, which itself is proportional to mass fraction of dust, $f\ll 1$. This
clarifies why RDI growth rate $\propto f^{1/2}$. When SDW has positive energy,
its resonance with IW provides an avoided crossing instead of the mode
coupling. In the high wavenumber limit RDI with unbounded growth rate $\propto
f^{1/3}$ is explained by the triple mode coupling, which is coupling of SDW
with two IW. It coexists with a new quasi-resonant instability accompanied by
bonding of two oppositely propagating low-frequency IW. The mode coupling does
not exist for dust streaming only radially in a disc. In this case RDI is
provided by the obscured mechanism associated with the inertia of solids.


  This paper describes the formal verification of NP-hardness reduction
functions of two key problems relevant in algebraic lattice theory: the closest
vector problem and the shortest vector problem, both in the infinity norm. The
formalization uncovered a number of problems with the existing proofs in the
literature. The paper describes how these problems were corrected in the
formalization. The work was carried out in the proof assistant Isabelle.


  We present a comprehensive study of the star-formation properties of the
infrared luminous galaxy Arp299 (IC694 + NGC3690). The observations include
HST/NICMOS imaging and MMT optical and near-infrared spectroscopy together with
HST archival data. We correct the galaxy parameters for extinction and use the
results as boundary conditions for evolutionary starburst models.
  These models and other arguments show that Arp 299 has been going through a
broad variety of interaction-induced star formation for the last ~15 Myr. In
addition to nuclear starbursts in the two colliding galaxies, there are nearby
luminous star forming regions that may be the result of molecular clouds
breaking up and starting to form massive stars as they approach the nuclear
potential. Two regions near the interaction region have very recently formed
massive stars (~4 Myr old). One of these regions in particular appears to have
undergone a starburst of very short duration, in contrast with the 5-10 Myr
durations that are typical of nuclear events. These regions will probably form
gravitationally bound new dwarf galaxies, although they are close enough to the
more massive original galaxies that they will probably eventually be subsumed
into them. In addition, we find 19 HII regions at least as energetic as 30
Doradus, and 21 older star clusters that likely are the products of similar HII
regions formed in the past. This population of supergiant HII regions is
unprecedented in normal galaxies and emphasizes that the effects of the
interaction propagate through the entire pair of galaxies.


  If dark matter particles pair annihilate into stable Standard Model
particles, a population of energetic, non-thermal electrons and positrons is
necessarily produced. The secondary radiation resulting from the energy losses
of this non-standard population (including synchrotron and inverse Compton
up-scattering of background radiation photons) in turn produces a peculiar
multi-wavelength spectrum, extending from radio to gamma-ray frequencies. We
give here an overview of multi-wavelength searches for dark matter, including
the expected injection spectrum and production rate of electrons and positrons
from dark matter annihilation, the computation of the effects of propagation
and energy losses and the actual multi-wavelength emission. We then outline the
application of this general framework to the case of galaxy clusters
(specifically Coma, 1E 0657-56 - the so-called Bullet cluster - and Ophiuchus)
and of dwarf spheroidal galaxies (including Draco, Fornax, Ursa Minor and
Carina). We also review the application of multi-wavelength search strategies
to our own Milky Way, and more specifically to the Galactic center environment,
to dark galactic mini-halos and to the so-called WMAP haze and other radio
data. We argue that multi-wavelength observations will complement gamma-ray
observations as probes of particle dark matter, since the expected luminosities
at different frequencies are generically comparable. The indirect search for
dark matter with astronomical observations at various frequencies is therefore
a crucial and indispensable element in the quest for the fundamental nature of
dark matter.


  This work describes simple and efficient algorithms for interactively
learning non-binary concepts in the learning from random counter-examples (LRC)
model. Here, learning takes place from random counter-examples that the learner
receives in response to their proper equivalence queries. In this context, the
learning time is defined as the number of counter-examples needed by the
learner to identify the target concept. Such learning is particularly suited
for online ranking, classification, clustering, etc., where machine learning
models must be used before they are fully trained.
  We provide two simple LRC algorithms, deterministic and randomized, for
exactly learning non-binary target concepts for any concept class $H$. We show
that both of these algorithms have an $\mathcal{O}(\log{}|H|)$ asymptotically
optimal average learning time. This solves an open problem on the existence of
an efficient LRC randomized algorithm while simplifying and generalizing
previous results. We also show that the expected learning time of any arbitrary
LRC algorithm can be upper bounded by
$\mathcal{O}(\frac{1}{\epsilon}\log{\frac{|H|}{\delta}})$, where $\epsilon$ and
$\delta$ are the allowed learning error and failure probability respectively.
This shows that LRC interactive learning is at least as efficient as
non-interactive Probably Approximately Correct (PAC) learning. Our simulations
show that in practice, these algorithms outperform their theoretical bounds.


  Graph contrastive learning (GCL), as a popular approach to graph
self-supervised learning, has recently achieved a non-negligible effect. To
achieve superior performance, the majority of existing GCL methods elaborate on
graph data augmentation to construct appropriate contrastive pairs. However,
existing methods place more emphasis on the complex graph data augmentation
which requires extra time overhead, and pay less attention to developing
contrastive schemes specific to encoder characteristics. We argue that a better
contrastive scheme should be tailored to the characteristics of graph neural
networks (e.g., neighborhood aggregation) and propose a simple yet effective
method named FastGCL. Specifically, by constructing weighted-aggregated and
non-aggregated neighborhood information as positive and negative samples
respectively, FastGCL identifies the potential semantic information of data
without disturbing the graph topology and node attributes, resulting in faster
training and convergence speeds. Extensive experiments have been conducted on
node classification and graph classification tasks, showing that FastGCL has
competitive classification performance and significant training speedup
compared to existing state-of-the-art methods.


  We present systematic spectral analyses of GRBs detected by the Fermi
Gamma-Ray Burst Monitor (GBM) during its first two years of operation. This
catalog contains two types of spectra extracted from 487 GRBs, and by fitting
four different spectral models, this results in a compendium of over 3800
spectra. The models were selected based on their empirical importance to the
spectral shape of many GRBs, and the analysis performed was devised to be as
thorough and objective as possible. We describe in detail our procedure and
criteria for the analyses, and present the bulk results in the form of
parameter distributions. This catalog should be considered an official product
from the Fermi GBM Science Team, and the data files containing the complete
results are available from the High-Energy Astrophysics Science Archive
Research Center (HEASARC).


  Formation, stability patterns, and decay channels of silver dianionic and
gold trianionic clusters are investigated with Penning-trap experiments and a
shell-correction method including shape deformations. The theoretical
predictions pertaining to the appearance sizes and electronic shell effects are
in remarkable agreement with the experiments. Decay of the multiply anionic
clusters occurs predominantly by electron tunneling through a Coulomb barrier,
rather than via fission, leading to appearance sizes unrelated to those of
multiply cationic clusters.


  In this work we address the re-entrance of magic-angle phenomena (band
flatness and quantum-geometric transport) in twisted bilayer graphene (TBG)
subjected to strong magnetic fluxes $\pm \Phi_0$, $\pm 2 \Phi_0$, $\pm 3
\Phi_0$... ($\Phi_0 = h/e$ is the flux quantum per moir\'e cell). The moir\'e
translation invariance is restored at the integer fluxes, for which we
calculate the TBG band structure using accurate atomistic models with lattice
relaxations. Similarly to the zero-flux physics outside the magic angle
condition, the reported effect breaks down rapidly with the twist. We conclude
that the magic-angle physics re-emerges in high magnetic fields, witnessed by
the appearance of flat electronic bands distinct from Landau levels, and
manifesting non-trivial quantum geometry. We further discuss the possible
flat-band quantum geometric contribution to the superfluid weight in strong
magnetic fields (28 T at 1.08$^\circ$ twist), according to Peotta-T\"{o}rm\"{a}
mechanism.


  Finite-thickness effects are analyzed theoretically for the plasma frequency
and associated dielectric response function of plasmonic films formed by
periodically aligned, infinitely thin, identical metallic cylinders. The plasma
frequency of the system is shown to have the unidirectional
square-root-of-momentum and quasilinear momentum spatial dispersion for the
thick and ultrathin films, respectively. This spatial dispersion and the
unidirectional dielectric response nonlocality associated with it can be
adjusted not only by the film material composition but also by varying the film
thickness, the cylinder length, the cylinder-radius-to-film-thickness ratio,
and by choosing the substrates and superstrates of the film appropriately.
Application of the theory developed to the finite-thickness periodically
aligned carbon nanotube films is discussed.


  Oxygen doping dimples the CuO_2 planes of YBa_2Cu_3O_{6.8-6.92} by displacing
copper normal to the planes and further towards Ba. The correlated oxygen
displacements, however, are constrained to the in-plane axes. This displacement
pattern is discussed in terms of doping dependent Cu 4s - 3d hybridizations.


  The ability to accurately predict the evolution of tidally stripped haloes is
important for understanding galaxy formation and testing the properties of dark
matter. Most studies of substructure evolution make predictions based on
empirical models of tidal mass loss that are calibrated using numerical
simulations. This approach can be accurate in the cases considered, but lacks
generality and does not provide a physical understanding of the processes
involved. Recently, we demonstrated that truncating NFW distribution functions
sharply in energy results in density profiles that resemble those of tidally
stripped systems, offering a path to constructing physically motivated models
of tidal mass loss. In this work, we review calculations of mass loss based on
energy truncation alone, and then consider what secondary effects may modulate
mass loss beyond this. We find that a combination of dependence on additional
orbital parameters and variations in individual particle energies over an orbit
results in a less abrupt truncation in energy space as a subhalo loses mass.
Combining the energy truncation approach with a simple prediction for the
mass-loss rate, we construct a full model of mass loss that can accurately
predict the evolution of a subhalo in terms of a single parameter $\eta_{\rm
eff}$. This parameter can be fully determined from the initial orbital and halo
properties, and does not require calibration with numerical simulations.


  A partition of a group is a dioid partition if the following three conditions
are met: The setwise product of any two parts is a union of parts, there is a
part that multiplies as an identity element, and the inverse of a part is a
part. This kind of a group partition was first introduced by Tamaschke in 1968.
We show that a dioid partition defines a dioid structure over the group,
analogously to the way a Schur ring over a group is defined. After proving
fundamental properties of dioid partitions, we focus on three part dioid
partitions of cyclic groups of prime order. We provide classification results
for their isomorphism types as well as for the partitions themselves.


  The magnetization at low temperatures for Nd0.5Sr0.5MnO3 and Nd0.5Ca0.5MnO3
samples showed a rapid increase with decreasing temperatures, contrary to a
La0.5Ca0.5MnO3 sample. Specific heat measurement at low temperatures showed a
Schottky-like anomaly for the first two samples. However, there is not a
straight forward correlation between the intrinsic magnetic moment of the Nd3+
ions and the Schottky-like anomaly.


  In the hydrodynamical description of heavy-ion collisions, the elliptic flow
$\mathrm{v_{2}}$ and triangular flow $\mathrm{v_{3}}$ are sensitive to the
quadrupole deformation $\mathrm{\beta_{2}}$ of the colliding nuclei. We produce
$\mathrm{v_{2}}$ and $\mathrm{v_{3}}$ ratios qualitatively and quantitatively
in most-central Xe-Xe collisions at 5.44 TeV. By employing HYDJET++ model, we
study the sensitivity of anisotropic flow coefficients and mean transverse
momentum to the quadrupole deformation and system-size in isotopic Xe-Xe
collisions. Flow observables strongly depend on the strength of nucleon-nucleon
scattering occuring in even-A and odd-A nuclei. Flow for odd-A nuclei is
suppressed in comparison to flow in even-A collisions. There exists a linear
inter-dependence between $\mathrm{p_{T}}$ integrated anisotropic flow and
nuclear deformation. Mean transverse momentum signifies the fireball
temperature in body-body and tip-tip collisions. There exists a negative linear
correlation of $\mathrm{\langle p_{T} \rangle}$ with collision system-size and
a positive correlation with nuclear deformation. Flow measurements in
high-energy, heavy-ion collisions using isotopic collision systems, offer a new
precision tool to study nuclear structure physics. Observation of nuclear
structure properties like nuclear deformation in a heavy-ion collision such as
this would be very interesting.


  Encryption on the internet with the shift to HTTPS has been an important step
to improve the privacy of internet users. However, there is an increasing body
of work about extracting information from encrypted internet traffic without
having to decrypt it. Such attacks bypass security guarantees assumed to be
given by HTTPS and thus need to be understood. Prior works showed that the
variable bitrates of video streams are sufficient to identify which video
someone is watching. These works generally have to make trade-offs in aspects
such as accuracy, scalability, robustness, etc. These trade-offs complicate the
practical use of these attacks. To that end, we propose a deep metric learning
framework based on the triplet loss method. Through this framework, we achieve
robust, generalisable, scalable and transferable encrypted video stream
detection. First, the triplet loss is better able to deal with video streams
not seen during training. Second, our approach can accurately classify videos
not seen during training. Third, we show that our method scales well to a
dataset of over 1000 videos. Finally, we show that a model trained on video
streams over Chrome can also classify streams over Firefox. Our results suggest
that this side-channel attack is more broadly applicable than originally
thought. We provide our code alongside a diverse and up-to-date dataset for
future research.


  The failure of conventional quantum theory to recognize time as an observable
and to admit time operators is addressed. Instead of focusing on the existence
of a time operator for a given Hamiltonian, we emphasize the role of the
Hamiltonian as the generator of translations in time to construct time states.
Taken together, these states constitute what we call a timeline, or quantum
history, that is adequate for the representation of any physical state of the
system. Such timelines appear to exist even for the semi-bounded and discrete
Hamiltonian systems ruled out by Pauli's theorem. However, the step from a
timeline to a valid time operator requires additional assumptions that are not
always met. Still, this approach illuminates the crucial issue surrounding the
construction of time operators, and establishes quantum histories as legitimate
alternatives to the familiar coordinate and momentum bases of standard quantum
theory.


  We apply the spectral method, recently developed by the authors, to calculate
the statistics of a reaction-limited multi-step birth-death process, or
chemical reaction, that includes as elementary steps branching A->2A and
annihilation 2A->0. The spectral method employs the generating function
technique in conjunction with the Sturm-Liouville theory of linear differential
operators. We focus on the limit when the branching rate is much higher than
the annihilation rate, and obtain accurate analytical results for the complete
probability distribution (including large deviations) of the metastable
long-lived state, and for the extinction time statistics. The analytical
results are in very good agreement with numerical calculations. Furthermore, we
use this example to settle the issue of the "lacking" boundary condition in the
spectral formulation.


  Pothole detection is crucial for road safety and maintenance, traditionally
relying on 2D image segmentation. However, existing 3D Semantic Pothole
Segmentation research often overlooks point cloud sparsity, leading to
suboptimal local feature capture and segmentation accuracy. Our research
presents an innovative point cloud-based pothole segmentation architecture. Our
model efficiently identifies hidden features and uses a feedback mechanism to
enhance local characteristics, improving feature presentation. We introduce a
local relationship learning module to understand local shape relationships,
enhancing structural insights. Additionally, we propose a lightweight adaptive
structure for refining local point features using the K nearest neighbor
algorithm, addressing point cloud density differences and domain selection.
Shared MLP Pooling is integrated to learn deep aggregation features,
facilitating semantic data exploration and segmentation guidance. Extensive
experiments on three public datasets confirm PotholeGuard's superior
performance over state-of-the-art methods. Our approach offers a promising
solution for robust and accurate 3D pothole segmentation, with applications in
road maintenance and safety.


  Recently, end-to-end neural diarization (EEND) is introduced and achieves
promising results in speaker-overlapped scenarios. In EEND, speaker diarization
is formulated as a multi-label prediction problem, where speaker activities are
estimated independently and their dependency are not well considered. To
overcome these disadvantages, we employ the power set encoding to reformulate
speaker diarization as a single-label classification problem and propose the
overlap-aware EEND (EEND-OLA) model, in which speaker overlaps and dependency
can be modeled explicitly. Inspired by the success of two-stage hybrid systems,
we further propose a novel Two-stage OverLap-aware Diarization framework (TOLD)
by involving a speaker overlap-aware post-processing (SOAP) model to
iteratively refine the diarization results of EEND-OLA. Experimental results
show that, compared with the original EEND, the proposed EEND-OLA achieves a
14.39% relative improvement in terms of diarization error rates (DER), and
utilizing SOAP provides another 19.33% relative improvement. As a result, our
method TOLD achieves a DER of 10.14% on the CALLHOME dataset, which is a new
state-of-the-art result on this benchmark to the best of our knowledge.


  Humans possess the remarkable ability to foresee the future to a certain
extent based on present observations, a skill we term as foresight minds.
However, this capability remains largely under explored within existing
Multimodal Large Language Models (MLLMs), hindering their capacity to learn the
fundamental principles of how things operate and the intentions behind the
observed subjects. To address this issue, we introduce the integration of
future modeling into the existing learning frameworks of MLLMs. By utilizing
the subject trajectory, a highly structured representation of a consecutive
frame sequence, as a learning objective, we aim to bridge the gap between the
past and the future. We propose two innovative methods to empower MLLMs with
foresight minds, Foresight Pre-Training (FPT) and Foresight Instruction-Tuning
(FIT), which are inspired by the modern learning paradigm of LLMs.
Specifically, FPT jointly training various tasks centered on trajectories,
enabling MLLMs to learn how to attend and predict entire trajectories from a
given initial observation. Then, FIT requires MLLMs to first predict
trajectories of related objects and then reason about potential future events
based on them. Aided by FPT and FIT, we build a novel and unified MLLM named
Merlin that supports multi-images input and analysis about potential actions of
multiple objects for the future reasoning. Experimental results show Merlin
powerful foresight minds with impressive performance on both future reasoning
and visual comprehension tasks.


  We design an all-carbon membrane for the filtration and desalination of
water. A unique layered assembly of carbon nanostructures including graphite
oxide (GO), buckypaper consisting of carbon nanotubes, and a strong carbon
fabric provides high mechanical strength and thermal stability, resilience to
harsh chemical cleaning agents and electrical conductivity, thus addressing
major shortcomings of commercial reverse osmosis membranes. We use ab initio
density functional theory calculations to obtain atomic-level insight into the
permeation of water molecules in-between GO layers and across in-layer vacancy
defects. Our calculations elucidate the reason for selective rejection of
solvated Na$^+$ ions in an optimized GO membrane that is structurally
stabilized in a sandwich arrangement in-between layers of buckypaper, which are
protected on both sides by strong carbon fabric layers.


  We introduce a suitable concept of weak evolution in the context of the
radial quintic focussing semilinear wave equation on $\mathbb{R}^{3+1}$, that
is adapted to continuation past type II singularities. We show that the weak
extension leads to type I singularity formation for initial data corresponding
to: (i) the Kenig-Merle blow-up solutions with initial energy below the ground
state and (ii) the Krieger-Nakanishi-Schlag blow-up solutions sitting initially
near and "above" the ground state static solution.


  We study some model-theoretic notions in NIP by means of spectral topology.
In the o-minimal setting we relate the o-minimal spectrum with other
topological spaces such as the real spectrum and the space of infinitesimal
types of Peterzil and Starchenko. In particular, we prove for definably compact
groups that the space of closed points is homeomorphic to the space of
infinitesimal types. We also prove that with the spectral topology the set of
invariant types concentrated in a definably compact set is a normal spectral
space whose closed points are the finitely satisfiable types.
  On the other hand, for arbitrary NIP structures we equip the set of invariant
types with a new topology, called the {\em honest topology}. With this topology
the set of invariant types is a normal spectral space whose closed points are
the finitely satisfiable ones, and the natural retraction from invariant types
onto finitely satisfiable types coincides with Simon's $F_M$ retraction.


  We present broad band surface photometry of the radio galaxy 3C270
(NGC~4261). We find a distinct dust lane in the $V-R$ image of the galaxy, and
determine its orientation and size. We use the major axis profile of the galaxy
to estimate the optical depth of the dust lane, and discuss the significance of
the lane to the shape of the galaxy.


  In recent years, large-scale numerical simulations played an essential role
in estimating the effects of explosion events in urban environments, for the
purpose of ensuring the security and safety of cities. Such simulations are
computationally expensive and, often, the time taken for one single computation
is large and does not permit parametric studies. The aim of this work is
therefore to facilitate real-time and multi-query calculations by employing a
non-intrusive Reduced Order Method (ROM). We propose a deep learning-based (DL)
ROM scheme able to deal with fast transient dynamics. In the case of blast
waves, the parametrised PDEs are time-dependent and non-linear. For such
problems, the Proper Orthogonal Decomposition (POD), which relies on a linear
superposition of modes, cannot approximate the solutions efficiently. The
piecewise POD-DL scheme developed here is a local ROM based on time-domain
partitioning and a first dimensionality reduction obtained through the POD.
Autoencoders are used as a second and non-linear dimensionality reduction. The
latent space obtained is then reconstructed from the time and parameter space
through deep forward neural networks. The proposed scheme is applied to an
example consisting of a blast wave propagating in air and impacting on the
outside of a building. The efficiency of the deep learning-based ROM in
approximating the time-dependent pressure field is shown.


  Stabbing queries in sets of intervals are usually answered using segment
trees. A dynamic variant of segment trees has been presented by van Kreveld and
Overmars, which uses red-black trees to do rebalancing operations. This paper
presents zipping segment trees - dynamic segment trees based on zip trees,
which were recently introduced by Tarjan et al. To facilitate zipping segment
trees, we show how to uphold certain segment tree properties during the
operations of a zip tree. We present an in-depth experimental evaluation and
comparison of dynamic segment trees based on red-black trees, weight-balanced
trees and several variants of the novel zipping segment trees. Our results
indicate that zipping segment trees perform better than rotation-based
alternatives.


  The Lyapunov exponent characterizes an exponential growth rate of the
difference of nearby orbits. A positive Lyapunov exponent is a manifestation of
chaos. Here, we propose the Lyapunov pair, which is based on the generalized
Lyapunov exponent, as a unified characterization of non-exponential and
exponential dynamical instabilities in one-dimensional maps. Chaos is
classified into three different types, i.e., super-exponential, exponential,
and sub-exponential dynamical instabilities. Using one-dimensional maps, we
demonstrate super-exponential and sub-exponential chaos and quantify the
dynamical instabilities by the Lyapunov pair. In sub-exponential chaos, we show
super-weak chaos, which means that the growth of the difference of nearby
orbits is slower than a stretched exponential growth. The scaling of the growth
is analytically studied by a recently developed theory of a continuous
accumulation process, which is related to infinite ergodic theory.


  It is known that there is a one-to-one correspondence between equivalence
classes of primitive indefinite binary quadratic forms and primitive hyperbolic
conjugacy classes of the modular group. Due to such a correspondence, Sarnak
obtained the asymptotic formula for the class number sum in order of the
fundamental unit by using the prime geodesic theorem for the modular group. In
the present paper, we propose asymptotic formulas of the class number sums over
discriminants in arithmetic progressions. Since there are relations between the
arithmetic properties of the discriminants and the conjugacy classes in the
finite groups given by the modular group and its congruence subgroups, we can
get the desired asymptotic formulas by arranging the Tchebotarev-type prime
geodesic theorem. While such asymptotic formulas were already given by Raulf,
the approaches are quite different, the expressions of the leading terms of our
asymptotic formulas are simpler and the estimates of the reminder terms are
sharper.


  Off-policy evaluation often refers to two related tasks: estimating the
expected return of a policy and estimating its value function (or other
functions of interest, such as density ratios). While recent works on
marginalized importance sampling (MIS) show that the former can enjoy provable
guarantees under realizable function approximation, the latter is only known to
be feasible under much stronger assumptions such as prohibitively expressive
discriminators. In this work, we provide guarantees for off-policy function
estimation under only realizability, by imposing proper regularization on the
MIS objectives. Compared to commonly used regularization in MIS, our
regularizer is much more flexible and can account for an arbitrary
user-specified distribution, under which the learned function will be close to
the groundtruth. We provide exact characterization of the optimal dual solution
that needs to be realized by the discriminator class, which determines the
data-coverage assumption in the case of value-function learning. As another
surprising observation, the regularizer can be altered to relax the
data-coverage requirement, and completely eliminate it in the ideal case with
strong side information.


  I give a brief overview of our current understanding of the QCD phase diagram
at finite temperature and baryon density. I emphasize phase transitions which
occur at high density and low temperature (relative to the QCD scale), such as
transition(s) to the color superconducting and chirally symmetric phases. These
transitions might occur during violent astrophysical activity such as type II
supernovae core bounce or neutron star mergers, and I discuss the possible
consequences.


  We characterize the generator matrix in standard form of generalized
Gabidulin codes. The parametrization we get for the non-systematic part of this
matrix coincides with the $q$-analogue of generalized Cauchy matrices, leading
to the definition of $q$-Cauchy matrices. These matrices can be represented
very conveniently and their representation allows to define new interesting
subfamilies of generalized Gabidulin codes whose generator matrix is a
structured matrix. In particular, as an application, we construct Gabidulin
codes whose generator matrix is the concatenation of an identity block and a
Toeplitz/Hankel matrix. In addition, our results allow to give a new efficient
criterion to verify whether a rank metric code of dimension $k$ and length $n$
is a generalized Gabidulin code. This criterion is only based on the
computation of the rank of one matrix and on the verification of the linear
independence of two sets of elements and it requires $\mathcal O(m\cdot
F(k,n))$ field operations, where $F(k,n)$ is the cost of computing the reduced
row echelon form of a $k \times n$ matrix. Moreover, we also provide a
characterization of the generator matrix in standard form of general MRD codes.


  We study the zero-temperature phase diagram of a one-dimensional array of QED
cavities where, besides the single-photon hopping, an additional coupling
between neighboring cavities is mediated by an N-type four-level system. By
varying the relative strength of the various couplings, the array is shown to
exhibit a variety of quantum phases including a polaritonic Mott insulator, a
density-wave and a superfluid phase. Our results have been obtained by means of
numerical density-matrix renormalization group calculations. The phase diagram
was obtained by analyzing the energy gaps for the polaritons, as well as
through a study of two-point correlation functions.


  Neural models have shown impressive performance gains in answering queries
from natural language text. However, existing works are unable to support
database queries, such as "List/Count all female athletes who were born in 20th
century", which require reasoning over sets of relevant facts with operations
such as join, filtering and aggregation. We show that while state-of-the-art
transformer models perform very well for small databases, they exhibit
limitations in processing noisy data, numerical operations, and queries that
aggregate facts. We propose a modular architecture to answer these
database-style queries over multiple spans from text and aggregating these at
scale. We evaluate the architecture using WikiNLDB, a novel dataset for
exploring such queries. Our architecture scales to databases containing
thousands of facts whereas contemporary models are limited by how many facts
can be encoded. In direct comparison on small databases, our approach increases
overall answer accuracy from 85% to 90%. On larger databases, our approach
retains its accuracy whereas transformer baselines could not encode the
context.


  Choosing a publication venue for an academic paper is a crucial step in the
research process. However, in many cases, decisions are based solely on the
experience of researchers, which often leads to suboptimal results. Although
there exist venue recommender systems for academic papers, they recommend
venues where the paper is expected to be published. In this study, we aim to
recommend publication venues from a different perspective. We estimate the
number of citations a paper will receive if the paper is published in each
venue and recommend the venue where the paper has the most potential impact.
However, there are two challenges to this task. First, a paper is published in
only one venue, and thus, we cannot observe the number of citations the paper
would receive if the paper were published in another venue. Secondly, the
contents of a paper and the publication venue are not statistically
independent; that is, there exist selection biases in choosing publication
venues. In this paper, we formulate the venue recommendation problem as a
treatment effect estimation problem. We use a bias correction method to
estimate the potential impact of choosing a publication venue effectively and
to recommend venues based on the potential impact of papers in each venue. We
highlight the effectiveness of our method using paper data from computer
science conferences.


  We perform a comprehensive study of stability of a pumped atom laser in the
presence of pumping, damping and outcoupling. We also introduce a realistic
feedback scheme to improve stability by extracting energy from the condensate
and determine its effectiveness. We find that while the feedback scheme is
highly efficient in reducing condensate fluctuations, it usually does not alter
the stability class of a particular set of pumping, damping and outcoupling
parameters.


  We prove that a bi-Lipschitz image of a planar $BV$-extension domain is also
a $BV$-extension domain, and that a bi-Lipschitz image of a planar
$W^{1,1}$-extension domain is again a $W^{1,1}$-extension domain.


  To help mitigate road congestion caused by the unrelenting growth of traffic
demand, many transit authorities have implemented managed lane policies.
Managed lanes typically run parallel to a freeway's standard, general-purpose
(GP) lanes, but are restricted to certain types of vehicles. It was originally
thought that managed lanes would improve the use of existing infrastructure
through incentivization of demand-management behaviors like carpooling, but
implementations have often been characterized by unpredicted phenomena that is
often to detrimental system performance.
  This paper presents several macroscopic traffic modeling tools we have used
for study of freeways equipped with managed lanes, or "managed lane-freeway
networks." The proposed framework is based on the widely-used first-order
kinematic wave theory. In this model, the GP and the managed lanes are modeled
as parallel links connected by nodes, where certain type of traffic may switch
between GP and managed lane links. Two types of managed lane topologies are
considered: full-access, where vehicles can switch between the GP and the
managed lanes anywhere; and separated, where such switching is allowed only at
certain locations called gates.
  We also describe methods to incorporate in three phenomena into our model
that are particular to managed lane-freeway networks. The inertia effect
reflects drivers' inclination to stay in their lane as long as possible and
switch only if this would obviously improve their travel condition. The
friction effect reflects the empirically-observed driver fear of moving fast in
a managed lane while traffic in the adjacent GP lanes moves slowly due to
congestion. The smoothing effect describes how managed lanes can increase
throughput at bottlenecks by reducing lane changes. We present simple models
for each of these phenomena that fit within the general macroscopic theory.


  The construction of the non-logarithmic conformal field theory based on
sl^(2)_{-1/2} is revisited. Without resorting to free-field methods, the
determination of the spectrum and fusion rules is streamlined and the beta
gamma ghost system is carefully derived as the extended algebra generated by
the unique finite-order simple current. A brief discussion of modular
invariance is given and the Verlinde formula is explicitly verified.


  In this article, we define a symmetric 2-tensor canonically associated to
Q-curvature called J-tensor on any Riemannian manifold with dimension at least
three. The relation between J-tensor and Q-curvature is precisely like Ricci
tensor and scalar curvature. Thus it can be interpreted as a higher-order
analogue of Ricci tensor. This tensor can also be used to understand
Chang-Gursky-Yang's theorem on 4-dimensional Q-singular metrics. Moreover, we
show an Almost-Schur Lemma holds for Q-curvature, which gives an estimate of
Q-curvature on closed manifolds.


  Open-set face recognition describes a scenario where unknown subjects, unseen
during the training stage, appear on test time. Not only it requires methods
that accurately identify individuals of interest, but also demands approaches
that effectively deal with unfamiliar faces. This work details a scalable
open-set face identification approach to galleries composed of hundreds and
thousands of subjects. It is composed of clustering and an ensemble of binary
learning algorithms that estimates when query face samples belong to the face
gallery and then retrieves their correct identity. The approach selects the
most suitable gallery subjects and uses the ensemble to improve prediction
performance. We carry out experiments on well-known LFW and YTF benchmarks.
Results show that competitive performance can be achieved even when targeting
scalability.


  We present theoretical study of morphology of Fe islands grown at Mo(110)
surface in sub-monolayer MBE mode. We utilize atomistic SOS model with bond
counting, and interactions of Fe adatom up to third nearest neighbors. We
performed KMC simulations for different values of adatom interactions and
varying temperatures. We have found that, while for the low temperature islands
are fat fractals, for the temperature 500K islands have faceted rhombic-like
shape. For the higher temperature, islands acquire a rounded shape. In order to
evaluated qualitatively morphological changes, we measured averaged aspect
ration of islands. We calculated dependence of the average aspect ratio on the
temperature, and on the strength of interactions of an adatom with neighbors.


  In this paper, motivated by finding sharp Li-Yau type gradient estimate for
positive solution of heat equations on complete Riemannian manifolds with
negative Ricci curvature lower bound, we first introduce the notion of Li-Yau
multiplier set and show that it can be computed by heat kernel of the manifold.
Then, an optimal Li-Yau type gradient estimate is obtained on hyperbolic spaces
by using recurrence relations of heat kernels on hyperbolic spaces. Finally, as
an application, we obtain sharp Harnack inequalities on hyperbolic spaces.


  We theoretically investigate the interaction of a single quantum dipole with
the modes of a fiber-coupled semiconductor waveguide. Through a combination of
tight modal confinement and phase-matched evanescent coupling, we predict that
approximately 70 % of the dipole's emission can be collected into a single mode
optical fiber. Calculations further show that the dipole strongly modifies
resonant light transmission through the system, with over an order of magnitude
change possible for an appropriate choice of fiber-waveguide coupler geometry.


  It is often useful to sort words into an order that reflects relations among
their meanings as obtained by using a thesaurus. In this paper, we introduce a
method of arranging words semantically by using several types of `{\sf is-a}'
thesauri and a multi-dimensional thesaurus. We also describe three major
applications where a meaning sort is useful and show the effectiveness of a
meaning sort. Since there is no doubt that a word list in meaning-order is
easier to use than a word list in some random order, a meaning sort, which can
easily produce a word list in meaning-order, must be useful and effective.


  An infinite word x is said to be quasiperiodic if there exists a finite word
q such that x is covered by occurrences of q (such a q is called a quasiperiod
of x). Using the notion of derivation, we show that this definition is not
sufficient to imply any symmetry in an infinite word. Therefore we introduce
multi-scale quasiperiodic words, i.e. quasiperiodic words that admit an
infinite number of quasiperiods. Such words are uniformly recurrent, this
allows us to study the subshift they generate. We prove that multi-scale
quasiperiodic subshifts are uniquely ergodic and have zero topological entropy
as well as zero Kolmogorov complexity. Sturmian subshifts are shown to be
multi-scale quasiperiodic.


  Language data and models demonstrate various types of bias, be it ethnic,
religious, gender, or socioeconomic. AI/NLP models, when trained on the
racially biased dataset, AI/NLP models instigate poor model explainability,
influence user experience during decision making and thus further magnifies
societal biases, raising profound ethical implications for society. The
motivation of the study is to investigate how AI systems imbibe bias from data
and produce unexplainable discriminatory outcomes and influence an individual's
articulateness of system outcome due to the presence of racial bias features in
datasets. The design of the experiment involves studying the counterfactual
impact of racial bias features present in language datasets and its associated
effect on the model outcome. A mixed research methodology is adopted to
investigate the cross implication of biased model outcome on user experience,
effect on decision-making through controlled lab experimentation. The findings
provide foundation support for correlating the implication of carry-over an
artificial intelligence model solving NLP task due to biased concept presented
in the dataset. Further, the research outcomes justify the negative influence
on users' persuasiveness that leads to alter the decision-making quotient of an
individual when trying to rely on the model outcome to act. The paper bridges
the gap across the harm caused in establishing poor customer trustworthiness
due to an inequitable system design and provides strong support for
researchers, policymakers, and data scientists to build responsible AI
frameworks within organizations.


  Elastic scattering is a very important process to understand nuclear
interactions in finite nuclei. Despite decades of efforts, the goal of reaching
a coherent description of this physical process in terms of microscopic forces
is still far from being completed.
  In previous papers (Phys. Rev. C93, 034619 (2016), Phys. Rev. C96, 044001
(2017)) we derived a nonrelativistic theoretical optical potential from
nucleon-nucleon chiral potentials at fourth (N3LO) and fifth order (N4LO). We
checked convergence patterns and established theoretical error bands. With this
work we study the performances of our optical potential in comparison with
those of a successful nonrelativistic phenomenological optical potential in the
description of elastic proton scattering data on several isotopic chains at
energies around and above 200 MeV.
  We use the same framework and the same approximations of our previous papers,
where the nonrelativistic optical potential is derived at the first-order term
within the spectator expansion of the multiple scattering theory and adopting
the impulse approximation and the optimum factorization approximation.
  The cross sections and analyzing powers for elastic proton scattering off
calcium, nickel, tin, and lead isotopes are presented for several incident
proton energies, exploring the range $156 \le E \le 333$ MeV, where
experimental data are available. In addition, we provide theoretical
predictions for Ni56 at 400 MeV, which is of interest for the future
experiments at EXL.
  Our results indicate that microscopic optical potentials derived from
nucleon-nucleon chiral potentials at N4LO can provide reliable predictions for
the cross section and the analyzing power both of stable and exotic nuclei,
even at energies where the reliability of the chiral expansion starts to be
questionable.


  We present a comparison of three different types of Langevin equation
exhibiting absorbing states: the Langevin equation defining the Reggeon field
theory, one with multiplicative noise, and a third type in which the noise is
complex. Each one is found to describe a different underlying physical
mechanism; in particular, the nature of the different absorbing states depends
on the type of noise considered.
  By studying the stationary single-site effective potential, we analyze the
impossibility of finding a reaction-diffusion model in the multiplicative noise
universality class. We also discuss some theoretical questions related to the
nature of complex noise, as for example, whether it is necessary or not to
consider a complex equation in order to describe processes as the annihilation
reaction, $A +A \to 0$.


  Antiferromagnetism of ultracold fermions in an optical lattice can be
detected by Bragg diffraction of light, in analogy to the diffraction of
neutrons from solid state materials. A finite sublattice magnetization will
lead to a Bragg peak from the (1/2 1/2 1/2) crystal plane with an intensity
depending on details of the atomic states, the frequency and polarization of
the probe beam, the direction and magnitude of the sublattice magnetization,
and the finite optical density of the sample. Accounting for these effects we
make quantitative predictions about the scattering intensity and find that with
experimentally feasible parameters the signal can be readily measured with a
CCD camera or a photodiode and used to detect antiferromagnetic order.


  Carbon and oxygen are key tracers of the Galactic chemical evolution; in
particular, a reported upturn in [C/O] towards decreasing [O/H] in metal-poor
halo stars could be a signature of nucleosynthesis by massive Population III
stars. We reanalyse carbon, oxygen, and iron abundances in thirty-nine
metal-poor turn-off stars. For the first time, we take into account
three-dimensional (3D) hydrodynamic effects together with departures from local
thermodynamic equilibrium (LTE) when determining both the stellar parameters
and the elemental abundances, by deriving effective temperatures from 3D
non-LTE H$\beta$ profiles, surface gravities from Gaia parallaxes, iron
abundances from 3D LTE Feii equivalent widths, and carbon and oxygen abundances
from 3D non-LTE Ci and Oi equivalent widths. We find that [C/Fe] stays flat
with [Fe/H], whereas [O/Fe] increases linearly up to $0.75$ dex with decreasing
[Fe/H] down to $-3.0$ dex. As such [C/O] monotonically decreases towards
decreasing [O/H], in contrast to previous findings, mainly by virtue of less
severe non-LTE effects for Oi at low [Fe/H] with our improved calculations.